From 8572750e835056282e899cd76c9896f256c6ba34 Mon Sep 17 00:00:00 2001
From:  <>
Date: Tue, 17 Dec 2024 19:11:41 +0000
Subject: [PATCH] Deployed e81ac22 with MkDocs version: 1.3.0

---
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diff --git a/.nojekyll b/.nojekyll
new file mode 100644
index 0000000..e69de29
diff --git a/404.html b/404.html
new file mode 100644
index 0000000..90f32dc
--- /dev/null
+++ b/404.html
@@ -0,0 +1,287 @@
+
+<!doctype html>
+<html lang="en" class="no-js">
+  <head>
+    
+      <meta charset="utf-8">
+      <meta name="viewport" content="width=device-width,initial-scale=1">
+      
+        <meta name="description" content="QuakeFlow: a Scalable Deep-learning-based Earthquake Monitoring Workflow with Cloud Computing">
+      
+      
+        <meta name="author" content="Weiqiang Zhu">
+      
+      
+      <link rel="icon" href="/assets/images/favicon.png">
+      <meta name="generator" content="mkdocs-1.3.0, mkdocs-material-8.4.1">
+    
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+        <title>QuakeFlow</title>
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+      
+        
+        <link rel="stylesheet" href="/assets/stylesheets/palette.cbb835fc.min.css">
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+  
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+
+<header class="md-header" data-md-component="header">
+  <nav class="md-header__inner md-grid" aria-label="Header">
+    <a href="/." title="QuakeFlow" class="md-header__button md-logo" aria-label="QuakeFlow" data-md-component="logo">
+      
+  
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+  <h1>404 - Not found</h1>
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+            
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+        <footer class="md-footer">
+  
+  <div class="md-footer-meta md-typeset">
+    <div class="md-footer-meta__inner md-grid">
+      <div class="md-copyright">
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+  
+    Made with
+    <a href="https://squidfunk.github.io/mkdocs-material/" target="_blank" rel="noopener">
+      Material for MkDocs
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+      <script src="/assets/javascripts/bundle.9c69f0bc.min.js"></script>
+      
+    
+  </body>
+</html>
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t!="undefined"){let r=`https://api.github.com/repos/${e}/${t}`;return _t(ke(`${r}/releases/latest`).pipe(ae(()=>C),m(n=>({version:n.tag_name})),je({})),ke(r).pipe(ae(()=>C),m(n=>({stars:n.stargazers_count,forks:n.forks_count})),je({}))).pipe(m(([n,o])=>H(H({},n),o)))}else{let r=`https://api.github.com/users/${e}`;return ke(r).pipe(m(n=>({repositories:n.public_repos})),je({}))}}function Pi(e,t){let r=`https://${e}/api/v4/projects/${encodeURIComponent(t)}`;return ke(r).pipe(ae(()=>C),m(({star_count:n,forks_count:o})=>({stars:n,forks:o})),je({}))}function Ii(e){let[t]=e.match(/(git(?:hub|lab))/i)||[];switch(t.toLowerCase()){case"github":let[,r,n]=e.match(/^.+github\.com\/([^/]+)\/?([^/]+)?/i);return Hi(r,n);case"gitlab":let[,o,i]=e.match(/^.+?([^/]*gitlab[^/]+)\/(.+?)\/?$/i);return Pi(o,i);default:return C}}var Hs;function Ps(e){return Hs||(Hs=P(()=>{let t=__md_get("__source",sessionStorage);return 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r!="undefined"&&r.click();break}});Di({document$:tt,tablet$:vr});Wi({document$:tt});Vi({viewport$:Ee,tablet$:vr});var Qe=di(we("header"),{viewport$:Ee}),hr=tt.pipe(m(()=>we("main")),S(e=>vi(e,{viewport$:Ee,header$:Qe})),X(1)),Us=R(...ne("consent").map(e=>Qo(e,{target$:gt})),...ne("dialog").map(e=>mi(e,{alert$:nn})),...ne("header").map(e=>hi(e,{viewport$:Ee,header$:Qe,main$:hr})),...ne("palette").map(e=>gi(e)),...ne("search").map(e=>Ri(e,{index$:Yi,keyboard$:rn})),...ne("source").map(e=>$i(e))),Ds=P(()=>R(...ne("announce").map(e=>qo(e)),...ne("content").map(e=>li(e,{target$:gt,print$:qi})),...ne("content").map(e=>ee("search.highlight")?ki(e,{index$:Yi,location$:br}):C),...ne("header-title").map(e=>bi(e,{viewport$:Ee,header$:Qe})),...ne("sidebar").map(e=>e.getAttribute("data-md-type")==="navigation"?Qr(zi,()=>tn(e,{viewport$:Ee,header$:Qe,main$:hr})):Qr(vr,()=>tn(e,{viewport$:Ee,header$:Qe,main$:hr}))),...ne("tabs").map(e=>ji(e,{viewport$:Ee,header$:Qe})),...ne("toc").map(e=>Fi(e,{viewport$:Ee,header$:Qe,target$:gt})),...ne("top").map(e=>Ui(e,{viewport$:Ee,header$:Qe,main$:hr,target$:gt})))),Ki=tt.pipe(S(()=>Ds),et(Us),X(1));Ki.subscribe();window.document$=tt;window.location$=br;window.target$=gt;window.keyboard$=rn;window.viewport$=Ee;window.tablet$=vr;window.screen$=zi;window.print$=qi;window.alert$=nn;window.component$=Ki;})();
+//# sourceMappingURL=bundle.9c69f0bc.min.js.map
+
diff --git a/assets/javascripts/bundle.9c69f0bc.min.js.map b/assets/javascripts/bundle.9c69f0bc.min.js.map
new file mode 100644
index 0000000..27a0842
--- /dev/null
+++ b/assets/javascripts/bundle.9c69f0bc.min.js.map
@@ -0,0 +1,8 @@
+{
+  "version": 3,
+  "sources": ["node_modules/focus-visible/dist/focus-visible.js", "node_modules/url-polyfill/url-polyfill.js", "node_modules/rxjs/node_modules/tslib/tslib.js", "node_modules/clipboard/dist/clipboard.js", "node_modules/escape-html/index.js", "node_modules/array-flat-polyfill/index.mjs", "src/assets/javascripts/bundle.ts", "node_modules/unfetch/polyfill/index.js", "node_modules/rxjs/node_modules/tslib/modules/index.js", "node_modules/rxjs/src/internal/util/isFunction.ts", "node_modules/rxjs/src/internal/util/createErrorClass.ts", "node_modules/rxjs/src/internal/util/UnsubscriptionError.ts", "node_modules/rxjs/src/internal/util/arrRemove.ts", "node_modules/rxjs/src/internal/Subscription.ts", "node_modules/rxjs/src/internal/config.ts", "node_modules/rxjs/src/internal/scheduler/timeoutProvider.ts", "node_modules/rxjs/src/internal/util/reportUnhandledError.ts", "node_modules/rxjs/src/internal/util/noop.ts", "node_modules/rxjs/src/internal/NotificationFactories.ts", 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"node_modules/rxjs/src/internal/scheduler/AsyncScheduler.ts", "node_modules/rxjs/src/internal/scheduler/async.ts", "node_modules/rxjs/src/internal/scheduler/AnimationFrameAction.ts", "node_modules/rxjs/src/internal/scheduler/AnimationFrameScheduler.ts", "node_modules/rxjs/src/internal/scheduler/animationFrame.ts", "node_modules/rxjs/src/internal/observable/empty.ts", "node_modules/rxjs/src/internal/util/isScheduler.ts", "node_modules/rxjs/src/internal/util/args.ts", "node_modules/rxjs/src/internal/util/isArrayLike.ts", "node_modules/rxjs/src/internal/util/isPromise.ts", "node_modules/rxjs/src/internal/util/isInteropObservable.ts", "node_modules/rxjs/src/internal/util/isAsyncIterable.ts", "node_modules/rxjs/src/internal/util/throwUnobservableError.ts", "node_modules/rxjs/src/internal/symbol/iterator.ts", "node_modules/rxjs/src/internal/util/isIterable.ts", "node_modules/rxjs/src/internal/util/isReadableStreamLike.ts", "node_modules/rxjs/src/internal/observable/innerFrom.ts", "node_modules/rxjs/src/internal/util/executeSchedule.ts", "node_modules/rxjs/src/internal/operators/observeOn.ts", "node_modules/rxjs/src/internal/operators/subscribeOn.ts", "node_modules/rxjs/src/internal/scheduled/scheduleObservable.ts", "node_modules/rxjs/src/internal/scheduled/schedulePromise.ts", "node_modules/rxjs/src/internal/scheduled/scheduleArray.ts", "node_modules/rxjs/src/internal/scheduled/scheduleIterable.ts", "node_modules/rxjs/src/internal/scheduled/scheduleAsyncIterable.ts", "node_modules/rxjs/src/internal/scheduled/scheduleReadableStreamLike.ts", "node_modules/rxjs/src/internal/scheduled/scheduled.ts", "node_modules/rxjs/src/internal/observable/from.ts", "node_modules/rxjs/src/internal/observable/of.ts", "node_modules/rxjs/src/internal/observable/throwError.ts", "node_modules/rxjs/src/internal/util/isDate.ts", "node_modules/rxjs/src/internal/operators/map.ts", "node_modules/rxjs/src/internal/util/mapOneOrManyArgs.ts", 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+  "sourceRoot": "../../../..",
+  "sourcesContent": ["(function (global, factory) {\n  typeof exports === 'object' && typeof module !== 'undefined' ? factory() :\n  typeof define === 'function' && define.amd ? define(factory) :\n  (factory());\n}(this, (function () { 'use strict';\n\n  /**\n   * Applies the :focus-visible polyfill at the given scope.\n   * A scope in this case is either the top-level Document or a Shadow Root.\n   *\n   * @param {(Document|ShadowRoot)} scope\n   * @see https://github.com/WICG/focus-visible\n   */\n  function applyFocusVisiblePolyfill(scope) {\n    var hadKeyboardEvent = true;\n    var hadFocusVisibleRecently = false;\n    var hadFocusVisibleRecentlyTimeout = null;\n\n    var inputTypesAllowlist = {\n      text: true,\n      search: true,\n      url: true,\n      tel: true,\n      email: true,\n      password: true,\n      number: true,\n      date: true,\n      month: true,\n      week: true,\n      time: true,\n      datetime: true,\n      'datetime-local': true\n    };\n\n    /**\n     * Helper function for legacy browsers and iframes which sometimes focus\n     * elements like document, body, and non-interactive SVG.\n     * @param {Element} el\n     */\n    function isValidFocusTarget(el) {\n      if (\n        el &&\n        el !== document &&\n        el.nodeName !== 'HTML' &&\n        el.nodeName !== 'BODY' &&\n        'classList' in el &&\n        'contains' in el.classList\n      ) {\n        return true;\n      }\n      return false;\n    }\n\n    /**\n     * Computes whether the given element should automatically trigger the\n     * `focus-visible` class being added, i.e. whether it should always match\n     * `:focus-visible` when focused.\n     * @param {Element} el\n     * @return {boolean}\n     */\n    function focusTriggersKeyboardModality(el) {\n      var type = el.type;\n      var tagName = el.tagName;\n\n      if (tagName === 'INPUT' && inputTypesAllowlist[type] && !el.readOnly) {\n        return true;\n      }\n\n      if (tagName === 'TEXTAREA' && !el.readOnly) {\n        return true;\n      }\n\n      if (el.isContentEditable) {\n        return true;\n      }\n\n      return false;\n    }\n\n    /**\n     * Add the `focus-visible` class to the given element if it was not added by\n     * the author.\n     * @param {Element} el\n     */\n    function addFocusVisibleClass(el) {\n      if (el.classList.contains('focus-visible')) {\n        return;\n      }\n      el.classList.add('focus-visible');\n      el.setAttribute('data-focus-visible-added', '');\n    }\n\n    /**\n     * Remove the `focus-visible` class from the given element if it was not\n     * originally added by the author.\n     * @param {Element} el\n     */\n    function removeFocusVisibleClass(el) {\n      if (!el.hasAttribute('data-focus-visible-added')) {\n        return;\n      }\n      el.classList.remove('focus-visible');\n      el.removeAttribute('data-focus-visible-added');\n    }\n\n    /**\n     * If the most recent user interaction was via the keyboard;\n     * and the key press did not include a meta, alt/option, or control key;\n     * then the modality is keyboard. Otherwise, the modality is not keyboard.\n     * Apply `focus-visible` to any current active element and keep track\n     * of our keyboard modality state with `hadKeyboardEvent`.\n     * @param {KeyboardEvent} e\n     */\n    function onKeyDown(e) {\n      if (e.metaKey || e.altKey || e.ctrlKey) {\n        return;\n      }\n\n      if (isValidFocusTarget(scope.activeElement)) {\n        addFocusVisibleClass(scope.activeElement);\n      }\n\n      hadKeyboardEvent = true;\n    }\n\n    /**\n     * If at any point a user clicks with a pointing device, ensure that we change\n     * the modality away from keyboard.\n     * This avoids the situation where a user presses a key on an already focused\n     * element, and then clicks on a different element, focusing it with a\n     * pointing device, while we still think we're in keyboard modality.\n     * @param {Event} e\n     */\n    function onPointerDown(e) {\n      hadKeyboardEvent = false;\n    }\n\n    /**\n     * On `focus`, add the `focus-visible` class to the target if:\n     * - the target received focus as a result of keyboard navigation, or\n     * - the event target is an element that will likely require interaction\n     *   via the keyboard (e.g. a text box)\n     * @param {Event} e\n     */\n    function onFocus(e) {\n      // Prevent IE from focusing the document or HTML element.\n      if (!isValidFocusTarget(e.target)) {\n        return;\n      }\n\n      if (hadKeyboardEvent || focusTriggersKeyboardModality(e.target)) {\n        addFocusVisibleClass(e.target);\n      }\n    }\n\n    /**\n     * On `blur`, remove the `focus-visible` class from the target.\n     * @param {Event} e\n     */\n    function onBlur(e) {\n      if (!isValidFocusTarget(e.target)) {\n        return;\n      }\n\n      if (\n        e.target.classList.contains('focus-visible') ||\n        e.target.hasAttribute('data-focus-visible-added')\n      ) {\n        // To detect a tab/window switch, we look for a blur event followed\n        // rapidly by a visibility change.\n        // If we don't see a visibility change within 100ms, it's probably a\n        // regular focus change.\n        hadFocusVisibleRecently = true;\n        window.clearTimeout(hadFocusVisibleRecentlyTimeout);\n        hadFocusVisibleRecentlyTimeout = window.setTimeout(function() {\n          hadFocusVisibleRecently = false;\n        }, 100);\n        removeFocusVisibleClass(e.target);\n      }\n    }\n\n    /**\n     * If the user changes tabs, keep track of whether or not the previously\n     * focused element had .focus-visible.\n     * @param {Event} e\n     */\n    function onVisibilityChange(e) {\n      if (document.visibilityState === 'hidden') {\n        // If the tab becomes active again, the browser will handle calling focus\n        // on the element (Safari actually calls it twice).\n        // If this tab change caused a blur on an element with focus-visible,\n        // re-apply the class when the user switches back to the tab.\n        if (hadFocusVisibleRecently) {\n          hadKeyboardEvent = true;\n        }\n        addInitialPointerMoveListeners();\n      }\n    }\n\n    /**\n     * Add a group of listeners to detect usage of any pointing devices.\n     * These listeners will be added when the polyfill first loads, and anytime\n     * the window is blurred, so that they are active when the window regains\n     * focus.\n     */\n    function addInitialPointerMoveListeners() {\n      document.addEventListener('mousemove', onInitialPointerMove);\n      document.addEventListener('mousedown', onInitialPointerMove);\n      document.addEventListener('mouseup', onInitialPointerMove);\n      document.addEventListener('pointermove', onInitialPointerMove);\n      document.addEventListener('pointerdown', onInitialPointerMove);\n      document.addEventListener('pointerup', onInitialPointerMove);\n      document.addEventListener('touchmove', onInitialPointerMove);\n      document.addEventListener('touchstart', onInitialPointerMove);\n      document.addEventListener('touchend', onInitialPointerMove);\n    }\n\n    function removeInitialPointerMoveListeners() {\n      document.removeEventListener('mousemove', onInitialPointerMove);\n      document.removeEventListener('mousedown', onInitialPointerMove);\n      document.removeEventListener('mouseup', onInitialPointerMove);\n      document.removeEventListener('pointermove', onInitialPointerMove);\n      document.removeEventListener('pointerdown', onInitialPointerMove);\n      document.removeEventListener('pointerup', onInitialPointerMove);\n      document.removeEventListener('touchmove', onInitialPointerMove);\n      document.removeEventListener('touchstart', onInitialPointerMove);\n      document.removeEventListener('touchend', onInitialPointerMove);\n    }\n\n    /**\n     * When the polfyill first loads, assume the user is in keyboard modality.\n     * If any event is received from a pointing device (e.g. mouse, pointer,\n     * touch), turn off keyboard modality.\n     * This accounts for situations where focus enters the page from the URL bar.\n     * @param {Event} e\n     */\n    function onInitialPointerMove(e) {\n      // Work around a Safari quirk that fires a mousemove on <html> whenever the\n      // window blurs, even if you're tabbing out of the page. \u00AF\\_(\u30C4)_/\u00AF\n      if (e.target.nodeName && e.target.nodeName.toLowerCase() === 'html') {\n        return;\n      }\n\n      hadKeyboardEvent = false;\n      removeInitialPointerMoveListeners();\n    }\n\n    // For some kinds of state, we are interested in changes at the global scope\n    // only. For example, global pointer input, global key presses and global\n    // visibility change should affect the state at every scope:\n    document.addEventListener('keydown', onKeyDown, true);\n    document.addEventListener('mousedown', onPointerDown, true);\n    document.addEventListener('pointerdown', onPointerDown, true);\n    document.addEventListener('touchstart', onPointerDown, true);\n    document.addEventListener('visibilitychange', onVisibilityChange, true);\n\n    addInitialPointerMoveListeners();\n\n    // For focus and blur, we specifically care about state changes in the local\n    // scope. This is because focus / blur events that originate from within a\n    // shadow root are not re-dispatched from the host element if it was already\n    // the active element in its own scope:\n    scope.addEventListener('focus', onFocus, true);\n    scope.addEventListener('blur', onBlur, true);\n\n    // We detect that a node is a ShadowRoot by ensuring that it is a\n    // DocumentFragment and also has a host property. This check covers native\n    // implementation and polyfill implementation transparently. If we only cared\n    // about the native implementation, we could just check if the scope was\n    // an instance of a ShadowRoot.\n    if (scope.nodeType === Node.DOCUMENT_FRAGMENT_NODE && scope.host) {\n      // Since a ShadowRoot is a special kind of DocumentFragment, it does not\n      // have a root element to add a class to. So, we add this attribute to the\n      // host element instead:\n      scope.host.setAttribute('data-js-focus-visible', '');\n    } else if (scope.nodeType === Node.DOCUMENT_NODE) {\n      document.documentElement.classList.add('js-focus-visible');\n      document.documentElement.setAttribute('data-js-focus-visible', '');\n    }\n  }\n\n  // It is important to wrap all references to global window and document in\n  // these checks to support server-side rendering use cases\n  // @see https://github.com/WICG/focus-visible/issues/199\n  if (typeof window !== 'undefined' && typeof document !== 'undefined') {\n    // Make the polyfill helper globally available. This can be used as a signal\n    // to interested libraries that wish to coordinate with the polyfill for e.g.,\n    // applying the polyfill to a shadow root:\n    window.applyFocusVisiblePolyfill = applyFocusVisiblePolyfill;\n\n    // Notify interested libraries of the polyfill's presence, in case the\n    // polyfill was loaded lazily:\n    var event;\n\n    try {\n      event = new CustomEvent('focus-visible-polyfill-ready');\n    } catch (error) {\n      // IE11 does not support using CustomEvent as a constructor directly:\n      event = document.createEvent('CustomEvent');\n      event.initCustomEvent('focus-visible-polyfill-ready', false, false, {});\n    }\n\n    window.dispatchEvent(event);\n  }\n\n  if (typeof document !== 'undefined') {\n    // Apply the polyfill to the global document, so that no JavaScript\n    // coordination is required to use the polyfill in the top-level document:\n    applyFocusVisiblePolyfill(document);\n  }\n\n})));\n", "(function(global) {\r\n  /**\r\n   * Polyfill URLSearchParams\r\n   *\r\n   * Inspired from : https://github.com/WebReflection/url-search-params/blob/master/src/url-search-params.js\r\n   */\r\n\r\n  var checkIfIteratorIsSupported = function() {\r\n    try {\r\n      return !!Symbol.iterator;\r\n    } catch (error) {\r\n      return false;\r\n    }\r\n  };\r\n\r\n\r\n  var iteratorSupported = checkIfIteratorIsSupported();\r\n\r\n  var createIterator = function(items) {\r\n    var iterator = {\r\n      next: function() {\r\n        var value = items.shift();\r\n        return { done: value === void 0, value: value };\r\n      }\r\n    };\r\n\r\n    if (iteratorSupported) {\r\n      iterator[Symbol.iterator] = function() {\r\n        return iterator;\r\n      };\r\n    }\r\n\r\n    return iterator;\r\n  };\r\n\r\n  /**\r\n   * Search param name and values should be encoded according to https://url.spec.whatwg.org/#urlencoded-serializing\r\n   * encodeURIComponent() produces the same result except encoding spaces as `%20` instead of `+`.\r\n   */\r\n  var serializeParam = function(value) {\r\n    return encodeURIComponent(value).replace(/%20/g, '+');\r\n  };\r\n\r\n  var deserializeParam = function(value) {\r\n    return decodeURIComponent(String(value).replace(/\\+/g, ' '));\r\n  };\r\n\r\n  var polyfillURLSearchParams = function() {\r\n\r\n    var URLSearchParams = function(searchString) {\r\n      Object.defineProperty(this, '_entries', { writable: true, value: {} });\r\n      var typeofSearchString = typeof searchString;\r\n\r\n      if (typeofSearchString === 'undefined') {\r\n        // do nothing\r\n      } else if (typeofSearchString === 'string') {\r\n        if (searchString !== '') {\r\n          this._fromString(searchString);\r\n        }\r\n      } else if (searchString instanceof URLSearchParams) {\r\n        var _this = this;\r\n        searchString.forEach(function(value, name) {\r\n          _this.append(name, value);\r\n        });\r\n      } else if ((searchString !== null) && (typeofSearchString === 'object')) {\r\n        if (Object.prototype.toString.call(searchString) === '[object Array]') {\r\n          for (var i = 0; i < searchString.length; i++) {\r\n            var entry = searchString[i];\r\n            if ((Object.prototype.toString.call(entry) === '[object Array]') || (entry.length !== 2)) {\r\n              this.append(entry[0], entry[1]);\r\n            } else {\r\n              throw new TypeError('Expected [string, any] as entry at index ' + i + ' of URLSearchParams\\'s input');\r\n            }\r\n          }\r\n        } else {\r\n          for (var key in searchString) {\r\n            if (searchString.hasOwnProperty(key)) {\r\n              this.append(key, searchString[key]);\r\n            }\r\n          }\r\n        }\r\n      } else {\r\n        throw new TypeError('Unsupported input\\'s type for URLSearchParams');\r\n      }\r\n    };\r\n\r\n    var proto = URLSearchParams.prototype;\r\n\r\n    proto.append = function(name, value) {\r\n      if (name in this._entries) {\r\n        this._entries[name].push(String(value));\r\n      } else {\r\n        this._entries[name] = [String(value)];\r\n      }\r\n    };\r\n\r\n    proto.delete = function(name) {\r\n      delete this._entries[name];\r\n    };\r\n\r\n    proto.get = function(name) {\r\n      return (name in this._entries) ? this._entries[name][0] : null;\r\n    };\r\n\r\n    proto.getAll = function(name) {\r\n      return (name in this._entries) ? this._entries[name].slice(0) : [];\r\n    };\r\n\r\n    proto.has = function(name) {\r\n      return (name in this._entries);\r\n    };\r\n\r\n    proto.set = function(name, value) {\r\n      this._entries[name] = [String(value)];\r\n    };\r\n\r\n    proto.forEach = function(callback, thisArg) {\r\n      var entries;\r\n      for (var name in this._entries) {\r\n        if (this._entries.hasOwnProperty(name)) {\r\n          entries = this._entries[name];\r\n          for (var i = 0; i < entries.length; i++) {\r\n            callback.call(thisArg, entries[i], name, this);\r\n          }\r\n        }\r\n      }\r\n    };\r\n\r\n    proto.keys = function() {\r\n      var items = [];\r\n      this.forEach(function(value, name) {\r\n        items.push(name);\r\n      });\r\n      return createIterator(items);\r\n    };\r\n\r\n    proto.values = function() {\r\n      var items = [];\r\n      this.forEach(function(value) {\r\n        items.push(value);\r\n      });\r\n      return createIterator(items);\r\n    };\r\n\r\n    proto.entries = function() {\r\n      var items = [];\r\n      this.forEach(function(value, name) {\r\n        items.push([name, value]);\r\n      });\r\n      return createIterator(items);\r\n    };\r\n\r\n    if (iteratorSupported) {\r\n      proto[Symbol.iterator] = proto.entries;\r\n    }\r\n\r\n    proto.toString = function() {\r\n      var searchArray = [];\r\n      this.forEach(function(value, name) {\r\n        searchArray.push(serializeParam(name) + '=' + serializeParam(value));\r\n      });\r\n      return searchArray.join('&');\r\n    };\r\n\r\n\r\n    global.URLSearchParams = URLSearchParams;\r\n  };\r\n\r\n  var checkIfURLSearchParamsSupported = function() {\r\n    try {\r\n      var URLSearchParams = global.URLSearchParams;\r\n\r\n      return (\r\n        (new URLSearchParams('?a=1').toString() === 'a=1') &&\r\n        (typeof URLSearchParams.prototype.set === 'function') &&\r\n        (typeof URLSearchParams.prototype.entries === 'function')\r\n      );\r\n    } catch (e) {\r\n      return false;\r\n    }\r\n  };\r\n\r\n  if (!checkIfURLSearchParamsSupported()) {\r\n    polyfillURLSearchParams();\r\n  }\r\n\r\n  var proto = global.URLSearchParams.prototype;\r\n\r\n  if (typeof proto.sort !== 'function') {\r\n    proto.sort = function() {\r\n      var _this = this;\r\n      var items = [];\r\n      this.forEach(function(value, name) {\r\n        items.push([name, value]);\r\n        if (!_this._entries) {\r\n          _this.delete(name);\r\n        }\r\n      });\r\n      items.sort(function(a, b) {\r\n        if (a[0] < b[0]) {\r\n          return -1;\r\n        } else if (a[0] > b[0]) {\r\n          return +1;\r\n        } else {\r\n          return 0;\r\n        }\r\n      });\r\n      if (_this._entries) { // force reset because IE keeps keys index\r\n        _this._entries = {};\r\n      }\r\n      for (var i = 0; i < items.length; i++) {\r\n        this.append(items[i][0], items[i][1]);\r\n      }\r\n    };\r\n  }\r\n\r\n  if (typeof proto._fromString !== 'function') {\r\n    Object.defineProperty(proto, '_fromString', {\r\n      enumerable: false,\r\n      configurable: false,\r\n      writable: false,\r\n      value: function(searchString) {\r\n        if (this._entries) {\r\n          this._entries = {};\r\n        } else {\r\n          var keys = [];\r\n          this.forEach(function(value, name) {\r\n            keys.push(name);\r\n          });\r\n          for (var i = 0; i < keys.length; i++) {\r\n            this.delete(keys[i]);\r\n          }\r\n        }\r\n\r\n        searchString = searchString.replace(/^\\?/, '');\r\n        var attributes = searchString.split('&');\r\n        var attribute;\r\n        for (var i = 0; i < attributes.length; i++) {\r\n          attribute = attributes[i].split('=');\r\n          this.append(\r\n            deserializeParam(attribute[0]),\r\n            (attribute.length > 1) ? deserializeParam(attribute[1]) : ''\r\n          );\r\n        }\r\n      }\r\n    });\r\n  }\r\n\r\n  // HTMLAnchorElement\r\n\r\n})(\r\n  (typeof global !== 'undefined') ? global\r\n    : ((typeof window !== 'undefined') ? window\r\n    : ((typeof self !== 'undefined') ? self : this))\r\n);\r\n\r\n(function(global) {\r\n  /**\r\n   * Polyfill URL\r\n   *\r\n   * Inspired from : https://github.com/arv/DOM-URL-Polyfill/blob/master/src/url.js\r\n   */\r\n\r\n  var checkIfURLIsSupported = function() {\r\n    try {\r\n      var u = new global.URL('b', 'http://a');\r\n      u.pathname = 'c d';\r\n      return (u.href === 'http://a/c%20d') && u.searchParams;\r\n    } catch (e) {\r\n      return false;\r\n    }\r\n  };\r\n\r\n\r\n  var polyfillURL = function() {\r\n    var _URL = global.URL;\r\n\r\n    var URL = function(url, base) {\r\n      if (typeof url !== 'string') url = String(url);\r\n      if (base && typeof base !== 'string') base = String(base);\r\n\r\n      // Only create another document if the base is different from current location.\r\n      var doc = document, baseElement;\r\n      if (base && (global.location === void 0 || base !== global.location.href)) {\r\n        base = base.toLowerCase();\r\n        doc = document.implementation.createHTMLDocument('');\r\n        baseElement = doc.createElement('base');\r\n        baseElement.href = base;\r\n        doc.head.appendChild(baseElement);\r\n        try {\r\n          if (baseElement.href.indexOf(base) !== 0) throw new Error(baseElement.href);\r\n        } catch (err) {\r\n          throw new Error('URL unable to set base ' + base + ' due to ' + err);\r\n        }\r\n      }\r\n\r\n      var anchorElement = doc.createElement('a');\r\n      anchorElement.href = url;\r\n      if (baseElement) {\r\n        doc.body.appendChild(anchorElement);\r\n        anchorElement.href = anchorElement.href; // force href to refresh\r\n      }\r\n\r\n      var inputElement = doc.createElement('input');\r\n      inputElement.type = 'url';\r\n      inputElement.value = url;\r\n\r\n      if (anchorElement.protocol === ':' || !/:/.test(anchorElement.href) || (!inputElement.checkValidity() && !base)) {\r\n        throw new TypeError('Invalid URL');\r\n      }\r\n\r\n      Object.defineProperty(this, '_anchorElement', {\r\n        value: anchorElement\r\n      });\r\n\r\n\r\n      // create a linked searchParams which reflect its changes on URL\r\n      var searchParams = new global.URLSearchParams(this.search);\r\n      var enableSearchUpdate = true;\r\n      var enableSearchParamsUpdate = true;\r\n      var _this = this;\r\n      ['append', 'delete', 'set'].forEach(function(methodName) {\r\n        var method = searchParams[methodName];\r\n        searchParams[methodName] = function() {\r\n          method.apply(searchParams, arguments);\r\n          if (enableSearchUpdate) {\r\n            enableSearchParamsUpdate = false;\r\n            _this.search = searchParams.toString();\r\n            enableSearchParamsUpdate = true;\r\n          }\r\n        };\r\n      });\r\n\r\n      Object.defineProperty(this, 'searchParams', {\r\n        value: searchParams,\r\n        enumerable: true\r\n      });\r\n\r\n      var search = void 0;\r\n      Object.defineProperty(this, '_updateSearchParams', {\r\n        enumerable: false,\r\n        configurable: false,\r\n        writable: false,\r\n        value: function() {\r\n          if (this.search !== search) {\r\n            search = this.search;\r\n            if (enableSearchParamsUpdate) {\r\n              enableSearchUpdate = false;\r\n              this.searchParams._fromString(this.search);\r\n              enableSearchUpdate = true;\r\n            }\r\n          }\r\n        }\r\n      });\r\n    };\r\n\r\n    var proto = URL.prototype;\r\n\r\n    var linkURLWithAnchorAttribute = function(attributeName) {\r\n      Object.defineProperty(proto, attributeName, {\r\n        get: function() {\r\n          return this._anchorElement[attributeName];\r\n        },\r\n        set: function(value) {\r\n          this._anchorElement[attributeName] = value;\r\n        },\r\n        enumerable: true\r\n      });\r\n    };\r\n\r\n    ['hash', 'host', 'hostname', 'port', 'protocol']\r\n      .forEach(function(attributeName) {\r\n        linkURLWithAnchorAttribute(attributeName);\r\n      });\r\n\r\n    Object.defineProperty(proto, 'search', {\r\n      get: function() {\r\n        return this._anchorElement['search'];\r\n      },\r\n      set: function(value) {\r\n        this._anchorElement['search'] = value;\r\n        this._updateSearchParams();\r\n      },\r\n      enumerable: true\r\n    });\r\n\r\n    Object.defineProperties(proto, {\r\n\r\n      'toString': {\r\n        get: function() {\r\n          var _this = this;\r\n          return function() {\r\n            return _this.href;\r\n          };\r\n        }\r\n      },\r\n\r\n      'href': {\r\n        get: function() {\r\n          return this._anchorElement.href.replace(/\\?$/, '');\r\n        },\r\n        set: function(value) {\r\n          this._anchorElement.href = value;\r\n          this._updateSearchParams();\r\n        },\r\n        enumerable: true\r\n      },\r\n\r\n      'pathname': {\r\n        get: function() {\r\n          return this._anchorElement.pathname.replace(/(^\\/?)/, '/');\r\n        },\r\n        set: function(value) {\r\n          this._anchorElement.pathname = value;\r\n        },\r\n        enumerable: true\r\n      },\r\n\r\n      'origin': {\r\n        get: function() {\r\n          // get expected port from protocol\r\n          var expectedPort = { 'http:': 80, 'https:': 443, 'ftp:': 21 }[this._anchorElement.protocol];\r\n          // add port to origin if, expected port is different than actual port\r\n          // and it is not empty f.e http://foo:8080\r\n          // 8080 != 80 && 8080 != ''\r\n          var addPortToOrigin = this._anchorElement.port != expectedPort &&\r\n            this._anchorElement.port !== '';\r\n\r\n          return this._anchorElement.protocol +\r\n            '//' +\r\n            this._anchorElement.hostname +\r\n            (addPortToOrigin ? (':' + this._anchorElement.port) : '');\r\n        },\r\n        enumerable: true\r\n      },\r\n\r\n      'password': { // TODO\r\n        get: function() {\r\n          return '';\r\n        },\r\n        set: function(value) {\r\n        },\r\n        enumerable: true\r\n      },\r\n\r\n      'username': { // TODO\r\n        get: function() {\r\n          return '';\r\n        },\r\n        set: function(value) {\r\n        },\r\n        enumerable: true\r\n      },\r\n    });\r\n\r\n    URL.createObjectURL = function(blob) {\r\n      return _URL.createObjectURL.apply(_URL, arguments);\r\n    };\r\n\r\n    URL.revokeObjectURL = function(url) {\r\n      return _URL.revokeObjectURL.apply(_URL, arguments);\r\n    };\r\n\r\n    global.URL = URL;\r\n\r\n  };\r\n\r\n  if (!checkIfURLIsSupported()) {\r\n    polyfillURL();\r\n  }\r\n\r\n  if ((global.location !== void 0) && !('origin' in global.location)) {\r\n    var getOrigin = function() {\r\n      return global.location.protocol + '//' + global.location.hostname + (global.location.port ? 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'right' : 'left'] = '-9999px'; // Move element to the same position vertically\n\n  var yPosition = window.pageYOffset || document.documentElement.scrollTop;\n  fakeElement.style.top = \"\".concat(yPosition, \"px\");\n  fakeElement.setAttribute('readonly', '');\n  fakeElement.value = value;\n  return fakeElement;\n}\n;// CONCATENATED MODULE: ./src/actions/copy.js\n\n\n\n/**\n * Create fake copy action wrapper using a fake element.\n * @param {String} target\n * @param {Object} options\n * @return {String}\n */\n\nvar fakeCopyAction = function fakeCopyAction(value, options) {\n  var fakeElement = createFakeElement(value);\n  options.container.appendChild(fakeElement);\n  var selectedText = select_default()(fakeElement);\n  command('copy');\n  fakeElement.remove();\n  return selectedText;\n};\n/**\n * Copy action wrapper.\n * @param {String|HTMLElement} target\n * @param {Object} options\n * @return {String}\n */\n\n\nvar ClipboardActionCopy = function ClipboardActionCopy(target) {\n  var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {\n    container: document.body\n  };\n  var selectedText = '';\n\n  if (typeof target === 'string') {\n    selectedText = fakeCopyAction(target, options);\n  } else if (target instanceof HTMLInputElement && !['text', 'search', 'url', 'tel', 'password'].includes(target === null || target === void 0 ? void 0 : target.type)) {\n    // If input type doesn't support `setSelectionRange`. Simulate it. https://developer.mozilla.org/en-US/docs/Web/API/HTMLInputElement/setSelectionRange\n    selectedText = fakeCopyAction(target.value, options);\n  } else {\n    selectedText = select_default()(target);\n    command('copy');\n  }\n\n  return selectedText;\n};\n\n/* harmony default export */ var actions_copy = (ClipboardActionCopy);\n;// CONCATENATED MODULE: ./src/actions/default.js\nfunction _typeof(obj) { \"@babel/helpers - typeof\"; if (typeof Symbol === \"function\" && typeof Symbol.iterator === \"symbol\") { _typeof = function _typeof(obj) { return typeof obj; }; } else { _typeof = function _typeof(obj) { return obj && typeof Symbol === \"function\" && obj.constructor === Symbol && obj !== Symbol.prototype ? \"symbol\" : typeof obj; }; } return _typeof(obj); }\n\n\n\n/**\n * Inner function which performs selection from either `text` or `target`\n * properties and then executes copy or cut operations.\n * @param {Object} options\n */\n\nvar ClipboardActionDefault = function ClipboardActionDefault() {\n  var options = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : {};\n  // Defines base properties passed from constructor.\n  var _options$action = options.action,\n      action = _options$action === void 0 ? 'copy' : _options$action,\n      container = options.container,\n      target = options.target,\n      text = options.text; // Sets the `action` to be performed which can be either 'copy' or 'cut'.\n\n  if (action !== 'copy' && action !== 'cut') {\n    throw new Error('Invalid \"action\" value, use either \"copy\" or \"cut\"');\n  } // Sets the `target` property using an element that will be have its content copied.\n\n\n  if (target !== undefined) {\n    if (target && _typeof(target) === 'object' && target.nodeType === 1) {\n      if (action === 'copy' && target.hasAttribute('disabled')) {\n        throw new Error('Invalid \"target\" attribute. Please use \"readonly\" instead of \"disabled\" attribute');\n      }\n\n      if (action === 'cut' && (target.hasAttribute('readonly') || target.hasAttribute('disabled'))) {\n        throw new Error('Invalid \"target\" attribute. You can\\'t cut text from elements with \"readonly\" or \"disabled\" attributes');\n      }\n    } else {\n      throw new Error('Invalid \"target\" value, use a valid Element');\n    }\n  } // Define selection strategy based on `text` property.\n\n\n  if (text) {\n    return actions_copy(text, {\n      container: container\n    });\n  } // Defines which selection strategy based on `target` property.\n\n\n  if (target) {\n    return action === 'cut' ? actions_cut(target) : actions_copy(target, {\n      container: container\n    });\n  }\n};\n\n/* harmony default export */ var actions_default = (ClipboardActionDefault);\n;// CONCATENATED MODULE: ./src/clipboard.js\nfunction clipboard_typeof(obj) { \"@babel/helpers - typeof\"; if (typeof Symbol === \"function\" && typeof Symbol.iterator === \"symbol\") { clipboard_typeof = function _typeof(obj) { return typeof obj; }; } else { clipboard_typeof = function _typeof(obj) { return obj && typeof Symbol === \"function\" && obj.constructor === Symbol && obj !== Symbol.prototype ? \"symbol\" : typeof obj; }; } return clipboard_typeof(obj); }\n\nfunction _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError(\"Cannot call a class as a function\"); } }\n\nfunction _defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if (\"value\" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } }\n\nfunction _createClass(Constructor, protoProps, staticProps) { if (protoProps) _defineProperties(Constructor.prototype, protoProps); if (staticProps) _defineProperties(Constructor, staticProps); return Constructor; }\n\nfunction _inherits(subClass, superClass) { if (typeof superClass !== \"function\" && superClass !== null) { throw new TypeError(\"Super expression must either be null or a function\"); } subClass.prototype = Object.create(superClass && superClass.prototype, { constructor: { value: subClass, writable: true, configurable: true } }); if (superClass) _setPrototypeOf(subClass, superClass); }\n\nfunction _setPrototypeOf(o, p) { _setPrototypeOf = Object.setPrototypeOf || function _setPrototypeOf(o, p) { o.__proto__ = p; return o; }; return _setPrototypeOf(o, p); }\n\nfunction _createSuper(Derived) { var hasNativeReflectConstruct = _isNativeReflectConstruct(); return function _createSuperInternal() { var Super = _getPrototypeOf(Derived), result; if (hasNativeReflectConstruct) { var NewTarget = _getPrototypeOf(this).constructor; result = Reflect.construct(Super, arguments, NewTarget); } else { result = Super.apply(this, arguments); } return _possibleConstructorReturn(this, result); }; }\n\nfunction _possibleConstructorReturn(self, call) { if (call && (clipboard_typeof(call) === \"object\" || typeof call === \"function\")) { return call; } return _assertThisInitialized(self); }\n\nfunction _assertThisInitialized(self) { if (self === void 0) { throw new ReferenceError(\"this hasn't been initialised - super() hasn't been called\"); } return self; }\n\nfunction _isNativeReflectConstruct() { if (typeof Reflect === \"undefined\" || !Reflect.construct) return false; if (Reflect.construct.sham) return false; if (typeof Proxy === \"function\") return true; try { Date.prototype.toString.call(Reflect.construct(Date, [], function () {})); return true; } catch (e) { return false; } }\n\nfunction _getPrototypeOf(o) { _getPrototypeOf = Object.setPrototypeOf ? Object.getPrototypeOf : function _getPrototypeOf(o) { return o.__proto__ || Object.getPrototypeOf(o); }; return _getPrototypeOf(o); }\n\n\n\n\n\n\n/**\n * Helper function to retrieve attribute value.\n * @param {String} suffix\n * @param {Element} element\n */\n\nfunction getAttributeValue(suffix, element) {\n  var attribute = \"data-clipboard-\".concat(suffix);\n\n  if (!element.hasAttribute(attribute)) {\n    return;\n  }\n\n  return element.getAttribute(attribute);\n}\n/**\n * Base class which takes one or more elements, adds event listeners to them,\n * and instantiates a new `ClipboardAction` on each click.\n */\n\n\nvar Clipboard = /*#__PURE__*/function (_Emitter) {\n  _inherits(Clipboard, _Emitter);\n\n  var _super = _createSuper(Clipboard);\n\n  /**\n   * @param {String|HTMLElement|HTMLCollection|NodeList} trigger\n   * @param {Object} options\n   */\n  function Clipboard(trigger, options) {\n    var _this;\n\n    _classCallCheck(this, Clipboard);\n\n    _this = _super.call(this);\n\n    _this.resolveOptions(options);\n\n    _this.listenClick(trigger);\n\n    return _this;\n  }\n  /**\n   * Defines if attributes would be resolved using internal setter functions\n   * or custom functions that were passed in the constructor.\n   * @param {Object} options\n   */\n\n\n  _createClass(Clipboard, [{\n    key: \"resolveOptions\",\n    value: function resolveOptions() {\n      var options = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : {};\n      this.action = typeof options.action === 'function' ? options.action : this.defaultAction;\n      this.target = typeof options.target === 'function' ? options.target : this.defaultTarget;\n      this.text = typeof options.text === 'function' ? options.text : this.defaultText;\n      this.container = clipboard_typeof(options.container) === 'object' ? options.container : document.body;\n    }\n    /**\n     * Adds a click event listener to the passed trigger.\n     * @param {String|HTMLElement|HTMLCollection|NodeList} trigger\n     */\n\n  }, {\n    key: \"listenClick\",\n    value: function listenClick(trigger) {\n      var _this2 = this;\n\n      this.listener = listen_default()(trigger, 'click', function (e) {\n        return _this2.onClick(e);\n      });\n    }\n    /**\n     * Defines a new `ClipboardAction` on each click event.\n     * @param {Event} e\n     */\n\n  }, {\n    key: \"onClick\",\n    value: function onClick(e) {\n      var trigger = e.delegateTarget || e.currentTarget;\n      var action = this.action(trigger) || 'copy';\n      var text = actions_default({\n        action: action,\n        container: this.container,\n        target: this.target(trigger),\n        text: this.text(trigger)\n      }); // Fires an event based on the copy operation result.\n\n      this.emit(text ? 'success' : 'error', {\n        action: action,\n        text: text,\n        trigger: trigger,\n        clearSelection: function clearSelection() {\n          if (trigger) {\n            trigger.focus();\n          }\n\n          window.getSelection().removeAllRanges();\n        }\n      });\n    }\n    /**\n     * Default `action` lookup function.\n     * @param {Element} trigger\n     */\n\n  }, {\n    key: \"defaultAction\",\n    value: function defaultAction(trigger) {\n      return getAttributeValue('action', trigger);\n    }\n    /**\n     * Default `target` lookup function.\n     * @param {Element} trigger\n     */\n\n  }, {\n    key: \"defaultTarget\",\n    value: function defaultTarget(trigger) {\n      var selector = getAttributeValue('target', trigger);\n\n      if (selector) {\n        return document.querySelector(selector);\n      }\n    }\n    /**\n     * Allow fire programmatically a copy action\n     * @param {String|HTMLElement} target\n     * @param {Object} options\n     * @returns Text copied.\n     */\n\n  }, {\n    key: \"defaultText\",\n\n    /**\n     * Default `text` lookup function.\n     * @param {Element} trigger\n     */\n    value: function defaultText(trigger) {\n      return getAttributeValue('text', trigger);\n    }\n    /**\n     * Destroy lifecycle.\n     */\n\n  }, {\n    key: \"destroy\",\n    value: function destroy() {\n      this.listener.destroy();\n    }\n  }], [{\n    key: \"copy\",\n    value: function copy(target) {\n      var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {\n        container: document.body\n      };\n      return actions_copy(target, options);\n    }\n    /**\n     * Allow fire programmatically a cut action\n     * @param {String|HTMLElement} target\n     * @returns Text cutted.\n     */\n\n  }, {\n    key: \"cut\",\n    value: function cut(target) {\n      return actions_cut(target);\n    }\n    /**\n     * Returns the support of the given action, or all actions if no action is\n     * given.\n     * @param {String} [action]\n     */\n\n  }, {\n    key: \"isSupported\",\n    value: function isSupported() {\n      var action = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : ['copy', 'cut'];\n      var actions = typeof action === 'string' ? [action] : action;\n      var support = !!document.queryCommandSupported;\n      actions.forEach(function (action) {\n        support = support && !!document.queryCommandSupported(action);\n      });\n      return support;\n    }\n  }]);\n\n  return Clipboard;\n}((tiny_emitter_default()));\n\n/* harmony default export */ var clipboard = (Clipboard);\n\n/***/ }),\n\n/***/ 828:\n/***/ (function(module) {\n\nvar DOCUMENT_NODE_TYPE = 9;\n\n/**\n * A polyfill for Element.matches()\n */\nif (typeof Element !== 'undefined' && !Element.prototype.matches) {\n    var proto = Element.prototype;\n\n    proto.matches = proto.matchesSelector ||\n                    proto.mozMatchesSelector ||\n                    proto.msMatchesSelector ||\n                    proto.oMatchesSelector ||\n                    proto.webkitMatchesSelector;\n}\n\n/**\n * Finds the closest parent that matches a selector.\n *\n * @param {Element} element\n * @param {String} selector\n * @return {Function}\n */\nfunction closest (element, selector) {\n    while (element && element.nodeType !== DOCUMENT_NODE_TYPE) {\n        if (typeof element.matches === 'function' &&\n            element.matches(selector)) {\n          return element;\n        }\n        element = element.parentNode;\n    }\n}\n\nmodule.exports = closest;\n\n\n/***/ }),\n\n/***/ 438:\n/***/ (function(module, __unused_webpack_exports, __webpack_require__) {\n\nvar closest = __webpack_require__(828);\n\n/**\n * Delegates event to a selector.\n *\n * @param {Element} element\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @param {Boolean} useCapture\n * @return {Object}\n */\nfunction _delegate(element, selector, type, callback, useCapture) {\n    var listenerFn = listener.apply(this, arguments);\n\n    element.addEventListener(type, listenerFn, useCapture);\n\n    return {\n        destroy: function() {\n            element.removeEventListener(type, listenerFn, useCapture);\n        }\n    }\n}\n\n/**\n * Delegates event to a selector.\n *\n * @param {Element|String|Array} [elements]\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @param {Boolean} useCapture\n * @return {Object}\n */\nfunction delegate(elements, selector, type, callback, useCapture) {\n    // Handle the regular Element usage\n    if (typeof elements.addEventListener === 'function') {\n        return _delegate.apply(null, arguments);\n    }\n\n    // Handle Element-less usage, it defaults to global delegation\n    if (typeof type === 'function') {\n        // Use `document` as the first parameter, then apply arguments\n        // This is a short way to .unshift `arguments` without running into deoptimizations\n        return _delegate.bind(null, document).apply(null, arguments);\n    }\n\n    // Handle Selector-based usage\n    if (typeof elements === 'string') {\n        elements = document.querySelectorAll(elements);\n    }\n\n    // Handle Array-like based usage\n    return Array.prototype.map.call(elements, function (element) {\n        return _delegate(element, selector, type, callback, useCapture);\n    });\n}\n\n/**\n * Finds closest match and invokes callback.\n *\n * @param {Element} element\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @return {Function}\n */\nfunction listener(element, selector, type, callback) {\n    return function(e) {\n        e.delegateTarget = closest(e.target, selector);\n\n        if (e.delegateTarget) {\n            callback.call(element, e);\n        }\n    }\n}\n\nmodule.exports = delegate;\n\n\n/***/ }),\n\n/***/ 879:\n/***/ (function(__unused_webpack_module, exports) {\n\n/**\n * Check if argument is a HTML element.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.node = function(value) {\n    return value !== undefined\n        && value instanceof HTMLElement\n        && value.nodeType === 1;\n};\n\n/**\n * Check if argument is a list of HTML elements.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.nodeList = function(value) {\n    var type = Object.prototype.toString.call(value);\n\n    return value !== undefined\n        && (type === '[object NodeList]' || type === '[object HTMLCollection]')\n        && ('length' in value)\n        && (value.length === 0 || exports.node(value[0]));\n};\n\n/**\n * Check if argument is a string.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.string = function(value) {\n    return typeof value === 'string'\n        || value instanceof String;\n};\n\n/**\n * Check if argument is a function.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.fn = function(value) {\n    var type = Object.prototype.toString.call(value);\n\n    return type === '[object Function]';\n};\n\n\n/***/ }),\n\n/***/ 370:\n/***/ (function(module, __unused_webpack_exports, __webpack_require__) {\n\nvar is = __webpack_require__(879);\nvar delegate = __webpack_require__(438);\n\n/**\n * Validates all params and calls the right\n * listener function based on its target type.\n *\n * @param {String|HTMLElement|HTMLCollection|NodeList} target\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listen(target, type, callback) {\n    if (!target && !type && !callback) {\n        throw new Error('Missing required arguments');\n    }\n\n    if (!is.string(type)) {\n        throw new TypeError('Second argument must be a String');\n    }\n\n    if (!is.fn(callback)) {\n        throw new TypeError('Third argument must be a Function');\n    }\n\n    if (is.node(target)) {\n        return listenNode(target, type, callback);\n    }\n    else if (is.nodeList(target)) {\n        return listenNodeList(target, type, callback);\n    }\n    else if (is.string(target)) {\n        return listenSelector(target, type, callback);\n    }\n    else {\n        throw new TypeError('First argument must be a String, HTMLElement, HTMLCollection, or NodeList');\n    }\n}\n\n/**\n * Adds an event listener to a HTML element\n * and returns a remove listener function.\n *\n * @param {HTMLElement} node\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listenNode(node, type, callback) {\n    node.addEventListener(type, callback);\n\n    return {\n        destroy: function() {\n            node.removeEventListener(type, callback);\n        }\n    }\n}\n\n/**\n * Add an event listener to a list of HTML elements\n * and returns a remove listener function.\n *\n * @param {NodeList|HTMLCollection} nodeList\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listenNodeList(nodeList, type, callback) {\n    Array.prototype.forEach.call(nodeList, function(node) {\n        node.addEventListener(type, callback);\n    });\n\n    return {\n        destroy: function() {\n            Array.prototype.forEach.call(nodeList, function(node) {\n                node.removeEventListener(type, callback);\n            });\n        }\n    }\n}\n\n/**\n * Add an event listener to a selector\n * and returns a remove listener function.\n *\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listenSelector(selector, type, callback) {\n    return delegate(document.body, selector, type, callback);\n}\n\nmodule.exports = listen;\n\n\n/***/ }),\n\n/***/ 817:\n/***/ (function(module) {\n\nfunction select(element) {\n    var selectedText;\n\n    if (element.nodeName === 'SELECT') {\n        element.focus();\n\n        selectedText = element.value;\n    }\n    else if (element.nodeName === 'INPUT' || element.nodeName === 'TEXTAREA') {\n        var isReadOnly = element.hasAttribute('readonly');\n\n        if (!isReadOnly) {\n            element.setAttribute('readonly', '');\n        }\n\n        element.select();\n        element.setSelectionRange(0, element.value.length);\n\n        if (!isReadOnly) {\n            element.removeAttribute('readonly');\n        }\n\n        selectedText = element.value;\n    }\n    else {\n        if (element.hasAttribute('contenteditable')) {\n            element.focus();\n        }\n\n        var selection = window.getSelection();\n        var range = document.createRange();\n\n        range.selectNodeContents(element);\n        selection.removeAllRanges();\n        selection.addRange(range);\n\n        selectedText = selection.toString();\n    }\n\n    return selectedText;\n}\n\nmodule.exports = select;\n\n\n/***/ }),\n\n/***/ 279:\n/***/ (function(module) {\n\nfunction E () {\n  // Keep this empty so it's easier to inherit from\n  // (via https://github.com/lipsmack from https://github.com/scottcorgan/tiny-emitter/issues/3)\n}\n\nE.prototype = {\n  on: function (name, callback, ctx) {\n    var e = this.e || (this.e = {});\n\n    (e[name] || (e[name] = [])).push({\n      fn: callback,\n      ctx: ctx\n    });\n\n    return this;\n  },\n\n  once: function (name, callback, ctx) {\n    var self = this;\n    function listener () {\n      self.off(name, listener);\n      callback.apply(ctx, arguments);\n    };\n\n    listener._ = callback\n    return this.on(name, listener, ctx);\n  },\n\n  emit: function (name) {\n    var data = [].slice.call(arguments, 1);\n    var evtArr = ((this.e || (this.e = {}))[name] || []).slice();\n    var i = 0;\n    var len = evtArr.length;\n\n    for (i; i < len; i++) {\n      evtArr[i].fn.apply(evtArr[i].ctx, data);\n    }\n\n    return this;\n  },\n\n  off: function (name, callback) {\n    var e = this.e || (this.e = {});\n    var evts = e[name];\n    var liveEvents = [];\n\n    if (evts && callback) {\n      for (var i = 0, len = evts.length; i < len; i++) {\n        if (evts[i].fn !== callback && evts[i].fn._ !== callback)\n          liveEvents.push(evts[i]);\n      }\n    }\n\n    // Remove event from queue to prevent memory leak\n    // Suggested by https://github.com/lazd\n    // Ref: https://github.com/scottcorgan/tiny-emitter/commit/c6ebfaa9bc973b33d110a84a307742b7cf94c953#commitcomment-5024910\n\n    (liveEvents.length)\n      ? e[name] = liveEvents\n      : delete e[name];\n\n    return this;\n  }\n};\n\nmodule.exports = E;\nmodule.exports.TinyEmitter = E;\n\n\n/***/ })\n\n/******/ \t});\n/************************************************************************/\n/******/ \t// The module cache\n/******/ \tvar __webpack_module_cache__ = {};\n/******/ \t\n/******/ \t// The require function\n/******/ \tfunction __webpack_require__(moduleId) {\n/******/ \t\t// Check if module is in cache\n/******/ \t\tif(__webpack_module_cache__[moduleId]) {\n/******/ \t\t\treturn __webpack_module_cache__[moduleId].exports;\n/******/ \t\t}\n/******/ \t\t// Create a new module (and put it into the cache)\n/******/ \t\tvar module = __webpack_module_cache__[moduleId] = {\n/******/ \t\t\t// no module.id needed\n/******/ \t\t\t// no module.loaded needed\n/******/ \t\t\texports: {}\n/******/ \t\t};\n/******/ \t\n/******/ \t\t// Execute the module function\n/******/ \t\t__webpack_modules__[moduleId](module, module.exports, __webpack_require__);\n/******/ \t\n/******/ \t\t// Return the exports of the module\n/******/ \t\treturn module.exports;\n/******/ \t}\n/******/ \t\n/************************************************************************/\n/******/ \t/* webpack/runtime/compat get default export */\n/******/ \t!function() {\n/******/ \t\t// getDefaultExport function for compatibility with non-harmony modules\n/******/ \t\t__webpack_require__.n = function(module) {\n/******/ \t\t\tvar getter = module && module.__esModule ?\n/******/ \t\t\t\tfunction() { return module['default']; } :\n/******/ \t\t\t\tfunction() { return module; };\n/******/ \t\t\t__webpack_require__.d(getter, { a: getter });\n/******/ \t\t\treturn getter;\n/******/ \t\t};\n/******/ \t}();\n/******/ \t\n/******/ \t/* webpack/runtime/define property getters */\n/******/ \t!function() {\n/******/ \t\t// define getter functions for harmony exports\n/******/ \t\t__webpack_require__.d = function(exports, definition) {\n/******/ \t\t\tfor(var key in definition) {\n/******/ \t\t\t\tif(__webpack_require__.o(definition, key) && !__webpack_require__.o(exports, key)) {\n/******/ \t\t\t\t\tObject.defineProperty(exports, key, { enumerable: true, get: definition[key] });\n/******/ \t\t\t\t}\n/******/ \t\t\t}\n/******/ \t\t};\n/******/ \t}();\n/******/ \t\n/******/ \t/* webpack/runtime/hasOwnProperty shorthand */\n/******/ \t!function() {\n/******/ \t\t__webpack_require__.o = function(obj, prop) { return Object.prototype.hasOwnProperty.call(obj, prop); }\n/******/ \t}();\n/******/ \t\n/************************************************************************/\n/******/ \t// module exports must be returned from runtime so entry inlining is disabled\n/******/ \t// startup\n/******/ \t// Load entry module and return exports\n/******/ \treturn __webpack_require__(686);\n/******/ })()\n.default;\n});", "/*!\n * escape-html\n * Copyright(c) 2012-2013 TJ Holowaychuk\n * Copyright(c) 2015 Andreas Lubbe\n * Copyright(c) 2015 Tiancheng \"Timothy\" Gu\n * MIT Licensed\n */\n\n'use strict';\n\n/**\n * Module variables.\n * @private\n */\n\nvar matchHtmlRegExp = /[\"'&<>]/;\n\n/**\n * Module exports.\n * @public\n */\n\nmodule.exports = escapeHtml;\n\n/**\n * Escape special characters in the given string of html.\n *\n * @param  {string} string The string to escape for inserting into HTML\n * @return {string}\n * @public\n */\n\nfunction escapeHtml(string) {\n  var str = '' + string;\n  var match = matchHtmlRegExp.exec(str);\n\n  if (!match) {\n    return str;\n  }\n\n  var escape;\n  var html = '';\n  var index = 0;\n  var lastIndex = 0;\n\n  for (index = match.index; index < str.length; index++) {\n    switch (str.charCodeAt(index)) {\n      case 34: // \"\n        escape = '&quot;';\n        break;\n      case 38: // &\n        escape = '&amp;';\n        break;\n      case 39: // '\n        escape = '&#39;';\n        break;\n      case 60: // <\n        escape = '&lt;';\n        break;\n      case 62: // >\n        escape = '&gt;';\n        break;\n      default:\n        continue;\n    }\n\n    if (lastIndex !== index) {\n      html += str.substring(lastIndex, index);\n    }\n\n    lastIndex = index + 1;\n    html += escape;\n  }\n\n  return lastIndex !== index\n    ? html + str.substring(lastIndex, index)\n    : html;\n}\n", "Array.prototype.flat||Object.defineProperty(Array.prototype,\"flat\",{configurable:!0,value:function r(){var t=isNaN(arguments[0])?1:Number(arguments[0]);return t?Array.prototype.reduce.call(this,function(a,e){return Array.isArray(e)?a.push.apply(a,r.call(e,t-1)):a.push(e),a},[]):Array.prototype.slice.call(this)},writable:!0}),Array.prototype.flatMap||Object.defineProperty(Array.prototype,\"flatMap\",{configurable:!0,value:function(r){return Array.prototype.map.apply(this,arguments).flat()},writable:!0})\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport \"array-flat-polyfill\"\nimport \"focus-visible\"\nimport \"unfetch/polyfill\"\nimport \"url-polyfill\"\n\nimport {\n  EMPTY,\n  NEVER,\n  Subject,\n  defer,\n  delay,\n  filter,\n  map,\n  merge,\n  mergeWith,\n  shareReplay,\n  switchMap\n} from \"rxjs\"\n\nimport { configuration, feature } from \"./_\"\nimport {\n  at,\n  getOptionalElement,\n  requestJSON,\n  setToggle,\n  watchDocument,\n  watchKeyboard,\n  watchLocation,\n  watchLocationTarget,\n  watchMedia,\n  watchPrint,\n  watchViewport\n} from \"./browser\"\nimport {\n  getComponentElement,\n  getComponentElements,\n  mountAnnounce,\n  mountBackToTop,\n  mountConsent,\n  mountContent,\n  mountDialog,\n  mountHeader,\n  mountHeaderTitle,\n  mountPalette,\n  mountSearch,\n  mountSearchHiglight,\n  mountSidebar,\n  mountSource,\n  mountTableOfContents,\n  mountTabs,\n  watchHeader,\n  watchMain\n} from \"./components\"\nimport {\n  SearchIndex,\n  setupClipboardJS,\n  setupInstantLoading,\n  setupVersionSelector\n} from \"./integrations\"\nimport {\n  patchIndeterminate,\n  patchScrollfix,\n  patchScrolllock\n} from \"./patches\"\nimport \"./polyfills\"\n\n/* ----------------------------------------------------------------------------\n * Application\n * ------------------------------------------------------------------------- */\n\n/* Yay, JavaScript is available */\ndocument.documentElement.classList.remove(\"no-js\")\ndocument.documentElement.classList.add(\"js\")\n\n/* Set up navigation observables and subjects */\nconst document$ = watchDocument()\nconst location$ = watchLocation()\nconst target$   = watchLocationTarget()\nconst keyboard$ = watchKeyboard()\n\n/* Set up media observables */\nconst viewport$ = watchViewport()\nconst tablet$   = watchMedia(\"(min-width: 960px)\")\nconst screen$   = watchMedia(\"(min-width: 1220px)\")\nconst print$    = watchPrint()\n\n/* Retrieve search index, if search is enabled */\nconst config = configuration()\nconst index$ = document.forms.namedItem(\"search\")\n  ? __search?.index || requestJSON<SearchIndex>(\n    new URL(\"search/search_index.json\", config.base)\n  )\n  : NEVER\n\n/* Set up Clipboard.js integration */\nconst alert$ = new Subject<string>()\nsetupClipboardJS({ alert$ })\n\n/* Set up instant loading, if enabled */\nif (feature(\"navigation.instant\"))\n  setupInstantLoading({ document$, location$, viewport$ })\n\n/* Set up version selector */\nif (config.version?.provider === \"mike\")\n  setupVersionSelector({ document$ })\n\n/* Always close drawer and search on navigation */\nmerge(location$, target$)\n  .pipe(\n    delay(125)\n  )\n    .subscribe(() => {\n      setToggle(\"drawer\", false)\n      setToggle(\"search\", false)\n    })\n\n/* Set up global keyboard handlers */\nkeyboard$\n  .pipe(\n    filter(({ mode }) => mode === \"global\")\n  )\n    .subscribe(key => {\n      switch (key.type) {\n\n        /* Go to previous page */\n        case \"p\":\n        case \",\":\n          const prev = getOptionalElement(\"[href][rel=prev]\")\n          if (typeof prev !== \"undefined\")\n            prev.click()\n          break\n\n        /* Go to next page */\n        case \"n\":\n        case \".\":\n          const next = getOptionalElement(\"[href][rel=next]\")\n          if (typeof next !== \"undefined\")\n            next.click()\n          break\n      }\n    })\n\n/* Set up patches */\npatchIndeterminate({ document$, tablet$ })\npatchScrollfix({ document$ })\npatchScrolllock({ viewport$, tablet$ })\n\n/* Set up header and main area observable */\nconst header$ = watchHeader(getComponentElement(\"header\"), { viewport$ })\nconst main$ = document$\n  .pipe(\n    map(() => getComponentElement(\"main\")),\n    switchMap(el => watchMain(el, { viewport$, header$ })),\n    shareReplay(1)\n  )\n\n/* Set up control component observables */\nconst control$ = merge(\n\n  /* Consent */\n  ...getComponentElements(\"consent\")\n    .map(el => mountConsent(el, { target$ })),\n\n  /* Dialog */\n  ...getComponentElements(\"dialog\")\n    .map(el => mountDialog(el, { alert$ })),\n\n  /* Header */\n  ...getComponentElements(\"header\")\n    .map(el => mountHeader(el, { viewport$, header$, main$ })),\n\n  /* Color palette */\n  ...getComponentElements(\"palette\")\n    .map(el => mountPalette(el)),\n\n  /* Search */\n  ...getComponentElements(\"search\")\n    .map(el => mountSearch(el, { index$, keyboard$ })),\n\n  /* Repository information */\n  ...getComponentElements(\"source\")\n    .map(el => mountSource(el))\n)\n\n/* Set up content component observables */\nconst content$ = defer(() => merge(\n\n  /* Announcement bar */\n  ...getComponentElements(\"announce\")\n    .map(el => mountAnnounce(el)),\n\n  /* Content */\n  ...getComponentElements(\"content\")\n    .map(el => mountContent(el, { target$, print$ })),\n\n  /* Search highlighting */\n  ...getComponentElements(\"content\")\n    .map(el => feature(\"search.highlight\")\n      ? mountSearchHiglight(el, { index$, location$ })\n      : EMPTY\n    ),\n\n  /* Header title */\n  ...getComponentElements(\"header-title\")\n    .map(el => mountHeaderTitle(el, { viewport$, header$ })),\n\n  /* Sidebar */\n  ...getComponentElements(\"sidebar\")\n    .map(el => el.getAttribute(\"data-md-type\") === \"navigation\"\n      ? at(screen$, () => mountSidebar(el, { viewport$, header$, main$ }))\n      : at(tablet$, () => mountSidebar(el, { viewport$, header$, main$ }))\n    ),\n\n  /* Navigation tabs */\n  ...getComponentElements(\"tabs\")\n    .map(el => mountTabs(el, { viewport$, header$ })),\n\n  /* Table of contents */\n  ...getComponentElements(\"toc\")\n    .map(el => mountTableOfContents(el, { viewport$, header$, target$ })),\n\n  /* Back-to-top button */\n  ...getComponentElements(\"top\")\n    .map(el => mountBackToTop(el, { viewport$, header$, main$, target$ }))\n))\n\n/* Set up component observables */\nconst component$ = document$\n  .pipe(\n    switchMap(() => content$),\n    mergeWith(control$),\n    shareReplay(1)\n  )\n\n/* Subscribe to all components */\ncomponent$.subscribe()\n\n/* ----------------------------------------------------------------------------\n * Exports\n * ------------------------------------------------------------------------- */\n\nwindow.document$  = document$          /* Document observable */\nwindow.location$  = location$          /* Location subject */\nwindow.target$    = target$            /* Location target observable */\nwindow.keyboard$  = keyboard$          /* Keyboard observable */\nwindow.viewport$  = viewport$          /* Viewport observable */\nwindow.tablet$    = tablet$            /* Media tablet observable */\nwindow.screen$    = screen$            /* Media screen observable */\nwindow.print$     = print$             /* Media print observable */\nwindow.alert$     = alert$             /* Alert subject */\nwindow.component$ = component$         /* Component observable */\n", "self.fetch||(self.fetch=function(e,n){return n=n||{},new Promise(function(t,s){var r=new XMLHttpRequest,o=[],u=[],i={},a=function(){return{ok:2==(r.status/100|0),statusText:r.statusText,status:r.status,url:r.responseURL,text:function(){return Promise.resolve(r.responseText)},json:function(){return Promise.resolve(r.responseText).then(JSON.parse)},blob:function(){return Promise.resolve(new Blob([r.response]))},clone:a,headers:{keys:function(){return o},entries:function(){return u},get:function(e){return i[e.toLowerCase()]},has:function(e){return e.toLowerCase()in i}}}};for(var c in r.open(n.method||\"get\",e,!0),r.onload=function(){r.getAllResponseHeaders().replace(/^(.*?):[^\\S\\n]*([\\s\\S]*?)$/gm,function(e,n,t){o.push(n=n.toLowerCase()),u.push([n,t]),i[n]=i[n]?i[n]+\",\"+t:t}),t(a())},r.onerror=s,r.withCredentials=\"include\"==n.credentials,n.headers)r.setRequestHeader(c,n.headers[c]);r.send(n.body||null)})});\n", "import tslib from '../tslib.js';\r\nconst {\r\n    __extends,\r\n    __assign,\r\n    __rest,\r\n    __decorate,\r\n    __param,\r\n    __metadata,\r\n    __awaiter,\r\n    __generator,\r\n    __exportStar,\r\n    __createBinding,\r\n    __values,\r\n    __read,\r\n    __spread,\r\n    __spreadArrays,\r\n    __spreadArray,\r\n    __await,\r\n    __asyncGenerator,\r\n    __asyncDelegator,\r\n    __asyncValues,\r\n    __makeTemplateObject,\r\n    __importStar,\r\n    __importDefault,\r\n    __classPrivateFieldGet,\r\n    __classPrivateFieldSet,\r\n} = tslib;\r\nexport {\r\n    __extends,\r\n    __assign,\r\n    __rest,\r\n    __decorate,\r\n    __param,\r\n    __metadata,\r\n    __awaiter,\r\n    __generator,\r\n    __exportStar,\r\n    __createBinding,\r\n    __values,\r\n    __read,\r\n    __spread,\r\n    __spreadArrays,\r\n    __spreadArray,\r\n    __await,\r\n    __asyncGenerator,\r\n    __asyncDelegator,\r\n    __asyncValues,\r\n    __makeTemplateObject,\r\n    __importStar,\r\n    __importDefault,\r\n    __classPrivateFieldGet,\r\n    __classPrivateFieldSet,\r\n};\r\n", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  ReplaySubject,\n  Subject,\n  fromEvent\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch document\n *\n * Documents are implemented as subjects, so all downstream observables are\n * automatically updated when a new document is emitted.\n *\n * @returns Document subject\n */\nexport function watchDocument(): Subject<Document> {\n  const document$ = new ReplaySubject<Document>(1)\n  fromEvent(document, \"DOMContentLoaded\", { once: true })\n    .subscribe(() => document$.next(document))\n\n  /* Return document */\n  return document$\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve all elements matching the query selector\n *\n * @template T - Element type\n *\n * @param selector - Query selector\n * @param node - Node of reference\n *\n * @returns Elements\n */\nexport function getElements<T extends keyof HTMLElementTagNameMap>(\n  selector: T, node?: ParentNode\n): HTMLElementTagNameMap[T][]\n\nexport function getElements<T extends HTMLElement>(\n  selector: string, node?: ParentNode\n): T[]\n\nexport function getElements<T extends HTMLElement>(\n  selector: string, node: ParentNode = document\n): T[] {\n  return Array.from(node.querySelectorAll<T>(selector))\n}\n\n/**\n * Retrieve an element matching a query selector or throw a reference error\n *\n * Note that this function assumes that the element is present. If unsure if an\n * element is existent, use the `getOptionalElement` function instead.\n *\n * @template T - Element type\n *\n * @param selector - Query selector\n * @param node - Node of reference\n *\n * @returns Element\n */\nexport function getElement<T extends keyof HTMLElementTagNameMap>(\n  selector: T, node?: ParentNode\n): HTMLElementTagNameMap[T]\n\nexport function getElement<T extends HTMLElement>(\n  selector: string, node?: ParentNode\n): T\n\nexport function getElement<T extends HTMLElement>(\n  selector: string, node: ParentNode = document\n): T {\n  const el = getOptionalElement<T>(selector, node)\n  if (typeof el === \"undefined\")\n    throw new ReferenceError(\n      `Missing element: expected \"${selector}\" to be present`\n    )\n\n  /* Return element */\n  return el\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Retrieve an optional element matching the query selector\n *\n * @template T - Element type\n *\n * @param selector - Query selector\n * @param node - Node of reference\n *\n * @returns Element or nothing\n */\nexport function getOptionalElement<T extends keyof HTMLElementTagNameMap>(\n  selector: T, node?: ParentNode\n): HTMLElementTagNameMap[T] | undefined\n\nexport function getOptionalElement<T extends HTMLElement>(\n  selector: string, node?: ParentNode\n): T | undefined\n\nexport function getOptionalElement<T extends HTMLElement>(\n  selector: string, node: ParentNode = document\n): T | undefined {\n  return node.querySelector<T>(selector) || undefined\n}\n\n/**\n * Retrieve the currently active element\n *\n * @returns Element or nothing\n */\nexport function getActiveElement(): HTMLElement | undefined {\n  return document.activeElement instanceof HTMLElement\n    ? document.activeElement || undefined\n    : undefined\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  debounceTime,\n  distinctUntilChanged,\n  fromEvent,\n  map,\n  merge,\n  startWith\n} from \"rxjs\"\n\nimport { getActiveElement } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch element focus\n *\n * Previously, this function used `focus` and `blur` events to determine whether\n * an element is focused, but this doesn't work if there are focusable elements\n * within the elements itself. A better solutions are `focusin` and `focusout`\n * events, which bubble up the tree and allow for more fine-grained control.\n *\n * `debounceTime` is necessary, because when a focus change happens inside an\n * element, the observable would first emit `false` and then `true` again.\n *\n * @param el - Element\n *\n * @returns Element focus observable\n */\nexport function watchElementFocus(\n  el: HTMLElement\n): Observable<boolean> {\n  return merge(\n    fromEvent(document.body, \"focusin\"),\n    fromEvent(document.body, \"focusout\")\n  )\n    .pipe(\n      debounceTime(1),\n      map(() => {\n        const active = getActiveElement()\n        return typeof active !== \"undefined\"\n          ? el.contains(active)\n          : false\n      }),\n      startWith(el === getActiveElement()),\n      distinctUntilChanged()\n    )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  animationFrameScheduler,\n  auditTime,\n  fromEvent,\n  map,\n  merge,\n  startWith\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Element offset\n */\nexport interface ElementOffset {\n  x: number                            /* Horizontal offset */\n  y: number                            /* Vertical offset */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve element offset\n *\n * @param el - Element\n *\n * @returns Element offset\n */\nexport function getElementOffset(\n  el: HTMLElement\n): ElementOffset {\n  return {\n    x: el.offsetLeft,\n    y: el.offsetTop\n  }\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch element offset\n *\n * @param el - Element\n *\n * @returns Element offset observable\n */\nexport function watchElementOffset(\n  el: HTMLElement\n): Observable<ElementOffset> {\n  return merge(\n    fromEvent(window, \"load\"),\n    fromEvent(window, \"resize\")\n  )\n    .pipe(\n      auditTime(0, animationFrameScheduler),\n      map(() => getElementOffset(el)),\n      startWith(getElementOffset(el))\n    )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  animationFrameScheduler,\n  auditTime,\n  fromEvent,\n  map,\n  merge,\n  startWith\n} from \"rxjs\"\n\nimport { ElementOffset } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve element content offset (= scroll offset)\n *\n * @param el - Element\n *\n * @returns Element content offset\n */\nexport function getElementContentOffset(\n  el: HTMLElement\n): ElementOffset {\n  return {\n    x: el.scrollLeft,\n    y: el.scrollTop\n  }\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch element content offset\n *\n * @param el - Element\n *\n * @returns Element content offset observable\n */\nexport function watchElementContentOffset(\n  el: HTMLElement\n): Observable<ElementOffset> {\n  return merge(\n    fromEvent(el, \"scroll\"),\n    fromEvent(window, \"resize\")\n  )\n    .pipe(\n      auditTime(0, animationFrameScheduler),\n      map(() => getElementContentOffset(el)),\n      startWith(getElementContentOffset(el))\n    )\n}\n", "/**\r\n * A collection of shims that provide minimal functionality of the ES6 collections.\r\n *\r\n * These implementations are not meant to be used outside of the ResizeObserver\r\n * modules as they cover only a limited range of use cases.\r\n */\r\n/* eslint-disable require-jsdoc, valid-jsdoc */\r\nvar MapShim = (function () {\r\n    if (typeof Map !== 'undefined') {\r\n        return Map;\r\n    }\r\n    /**\r\n     * Returns index in provided array that matches the specified key.\r\n     *\r\n     * @param {Array<Array>} arr\r\n     * @param {*} key\r\n     * @returns {number}\r\n     */\r\n    function getIndex(arr, key) {\r\n        var result = -1;\r\n        arr.some(function (entry, index) {\r\n            if (entry[0] === key) {\r\n                result = index;\r\n                return true;\r\n            }\r\n            return false;\r\n        });\r\n        return result;\r\n    }\r\n    return /** @class */ (function () {\r\n        function class_1() {\r\n            this.__entries__ = [];\r\n        }\r\n        Object.defineProperty(class_1.prototype, \"size\", {\r\n            /**\r\n             * @returns {boolean}\r\n             */\r\n            get: function () {\r\n                return this.__entries__.length;\r\n            },\r\n            enumerable: true,\r\n            configurable: true\r\n        });\r\n        /**\r\n         * @param {*} key\r\n         * @returns {*}\r\n         */\r\n        class_1.prototype.get = function (key) {\r\n            var index = getIndex(this.__entries__, key);\r\n            var entry = this.__entries__[index];\r\n            return entry && entry[1];\r\n        };\r\n        /**\r\n         * @param {*} key\r\n         * @param {*} value\r\n         * @returns {void}\r\n         */\r\n        class_1.prototype.set = function (key, value) {\r\n            var index = getIndex(this.__entries__, key);\r\n            if (~index) {\r\n                this.__entries__[index][1] = value;\r\n            }\r\n            else {\r\n                this.__entries__.push([key, value]);\r\n            }\r\n        };\r\n        /**\r\n         * @param {*} key\r\n         * @returns {void}\r\n         */\r\n        class_1.prototype.delete = function (key) {\r\n            var entries = this.__entries__;\r\n            var index = getIndex(entries, key);\r\n            if (~index) {\r\n                entries.splice(index, 1);\r\n            }\r\n        };\r\n        /**\r\n         * @param {*} key\r\n         * @returns {void}\r\n         */\r\n        class_1.prototype.has = function (key) {\r\n            return !!~getIndex(this.__entries__, key);\r\n        };\r\n        /**\r\n         * @returns {void}\r\n         */\r\n        class_1.prototype.clear = function () {\r\n            this.__entries__.splice(0);\r\n        };\r\n        /**\r\n         * @param {Function} callback\r\n         * @param {*} [ctx=null]\r\n         * @returns {void}\r\n         */\r\n        class_1.prototype.forEach = function (callback, ctx) {\r\n            if (ctx === void 0) { ctx = null; }\r\n            for (var _i = 0, _a = this.__entries__; _i < _a.length; _i++) {\r\n                var entry = _a[_i];\r\n                callback.call(ctx, entry[1], entry[0]);\r\n            }\r\n        };\r\n        return class_1;\r\n    }());\r\n})();\n\n/**\r\n * Detects whether window and document objects are available in current environment.\r\n */\r\nvar isBrowser = typeof window !== 'undefined' && typeof document !== 'undefined' && window.document === document;\n\n// Returns global object of a current environment.\r\nvar global$1 = (function () {\r\n    if (typeof global !== 'undefined' && global.Math === Math) {\r\n        return global;\r\n    }\r\n    if (typeof self !== 'undefined' && self.Math === Math) {\r\n        return self;\r\n    }\r\n    if (typeof window !== 'undefined' && window.Math === Math) {\r\n        return window;\r\n    }\r\n    // eslint-disable-next-line no-new-func\r\n    return Function('return this')();\r\n})();\n\n/**\r\n * A shim for the requestAnimationFrame which falls back to the setTimeout if\r\n * first one is not supported.\r\n *\r\n * @returns {number} Requests' identifier.\r\n */\r\nvar requestAnimationFrame$1 = (function () {\r\n    if (typeof requestAnimationFrame === 'function') {\r\n        // It's required to use a bounded function because IE sometimes throws\r\n        // an \"Invalid calling object\" error if rAF is invoked without the global\r\n        // object on the left hand side.\r\n        return requestAnimationFrame.bind(global$1);\r\n    }\r\n    return function (callback) { return setTimeout(function () { return callback(Date.now()); }, 1000 / 60); };\r\n})();\n\n// Defines minimum timeout before adding a trailing call.\r\nvar trailingTimeout = 2;\r\n/**\r\n * Creates a wrapper function which ensures that provided callback will be\r\n * invoked only once during the specified delay period.\r\n *\r\n * @param {Function} callback - Function to be invoked after the delay period.\r\n * @param {number} delay - Delay after which to invoke callback.\r\n * @returns {Function}\r\n */\r\nfunction throttle (callback, delay) {\r\n    var leadingCall = false, trailingCall = false, lastCallTime = 0;\r\n    /**\r\n     * Invokes the original callback function and schedules new invocation if\r\n     * the \"proxy\" was called during current request.\r\n     *\r\n     * @returns {void}\r\n     */\r\n    function resolvePending() {\r\n        if (leadingCall) {\r\n            leadingCall = false;\r\n            callback();\r\n        }\r\n        if (trailingCall) {\r\n            proxy();\r\n        }\r\n    }\r\n    /**\r\n     * Callback invoked after the specified delay. It will further postpone\r\n     * invocation of the original function delegating it to the\r\n     * requestAnimationFrame.\r\n     *\r\n     * @returns {void}\r\n     */\r\n    function timeoutCallback() {\r\n        requestAnimationFrame$1(resolvePending);\r\n    }\r\n    /**\r\n     * Schedules invocation of the original function.\r\n     *\r\n     * @returns {void}\r\n     */\r\n    function proxy() {\r\n        var timeStamp = Date.now();\r\n        if (leadingCall) {\r\n            // Reject immediately following calls.\r\n            if (timeStamp - lastCallTime < trailingTimeout) {\r\n                return;\r\n            }\r\n            // Schedule new call to be in invoked when the pending one is resolved.\r\n            // This is important for \"transitions\" which never actually start\r\n            // immediately so there is a chance that we might miss one if change\r\n            // happens amids the pending invocation.\r\n            trailingCall = true;\r\n        }\r\n        else {\r\n            leadingCall = true;\r\n            trailingCall = false;\r\n            setTimeout(timeoutCallback, delay);\r\n        }\r\n        lastCallTime = timeStamp;\r\n    }\r\n    return proxy;\r\n}\n\n// Minimum delay before invoking the update of observers.\r\nvar REFRESH_DELAY = 20;\r\n// A list of substrings of CSS properties used to find transition events that\r\n// might affect dimensions of observed elements.\r\nvar transitionKeys = ['top', 'right', 'bottom', 'left', 'width', 'height', 'size', 'weight'];\r\n// Check if MutationObserver is available.\r\nvar mutationObserverSupported = typeof MutationObserver !== 'undefined';\r\n/**\r\n * Singleton controller class which handles updates of ResizeObserver instances.\r\n */\r\nvar ResizeObserverController = /** @class */ (function () {\r\n    /**\r\n     * Creates a new instance of ResizeObserverController.\r\n     *\r\n     * @private\r\n     */\r\n    function ResizeObserverController() {\r\n        /**\r\n         * Indicates whether DOM listeners have been added.\r\n         *\r\n         * @private {boolean}\r\n         */\r\n        this.connected_ = false;\r\n        /**\r\n         * Tells that controller has subscribed for Mutation Events.\r\n         *\r\n         * @private {boolean}\r\n         */\r\n        this.mutationEventsAdded_ = false;\r\n        /**\r\n         * Keeps reference to the instance of MutationObserver.\r\n         *\r\n         * @private {MutationObserver}\r\n         */\r\n        this.mutationsObserver_ = null;\r\n        /**\r\n         * A list of connected observers.\r\n         *\r\n         * @private {Array<ResizeObserverSPI>}\r\n         */\r\n        this.observers_ = [];\r\n        this.onTransitionEnd_ = this.onTransitionEnd_.bind(this);\r\n        this.refresh = throttle(this.refresh.bind(this), REFRESH_DELAY);\r\n    }\r\n    /**\r\n     * Adds observer to observers list.\r\n     *\r\n     * @param {ResizeObserverSPI} observer - Observer to be added.\r\n     * @returns {void}\r\n     */\r\n    ResizeObserverController.prototype.addObserver = function (observer) {\r\n        if (!~this.observers_.indexOf(observer)) {\r\n            this.observers_.push(observer);\r\n        }\r\n        // Add listeners if they haven't been added yet.\r\n        if (!this.connected_) {\r\n            this.connect_();\r\n        }\r\n    };\r\n    /**\r\n     * Removes observer from observers list.\r\n     *\r\n     * @param {ResizeObserverSPI} observer - Observer to be removed.\r\n     * @returns {void}\r\n     */\r\n    ResizeObserverController.prototype.removeObserver = function (observer) {\r\n        var observers = this.observers_;\r\n        var index = observers.indexOf(observer);\r\n        // Remove observer if it's present in registry.\r\n        if (~index) {\r\n            observers.splice(index, 1);\r\n        }\r\n        // Remove listeners if controller has no connected observers.\r\n        if (!observers.length && this.connected_) {\r\n            this.disconnect_();\r\n        }\r\n    };\r\n    /**\r\n     * Invokes the update of observers. It will continue running updates insofar\r\n     * it detects changes.\r\n     *\r\n     * @returns {void}\r\n     */\r\n    ResizeObserverController.prototype.refresh = function () {\r\n        var changesDetected = this.updateObservers_();\r\n        // Continue running updates if changes have been detected as there might\r\n        // be future ones caused by CSS transitions.\r\n        if (changesDetected) {\r\n            this.refresh();\r\n        }\r\n    };\r\n    /**\r\n     * Updates every observer from observers list and notifies them of queued\r\n     * entries.\r\n     *\r\n     * @private\r\n     * @returns {boolean} Returns \"true\" if any observer has detected changes in\r\n     *      dimensions of it's elements.\r\n     */\r\n    ResizeObserverController.prototype.updateObservers_ = function () {\r\n        // Collect observers that have active observations.\r\n        var activeObservers = this.observers_.filter(function (observer) {\r\n            return observer.gatherActive(), observer.hasActive();\r\n        });\r\n        // Deliver notifications in a separate cycle in order to avoid any\r\n        // collisions between observers, e.g. when multiple instances of\r\n        // ResizeObserver are tracking the same element and the callback of one\r\n        // of them changes content dimensions of the observed target. Sometimes\r\n        // this may result in notifications being blocked for the rest of observers.\r\n        activeObservers.forEach(function (observer) { return observer.broadcastActive(); });\r\n        return activeObservers.length > 0;\r\n    };\r\n    /**\r\n     * Initializes DOM listeners.\r\n     *\r\n     * @private\r\n     * @returns {void}\r\n     */\r\n    ResizeObserverController.prototype.connect_ = function () {\r\n        // Do nothing if running in a non-browser environment or if listeners\r\n        // have been already added.\r\n        if (!isBrowser || this.connected_) {\r\n            return;\r\n        }\r\n        // Subscription to the \"Transitionend\" event is used as a workaround for\r\n        // delayed transitions. This way it's possible to capture at least the\r\n        // final state of an element.\r\n        document.addEventListener('transitionend', this.onTransitionEnd_);\r\n        window.addEventListener('resize', this.refresh);\r\n        if (mutationObserverSupported) {\r\n            this.mutationsObserver_ = new MutationObserver(this.refresh);\r\n            this.mutationsObserver_.observe(document, {\r\n                attributes: true,\r\n                childList: true,\r\n                characterData: true,\r\n                subtree: true\r\n            });\r\n        }\r\n        else {\r\n            document.addEventListener('DOMSubtreeModified', this.refresh);\r\n            this.mutationEventsAdded_ = true;\r\n        }\r\n        this.connected_ = true;\r\n    };\r\n    /**\r\n     * Removes DOM listeners.\r\n     *\r\n     * @private\r\n     * @returns {void}\r\n     */\r\n    ResizeObserverController.prototype.disconnect_ = function () {\r\n        // Do nothing if running in a non-browser environment or if listeners\r\n        // have been already removed.\r\n        if (!isBrowser || !this.connected_) {\r\n            return;\r\n        }\r\n        document.removeEventListener('transitionend', this.onTransitionEnd_);\r\n        window.removeEventListener('resize', this.refresh);\r\n        if (this.mutationsObserver_) {\r\n            this.mutationsObserver_.disconnect();\r\n        }\r\n        if (this.mutationEventsAdded_) {\r\n            document.removeEventListener('DOMSubtreeModified', this.refresh);\r\n        }\r\n        this.mutationsObserver_ = null;\r\n        this.mutationEventsAdded_ = false;\r\n        this.connected_ = false;\r\n    };\r\n    /**\r\n     * \"Transitionend\" event handler.\r\n     *\r\n     * @private\r\n     * @param {TransitionEvent} event\r\n     * @returns {void}\r\n     */\r\n    ResizeObserverController.prototype.onTransitionEnd_ = function (_a) {\r\n        var _b = _a.propertyName, propertyName = _b === void 0 ? '' : _b;\r\n        // Detect whether transition may affect dimensions of an element.\r\n        var isReflowProperty = transitionKeys.some(function (key) {\r\n            return !!~propertyName.indexOf(key);\r\n        });\r\n        if (isReflowProperty) {\r\n            this.refresh();\r\n        }\r\n    };\r\n    /**\r\n     * Returns instance of the ResizeObserverController.\r\n     *\r\n     * @returns {ResizeObserverController}\r\n     */\r\n    ResizeObserverController.getInstance = function () {\r\n        if (!this.instance_) {\r\n            this.instance_ = new ResizeObserverController();\r\n        }\r\n        return this.instance_;\r\n    };\r\n    /**\r\n     * Holds reference to the controller's instance.\r\n     *\r\n     * @private {ResizeObserverController}\r\n     */\r\n    ResizeObserverController.instance_ = null;\r\n    return ResizeObserverController;\r\n}());\n\n/**\r\n * Defines non-writable/enumerable properties of the provided target object.\r\n *\r\n * @param {Object} target - Object for which to define properties.\r\n * @param {Object} props - Properties to be defined.\r\n * @returns {Object} Target object.\r\n */\r\nvar defineConfigurable = (function (target, props) {\r\n    for (var _i = 0, _a = Object.keys(props); _i < _a.length; _i++) {\r\n        var key = _a[_i];\r\n        Object.defineProperty(target, key, {\r\n            value: props[key],\r\n            enumerable: false,\r\n            writable: false,\r\n            configurable: true\r\n        });\r\n    }\r\n    return target;\r\n});\n\n/**\r\n * Returns the global object associated with provided element.\r\n *\r\n * @param {Object} target\r\n * @returns {Object}\r\n */\r\nvar getWindowOf = (function (target) {\r\n    // Assume that the element is an instance of Node, which means that it\r\n    // has the \"ownerDocument\" property from which we can retrieve a\r\n    // corresponding global object.\r\n    var ownerGlobal = target && target.ownerDocument && target.ownerDocument.defaultView;\r\n    // Return the local global object if it's not possible extract one from\r\n    // provided element.\r\n    return ownerGlobal || global$1;\r\n});\n\n// Placeholder of an empty content rectangle.\r\nvar emptyRect = createRectInit(0, 0, 0, 0);\r\n/**\r\n * Converts provided string to a number.\r\n *\r\n * @param {number|string} value\r\n * @returns {number}\r\n */\r\nfunction toFloat(value) {\r\n    return parseFloat(value) || 0;\r\n}\r\n/**\r\n * Extracts borders size from provided styles.\r\n *\r\n * @param {CSSStyleDeclaration} styles\r\n * @param {...string} positions - Borders positions (top, right, ...)\r\n * @returns {number}\r\n */\r\nfunction getBordersSize(styles) {\r\n    var positions = [];\r\n    for (var _i = 1; _i < arguments.length; _i++) {\r\n        positions[_i - 1] = arguments[_i];\r\n    }\r\n    return positions.reduce(function (size, position) {\r\n        var value = styles['border-' + position + '-width'];\r\n        return size + toFloat(value);\r\n    }, 0);\r\n}\r\n/**\r\n * Extracts paddings sizes from provided styles.\r\n *\r\n * @param {CSSStyleDeclaration} styles\r\n * @returns {Object} Paddings box.\r\n */\r\nfunction getPaddings(styles) {\r\n    var positions = ['top', 'right', 'bottom', 'left'];\r\n    var paddings = {};\r\n    for (var _i = 0, positions_1 = positions; _i < positions_1.length; _i++) {\r\n        var position = positions_1[_i];\r\n        var value = styles['padding-' + position];\r\n        paddings[position] = toFloat(value);\r\n    }\r\n    return paddings;\r\n}\r\n/**\r\n * Calculates content rectangle of provided SVG element.\r\n *\r\n * @param {SVGGraphicsElement} target - Element content rectangle of which needs\r\n *      to be calculated.\r\n * @returns {DOMRectInit}\r\n */\r\nfunction getSVGContentRect(target) {\r\n    var bbox = target.getBBox();\r\n    return createRectInit(0, 0, bbox.width, bbox.height);\r\n}\r\n/**\r\n * Calculates content rectangle of provided HTMLElement.\r\n *\r\n * @param {HTMLElement} target - Element for which to calculate the content rectangle.\r\n * @returns {DOMRectInit}\r\n */\r\nfunction getHTMLElementContentRect(target) {\r\n    // Client width & height properties can't be\r\n    // used exclusively as they provide rounded values.\r\n    var clientWidth = target.clientWidth, clientHeight = target.clientHeight;\r\n    // By this condition we can catch all non-replaced inline, hidden and\r\n    // detached elements. Though elements with width & height properties less\r\n    // than 0.5 will be discarded as well.\r\n    //\r\n    // Without it we would need to implement separate methods for each of\r\n    // those cases and it's not possible to perform a precise and performance\r\n    // effective test for hidden elements. E.g. even jQuery's ':visible' filter\r\n    // gives wrong results for elements with width & height less than 0.5.\r\n    if (!clientWidth && !clientHeight) {\r\n        return emptyRect;\r\n    }\r\n    var styles = getWindowOf(target).getComputedStyle(target);\r\n    var paddings = getPaddings(styles);\r\n    var horizPad = paddings.left + paddings.right;\r\n    var vertPad = paddings.top + paddings.bottom;\r\n    // Computed styles of width & height are being used because they are the\r\n    // only dimensions available to JS that contain non-rounded values. It could\r\n    // be possible to utilize the getBoundingClientRect if only it's data wasn't\r\n    // affected by CSS transformations let alone paddings, borders and scroll bars.\r\n    var width = toFloat(styles.width), height = toFloat(styles.height);\r\n    // Width & height include paddings and borders when the 'border-box' box\r\n    // model is applied (except for IE).\r\n    if (styles.boxSizing === 'border-box') {\r\n        // Following conditions are required to handle Internet Explorer which\r\n        // doesn't include paddings and borders to computed CSS dimensions.\r\n        //\r\n        // We can say that if CSS dimensions + paddings are equal to the \"client\"\r\n        // properties then it's either IE, and thus we don't need to subtract\r\n        // anything, or an element merely doesn't have paddings/borders styles.\r\n        if (Math.round(width + horizPad) !== clientWidth) {\r\n            width -= getBordersSize(styles, 'left', 'right') + horizPad;\r\n        }\r\n        if (Math.round(height + vertPad) !== clientHeight) {\r\n            height -= getBordersSize(styles, 'top', 'bottom') + vertPad;\r\n        }\r\n    }\r\n    // Following steps can't be applied to the document's root element as its\r\n    // client[Width/Height] properties represent viewport area of the window.\r\n    // Besides, it's as well not necessary as the <html> itself neither has\r\n    // rendered scroll bars nor it can be clipped.\r\n    if (!isDocumentElement(target)) {\r\n        // In some browsers (only in Firefox, actually) CSS width & height\r\n        // include scroll bars size which can be removed at this step as scroll\r\n        // bars are the only difference between rounded dimensions + paddings\r\n        // and \"client\" properties, though that is not always true in Chrome.\r\n        var vertScrollbar = Math.round(width + horizPad) - clientWidth;\r\n        var horizScrollbar = Math.round(height + vertPad) - clientHeight;\r\n        // Chrome has a rather weird rounding of \"client\" properties.\r\n        // E.g. for an element with content width of 314.2px it sometimes gives\r\n        // the client width of 315px and for the width of 314.7px it may give\r\n        // 314px. And it doesn't happen all the time. So just ignore this delta\r\n        // as a non-relevant.\r\n        if (Math.abs(vertScrollbar) !== 1) {\r\n            width -= vertScrollbar;\r\n        }\r\n        if (Math.abs(horizScrollbar) !== 1) {\r\n            height -= horizScrollbar;\r\n        }\r\n    }\r\n    return createRectInit(paddings.left, paddings.top, width, height);\r\n}\r\n/**\r\n * Checks whether provided element is an instance of the SVGGraphicsElement.\r\n *\r\n * @param {Element} target - Element to be checked.\r\n * @returns {boolean}\r\n */\r\nvar isSVGGraphicsElement = (function () {\r\n    // Some browsers, namely IE and Edge, don't have the SVGGraphicsElement\r\n    // interface.\r\n    if (typeof SVGGraphicsElement !== 'undefined') {\r\n        return function (target) { return target instanceof getWindowOf(target).SVGGraphicsElement; };\r\n    }\r\n    // If it's so, then check that element is at least an instance of the\r\n    // SVGElement and that it has the \"getBBox\" method.\r\n    // eslint-disable-next-line no-extra-parens\r\n    return function (target) { return (target instanceof getWindowOf(target).SVGElement &&\r\n        typeof target.getBBox === 'function'); };\r\n})();\r\n/**\r\n * Checks whether provided element is a document element (<html>).\r\n *\r\n * @param {Element} target - Element to be checked.\r\n * @returns {boolean}\r\n */\r\nfunction isDocumentElement(target) {\r\n    return target === getWindowOf(target).document.documentElement;\r\n}\r\n/**\r\n * Calculates an appropriate content rectangle for provided html or svg element.\r\n *\r\n * @param {Element} target - Element content rectangle of which needs to be calculated.\r\n * @returns {DOMRectInit}\r\n */\r\nfunction getContentRect(target) {\r\n    if (!isBrowser) {\r\n        return emptyRect;\r\n    }\r\n    if (isSVGGraphicsElement(target)) {\r\n        return getSVGContentRect(target);\r\n    }\r\n    return getHTMLElementContentRect(target);\r\n}\r\n/**\r\n * Creates rectangle with an interface of the DOMRectReadOnly.\r\n * Spec: https://drafts.fxtf.org/geometry/#domrectreadonly\r\n *\r\n * @param {DOMRectInit} rectInit - Object with rectangle's x/y coordinates and dimensions.\r\n * @returns {DOMRectReadOnly}\r\n */\r\nfunction createReadOnlyRect(_a) {\r\n    var x = _a.x, y = _a.y, width = _a.width, height = _a.height;\r\n    // If DOMRectReadOnly is available use it as a prototype for the rectangle.\r\n    var Constr = typeof DOMRectReadOnly !== 'undefined' ? DOMRectReadOnly : Object;\r\n    var rect = Object.create(Constr.prototype);\r\n    // Rectangle's properties are not writable and non-enumerable.\r\n    defineConfigurable(rect, {\r\n        x: x, y: y, width: width, height: height,\r\n        top: y,\r\n        right: x + width,\r\n        bottom: height + y,\r\n        left: x\r\n    });\r\n    return rect;\r\n}\r\n/**\r\n * Creates DOMRectInit object based on the provided dimensions and the x/y coordinates.\r\n * Spec: https://drafts.fxtf.org/geometry/#dictdef-domrectinit\r\n *\r\n * @param {number} x - X coordinate.\r\n * @param {number} y - Y coordinate.\r\n * @param {number} width - Rectangle's width.\r\n * @param {number} height - Rectangle's height.\r\n * @returns {DOMRectInit}\r\n */\r\nfunction createRectInit(x, y, width, height) {\r\n    return { x: x, y: y, width: width, height: height };\r\n}\n\n/**\r\n * Class that is responsible for computations of the content rectangle of\r\n * provided DOM element and for keeping track of it's changes.\r\n */\r\nvar ResizeObservation = /** @class */ (function () {\r\n    /**\r\n     * Creates an instance of ResizeObservation.\r\n     *\r\n     * @param {Element} target - Element to be observed.\r\n     */\r\n    function ResizeObservation(target) {\r\n        /**\r\n         * Broadcasted width of content rectangle.\r\n         *\r\n         * @type {number}\r\n         */\r\n        this.broadcastWidth = 0;\r\n        /**\r\n         * Broadcasted height of content rectangle.\r\n         *\r\n         * @type {number}\r\n         */\r\n        this.broadcastHeight = 0;\r\n        /**\r\n         * Reference to the last observed content rectangle.\r\n         *\r\n         * @private {DOMRectInit}\r\n         */\r\n        this.contentRect_ = createRectInit(0, 0, 0, 0);\r\n        this.target = target;\r\n    }\r\n    /**\r\n     * Updates content rectangle and tells whether it's width or height properties\r\n     * have changed since the last broadcast.\r\n     *\r\n     * @returns {boolean}\r\n     */\r\n    ResizeObservation.prototype.isActive = function () {\r\n        var rect = getContentRect(this.target);\r\n        this.contentRect_ = rect;\r\n        return (rect.width !== this.broadcastWidth ||\r\n            rect.height !== this.broadcastHeight);\r\n    };\r\n    /**\r\n     * Updates 'broadcastWidth' and 'broadcastHeight' properties with a data\r\n     * from the corresponding properties of the last observed content rectangle.\r\n     *\r\n     * @returns {DOMRectInit} Last observed content rectangle.\r\n     */\r\n    ResizeObservation.prototype.broadcastRect = function () {\r\n        var rect = this.contentRect_;\r\n        this.broadcastWidth = rect.width;\r\n        this.broadcastHeight = rect.height;\r\n        return rect;\r\n    };\r\n    return ResizeObservation;\r\n}());\n\nvar ResizeObserverEntry = /** @class */ (function () {\r\n    /**\r\n     * Creates an instance of ResizeObserverEntry.\r\n     *\r\n     * @param {Element} target - Element that is being observed.\r\n     * @param {DOMRectInit} rectInit - Data of the element's content rectangle.\r\n     */\r\n    function ResizeObserverEntry(target, rectInit) {\r\n        var contentRect = createReadOnlyRect(rectInit);\r\n        // According to the specification following properties are not writable\r\n        // and are also not enumerable in the native implementation.\r\n        //\r\n        // Property accessors are not being used as they'd require to define a\r\n        // private WeakMap storage which may cause memory leaks in browsers that\r\n        // don't support this type of collections.\r\n        defineConfigurable(this, { target: target, contentRect: contentRect });\r\n    }\r\n    return ResizeObserverEntry;\r\n}());\n\nvar ResizeObserverSPI = /** @class */ (function () {\r\n    /**\r\n     * Creates a new instance of ResizeObserver.\r\n     *\r\n     * @param {ResizeObserverCallback} callback - Callback function that is invoked\r\n     *      when one of the observed elements changes it's content dimensions.\r\n     * @param {ResizeObserverController} controller - Controller instance which\r\n     *      is responsible for the updates of observer.\r\n     * @param {ResizeObserver} callbackCtx - Reference to the public\r\n     *      ResizeObserver instance which will be passed to callback function.\r\n     */\r\n    function ResizeObserverSPI(callback, controller, callbackCtx) {\r\n        /**\r\n         * Collection of resize observations that have detected changes in dimensions\r\n         * of elements.\r\n         *\r\n         * @private {Array<ResizeObservation>}\r\n         */\r\n        this.activeObservations_ = [];\r\n        /**\r\n         * Registry of the ResizeObservation instances.\r\n         *\r\n         * @private {Map<Element, ResizeObservation>}\r\n         */\r\n        this.observations_ = new MapShim();\r\n        if (typeof callback !== 'function') {\r\n            throw new TypeError('The callback provided as parameter 1 is not a function.');\r\n        }\r\n        this.callback_ = callback;\r\n        this.controller_ = controller;\r\n        this.callbackCtx_ = callbackCtx;\r\n    }\r\n    /**\r\n     * Starts observing provided element.\r\n     *\r\n     * @param {Element} target - Element to be observed.\r\n     * @returns {void}\r\n     */\r\n    ResizeObserverSPI.prototype.observe = function (target) {\r\n        if (!arguments.length) {\r\n            throw new TypeError('1 argument required, but only 0 present.');\r\n        }\r\n        // Do nothing if current environment doesn't have the Element interface.\r\n        if (typeof Element === 'undefined' || !(Element instanceof Object)) {\r\n            return;\r\n        }\r\n        if (!(target instanceof getWindowOf(target).Element)) {\r\n            throw new TypeError('parameter 1 is not of type \"Element\".');\r\n        }\r\n        var observations = this.observations_;\r\n        // Do nothing if element is already being observed.\r\n        if (observations.has(target)) {\r\n            return;\r\n        }\r\n        observations.set(target, new ResizeObservation(target));\r\n        this.controller_.addObserver(this);\r\n        // Force the update of observations.\r\n        this.controller_.refresh();\r\n    };\r\n    /**\r\n     * Stops observing provided element.\r\n     *\r\n     * @param {Element} target - Element to stop observing.\r\n     * @returns {void}\r\n     */\r\n    ResizeObserverSPI.prototype.unobserve = function (target) {\r\n        if (!arguments.length) {\r\n            throw new TypeError('1 argument required, but only 0 present.');\r\n        }\r\n        // Do nothing if current environment doesn't have the Element interface.\r\n        if (typeof Element === 'undefined' || !(Element instanceof Object)) {\r\n            return;\r\n        }\r\n        if (!(target instanceof getWindowOf(target).Element)) {\r\n            throw new TypeError('parameter 1 is not of type \"Element\".');\r\n        }\r\n        var observations = this.observations_;\r\n        // Do nothing if element is not being observed.\r\n        if (!observations.has(target)) {\r\n            return;\r\n        }\r\n        observations.delete(target);\r\n        if (!observations.size) {\r\n            this.controller_.removeObserver(this);\r\n        }\r\n    };\r\n    /**\r\n     * Stops observing all elements.\r\n     *\r\n     * @returns {void}\r\n     */\r\n    ResizeObserverSPI.prototype.disconnect = function () {\r\n        this.clearActive();\r\n        this.observations_.clear();\r\n        this.controller_.removeObserver(this);\r\n    };\r\n    /**\r\n     * Collects observation instances the associated element of which has changed\r\n     * it's content rectangle.\r\n     *\r\n     * @returns {void}\r\n     */\r\n    ResizeObserverSPI.prototype.gatherActive = function () {\r\n        var _this = this;\r\n        this.clearActive();\r\n        this.observations_.forEach(function (observation) {\r\n            if (observation.isActive()) {\r\n                _this.activeObservations_.push(observation);\r\n            }\r\n        });\r\n    };\r\n    /**\r\n     * Invokes initial callback function with a list of ResizeObserverEntry\r\n     * instances collected from active resize observations.\r\n     *\r\n     * @returns {void}\r\n     */\r\n    ResizeObserverSPI.prototype.broadcastActive = function () {\r\n        // Do nothing if observer doesn't have active observations.\r\n        if (!this.hasActive()) {\r\n            return;\r\n        }\r\n        var ctx = this.callbackCtx_;\r\n        // Create ResizeObserverEntry instance for every active observation.\r\n        var entries = this.activeObservations_.map(function (observation) {\r\n            return new ResizeObserverEntry(observation.target, observation.broadcastRect());\r\n        });\r\n        this.callback_.call(ctx, entries, ctx);\r\n        this.clearActive();\r\n    };\r\n    /**\r\n     * Clears the collection of active observations.\r\n     *\r\n     * @returns {void}\r\n     */\r\n    ResizeObserverSPI.prototype.clearActive = function () {\r\n        this.activeObservations_.splice(0);\r\n    };\r\n    /**\r\n     * Tells whether observer has active observations.\r\n     *\r\n     * @returns {boolean}\r\n     */\r\n    ResizeObserverSPI.prototype.hasActive = function () {\r\n        return this.activeObservations_.length > 0;\r\n    };\r\n    return ResizeObserverSPI;\r\n}());\n\n// Registry of internal observers. If WeakMap is not available use current shim\r\n// for the Map collection as it has all required methods and because WeakMap\r\n// can't be fully polyfilled anyway.\r\nvar observers = typeof WeakMap !== 'undefined' ? new WeakMap() : new MapShim();\r\n/**\r\n * ResizeObserver API. Encapsulates the ResizeObserver SPI implementation\r\n * exposing only those methods and properties that are defined in the spec.\r\n */\r\nvar ResizeObserver = /** @class */ (function () {\r\n    /**\r\n     * Creates a new instance of ResizeObserver.\r\n     *\r\n     * @param {ResizeObserverCallback} callback - Callback that is invoked when\r\n     *      dimensions of the observed elements change.\r\n     */\r\n    function ResizeObserver(callback) {\r\n        if (!(this instanceof ResizeObserver)) {\r\n            throw new TypeError('Cannot call a class as a function.');\r\n        }\r\n        if (!arguments.length) {\r\n            throw new TypeError('1 argument required, but only 0 present.');\r\n        }\r\n        var controller = ResizeObserverController.getInstance();\r\n        var observer = new ResizeObserverSPI(callback, controller, this);\r\n        observers.set(this, observer);\r\n    }\r\n    return ResizeObserver;\r\n}());\r\n// Expose public methods of ResizeObserver.\r\n[\r\n    'observe',\r\n    'unobserve',\r\n    'disconnect'\r\n].forEach(function (method) {\r\n    ResizeObserver.prototype[method] = function () {\r\n        var _a;\r\n        return (_a = observers.get(this))[method].apply(_a, arguments);\r\n    };\r\n});\n\nvar index = (function () {\r\n    // Export existing implementation if available.\r\n    if (typeof global$1.ResizeObserver !== 'undefined') {\r\n        return global$1.ResizeObserver;\r\n    }\r\n    return ResizeObserver;\r\n})();\n\nexport default index;\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport ResizeObserver from \"resize-observer-polyfill\"\nimport {\n  NEVER,\n  Observable,\n  Subject,\n  defer,\n  filter,\n  finalize,\n  map,\n  merge,\n  of,\n  shareReplay,\n  startWith,\n  switchMap,\n  tap\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Element offset\n */\nexport interface ElementSize {\n  width: number                        /* Element width */\n  height: number                       /* Element height */\n}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Resize observer entry subject\n */\nconst entry$ = new Subject<ResizeObserverEntry>()\n\n/**\n * Resize observer observable\n *\n * This observable will create a `ResizeObserver` on the first subscription\n * and will automatically terminate it when there are no more subscribers.\n * It's quite important to centralize observation in a single `ResizeObserver`,\n * as the performance difference can be quite dramatic, as the link shows.\n *\n * @see https://bit.ly/3iIYfEm - Google Groups on performance\n */\nconst observer$ = defer(() => of(\n  new ResizeObserver(entries => {\n    for (const entry of entries)\n      entry$.next(entry)\n  })\n))\n  .pipe(\n    switchMap(observer => merge(NEVER, of(observer))\n      .pipe(\n        finalize(() => observer.disconnect())\n      )\n    ),\n    shareReplay(1)\n  )\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve element size\n *\n * @param el - Element\n *\n * @returns Element size\n */\nexport function getElementSize(\n  el: HTMLElement\n): ElementSize {\n  return {\n    width:  el.offsetWidth,\n    height: el.offsetHeight\n  }\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch element size\n *\n * This function returns an observable that subscribes to a single internal\n * instance of `ResizeObserver` upon subscription, and emit resize events until\n * termination. Note that this function should not be called with the same\n * element twice, as the first unsubscription will terminate observation.\n *\n * Sadly, we can't use the `DOMRect` objects returned by the observer, because\n * we need the emitted values to be consistent with `getElementSize`, which will\n * return the used values (rounded) and not actual values (unrounded). Thus, we\n * use the `offset*` properties. See the linked GitHub issue.\n *\n * @see https://bit.ly/3m0k3he - GitHub issue\n *\n * @param el - Element\n *\n * @returns Element size observable\n */\nexport function watchElementSize(\n  el: HTMLElement\n): Observable<ElementSize> {\n  return observer$\n    .pipe(\n      tap(observer => observer.observe(el)),\n      switchMap(observer => entry$\n        .pipe(\n          filter(({ target }) => target === el),\n          finalize(() => observer.unobserve(el)),\n          map(() => getElementSize(el))\n        )\n      ),\n      startWith(getElementSize(el))\n    )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { ElementSize } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve element content size (= scroll width and height)\n *\n * @param el - Element\n *\n * @returns Element content size\n */\nexport function getElementContentSize(\n  el: HTMLElement\n): ElementSize {\n  return {\n    width:  el.scrollWidth,\n    height: el.scrollHeight\n  }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  NEVER,\n  Observable,\n  Subject,\n  defer,\n  distinctUntilChanged,\n  filter,\n  finalize,\n  map,\n  merge,\n  of,\n  shareReplay,\n  switchMap,\n  tap\n} from \"rxjs\"\n\nimport {\n  getElementContentSize,\n  getElementSize,\n  watchElementContentOffset\n} from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Intersection observer entry subject\n */\nconst entry$ = new Subject<IntersectionObserverEntry>()\n\n/**\n * Intersection observer observable\n *\n * This observable will create an `IntersectionObserver` on first subscription\n * and will automatically terminate it when there are no more subscribers.\n *\n * @see https://bit.ly/3iIYfEm - Google Groups on performance\n */\nconst observer$ = defer(() => of(\n  new IntersectionObserver(entries => {\n    for (const entry of entries)\n      entry$.next(entry)\n  }, {\n    threshold: 0\n  })\n))\n  .pipe(\n    switchMap(observer => merge(NEVER, of(observer))\n      .pipe(\n        finalize(() => observer.disconnect())\n      )\n    ),\n    shareReplay(1)\n  )\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch element visibility\n *\n * @param el - Element\n *\n * @returns Element visibility observable\n */\nexport function watchElementVisibility(\n  el: HTMLElement\n): Observable<boolean> {\n  return observer$\n    .pipe(\n      tap(observer => observer.observe(el)),\n      switchMap(observer => entry$\n        .pipe(\n          filter(({ target }) => target === el),\n          finalize(() => observer.unobserve(el)),\n          map(({ isIntersecting }) => isIntersecting)\n        )\n      )\n    )\n}\n\n/**\n * Watch element boundary\n *\n * This function returns an observable which emits whether the bottom content\n * boundary (= scroll offset) of an element is within a certain threshold.\n *\n * @param el - Element\n * @param threshold - Threshold\n *\n * @returns Element boundary observable\n */\nexport function watchElementBoundary(\n  el: HTMLElement, threshold = 16\n): Observable<boolean> {\n  return watchElementContentOffset(el)\n    .pipe(\n      map(({ y }) => {\n        const visible = getElementSize(el)\n        const content = getElementContentSize(el)\n        return y >= (\n          content.height - visible.height - threshold\n        )\n      }),\n      distinctUntilChanged()\n    )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  fromEvent,\n  map,\n  startWith\n} from \"rxjs\"\n\nimport { getElement } from \"../element\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Toggle\n */\nexport type Toggle =\n  | \"drawer\"                           /* Toggle for drawer */\n  | \"search\"                           /* Toggle for search */\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Toggle map\n */\nconst toggles: Record<Toggle, HTMLInputElement> = {\n  drawer: getElement(\"[data-md-toggle=drawer]\"),\n  search: getElement(\"[data-md-toggle=search]\")\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve the value of a toggle\n *\n * @param name - Toggle\n *\n * @returns Toggle value\n */\nexport function getToggle(name: Toggle): boolean {\n  return toggles[name].checked\n}\n\n/**\n * Set toggle\n *\n * Simulating a click event seems to be the most cross-browser compatible way\n * of changing the value while also emitting a `change` event. Before, Material\n * used `CustomEvent` to programmatically change the value of a toggle, but this\n * is a much simpler and cleaner solution which doesn't require a polyfill.\n *\n * @param name - Toggle\n * @param value - Toggle value\n */\nexport function setToggle(name: Toggle, value: boolean): void {\n  if (toggles[name].checked !== value)\n    toggles[name].click()\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch toggle\n *\n * @param name - Toggle\n *\n * @returns Toggle value observable\n */\nexport function watchToggle(name: Toggle): Observable<boolean> {\n  const el = toggles[name]\n  return fromEvent(el, \"change\")\n    .pipe(\n      map(() => el.checked),\n      startWith(el.checked)\n    )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  filter,\n  fromEvent,\n  map,\n  share\n} from \"rxjs\"\n\nimport { getActiveElement } from \"../element\"\nimport { getToggle } from \"../toggle\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Keyboard mode\n */\nexport type KeyboardMode =\n  | \"global\"                           /* Global */\n  | \"search\"                           /* Search is open */\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Keyboard\n */\nexport interface Keyboard {\n  mode: KeyboardMode                   /* Keyboard mode */\n  type: string                         /* Key type */\n  claim(): void                        /* Key claim */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Check whether an element may receive keyboard input\n *\n * @param el - Element\n * @param type - Key type\n *\n * @returns Test result\n */\nfunction isSusceptibleToKeyboard(\n  el: HTMLElement, type: string\n): boolean {\n  switch (el.constructor) {\n\n    /* Input elements */\n    case HTMLInputElement:\n      /* @ts-expect-error - omit unnecessary type cast */\n      if (el.type === \"radio\")\n        return /^Arrow/.test(type)\n      else\n        return true\n\n    /* Select element and textarea */\n    case HTMLSelectElement:\n    case HTMLTextAreaElement:\n      return true\n\n    /* Everything else */\n    default:\n      return el.isContentEditable\n  }\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch keyboard\n *\n * @returns Keyboard observable\n */\nexport function watchKeyboard(): Observable<Keyboard> {\n  return fromEvent<KeyboardEvent>(window, \"keydown\")\n    .pipe(\n      filter(ev => !(ev.metaKey || ev.ctrlKey)),\n      map(ev => ({\n        mode: getToggle(\"search\") ? \"search\" : \"global\",\n        type: ev.key,\n        claim() {\n          ev.preventDefault()\n          ev.stopPropagation()\n        }\n      } as Keyboard)),\n      filter(({ mode, type }) => {\n        if (mode === \"global\") {\n          const active = getActiveElement()\n          if (typeof active !== \"undefined\")\n            return !isSusceptibleToKeyboard(active, type)\n        }\n        return true\n      }),\n      share()\n    )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { Subject } from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve location\n *\n * This function returns a `URL` object (and not `Location`) to normalize the\n * typings across the application. Furthermore, locations need to be tracked\n * without setting them and `Location` is a singleton which represents the\n * current location.\n *\n * @returns URL\n */\nexport function getLocation(): URL {\n  return new URL(location.href)\n}\n\n/**\n * Set location\n *\n * @param url - URL to change to\n */\nexport function setLocation(url: URL): void {\n  location.href = url.href\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch location\n *\n * @returns Location subject\n */\nexport function watchLocation(): Subject<URL> {\n  return new Subject<URL>()\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { JSX as JSXInternal } from \"preact\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * HTML attributes\n */\ntype Attributes =\n  & JSXInternal.HTMLAttributes\n  & JSXInternal.SVGAttributes\n  & Record<string, any>\n\n/**\n * Child element\n */\ntype Child =\n  | HTMLElement\n  | Text\n  | string\n  | number\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Append a child node to an element\n *\n * @param el - Element\n * @param child - Child node(s)\n */\nfunction appendChild(el: HTMLElement, child: Child | Child[]): void {\n\n  /* Handle primitive types (including raw HTML) */\n  if (typeof child === \"string\" || typeof child === \"number\") {\n    el.innerHTML += child.toString()\n\n  /* Handle nodes */\n  } else if (child instanceof Node) {\n    el.appendChild(child)\n\n  /* Handle nested children */\n  } else if (Array.isArray(child)) {\n    for (const node of child)\n      appendChild(el, node)\n  }\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * JSX factory\n *\n * @template T - Element type\n *\n * @param tag - HTML tag\n * @param attributes - HTML attributes\n * @param children - Child elements\n *\n * @returns Element\n */\nexport function h<T extends keyof HTMLElementTagNameMap>(\n  tag: T, attributes?: Attributes | null, ...children: Child[]\n): HTMLElementTagNameMap[T]\n\nexport function h<T extends h.JSX.Element>(\n  tag: string, attributes?: Attributes | null, ...children: Child[]\n): T\n\nexport function h<T extends h.JSX.Element>(\n  tag: string, attributes?: Attributes | null, ...children: Child[]\n): T {\n  const el = document.createElement(tag)\n\n  /* Set attributes, if any */\n  if (attributes)\n    for (const attr of Object.keys(attributes)) {\n      if (typeof attributes[attr] === \"undefined\")\n        continue\n\n      /* Set default attribute or boolean */\n      if (typeof attributes[attr] !== \"boolean\")\n        el.setAttribute(attr, attributes[attr])\n      else\n        el.setAttribute(attr, \"\")\n    }\n\n  /* Append child nodes */\n  for (const child of children)\n    appendChild(el, child)\n\n  /* Return element */\n  return el as T\n}\n\n/* ----------------------------------------------------------------------------\n * Namespace\n * ------------------------------------------------------------------------- */\n\nexport declare namespace h {\n  namespace JSX {\n    type Element = HTMLElement\n    type IntrinsicElements = JSXInternal.IntrinsicElements\n  }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Truncate a string after the given number of characters\n *\n * This is not a very reasonable approach, since the summaries kind of suck.\n * It would be better to create something more intelligent, highlighting the\n * search occurrences and making a better summary out of it, but this note was\n * written three years ago, so who knows if we'll ever fix it.\n *\n * @param value - Value to be truncated\n * @param n - Number of characters\n *\n * @returns Truncated value\n */\nexport function truncate(value: string, n: number): string {\n  let i = n\n  if (value.length > i) {\n    while (value[i] !== \" \" && --i > 0) { /* keep eating */ }\n    return `${value.substring(0, i)}...`\n  }\n  return value\n}\n\n/**\n * Round a number for display with repository facts\n *\n * This is a reverse-engineered version of GitHub's weird rounding algorithm\n * for stars, forks and all other numbers. While all numbers below `1,000` are\n * returned as-is, bigger numbers are converted to fixed numbers:\n *\n * - `1,049` => `1k`\n * - `1,050` => `1.1k`\n * - `1,949` => `1.9k`\n * - `1,950` => `2k`\n *\n * @param value - Original value\n *\n * @returns Rounded value\n */\nexport function round(value: number): string {\n  if (value > 999) {\n    const digits = +((value - 950) % 1000 > 99)\n    return `${((value + 0.000001) / 1000).toFixed(digits)}k`\n  } else {\n    return value.toString()\n  }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  filter,\n  fromEvent,\n  map,\n  shareReplay,\n  startWith\n} from \"rxjs\"\n\nimport { getOptionalElement } from \"~/browser\"\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve location hash\n *\n * @returns Location hash\n */\nexport function getLocationHash(): string {\n  return location.hash.substring(1)\n}\n\n/**\n * Set location hash\n *\n * Setting a new fragment identifier via `location.hash` will have no effect\n * if the value doesn't change. When a new fragment identifier is set, we want\n * the browser to target the respective element at all times, which is why we\n * use this dirty little trick.\n *\n * @param hash - Location hash\n */\nexport function setLocationHash(hash: string): void {\n  const el = h(\"a\", { href: hash })\n  el.addEventListener(\"click\", ev => ev.stopPropagation())\n  el.click()\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch location hash\n *\n * @returns Location hash observable\n */\nexport function watchLocationHash(): Observable<string> {\n  return fromEvent<HashChangeEvent>(window, \"hashchange\")\n    .pipe(\n      map(getLocationHash),\n      startWith(getLocationHash()),\n      filter(hash => hash.length > 0),\n      shareReplay(1)\n    )\n}\n\n/**\n * Watch location target\n *\n * @returns Location target observable\n */\nexport function watchLocationTarget(): Observable<HTMLElement> {\n  return watchLocationHash()\n    .pipe(\n      map(id => getOptionalElement(`[id=\"${id}\"]`)!),\n      filter(el => typeof el !== \"undefined\")\n    )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  EMPTY,\n  Observable,\n  fromEvent,\n  fromEventPattern,\n  map,\n  merge,\n  startWith,\n  switchMap\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch media query\n *\n * Note that although `MediaQueryList.addListener` is deprecated we have to\n * use it, because it's the only way to ensure proper downward compatibility.\n *\n * @see https://bit.ly/3dUBH2m - GitHub issue\n *\n * @param query - Media query\n *\n * @returns Media observable\n */\nexport function watchMedia(query: string): Observable<boolean> {\n  const media = matchMedia(query)\n  return fromEventPattern<boolean>(next => (\n    media.addListener(() => next(media.matches))\n  ))\n    .pipe(\n      startWith(media.matches)\n    )\n}\n\n/**\n * Watch print mode\n *\n * @returns Print observable\n */\nexport function watchPrint(): Observable<boolean> {\n  const media = matchMedia(\"print\")\n  return merge(\n    fromEvent(window, \"beforeprint\").pipe(map(() => true)),\n    fromEvent(window, \"afterprint\").pipe(map(() => false))\n  )\n    .pipe(\n      startWith(media.matches)\n    )\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Toggle an observable with a media observable\n *\n * @template T - Data type\n *\n * @param query$ - Media observable\n * @param factory - Observable factory\n *\n * @returns Toggled observable\n */\nexport function at<T>(\n  query$: Observable<boolean>, factory: () => Observable<T>\n): Observable<T> {\n  return query$\n    .pipe(\n      switchMap(active => active ? factory() : EMPTY)\n    )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  EMPTY,\n  Observable,\n  catchError,\n  from,\n  map,\n  of,\n  shareReplay,\n  switchMap,\n  throwError\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch the given URL\n *\n * If the request fails (e.g. when dispatched from `file://` locations), the\n * observable will complete without emitting a value.\n *\n * @param url - Request URL\n * @param options - Options\n *\n * @returns Response observable\n */\nexport function request(\n  url: URL | string, options: RequestInit = { credentials: \"same-origin\" }\n): Observable<Response> {\n  return from(fetch(`${url}`, options))\n    .pipe(\n      catchError(() => EMPTY),\n      switchMap(res => res.status !== 200\n        ? throwError(() => new Error(res.statusText))\n        : of(res)\n      )\n    )\n}\n\n/**\n * Fetch JSON from the given URL\n *\n * @template T - Data type\n *\n * @param url - Request URL\n * @param options - Options\n *\n * @returns Data observable\n */\nexport function requestJSON<T>(\n  url: URL | string, options?: RequestInit\n): Observable<T> {\n  return request(url, options)\n    .pipe(\n      switchMap(res => res.json()),\n      shareReplay(1)\n    )\n}\n\n/**\n * Fetch XML from the given URL\n *\n * @param url - Request URL\n * @param options - Options\n *\n * @returns Data observable\n */\nexport function requestXML(\n  url: URL | string, options?: RequestInit\n): Observable<Document> {\n  const dom = new DOMParser()\n  return request(url, options)\n    .pipe(\n      switchMap(res => res.text()),\n      map(res => dom.parseFromString(res, \"text/xml\")),\n      shareReplay(1)\n    )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  defer,\n  finalize,\n  fromEvent,\n  map,\n  merge,\n  switchMap,\n  take,\n  throwError\n} from \"rxjs\"\n\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Create and load a `script` element\n *\n * This function returns an observable that will emit when the script was\n * successfully loaded, or throw an error if it didn't.\n *\n * @param src - Script URL\n *\n * @returns Script observable\n */\nexport function watchScript(src: string): Observable<void> {\n  const script = h(\"script\", { src })\n  return defer(() => {\n    document.head.appendChild(script)\n    return merge(\n      fromEvent(script, \"load\"),\n      fromEvent(script, \"error\")\n        .pipe(\n          switchMap(() => (\n            throwError(() => new ReferenceError(`Invalid script: ${src}`))\n          ))\n        )\n    )\n      .pipe(\n        map(() => undefined),\n        finalize(() => document.head.removeChild(script)),\n        take(1)\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  fromEvent,\n  map,\n  merge,\n  startWith\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Viewport offset\n */\nexport interface ViewportOffset {\n  x: number                            /* Horizontal offset */\n  y: number                            /* Vertical offset */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve viewport offset\n *\n * On iOS Safari, viewport offset can be negative due to overflow scrolling.\n * As this may induce strange behaviors downstream, we'll just limit it to 0.\n *\n * @returns Viewport offset\n */\nexport function getViewportOffset(): ViewportOffset {\n  return {\n    x: Math.max(0, scrollX),\n    y: Math.max(0, scrollY)\n  }\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch viewport offset\n *\n * @returns Viewport offset observable\n */\nexport function watchViewportOffset(): Observable<ViewportOffset> {\n  return merge(\n    fromEvent(window, \"scroll\", { passive: true }),\n    fromEvent(window, \"resize\", { passive: true })\n  )\n    .pipe(\n      map(getViewportOffset),\n      startWith(getViewportOffset())\n    )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  fromEvent,\n  map,\n  startWith\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Viewport size\n */\nexport interface ViewportSize {\n  width: number                        /* Viewport width */\n  height: number                       /* Viewport height */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve viewport size\n *\n * @returns Viewport size\n */\nexport function getViewportSize(): ViewportSize {\n  return {\n    width:  innerWidth,\n    height: innerHeight\n  }\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch viewport size\n *\n * @returns Viewport size observable\n */\nexport function watchViewportSize(): Observable<ViewportSize> {\n  return fromEvent(window, \"resize\", { passive: true })\n    .pipe(\n      map(getViewportSize),\n      startWith(getViewportSize())\n    )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  combineLatest,\n  map,\n  shareReplay\n} from \"rxjs\"\n\nimport {\n  ViewportOffset,\n  watchViewportOffset\n} from \"../offset\"\nimport {\n  ViewportSize,\n  watchViewportSize\n} from \"../size\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Viewport\n */\nexport interface Viewport {\n  offset: ViewportOffset               /* Viewport offset */\n  size: ViewportSize                   /* Viewport size */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch viewport\n *\n * @returns Viewport observable\n */\nexport function watchViewport(): Observable<Viewport> {\n  return combineLatest([\n    watchViewportOffset(),\n    watchViewportSize()\n  ])\n    .pipe(\n      map(([offset, size]) => ({ offset, size })),\n      shareReplay(1)\n    )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  combineLatest,\n  distinctUntilKeyChanged,\n  map\n} from \"rxjs\"\n\nimport { Header } from \"~/components\"\n\nimport { getElementOffset } from \"../../element\"\nimport { Viewport } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  header$: Observable<Header>          /* Header observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch viewport relative to element\n *\n * @param el - Element\n * @param options - Options\n *\n * @returns Viewport observable\n */\nexport function watchViewportAt(\n  el: HTMLElement, { viewport$, header$ }: WatchOptions\n): Observable<Viewport> {\n  const size$ = viewport$\n    .pipe(\n      distinctUntilKeyChanged(\"size\")\n    )\n\n  /* Compute element offset */\n  const offset$ = combineLatest([size$, header$])\n    .pipe(\n      map(() => getElementOffset(el))\n    )\n\n  /* Compute relative viewport, return hot observable */\n  return combineLatest([header$, viewport$, offset$])\n    .pipe(\n      map(([{ height }, { offset, size }, { x, y }]) => ({\n        offset: {\n          x: offset.x - x,\n          y: offset.y - y + height\n        },\n        size\n      }))\n    )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  fromEvent,\n  map,\n  share,\n  switchMap,\n  tap,\n  throttle\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Worker message\n */\nexport interface WorkerMessage {\n  type: unknown                        /* Message type */\n  data?: unknown                       /* Message data */\n}\n\n/**\n * Worker handler\n *\n * @template T - Message type\n */\nexport interface WorkerHandler<\n  T extends WorkerMessage\n> {\n  tx$: Subject<T>                      /* Message transmission subject */\n  rx$: Observable<T>                   /* Message receive observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n *\n * @template T - Worker message type\n */\ninterface WatchOptions<T extends WorkerMessage> {\n  tx$: Observable<T>                   /* Message transmission observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch a web worker\n *\n * This function returns an observable that sends all values emitted by the\n * message observable to the web worker. Web worker communication is expected\n * to be bidirectional (request-response) and synchronous. Messages that are\n * emitted during a pending request are throttled, the last one is emitted.\n *\n * @param worker - Web worker\n * @param options - Options\n *\n * @returns Worker message observable\n */\nexport function watchWorker<T extends WorkerMessage>(\n  worker: Worker, { tx$ }: WatchOptions<T>\n): Observable<T> {\n\n  /* Intercept messages from worker-like objects */\n  const rx$ = fromEvent<MessageEvent>(worker, \"message\")\n    .pipe(\n      map(({ data }) => data as T)\n    )\n\n  /* Send and receive messages, return hot observable */\n  return tx$\n    .pipe(\n      throttle(() => rx$, { leading: true, trailing: true }),\n      tap(message => worker.postMessage(message)),\n      switchMap(() => rx$),\n      share()\n    )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { getElement, getLocation } from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Feature flag\n */\nexport type Flag =\n  | \"announce.dismiss\"                 /* Dismissable announcement bar */\n  | \"content.code.annotate\"            /* Code annotations */\n  | \"content.tabs.link\"                /* Link content tabs */\n  | \"header.autohide\"                  /* Hide header */\n  | \"navigation.expand\"                /* Automatic expansion */\n  | \"navigation.indexes\"               /* Section pages */\n  | \"navigation.instant\"               /* Instant loading */\n  | \"navigation.sections\"              /* Section navigation */\n  | \"navigation.tabs\"                  /* Tabs navigation */\n  | \"navigation.tabs.sticky\"           /* Tabs navigation (sticky) */\n  | \"navigation.top\"                   /* Back-to-top button */\n  | \"navigation.tracking\"              /* Anchor tracking */\n  | \"search.highlight\"                 /* Search highlighting */\n  | \"search.share\"                     /* Search sharing */\n  | \"search.suggest\"                   /* Search suggestions */\n  | \"toc.integrate\"                    /* Integrated table of contents */\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Translation\n */\nexport type Translation =\n  | \"clipboard.copy\"                   /* Copy to clipboard */\n  | \"clipboard.copied\"                 /* Copied to clipboard */\n  | \"search.config.lang\"               /* Search language */\n  | \"search.config.pipeline\"           /* Search pipeline */\n  | \"search.config.separator\"          /* Search separator */\n  | \"search.placeholder\"               /* Search */\n  | \"search.result.placeholder\"        /* Type to start searching */\n  | \"search.result.none\"               /* No matching documents */\n  | \"search.result.one\"                /* 1 matching document */\n  | \"search.result.other\"              /* # matching documents */\n  | \"search.result.more.one\"           /* 1 more on this page */\n  | \"search.result.more.other\"         /* # more on this page */\n  | \"search.result.term.missing\"       /* Missing */\n  | \"select.version.title\"             /* Version selector */\n\n/**\n * Translations\n */\nexport type Translations = Record<Translation, string>\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Versioning\n */\nexport interface Versioning {\n  provider: \"mike\"                     /* Version provider */\n  default?: string                     /* Default version */\n}\n\n/**\n * Configuration\n */\nexport interface Config {\n  base: string                         /* Base URL */\n  features: Flag[]                     /* Feature flags */\n  translations: Translations           /* Translations */\n  search: string                       /* Search worker URL */\n  version?: Versioning                 /* Versioning */\n}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve global configuration and make base URL absolute\n */\nconst script = getElement(\"#__config\")\nconst config: Config = JSON.parse(script.textContent!)\nconfig.base = `${new URL(config.base, getLocation())}`\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve global configuration\n *\n * @returns Global configuration\n */\nexport function configuration(): Config {\n  return config\n}\n\n/**\n * Check whether a feature flag is enabled\n *\n * @param flag - Feature flag\n *\n * @returns Test result\n */\nexport function feature(flag: Flag): boolean {\n  return config.features.includes(flag)\n}\n\n/**\n * Retrieve the translation for the given key\n *\n * @param key - Key to be translated\n * @param value - Positional value, if any\n *\n * @returns Translation\n */\nexport function translation(\n  key: Translation, value?: string | number\n): string {\n  return typeof value !== \"undefined\"\n    ? config.translations[key].replace(\"#\", value.toString())\n    : config.translations[key]\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { getElement, getElements } from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Component type\n */\nexport type ComponentType =\n  | \"announce\"                         /* Announcement bar */\n  | \"container\"                        /* Container */\n  | \"consent\"                          /* Consent */\n  | \"content\"                          /* Content */\n  | \"dialog\"                           /* Dialog */\n  | \"header\"                           /* Header */\n  | \"header-title\"                     /* Header title */\n  | \"header-topic\"                     /* Header topic */\n  | \"main\"                             /* Main area */\n  | \"outdated\"                         /* Version warning */\n  | \"palette\"                          /* Color palette */\n  | \"search\"                           /* Search */\n  | \"search-query\"                     /* Search input */\n  | \"search-result\"                    /* Search results */\n  | \"search-share\"                     /* Search sharing */\n  | \"search-suggest\"                   /* Search suggestions */\n  | \"sidebar\"                          /* Sidebar */\n  | \"skip\"                             /* Skip link */\n  | \"source\"                           /* Repository information */\n  | \"tabs\"                             /* Navigation tabs */\n  | \"toc\"                              /* Table of contents */\n  | \"top\"                              /* Back-to-top button */\n\n/**\n * Component\n *\n * @template T - Component type\n * @template U - Reference type\n */\nexport type Component<\n  T extends {} = {},\n  U extends HTMLElement = HTMLElement\n> =\n  T & {\n    ref: U                             /* Component reference */\n  }\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Component type map\n */\ninterface ComponentTypeMap {\n  \"announce\": HTMLElement              /* Announcement bar */\n  \"container\": HTMLElement             /* Container */\n  \"consent\": HTMLElement               /* Consent */\n  \"content\": HTMLElement               /* Content */\n  \"dialog\": HTMLElement                /* Dialog */\n  \"header\": HTMLElement                /* Header */\n  \"header-title\": HTMLElement          /* Header title */\n  \"header-topic\": HTMLElement          /* Header topic */\n  \"main\": HTMLElement                  /* Main area */\n  \"outdated\": HTMLElement              /* Version warning */\n  \"palette\": HTMLElement               /* Color palette */\n  \"search\": HTMLElement                /* Search */\n  \"search-query\": HTMLInputElement     /* Search input */\n  \"search-result\": HTMLElement         /* Search results */\n  \"search-share\": HTMLAnchorElement    /* Search sharing */\n  \"search-suggest\": HTMLElement        /* Search suggestions */\n  \"sidebar\": HTMLElement               /* Sidebar */\n  \"skip\": HTMLAnchorElement            /* Skip link */\n  \"source\": HTMLAnchorElement          /* Repository information */\n  \"tabs\": HTMLElement                  /* Navigation tabs */\n  \"toc\": HTMLElement                   /* Table of contents */\n  \"top\": HTMLAnchorElement             /* Back-to-top button */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve the element for a given component or throw a reference error\n *\n * @template T - Component type\n *\n * @param type - Component type\n * @param node - Node of reference\n *\n * @returns Element\n */\nexport function getComponentElement<T extends ComponentType>(\n  type: T, node: ParentNode = document\n): ComponentTypeMap[T] {\n  return getElement(`[data-md-component=${type}]`, node)\n}\n\n/**\n * Retrieve all elements for a given component\n *\n * @template T - Component type\n *\n * @param type - Component type\n * @param node - Node of reference\n *\n * @returns Elements\n */\nexport function getComponentElements<T extends ComponentType>(\n  type: T, node: ParentNode = document\n): ComponentTypeMap[T][] {\n  return getElements(`[data-md-component=${type}]`, node)\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  EMPTY,\n  Observable,\n  Subject,\n  defer,\n  finalize,\n  fromEvent,\n  map,\n  startWith,\n  tap\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport { getElement } from \"~/browser\"\n\nimport { Component } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Announcement bar\n */\nexport interface Announce {\n  hash: number                        /* Content hash */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch announcement bar\n *\n * @param el - Announcement bar element\n *\n * @returns Announcement bar observable\n */\nexport function watchAnnounce(\n  el: HTMLElement\n): Observable<Announce> {\n  const button = getElement(\".md-typeset > :first-child\", el)\n  return fromEvent(button, \"click\", { once: true })\n    .pipe(\n      map(() => getElement(\".md-typeset\", el)),\n      map(content => ({ hash: __md_hash(content.innerHTML) }))\n    )\n}\n\n/**\n * Mount announcement bar\n *\n * @param el - Announcement bar element\n *\n * @returns Announcement bar component observable\n */\nexport function mountAnnounce(\n  el: HTMLElement\n): Observable<Component<Announce>> {\n  if (!feature(\"announce.dismiss\") || !el.childElementCount)\n    return EMPTY\n\n  /* Mount component on subscription */\n  return defer(() => {\n    const push$ = new Subject<Announce>()\n    push$\n      .pipe(\n        startWith({ hash: __md_get<number>(\"__announce\") })\n      )\n        .subscribe(({ hash }) => {\n          if (hash && hash === (__md_get<number>(\"__announce\") ?? hash)) {\n            el.hidden = true\n\n            /* Persist preference in local storage */\n            __md_set<number>(\"__announce\", hash)\n          }\n        })\n\n    /* Create and return component */\n    return watchAnnounce(el)\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  finalize,\n  map,\n  tap\n} from \"rxjs\"\n\nimport { Component } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Consent\n */\nexport interface Consent {\n  hidden: boolean                      /* Consent is hidden */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  target$: Observable<HTMLElement>     /* Target observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  target$: Observable<HTMLElement>     /* Target observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch consent\n *\n * @param el - Consent element\n * @param options - Options\n *\n * @returns Consent observable\n */\nexport function watchConsent(\n  el: HTMLElement, { target$ }: WatchOptions\n): Observable<Consent> {\n  return target$\n    .pipe(\n      map(target => ({ hidden: target !== el }))\n    )\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Mount consent\n *\n * @param el - Consent element\n * @param options - Options\n *\n * @returns Consent component observable\n */\nexport function mountConsent(\n  el: HTMLElement, options: MountOptions\n): Observable<Component<Consent>> {\n  const internal$ = new Subject<Consent>()\n  internal$.subscribe(({ hidden }) => {\n    el.hidden = hidden\n  })\n\n  /* Create and return component */\n  return watchConsent(el, options)\n    .pipe(\n      tap(state => internal$.next(state)),\n      finalize(() => internal$.complete()),\n      map(state => ({ ref: el, ...state }))\n    )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport ClipboardJS from \"clipboard\"\nimport {\n  EMPTY,\n  Observable,\n  Subject,\n  defer,\n  distinctUntilChanged,\n  distinctUntilKeyChanged,\n  filter,\n  finalize,\n  map,\n  mergeWith,\n  switchMap,\n  take,\n  takeLast,\n  takeUntil,\n  tap\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport {\n  getElementContentSize,\n  watchElementSize,\n  watchElementVisibility\n} from \"~/browser\"\nimport { renderClipboardButton } from \"~/templates\"\n\nimport { Component } from \"../../../_\"\nimport {\n  Annotation,\n  mountAnnotationList\n} from \"../../annotation\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Code block\n */\nexport interface CodeBlock {\n  scrollable: boolean                  /* Code block overflows */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  print$: Observable<boolean>          /* Media print observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Global sequence number for Clipboard.js integration\n */\nlet sequence = 0\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Find candidate list element directly following a code block\n *\n * @param el - Code block element\n *\n * @returns List element or nothing\n */\nfunction findCandidateList(el: HTMLElement): HTMLElement | undefined {\n  if (el.nextElementSibling) {\n    const sibling = el.nextElementSibling as HTMLElement\n    if (sibling.tagName === \"OL\")\n      return sibling\n\n    /* Skip empty paragraphs - see https://bit.ly/3r4ZJ2O */\n    else if (sibling.tagName === \"P\" && !sibling.children.length)\n      return findCandidateList(sibling)\n  }\n\n  /* Everything else */\n  return undefined\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch code block\n *\n * This function monitors size changes of the viewport, as well as switches of\n * content tabs with embedded code blocks, as both may trigger overflow.\n *\n * @param el - Code block element\n *\n * @returns Code block observable\n */\nexport function watchCodeBlock(\n  el: HTMLElement\n): Observable<CodeBlock> {\n  return watchElementSize(el)\n    .pipe(\n      map(({ width }) => {\n        const content = getElementContentSize(el)\n        return {\n          scrollable: content.width > width\n        }\n      }),\n      distinctUntilKeyChanged(\"scrollable\")\n    )\n}\n\n/**\n * Mount code block\n *\n * This function ensures that an overflowing code block is focusable through\n * keyboard, so it can be scrolled without a mouse to improve on accessibility.\n * Furthermore, if code annotations are enabled, they are mounted if and only\n * if the code block is currently visible, e.g., not in a hidden content tab.\n *\n * @param el - Code block element\n * @param options - Options\n *\n * @returns Code block and annotation component observable\n */\nexport function mountCodeBlock(\n  el: HTMLElement, options: MountOptions\n): Observable<Component<CodeBlock | Annotation>> {\n  const { matches: hover } = matchMedia(\"(hover)\")\n\n  /* Defer mounting of code block - see https://bit.ly/3vHVoVD */\n  const factory$ = defer(() => {\n    const push$ = new Subject<CodeBlock>()\n    push$.subscribe(({ scrollable }) => {\n      if (scrollable && hover)\n        el.setAttribute(\"tabindex\", \"0\")\n      else\n        el.removeAttribute(\"tabindex\")\n    })\n\n    /* Render button for Clipboard.js integration */\n    if (ClipboardJS.isSupported()) {\n      const parent = el.closest(\"pre\")!\n      parent.id = `__code_${++sequence}`\n      parent.insertBefore(\n        renderClipboardButton(parent.id),\n        el\n      )\n    }\n\n    /* Handle code annotations */\n    const container = el.closest(\".highlight\")\n    if (container instanceof HTMLElement) {\n      const list = findCandidateList(container)\n\n      /* Mount code annotations, if enabled */\n      if (typeof list !== \"undefined\" && (\n        container.classList.contains(\"annotate\") ||\n        feature(\"content.code.annotate\")\n      )) {\n        const annotations$ = mountAnnotationList(list, el, options)\n\n        /* Create and return component */\n        return watchCodeBlock(el)\n          .pipe(\n            tap(state => push$.next(state)),\n            finalize(() => push$.complete()),\n            map(state => ({ ref: el, ...state })),\n            mergeWith(\n              watchElementSize(container)\n                .pipe(\n                  takeUntil(push$.pipe(takeLast(1))),\n                  map(({ width, height }) => width && height),\n                  distinctUntilChanged(),\n                  switchMap(active => active ? annotations$ : EMPTY)\n                )\n            )\n          )\n      }\n    }\n\n    /* Create and return component */\n    return watchCodeBlock(el)\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n\n  /* Mount code block on first sight */\n  return watchElementVisibility(el)\n    .pipe(\n      filter(visible => visible),\n      take(1),\n      switchMap(() => factory$)\n    )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render an empty annotation\n *\n * @param id - Annotation identifier\n *\n * @returns Element\n */\nexport function renderAnnotation(id: number): HTMLElement {\n  return (\n    <aside class=\"md-annotation\" tabIndex={0}>\n      <div class=\"md-annotation__inner md-tooltip\">\n        <div class=\"md-tooltip__inner md-typeset\"></div>\n      </div>\n      <span class=\"md-annotation__index\">\n        <span data-md-annotation-id={id}></span>\n      </span>\n    </aside>\n  )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { translation } from \"~/_\"\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a 'copy-to-clipboard' button\n *\n * @param id - Unique identifier\n *\n * @returns Element\n */\nexport function renderClipboardButton(id: string): HTMLElement {\n  return (\n    <button\n      class=\"md-clipboard md-icon\"\n      title={translation(\"clipboard.copy\")}\n      data-clipboard-target={`#${id} > code`}\n    ></button>\n  )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { ComponentChild } from \"preact\"\n\nimport { feature, translation } from \"~/_\"\nimport {\n  SearchDocument,\n  SearchMetadata,\n  SearchResultItem\n} from \"~/integrations/search\"\nimport { h, truncate } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Render flag\n */\nconst enum Flag {\n  TEASER = 1,                          /* Render teaser */\n  PARENT = 2                           /* Render as parent */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper function\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a search document\n *\n * @param document - Search document\n * @param flag - Render flags\n *\n * @returns Element\n */\nfunction renderSearchDocument(\n  document: SearchDocument & SearchMetadata, flag: Flag\n): HTMLElement {\n  const parent = flag & Flag.PARENT\n  const teaser = flag & Flag.TEASER\n\n  /* Render missing query terms */\n  const missing = Object.keys(document.terms)\n    .filter(key => !document.terms[key])\n    .reduce<ComponentChild[]>((list, key) => [\n      ...list, <del>{key}</del>, \" \"\n    ], [])\n    .slice(0, -1)\n\n  /* Assemble query string for highlighting */\n  const url = new URL(document.location)\n  if (feature(\"search.highlight\"))\n    url.searchParams.set(\"h\", Object.entries(document.terms)\n      .filter(([, match]) => match)\n      .reduce((highlight, [value]) => `${highlight} ${value}`.trim(), \"\")\n    )\n\n  /* Render article or section, depending on flags */\n  return (\n    <a href={`${url}`} class=\"md-search-result__link\" tabIndex={-1}>\n      <article\n        class={[\"md-search-result__article\", ...parent\n          ? [\"md-search-result__article--document\"]\n          : []\n        ].join(\" \")}\n        data-md-score={document.score.toFixed(2)}\n      >\n        {parent > 0 && <div class=\"md-search-result__icon md-icon\"></div>}\n        <h1 class=\"md-search-result__title\">{document.title}</h1>\n        {teaser > 0 && document.text.length > 0 &&\n          <p class=\"md-search-result__teaser\">\n            {truncate(document.text, 320)}\n          </p>\n        }\n        {document.tags && document.tags.map(tag => (\n          <span class=\"md-tag\">{tag}</span>\n        ))}\n        {teaser > 0 && missing.length > 0 &&\n          <p class=\"md-search-result__terms\">\n            {translation(\"search.result.term.missing\")}: {...missing}\n          </p>\n        }\n      </article>\n    </a>\n  )\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a search result\n *\n * @param result - Search result\n *\n * @returns Element\n */\nexport function renderSearchResultItem(\n  result: SearchResultItem\n): HTMLElement {\n  const threshold = result[0].score\n  const docs = [...result]\n\n  /* Find and extract parent article */\n  const parent = docs.findIndex(doc => !doc.location.includes(\"#\"))\n  const [article] = docs.splice(parent, 1)\n\n  /* Determine last index above threshold */\n  let index = docs.findIndex(doc => doc.score < threshold)\n  if (index === -1)\n    index = docs.length\n\n  /* Partition sections */\n  const best = docs.slice(0, index)\n  const more = docs.slice(index)\n\n  /* Render children */\n  const children = [\n    renderSearchDocument(article, Flag.PARENT | +(!parent && index === 0)),\n    ...best.map(section => renderSearchDocument(section, Flag.TEASER)),\n    ...more.length ? [\n      <details class=\"md-search-result__more\">\n        <summary tabIndex={-1}>\n          {more.length > 0 && more.length === 1\n            ? translation(\"search.result.more.one\")\n            : translation(\"search.result.more.other\", more.length)\n          }\n        </summary>\n        {...more.map(section => renderSearchDocument(section, Flag.TEASER))}\n      </details>\n    ] : []\n  ]\n\n  /* Render search result */\n  return (\n    <li class=\"md-search-result__item\">\n      {children}\n    </li>\n  )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { SourceFacts } from \"~/components\"\nimport { h, round } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render repository facts\n *\n * @param facts - Repository facts\n *\n * @returns Element\n */\nexport function renderSourceFacts(facts: SourceFacts): HTMLElement {\n  return (\n    <ul class=\"md-source__facts\">\n      {Object.entries(facts).map(([key, value]) => (\n        <li class={`md-source__fact md-source__fact--${key}`}>\n          {typeof value === \"number\" ? round(value) : value}\n        </li>\n      ))}\n    </ul>\n  )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Tabbed control type\n */\ntype TabbedControlType =\n  | \"prev\"\n  | \"next\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render control for content tabs\n *\n * @param type - Control type\n *\n * @returns Element\n */\nexport function renderTabbedControl(\n  type: TabbedControlType\n): HTMLElement {\n  const classes = `tabbed-control tabbed-control--${type}`\n  return (\n    <div class={classes} hidden>\n      <button class=\"tabbed-button\" tabIndex={-1}></button>\n    </div>\n  )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a table inside a wrapper to improve scrolling on mobile\n *\n * @param table - Table element\n *\n * @returns Element\n */\nexport function renderTable(table: HTMLElement): HTMLElement {\n  return (\n    <div class=\"md-typeset__scrollwrap\">\n      <div class=\"md-typeset__table\">\n        {table}\n      </div>\n    </div>\n  )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { configuration, translation } from \"~/_\"\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Version\n */\nexport interface Version {\n  version: string                      /* Version identifier */\n  title: string                        /* Version title */\n  aliases: string[]                    /* Version aliases */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a version\n *\n * @param version - Version\n *\n * @returns Element\n */\nfunction renderVersion(version: Version): HTMLElement {\n  const config = configuration()\n\n  /* Ensure trailing slash, see https://bit.ly/3rL5u3f */\n  const url = new URL(`../${version.version}/`, config.base)\n  return (\n    <li class=\"md-version__item\">\n      <a href={`${url}`} class=\"md-version__link\">\n        {version.title}\n      </a>\n    </li>\n  )\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a version selector\n *\n * @param versions - Versions\n * @param active - Active version\n *\n * @returns Element\n */\nexport function renderVersionSelector(\n  versions: Version[], active: Version\n): HTMLElement {\n  return (\n    <div class=\"md-version\">\n      <button\n        class=\"md-version__current\"\n        aria-label={translation(\"select.version.title\")}\n      >\n        {active.title}\n      </button>\n      <ul class=\"md-version__list\">\n        {versions.map(renderVersion)}\n      </ul>\n    </div>\n  )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  EMPTY,\n  Observable,\n  Subject,\n  animationFrameScheduler,\n  combineLatest,\n  defer,\n  finalize,\n  fromEvent,\n  map,\n  switchMap,\n  take,\n  takeLast,\n  takeUntil,\n  tap,\n  throttleTime\n} from \"rxjs\"\n\nimport {\n  ElementOffset,\n  getElement,\n  getElementSize,\n  watchElementContentOffset,\n  watchElementFocus,\n  watchElementOffset,\n  watchElementVisibility\n} from \"~/browser\"\n\nimport { Component } from \"../../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Annotation\n */\nexport interface Annotation {\n  active: boolean                      /* Annotation is active */\n  offset: ElementOffset                /* Annotation offset */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch annotation\n *\n * @param el - Annotation element\n * @param container - Containing element\n *\n * @returns Annotation observable\n */\nexport function watchAnnotation(\n  el: HTMLElement, container: HTMLElement\n): Observable<Annotation> {\n  const offset$ = defer(() => combineLatest([\n    watchElementOffset(el),\n    watchElementContentOffset(container)\n  ]))\n    .pipe(\n      map(([{ x, y }, scroll]) => {\n        const { width } = getElementSize(el)\n        return ({\n          x: x - scroll.x + width / 2,\n          y: y - scroll.y\n        })\n      })\n    )\n\n  /* Actively watch annotation on focus */\n  return watchElementFocus(el)\n    .pipe(\n      switchMap(active => offset$\n        .pipe(\n          map(offset => ({ active, offset })),\n          take(+!active || Infinity)\n        )\n      )\n    )\n}\n\n/**\n * Mount annotation\n *\n * @param el - Annotation element\n * @param container - Containing element\n *\n * @returns Annotation component observable\n */\nexport function mountAnnotation(\n  el: HTMLElement, container: HTMLElement\n): Observable<Component<Annotation>> {\n  return defer(() => {\n    const push$ = new Subject<Annotation>()\n    push$.subscribe({\n\n      /* Handle emission */\n      next({ offset }) {\n        el.style.setProperty(\"--md-tooltip-x\", `${offset.x}px`)\n        el.style.setProperty(\"--md-tooltip-y\", `${offset.y}px`)\n      },\n\n      /* Handle complete */\n      complete() {\n        el.style.removeProperty(\"--md-tooltip-x\")\n        el.style.removeProperty(\"--md-tooltip-y\")\n      }\n    })\n\n    /* Start animation only when annotation is visible */\n    const done$ = push$.pipe(takeLast(1))\n    watchElementVisibility(el)\n      .pipe(\n        takeUntil(done$)\n      )\n        .subscribe(visible => {\n          el.toggleAttribute(\"data-md-visible\", visible)\n        })\n\n    /* Track relative origin of tooltip */\n    push$\n      .pipe(\n        throttleTime(500, animationFrameScheduler),\n        map(() => container.getBoundingClientRect()),\n        map(({ x }) => x)\n      )\n        .subscribe({\n\n          /* Handle emission */\n          next(origin) {\n            if (origin)\n              el.style.setProperty(\"--md-tooltip-0\", `${-origin}px`)\n            else\n              el.style.removeProperty(\"--md-tooltip-0\")\n          },\n\n          /* Handle complete */\n          complete() {\n            el.style.removeProperty(\"--md-tooltip-0\")\n          }\n        })\n\n    /* Close open annotation on click */\n    const index = getElement(\":scope > :last-child\", el)\n    const blur$ = fromEvent(index, \"mousedown\", { once: true })\n    push$\n      .pipe(\n        switchMap(({ active }) => active ? blur$ : EMPTY),\n        tap(ev => ev.preventDefault())\n      )\n        .subscribe(() => el.blur())\n\n    /* Create and return component */\n    return watchAnnotation(el, container)\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  EMPTY,\n  Observable,\n  Subject,\n  defer,\n  finalize,\n  merge,\n  share,\n  takeLast,\n  takeUntil\n} from \"rxjs\"\n\nimport {\n  getElement,\n  getElements,\n  getOptionalElement\n} from \"~/browser\"\nimport { renderAnnotation } from \"~/templates\"\n\nimport { Component } from \"../../../_\"\nimport {\n  Annotation,\n  mountAnnotation\n} from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  print$: Observable<boolean>          /* Media print observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Find all annotation markers in the given code block\n *\n * @param container - Containing element\n *\n * @returns Annotation markers\n */\nfunction findAnnotationMarkers(container: HTMLElement): Text[] {\n  const markers: Text[] = []\n  for (const comment of getElements(\".c, .c1, .cm\", container)) {\n    let match: RegExpExecArray | null\n\n    /* Split text at marker and add to list */\n    let text = comment.firstChild as Text\n    if (text instanceof Text)\n      while ((match = /\\((\\d+)\\)/.exec(text.textContent!))) {\n        const marker = text.splitText(match.index)\n        text = marker.splitText(match[0].length)\n        markers.push(marker)\n      }\n  }\n  return markers\n}\n\n/**\n * Swap the child nodes of two elements\n *\n * @param source - Source element\n * @param target - Target element\n */\nfunction swap(source: HTMLElement, target: HTMLElement): void {\n  target.append(...Array.from(source.childNodes))\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount annotation list\n *\n * This function analyzes the containing code block and checks for markers\n * referring to elements in the given annotation list. If no markers are found,\n * the list is left untouched. Otherwise, list elements are rendered as\n * annotations inside the code block.\n *\n * @param el - Annotation list element\n * @param container - Containing element\n * @param options - Options\n *\n * @returns Annotation component observable\n */\nexport function mountAnnotationList(\n  el: HTMLElement, container: HTMLElement, { print$ }: MountOptions\n): Observable<Component<Annotation>> {\n\n  /* Find and replace all markers with empty annotations */\n  const annotations = new Map<number, HTMLElement>()\n  for (const marker of findAnnotationMarkers(container)) {\n    const [, id] = marker.textContent!.match(/\\((\\d+)\\)/)!\n    if (getOptionalElement(`li:nth-child(${id})`, el)) {\n      annotations.set(+id, renderAnnotation(+id))\n      marker.replaceWith(annotations.get(+id)!)\n    }\n  }\n\n  /* Keep list if there are no annotations to render */\n  if (annotations.size === 0)\n    return EMPTY\n\n  /* Create and return component */\n  return defer(() => {\n    const done$ = new Subject<void>()\n\n    /* Handle print mode - see https://bit.ly/3rgPdpt */\n    print$\n      .pipe(\n        takeUntil(done$.pipe(takeLast(1)))\n      )\n        .subscribe(active => {\n          el.hidden = !active\n\n          /* Show annotations in code block or list (print) */\n          for (const [id, annotation] of annotations) {\n            const inner = getElement(\".md-typeset\", annotation)\n            const child = getElement(`li:nth-child(${id})`, el)\n            if (!active)\n              swap(child, inner)\n            else\n              swap(inner, child)\n          }\n        })\n\n    /* Create and return component */\n    return merge(...[...annotations]\n      .map(([, annotation]) => (\n        mountAnnotation(annotation, container)\n      ))\n    )\n      .pipe(\n        finalize(() => done$.complete()),\n        share()\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  map,\n  of,\n  shareReplay,\n  tap\n} from \"rxjs\"\n\nimport { watchScript } from \"~/browser\"\nimport { h } from \"~/utilities\"\n\nimport { Component } from \"../../../_\"\n\nimport themeCSS from \"./index.css\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mermaid diagram\n */\nexport interface Mermaid {}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Mermaid instance observable\n */\nlet mermaid$: Observable<void>\n\n/**\n * Global sequence number for diagrams\n */\nlet sequence = 0\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch Mermaid script\n *\n * @returns Mermaid scripts observable\n */\nfunction fetchScripts(): Observable<void> {\n  return typeof mermaid === \"undefined\" || mermaid instanceof Element\n    ? watchScript(\"https://unpkg.com/mermaid@9.0.1/dist/mermaid.min.js\")\n    : of(undefined)\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount Mermaid diagram\n *\n * @param el - Code block element\n *\n * @returns Mermaid diagram component observable\n */\nexport function mountMermaid(\n  el: HTMLElement\n): Observable<Component<Mermaid>> {\n  el.classList.remove(\"mermaid\") // Hack: mitigate https://bit.ly/3CiN6Du\n  mermaid$ ||= fetchScripts()\n    .pipe(\n      tap(() => mermaid.initialize({\n        startOnLoad: false,\n        themeCSS\n      })),\n      map(() => undefined),\n      shareReplay(1)\n    )\n\n  /* Render diagram */\n  mermaid$.subscribe(() => {\n    el.classList.add(\"mermaid\") // Hack: mitigate https://bit.ly/3CiN6Du\n    const id = `__mermaid_${sequence++}`\n    const host = h(\"div\", { class: \"mermaid\" })\n    mermaid.mermaidAPI.render(id, el.textContent, (svg: string) => {\n\n      /* Create a shadow root and inject diagram */\n      const shadow = host.attachShadow({ mode: \"closed\" })\n      shadow.innerHTML = svg\n\n      /* Replace code block with diagram */\n      el.replaceWith(host)\n    })\n  })\n\n  /* Create and return component */\n  return mermaid$\n    .pipe(\n      map(() => ({ ref: el }))\n    )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  defer,\n  filter,\n  finalize,\n  map,\n  merge,\n  tap\n} from \"rxjs\"\n\nimport { Component } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Details\n */\nexport interface Details {\n  action: \"open\" | \"close\"             /* Details state */\n  reveal?: boolean                     /* Details is revealed */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  target$: Observable<HTMLElement>     /* Location target observable */\n  print$: Observable<boolean>          /* Media print observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  target$: Observable<HTMLElement>     /* Location target observable */\n  print$: Observable<boolean>          /* Media print observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch details\n *\n * @param el - Details element\n * @param options - Options\n *\n * @returns Details observable\n */\nexport function watchDetails(\n  el: HTMLDetailsElement, { target$, print$ }: WatchOptions\n): Observable<Details> {\n  let open = true\n  return merge(\n\n    /* Open and focus details on location target */\n    target$\n      .pipe(\n        map(target => target.closest(\"details:not([open])\")!),\n        filter(details => el === details),\n        map(() => ({\n          action: \"open\", reveal: true\n        }) as Details)\n      ),\n\n    /* Open details on print and close afterwards */\n    print$\n      .pipe(\n        filter(active => active || !open),\n        tap(() => open = el.open),\n        map(active => ({\n          action: active ? \"open\" : \"close\"\n        }) as Details)\n      )\n  )\n}\n\n/**\n * Mount details\n *\n * This function ensures that `details` tags are opened on anchor jumps and\n * prior to printing, so the whole content of the page is visible.\n *\n * @param el - Details element\n * @param options - Options\n *\n * @returns Details component observable\n */\nexport function mountDetails(\n  el: HTMLDetailsElement, options: MountOptions\n): Observable<Component<Details>> {\n  return defer(() => {\n    const push$ = new Subject<Details>()\n    push$.subscribe(({ action, reveal }) => {\n      if (action === \"open\")\n        el.setAttribute(\"open\", \"\")\n      else\n        el.removeAttribute(\"open\")\n      if (reveal)\n        el.scrollIntoView()\n    })\n\n    /* Create and return component */\n    return watchDetails(el, options)\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { Observable, of } from \"rxjs\"\n\nimport { renderTable } from \"~/templates\"\nimport { h } from \"~/utilities\"\n\nimport { Component } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Data table\n */\nexport interface DataTable {}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Sentinel for replacement\n */\nconst sentinel = h(\"table\")\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount data table\n *\n * This function wraps a data table in another scrollable container, so it can\n * be smoothly scrolled on smaller screen sizes and won't break the layout.\n *\n * @param el - Data table element\n *\n * @returns Data table component observable\n */\nexport function mountDataTable(\n  el: HTMLElement\n): Observable<Component<DataTable>> {\n  el.replaceWith(sentinel)\n  sentinel.replaceWith(renderTable(el))\n\n  /* Create and return component */\n  return of({ ref: el })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  animationFrameScheduler,\n  asyncScheduler,\n  auditTime,\n  combineLatest,\n  defer,\n  finalize,\n  fromEvent,\n  map,\n  merge,\n  skip,\n  startWith,\n  subscribeOn,\n  takeLast,\n  takeUntil,\n  tap\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport {\n  getElement,\n  getElementContentOffset,\n  getElementContentSize,\n  getElementOffset,\n  getElementSize,\n  getElements,\n  watchElementContentOffset,\n  watchElementSize\n} from \"~/browser\"\nimport { renderTabbedControl } from \"~/templates\"\n\nimport { Component } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Content tabs\n */\nexport interface ContentTabs {\n  active: HTMLLabelElement             /* Active tab label */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch content tabs\n *\n * @param el - Content tabs element\n *\n * @returns Content tabs observable\n */\nexport function watchContentTabs(\n  el: HTMLElement\n): Observable<ContentTabs> {\n  const inputs = getElements<HTMLInputElement>(\":scope > input\", el)\n  const initial = inputs.find(input => input.checked) || inputs[0]\n  return merge(...inputs.map(input => fromEvent(input, \"change\")\n    .pipe(\n      map(() => getElement<HTMLLabelElement>(`label[for=\"${input.id}\"]`))\n    )\n  ))\n    .pipe(\n      startWith(getElement<HTMLLabelElement>(`label[for=\"${initial.id}\"]`)),\n      map(active => ({ active }))\n    )\n}\n\n/**\n * Mount content tabs\n *\n * This function scrolls the active tab into view. While this functionality is\n * provided by browsers as part of `scrollInfoView`, browsers will always also\n * scroll the vertical axis, which we do not want. Thus, we decided to provide\n * this functionality ourselves.\n *\n * @param el - Content tabs element\n *\n * @returns Content tabs component observable\n */\nexport function mountContentTabs(\n  el: HTMLElement\n): Observable<Component<ContentTabs>> {\n\n  /* Render content tab previous button for pagination */\n  const prev = renderTabbedControl(\"prev\")\n  el.append(prev)\n\n  /* Render content tab next button for pagination */\n  const next = renderTabbedControl(\"next\")\n  el.append(next)\n\n  /* Mount component on subscription */\n  const container = getElement(\".tabbed-labels\", el)\n  return defer(() => {\n    const push$ = new Subject<ContentTabs>()\n    const done$ = push$.pipe(takeLast(1))\n    combineLatest([push$, watchElementSize(el)])\n      .pipe(\n        auditTime(1, animationFrameScheduler),\n        takeUntil(done$)\n      )\n        .subscribe({\n\n          /* Handle emission */\n          next([{ active }, size]) {\n            const offset = getElementOffset(active)\n            const { width } = getElementSize(active)\n\n            /* Set tab indicator offset and width */\n            el.style.setProperty(\"--md-indicator-x\", `${offset.x}px`)\n            el.style.setProperty(\"--md-indicator-width\", `${width}px`)\n\n            /* Scroll container to active content tab */\n            const content = getElementContentOffset(container)\n            if (\n              offset.x         < content.x              ||\n              offset.x + width > content.x + size.width\n            )\n              container.scrollTo({\n                left: Math.max(0, offset.x - 16),\n                behavior: \"smooth\"\n              })\n          },\n\n          /* Handle complete */\n          complete() {\n            el.style.removeProperty(\"--md-indicator-x\")\n            el.style.removeProperty(\"--md-indicator-width\")\n          }\n        })\n\n    /* Hide content tab buttons on borders */\n    combineLatest([\n      watchElementContentOffset(container),\n      watchElementSize(container)\n    ])\n      .pipe(\n        takeUntil(done$)\n      )\n        .subscribe(([offset, size]) => {\n          const content = getElementContentSize(container)\n          prev.hidden = offset.x < 16\n          next.hidden = offset.x > content.width - size.width - 16\n        })\n\n    /* Paginate content tab container on click */\n    merge(\n      fromEvent(prev, \"click\").pipe(map(() => -1)),\n      fromEvent(next, \"click\").pipe(map(() => +1))\n    )\n      .pipe(\n        takeUntil(done$)\n      )\n        .subscribe(direction => {\n          const { width } = getElementSize(container)\n          container.scrollBy({\n            left: width * direction,\n            behavior: \"smooth\"\n          })\n        })\n\n    /* Set up linking of content tabs, if enabled */\n    if (feature(\"content.tabs.link\"))\n      push$.pipe(skip(1))\n        .subscribe(({ active }) => {\n          const tab = active.innerText.trim()\n          for (const set of getElements(\"[data-tabs]\"))\n            for (const input of getElements<HTMLInputElement>(\n              \":scope > input\", set\n            )) {\n              const label = getElement(`label[for=\"${input.id}\"]`)\n              if (label.innerText.trim() === tab) {\n                input.click()\n                break\n              }\n            }\n\n          /* Persist active tabs in local storage */\n          const tabs = __md_get<string[]>(\"__tabs\") || []\n          __md_set(\"__tabs\", [...new Set([tab, ...tabs])])\n        })\n\n    /* Create and return component */\n    return watchContentTabs(el)\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n    .pipe(\n      subscribeOn(asyncScheduler)\n    )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { Observable, merge } from \"rxjs\"\n\nimport { getElements } from \"~/browser\"\n\nimport { Component } from \"../../_\"\nimport { Annotation } from \"../annotation\"\nimport {\n  CodeBlock,\n  Mermaid,\n  mountCodeBlock,\n  mountMermaid\n} from \"../code\"\nimport {\n  Details,\n  mountDetails\n} from \"../details\"\nimport {\n  DataTable,\n  mountDataTable\n} from \"../table\"\nimport {\n  ContentTabs,\n  mountContentTabs\n} from \"../tabs\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Content\n */\nexport type Content =\n  | Annotation\n  | ContentTabs\n  | CodeBlock\n  | Mermaid\n  | DataTable\n  | Details\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  target$: Observable<HTMLElement>     /* Location target observable */\n  print$: Observable<boolean>          /* Media print observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount content\n *\n * This function mounts all components that are found in the content of the\n * actual article, including code blocks, data tables and details.\n *\n * @param el - Content element\n * @param options - Options\n *\n * @returns Content component observable\n */\nexport function mountContent(\n  el: HTMLElement, { target$, print$ }: MountOptions\n): Observable<Component<Content>> {\n  return merge(\n\n    /* Code blocks */\n    ...getElements(\"pre:not(.mermaid) > code\", el)\n      .map(child => mountCodeBlock(child, { print$ })),\n\n    /* Mermaid diagrams */\n    ...getElements(\"pre.mermaid\", el)\n      .map(child => mountMermaid(child)),\n\n    /* Data tables */\n    ...getElements(\"table:not([class])\", el)\n      .map(child => mountDataTable(child)),\n\n    /* Details */\n    ...getElements(\"details\", el)\n      .map(child => mountDetails(child, { target$, print$ })),\n\n    /* Content tabs */\n    ...getElements(\"[data-tabs]\", el)\n      .map(child => mountContentTabs(child))\n  )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  defer,\n  delay,\n  finalize,\n  map,\n  merge,\n  of,\n  switchMap,\n  tap\n} from \"rxjs\"\n\nimport { getElement } from \"~/browser\"\n\nimport { Component } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Dialog\n */\nexport interface Dialog {\n  message: string                      /* Dialog message */\n  active: boolean                      /* Dialog is active */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  alert$: Subject<string>              /* Alert subject */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  alert$: Subject<string>              /* Alert subject */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch dialog\n *\n * @param _el - Dialog element\n * @param options - Options\n *\n * @returns Dialog observable\n */\nexport function watchDialog(\n  _el: HTMLElement, { alert$ }: WatchOptions\n): Observable<Dialog> {\n  return alert$\n    .pipe(\n      switchMap(message => merge(\n        of(true),\n        of(false).pipe(delay(2000))\n      )\n        .pipe(\n          map(active => ({ message, active }))\n        )\n      )\n    )\n}\n\n/**\n * Mount dialog\n *\n * This function reveals the dialog in the right corner when a new alert is\n * emitted through the subject that is passed as part of the options.\n *\n * @param el - Dialog element\n * @param options - Options\n *\n * @returns Dialog component observable\n */\nexport function mountDialog(\n  el: HTMLElement, options: MountOptions\n): Observable<Component<Dialog>> {\n  const inner = getElement(\".md-typeset\", el)\n  return defer(() => {\n    const push$ = new Subject<Dialog>()\n    push$.subscribe(({ message, active }) => {\n      el.classList.toggle(\"md-dialog--active\", active)\n      inner.textContent = message\n    })\n\n    /* Create and return component */\n    return watchDialog(el, options)\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  bufferCount,\n  combineLatest,\n  combineLatestWith,\n  defer,\n  distinctUntilChanged,\n  distinctUntilKeyChanged,\n  filter,\n  map,\n  of,\n  shareReplay,\n  startWith,\n  switchMap,\n  takeLast,\n  takeUntil\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport {\n  Viewport,\n  watchElementSize,\n  watchToggle\n} from \"~/browser\"\n\nimport { Component } from \"../../_\"\nimport { Main } from \"../../main\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Header\n */\nexport interface Header {\n  height: number                       /* Header visible height */\n  hidden: boolean                      /* Header is hidden */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  header$: Observable<Header>          /* Header observable */\n  main$: Observable<Main>              /* Main area observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Compute whether the header is hidden\n *\n * If the user scrolls past a certain threshold, the header can be hidden when\n * scrolling down, and shown when scrolling up.\n *\n * @param options - Options\n *\n * @returns Toggle observable\n */\nfunction isHidden({ viewport$ }: WatchOptions): Observable<boolean> {\n  if (!feature(\"header.autohide\"))\n    return of(false)\n\n  /* Compute direction and turning point */\n  const direction$ = viewport$\n    .pipe(\n      map(({ offset: { y } }) => y),\n      bufferCount(2, 1),\n      map(([a, b]) => [a < b, b] as const),\n      distinctUntilKeyChanged(0)\n    )\n\n  /* Compute whether header should be hidden */\n  const hidden$ = combineLatest([viewport$, direction$])\n    .pipe(\n      filter(([{ offset }, [, y]]) => Math.abs(y - offset.y) > 100),\n      map(([, [direction]]) => direction),\n      distinctUntilChanged()\n    )\n\n  /* Compute threshold for hiding */\n  const search$ = watchToggle(\"search\")\n  return combineLatest([viewport$, search$])\n    .pipe(\n      map(([{ offset }, search]) => offset.y > 400 && !search),\n      distinctUntilChanged(),\n      switchMap(active => active ? hidden$ : of(false)),\n      startWith(false)\n    )\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch header\n *\n * @param el - Header element\n * @param options - Options\n *\n * @returns Header observable\n */\nexport function watchHeader(\n  el: HTMLElement, options: WatchOptions\n): Observable<Header> {\n  return defer(() => combineLatest([\n    watchElementSize(el),\n    isHidden(options)\n  ]))\n    .pipe(\n      map(([{ height }, hidden]) => ({\n        height,\n        hidden\n      })),\n      distinctUntilChanged((a, b) => (\n        a.height === b.height &&\n        a.hidden === b.hidden\n      )),\n      shareReplay(1)\n    )\n}\n\n/**\n * Mount header\n *\n * This function manages the different states of the header, i.e. whether it's\n * hidden or rendered with a shadow. This depends heavily on the main area.\n *\n * @param el - Header element\n * @param options - Options\n *\n * @returns Header component observable\n */\nexport function mountHeader(\n  el: HTMLElement, { header$, main$ }: MountOptions\n): Observable<Component<Header>> {\n  return defer(() => {\n    const push$ = new Subject<Main>()\n    const done$ = push$.pipe(takeLast(1))\n    push$\n      .pipe(\n        distinctUntilKeyChanged(\"active\"),\n        combineLatestWith(header$)\n      )\n        .subscribe(([{ active }, { hidden }]) => {\n          el.classList.toggle(\"md-header--shadow\", active && !hidden)\n          el.hidden = hidden\n        })\n\n    /* Link to main area */\n    main$.subscribe(push$)\n\n    /* Create and return component */\n    return header$\n      .pipe(\n        takeUntil(done$),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  EMPTY,\n  Observable,\n  Subject,\n  defer,\n  distinctUntilKeyChanged,\n  finalize,\n  map,\n  tap\n} from \"rxjs\"\n\nimport {\n  Viewport,\n  getElementSize,\n  getOptionalElement,\n  watchViewportAt\n} from \"~/browser\"\n\nimport { Component } from \"../../_\"\nimport { Header } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Header\n */\nexport interface HeaderTitle {\n  active: boolean                      /* Header title is active */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  header$: Observable<Header>          /* Header observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  header$: Observable<Header>          /* Header observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch header title\n *\n * @param el - Heading element\n * @param options - Options\n *\n * @returns Header title observable\n */\nexport function watchHeaderTitle(\n  el: HTMLElement, { viewport$, header$ }: WatchOptions\n): Observable<HeaderTitle> {\n  return watchViewportAt(el, { viewport$, header$ })\n    .pipe(\n      map(({ offset: { y } }) => {\n        const { height } = getElementSize(el)\n        return {\n          active: y >= height\n        }\n      }),\n      distinctUntilKeyChanged(\"active\")\n    )\n}\n\n/**\n * Mount header title\n *\n * This function swaps the header title from the site title to the title of the\n * current page when the user scrolls past the first headline.\n *\n * @param el - Header title element\n * @param options - Options\n *\n * @returns Header title component observable\n */\nexport function mountHeaderTitle(\n  el: HTMLElement, options: MountOptions\n): Observable<Component<HeaderTitle>> {\n  return defer(() => {\n    const push$ = new Subject<HeaderTitle>()\n    push$.subscribe(({ active }) => {\n      el.classList.toggle(\"md-header__title--active\", active)\n    })\n\n    /* Obtain headline, if any */\n    const heading = getOptionalElement(\"article h1\")\n    if (typeof heading === \"undefined\")\n      return EMPTY\n\n    /* Create and return component */\n    return watchHeaderTitle(heading, options)\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  combineLatest,\n  distinctUntilChanged,\n  distinctUntilKeyChanged,\n  map,\n  switchMap\n} from \"rxjs\"\n\nimport {\n  Viewport,\n  watchElementSize\n} from \"~/browser\"\n\nimport { Header } from \"../header\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Main area\n */\nexport interface Main {\n  offset: number                       /* Main area top offset */\n  height: number                       /* Main area visible height */\n  active: boolean                      /* Main area is active */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  header$: Observable<Header>          /* Header observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch main area\n *\n * This function returns an observable that computes the visual parameters of\n * the main area which depends on the viewport vertical offset and height, as\n * well as the height of the header element, if the header is fixed.\n *\n * @param el - Main area element\n * @param options - Options\n *\n * @returns Main area observable\n */\nexport function watchMain(\n  el: HTMLElement, { viewport$, header$ }: WatchOptions\n): Observable<Main> {\n\n  /* Compute necessary adjustment for header */\n  const adjust$ = header$\n    .pipe(\n      map(({ height }) => height),\n      distinctUntilChanged()\n    )\n\n  /* Compute the main area's top and bottom borders */\n  const border$ = adjust$\n    .pipe(\n      switchMap(() => watchElementSize(el)\n        .pipe(\n          map(({ height }) => ({\n            top:    el.offsetTop,\n            bottom: el.offsetTop + height\n          })),\n          distinctUntilKeyChanged(\"bottom\")\n        )\n      )\n    )\n\n  /* Compute the main area's offset, visible height and if we scrolled past */\n  return combineLatest([adjust$, border$, viewport$])\n    .pipe(\n      map(([header, { top, bottom }, { offset: { y }, size: { height } }]) => {\n        height = Math.max(0, height\n          - Math.max(0, top    - y,  header)\n          - Math.max(0, height + y - bottom)\n        )\n        return {\n          offset: top - header,\n          height,\n          active: top - header <= y\n        }\n      }),\n      distinctUntilChanged((a, b) => (\n        a.offset === b.offset &&\n        a.height === b.height &&\n        a.active === b.active\n      ))\n    )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  asyncScheduler,\n  defer,\n  finalize,\n  fromEvent,\n  map,\n  mergeMap,\n  observeOn,\n  of,\n  shareReplay,\n  startWith,\n  tap\n} from \"rxjs\"\n\nimport { getElements } from \"~/browser\"\n\nimport { Component } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Palette colors\n */\nexport interface PaletteColor {\n  scheme?: string                      /* Color scheme */\n  primary?: string                     /* Primary color */\n  accent?: string                      /* Accent color */\n}\n\n/**\n * Palette\n */\nexport interface Palette {\n  index: number                        /* Palette index */\n  color: PaletteColor                  /* Palette colors */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch color palette\n *\n * @param inputs - Color palette element\n *\n * @returns Color palette observable\n */\nexport function watchPalette(\n  inputs: HTMLInputElement[]\n): Observable<Palette> {\n  const current = __md_get<Palette>(\"__palette\") || {\n    index: inputs.findIndex(input => matchMedia(\n      input.getAttribute(\"data-md-color-media\")!\n    ).matches)\n  }\n\n  /* Emit changes in color palette */\n  return of(...inputs)\n    .pipe(\n      mergeMap(input => fromEvent(input, \"change\")\n        .pipe(\n          map(() => input)\n        )\n      ),\n      startWith(inputs[Math.max(0, current.index)]),\n      map(input => ({\n        index: inputs.indexOf(input),\n        color: {\n          scheme:  input.getAttribute(\"data-md-color-scheme\"),\n          primary: input.getAttribute(\"data-md-color-primary\"),\n          accent:  input.getAttribute(\"data-md-color-accent\")\n        }\n      } as Palette)),\n      shareReplay(1)\n    )\n}\n\n/**\n * Mount color palette\n *\n * @param el - Color palette element\n *\n * @returns Color palette component observable\n */\nexport function mountPalette(\n  el: HTMLElement\n): Observable<Component<Palette>> {\n  return defer(() => {\n    const push$ = new Subject<Palette>()\n    push$.subscribe(palette => {\n      document.body.setAttribute(\"data-md-color-switching\", \"\")\n\n      /* Set color palette */\n      for (const [key, value] of Object.entries(palette.color))\n        document.body.setAttribute(`data-md-color-${key}`, value)\n\n      /* Toggle visibility */\n      for (let index = 0; index < inputs.length; index++) {\n        const label = inputs[index].nextElementSibling\n        if (label instanceof HTMLElement)\n          label.hidden = palette.index !== index\n      }\n\n      /* Persist preference in local storage */\n      __md_set(\"__palette\", palette)\n    })\n\n    /* Revert transition durations after color switch */\n    push$.pipe(observeOn(asyncScheduler))\n      .subscribe(() => {\n        document.body.removeAttribute(\"data-md-color-switching\")\n      })\n\n    /* Create and return component */\n    const inputs = getElements<HTMLInputElement>(\"input\", el)\n    return watchPalette(inputs)\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport ClipboardJS from \"clipboard\"\nimport {\n  Observable,\n  Subject,\n  map,\n  tap\n} from \"rxjs\"\n\nimport { translation } from \"~/_\"\nimport { getElement } from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Setup options\n */\ninterface SetupOptions {\n  alert$: Subject<string>              /* Alert subject */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Extract text to copy\n *\n * @param el - HTML element\n *\n * @returns Extracted text\n */\nfunction extract(el: HTMLElement): string {\n  el.setAttribute(\"data-md-copying\", \"\")\n  const text = el.innerText\n  el.removeAttribute(\"data-md-copying\")\n  return text\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Set up Clipboard.js integration\n *\n * @param options - Options\n */\nexport function setupClipboardJS(\n  { alert$ }: SetupOptions\n): void {\n  if (ClipboardJS.isSupported()) {\n    new Observable<ClipboardJS.Event>(subscriber => {\n      new ClipboardJS(\"[data-clipboard-target], [data-clipboard-text]\", {\n        text: el => (\n          el.getAttribute(\"data-clipboard-text\")! ||\n          extract(getElement(\n            el.getAttribute(\"data-clipboard-target\")!\n          ))\n        )\n      })\n        .on(\"success\", ev => subscriber.next(ev))\n    })\n      .pipe(\n        tap(ev => {\n          const trigger = ev.trigger as HTMLElement\n          trigger.focus()\n        }),\n        map(() => translation(\"clipboard.copied\"))\n      )\n        .subscribe(alert$)\n  }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  EMPTY,\n  Observable,\n  catchError,\n  defaultIfEmpty,\n  map,\n  of,\n  tap\n} from \"rxjs\"\n\nimport { configuration } from \"~/_\"\nimport { getElements, requestXML } from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Sitemap, i.e. a list of URLs\n */\nexport type Sitemap = string[]\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Preprocess a list of URLs\n *\n * This function replaces the `site_url` in the sitemap with the actual base\n * URL, to allow instant loading to work in occasions like Netlify previews.\n *\n * @param urls - URLs\n *\n * @returns URL path parts\n */\nfunction preprocess(urls: Sitemap): Sitemap {\n  if (urls.length < 2)\n    return [\"\"]\n\n  /* Take the first two URLs and remove everything after the last slash */\n  const [root, next] = [...urls]\n    .sort((a, b) => a.length - b.length)\n    .map(url => url.replace(/[^/]+$/, \"\"))\n\n  /* Compute common prefix */\n  let index = 0\n  if (root === next)\n    index = root.length\n  else\n    while (root.charCodeAt(index) === next.charCodeAt(index))\n      index++\n\n  /* Remove common prefix and return in original order */\n  return urls.map(url => url.replace(root.slice(0, index), \"\"))\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch the sitemap for the given base URL\n *\n * @param base - Base URL\n *\n * @returns Sitemap observable\n */\nexport function fetchSitemap(base?: URL): Observable<Sitemap> {\n  const cached = __md_get<Sitemap>(\"__sitemap\", sessionStorage, base)\n  if (cached) {\n    return of(cached)\n  } else {\n    const config = configuration()\n    return requestXML(new URL(\"sitemap.xml\", base || config.base))\n      .pipe(\n        map(sitemap => preprocess(getElements(\"loc\", sitemap)\n          .map(node => node.textContent!)\n        )),\n        catchError(() => EMPTY), // @todo refactor instant loading\n        defaultIfEmpty([]),\n        tap(sitemap => __md_set(\"__sitemap\", sitemap, sessionStorage, base))\n      )\n  }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  EMPTY,\n  NEVER,\n  Observable,\n  Subject,\n  bufferCount,\n  catchError,\n  concatMap,\n  debounceTime,\n  distinctUntilChanged,\n  distinctUntilKeyChanged,\n  filter,\n  fromEvent,\n  map,\n  merge,\n  of,\n  sample,\n  share,\n  skip,\n  skipUntil,\n  switchMap\n} from \"rxjs\"\n\nimport { configuration, feature } from \"~/_\"\nimport {\n  Viewport,\n  ViewportOffset,\n  getElements,\n  getOptionalElement,\n  request,\n  setLocation,\n  setLocationHash\n} from \"~/browser\"\nimport { getComponentElement } from \"~/components\"\nimport { h } from \"~/utilities\"\n\nimport { fetchSitemap } from \"../sitemap\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * History state\n */\nexport interface HistoryState {\n  url: URL                             /* State URL */\n  offset?: ViewportOffset              /* State viewport offset */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Setup options\n */\ninterface SetupOptions {\n  document$: Subject<Document>         /* Document subject */\n  location$: Subject<URL>              /* Location subject */\n  viewport$: Observable<Viewport>      /* Viewport observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Set up instant loading\n *\n * When fetching, theoretically, we could use `responseType: \"document\"`, but\n * since all MkDocs links are relative, we need to make sure that the current\n * location matches the document we just loaded. Otherwise any relative links\n * in the document could use the old location.\n *\n * This is the reason why we need to synchronize history events and the process\n * of fetching the document for navigation changes (except `popstate` events):\n *\n * 1. Fetch document via `XMLHTTPRequest`\n * 2. Set new location via `history.pushState`\n * 3. Parse and emit fetched document\n *\n * For `popstate` events, we must not use `history.pushState`, or the forward\n * history will be irreversibly overwritten. In case the request fails, the\n * location change is dispatched regularly.\n *\n * @param options - Options\n */\nexport function setupInstantLoading(\n  { document$, location$, viewport$ }: SetupOptions\n): void {\n  const config = configuration()\n  if (location.protocol === \"file:\")\n    return\n\n  /* Disable automatic scroll restoration */\n  if (\"scrollRestoration\" in history) {\n    history.scrollRestoration = \"manual\"\n\n    /* Hack: ensure that reloads restore viewport offset */\n    fromEvent(window, \"beforeunload\")\n      .subscribe(() => {\n        history.scrollRestoration = \"auto\"\n      })\n  }\n\n  /* Hack: ensure absolute favicon link to omit 404s when switching */\n  const favicon = getOptionalElement<HTMLLinkElement>(\"link[rel=icon]\")\n  if (typeof favicon !== \"undefined\")\n    favicon.href = favicon.href\n\n  /* Intercept internal navigation */\n  const push$ = fetchSitemap()\n    .pipe(\n      map(paths => paths.map(path => `${new URL(path, config.base)}`)),\n      switchMap(urls => fromEvent<MouseEvent>(document.body, \"click\")\n        .pipe(\n          filter(ev => !ev.metaKey && !ev.ctrlKey),\n          switchMap(ev => {\n            if (ev.target instanceof Element) {\n              const el = ev.target.closest(\"a\")\n              if (el && !el.target) {\n                const url = new URL(el.href)\n\n                /* Canonicalize URL */\n                url.search = \"\"\n                url.hash = \"\"\n\n                /* Check if URL should be intercepted */\n                if (\n                  url.pathname !== location.pathname &&\n                  urls.includes(url.toString())\n                ) {\n                  ev.preventDefault()\n                  return of({\n                    url: new URL(el.href)\n                  })\n                }\n              }\n            }\n            return NEVER\n          })\n        )\n      ),\n      share<HistoryState>()\n    )\n\n  /* Intercept history back and forward */\n  const pop$ = fromEvent<PopStateEvent>(window, \"popstate\")\n    .pipe(\n      filter(ev => ev.state !== null),\n      map(ev => ({\n        url: new URL(location.href),\n        offset: ev.state\n      })),\n      share<HistoryState>()\n    )\n\n  /* Emit location change */\n  merge(push$, pop$)\n    .pipe(\n      distinctUntilChanged((a, b) => a.url.href === b.url.href),\n      map(({ url }) => url)\n    )\n      .subscribe(location$)\n\n  /* Fetch document via `XMLHTTPRequest` */\n  const response$ = location$\n    .pipe(\n      distinctUntilKeyChanged(\"pathname\"),\n      switchMap(url => request(url.href)\n        .pipe(\n          catchError(() => {\n            setLocation(url)\n            return NEVER\n          })\n        )\n      ),\n      share()\n    )\n\n  /* Set new location via `history.pushState` */\n  push$\n    .pipe(\n      sample(response$)\n    )\n      .subscribe(({ url }) => {\n        history.pushState({}, \"\", `${url}`)\n      })\n\n  /* Parse and emit fetched document */\n  const dom = new DOMParser()\n  response$\n    .pipe(\n      switchMap(res => res.text()),\n      map(res => dom.parseFromString(res, \"text/html\"))\n    )\n      .subscribe(document$)\n\n  /* Replace meta tags and components */\n  document$\n    .pipe(\n      skip(1)\n    )\n      .subscribe(replacement => {\n        for (const selector of [\n\n          /* Meta tags */\n          \"title\",\n          \"link[rel=canonical]\",\n          \"meta[name=author]\",\n          \"meta[name=description]\",\n\n          /* Components */\n          \"[data-md-component=announce]\",\n          \"[data-md-component=container]\",\n          \"[data-md-component=header-topic]\",\n          \"[data-md-component=outdated]\",\n          \"[data-md-component=logo]\",\n          \"[data-md-component=skip]\",\n          ...feature(\"navigation.tabs.sticky\")\n            ? [\"[data-md-component=tabs]\"]\n            : []\n        ]) {\n          const source = getOptionalElement(selector)\n          const target = getOptionalElement(selector, replacement)\n          if (\n            typeof source !== \"undefined\" &&\n            typeof target !== \"undefined\"\n          ) {\n            source.replaceWith(target)\n          }\n        }\n      })\n\n  /* Re-evaluate scripts */\n  document$\n    .pipe(\n      skip(1),\n      map(() => getComponentElement(\"container\")),\n      switchMap(el => getElements(\"script\", el)),\n      concatMap(el => {\n        const script = h(\"script\")\n        if (el.src) {\n          for (const name of el.getAttributeNames())\n            script.setAttribute(name, el.getAttribute(name)!)\n          el.replaceWith(script)\n\n          /* Complete when script is loaded */\n          return new Observable(observer => {\n            script.onload = () => observer.complete()\n          })\n\n        /* Complete immediately */\n        } else {\n          script.textContent = el.textContent\n          el.replaceWith(script)\n          return EMPTY\n        }\n      })\n    )\n      .subscribe()\n\n  /* Emit history state change */\n  merge(push$, pop$)\n    .pipe(\n      sample(document$)\n    )\n      .subscribe(({ url, offset }) => {\n        if (url.hash && !offset) {\n          setLocationHash(url.hash)\n        } else {\n          window.scrollTo(0, offset?.y || 0)\n        }\n      })\n\n  /* Debounce update of viewport offset */\n  viewport$\n    .pipe(\n      skipUntil(push$),\n      debounceTime(250),\n      distinctUntilKeyChanged(\"offset\")\n    )\n      .subscribe(({ offset }) => {\n        history.replaceState(offset, \"\")\n      })\n\n  /* Set viewport offset from history */\n  merge(push$, pop$)\n    .pipe(\n      bufferCount(2, 1),\n      filter(([a, b]) => a.url.pathname === b.url.pathname),\n      map(([, state]) => state)\n    )\n      .subscribe(({ offset }) => {\n        window.scrollTo(0, offset?.y || 0)\n      })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport escapeHTML from \"escape-html\"\n\nimport { SearchIndexDocument } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search document\n */\nexport interface SearchDocument extends SearchIndexDocument {\n  parent?: SearchIndexDocument         /* Parent article */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Search document mapping\n */\nexport type SearchDocumentMap = Map<string, SearchDocument>\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Create a search document mapping\n *\n * @param docs - Search index documents\n *\n * @returns Search document map\n */\nexport function setupSearchDocumentMap(\n  docs: SearchIndexDocument[]\n): SearchDocumentMap {\n  const documents = new Map<string, SearchDocument>()\n  const parents   = new Set<SearchDocument>()\n  for (const doc of docs) {\n    const [path, hash] = doc.location.split(\"#\")\n\n    /* Extract location, title and tags */\n    const location = doc.location\n    const title    = doc.title\n    const tags     = doc.tags\n\n    /* Escape and cleanup text */\n    const text = escapeHTML(doc.text)\n      .replace(/\\s+(?=[,.:;!?])/g, \"\")\n      .replace(/\\s+/g, \" \")\n\n    /* Handle section */\n    if (hash) {\n      const parent = documents.get(path)!\n\n      /* Ignore first section, override article */\n      if (!parents.has(parent)) {\n        parent.title = doc.title\n        parent.text  = text\n\n        /* Remember that we processed the article */\n        parents.add(parent)\n\n      /* Add subsequent section */\n      } else {\n        documents.set(location, {\n          location,\n          title,\n          text,\n          parent\n        })\n      }\n\n    /* Add article */\n    } else {\n      documents.set(location, {\n        location,\n        title,\n        text,\n        ...tags && { tags }\n      })\n    }\n  }\n  return documents\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport escapeHTML from \"escape-html\"\n\nimport { SearchIndexConfig } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search highlight function\n *\n * @param value - Value\n *\n * @returns Highlighted value\n */\nexport type SearchHighlightFn = (value: string) => string\n\n/**\n * Search highlight factory function\n *\n * @param query - Query value\n *\n * @returns Search highlight function\n */\nexport type SearchHighlightFactoryFn = (query: string) => SearchHighlightFn\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Create a search highlighter\n *\n * @param config - Search index configuration\n * @param escape - Whether to escape HTML\n *\n * @returns Search highlight factory function\n */\nexport function setupSearchHighlighter(\n  config: SearchIndexConfig, escape: boolean\n): SearchHighlightFactoryFn {\n  const separator = new RegExp(config.separator, \"img\")\n  const highlight = (_: unknown, data: string, term: string) => {\n    return `${data}<mark data-md-highlight>${term}</mark>`\n  }\n\n  /* Return factory function */\n  return (query: string) => {\n    query = query\n      .replace(/[\\s*+\\-:~^]+/g, \" \")\n      .trim()\n\n    /* Create search term match expression */\n    const match = new RegExp(`(^|${config.separator})(${\n      query\n        .replace(/[|\\\\{}()[\\]^$+*?.-]/g, \"\\\\$&\")\n        .replace(separator, \"|\")\n    })`, \"img\")\n\n    /* Highlight string value */\n    return value => (\n      escape\n        ? escapeHTML(value)\n        : value\n      )\n        .replace(match, highlight)\n        .replace(/<\\/mark>(\\s+)<mark[^>]*>/img, \"$1\")\n  }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search transformation function\n *\n * @param value - Query value\n *\n * @returns Transformed query value\n */\nexport type SearchTransformFn = (value: string) => string\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Default transformation function\n *\n * 1. Search for terms in quotation marks and prepend a `+` modifier to denote\n *    that the resulting document must contain all terms, converting the query\n *    to an `AND` query (as opposed to the default `OR` behavior). While users\n *    may expect terms enclosed in quotation marks to map to span queries, i.e.\n *    for which order is important, Lunr.js doesn't support them, so the best\n *    we can do is to convert the terms to an `AND` query.\n *\n * 2. Replace control characters which are not located at the beginning of the\n *    query or preceded by white space, or are not followed by a non-whitespace\n *    character or are at the end of the query string. Furthermore, filter\n *    unmatched quotation marks.\n *\n * 3. Trim excess whitespace from left and right.\n *\n * @param query - Query value\n *\n * @returns Transformed query value\n */\nexport function defaultTransform(query: string): string {\n  return query\n    .split(/\"([^\"]+)\"/g)                            /* => 1 */\n      .map((terms, index) => index & 1\n        ? terms.replace(/^\\b|^(?![^\\x00-\\x7F]|$)|\\s+/g, \" +\")\n        : terms\n      )\n      .join(\"\")\n    .replace(/\"|(?:^|\\s+)[*+\\-:^~]+(?=\\s+|$)/g, \"\") /* => 2 */\n    .trim()                                         /* => 3 */\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A RTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { SearchIndex, SearchResult } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search message type\n */\nexport const enum SearchMessageType {\n  SETUP,                               /* Search index setup */\n  READY,                               /* Search index ready */\n  QUERY,                               /* Search query */\n  RESULT                               /* Search results */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Message containing the data necessary to setup the search index\n */\nexport interface SearchSetupMessage {\n  type: SearchMessageType.SETUP        /* Message type */\n  data: SearchIndex                    /* Message data */\n}\n\n/**\n * Message indicating the search index is ready\n */\nexport interface SearchReadyMessage {\n  type: SearchMessageType.READY        /* Message type */\n}\n\n/**\n * Message containing a search query\n */\nexport interface SearchQueryMessage {\n  type: SearchMessageType.QUERY        /* Message type */\n  data: string                         /* Message data */\n}\n\n/**\n * Message containing results for a search query\n */\nexport interface SearchResultMessage {\n  type: SearchMessageType.RESULT       /* Message type */\n  data: SearchResult                   /* Message data */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Message exchanged with the search worker\n */\nexport type SearchMessage =\n  | SearchSetupMessage\n  | SearchReadyMessage\n  | SearchQueryMessage\n  | SearchResultMessage\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Type guard for search setup messages\n *\n * @param message - Search worker message\n *\n * @returns Test result\n */\nexport function isSearchSetupMessage(\n  message: SearchMessage\n): message is SearchSetupMessage {\n  return message.type === SearchMessageType.SETUP\n}\n\n/**\n * Type guard for search ready messages\n *\n * @param message - Search worker message\n *\n * @returns Test result\n */\nexport function isSearchReadyMessage(\n  message: SearchMessage\n): message is SearchReadyMessage {\n  return message.type === SearchMessageType.READY\n}\n\n/**\n * Type guard for search query messages\n *\n * @param message - Search worker message\n *\n * @returns Test result\n */\nexport function isSearchQueryMessage(\n  message: SearchMessage\n): message is SearchQueryMessage {\n  return message.type === SearchMessageType.QUERY\n}\n\n/**\n * Type guard for search result messages\n *\n * @param message - Search worker message\n *\n * @returns Test result\n */\nexport function isSearchResultMessage(\n  message: SearchMessage\n): message is SearchResultMessage {\n  return message.type === SearchMessageType.RESULT\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A RTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  ObservableInput,\n  Subject,\n  from,\n  map,\n  share\n} from \"rxjs\"\n\nimport { configuration, feature, translation } from \"~/_\"\nimport { WorkerHandler, watchWorker } from \"~/browser\"\n\nimport { SearchIndex } from \"../../_\"\nimport {\n  SearchOptions,\n  SearchPipeline\n} from \"../../options\"\nimport {\n  SearchMessage,\n  SearchMessageType,\n  SearchSetupMessage,\n  isSearchResultMessage\n} from \"../message\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search worker\n */\nexport type SearchWorker = WorkerHandler<SearchMessage>\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Set up search index\n *\n * @param data - Search index\n *\n * @returns Search index\n */\nfunction setupSearchIndex({ config, docs }: SearchIndex): SearchIndex {\n\n  /* Override default language with value from translation */\n  if (config.lang.length === 1 && config.lang[0] === \"en\")\n    config.lang = [\n      translation(\"search.config.lang\")\n    ]\n\n  /* Override default separator with value from translation */\n  if (config.separator === \"[\\\\s\\\\-]+\")\n    config.separator = translation(\"search.config.separator\")\n\n  /* Set pipeline from translation */\n  const pipeline = translation(\"search.config.pipeline\")\n    .split(/\\s*,\\s*/)\n    .filter(Boolean) as SearchPipeline\n\n  /* Determine search options */\n  const options: SearchOptions = {\n    pipeline,\n    suggestions: feature(\"search.suggest\")\n  }\n\n  /* Return search index after defaulting */\n  return { config, docs, options }\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Set up search worker\n *\n * This function creates a web worker to set up and query the search index,\n * which is done using Lunr.js. The index must be passed as an observable to\n * enable hacks like _localsearch_ via search index embedding as JSON.\n *\n * @param url - Worker URL\n * @param index - Search index observable input\n *\n * @returns Search worker\n */\nexport function setupSearchWorker(\n  url: string, index: ObservableInput<SearchIndex>\n): SearchWorker {\n  const config = configuration()\n  const worker = new Worker(url)\n\n  /* Create communication channels and resolve relative links */\n  const tx$ = new Subject<SearchMessage>()\n  const rx$ = watchWorker(worker, { tx$ })\n    .pipe(\n      map(message => {\n        if (isSearchResultMessage(message)) {\n          for (const result of message.data.items)\n            for (const document of result)\n              document.location = `${new URL(document.location, config.base)}`\n        }\n        return message\n      }),\n      share()\n    )\n\n  /* Set up search index */\n  from(index)\n    .pipe(\n      map(data => ({\n        type: SearchMessageType.SETUP,\n        data: setupSearchIndex(data)\n      } as SearchSetupMessage))\n    )\n      .subscribe(tx$.next.bind(tx$))\n\n  /* Return search worker */\n  return { tx$, rx$ }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  EMPTY,\n  Subject,\n  catchError,\n  combineLatest,\n  filter,\n  fromEvent,\n  map,\n  of,\n  switchMap,\n  withLatestFrom\n} from \"rxjs\"\n\nimport { configuration } from \"~/_\"\nimport {\n  getElement,\n  getLocation,\n  requestJSON,\n  setLocation\n} from \"~/browser\"\nimport { getComponentElements } from \"~/components\"\nimport {\n  Version,\n  renderVersionSelector\n} from \"~/templates\"\n\nimport { fetchSitemap } from \"../sitemap\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Setup options\n */\ninterface SetupOptions {\n  document$: Subject<Document>         /* Document subject */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Set up version selector\n *\n * @param options - Options\n */\nexport function setupVersionSelector(\n  { document$ }: SetupOptions\n): void {\n  const config = configuration()\n  const versions$ = requestJSON<Version[]>(\n    new URL(\"../versions.json\", config.base)\n  )\n    .pipe(\n      catchError(() => EMPTY) // @todo refactor instant loading\n    )\n\n  /* Determine current version */\n  const current$ = versions$\n    .pipe(\n      map(versions => {\n        const [, current] = config.base.match(/([^/]+)\\/?$/)!\n        return versions.find(({ version, aliases }) => (\n          version === current || aliases.includes(current)\n        )) || versions[0]\n      })\n    )\n\n  /* Intercept inter-version navigation */\n  versions$\n    .pipe(\n      map(versions => new Map(versions.map(version => [\n        `${new URL(`../${version.version}/`, config.base)}`,\n        version\n      ]))),\n      switchMap(urls => fromEvent<MouseEvent>(document.body, \"click\")\n        .pipe(\n          filter(ev => !ev.metaKey && !ev.ctrlKey),\n          withLatestFrom(current$),\n          switchMap(([ev, current]) => {\n            if (ev.target instanceof Element) {\n              const el = ev.target.closest(\"a\")\n              if (el && !el.target && urls.has(el.href)) {\n                const url = el.href\n                // This is a temporary hack to detect if a version inside the\n                // version selector or on another part of the site was clicked.\n                // If we're inside the version selector, we definitely want to\n                // find the same page, as we might have different deployments\n                // due to aliases. However, if we're outside the version\n                // selector, we must abort here, because we might otherwise\n                // interfere with instant loading. We need to refactor this\n                // at some point together with instant loading.\n                //\n                // See https://github.com/squidfunk/mkdocs-material/issues/4012\n                if (!ev.target.closest(\".md-version\")) {\n                  const version = urls.get(url)!\n                  if (version === current)\n                    return EMPTY\n                }\n                ev.preventDefault()\n                return of(url)\n              }\n            }\n            return EMPTY\n          }),\n          switchMap(url => {\n            const { version } = urls.get(url)!\n            return fetchSitemap(new URL(url))\n              .pipe(\n                map(sitemap => {\n                  const location = getLocation()\n                  const path = location.href.replace(config.base, \"\")\n                  return sitemap.includes(path)\n                    ? new URL(`../${version}/${path}`, config.base)\n                    : new URL(url)\n                })\n              )\n          })\n        )\n      )\n    )\n      .subscribe(url => setLocation(url))\n\n  /* Render version selector and warning */\n  combineLatest([versions$, current$])\n    .subscribe(([versions, current]) => {\n      const topic = getElement(\".md-header__topic\")\n      topic.appendChild(renderVersionSelector(versions, current))\n    })\n\n  /* Integrate outdated version banner with instant loading */\n  document$.pipe(switchMap(() => current$))\n    .subscribe(current => {\n\n      /* Check if version state was already determined */\n      let outdated = __md_get(\"__outdated\", sessionStorage)\n      if (outdated === null) {\n        const latest = config.version?.default || \"latest\"\n        outdated = !current.aliases.includes(latest)\n\n        /* Persist version state in session storage */\n        __md_set(\"__outdated\", outdated, sessionStorage)\n      }\n\n      /* Unhide outdated version banner */\n      if (outdated)\n        for (const warning of getComponentElements(\"outdated\"))\n          warning.hidden = false\n    })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  combineLatest,\n  delay,\n  distinctUntilChanged,\n  distinctUntilKeyChanged,\n  filter,\n  finalize,\n  fromEvent,\n  map,\n  merge,\n  share,\n  shareReplay,\n  startWith,\n  take,\n  takeLast,\n  takeUntil,\n  tap\n} from \"rxjs\"\n\nimport { translation } from \"~/_\"\nimport {\n  getLocation,\n  setToggle,\n  watchElementFocus,\n  watchToggle\n} from \"~/browser\"\nimport {\n  SearchMessageType,\n  SearchQueryMessage,\n  SearchWorker,\n  defaultTransform,\n  isSearchReadyMessage\n} from \"~/integrations\"\n\nimport { Component } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search query\n */\nexport interface SearchQuery {\n  value: string                        /* Query value */\n  focus: boolean                       /* Query focus */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch search query\n *\n * Note that the focus event which triggers re-reading the current query value\n * is delayed by `1ms` so the input's empty state is allowed to propagate.\n *\n * @param el - Search query element\n * @param worker - Search worker\n *\n * @returns Search query observable\n */\nexport function watchSearchQuery(\n  el: HTMLInputElement, { rx$ }: SearchWorker\n): Observable<SearchQuery> {\n  const fn = __search?.transform || defaultTransform\n\n  /* Immediately show search dialog */\n  const { searchParams } = getLocation()\n  if (searchParams.has(\"q\"))\n    setToggle(\"search\", true)\n\n  /* Intercept query parameter (deep link) */\n  const param$ = rx$\n    .pipe(\n      filter(isSearchReadyMessage),\n      take(1),\n      map(() => searchParams.get(\"q\") || \"\")\n    )\n\n  /* Remove query parameter when search is closed */\n  watchToggle(\"search\")\n    .pipe(\n      filter(active => !active),\n      take(1)\n    )\n      .subscribe(() => {\n        const url = new URL(location.href)\n        url.searchParams.delete(\"q\")\n        history.replaceState({}, \"\", `${url}`)\n      })\n\n  /* Set query from parameter */\n  param$.subscribe(value => { // TODO: not ideal - find a better way\n    if (value) {\n      el.value = value\n      el.focus()\n    }\n  })\n\n  /* Intercept focus and input events */\n  const focus$ = watchElementFocus(el)\n  const value$ = merge(\n    fromEvent(el, \"keyup\"),\n    fromEvent(el, \"focus\").pipe(delay(1)),\n    param$\n  )\n    .pipe(\n      map(() => fn(el.value)),\n      startWith(\"\"),\n      distinctUntilChanged(),\n    )\n\n  /* Combine into single observable */\n  return combineLatest([value$, focus$])\n    .pipe(\n      map(([value, focus]) => ({ value, focus })),\n      shareReplay(1)\n    )\n}\n\n/**\n * Mount search query\n *\n * @param el - Search query element\n * @param worker - Search worker\n *\n * @returns Search query component observable\n */\nexport function mountSearchQuery(\n  el: HTMLInputElement, { tx$, rx$ }: SearchWorker\n): Observable<Component<SearchQuery, HTMLInputElement>> {\n  const push$ = new Subject<SearchQuery>()\n  const done$ = push$.pipe(takeLast(1))\n\n  /* Handle value changes */\n  push$\n    .pipe(\n      distinctUntilKeyChanged(\"value\"),\n      map(({ value }): SearchQueryMessage => ({\n        type: SearchMessageType.QUERY,\n        data: value\n      }))\n    )\n      .subscribe(tx$.next.bind(tx$))\n\n  /* Handle focus changes */\n  push$\n    .pipe(\n      distinctUntilKeyChanged(\"focus\")\n    )\n      .subscribe(({ focus }) => {\n        if (focus) {\n          setToggle(\"search\", focus)\n          el.placeholder = \"\"\n        } else {\n          el.placeholder = translation(\"search.placeholder\")\n        }\n      })\n\n  /* Handle reset */\n  fromEvent(el.form!, \"reset\")\n    .pipe(\n      takeUntil(done$)\n    )\n      .subscribe(() => el.focus())\n\n  /* Create and return component */\n  return watchSearchQuery(el, { tx$, rx$ })\n    .pipe(\n      tap(state => push$.next(state)),\n      finalize(() => push$.complete()),\n      map(state => ({ ref: el, ...state })),\n      share()\n    )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  bufferCount,\n  filter,\n  finalize,\n  map,\n  merge,\n  of,\n  skipUntil,\n  switchMap,\n  take,\n  tap,\n  withLatestFrom,\n  zipWith\n} from \"rxjs\"\n\nimport { translation } from \"~/_\"\nimport {\n  getElement,\n  watchElementBoundary\n} from \"~/browser\"\nimport {\n  SearchResult,\n  SearchWorker,\n  isSearchReadyMessage,\n  isSearchResultMessage\n} from \"~/integrations\"\nimport { renderSearchResultItem } from \"~/templates\"\nimport { round } from \"~/utilities\"\n\nimport { Component } from \"../../_\"\nimport { SearchQuery } from \"../query\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  query$: Observable<SearchQuery>      /* Search query observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount search result list\n *\n * This function performs a lazy rendering of the search results, depending on\n * the vertical offset of the search result container.\n *\n * @param el - Search result list element\n * @param worker - Search worker\n * @param options - Options\n *\n * @returns Search result list component observable\n */\nexport function mountSearchResult(\n  el: HTMLElement, { rx$ }: SearchWorker, { query$ }: MountOptions\n): Observable<Component<SearchResult>> {\n  const push$ = new Subject<SearchResult>()\n  const boundary$ = watchElementBoundary(el.parentElement!)\n    .pipe(\n      filter(Boolean)\n    )\n\n  /* Retrieve nested components */\n  const meta = getElement(\":scope > :first-child\", el)\n  const list = getElement(\":scope > :last-child\", el)\n\n  /* Wait until search is ready */\n  const ready$ = rx$\n    .pipe(\n      filter(isSearchReadyMessage),\n      take(1)\n    )\n\n  /* Update search result metadata */\n  push$\n    .pipe(\n      withLatestFrom(query$),\n      skipUntil(ready$)\n    )\n      .subscribe(([{ items }, { value }]) => {\n        if (value) {\n          switch (items.length) {\n\n            /* No results */\n            case 0:\n              meta.textContent = translation(\"search.result.none\")\n              break\n\n            /* One result */\n            case 1:\n              meta.textContent = translation(\"search.result.one\")\n              break\n\n            /* Multiple result */\n            default:\n              meta.textContent = translation(\n                \"search.result.other\",\n                round(items.length)\n              )\n          }\n        } else {\n          meta.textContent = translation(\"search.result.placeholder\")\n        }\n      })\n\n  /* Update search result list */\n  push$\n    .pipe(\n      tap(() => list.innerHTML = \"\"),\n      switchMap(({ items }) => merge(\n        of(...items.slice(0, 10)),\n        of(...items.slice(10))\n          .pipe(\n            bufferCount(4),\n            zipWith(boundary$),\n            switchMap(([chunk]) => chunk)\n          )\n      ))\n    )\n      .subscribe(result => list.appendChild(\n        renderSearchResultItem(result)\n      ))\n\n  /* Filter search result message */\n  const result$ = rx$\n    .pipe(\n      filter(isSearchResultMessage),\n      map(({ data }) => data)\n    )\n\n  /* Create and return component */\n  return result$\n    .pipe(\n      tap(state => push$.next(state)),\n      finalize(() => push$.complete()),\n      map(state => ({ ref: el, ...state }))\n    )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  finalize,\n  fromEvent,\n  map,\n  tap\n} from \"rxjs\"\n\nimport { getLocation } from \"~/browser\"\n\nimport { Component } from \"../../_\"\nimport { SearchQuery } from \"../query\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search sharing\n */\nexport interface SearchShare {\n  url: URL                             /* Deep link for sharing */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  query$: Observable<SearchQuery>      /* Search query observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  query$: Observable<SearchQuery>      /* Search query observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount search sharing\n *\n * @param _el - Search sharing element\n * @param options - Options\n *\n * @returns Search sharing observable\n */\nexport function watchSearchShare(\n  _el: HTMLElement, { query$ }: WatchOptions\n): Observable<SearchShare> {\n  return query$\n    .pipe(\n      map(({ value }) => {\n        const url = getLocation()\n        url.hash = \"\"\n        url.searchParams.delete(\"h\")\n        url.searchParams.set(\"q\", value)\n        return { url }\n      })\n    )\n}\n\n/**\n * Mount search sharing\n *\n * @param el - Search sharing element\n * @param options - Options\n *\n * @returns Search sharing component observable\n */\nexport function mountSearchShare(\n  el: HTMLAnchorElement, options: MountOptions\n): Observable<Component<SearchShare>> {\n  const push$ = new Subject<SearchShare>()\n  push$.subscribe(({ url }) => {\n    el.setAttribute(\"data-clipboard-text\", el.href)\n    el.href = `${url}`\n  })\n\n  /* Prevent following of link */\n  fromEvent(el, \"click\")\n    .subscribe(ev => ev.preventDefault())\n\n  /* Create and return component */\n  return watchSearchShare(el, options)\n    .pipe(\n      tap(state => push$.next(state)),\n      finalize(() => push$.complete()),\n      map(state => ({ ref: el, ...state }))\n    )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  asyncScheduler,\n  combineLatestWith,\n  distinctUntilChanged,\n  filter,\n  finalize,\n  fromEvent,\n  map,\n  merge,\n  observeOn,\n  tap\n} from \"rxjs\"\n\nimport { Keyboard } from \"~/browser\"\nimport {\n  SearchResult,\n  SearchWorker,\n  isSearchResultMessage\n} from \"~/integrations\"\n\nimport { Component, getComponentElement } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search suggestions\n */\nexport interface SearchSuggest {}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  keyboard$: Observable<Keyboard>      /* Keyboard observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount search suggestions\n *\n * This function will perform a lazy rendering of the search results, depending\n * on the vertical offset of the search result container.\n *\n * @param el - Search result list element\n * @param worker - Search worker\n * @param options - Options\n *\n * @returns Search result list component observable\n */\nexport function mountSearchSuggest(\n  el: HTMLElement, { rx$ }: SearchWorker, { keyboard$ }: MountOptions\n): Observable<Component<SearchSuggest>> {\n  const push$ = new Subject<SearchResult>()\n\n  /* Retrieve query component and track all changes */\n  const query  = getComponentElement(\"search-query\")\n  const query$ = merge(\n    fromEvent(query, \"keydown\"),\n    fromEvent(query, \"focus\")\n  )\n    .pipe(\n      observeOn(asyncScheduler),\n      map(() => query.value),\n      distinctUntilChanged(),\n    )\n\n  /* Update search suggestions */\n  push$\n    .pipe(\n      combineLatestWith(query$),\n      map(([{ suggestions }, value]) => {\n        const words = value.split(/([\\s-]+)/)\n        if (suggestions?.length && words[words.length - 1]) {\n          const last = suggestions[suggestions.length - 1]\n          if (last.startsWith(words[words.length - 1]))\n            words[words.length - 1] = last\n        } else {\n          words.length = 0\n        }\n        return words\n      })\n    )\n      .subscribe(words => el.innerHTML = words\n        .join(\"\")\n        .replace(/\\s/g, \"&nbsp;\")\n      )\n\n  /* Set up search keyboard handlers */\n  keyboard$\n    .pipe(\n      filter(({ mode }) => mode === \"search\")\n    )\n      .subscribe(key => {\n        switch (key.type) {\n\n          /* Right arrow: accept current suggestion */\n          case \"ArrowRight\":\n            if (\n              el.innerText.length &&\n              query.selectionStart === query.value.length\n            )\n              query.value = el.innerText\n            break\n        }\n      })\n\n  /* Filter search result message */\n  const result$ = rx$\n    .pipe(\n      filter(isSearchResultMessage),\n      map(({ data }) => data)\n    )\n\n  /* Create and return component */\n  return result$\n    .pipe(\n      tap(state => push$.next(state)),\n      finalize(() => push$.complete()),\n      map(() => ({ ref: el }))\n    )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  NEVER,\n  Observable,\n  ObservableInput,\n  filter,\n  merge,\n  mergeWith,\n  sample,\n  take\n} from \"rxjs\"\n\nimport { configuration } from \"~/_\"\nimport {\n  Keyboard,\n  getActiveElement,\n  getElements,\n  setToggle\n} from \"~/browser\"\nimport {\n  SearchIndex,\n  SearchResult,\n  isSearchQueryMessage,\n  isSearchReadyMessage,\n  setupSearchWorker\n} from \"~/integrations\"\n\nimport {\n  Component,\n  getComponentElement,\n  getComponentElements\n} from \"../../_\"\nimport {\n  SearchQuery,\n  mountSearchQuery\n} from \"../query\"\nimport { mountSearchResult } from \"../result\"\nimport {\n  SearchShare,\n  mountSearchShare\n} from \"../share\"\nimport {\n  SearchSuggest,\n  mountSearchSuggest\n} from \"../suggest\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search\n */\nexport type Search =\n  | SearchQuery\n  | SearchResult\n  | SearchShare\n  | SearchSuggest\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  index$: ObservableInput<SearchIndex> /* Search index observable */\n  keyboard$: Observable<Keyboard>      /* Keyboard observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount search\n *\n * This function sets up the search functionality, including the underlying\n * web worker and all keyboard bindings.\n *\n * @param el - Search element\n * @param options - Options\n *\n * @returns Search component observable\n */\nexport function mountSearch(\n  el: HTMLElement, { index$, keyboard$ }: MountOptions\n): Observable<Component<Search>> {\n  const config = configuration()\n  try {\n    const url = __search?.worker || config.search\n    const worker = setupSearchWorker(url, index$)\n\n    /* Retrieve query and result components */\n    const query  = getComponentElement(\"search-query\", el)\n    const result = getComponentElement(\"search-result\", el)\n\n    /* Re-emit query when search is ready */\n    const { tx$, rx$ } = worker\n    tx$\n      .pipe(\n        filter(isSearchQueryMessage),\n        sample(rx$.pipe(filter(isSearchReadyMessage))),\n        take(1)\n      )\n        .subscribe(tx$.next.bind(tx$))\n\n    /* Set up search keyboard handlers */\n    keyboard$\n      .pipe(\n        filter(({ mode }) => mode === \"search\")\n      )\n        .subscribe(key => {\n          const active = getActiveElement()\n          switch (key.type) {\n\n            /* Enter: go to first (best) result */\n            case \"Enter\":\n              if (active === query) {\n                const anchors = new Map<HTMLAnchorElement, number>()\n                for (const anchor of getElements<HTMLAnchorElement>(\n                  \":first-child [href]\", result\n                )) {\n                  const article = anchor.firstElementChild!\n                  anchors.set(anchor, parseFloat(\n                    article.getAttribute(\"data-md-score\")!\n                  ))\n                }\n\n                /* Go to result with highest score, if any */\n                if (anchors.size) {\n                  const [[best]] = [...anchors].sort(([, a], [, b]) => b - a)\n                  best.click()\n                }\n\n                /* Otherwise omit form submission */\n                key.claim()\n              }\n              break\n\n            /* Escape or Tab: close search */\n            case \"Escape\":\n            case \"Tab\":\n              setToggle(\"search\", false)\n              query.blur()\n              break\n\n            /* Vertical arrows: select previous or next search result */\n            case \"ArrowUp\":\n            case \"ArrowDown\":\n              if (typeof active === \"undefined\") {\n                query.focus()\n              } else {\n                const els = [query, ...getElements(\n                  \":not(details) > [href], summary, details[open] [href]\",\n                  result\n                )]\n                const i = Math.max(0, (\n                  Math.max(0, els.indexOf(active)) + els.length + (\n                    key.type === \"ArrowUp\" ? -1 : +1\n                  )\n                ) % els.length)\n                els[i].focus()\n              }\n\n              /* Prevent scrolling of page */\n              key.claim()\n              break\n\n            /* All other keys: hand to search query */\n            default:\n              if (query !== getActiveElement())\n                query.focus()\n          }\n        })\n\n    /* Set up global keyboard handlers */\n    keyboard$\n      .pipe(\n        filter(({ mode }) => mode === \"global\"),\n      )\n        .subscribe(key => {\n          switch (key.type) {\n\n            /* Open search and select query */\n            case \"f\":\n            case \"s\":\n            case \"/\":\n              query.focus()\n              query.select()\n\n              /* Prevent scrolling of page */\n              key.claim()\n              break\n          }\n        })\n\n    /* Create and return component */\n    const query$  = mountSearchQuery(query, worker)\n    const result$ = mountSearchResult(result, worker, { query$ })\n    return merge(query$, result$)\n      .pipe(\n        mergeWith(\n\n          /* Search sharing */\n          ...getComponentElements(\"search-share\", el)\n            .map(child => mountSearchShare(child, { query$ })),\n\n          /* Search suggestions */\n          ...getComponentElements(\"search-suggest\", el)\n            .map(child => mountSearchSuggest(child, worker, { keyboard$ }))\n        )\n      )\n\n  /* Gracefully handle broken search */\n  } catch (err) {\n    el.hidden = true\n    return NEVER\n  }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  ObservableInput,\n  combineLatest,\n  filter,\n  map,\n  startWith\n} from \"rxjs\"\n\nimport { getLocation } from \"~/browser\"\nimport {\n  SearchIndex,\n  setupSearchHighlighter\n} from \"~/integrations\"\nimport { h } from \"~/utilities\"\n\nimport { Component } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search highlighting\n */\nexport interface SearchHighlight {\n  nodes: Map<ChildNode, string>        /* Map of replacements */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  index$: ObservableInput<SearchIndex> /* Search index observable */\n  location$: Observable<URL>           /* Location observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount search highlighting\n *\n * @param el - Content element\n * @param options - Options\n *\n * @returns Search highlighting component observable\n */\nexport function mountSearchHiglight(\n  el: HTMLElement, { index$, location$ }: MountOptions\n): Observable<Component<SearchHighlight>> {\n  return combineLatest([\n    index$,\n    location$\n      .pipe(\n        startWith(getLocation()),\n        filter(url => !!url.searchParams.get(\"h\"))\n      )\n  ])\n    .pipe(\n      map(([index, url]) => setupSearchHighlighter(index.config, true)(\n        url.searchParams.get(\"h\")!\n      )),\n      map(fn => {\n        const nodes = new Map<ChildNode, string>()\n\n        /* Traverse text nodes and collect matches */\n        const it = document.createNodeIterator(el, NodeFilter.SHOW_TEXT)\n        for (let node = it.nextNode(); node; node = it.nextNode()) {\n          if (node.parentElement?.offsetHeight) {\n            const original = node.textContent!\n            const replaced = fn(original)\n            if (replaced.length > original.length)\n              nodes.set(node as ChildNode, replaced)\n          }\n        }\n\n        /* Replace original nodes with matches */\n        for (const [node, text] of nodes) {\n          const { childNodes } = h(\"span\", null, text)\n          node.replaceWith(...Array.from(childNodes))\n        }\n\n        /* Return component */\n        return { ref: el, nodes }\n      })\n    )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  animationFrameScheduler,\n  auditTime,\n  combineLatest,\n  defer,\n  distinctUntilChanged,\n  finalize,\n  map,\n  tap,\n  withLatestFrom\n} from \"rxjs\"\n\nimport {\n  Viewport,\n  getElement,\n  getElementOffset\n} from \"~/browser\"\n\nimport { Component } from \"../_\"\nimport { Header } from \"../header\"\nimport { Main } from \"../main\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Sidebar\n */\nexport interface Sidebar {\n  height: number                       /* Sidebar height */\n  locked: boolean                      /* Sidebar is locked */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  main$: Observable<Main>              /* Main area observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  header$: Observable<Header>          /* Header observable */\n  main$: Observable<Main>              /* Main area observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch sidebar\n *\n * This function returns an observable that computes the visual parameters of\n * the sidebar which depends on the vertical viewport offset, as well as the\n * height of the main area. When the page is scrolled beyond the header, the\n * sidebar is locked and fills the remaining space.\n *\n * @param el - Sidebar element\n * @param options - Options\n *\n * @returns Sidebar observable\n */\nexport function watchSidebar(\n  el: HTMLElement, { viewport$, main$ }: WatchOptions\n): Observable<Sidebar> {\n  const parent = el.parentElement!\n  const adjust =\n    parent.offsetTop -\n    parent.parentElement!.offsetTop\n\n  /* Compute the sidebar's available height and if it should be locked */\n  return combineLatest([main$, viewport$])\n    .pipe(\n      map(([{ offset, height }, { offset: { y } }]) => {\n        height = height\n          + Math.min(adjust, Math.max(0, y - offset))\n          - adjust\n        return {\n          height,\n          locked: y >= offset + adjust\n        }\n      }),\n      distinctUntilChanged((a, b) => (\n        a.height === b.height &&\n        a.locked === b.locked\n      ))\n    )\n}\n\n/**\n * Mount sidebar\n *\n * This function doesn't set the height of the actual sidebar, but of its first\n * child \u2013 the `.md-sidebar__scrollwrap` element in order to mitigiate jittery\n * sidebars when the footer is scrolled into view. At some point we switched\n * from `absolute` / `fixed` positioning to `sticky` positioning, significantly\n * reducing jitter in some browsers (respectively Firefox and Safari) when\n * scrolling from the top. However, top-aligned sticky positioning means that\n * the sidebar snaps to the bottom when the end of the container is reached.\n * This is what leads to the mentioned jitter, as the sidebar's height may be\n * updated too slowly.\n *\n * This behaviour can be mitigiated by setting the height of the sidebar to `0`\n * while preserving the padding, and the height on its first element.\n *\n * @param el - Sidebar element\n * @param options - Options\n *\n * @returns Sidebar component observable\n */\nexport function mountSidebar(\n  el: HTMLElement, { header$, ...options }: MountOptions\n): Observable<Component<Sidebar>> {\n  const inner = getElement(\".md-sidebar__scrollwrap\", el)\n  const { y } = getElementOffset(inner)\n  return defer(() => {\n    const push$ = new Subject<Sidebar>()\n    push$\n      .pipe(\n        auditTime(0, animationFrameScheduler),\n        withLatestFrom(header$)\n      )\n        .subscribe({\n\n          /* Handle emission */\n          next([{ height }, { height: offset }]) {\n            inner.style.height = `${height - 2 * y}px`\n            el.style.top       = `${offset}px`\n          },\n\n          /* Handle complete */\n          complete() {\n            inner.style.height = \"\"\n            el.style.top       = \"\"\n          }\n        })\n\n    /* Create and return component */\n    return watchSidebar(el, options)\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { Repo, User } from \"github-types\"\nimport {\n  EMPTY,\n  Observable,\n  catchError,\n  defaultIfEmpty,\n  map,\n  zip\n} from \"rxjs\"\n\nimport { requestJSON } from \"~/browser\"\n\nimport { SourceFacts } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * GitHub release (partial)\n */\ninterface Release {\n  tag_name: string                     /* Tag name */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch GitHub repository facts\n *\n * @param user - GitHub user or organization\n * @param repo - GitHub repository\n *\n * @returns Repository facts observable\n */\nexport function fetchSourceFactsFromGitHub(\n  user: string, repo?: string\n): Observable<SourceFacts> {\n  if (typeof repo !== \"undefined\") {\n    const url = `https://api.github.com/repos/${user}/${repo}`\n    return zip(\n\n      /* Fetch version */\n      requestJSON<Release>(`${url}/releases/latest`)\n        .pipe(\n          catchError(() => EMPTY), // @todo refactor instant loading\n          map(release => ({\n            version: release.tag_name\n          })),\n          defaultIfEmpty({})\n        ),\n\n      /* Fetch stars and forks */\n      requestJSON<Repo>(url)\n        .pipe(\n          catchError(() => EMPTY), // @todo refactor instant loading\n          map(info => ({\n            stars: info.stargazers_count,\n            forks: info.forks_count\n          })),\n          defaultIfEmpty({})\n        )\n    )\n      .pipe(\n        map(([release, info]) => ({ ...release, ...info }))\n      )\n\n  /* User or organization */\n  } else {\n    const url = `https://api.github.com/users/${user}`\n    return requestJSON<User>(url)\n      .pipe(\n        map(info => ({\n          repositories: info.public_repos\n        })),\n        defaultIfEmpty({})\n      )\n  }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { ProjectSchema } from \"gitlab\"\nimport {\n  EMPTY,\n  Observable,\n  catchError,\n  defaultIfEmpty,\n  map\n} from \"rxjs\"\n\nimport { requestJSON } from \"~/browser\"\n\nimport { SourceFacts } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch GitLab repository facts\n *\n * @param base - GitLab base\n * @param project - GitLab project\n *\n * @returns Repository facts observable\n */\nexport function fetchSourceFactsFromGitLab(\n  base: string, project: string\n): Observable<SourceFacts> {\n  const url = `https://${base}/api/v4/projects/${encodeURIComponent(project)}`\n  return requestJSON<ProjectSchema>(url)\n    .pipe(\n      catchError(() => EMPTY), // @todo refactor instant loading\n      map(({ star_count, forks_count }) => ({\n        stars: star_count,\n        forks: forks_count\n      })),\n      defaultIfEmpty({})\n    )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { EMPTY, Observable } from \"rxjs\"\n\nimport { fetchSourceFactsFromGitHub } from \"../github\"\nimport { fetchSourceFactsFromGitLab } from \"../gitlab\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Repository facts for repositories\n */\nexport interface RepositoryFacts {\n  stars?: number                       /* Number of stars */\n  forks?: number                       /* Number of forks */\n  version?: string                     /* Latest version */\n}\n\n/**\n * Repository facts for organizations\n */\nexport interface OrganizationFacts {\n  repositories?: number                /* Number of repositories */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Repository facts\n */\nexport type SourceFacts =\n  | RepositoryFacts\n  | OrganizationFacts\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch repository facts\n *\n * @param url - Repository URL\n *\n * @returns Repository facts observable\n */\nexport function fetchSourceFacts(\n  url: string\n): Observable<SourceFacts> {\n  const [type] = url.match(/(git(?:hub|lab))/i) || []\n  switch (type.toLowerCase()) {\n\n    /* GitHub repository */\n    case \"github\":\n      const [, user, repo] = url.match(/^.+github\\.com\\/([^/]+)\\/?([^/]+)?/i)!\n      return fetchSourceFactsFromGitHub(user, repo)\n\n    /* GitLab repository */\n    case \"gitlab\":\n      const [, base, slug] = url.match(/^.+?([^/]*gitlab[^/]+)\\/(.+?)\\/?$/i)!\n      return fetchSourceFactsFromGitLab(base, slug)\n\n    /* Everything else */\n    default:\n      return EMPTY\n  }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  EMPTY,\n  Observable,\n  Subject,\n  catchError,\n  defer,\n  filter,\n  finalize,\n  map,\n  of,\n  shareReplay,\n  tap\n} from \"rxjs\"\n\nimport { getElement } from \"~/browser\"\nimport { renderSourceFacts } from \"~/templates\"\n\nimport { Component } from \"../../_\"\nimport {\n  SourceFacts,\n  fetchSourceFacts\n} from \"../facts\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Repository information\n */\nexport interface Source {\n  facts: SourceFacts                   /* Repository facts */\n}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Repository information observable\n */\nlet fetch$: Observable<Source>\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch repository information\n *\n * This function tries to read the repository facts from session storage, and\n * if unsuccessful, fetches them from the underlying provider.\n *\n * @param el - Repository information element\n *\n * @returns Repository information observable\n */\nexport function watchSource(\n  el: HTMLAnchorElement\n): Observable<Source> {\n  return fetch$ ||= defer(() => {\n    const cached = __md_get<SourceFacts>(\"__source\", sessionStorage)\n    if (cached)\n      return of(cached)\n    else\n      return fetchSourceFacts(el.href)\n        .pipe(\n          tap(facts => __md_set(\"__source\", facts, sessionStorage))\n        )\n  })\n    .pipe(\n      catchError(() => EMPTY),\n      filter(facts => Object.keys(facts).length > 0),\n      map(facts => ({ facts })),\n      shareReplay(1)\n    )\n}\n\n/**\n * Mount repository information\n *\n * @param el - Repository information element\n *\n * @returns Repository information component observable\n */\nexport function mountSource(\n  el: HTMLAnchorElement\n): Observable<Component<Source>> {\n  const inner = getElement(\":scope > :last-child\", el)\n  return defer(() => {\n    const push$ = new Subject<Source>()\n    push$.subscribe(({ facts }) => {\n      inner.appendChild(renderSourceFacts(facts))\n      inner.classList.add(\"md-source__repository--active\")\n    })\n\n    /* Create and return component */\n    return watchSource(el)\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  defer,\n  distinctUntilKeyChanged,\n  finalize,\n  map,\n  of,\n  switchMap,\n  tap\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport {\n  Viewport,\n  watchElementSize,\n  watchViewportAt\n} from \"~/browser\"\n\nimport { Component } from \"../_\"\nimport { Header } from \"../header\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Navigation tabs\n */\nexport interface Tabs {\n  hidden: boolean                      /* Navigation tabs are hidden */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  header$: Observable<Header>          /* Header observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  header$: Observable<Header>          /* Header observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch navigation tabs\n *\n * @param el - Navigation tabs element\n * @param options - Options\n *\n * @returns Navigation tabs observable\n */\nexport function watchTabs(\n  el: HTMLElement, { viewport$, header$ }: WatchOptions\n): Observable<Tabs> {\n  return watchElementSize(document.body)\n    .pipe(\n      switchMap(() => watchViewportAt(el, { header$, viewport$ })),\n      map(({ offset: { y } }) => {\n        return {\n          hidden: y >= 10\n        }\n      }),\n      distinctUntilKeyChanged(\"hidden\")\n    )\n}\n\n/**\n * Mount navigation tabs\n *\n * This function hides the navigation tabs when scrolling past the threshold\n * and makes them reappear in a nice CSS animation when scrolling back up.\n *\n * @param el - Navigation tabs element\n * @param options - Options\n *\n * @returns Navigation tabs component observable\n */\nexport function mountTabs(\n  el: HTMLElement, options: MountOptions\n): Observable<Component<Tabs>> {\n  return defer(() => {\n    const push$ = new Subject<Tabs>()\n    push$.subscribe({\n\n      /* Handle emission */\n      next({ hidden }) {\n        el.hidden = hidden\n      },\n\n      /* Handle complete */\n      complete() {\n        el.hidden = false\n      }\n    })\n\n    /* Create and return component */\n    return (\n      feature(\"navigation.tabs.sticky\")\n        ? of({ hidden: false })\n        : watchTabs(el, options)\n    )\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  bufferCount,\n  combineLatestWith,\n  debounceTime,\n  defer,\n  distinctUntilChanged,\n  distinctUntilKeyChanged,\n  finalize,\n  map,\n  of,\n  repeat,\n  scan,\n  share,\n  skip,\n  startWith,\n  switchMap,\n  takeLast,\n  takeUntil,\n  tap,\n  withLatestFrom\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport {\n  Viewport,\n  getElement,\n  getElements,\n  getLocation,\n  getOptionalElement,\n  watchElementSize\n} from \"~/browser\"\n\nimport {\n  Component,\n  getComponentElement\n} from \"../_\"\nimport { Header } from \"../header\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Table of contents\n */\nexport interface TableOfContents {\n  prev: HTMLAnchorElement[][]          /* Anchors (previous) */\n  next: HTMLAnchorElement[][]          /* Anchors (next) */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  header$: Observable<Header>          /* Header observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  header$: Observable<Header>          /* Header observable */\n  target$: Observable<HTMLElement>     /* Location target observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch table of contents\n *\n * This is effectively a scroll spy implementation which will account for the\n * fixed header and automatically re-calculate anchor offsets when the viewport\n * is resized. The returned observable will only emit if the table of contents\n * needs to be repainted.\n *\n * This implementation tracks an anchor element's entire path starting from its\n * level up to the top-most anchor element, e.g. `[h3, h2, h1]`. Although the\n * Material theme currently doesn't make use of this information, it enables\n * the styling of the entire hierarchy through customization.\n *\n * Note that the current anchor is the last item of the `prev` anchor list.\n *\n * @param el - Table of contents element\n * @param options - Options\n *\n * @returns Table of contents observable\n */\nexport function watchTableOfContents(\n  el: HTMLElement, { viewport$, header$ }: WatchOptions\n): Observable<TableOfContents> {\n  const table = new Map<HTMLAnchorElement, HTMLElement>()\n\n  /* Compute anchor-to-target mapping */\n  const anchors = getElements<HTMLAnchorElement>(\"[href^=\\\\#]\", el)\n  for (const anchor of anchors) {\n    const id = decodeURIComponent(anchor.hash.substring(1))\n    const target = getOptionalElement(`[id=\"${id}\"]`)\n    if (typeof target !== \"undefined\")\n      table.set(anchor, target)\n  }\n\n  /* Compute necessary adjustment for header */\n  const adjust$ = header$\n    .pipe(\n      distinctUntilKeyChanged(\"height\"),\n      map(({ height }) => {\n        const main = getComponentElement(\"main\")\n        const grid = getElement(\":scope > :first-child\", main)\n        return height + 0.8 * (\n          grid.offsetTop -\n          main.offsetTop\n        )\n      }),\n      share()\n    )\n\n  /* Compute partition of previous and next anchors */\n  const partition$ = watchElementSize(document.body)\n    .pipe(\n      distinctUntilKeyChanged(\"height\"),\n\n      /* Build index to map anchor paths to vertical offsets */\n      switchMap(body => defer(() => {\n        let path: HTMLAnchorElement[] = []\n        return of([...table].reduce((index, [anchor, target]) => {\n          while (path.length) {\n            const last = table.get(path[path.length - 1])!\n            if (last.tagName >= target.tagName) {\n              path.pop()\n            } else {\n              break\n            }\n          }\n\n          /* If the current anchor is hidden, continue with its parent */\n          let offset = target.offsetTop\n          while (!offset && target.parentElement) {\n            target = target.parentElement\n            offset = target.offsetTop\n          }\n\n          /* Map reversed anchor path to vertical offset */\n          return index.set(\n            [...path = [...path, anchor]].reverse(),\n            offset\n          )\n        }, new Map<HTMLAnchorElement[], number>()))\n      })\n        .pipe(\n\n          /* Sort index by vertical offset (see https://bit.ly/30z6QSO) */\n          map(index => new Map([...index].sort(([, a], [, b]) => a - b))),\n          combineLatestWith(adjust$),\n\n          /* Re-compute partition when viewport offset changes */\n          switchMap(([index, adjust]) => viewport$\n            .pipe(\n              scan(([prev, next], { offset: { y }, size }) => {\n                const last = y + size.height >= Math.floor(body.height)\n\n                /* Look forward */\n                while (next.length) {\n                  const [, offset] = next[0]\n                  if (offset - adjust < y || last) {\n                    prev = [...prev, next.shift()!]\n                  } else {\n                    break\n                  }\n                }\n\n                /* Look backward */\n                while (prev.length) {\n                  const [, offset] = prev[prev.length - 1]\n                  if (offset - adjust >= y && !last) {\n                    next = [prev.pop()!, ...next]\n                  } else {\n                    break\n                  }\n                }\n\n                /* Return partition */\n                return [prev, next]\n              }, [[], [...index]]),\n              distinctUntilChanged((a, b) => (\n                a[0] === b[0] &&\n                a[1] === b[1]\n              ))\n            )\n          )\n        )\n      )\n    )\n\n  /* Compute and return anchor list migrations */\n  return partition$\n    .pipe(\n      map(([prev, next]) => ({\n        prev: prev.map(([path]) => path),\n        next: next.map(([path]) => path)\n      })),\n\n      /* Extract anchor list migrations */\n      startWith({ prev: [], next: [] }),\n      bufferCount(2, 1),\n      map(([a, b]) => {\n\n        /* Moving down */\n        if (a.prev.length < b.prev.length) {\n          return {\n            prev: b.prev.slice(Math.max(0, a.prev.length - 1), b.prev.length),\n            next: []\n          }\n\n        /* Moving up */\n        } else {\n          return {\n            prev: b.prev.slice(-1),\n            next: b.next.slice(0, b.next.length - a.next.length)\n          }\n        }\n      })\n    )\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Mount table of contents\n *\n * @param el - Table of contents element\n * @param options - Options\n *\n * @returns Table of contents component observable\n */\nexport function mountTableOfContents(\n  el: HTMLElement, { viewport$, header$, target$ }: MountOptions\n): Observable<Component<TableOfContents>> {\n  return defer(() => {\n    const push$ = new Subject<TableOfContents>()\n    const done$ = push$.pipe(takeLast(1))\n    push$.subscribe(({ prev, next }) => {\n\n      /* Look forward */\n      for (const [anchor] of next) {\n        anchor.classList.remove(\"md-nav__link--passed\")\n        anchor.classList.remove(\"md-nav__link--active\")\n      }\n\n      /* Look backward */\n      for (const [index, [anchor]] of prev.entries()) {\n        anchor.classList.add(\"md-nav__link--passed\")\n        anchor.classList.toggle(\n          \"md-nav__link--active\",\n          index === prev.length - 1\n        )\n      }\n    })\n\n    /* Set up anchor tracking, if enabled */\n    if (feature(\"navigation.tracking\"))\n      viewport$\n        .pipe(\n          takeUntil(done$),\n          distinctUntilKeyChanged(\"offset\"),\n          debounceTime(250),\n          skip(1),\n          takeUntil(target$.pipe(skip(1))),\n          repeat({ delay: 250 }),\n          withLatestFrom(push$)\n        )\n          .subscribe(([, { prev }]) => {\n            const url = getLocation()\n\n            /* Set hash fragment to active anchor */\n            const anchor = prev[prev.length - 1]\n            if (anchor && anchor.length) {\n              const [active] = anchor\n              const { hash } = new URL(active.href)\n              if (url.hash !== hash) {\n                url.hash = hash\n                history.replaceState({}, \"\", `${url}`)\n              }\n\n            /* Reset anchor when at the top */\n            } else {\n              url.hash = \"\"\n              history.replaceState({}, \"\", `${url}`)\n            }\n          })\n\n    /* Create and return component */\n    return watchTableOfContents(el, { viewport$, header$ })\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  bufferCount,\n  combineLatest,\n  distinctUntilChanged,\n  distinctUntilKeyChanged,\n  endWith,\n  finalize,\n  map,\n  repeat,\n  skip,\n  takeLast,\n  takeUntil,\n  tap\n} from \"rxjs\"\n\nimport { Viewport } from \"~/browser\"\n\nimport { Component } from \"../_\"\nimport { Header } from \"../header\"\nimport { Main } from \"../main\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Back-to-top button\n */\nexport interface BackToTop {\n  hidden: boolean                      /* Back-to-top button is hidden */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  main$: Observable<Main>              /* Main area observable */\n  target$: Observable<HTMLElement>     /* Location target observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  header$: Observable<Header>          /* Header observable */\n  main$: Observable<Main>              /* Main area observable */\n  target$: Observable<HTMLElement>     /* Location target observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch back-to-top\n *\n * @param _el - Back-to-top element\n * @param options - Options\n *\n * @returns Back-to-top observable\n */\nexport function watchBackToTop(\n  _el: HTMLElement, { viewport$, main$, target$ }: WatchOptions\n): Observable<BackToTop> {\n\n  /* Compute direction */\n  const direction$ = viewport$\n    .pipe(\n      map(({ offset: { y } }) => y),\n      bufferCount(2, 1),\n      map(([a, b]) => a > b && b > 0),\n      distinctUntilChanged()\n    )\n\n  /* Compute whether main area is active */\n  const active$ = main$\n    .pipe(\n      map(({ active }) => active)\n    )\n\n  /* Compute threshold for hiding */\n  return combineLatest([active$, direction$])\n    .pipe(\n      map(([active, direction]) => !(active && direction)),\n      distinctUntilChanged(),\n      takeUntil(target$.pipe(skip(1))),\n      endWith(true),\n      repeat({ delay: 250 }),\n      map(hidden => ({ hidden }))\n    )\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Mount back-to-top\n *\n * @param el - Back-to-top element\n * @param options - Options\n *\n * @returns Back-to-top component observable\n */\nexport function mountBackToTop(\n  el: HTMLElement, { viewport$, header$, main$, target$ }: MountOptions\n): Observable<Component<BackToTop>> {\n  const push$ = new Subject<BackToTop>()\n  const done$ = push$.pipe(takeLast(1))\n  push$.subscribe({\n\n    /* Handle emission */\n    next({ hidden }) {\n      el.hidden = hidden\n      if (hidden) {\n        el.setAttribute(\"tabindex\", \"-1\")\n        el.blur()\n      } else {\n        el.removeAttribute(\"tabindex\")\n      }\n    },\n\n    /* Handle complete */\n    complete() {\n      el.style.top = \"\"\n      el.hidden = true\n      el.removeAttribute(\"tabindex\")\n    }\n  })\n\n  /* Watch header height */\n  header$\n    .pipe(\n      takeUntil(done$),\n      distinctUntilKeyChanged(\"height\")\n    )\n      .subscribe(({ height }) => {\n        el.style.top = `${height + 16}px`\n      })\n\n  /* Create and return component */\n  return watchBackToTop(el, { viewport$, main$, target$ })\n    .pipe(\n      tap(state => push$.next(state)),\n      finalize(() => push$.complete()),\n      map(state => ({ ref: el, ...state }))\n    )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  fromEvent,\n  map,\n  mergeMap,\n  switchMap,\n  takeWhile,\n  tap,\n  withLatestFrom\n} from \"rxjs\"\n\nimport { getElements } from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch options\n */\ninterface PatchOptions {\n  document$: Observable<Document>      /* Document observable */\n  tablet$: Observable<boolean>         /* Media tablet observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch indeterminate checkboxes\n *\n * This function replaces the indeterminate \"pseudo state\" with the actual\n * indeterminate state, which is used to keep navigation always expanded.\n *\n * @param options - Options\n */\nexport function patchIndeterminate(\n  { document$, tablet$ }: PatchOptions\n): void {\n  document$\n    .pipe(\n      switchMap(() => getElements<HTMLInputElement>(\n        // @todo `data-md-state` is deprecated and removed in v9\n        \".md-toggle--indeterminate, [data-md-state=indeterminate]\"\n      )),\n      tap(el => {\n        el.indeterminate = true\n        el.checked = false\n      }),\n      mergeMap(el => fromEvent(el, \"change\")\n        .pipe(\n          takeWhile(() => el.classList.contains(\"md-toggle--indeterminate\")),\n          map(() => el)\n        )\n      ),\n      withLatestFrom(tablet$)\n    )\n      .subscribe(([el, tablet]) => {\n        el.classList.remove(\"md-toggle--indeterminate\")\n        if (tablet)\n          el.checked = false\n      })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  filter,\n  fromEvent,\n  map,\n  mergeMap,\n  switchMap,\n  tap\n} from \"rxjs\"\n\nimport { getElements } from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch options\n */\ninterface PatchOptions {\n  document$: Observable<Document>      /* Document observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Check whether the given device is an Apple device\n *\n * @returns Test result\n */\nfunction isAppleDevice(): boolean {\n  return /(iPad|iPhone|iPod)/.test(navigator.userAgent)\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch all elements with `data-md-scrollfix` attributes\n *\n * This is a year-old patch which ensures that overflow scrolling works at the\n * top and bottom of containers on iOS by ensuring a `1px` scroll offset upon\n * the start of a touch event.\n *\n * @see https://bit.ly/2SCtAOO - Original source\n *\n * @param options - Options\n */\nexport function patchScrollfix(\n  { document$ }: PatchOptions\n): void {\n  document$\n    .pipe(\n      switchMap(() => getElements(\"[data-md-scrollfix]\")),\n      tap(el => el.removeAttribute(\"data-md-scrollfix\")),\n      filter(isAppleDevice),\n      mergeMap(el => fromEvent(el, \"touchstart\")\n        .pipe(\n          map(() => el)\n        )\n      )\n    )\n      .subscribe(el => {\n        const top = el.scrollTop\n\n        /* We're at the top of the container */\n        if (top === 0) {\n          el.scrollTop = 1\n\n        /* We're at the bottom of the container */\n        } else if (top + el.offsetHeight === el.scrollHeight) {\n          el.scrollTop = top - 1\n        }\n      })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  combineLatest,\n  delay,\n  map,\n  of,\n  switchMap,\n  withLatestFrom\n} from \"rxjs\"\n\nimport {\n  Viewport,\n  watchToggle\n} from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch options\n */\ninterface PatchOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  tablet$: Observable<boolean>         /* Media tablet observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch the document body to lock when search is open\n *\n * For mobile and tablet viewports, the search is rendered full screen, which\n * leads to scroll leaking when at the top or bottom of the search result. This\n * function locks the body when the search is in full screen mode, and restores\n * the scroll position when leaving.\n *\n * @param options - Options\n */\nexport function patchScrolllock(\n  { viewport$, tablet$ }: PatchOptions\n): void {\n  combineLatest([watchToggle(\"search\"), tablet$])\n    .pipe(\n      map(([active, tablet]) => active && !tablet),\n      switchMap(active => of(active)\n        .pipe(\n          delay(active ? 400 : 100)\n        )\n      ),\n      withLatestFrom(viewport$)\n    )\n      .subscribe(([active, { offset: { y }}]) => {\n        if (active) {\n          document.body.setAttribute(\"data-md-scrolllock\", \"\")\n          document.body.style.top = `-${y}px`\n        } else {\n          const value = -1 * parseInt(document.body.style.top, 10)\n          document.body.removeAttribute(\"data-md-scrolllock\")\n          document.body.style.top = \"\"\n          if (value)\n            window.scrollTo(0, value)\n        }\n      })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Polyfills\n * ------------------------------------------------------------------------- */\n\n/* Polyfill `Object.entries` */\nif (!Object.entries)\n  Object.entries = function (obj: object) {\n    const data: [string, string][] = []\n    for (const key of Object.keys(obj))\n      // @ts-expect-error - ignore property access warning\n      data.push([key, obj[key]])\n\n    /* Return entries */\n    return data\n  }\n\n/* Polyfill `Object.values` */\nif (!Object.values)\n  Object.values = function (obj: object) {\n    const data: string[] = []\n    for (const key of Object.keys(obj))\n      // @ts-expect-error - ignore property access warning\n      data.push(obj[key])\n\n    /* Return values */\n    return data\n  }\n\n/* ------------------------------------------------------------------------- */\n\n/* Polyfills for `Element` */\nif (typeof Element !== \"undefined\") {\n\n  /* Polyfill `Element.scrollTo` */\n  if (!Element.prototype.scrollTo)\n    Element.prototype.scrollTo = function (\n      x?: ScrollToOptions | number, y?: number\n    ): void {\n      if (typeof x === \"object\") {\n        this.scrollLeft = x.left!\n        this.scrollTop = x.top!\n      } else {\n        this.scrollLeft = x!\n        this.scrollTop = y!\n      }\n    }\n\n  /* Polyfill `Element.replaceWith` */\n  if (!Element.prototype.replaceWith)\n    Element.prototype.replaceWith = function (\n      ...nodes: Array<string | Node>\n    ): void {\n      const parent = this.parentNode\n      if (parent) {\n        if (nodes.length === 0)\n          parent.removeChild(this)\n\n        /* Replace children and create text nodes */\n        for (let i = nodes.length - 1; i >= 0; i--) {\n          let node = nodes[i]\n          if (typeof node !== \"object\")\n            node = document.createTextNode(node)\n          else if (node.parentNode)\n            node.parentNode.removeChild(node)\n\n          /* Replace child or insert before previous sibling */\n          if (!i)\n            parent.replaceChild(node, this)\n          else\n            parent.insertBefore(this.previousSibling!, node)\n        }\n      }\n    }\n}\n"],
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+}
diff --git a/assets/javascripts/lunr/min/lunr.ar.min.js b/assets/javascripts/lunr/min/lunr.ar.min.js
new file mode 100644
index 0000000..248ddc5
--- /dev/null
+++ b/assets/javascripts/lunr/min/lunr.ar.min.js
@@ -0,0 +1 @@
+!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. 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\ No newline at end of file
diff --git a/assets/javascripts/lunr/min/lunr.da.min.js b/assets/javascripts/lunr/min/lunr.da.min.js
new file mode 100644
index 0000000..b9d8509
--- /dev/null
+++ b/assets/javascripts/lunr/min/lunr.da.min.js
@@ -0,0 +1,18 @@
+/*!
+ * Lunr languages, `Danish` language
+ * https://github.com/MihaiValentin/lunr-languages
+ *
+ * Copyright 2014, Mihai Valentin
+ * http://www.mozilla.org/MPL/
+ */
+/*!
+ * based on
+ * Snowball JavaScript Library v0.3
+ * http://code.google.com/p/urim/
+ * http://snowball.tartarus.org/
+ *
+ * Copyright 2010, Oleg Mazko
+ * http://www.mozilla.org/MPL/
+ */
+
+!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.da=function(){this.pipeline.reset(),this.pipeline.add(e.da.trimmer,e.da.stopWordFilter,e.da.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.da.stemmer))},e.da.wordCharacters="A-Za-zªºÀ-ÖØ-öø-ʸˠ-ˤᴀ-ᴥᴬ-ᵜᵢ-ᵥᵫ-ᵷᵹ-ᶾḀ-ỿⁱⁿₐ-ₜKÅℲⅎⅠ-ↈⱠ-ⱿꜢ-ꞇꞋ-ꞭꞰ-ꞷꟷ-ꟿꬰ-ꭚꭜ-ꭤff-stＡ-Ｚａ-ｚ",e.da.trimmer=e.trimmerSupport.generateTrimmer(e.da.wordCharacters),e.Pipeline.registerFunction(e.da.trimmer,"trimmer-da"),e.da.stemmer=function(){var r=e.stemmerSupport.Among,i=e.stemmerSupport.SnowballProgram,n=new function(){function e(){var e,r=f.cursor+3;if(d=f.limit,0<=r&&r<=f.limit){for(a=r;;){if(e=f.cursor,f.in_grouping(w,97,248)){f.cursor=e;break}if(f.cursor=e,e>=f.limit)return;f.cursor++}for(;!f.out_grouping(w,97,248);){if(f.cursor>=f.limit)return;f.cursor++}d=f.cursor,d<a&&(d=a)}}function n(){var e,r;if(f.cursor>=d&&(r=f.limit_backward,f.limit_backward=d,f.ket=f.cursor,e=f.find_among_b(c,32),f.limit_backward=r,e))switch(f.bra=f.cursor,e){case 1:f.slice_del();break;case 2:f.in_grouping_b(p,97,229)&&f.slice_del()}}function t(){var e,r=f.limit-f.cursor;f.cursor>=d&&(e=f.limit_backward,f.limit_backward=d,f.ket=f.cursor,f.find_among_b(l,4)?(f.bra=f.cursor,f.limit_backward=e,f.cursor=f.limit-r,f.cursor>f.limit_backward&&(f.cursor--,f.bra=f.cursor,f.slice_del())):f.limit_backward=e)}function s(){var e,r,i,n=f.limit-f.cursor;if(f.ket=f.cursor,f.eq_s_b(2,"st")&&(f.bra=f.cursor,f.eq_s_b(2,"ig")&&f.slice_del()),f.cursor=f.limit-n,f.cursor>=d&&(r=f.limit_backward,f.limit_backward=d,f.ket=f.cursor,e=f.find_among_b(m,5),f.limit_backward=r,e))switch(f.bra=f.cursor,e){case 1:f.slice_del(),i=f.limit-f.cursor,t(),f.cursor=f.limit-i;break;case 2:f.slice_from("løs")}}function o(){var e;f.cursor>=d&&(e=f.limit_backward,f.limit_backward=d,f.ket=f.cursor,f.out_grouping_b(w,97,248)?(f.bra=f.cursor,u=f.slice_to(u),f.limit_backward=e,f.eq_v_b(u)&&f.slice_del()):f.limit_backward=e)}var a,d,u,c=[new r("hed",-1,1),new r("ethed",0,1),new r("ered",-1,1),new r("e",-1,1),new r("erede",3,1),new r("ende",3,1),new r("erende",5,1),new r("ene",3,1),new r("erne",3,1),new r("ere",3,1),new r("en",-1,1),new r("heden",10,1),new r("eren",10,1),new r("er",-1,1),new r("heder",13,1),new r("erer",13,1),new r("s",-1,2),new r("heds",16,1),new r("es",16,1),new r("endes",18,1),new r("erendes",19,1),new r("enes",18,1),new r("ernes",18,1),new r("eres",18,1),new r("ens",16,1),new r("hedens",24,1),new r("erens",24,1),new r("ers",16,1),new r("ets",16,1),new r("erets",28,1),new r("et",-1,1),new r("eret",30,1)],l=[new r("gd",-1,-1),new r("dt",-1,-1),new r("gt",-1,-1),new r("kt",-1,-1)],m=[new r("ig",-1,1),new r("lig",0,1),new r("elig",1,1),new r("els",-1,1),new r("løst",-1,2)],w=[17,65,16,1,0,0,0,0,0,0,0,0,0,0,0,0,48,0,128],p=[239,254,42,3,0,0,0,0,0,0,0,0,0,0,0,0,16],f=new i;this.setCurrent=function(e){f.setCurrent(e)},this.getCurrent=function(){return f.getCurrent()},this.stem=function(){var r=f.cursor;return e(),f.limit_backward=r,f.cursor=f.limit,n(),f.cursor=f.limit,t(),f.cursor=f.limit,s(),f.cursor=f.limit,o(),!0}};return function(e){return"function"==typeof e.update?e.update(function(e){return n.setCurrent(e),n.stem(),n.getCurrent()}):(n.setCurrent(e),n.stem(),n.getCurrent())}}(),e.Pipeline.registerFunction(e.da.stemmer,"stemmer-da"),e.da.stopWordFilter=e.generateStopWordFilter("ad af alle alt anden at blev blive bliver da de dem den denne der deres det dette dig din disse dog du efter eller en end er et for fra ham han hans har havde have hende hendes her hos hun hvad hvis hvor i ikke ind jeg jer jo kunne man mange med meget men mig min mine mit mod ned noget nogle nu når og også om op os over på selv sig sin sine sit skal skulle som sådan thi til ud under var vi vil ville vor være været".split(" ")),e.Pipeline.registerFunction(e.da.stopWordFilter,"stopWordFilter-da")}});
\ No newline at end of file
diff --git a/assets/javascripts/lunr/min/lunr.de.min.js b/assets/javascripts/lunr/min/lunr.de.min.js
new file mode 100644
index 0000000..f3b5c10
--- /dev/null
+++ b/assets/javascripts/lunr/min/lunr.de.min.js
@@ -0,0 +1,18 @@
+/*!
+ * Lunr languages, `German` language
+ * https://github.com/MihaiValentin/lunr-languages
+ *
+ * Copyright 2014, Mihai Valentin
+ * http://www.mozilla.org/MPL/
+ */
+/*!
+ * based on
+ * Snowball JavaScript Library v0.3
+ * http://code.google.com/p/urim/
+ * http://snowball.tartarus.org/
+ *
+ * Copyright 2010, Oleg Mazko
+ * http://www.mozilla.org/MPL/
+ */
+
+!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.de=function(){this.pipeline.reset(),this.pipeline.add(e.de.trimmer,e.de.stopWordFilter,e.de.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.de.stemmer))},e.de.wordCharacters="A-Za-zªºÀ-ÖØ-öø-ʸˠ-ˤᴀ-ᴥᴬ-ᵜᵢ-ᵥᵫ-ᵷᵹ-ᶾḀ-ỿⁱⁿₐ-ₜKÅℲⅎⅠ-ↈⱠ-ⱿꜢ-ꞇꞋ-ꞭꞰ-ꞷꟷ-ꟿꬰ-ꭚꭜ-ꭤff-stＡ-Ｚａ-ｚ",e.de.trimmer=e.trimmerSupport.generateTrimmer(e.de.wordCharacters),e.Pipeline.registerFunction(e.de.trimmer,"trimmer-de"),e.de.stemmer=function(){var r=e.stemmerSupport.Among,n=e.stemmerSupport.SnowballProgram,i=new function(){function e(e,r,n){return!(!v.eq_s(1,e)||(v.ket=v.cursor,!v.in_grouping(p,97,252)))&&(v.slice_from(r),v.cursor=n,!0)}function i(){for(var r,n,i,s,t=v.cursor;;)if(r=v.cursor,v.bra=r,v.eq_s(1,"ß"))v.ket=v.cursor,v.slice_from("ss");else{if(r>=v.limit)break;v.cursor=r+1}for(v.cursor=t;;)for(n=v.cursor;;){if(i=v.cursor,v.in_grouping(p,97,252)){if(s=v.cursor,v.bra=s,e("u","U",i))break;if(v.cursor=s,e("y","Y",i))break}if(i>=v.limit)return void(v.cursor=n);v.cursor=i+1}}function s(){for(;!v.in_grouping(p,97,252);){if(v.cursor>=v.limit)return!0;v.cursor++}for(;!v.out_grouping(p,97,252);){if(v.cursor>=v.limit)return!0;v.cursor++}return!1}function t(){m=v.limit,l=m;var e=v.cursor+3;0<=e&&e<=v.limit&&(d=e,s()||(m=v.cursor,m<d&&(m=d),s()||(l=v.cursor)))}function o(){for(var e,r;;){if(r=v.cursor,v.bra=r,!(e=v.find_among(h,6)))return;switch(v.ket=v.cursor,e){case 1:v.slice_from("y");break;case 2:case 5:v.slice_from("u");break;case 3:v.slice_from("a");break;case 4:v.slice_from("o");break;case 6:if(v.cursor>=v.limit)return;v.cursor++}}}function c(){return m<=v.cursor}function u(){return l<=v.cursor}function a(){var e,r,n,i,s=v.limit-v.cursor;if(v.ket=v.cursor,(e=v.find_among_b(w,7))&&(v.bra=v.cursor,c()))switch(e){case 1:v.slice_del();break;case 2:v.slice_del(),v.ket=v.cursor,v.eq_s_b(1,"s")&&(v.bra=v.cursor,v.eq_s_b(3,"nis")&&v.slice_del());break;case 3:v.in_grouping_b(g,98,116)&&v.slice_del()}if(v.cursor=v.limit-s,v.ket=v.cursor,(e=v.find_among_b(f,4))&&(v.bra=v.cursor,c()))switch(e){case 1:v.slice_del();break;case 2:if(v.in_grouping_b(k,98,116)){var t=v.cursor-3;v.limit_backward<=t&&t<=v.limit&&(v.cursor=t,v.slice_del())}}if(v.cursor=v.limit-s,v.ket=v.cursor,(e=v.find_among_b(_,8))&&(v.bra=v.cursor,u()))switch(e){case 1:v.slice_del(),v.ket=v.cursor,v.eq_s_b(2,"ig")&&(v.bra=v.cursor,r=v.limit-v.cursor,v.eq_s_b(1,"e")||(v.cursor=v.limit-r,u()&&v.slice_del()));break;case 2:n=v.limit-v.cursor,v.eq_s_b(1,"e")||(v.cursor=v.limit-n,v.slice_del());break;case 3:if(v.slice_del(),v.ket=v.cursor,i=v.limit-v.cursor,!v.eq_s_b(2,"er")&&(v.cursor=v.limit-i,!v.eq_s_b(2,"en")))break;v.bra=v.cursor,c()&&v.slice_del();break;case 4:v.slice_del(),v.ket=v.cursor,e=v.find_among_b(b,2),e&&(v.bra=v.cursor,u()&&1==e&&v.slice_del())}}var d,l,m,h=[new r("",-1,6),new r("U",0,2),new r("Y",0,1),new r("ä",0,3),new r("ö",0,4),new r("ü",0,5)],w=[new r("e",-1,2),new r("em",-1,1),new r("en",-1,2),new r("ern",-1,1),new r("er",-1,1),new r("s",-1,3),new r("es",5,2)],f=[new r("en",-1,1),new r("er",-1,1),new r("st",-1,2),new r("est",2,1)],b=[new r("ig",-1,1),new r("lich",-1,1)],_=[new r("end",-1,1),new r("ig",-1,2),new r("ung",-1,1),new r("lich",-1,3),new r("isch",-1,2),new r("ik",-1,2),new r("heit",-1,3),new r("keit",-1,4)],p=[17,65,16,1,0,0,0,0,0,0,0,0,0,0,0,0,8,0,32,8],g=[117,30,5],k=[117,30,4],v=new n;this.setCurrent=function(e){v.setCurrent(e)},this.getCurrent=function(){return v.getCurrent()},this.stem=function(){var e=v.cursor;return i(),v.cursor=e,t(),v.limit_backward=e,v.cursor=v.limit,a(),v.cursor=v.limit_backward,o(),!0}};return function(e){return"function"==typeof e.update?e.update(function(e){return i.setCurrent(e),i.stem(),i.getCurrent()}):(i.setCurrent(e),i.stem(),i.getCurrent())}}(),e.Pipeline.registerFunction(e.de.stemmer,"stemmer-de"),e.de.stopWordFilter=e.generateStopWordFilter("aber alle allem allen aller alles als also am an ander andere anderem anderen anderer anderes anderm andern anderr anders auch auf aus bei bin bis bist da damit dann das dasselbe dazu daß dein deine deinem deinen deiner deines dem demselben den denn denselben der derer derselbe derselben des desselben dessen dich die dies diese dieselbe dieselben diesem diesen dieser dieses dir doch dort du durch ein eine einem einen einer eines einig einige einigem einigen einiger einiges einmal er es etwas euch euer eure eurem euren eurer eures für gegen gewesen hab habe haben hat hatte hatten hier hin hinter ich ihm ihn ihnen ihr ihre ihrem ihren ihrer ihres im in indem ins ist jede jedem jeden jeder jedes jene jenem jenen jener jenes jetzt kann kein keine keinem keinen keiner keines können könnte machen man manche manchem manchen mancher manches mein meine meinem meinen meiner meines mich mir mit muss musste nach nicht nichts noch nun nur ob oder ohne sehr sein seine seinem seinen seiner seines selbst sich sie sind so solche solchem solchen solcher solches soll sollte sondern sonst um und uns unse unsem unsen unser unses unter viel vom von vor war waren warst was weg weil weiter welche welchem welchen welcher welches wenn werde werden wie wieder will wir wird wirst wo wollen wollte während würde würden zu zum zur zwar zwischen über".split(" ")),e.Pipeline.registerFunction(e.de.stopWordFilter,"stopWordFilter-de")}});
\ No newline at end of file
diff --git a/assets/javascripts/lunr/min/lunr.du.min.js b/assets/javascripts/lunr/min/lunr.du.min.js
new file mode 100644
index 0000000..49a0f3f
--- /dev/null
+++ b/assets/javascripts/lunr/min/lunr.du.min.js
@@ -0,0 +1,18 @@
+/*!
+ * Lunr languages, `Dutch` language
+ * https://github.com/MihaiValentin/lunr-languages
+ *
+ * Copyright 2014, Mihai Valentin
+ * http://www.mozilla.org/MPL/
+ */
+/*!
+ * based on
+ * Snowball JavaScript Library v0.3
+ * http://code.google.com/p/urim/
+ * http://snowball.tartarus.org/
+ *
+ * Copyright 2010, Oleg Mazko
+ * http://www.mozilla.org/MPL/
+ */
+
+!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");console.warn('[Lunr Languages] Please use the "nl" instead of the "du". The "nl" code is the standard code for Dutch language, and "du" will be removed in the next major versions.'),e.du=function(){this.pipeline.reset(),this.pipeline.add(e.du.trimmer,e.du.stopWordFilter,e.du.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.du.stemmer))},e.du.wordCharacters="A-Za-zªºÀ-ÖØ-öø-ʸˠ-ˤᴀ-ᴥᴬ-ᵜᵢ-ᵥᵫ-ᵷᵹ-ᶾḀ-ỿⁱⁿₐ-ₜKÅℲⅎⅠ-ↈⱠ-ⱿꜢ-ꞇꞋ-ꞭꞰ-ꞷꟷ-ꟿꬰ-ꭚꭜ-ꭤff-stＡ-Ｚａ-ｚ",e.du.trimmer=e.trimmerSupport.generateTrimmer(e.du.wordCharacters),e.Pipeline.registerFunction(e.du.trimmer,"trimmer-du"),e.du.stemmer=function(){var r=e.stemmerSupport.Among,i=e.stemmerSupport.SnowballProgram,n=new function(){function e(){for(var e,r,i,o=C.cursor;;){if(C.bra=C.cursor,e=C.find_among(b,11))switch(C.ket=C.cursor,e){case 1:C.slice_from("a");continue;case 2:C.slice_from("e");continue;case 3:C.slice_from("i");continue;case 4:C.slice_from("o");continue;case 5:C.slice_from("u");continue;case 6:if(C.cursor>=C.limit)break;C.cursor++;continue}break}for(C.cursor=o,C.bra=o,C.eq_s(1,"y")?(C.ket=C.cursor,C.slice_from("Y")):C.cursor=o;;)if(r=C.cursor,C.in_grouping(q,97,232)){if(i=C.cursor,C.bra=i,C.eq_s(1,"i"))C.ket=C.cursor,C.in_grouping(q,97,232)&&(C.slice_from("I"),C.cursor=r);else if(C.cursor=i,C.eq_s(1,"y"))C.ket=C.cursor,C.slice_from("Y"),C.cursor=r;else if(n(r))break}else if(n(r))break}function n(e){return C.cursor=e,e>=C.limit||(C.cursor++,!1)}function o(){_=C.limit,f=_,t()||(_=C.cursor,_<3&&(_=3),t()||(f=C.cursor))}function t(){for(;!C.in_grouping(q,97,232);){if(C.cursor>=C.limit)return!0;C.cursor++}for(;!C.out_grouping(q,97,232);){if(C.cursor>=C.limit)return!0;C.cursor++}return!1}function s(){for(var e;;)if(C.bra=C.cursor,e=C.find_among(p,3))switch(C.ket=C.cursor,e){case 1:C.slice_from("y");break;case 2:C.slice_from("i");break;case 3:if(C.cursor>=C.limit)return;C.cursor++}}function u(){return _<=C.cursor}function c(){return f<=C.cursor}function a(){var e=C.limit-C.cursor;C.find_among_b(g,3)&&(C.cursor=C.limit-e,C.ket=C.cursor,C.cursor>C.limit_backward&&(C.cursor--,C.bra=C.cursor,C.slice_del()))}function l(){var e;w=!1,C.ket=C.cursor,C.eq_s_b(1,"e")&&(C.bra=C.cursor,u()&&(e=C.limit-C.cursor,C.out_grouping_b(q,97,232)&&(C.cursor=C.limit-e,C.slice_del(),w=!0,a())))}function m(){var e;u()&&(e=C.limit-C.cursor,C.out_grouping_b(q,97,232)&&(C.cursor=C.limit-e,C.eq_s_b(3,"gem")||(C.cursor=C.limit-e,C.slice_del(),a())))}function d(){var e,r,i,n,o,t,s=C.limit-C.cursor;if(C.ket=C.cursor,e=C.find_among_b(h,5))switch(C.bra=C.cursor,e){case 1:u()&&C.slice_from("heid");break;case 2:m();break;case 3:u()&&C.out_grouping_b(z,97,232)&&C.slice_del()}if(C.cursor=C.limit-s,l(),C.cursor=C.limit-s,C.ket=C.cursor,C.eq_s_b(4,"heid")&&(C.bra=C.cursor,c()&&(r=C.limit-C.cursor,C.eq_s_b(1,"c")||(C.cursor=C.limit-r,C.slice_del(),C.ket=C.cursor,C.eq_s_b(2,"en")&&(C.bra=C.cursor,m())))),C.cursor=C.limit-s,C.ket=C.cursor,e=C.find_among_b(k,6))switch(C.bra=C.cursor,e){case 1:if(c()){if(C.slice_del(),i=C.limit-C.cursor,C.ket=C.cursor,C.eq_s_b(2,"ig")&&(C.bra=C.cursor,c()&&(n=C.limit-C.cursor,!C.eq_s_b(1,"e")))){C.cursor=C.limit-n,C.slice_del();break}C.cursor=C.limit-i,a()}break;case 2:c()&&(o=C.limit-C.cursor,C.eq_s_b(1,"e")||(C.cursor=C.limit-o,C.slice_del()));break;case 3:c()&&(C.slice_del(),l());break;case 4:c()&&C.slice_del();break;case 5:c()&&w&&C.slice_del()}C.cursor=C.limit-s,C.out_grouping_b(j,73,232)&&(t=C.limit-C.cursor,C.find_among_b(v,4)&&C.out_grouping_b(q,97,232)&&(C.cursor=C.limit-t,C.ket=C.cursor,C.cursor>C.limit_backward&&(C.cursor--,C.bra=C.cursor,C.slice_del())))}var f,_,w,b=[new r("",-1,6),new r("á",0,1),new r("ä",0,1),new r("é",0,2),new r("ë",0,2),new r("í",0,3),new r("ï",0,3),new r("ó",0,4),new r("ö",0,4),new r("ú",0,5),new r("ü",0,5)],p=[new r("",-1,3),new r("I",0,2),new r("Y",0,1)],g=[new r("dd",-1,-1),new r("kk",-1,-1),new r("tt",-1,-1)],h=[new r("ene",-1,2),new r("se",-1,3),new r("en",-1,2),new r("heden",2,1),new r("s",-1,3)],k=[new r("end",-1,1),new r("ig",-1,2),new r("ing",-1,1),new r("lijk",-1,3),new r("baar",-1,4),new r("bar",-1,5)],v=[new r("aa",-1,-1),new r("ee",-1,-1),new r("oo",-1,-1),new r("uu",-1,-1)],q=[17,65,16,1,0,0,0,0,0,0,0,0,0,0,0,0,128],j=[1,0,0,17,65,16,1,0,0,0,0,0,0,0,0,0,0,0,0,128],z=[17,67,16,1,0,0,0,0,0,0,0,0,0,0,0,0,128],C=new i;this.setCurrent=function(e){C.setCurrent(e)},this.getCurrent=function(){return C.getCurrent()},this.stem=function(){var r=C.cursor;return e(),C.cursor=r,o(),C.limit_backward=r,C.cursor=C.limit,d(),C.cursor=C.limit_backward,s(),!0}};return function(e){return"function"==typeof e.update?e.update(function(e){return n.setCurrent(e),n.stem(),n.getCurrent()}):(n.setCurrent(e),n.stem(),n.getCurrent())}}(),e.Pipeline.registerFunction(e.du.stemmer,"stemmer-du"),e.du.stopWordFilter=e.generateStopWordFilter(" aan al alles als altijd andere ben bij daar dan dat de der deze die dit doch doen door dus een eens en er ge geen geweest haar had heb hebben heeft hem het hier hij hoe hun iemand iets ik in is ja je kan kon kunnen maar me meer men met mij mijn moet na naar niet niets nog nu of om omdat onder ons ook op over reeds te tegen toch toen tot u uit uw van veel voor want waren was wat werd wezen wie wil worden wordt zal ze zelf zich zij zijn zo zonder zou".split(" ")),e.Pipeline.registerFunction(e.du.stopWordFilter,"stopWordFilter-du")}});
\ No newline at end of file
diff --git a/assets/javascripts/lunr/min/lunr.es.min.js b/assets/javascripts/lunr/min/lunr.es.min.js
new file mode 100644
index 0000000..2989d34
--- /dev/null
+++ b/assets/javascripts/lunr/min/lunr.es.min.js
@@ -0,0 +1,18 @@
+/*!
+ * Lunr languages, `Spanish` language
+ * https://github.com/MihaiValentin/lunr-languages
+ *
+ * Copyright 2014, Mihai Valentin
+ * http://www.mozilla.org/MPL/
+ */
+/*!
+ * based on
+ * Snowball JavaScript Library v0.3
+ * http://code.google.com/p/urim/
+ * http://snowball.tartarus.org/
+ *
+ * Copyright 2010, Oleg Mazko
+ * http://www.mozilla.org/MPL/
+ */
+
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\ No newline at end of file
diff --git a/assets/javascripts/lunr/min/lunr.fi.min.js b/assets/javascripts/lunr/min/lunr.fi.min.js
new file mode 100644
index 0000000..29f5dfc
--- /dev/null
+++ b/assets/javascripts/lunr/min/lunr.fi.min.js
@@ -0,0 +1,18 @@
+/*!
+ * Lunr languages, `Finnish` language
+ * https://github.com/MihaiValentin/lunr-languages
+ *
+ * Copyright 2014, Mihai Valentin
+ * http://www.mozilla.org/MPL/
+ */
+/*!
+ * based on
+ * Snowball JavaScript Library v0.3
+ * http://code.google.com/p/urim/
+ * http://snowball.tartarus.org/
+ *
+ * Copyright 2010, Oleg Mazko
+ * http://www.mozilla.org/MPL/
+ */
+
+!function(i,e){"function"==typeof define&&define.amd?define(e):"object"==typeof exports?module.exports=e():e()(i.lunr)}(this,function(){return function(i){if(void 0===i)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===i.stemmerSupport)throw new Error("Lunr stemmer support is not present. 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\ No newline at end of file
diff --git a/assets/javascripts/lunr/min/lunr.fr.min.js b/assets/javascripts/lunr/min/lunr.fr.min.js
new file mode 100644
index 0000000..68cd009
--- /dev/null
+++ b/assets/javascripts/lunr/min/lunr.fr.min.js
@@ -0,0 +1,18 @@
+/*!
+ * Lunr languages, `French` language
+ * https://github.com/MihaiValentin/lunr-languages
+ *
+ * Copyright 2014, Mihai Valentin
+ * http://www.mozilla.org/MPL/
+ */
+/*!
+ * based on
+ * Snowball JavaScript Library v0.3
+ * http://code.google.com/p/urim/
+ * http://snowball.tartarus.org/
+ *
+ * Copyright 2010, Oleg Mazko
+ * http://www.mozilla.org/MPL/
+ */
+
+!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. 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\ No newline at end of file
diff --git a/assets/javascripts/lunr/min/lunr.hi.min.js b/assets/javascripts/lunr/min/lunr.hi.min.js
new file mode 100644
index 0000000..7dbc414
--- /dev/null
+++ b/assets/javascripts/lunr/min/lunr.hi.min.js
@@ -0,0 +1 @@
+!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.hi=function(){this.pipeline.reset(),this.pipeline.add(e.hi.trimmer,e.hi.stopWordFilter,e.hi.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.hi.stemmer))},e.hi.wordCharacters="ऀ-ःऄ-एऐ-टठ-यर-िी-ॏॐ-य़ॠ-९॰-ॿa-zA-Zａ-ｚＡ-Ｚ0-9０-９",e.hi.trimmer=e.trimmerSupport.generateTrimmer(e.hi.wordCharacters),e.Pipeline.registerFunction(e.hi.trimmer,"trimmer-hi"),e.hi.stopWordFilter=e.generateStopWordFilter("अत अपना अपनी अपने अभी अंदर आदि आप इत्यादि इन इनका इन्हीं इन्हें इन्हों इस इसका इसकी इसके इसमें इसी इसे उन उनका उनकी उनके उनको उन्हीं उन्हें उन्हों उस उसके उसी उसे एक एवं एस ऐसे और कई कर करता करते करना करने करें कहते कहा का काफ़ी कि कितना किन्हें किन्हों किया किर किस किसी किसे की कुछ कुल के को कोई कौन कौनसा गया घर जब जहाँ जा जितना जिन जिन्हें जिन्हों जिस जिसे जीधर जैसा जैसे जो तक तब तरह तिन तिन्हें तिन्हों तिस तिसे तो था थी थे दबारा दिया दुसरा दूसरे दो द्वारा न नके नहीं ना निहायत नीचे ने पर पहले पूरा पे फिर बनी बही बहुत बाद बाला बिलकुल भी भीतर मगर मानो मे में यदि यह यहाँ यही या यिह ये रखें रहा रहे ऱ्वासा लिए लिये लेकिन व वग़ैरह वर्ग वह वहाँ वहीं वाले वुह वे वो सकता सकते सबसे सभी साथ साबुत साभ सारा से सो संग ही हुआ हुई हुए है हैं हो होता होती होते होना होने".split(" ")),e.hi.stemmer=function(){return function(e){return"function"==typeof e.update?e.update(function(e){return e}):e}}();var r=e.wordcut;r.init(),e.hi.tokenizer=function(i){if(!arguments.length||null==i||void 0==i)return[];if(Array.isArray(i))return i.map(function(r){return isLunr2?new e.Token(r.toLowerCase()):r.toLowerCase()});var t=i.toString().toLowerCase().replace(/^\s+/,"");return r.cut(t).split("|")},e.Pipeline.registerFunction(e.hi.stemmer,"stemmer-hi"),e.Pipeline.registerFunction(e.hi.stopWordFilter,"stopWordFilter-hi")}});
\ No newline at end of file
diff --git a/assets/javascripts/lunr/min/lunr.hu.min.js b/assets/javascripts/lunr/min/lunr.hu.min.js
new file mode 100644
index 0000000..ed9d909
--- /dev/null
+++ b/assets/javascripts/lunr/min/lunr.hu.min.js
@@ -0,0 +1,18 @@
+/*!
+ * Lunr languages, `Hungarian` language
+ * https://github.com/MihaiValentin/lunr-languages
+ *
+ * Copyright 2014, Mihai Valentin
+ * http://www.mozilla.org/MPL/
+ */
+/*!
+ * based on
+ * Snowball JavaScript Library v0.3
+ * http://code.google.com/p/urim/
+ * http://snowball.tartarus.org/
+ *
+ * Copyright 2010, Oleg Mazko
+ * http://www.mozilla.org/MPL/
+ */
+
+!function(e,n){"function"==typeof define&&define.amd?define(n):"object"==typeof exports?module.exports=n():n()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.hu=function(){this.pipeline.reset(),this.pipeline.add(e.hu.trimmer,e.hu.stopWordFilter,e.hu.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.hu.stemmer))},e.hu.wordCharacters="A-Za-zªºÀ-ÖØ-öø-ʸˠ-ˤᴀ-ᴥᴬ-ᵜᵢ-ᵥᵫ-ᵷᵹ-ᶾḀ-ỿⁱⁿₐ-ₜKÅℲⅎⅠ-ↈⱠ-ⱿꜢ-ꞇꞋ-ꞭꞰ-ꞷꟷ-ꟿꬰ-ꭚꭜ-ꭤff-stＡ-Ｚａ-ｚ",e.hu.trimmer=e.trimmerSupport.generateTrimmer(e.hu.wordCharacters),e.Pipeline.registerFunction(e.hu.trimmer,"trimmer-hu"),e.hu.stemmer=function(){var n=e.stemmerSupport.Among,r=e.stemmerSupport.SnowballProgram,i=new function(){function e(){var e,n=L.cursor;if(d=L.limit,L.in_grouping(W,97,252))for(;;){if(e=L.cursor,L.out_grouping(W,97,252))return L.cursor=e,L.find_among(g,8)||(L.cursor=e,e<L.limit&&L.cursor++),void(d=L.cursor);if(L.cursor=e,e>=L.limit)return void(d=e);L.cursor++}if(L.cursor=n,L.out_grouping(W,97,252)){for(;!L.in_grouping(W,97,252);){if(L.cursor>=L.limit)return;L.cursor++}d=L.cursor}}function i(){return d<=L.cursor}function a(){var e;if(L.ket=L.cursor,(e=L.find_among_b(h,2))&&(L.bra=L.cursor,i()))switch(e){case 1:L.slice_from("a");break;case 2:L.slice_from("e")}}function t(){var e=L.limit-L.cursor;return!!L.find_among_b(p,23)&&(L.cursor=L.limit-e,!0)}function s(){if(L.cursor>L.limit_backward){L.cursor--,L.ket=L.cursor;var e=L.cursor-1;L.limit_backward<=e&&e<=L.limit&&(L.cursor=e,L.bra=e,L.slice_del())}}function c(){var e;if(L.ket=L.cursor,(e=L.find_among_b(_,2))&&(L.bra=L.cursor,i())){if((1==e||2==e)&&!t())return;L.slice_del(),s()}}function o(){L.ket=L.cursor,L.find_among_b(v,44)&&(L.bra=L.cursor,i()&&(L.slice_del(),a()))}function w(){var e;if(L.ket=L.cursor,(e=L.find_among_b(z,3))&&(L.bra=L.cursor,i()))switch(e){case 1:L.slice_from("e");break;case 2:case 3:L.slice_from("a")}}function l(){var e;if(L.ket=L.cursor,(e=L.find_among_b(y,6))&&(L.bra=L.cursor,i()))switch(e){case 1:case 2:L.slice_del();break;case 3:L.slice_from("a");break;case 4:L.slice_from("e")}}function u(){var e;if(L.ket=L.cursor,(e=L.find_among_b(j,2))&&(L.bra=L.cursor,i())){if((1==e||2==e)&&!t())return;L.slice_del(),s()}}function m(){var e;if(L.ket=L.cursor,(e=L.find_among_b(C,7))&&(L.bra=L.cursor,i()))switch(e){case 1:L.slice_from("a");break;case 2:L.slice_from("e");break;case 3:case 4:case 5:case 6:case 7:L.slice_del()}}function k(){var e;if(L.ket=L.cursor,(e=L.find_among_b(P,12))&&(L.bra=L.cursor,i()))switch(e){case 1:case 4:case 7:case 9:L.slice_del();break;case 2:case 5:case 8:L.slice_from("e");break;case 3:case 6:L.slice_from("a")}}function f(){var e;if(L.ket=L.cursor,(e=L.find_among_b(F,31))&&(L.bra=L.cursor,i()))switch(e){case 1:case 4:case 7:case 8:case 9:case 12:case 13:case 16:case 17:case 18:L.slice_del();break;case 2:case 5:case 10:case 14:case 19:L.slice_from("a");break;case 3:case 6:case 11:case 15:case 20:L.slice_from("e")}}function b(){var e;if(L.ket=L.cursor,(e=L.find_among_b(S,42))&&(L.bra=L.cursor,i()))switch(e){case 1:case 4:case 5:case 6:case 9:case 10:case 11:case 14:case 15:case 16:case 17:case 20:case 21:case 24:case 25:case 26:case 29:L.slice_del();break;case 2:case 7:case 12:case 18:case 22:case 27:L.slice_from("a");break;case 3:case 8:case 13:case 19:case 23:case 28:L.slice_from("e")}}var d,g=[new n("cs",-1,-1),new n("dzs",-1,-1),new n("gy",-1,-1),new n("ly",-1,-1),new n("ny",-1,-1),new n("sz",-1,-1),new n("ty",-1,-1),new n("zs",-1,-1)],h=[new n("á",-1,1),new n("é",-1,2)],p=[new n("bb",-1,-1),new n("cc",-1,-1),new n("dd",-1,-1),new n("ff",-1,-1),new n("gg",-1,-1),new n("jj",-1,-1),new n("kk",-1,-1),new n("ll",-1,-1),new n("mm",-1,-1),new n("nn",-1,-1),new n("pp",-1,-1),new n("rr",-1,-1),new n("ccs",-1,-1),new n("ss",-1,-1),new n("zzs",-1,-1),new n("tt",-1,-1),new n("vv",-1,-1),new n("ggy",-1,-1),new n("lly",-1,-1),new n("nny",-1,-1),new n("tty",-1,-1),new n("ssz",-1,-1),new n("zz",-1,-1)],_=[new n("al",-1,1),new n("el",-1,2)],v=[new n("ba",-1,-1),new n("ra",-1,-1),new n("be",-1,-1),new n("re",-1,-1),new n("ig",-1,-1),new n("nak",-1,-1),new n("nek",-1,-1),new n("val",-1,-1),new n("vel",-1,-1),new n("ul",-1,-1),new n("nál",-1,-1),new n("nél",-1,-1),new n("ból",-1,-1),new n("ról",-1,-1),new n("tól",-1,-1),new n("bõl",-1,-1),new n("rõl",-1,-1),new n("tõl",-1,-1),new n("ül",-1,-1),new n("n",-1,-1),new n("an",19,-1),new n("ban",20,-1),new n("en",19,-1),new n("ben",22,-1),new n("képpen",22,-1),new n("on",19,-1),new n("ön",19,-1),new n("képp",-1,-1),new n("kor",-1,-1),new n("t",-1,-1),new n("at",29,-1),new n("et",29,-1),new n("ként",29,-1),new n("anként",32,-1),new n("enként",32,-1),new n("onként",32,-1),new n("ot",29,-1),new n("ért",29,-1),new n("öt",29,-1),new n("hez",-1,-1),new n("hoz",-1,-1),new n("höz",-1,-1),new n("vá",-1,-1),new n("vé",-1,-1)],z=[new n("án",-1,2),new n("én",-1,1),new n("ánként",-1,3)],y=[new n("stul",-1,2),new n("astul",0,1),new n("ástul",0,3),new n("stül",-1,2),new n("estül",3,1),new n("éstül",3,4)],j=[new n("á",-1,1),new n("é",-1,2)],C=[new n("k",-1,7),new n("ak",0,4),new n("ek",0,6),new n("ok",0,5),new n("ák",0,1),new n("ék",0,2),new n("ök",0,3)],P=[new n("éi",-1,7),new n("áéi",0,6),new n("ééi",0,5),new n("é",-1,9),new n("ké",3,4),new n("aké",4,1),new n("eké",4,1),new n("oké",4,1),new n("áké",4,3),new n("éké",4,2),new n("öké",4,1),new n("éé",3,8)],F=[new n("a",-1,18),new n("ja",0,17),new n("d",-1,16),new n("ad",2,13),new n("ed",2,13),new n("od",2,13),new n("ád",2,14),new n("éd",2,15),new n("öd",2,13),new n("e",-1,18),new n("je",9,17),new n("nk",-1,4),new n("unk",11,1),new n("ánk",11,2),new n("énk",11,3),new n("ünk",11,1),new n("uk",-1,8),new n("juk",16,7),new n("ájuk",17,5),new n("ük",-1,8),new n("jük",19,7),new n("éjük",20,6),new n("m",-1,12),new n("am",22,9),new n("em",22,9),new n("om",22,9),new n("ám",22,10),new n("ém",22,11),new n("o",-1,18),new n("á",-1,19),new n("é",-1,20)],S=[new n("id",-1,10),new n("aid",0,9),new n("jaid",1,6),new n("eid",0,9),new n("jeid",3,6),new n("áid",0,7),new n("éid",0,8),new n("i",-1,15),new n("ai",7,14),new n("jai",8,11),new n("ei",7,14),new n("jei",10,11),new n("ái",7,12),new n("éi",7,13),new n("itek",-1,24),new n("eitek",14,21),new n("jeitek",15,20),new n("éitek",14,23),new n("ik",-1,29),new n("aik",18,26),new n("jaik",19,25),new n("eik",18,26),new n("jeik",21,25),new n("áik",18,27),new n("éik",18,28),new n("ink",-1,20),new n("aink",25,17),new n("jaink",26,16),new n("eink",25,17),new n("jeink",28,16),new n("áink",25,18),new n("éink",25,19),new n("aitok",-1,21),new n("jaitok",32,20),new n("áitok",-1,22),new n("im",-1,5),new n("aim",35,4),new n("jaim",36,1),new n("eim",35,4),new n("jeim",38,1),new n("áim",35,2),new n("éim",35,3)],W=[17,65,16,0,0,0,0,0,0,0,0,0,0,0,0,0,1,17,52,14],L=new r;this.setCurrent=function(e){L.setCurrent(e)},this.getCurrent=function(){return L.getCurrent()},this.stem=function(){var n=L.cursor;return e(),L.limit_backward=n,L.cursor=L.limit,c(),L.cursor=L.limit,o(),L.cursor=L.limit,w(),L.cursor=L.limit,l(),L.cursor=L.limit,u(),L.cursor=L.limit,k(),L.cursor=L.limit,f(),L.cursor=L.limit,b(),L.cursor=L.limit,m(),!0}};return function(e){return"function"==typeof e.update?e.update(function(e){return i.setCurrent(e),i.stem(),i.getCurrent()}):(i.setCurrent(e),i.stem(),i.getCurrent())}}(),e.Pipeline.registerFunction(e.hu.stemmer,"stemmer-hu"),e.hu.stopWordFilter=e.generateStopWordFilter("a abban ahhoz ahogy ahol aki akik akkor alatt amely amelyek amelyekben amelyeket amelyet amelynek ami amikor amit amolyan amíg annak arra arról az azok azon azonban azt aztán azután azzal azért be belül benne bár cikk cikkek cikkeket csak de e ebben eddig egy egyes egyetlen egyik egyre egyéb egész ehhez ekkor el ellen elsõ elég elõ elõször elõtt emilyen ennek erre ez ezek ezen ezt ezzel ezért fel felé hanem hiszen hogy hogyan igen ill ill. illetve ilyen ilyenkor ismét ison itt jobban jó jól kell kellett keressünk keresztül ki kívül között közül legalább legyen lehet lehetett lenne lenni lesz lett maga magát majd majd meg mellett mely melyek mert mi mikor milyen minden mindenki mindent mindig mint mintha mit mivel miért most már más másik még míg nagy nagyobb nagyon ne nekem neki nem nincs néha néhány nélkül olyan ott pedig persze rá s saját sem semmi sok sokat sokkal szemben szerint szinte számára talán tehát teljes tovább továbbá több ugyanis utolsó után utána vagy vagyis vagyok valaki valami valamint való van vannak vele vissza viszont volna volt voltak voltam voltunk által általában át én éppen és így õ õk õket össze úgy új újabb újra".split(" ")),e.Pipeline.registerFunction(e.hu.stopWordFilter,"stopWordFilter-hu")}});
\ No newline at end of file
diff --git a/assets/javascripts/lunr/min/lunr.it.min.js b/assets/javascripts/lunr/min/lunr.it.min.js
new file mode 100644
index 0000000..344b6a3
--- /dev/null
+++ b/assets/javascripts/lunr/min/lunr.it.min.js
@@ -0,0 +1,18 @@
+/*!
+ * Lunr languages, `Italian` language
+ * https://github.com/MihaiValentin/lunr-languages
+ *
+ * Copyright 2014, Mihai Valentin
+ * http://www.mozilla.org/MPL/
+ */
+/*!
+ * based on
+ * Snowball JavaScript Library v0.3
+ * http://code.google.com/p/urim/
+ * http://snowball.tartarus.org/
+ *
+ * Copyright 2010, Oleg Mazko
+ * http://www.mozilla.org/MPL/
+ */
+
+!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.it=function(){this.pipeline.reset(),this.pipeline.add(e.it.trimmer,e.it.stopWordFilter,e.it.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.it.stemmer))},e.it.wordCharacters="A-Za-zªºÀ-ÖØ-öø-ʸˠ-ˤᴀ-ᴥᴬ-ᵜᵢ-ᵥᵫ-ᵷᵹ-ᶾḀ-ỿⁱⁿₐ-ₜKÅℲⅎⅠ-ↈⱠ-ⱿꜢ-ꞇꞋ-ꞭꞰ-ꞷꟷ-ꟿꬰ-ꭚꭜ-ꭤff-stＡ-Ｚａ-ｚ",e.it.trimmer=e.trimmerSupport.generateTrimmer(e.it.wordCharacters),e.Pipeline.registerFunction(e.it.trimmer,"trimmer-it"),e.it.stemmer=function(){var r=e.stemmerSupport.Among,n=e.stemmerSupport.SnowballProgram,i=new function(){function e(e,r,n){return!(!x.eq_s(1,e)||(x.ket=x.cursor,!x.in_grouping(L,97,249)))&&(x.slice_from(r),x.cursor=n,!0)}function i(){for(var r,n,i,o,t=x.cursor;;){if(x.bra=x.cursor,r=x.find_among(h,7))switch(x.ket=x.cursor,r){case 1:x.slice_from("à");continue;case 2:x.slice_from("è");continue;case 3:x.slice_from("ì");continue;case 4:x.slice_from("ò");continue;case 5:x.slice_from("ù");continue;case 6:x.slice_from("qU");continue;case 7:if(x.cursor>=x.limit)break;x.cursor++;continue}break}for(x.cursor=t;;)for(n=x.cursor;;){if(i=x.cursor,x.in_grouping(L,97,249)){if(x.bra=x.cursor,o=x.cursor,e("u","U",i))break;if(x.cursor=o,e("i","I",i))break}if(x.cursor=i,x.cursor>=x.limit)return void(x.cursor=n);x.cursor++}}function o(e){if(x.cursor=e,!x.in_grouping(L,97,249))return!1;for(;!x.out_grouping(L,97,249);){if(x.cursor>=x.limit)return!1;x.cursor++}return!0}function t(){if(x.in_grouping(L,97,249)){var e=x.cursor;if(x.out_grouping(L,97,249)){for(;!x.in_grouping(L,97,249);){if(x.cursor>=x.limit)return o(e);x.cursor++}return!0}return o(e)}return!1}function s(){var e,r=x.cursor;if(!t()){if(x.cursor=r,!x.out_grouping(L,97,249))return;if(e=x.cursor,x.out_grouping(L,97,249)){for(;!x.in_grouping(L,97,249);){if(x.cursor>=x.limit)return x.cursor=e,void(x.in_grouping(L,97,249)&&x.cursor<x.limit&&x.cursor++);x.cursor++}return void(k=x.cursor)}if(x.cursor=e,!x.in_grouping(L,97,249)||x.cursor>=x.limit)return;x.cursor++}k=x.cursor}function a(){for(;!x.in_grouping(L,97,249);){if(x.cursor>=x.limit)return!1;x.cursor++}for(;!x.out_grouping(L,97,249);){if(x.cursor>=x.limit)return!1;x.cursor++}return!0}function u(){var e=x.cursor;k=x.limit,p=k,g=k,s(),x.cursor=e,a()&&(p=x.cursor,a()&&(g=x.cursor))}function c(){for(var e;;){if(x.bra=x.cursor,!(e=x.find_among(q,3)))break;switch(x.ket=x.cursor,e){case 1:x.slice_from("i");break;case 2:x.slice_from("u");break;case 3:if(x.cursor>=x.limit)return;x.cursor++}}}function w(){return k<=x.cursor}function l(){return p<=x.cursor}function m(){return g<=x.cursor}function f(){var e;if(x.ket=x.cursor,x.find_among_b(C,37)&&(x.bra=x.cursor,(e=x.find_among_b(z,5))&&w()))switch(e){case 1:x.slice_del();break;case 2:x.slice_from("e")}}function v(){var e;if(x.ket=x.cursor,!(e=x.find_among_b(S,51)))return!1;switch(x.bra=x.cursor,e){case 1:if(!m())return!1;x.slice_del();break;case 2:if(!m())return!1;x.slice_del(),x.ket=x.cursor,x.eq_s_b(2,"ic")&&(x.bra=x.cursor,m()&&x.slice_del());break;case 3:if(!m())return!1;x.slice_from("log");break;case 4:if(!m())return!1;x.slice_from("u");break;case 5:if(!m())return!1;x.slice_from("ente");break;case 6:if(!w())return!1;x.slice_del();break;case 7:if(!l())return!1;x.slice_del(),x.ket=x.cursor,e=x.find_among_b(P,4),e&&(x.bra=x.cursor,m()&&(x.slice_del(),1==e&&(x.ket=x.cursor,x.eq_s_b(2,"at")&&(x.bra=x.cursor,m()&&x.slice_del()))));break;case 8:if(!m())return!1;x.slice_del(),x.ket=x.cursor,e=x.find_among_b(F,3),e&&(x.bra=x.cursor,1==e&&m()&&x.slice_del());break;case 9:if(!m())return!1;x.slice_del(),x.ket=x.cursor,x.eq_s_b(2,"at")&&(x.bra=x.cursor,m()&&(x.slice_del(),x.ket=x.cursor,x.eq_s_b(2,"ic")&&(x.bra=x.cursor,m()&&x.slice_del())))}return!0}function b(){var e,r;x.cursor>=k&&(r=x.limit_backward,x.limit_backward=k,x.ket=x.cursor,e=x.find_among_b(W,87),e&&(x.bra=x.cursor,1==e&&x.slice_del()),x.limit_backward=r)}function d(){var e=x.limit-x.cursor;if(x.ket=x.cursor,x.in_grouping_b(y,97,242)&&(x.bra=x.cursor,w()&&(x.slice_del(),x.ket=x.cursor,x.eq_s_b(1,"i")&&(x.bra=x.cursor,w()))))return void x.slice_del();x.cursor=x.limit-e}function _(){d(),x.ket=x.cursor,x.eq_s_b(1,"h")&&(x.bra=x.cursor,x.in_grouping_b(U,99,103)&&w()&&x.slice_del())}var g,p,k,h=[new r("",-1,7),new r("qu",0,6),new r("á",0,1),new r("é",0,2),new r("í",0,3),new r("ó",0,4),new r("ú",0,5)],q=[new r("",-1,3),new r("I",0,1),new r("U",0,2)],C=[new r("la",-1,-1),new r("cela",0,-1),new r("gliela",0,-1),new r("mela",0,-1),new r("tela",0,-1),new r("vela",0,-1),new r("le",-1,-1),new r("cele",6,-1),new r("gliele",6,-1),new r("mele",6,-1),new r("tele",6,-1),new r("vele",6,-1),new r("ne",-1,-1),new r("cene",12,-1),new r("gliene",12,-1),new r("mene",12,-1),new r("sene",12,-1),new r("tene",12,-1),new r("vene",12,-1),new r("ci",-1,-1),new r("li",-1,-1),new r("celi",20,-1),new r("glieli",20,-1),new r("meli",20,-1),new r("teli",20,-1),new r("veli",20,-1),new r("gli",20,-1),new r("mi",-1,-1),new r("si",-1,-1),new r("ti",-1,-1),new r("vi",-1,-1),new r("lo",-1,-1),new r("celo",31,-1),new r("glielo",31,-1),new r("melo",31,-1),new r("telo",31,-1),new r("velo",31,-1)],z=[new r("ando",-1,1),new r("endo",-1,1),new r("ar",-1,2),new r("er",-1,2),new r("ir",-1,2)],P=[new r("ic",-1,-1),new r("abil",-1,-1),new r("os",-1,-1),new r("iv",-1,1)],F=[new r("ic",-1,1),new r("abil",-1,1),new r("iv",-1,1)],S=[new r("ica",-1,1),new r("logia",-1,3),new r("osa",-1,1),new r("ista",-1,1),new r("iva",-1,9),new r("anza",-1,1),new r("enza",-1,5),new r("ice",-1,1),new r("atrice",7,1),new r("iche",-1,1),new r("logie",-1,3),new r("abile",-1,1),new r("ibile",-1,1),new r("usione",-1,4),new r("azione",-1,2),new r("uzione",-1,4),new r("atore",-1,2),new r("ose",-1,1),new r("ante",-1,1),new r("mente",-1,1),new r("amente",19,7),new r("iste",-1,1),new r("ive",-1,9),new r("anze",-1,1),new r("enze",-1,5),new r("ici",-1,1),new r("atrici",25,1),new r("ichi",-1,1),new r("abili",-1,1),new r("ibili",-1,1),new r("ismi",-1,1),new r("usioni",-1,4),new r("azioni",-1,2),new r("uzioni",-1,4),new r("atori",-1,2),new r("osi",-1,1),new r("anti",-1,1),new r("amenti",-1,6),new r("imenti",-1,6),new r("isti",-1,1),new r("ivi",-1,9),new r("ico",-1,1),new r("ismo",-1,1),new r("oso",-1,1),new r("amento",-1,6),new r("imento",-1,6),new r("ivo",-1,9),new r("ità",-1,8),new r("istà",-1,1),new r("istè",-1,1),new r("istì",-1,1)],W=[new r("isca",-1,1),new r("enda",-1,1),new r("ata",-1,1),new r("ita",-1,1),new r("uta",-1,1),new r("ava",-1,1),new r("eva",-1,1),new r("iva",-1,1),new r("erebbe",-1,1),new r("irebbe",-1,1),new r("isce",-1,1),new r("ende",-1,1),new r("are",-1,1),new r("ere",-1,1),new r("ire",-1,1),new r("asse",-1,1),new r("ate",-1,1),new r("avate",16,1),new r("evate",16,1),new r("ivate",16,1),new r("ete",-1,1),new r("erete",20,1),new r("irete",20,1),new r("ite",-1,1),new r("ereste",-1,1),new r("ireste",-1,1),new r("ute",-1,1),new r("erai",-1,1),new r("irai",-1,1),new r("isci",-1,1),new r("endi",-1,1),new r("erei",-1,1),new r("irei",-1,1),new r("assi",-1,1),new r("ati",-1,1),new r("iti",-1,1),new r("eresti",-1,1),new r("iresti",-1,1),new r("uti",-1,1),new r("avi",-1,1),new r("evi",-1,1),new r("ivi",-1,1),new r("isco",-1,1),new r("ando",-1,1),new r("endo",-1,1),new r("Yamo",-1,1),new r("iamo",-1,1),new r("avamo",-1,1),new r("evamo",-1,1),new r("ivamo",-1,1),new r("eremo",-1,1),new r("iremo",-1,1),new r("assimo",-1,1),new r("ammo",-1,1),new r("emmo",-1,1),new r("eremmo",54,1),new r("iremmo",54,1),new r("immo",-1,1),new r("ano",-1,1),new r("iscano",58,1),new r("avano",58,1),new r("evano",58,1),new r("ivano",58,1),new r("eranno",-1,1),new r("iranno",-1,1),new r("ono",-1,1),new r("iscono",65,1),new r("arono",65,1),new r("erono",65,1),new r("irono",65,1),new r("erebbero",-1,1),new r("irebbero",-1,1),new r("assero",-1,1),new r("essero",-1,1),new r("issero",-1,1),new r("ato",-1,1),new r("ito",-1,1),new r("uto",-1,1),new r("avo",-1,1),new r("evo",-1,1),new r("ivo",-1,1),new r("ar",-1,1),new r("ir",-1,1),new r("erà",-1,1),new r("irà",-1,1),new r("erò",-1,1),new r("irò",-1,1)],L=[17,65,16,0,0,0,0,0,0,0,0,0,0,0,0,128,128,8,2,1],y=[17,65,0,0,0,0,0,0,0,0,0,0,0,0,0,128,128,8,2],U=[17],x=new n;this.setCurrent=function(e){x.setCurrent(e)},this.getCurrent=function(){return x.getCurrent()},this.stem=function(){var e=x.cursor;return i(),x.cursor=e,u(),x.limit_backward=e,x.cursor=x.limit,f(),x.cursor=x.limit,v()||(x.cursor=x.limit,b()),x.cursor=x.limit,_(),x.cursor=x.limit_backward,c(),!0}};return function(e){return"function"==typeof e.update?e.update(function(e){return i.setCurrent(e),i.stem(),i.getCurrent()}):(i.setCurrent(e),i.stem(),i.getCurrent())}}(),e.Pipeline.registerFunction(e.it.stemmer,"stemmer-it"),e.it.stopWordFilter=e.generateStopWordFilter("a abbia abbiamo abbiano abbiate ad agl agli ai al all alla alle allo anche avemmo avendo avesse avessero avessi avessimo aveste avesti avete aveva avevamo avevano avevate avevi avevo avrai avranno avrebbe avrebbero avrei avremmo avremo avreste avresti avrete avrà avrò avuta avute avuti avuto c che chi ci coi col come con contro cui da dagl dagli dai dal dall dalla dalle dallo degl degli dei del dell della delle dello di dov dove e ebbe ebbero ebbi ed era erano eravamo eravate eri ero essendo faccia facciamo facciano facciate faccio facemmo facendo facesse facessero facessi facessimo faceste facesti faceva facevamo facevano facevate facevi facevo fai fanno farai faranno farebbe farebbero farei faremmo faremo fareste faresti farete farà farò fece fecero feci fosse fossero fossi fossimo foste fosti fu fui fummo furono gli ha hai hanno ho i il in io l la le lei li lo loro lui ma mi mia mie miei mio ne negl negli nei nel nell nella nelle nello noi non nostra nostre nostri nostro o per perché più quale quanta quante quanti quanto quella quelle quelli quello questa queste questi questo sarai saranno sarebbe sarebbero sarei saremmo saremo sareste saresti sarete sarà sarò se sei si sia siamo siano siate siete sono sta stai stando stanno starai staranno starebbe starebbero starei staremmo staremo stareste staresti starete starà starò stava stavamo stavano stavate stavi stavo stemmo stesse stessero stessi stessimo steste stesti stette stettero stetti stia stiamo stiano stiate sto su sua sue sugl sugli sui sul sull sulla sulle sullo suo suoi ti tra tu tua tue tuo tuoi tutti tutto un una uno vi voi vostra vostre vostri vostro è".split(" ")),e.Pipeline.registerFunction(e.it.stopWordFilter,"stopWordFilter-it")}});
\ No newline at end of file
diff --git a/assets/javascripts/lunr/min/lunr.ja.min.js b/assets/javascripts/lunr/min/lunr.ja.min.js
new file mode 100644
index 0000000..5f254eb
--- /dev/null
+++ b/assets/javascripts/lunr/min/lunr.ja.min.js
@@ -0,0 +1 @@
+!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");var r="2"==e.version[0];e.ja=function(){this.pipeline.reset(),this.pipeline.add(e.ja.trimmer,e.ja.stopWordFilter,e.ja.stemmer),r?this.tokenizer=e.ja.tokenizer:(e.tokenizer&&(e.tokenizer=e.ja.tokenizer),this.tokenizerFn&&(this.tokenizerFn=e.ja.tokenizer))};var t=new e.TinySegmenter;e.ja.tokenizer=function(i){var n,o,s,p,a,u,m,l,c,f;if(!arguments.length||null==i||void 0==i)return[];if(Array.isArray(i))return i.map(function(t){return r?new e.Token(t.toLowerCase()):t.toLowerCase()});for(o=i.toString().toLowerCase().replace(/^\s+/,""),n=o.length-1;n>=0;n--)if(/\S/.test(o.charAt(n))){o=o.substring(0,n+1);break}for(a=[],s=o.length,c=0,l=0;c<=s;c++)if(u=o.charAt(c),m=c-l,u.match(/\s/)||c==s){if(m>0)for(p=t.segment(o.slice(l,c)).filter(function(e){return!!e}),f=l,n=0;n<p.length;n++)r?a.push(new e.Token(p[n],{position:[f,p[n].length],index:a.length})):a.push(p[n]),f+=p[n].length;l=c+1}return a},e.ja.stemmer=function(){return function(e){return e}}(),e.Pipeline.registerFunction(e.ja.stemmer,"stemmer-ja"),e.ja.wordCharacters="一二三四五六七八九十百千万億兆一-龠々〆ヵヶぁ-んァ-ヴーｱ-ﾝﾞa-zA-Zａ-ｚＡ-Ｚ0-9０-９",e.ja.trimmer=e.trimmerSupport.generateTrimmer(e.ja.wordCharacters),e.Pipeline.registerFunction(e.ja.trimmer,"trimmer-ja"),e.ja.stopWordFilter=e.generateStopWordFilter("これ それ あれ この その あの ここ そこ あそこ こちら どこ だれ なに なん 何 私 貴方 貴方方 我々 私達 あの人 あのかた 彼女 彼 です あります おります います は が の に を で え から まで より も どの と し それで しかし".split(" ")),e.Pipeline.registerFunction(e.ja.stopWordFilter,"stopWordFilter-ja"),e.jp=e.ja,e.Pipeline.registerFunction(e.jp.stemmer,"stemmer-jp"),e.Pipeline.registerFunction(e.jp.trimmer,"trimmer-jp"),e.Pipeline.registerFunction(e.jp.stopWordFilter,"stopWordFilter-jp")}});
\ No newline at end of file
diff --git a/assets/javascripts/lunr/min/lunr.jp.min.js b/assets/javascripts/lunr/min/lunr.jp.min.js
new file mode 100644
index 0000000..c055eba
--- /dev/null
+++ b/assets/javascripts/lunr/min/lunr.jp.min.js
@@ -0,0 +1 @@
+module.exports=require("./lunr.ja");
\ No newline at end of file
diff --git a/assets/javascripts/lunr/min/lunr.multi.min.js b/assets/javascripts/lunr/min/lunr.multi.min.js
new file mode 100644
index 0000000..7debad0
--- /dev/null
+++ b/assets/javascripts/lunr/min/lunr.multi.min.js
@@ -0,0 +1 @@
+!function(e,t){"function"==typeof define&&define.amd?define(t):"object"==typeof exports?module.exports=t():t()(e.lunr)}(this,function(){return function(e){e.multiLanguage=function(){for(var t=Array.prototype.slice.call(arguments),i=t.join("-"),r="",n=[],s=[],p=0;p<t.length;++p)"en"==t[p]?(r+="\\w",n.unshift(e.stopWordFilter),n.push(e.stemmer),s.push(e.stemmer)):(r+=e[t[p]].wordCharacters,e[t[p]].stopWordFilter&&n.unshift(e[t[p]].stopWordFilter),e[t[p]].stemmer&&(n.push(e[t[p]].stemmer),s.push(e[t[p]].stemmer)));var o=e.trimmerSupport.generateTrimmer(r);return e.Pipeline.registerFunction(o,"lunr-multi-trimmer-"+i),n.unshift(o),function(){this.pipeline.reset(),this.pipeline.add.apply(this.pipeline,n),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add.apply(this.searchPipeline,s))}}}});
\ No newline at end of file
diff --git a/assets/javascripts/lunr/min/lunr.nl.min.js b/assets/javascripts/lunr/min/lunr.nl.min.js
new file mode 100644
index 0000000..c4a2535
--- /dev/null
+++ b/assets/javascripts/lunr/min/lunr.nl.min.js
@@ -0,0 +1,18 @@
+/*!
+ * Lunr languages, `Dutch` language
+ * https://github.com/MihaiValentin/lunr-languages
+ *
+ * Copyright 2014, Mihai Valentin
+ * http://www.mozilla.org/MPL/
+ */
+/*!
+ * based on
+ * Snowball JavaScript Library v0.3
+ * http://code.google.com/p/urim/
+ * http://snowball.tartarus.org/
+ *
+ * Copyright 2010, Oleg Mazko
+ * http://www.mozilla.org/MPL/
+ */
+
+!function(r,e){"function"==typeof define&&define.amd?define(e):"object"==typeof exports?module.exports=e():e()(r.lunr)}(this,function(){return function(r){if(void 0===r)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===r.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");r.nl=function(){this.pipeline.reset(),this.pipeline.add(r.nl.trimmer,r.nl.stopWordFilter,r.nl.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(r.nl.stemmer))},r.nl.wordCharacters="A-Za-zªºÀ-ÖØ-öø-ʸˠ-ˤᴀ-ᴥᴬ-ᵜᵢ-ᵥᵫ-ᵷᵹ-ᶾḀ-ỿⁱⁿₐ-ₜKÅℲⅎⅠ-ↈⱠ-ⱿꜢ-ꞇꞋ-ꞭꞰ-ꞷꟷ-ꟿꬰ-ꭚꭜ-ꭤff-stＡ-Ｚａ-ｚ",r.nl.trimmer=r.trimmerSupport.generateTrimmer(r.nl.wordCharacters),r.Pipeline.registerFunction(r.nl.trimmer,"trimmer-nl"),r.nl.stemmer=function(){var e=r.stemmerSupport.Among,i=r.stemmerSupport.SnowballProgram,n=new function(){function r(){for(var r,e,i,o=C.cursor;;){if(C.bra=C.cursor,r=C.find_among(b,11))switch(C.ket=C.cursor,r){case 1:C.slice_from("a");continue;case 2:C.slice_from("e");continue;case 3:C.slice_from("i");continue;case 4:C.slice_from("o");continue;case 5:C.slice_from("u");continue;case 6:if(C.cursor>=C.limit)break;C.cursor++;continue}break}for(C.cursor=o,C.bra=o,C.eq_s(1,"y")?(C.ket=C.cursor,C.slice_from("Y")):C.cursor=o;;)if(e=C.cursor,C.in_grouping(q,97,232)){if(i=C.cursor,C.bra=i,C.eq_s(1,"i"))C.ket=C.cursor,C.in_grouping(q,97,232)&&(C.slice_from("I"),C.cursor=e);else if(C.cursor=i,C.eq_s(1,"y"))C.ket=C.cursor,C.slice_from("Y"),C.cursor=e;else if(n(e))break}else if(n(e))break}function n(r){return C.cursor=r,r>=C.limit||(C.cursor++,!1)}function o(){_=C.limit,d=_,t()||(_=C.cursor,_<3&&(_=3),t()||(d=C.cursor))}function t(){for(;!C.in_grouping(q,97,232);){if(C.cursor>=C.limit)return!0;C.cursor++}for(;!C.out_grouping(q,97,232);){if(C.cursor>=C.limit)return!0;C.cursor++}return!1}function s(){for(var r;;)if(C.bra=C.cursor,r=C.find_among(p,3))switch(C.ket=C.cursor,r){case 1:C.slice_from("y");break;case 2:C.slice_from("i");break;case 3:if(C.cursor>=C.limit)return;C.cursor++}}function u(){return _<=C.cursor}function c(){return d<=C.cursor}function a(){var r=C.limit-C.cursor;C.find_among_b(g,3)&&(C.cursor=C.limit-r,C.ket=C.cursor,C.cursor>C.limit_backward&&(C.cursor--,C.bra=C.cursor,C.slice_del()))}function l(){var r;w=!1,C.ket=C.cursor,C.eq_s_b(1,"e")&&(C.bra=C.cursor,u()&&(r=C.limit-C.cursor,C.out_grouping_b(q,97,232)&&(C.cursor=C.limit-r,C.slice_del(),w=!0,a())))}function m(){var r;u()&&(r=C.limit-C.cursor,C.out_grouping_b(q,97,232)&&(C.cursor=C.limit-r,C.eq_s_b(3,"gem")||(C.cursor=C.limit-r,C.slice_del(),a())))}function f(){var r,e,i,n,o,t,s=C.limit-C.cursor;if(C.ket=C.cursor,r=C.find_among_b(h,5))switch(C.bra=C.cursor,r){case 1:u()&&C.slice_from("heid");break;case 2:m();break;case 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e("",-1,6),new e("á",0,1),new e("ä",0,1),new e("é",0,2),new e("ë",0,2),new e("í",0,3),new e("ï",0,3),new e("ó",0,4),new e("ö",0,4),new e("ú",0,5),new e("ü",0,5)],p=[new e("",-1,3),new e("I",0,2),new e("Y",0,1)],g=[new e("dd",-1,-1),new e("kk",-1,-1),new e("tt",-1,-1)],h=[new e("ene",-1,2),new e("se",-1,3),new e("en",-1,2),new e("heden",2,1),new e("s",-1,3)],k=[new e("end",-1,1),new e("ig",-1,2),new e("ing",-1,1),new e("lijk",-1,3),new e("baar",-1,4),new e("bar",-1,5)],v=[new e("aa",-1,-1),new e("ee",-1,-1),new e("oo",-1,-1),new e("uu",-1,-1)],q=[17,65,16,1,0,0,0,0,0,0,0,0,0,0,0,0,128],z=[1,0,0,17,65,16,1,0,0,0,0,0,0,0,0,0,0,0,0,128],j=[17,67,16,1,0,0,0,0,0,0,0,0,0,0,0,0,128],C=new i;this.setCurrent=function(r){C.setCurrent(r)},this.getCurrent=function(){return C.getCurrent()},this.stem=function(){var e=C.cursor;return r(),C.cursor=e,o(),C.limit_backward=e,C.cursor=C.limit,f(),C.cursor=C.limit_backward,s(),!0}};return function(r){return"function"==typeof r.update?r.update(function(r){return n.setCurrent(r),n.stem(),n.getCurrent()}):(n.setCurrent(r),n.stem(),n.getCurrent())}}(),r.Pipeline.registerFunction(r.nl.stemmer,"stemmer-nl"),r.nl.stopWordFilter=r.generateStopWordFilter(" aan al alles als altijd andere ben bij daar dan dat de der deze die dit doch doen door dus een eens en er ge geen geweest haar had heb hebben heeft hem het hier hij hoe hun iemand iets ik in is ja je kan kon kunnen maar me meer men met mij mijn moet na naar niet niets nog nu of om omdat onder ons ook op over reeds te tegen toch toen tot u uit uw van veel voor want waren was wat werd wezen wie wil worden wordt zal ze zelf zich zij zijn zo zonder zou".split(" ")),r.Pipeline.registerFunction(r.nl.stopWordFilter,"stopWordFilter-nl")}});
\ No newline at end of file
diff --git a/assets/javascripts/lunr/min/lunr.no.min.js b/assets/javascripts/lunr/min/lunr.no.min.js
new file mode 100644
index 0000000..92bc7e4
--- /dev/null
+++ b/assets/javascripts/lunr/min/lunr.no.min.js
@@ -0,0 +1,18 @@
+/*!
+ * Lunr languages, `Norwegian` language
+ * https://github.com/MihaiValentin/lunr-languages
+ *
+ * Copyright 2014, Mihai Valentin
+ * http://www.mozilla.org/MPL/
+ */
+/*!
+ * based on
+ * Snowball JavaScript Library v0.3
+ * http://code.google.com/p/urim/
+ * http://snowball.tartarus.org/
+ *
+ * Copyright 2010, Oleg Mazko
+ * http://www.mozilla.org/MPL/
+ */
+
+!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.no=function(){this.pipeline.reset(),this.pipeline.add(e.no.trimmer,e.no.stopWordFilter,e.no.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.no.stemmer))},e.no.wordCharacters="A-Za-zªºÀ-ÖØ-öø-ʸˠ-ˤᴀ-ᴥᴬ-ᵜᵢ-ᵥᵫ-ᵷᵹ-ᶾḀ-ỿⁱⁿₐ-ₜKÅℲⅎⅠ-ↈⱠ-ⱿꜢ-ꞇꞋ-ꞭꞰ-ꞷꟷ-ꟿꬰ-ꭚꭜ-ꭤff-stＡ-Ｚａ-ｚ",e.no.trimmer=e.trimmerSupport.generateTrimmer(e.no.wordCharacters),e.Pipeline.registerFunction(e.no.trimmer,"trimmer-no"),e.no.stemmer=function(){var r=e.stemmerSupport.Among,n=e.stemmerSupport.SnowballProgram,i=new function(){function e(){var e,r=w.cursor+3;if(a=w.limit,0<=r||r<=w.limit){for(s=r;;){if(e=w.cursor,w.in_grouping(d,97,248)){w.cursor=e;break}if(e>=w.limit)return;w.cursor=e+1}for(;!w.out_grouping(d,97,248);){if(w.cursor>=w.limit)return;w.cursor++}a=w.cursor,a<s&&(a=s)}}function i(){var e,r,n;if(w.cursor>=a&&(r=w.limit_backward,w.limit_backward=a,w.ket=w.cursor,e=w.find_among_b(m,29),w.limit_backward=r,e))switch(w.bra=w.cursor,e){case 1:w.slice_del();break;case 2:n=w.limit-w.cursor,w.in_grouping_b(c,98,122)?w.slice_del():(w.cursor=w.limit-n,w.eq_s_b(1,"k")&&w.out_grouping_b(d,97,248)&&w.slice_del());break;case 3:w.slice_from("er")}}function t(){var e,r=w.limit-w.cursor;w.cursor>=a&&(e=w.limit_backward,w.limit_backward=a,w.ket=w.cursor,w.find_among_b(u,2)?(w.bra=w.cursor,w.limit_backward=e,w.cursor=w.limit-r,w.cursor>w.limit_backward&&(w.cursor--,w.bra=w.cursor,w.slice_del())):w.limit_backward=e)}function o(){var e,r;w.cursor>=a&&(r=w.limit_backward,w.limit_backward=a,w.ket=w.cursor,e=w.find_among_b(l,11),e?(w.bra=w.cursor,w.limit_backward=r,1==e&&w.slice_del()):w.limit_backward=r)}var s,a,m=[new r("a",-1,1),new r("e",-1,1),new r("ede",1,1),new r("ande",1,1),new r("ende",1,1),new r("ane",1,1),new r("ene",1,1),new r("hetene",6,1),new r("erte",1,3),new r("en",-1,1),new r("heten",9,1),new r("ar",-1,1),new r("er",-1,1),new r("heter",12,1),new r("s",-1,2),new r("as",14,1),new r("es",14,1),new r("edes",16,1),new r("endes",16,1),new r("enes",16,1),new r("hetenes",19,1),new r("ens",14,1),new r("hetens",21,1),new r("ers",14,1),new r("ets",14,1),new r("et",-1,1),new r("het",25,1),new r("ert",-1,3),new r("ast",-1,1)],u=[new r("dt",-1,-1),new r("vt",-1,-1)],l=[new r("leg",-1,1),new r("eleg",0,1),new r("ig",-1,1),new r("eig",2,1),new r("lig",2,1),new r("elig",4,1),new r("els",-1,1),new r("lov",-1,1),new r("elov",7,1),new r("slov",7,1),new r("hetslov",9,1)],d=[17,65,16,1,0,0,0,0,0,0,0,0,0,0,0,0,48,0,128],c=[119,125,149,1],w=new n;this.setCurrent=function(e){w.setCurrent(e)},this.getCurrent=function(){return w.getCurrent()},this.stem=function(){var r=w.cursor;return e(),w.limit_backward=r,w.cursor=w.limit,i(),w.cursor=w.limit,t(),w.cursor=w.limit,o(),!0}};return function(e){return"function"==typeof e.update?e.update(function(e){return i.setCurrent(e),i.stem(),i.getCurrent()}):(i.setCurrent(e),i.stem(),i.getCurrent())}}(),e.Pipeline.registerFunction(e.no.stemmer,"stemmer-no"),e.no.stopWordFilter=e.generateStopWordFilter("alle at av bare begge ble blei bli blir blitt både båe da de deg dei deim deira deires dem den denne der dere deres det dette di din disse ditt du dykk dykkar då eg ein eit eitt eller elles en enn er et ett etter for fordi fra før ha hadde han hans har hennar henne hennes her hjå ho hoe honom hoss hossen hun hva hvem hver hvilke hvilken hvis hvor hvordan hvorfor i ikke ikkje ikkje ingen ingi inkje inn inni ja jeg kan kom korleis korso kun kunne kva kvar kvarhelst kven kvi kvifor man mange me med medan meg meget mellom men mi min mine mitt mot mykje ned no noe noen noka noko nokon nokor nokre nå når og også om opp oss over på samme seg selv si si sia sidan siden sin sine sitt sjøl skal skulle slik so som som somme somt så sånn til um upp ut uten var vart varte ved vere verte vi vil ville vore vors vort vår være være vært å".split(" ")),e.Pipeline.registerFunction(e.no.stopWordFilter,"stopWordFilter-no")}});
\ No newline at end of file
diff --git a/assets/javascripts/lunr/min/lunr.pt.min.js b/assets/javascripts/lunr/min/lunr.pt.min.js
new file mode 100644
index 0000000..6c16996
--- /dev/null
+++ b/assets/javascripts/lunr/min/lunr.pt.min.js
@@ -0,0 +1,18 @@
+/*!
+ * Lunr languages, `Portuguese` language
+ * https://github.com/MihaiValentin/lunr-languages
+ *
+ * Copyright 2014, Mihai Valentin
+ * http://www.mozilla.org/MPL/
+ */
+/*!
+ * based on
+ * Snowball JavaScript Library v0.3
+ * http://code.google.com/p/urim/
+ * http://snowball.tartarus.org/
+ *
+ * Copyright 2010, Oleg Mazko
+ * http://www.mozilla.org/MPL/
+ */
+
+!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. 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diff --git a/assets/javascripts/lunr/min/lunr.ro.min.js b/assets/javascripts/lunr/min/lunr.ro.min.js
new file mode 100644
index 0000000..7277140
--- /dev/null
+++ b/assets/javascripts/lunr/min/lunr.ro.min.js
@@ -0,0 +1,18 @@
+/*!
+ * Lunr languages, `Romanian` language
+ * https://github.com/MihaiValentin/lunr-languages
+ *
+ * Copyright 2014, Mihai Valentin
+ * http://www.mozilla.org/MPL/
+ */
+/*!
+ * based on
+ * Snowball JavaScript Library v0.3
+ * http://code.google.com/p/urim/
+ * http://snowball.tartarus.org/
+ *
+ * Copyright 2010, Oleg Mazko
+ * http://www.mozilla.org/MPL/
+ */
+
+!function(e,i){"function"==typeof define&&define.amd?define(i):"object"==typeof exports?module.exports=i():i()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. 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\ No newline at end of file
diff --git a/assets/javascripts/lunr/min/lunr.ru.min.js b/assets/javascripts/lunr/min/lunr.ru.min.js
new file mode 100644
index 0000000..186cc48
--- /dev/null
+++ b/assets/javascripts/lunr/min/lunr.ru.min.js
@@ -0,0 +1,18 @@
+/*!
+ * Lunr languages, `Russian` language
+ * https://github.com/MihaiValentin/lunr-languages
+ *
+ * Copyright 2014, Mihai Valentin
+ * http://www.mozilla.org/MPL/
+ */
+/*!
+ * based on
+ * Snowball JavaScript Library v0.3
+ * http://code.google.com/p/urim/
+ * http://snowball.tartarus.org/
+ *
+ * Copyright 2010, Oleg Mazko
+ * http://www.mozilla.org/MPL/
+ */
+
+!function(e,n){"function"==typeof define&&define.amd?define(n):"object"==typeof exports?module.exports=n():n()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.ru=function(){this.pipeline.reset(),this.pipeline.add(e.ru.trimmer,e.ru.stopWordFilter,e.ru.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.ru.stemmer))},e.ru.wordCharacters="Ѐ-҄҇-ԯᴫᵸⷠ-ⷿꙀ-ꚟ︮︯",e.ru.trimmer=e.trimmerSupport.generateTrimmer(e.ru.wordCharacters),e.Pipeline.registerFunction(e.ru.trimmer,"trimmer-ru"),e.ru.stemmer=function(){var n=e.stemmerSupport.Among,r=e.stemmerSupport.SnowballProgram,t=new function(){function e(){for(;!W.in_grouping(S,1072,1103);){if(W.cursor>=W.limit)return!1;W.cursor++}return!0}function t(){for(;!W.out_grouping(S,1072,1103);){if(W.cursor>=W.limit)return!1;W.cursor++}return!0}function w(){b=W.limit,_=b,e()&&(b=W.cursor,t()&&e()&&t()&&(_=W.cursor))}function i(){return _<=W.cursor}function u(e,n){var r,t;if(W.ket=W.cursor,r=W.find_among_b(e,n)){switch(W.bra=W.cursor,r){case 1:if(t=W.limit-W.cursor,!W.eq_s_b(1,"а")&&(W.cursor=W.limit-t,!W.eq_s_b(1,"я")))return!1;case 2:W.slice_del()}return!0}return!1}function o(){return u(h,9)}function s(e,n){var r;return W.ket=W.cursor,!!(r=W.find_among_b(e,n))&&(W.bra=W.cursor,1==r&&W.slice_del(),!0)}function c(){return s(g,26)}function m(){return!!c()&&(u(C,8),!0)}function f(){return s(k,2)}function l(){return u(P,46)}function a(){s(v,36)}function p(){var e;W.ket=W.cursor,(e=W.find_among_b(F,2))&&(W.bra=W.cursor,i()&&1==e&&W.slice_del())}function d(){var e;if(W.ket=W.cursor,e=W.find_among_b(q,4))switch(W.bra=W.cursor,e){case 1:if(W.slice_del(),W.ket=W.cursor,!W.eq_s_b(1,"н"))break;W.bra=W.cursor;case 2:if(!W.eq_s_b(1,"н"))break;case 3:W.slice_del()}}var _,b,h=[new n("в",-1,1),new n("ив",0,2),new n("ыв",0,2),new n("вши",-1,1),new n("ивши",3,2),new n("ывши",3,2),new n("вшись",-1,1),new n("ившись",6,2),new n("ывшись",6,2)],g=[new n("ее",-1,1),new n("ие",-1,1),new n("ое",-1,1),new n("ые",-1,1),new n("ими",-1,1),new n("ыми",-1,1),new n("ей",-1,1),new n("ий",-1,1),new n("ой",-1,1),new n("ый",-1,1),new n("ем",-1,1),new n("им",-1,1),new n("ом",-1,1),new n("ым",-1,1),new n("его",-1,1),new n("ого",-1,1),new n("ему",-1,1),new n("ому",-1,1),new n("их",-1,1),new n("ых",-1,1),new n("ею",-1,1),new n("ою",-1,1),new n("ую",-1,1),new n("юю",-1,1),new n("ая",-1,1),new n("яя",-1,1)],C=[new n("ем",-1,1),new n("нн",-1,1),new n("вш",-1,1),new n("ивш",2,2),new n("ывш",2,2),new n("щ",-1,1),new n("ющ",5,1),new n("ующ",6,2)],k=[new n("сь",-1,1),new n("ся",-1,1)],P=[new n("ла",-1,1),new n("ила",0,2),new n("ыла",0,2),new n("на",-1,1),new n("ена",3,2),new n("ете",-1,1),new n("ите",-1,2),new n("йте",-1,1),new n("ейте",7,2),new n("уйте",7,2),new n("ли",-1,1),new n("или",10,2),new n("ыли",10,2),new n("й",-1,1),new n("ей",13,2),new n("уй",13,2),new n("л",-1,1),new n("ил",16,2),new n("ыл",16,2),new n("ем",-1,1),new n("им",-1,2),new n("ым",-1,2),new n("н",-1,1),new n("ен",22,2),new n("ло",-1,1),new n("ило",24,2),new n("ыло",24,2),new n("но",-1,1),new n("ено",27,2),new n("нно",27,1),new n("ет",-1,1),new n("ует",30,2),new n("ит",-1,2),new n("ыт",-1,2),new n("ют",-1,1),new n("уют",34,2),new n("ят",-1,2),new n("ны",-1,1),new n("ены",37,2),new n("ть",-1,1),new n("ить",39,2),new n("ыть",39,2),new n("ешь",-1,1),new n("ишь",-1,2),new n("ю",-1,2),new n("ую",44,2)],v=[new n("а",-1,1),new n("ев",-1,1),new n("ов",-1,1),new n("е",-1,1),new n("ие",3,1),new n("ье",3,1),new n("и",-1,1),new n("еи",6,1),new n("ии",6,1),new n("ами",6,1),new n("ями",6,1),new n("иями",10,1),new n("й",-1,1),new n("ей",12,1),new n("ией",13,1),new n("ий",12,1),new n("ой",12,1),new n("ам",-1,1),new n("ем",-1,1),new n("ием",18,1),new n("ом",-1,1),new n("ям",-1,1),new n("иям",21,1),new n("о",-1,1),new n("у",-1,1),new n("ах",-1,1),new n("ях",-1,1),new n("иях",26,1),new n("ы",-1,1),new n("ь",-1,1),new n("ю",-1,1),new n("ию",30,1),new n("ью",30,1),new n("я",-1,1),new n("ия",33,1),new n("ья",33,1)],F=[new n("ост",-1,1),new n("ость",-1,1)],q=[new n("ейше",-1,1),new n("н",-1,2),new n("ейш",-1,1),new n("ь",-1,3)],S=[33,65,8,232],W=new r;this.setCurrent=function(e){W.setCurrent(e)},this.getCurrent=function(){return W.getCurrent()},this.stem=function(){return w(),W.cursor=W.limit,!(W.cursor<b)&&(W.limit_backward=b,o()||(W.cursor=W.limit,f()||(W.cursor=W.limit),m()||(W.cursor=W.limit,l()||(W.cursor=W.limit,a()))),W.cursor=W.limit,W.ket=W.cursor,W.eq_s_b(1,"и")?(W.bra=W.cursor,W.slice_del()):W.cursor=W.limit,p(),W.cursor=W.limit,d(),!0)}};return function(e){return"function"==typeof e.update?e.update(function(e){return t.setCurrent(e),t.stem(),t.getCurrent()}):(t.setCurrent(e),t.stem(),t.getCurrent())}}(),e.Pipeline.registerFunction(e.ru.stemmer,"stemmer-ru"),e.ru.stopWordFilter=e.generateStopWordFilter("алло без близко более больше будем будет будете будешь будто буду будут будь бы бывает бывь был была были было быть в важная важное важные важный вам вами вас ваш ваша ваше ваши вверх вдали вдруг ведь везде весь вниз внизу во вокруг вон восемнадцатый восемнадцать восемь восьмой вот впрочем времени время все всегда всего всем всеми всему всех всею всю всюду вся всё второй вы г где говорил говорит год года году да давно даже далеко дальше даром два двадцатый двадцать две двенадцатый двенадцать двух девятнадцатый девятнадцать девятый девять действительно дел день десятый десять для до довольно долго должно другая другие других друго другое другой е его ее ей ему если есть еще ещё ею её ж же жизнь за занят занята занято заняты затем зато зачем здесь значит и из или им именно иметь ими имя иногда их к каждая каждое каждые каждый кажется как какая какой кем когда кого ком кому конечно которая которого которой которые который которых кроме кругом кто куда лет ли лишь лучше люди м мало между меля менее меньше меня миллионов мимо мира мне много многочисленная многочисленное многочисленные многочисленный мной мною мог могут мож может можно можхо мои мой мор мочь моя моё мы на наверху над надо назад наиболее наконец нам нами нас начала наш наша наше наши не него недавно недалеко нее ней нельзя нем немного нему непрерывно нередко несколько нет нею неё ни нибудь ниже низко никогда никуда ними них ничего но ну нужно нх о об оба обычно один одиннадцатый одиннадцать однажды однако одного одной около он она они оно опять особенно от отовсюду отсюда очень первый перед по под пожалуйста позже пока пор пора после посреди потом потому почему почти прекрасно при про просто против процентов пятнадцатый пятнадцать пятый пять раз разве рано раньше рядом с сам сама сами самим самими самих само самого самой самом самому саму свое своего своей свои своих свою сеаой себе себя сегодня седьмой сейчас семнадцатый семнадцать семь сих сказал сказала сказать сколько слишком сначала снова со собой собою совсем спасибо стал суть т та так такая также такие такое такой там твой твоя твоё те тебе тебя тем теми теперь тех то тобой тобою тогда того тоже только том тому тот тою третий три тринадцатый тринадцать ту туда тут ты тысяч у уж уже уметь хорошо хотеть хоть хотя хочешь часто чаще чего человек чем чему через четвертый четыре четырнадцатый четырнадцать что чтоб чтобы чуть шестнадцатый шестнадцать шестой шесть эта эти этим этими этих это этого этой этом этому этот эту я \ufeffа".split(" ")),e.Pipeline.registerFunction(e.ru.stopWordFilter,"stopWordFilter-ru")}});
\ No newline at end of file
diff --git a/assets/javascripts/lunr/min/lunr.stemmer.support.min.js b/assets/javascripts/lunr/min/lunr.stemmer.support.min.js
new file mode 100644
index 0000000..abd4475
--- /dev/null
+++ b/assets/javascripts/lunr/min/lunr.stemmer.support.min.js
@@ -0,0 +1 @@
+!function(r,t){"function"==typeof define&&define.amd?define(t):"object"==typeof exports?module.exports=t():t()(r.lunr)}(this,function(){return function(r){r.stemmerSupport={Among:function(r,t,i,s){if(this.toCharArray=function(r){for(var t=r.length,i=new Array(t),s=0;s<t;s++)i[s]=r.charCodeAt(s);return i},!r&&""!=r||!t&&0!=t||!i)throw"Bad Among initialisation: s:"+r+", substring_i: "+t+", result: "+i;this.s_size=r.length,this.s=this.toCharArray(r),this.substring_i=t,this.result=i,this.method=s},SnowballProgram:function(){var r;return{bra:0,ket:0,limit:0,cursor:0,limit_backward:0,setCurrent:function(t){r=t,this.cursor=0,this.limit=t.length,this.limit_backward=0,this.bra=this.cursor,this.ket=this.limit},getCurrent:function(){var t=r;return r=null,t},in_grouping:function(t,i,s){if(this.cursor<this.limit){var e=r.charCodeAt(this.cursor);if(e<=s&&e>=i&&(e-=i,t[e>>3]&1<<(7&e)))return this.cursor++,!0}return!1},in_grouping_b:function(t,i,s){if(this.cursor>this.limit_backward){var e=r.charCodeAt(this.cursor-1);if(e<=s&&e>=i&&(e-=i,t[e>>3]&1<<(7&e)))return this.cursor--,!0}return!1},out_grouping:function(t,i,s){if(this.cursor<this.limit){var e=r.charCodeAt(this.cursor);if(e>s||e<i)return this.cursor++,!0;if(e-=i,!(t[e>>3]&1<<(7&e)))return this.cursor++,!0}return!1},out_grouping_b:function(t,i,s){if(this.cursor>this.limit_backward){var e=r.charCodeAt(this.cursor-1);if(e>s||e<i)return this.cursor--,!0;if(e-=i,!(t[e>>3]&1<<(7&e)))return this.cursor--,!0}return!1},eq_s:function(t,i){if(this.limit-this.cursor<t)return!1;for(var s=0;s<t;s++)if(r.charCodeAt(this.cursor+s)!=i.charCodeAt(s))return!1;return this.cursor+=t,!0},eq_s_b:function(t,i){if(this.cursor-this.limit_backward<t)return!1;for(var s=0;s<t;s++)if(r.charCodeAt(this.cursor-t+s)!=i.charCodeAt(s))return!1;return this.cursor-=t,!0},find_among:function(t,i){for(var s=0,e=i,n=this.cursor,u=this.limit,o=0,h=0,c=!1;;){for(var a=s+(e-s>>1),f=0,l=o<h?o:h,_=t[a],m=l;m<_.s_size;m++){if(n+l==u){f=-1;break}if(f=r.charCodeAt(n+l)-_.s[m])break;l++}if(f<0?(e=a,h=l):(s=a,o=l),e-s<=1){if(s>0||e==s||c)break;c=!0}}for(;;){var _=t[s];if(o>=_.s_size){if(this.cursor=n+_.s_size,!_.method)return _.result;var b=_.method();if(this.cursor=n+_.s_size,b)return _.result}if((s=_.substring_i)<0)return 0}},find_among_b:function(t,i){for(var s=0,e=i,n=this.cursor,u=this.limit_backward,o=0,h=0,c=!1;;){for(var a=s+(e-s>>1),f=0,l=o<h?o:h,_=t[a],m=_.s_size-1-l;m>=0;m--){if(n-l==u){f=-1;break}if(f=r.charCodeAt(n-1-l)-_.s[m])break;l++}if(f<0?(e=a,h=l):(s=a,o=l),e-s<=1){if(s>0||e==s||c)break;c=!0}}for(;;){var _=t[s];if(o>=_.s_size){if(this.cursor=n-_.s_size,!_.method)return _.result;var b=_.method();if(this.cursor=n-_.s_size,b)return _.result}if((s=_.substring_i)<0)return 0}},replace_s:function(t,i,s){var e=s.length-(i-t),n=r.substring(0,t),u=r.substring(i);return r=n+s+u,this.limit+=e,this.cursor>=i?this.cursor+=e:this.cursor>t&&(this.cursor=t),e},slice_check:function(){if(this.bra<0||this.bra>this.ket||this.ket>this.limit||this.limit>r.length)throw"faulty slice operation"},slice_from:function(r){this.slice_check(),this.replace_s(this.bra,this.ket,r)},slice_del:function(){this.slice_from("")},insert:function(r,t,i){var s=this.replace_s(r,t,i);r<=this.bra&&(this.bra+=s),r<=this.ket&&(this.ket+=s)},slice_to:function(){return this.slice_check(),r.substring(this.bra,this.ket)},eq_v_b:function(r){return this.eq_s_b(r.length,r)}}}},r.trimmerSupport={generateTrimmer:function(r){var t=new RegExp("^[^"+r+"]+"),i=new RegExp("[^"+r+"]+$");return function(r){return"function"==typeof r.update?r.update(function(r){return r.replace(t,"").replace(i,"")}):r.replace(t,"").replace(i,"")}}}}});
\ No newline at end of file
diff --git a/assets/javascripts/lunr/min/lunr.sv.min.js b/assets/javascripts/lunr/min/lunr.sv.min.js
new file mode 100644
index 0000000..3e5eb64
--- /dev/null
+++ b/assets/javascripts/lunr/min/lunr.sv.min.js
@@ -0,0 +1,18 @@
+/*!
+ * Lunr languages, `Swedish` language
+ * https://github.com/MihaiValentin/lunr-languages
+ *
+ * Copyright 2014, Mihai Valentin
+ * http://www.mozilla.org/MPL/
+ */
+/*!
+ * based on
+ * Snowball JavaScript Library v0.3
+ * http://code.google.com/p/urim/
+ * http://snowball.tartarus.org/
+ *
+ * Copyright 2010, Oleg Mazko
+ * http://www.mozilla.org/MPL/
+ */
+
+!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.sv=function(){this.pipeline.reset(),this.pipeline.add(e.sv.trimmer,e.sv.stopWordFilter,e.sv.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.sv.stemmer))},e.sv.wordCharacters="A-Za-zªºÀ-ÖØ-öø-ʸˠ-ˤᴀ-ᴥᴬ-ᵜᵢ-ᵥᵫ-ᵷᵹ-ᶾḀ-ỿⁱⁿₐ-ₜKÅℲⅎⅠ-ↈⱠ-ⱿꜢ-ꞇꞋ-ꞭꞰ-ꞷꟷ-ꟿꬰ-ꭚꭜ-ꭤff-stＡ-Ｚａ-ｚ",e.sv.trimmer=e.trimmerSupport.generateTrimmer(e.sv.wordCharacters),e.Pipeline.registerFunction(e.sv.trimmer,"trimmer-sv"),e.sv.stemmer=function(){var r=e.stemmerSupport.Among,n=e.stemmerSupport.SnowballProgram,t=new function(){function e(){var e,r=w.cursor+3;if(o=w.limit,0<=r||r<=w.limit){for(a=r;;){if(e=w.cursor,w.in_grouping(l,97,246)){w.cursor=e;break}if(w.cursor=e,w.cursor>=w.limit)return;w.cursor++}for(;!w.out_grouping(l,97,246);){if(w.cursor>=w.limit)return;w.cursor++}o=w.cursor,o<a&&(o=a)}}function t(){var e,r=w.limit_backward;if(w.cursor>=o&&(w.limit_backward=o,w.cursor=w.limit,w.ket=w.cursor,e=w.find_among_b(u,37),w.limit_backward=r,e))switch(w.bra=w.cursor,e){case 1:w.slice_del();break;case 2:w.in_grouping_b(d,98,121)&&w.slice_del()}}function i(){var e=w.limit_backward;w.cursor>=o&&(w.limit_backward=o,w.cursor=w.limit,w.find_among_b(c,7)&&(w.cursor=w.limit,w.ket=w.cursor,w.cursor>w.limit_backward&&(w.bra=--w.cursor,w.slice_del())),w.limit_backward=e)}function s(){var e,r;if(w.cursor>=o){if(r=w.limit_backward,w.limit_backward=o,w.cursor=w.limit,w.ket=w.cursor,e=w.find_among_b(m,5))switch(w.bra=w.cursor,e){case 1:w.slice_del();break;case 2:w.slice_from("lös");break;case 3:w.slice_from("full")}w.limit_backward=r}}var a,o,u=[new r("a",-1,1),new r("arna",0,1),new r("erna",0,1),new r("heterna",2,1),new r("orna",0,1),new r("ad",-1,1),new r("e",-1,1),new r("ade",6,1),new r("ande",6,1),new r("arne",6,1),new r("are",6,1),new r("aste",6,1),new r("en",-1,1),new r("anden",12,1),new r("aren",12,1),new r("heten",12,1),new r("ern",-1,1),new r("ar",-1,1),new r("er",-1,1),new r("heter",18,1),new r("or",-1,1),new r("s",-1,2),new r("as",21,1),new r("arnas",22,1),new r("ernas",22,1),new r("ornas",22,1),new r("es",21,1),new r("ades",26,1),new r("andes",26,1),new r("ens",21,1),new r("arens",29,1),new r("hetens",29,1),new r("erns",21,1),new r("at",-1,1),new r("andet",-1,1),new r("het",-1,1),new r("ast",-1,1)],c=[new r("dd",-1,-1),new r("gd",-1,-1),new r("nn",-1,-1),new r("dt",-1,-1),new r("gt",-1,-1),new r("kt",-1,-1),new r("tt",-1,-1)],m=[new r("ig",-1,1),new r("lig",0,1),new r("els",-1,1),new r("fullt",-1,3),new r("löst",-1,2)],l=[17,65,16,1,0,0,0,0,0,0,0,0,0,0,0,0,24,0,32],d=[119,127,149],w=new n;this.setCurrent=function(e){w.setCurrent(e)},this.getCurrent=function(){return w.getCurrent()},this.stem=function(){var r=w.cursor;return e(),w.limit_backward=r,w.cursor=w.limit,t(),w.cursor=w.limit,i(),w.cursor=w.limit,s(),!0}};return function(e){return"function"==typeof e.update?e.update(function(e){return t.setCurrent(e),t.stem(),t.getCurrent()}):(t.setCurrent(e),t.stem(),t.getCurrent())}}(),e.Pipeline.registerFunction(e.sv.stemmer,"stemmer-sv"),e.sv.stopWordFilter=e.generateStopWordFilter("alla allt att av blev bli blir blivit de dem den denna deras dess dessa det detta dig din dina ditt du där då efter ej eller en er era ert ett från för ha hade han hans har henne hennes hon honom hur här i icke ingen inom inte jag ju kan kunde man med mellan men mig min mina mitt mot mycket ni nu när någon något några och om oss på samma sedan sig sin sina sitta själv skulle som så sådan sådana sådant till under upp ut utan vad var vara varför varit varje vars vart vem vi vid vilka vilkas vilken vilket vår våra vårt än är åt över".split(" ")),e.Pipeline.registerFunction(e.sv.stopWordFilter,"stopWordFilter-sv")}});
\ No newline at end of file
diff --git a/assets/javascripts/lunr/min/lunr.th.min.js b/assets/javascripts/lunr/min/lunr.th.min.js
new file mode 100644
index 0000000..dee3aac
--- /dev/null
+++ b/assets/javascripts/lunr/min/lunr.th.min.js
@@ -0,0 +1 @@
+!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");var r="2"==e.version[0];e.th=function(){this.pipeline.reset(),this.pipeline.add(e.th.trimmer),r?this.tokenizer=e.th.tokenizer:(e.tokenizer&&(e.tokenizer=e.th.tokenizer),this.tokenizerFn&&(this.tokenizerFn=e.th.tokenizer))},e.th.wordCharacters="[฀-๿]",e.th.trimmer=e.trimmerSupport.generateTrimmer(e.th.wordCharacters),e.Pipeline.registerFunction(e.th.trimmer,"trimmer-th");var t=e.wordcut;t.init(),e.th.tokenizer=function(i){if(!arguments.length||null==i||void 0==i)return[];if(Array.isArray(i))return i.map(function(t){return r?new e.Token(t):t});var n=i.toString().replace(/^\s+/,"");return t.cut(n).split("|")}}});
\ No newline at end of file
diff --git a/assets/javascripts/lunr/min/lunr.tr.min.js b/assets/javascripts/lunr/min/lunr.tr.min.js
new file mode 100644
index 0000000..563f6ec
--- /dev/null
+++ b/assets/javascripts/lunr/min/lunr.tr.min.js
@@ -0,0 +1,18 @@
+/*!
+ * Lunr languages, `Turkish` language
+ * https://github.com/MihaiValentin/lunr-languages
+ *
+ * Copyright 2014, Mihai Valentin
+ * http://www.mozilla.org/MPL/
+ */
+/*!
+ * based on
+ * Snowball JavaScript Library v0.3
+ * http://code.google.com/p/urim/
+ * http://snowball.tartarus.org/
+ *
+ * Copyright 2010, Oleg Mazko
+ * http://www.mozilla.org/MPL/
+ */
+
+!function(r,i){"function"==typeof define&&define.amd?define(i):"object"==typeof exports?module.exports=i():i()(r.lunr)}(this,function(){return function(r){if(void 0===r)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===r.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");r.tr=function(){this.pipeline.reset(),this.pipeline.add(r.tr.trimmer,r.tr.stopWordFilter,r.tr.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(r.tr.stemmer))},r.tr.wordCharacters="A-Za-zªºÀ-ÖØ-öø-ʸˠ-ˤᴀ-ᴥᴬ-ᵜᵢ-ᵥᵫ-ᵷᵹ-ᶾḀ-ỿⁱⁿₐ-ₜKÅℲⅎⅠ-ↈⱠ-ⱿꜢ-ꞇꞋ-ꞭꞰ-ꞷꟷ-ꟿꬰ-ꭚꭜ-ꭤff-stＡ-Ｚａ-ｚ",r.tr.trimmer=r.trimmerSupport.generateTrimmer(r.tr.wordCharacters),r.Pipeline.registerFunction(r.tr.trimmer,"trimmer-tr"),r.tr.stemmer=function(){var i=r.stemmerSupport.Among,e=r.stemmerSupport.SnowballProgram,n=new function(){function r(r,i,e){for(;;){var n=Dr.limit-Dr.cursor;if(Dr.in_grouping_b(r,i,e)){Dr.cursor=Dr.limit-n;break}if(Dr.cursor=Dr.limit-n,Dr.cursor<=Dr.limit_backward)return!1;Dr.cursor--}return!0}function n(){var i,e;i=Dr.limit-Dr.cursor,r(Wr,97,305);for(var n=0;n<Br.length;n++){e=Dr.limit-Dr.cursor;var t=Br[n];if(Dr.eq_s_b(1,t[0])&&r(t[1],t[2],t[3]))return Dr.cursor=Dr.limit-i,!0;Dr.cursor=Dr.limit-e}return Dr.cursor=Dr.limit-e,!(!Dr.eq_s_b(1,"ü")||!r(Zr,246,252))&&(Dr.cursor=Dr.limit-i,!0)}function t(r,i){var e,n=Dr.limit-Dr.cursor;return r()&&(Dr.cursor=Dr.limit-n,Dr.cursor>Dr.limit_backward&&(Dr.cursor--,e=Dr.limit-Dr.cursor,i()))?(Dr.cursor=Dr.limit-e,!0):(Dr.cursor=Dr.limit-n,r()?(Dr.cursor=Dr.limit-n,!1):(Dr.cursor=Dr.limit-n,!(Dr.cursor<=Dr.limit_backward)&&(Dr.cursor--,!!i()&&(Dr.cursor=Dr.limit-n,!0))))}function u(r){return t(r,function(){return Dr.in_grouping_b(Wr,97,305)})}function o(){return u(function(){return Dr.eq_s_b(1,"n")})}function s(){return u(function(){return Dr.eq_s_b(1,"s")})}function c(){return u(function(){return Dr.eq_s_b(1,"y")})}function l(){return t(function(){return Dr.in_grouping_b(Lr,105,305)},function(){return Dr.out_grouping_b(Wr,97,305)})}function a(){return Dr.find_among_b(ur,10)&&l()}function m(){return n()&&Dr.in_grouping_b(Lr,105,305)&&s()}function d(){return Dr.find_among_b(or,2)}function f(){return n()&&Dr.in_grouping_b(Lr,105,305)&&c()}function 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Dr.find_among_b(Pr,8)&&c()}function T(){return n()&&Dr.find_among_b(Fr,4)&&c()}function Z(){return Dr.eq_s_b(3,"ken")&&c()}function B(){var r=Dr.limit-Dr.cursor;return!(T()||(Dr.cursor=Dr.limit-r,E()||(Dr.cursor=Dr.limit-r,j()||(Dr.cursor=Dr.limit-r,Z()))))}function D(){if(A()){var r=Dr.limit-Dr.cursor;if(S()||(Dr.cursor=Dr.limit-r,W()||(Dr.cursor=Dr.limit-r,C()||(Dr.cursor=Dr.limit-r,P()||(Dr.cursor=Dr.limit-r,F()||(Dr.cursor=Dr.limit-r))))),T())return!1}return!0}function G(){if(W()){Dr.bra=Dr.cursor,Dr.slice_del();var r=Dr.limit-Dr.cursor;return Dr.ket=Dr.cursor,x()||(Dr.cursor=Dr.limit-r,E()||(Dr.cursor=Dr.limit-r,j()||(Dr.cursor=Dr.limit-r,T()||(Dr.cursor=Dr.limit-r)))),nr=!1,!1}return!0}function H(){if(!L())return!0;var r=Dr.limit-Dr.cursor;return!E()&&(Dr.cursor=Dr.limit-r,!j())}function I(){var r,i=Dr.limit-Dr.cursor;return!(S()||(Dr.cursor=Dr.limit-i,F()||(Dr.cursor=Dr.limit-i,P()||(Dr.cursor=Dr.limit-i,C()))))||(Dr.bra=Dr.cursor,Dr.slice_del(),r=Dr.limit-Dr.cursor,Dr.ket=Dr.cursor,T()||(Dr.cursor=Dr.limit-r),!1)}function J(){var r,i=Dr.limit-Dr.cursor;if(Dr.ket=Dr.cursor,nr=!0,B()&&(Dr.cursor=Dr.limit-i,D()&&(Dr.cursor=Dr.limit-i,G()&&(Dr.cursor=Dr.limit-i,H()&&(Dr.cursor=Dr.limit-i,I()))))){if(Dr.cursor=Dr.limit-i,!x())return;Dr.bra=Dr.cursor,Dr.slice_del(),Dr.ket=Dr.cursor,r=Dr.limit-Dr.cursor,S()||(Dr.cursor=Dr.limit-r,W()||(Dr.cursor=Dr.limit-r,C()||(Dr.cursor=Dr.limit-r,P()||(Dr.cursor=Dr.limit-r,F()||(Dr.cursor=Dr.limit-r))))),T()||(Dr.cursor=Dr.limit-r)}Dr.bra=Dr.cursor,Dr.slice_del()}function K(){var r,i,e,n;if(Dr.ket=Dr.cursor,h()){if(r=Dr.limit-Dr.cursor,p())return 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r=Dr.limit-Dr.cursor;if(Dr.in_grouping_b(Wr,97,305)){Dr.cursor=Dr.limit-r;break}if(Dr.cursor=Dr.limit-r,Dr.cursor<=Dr.limit_backward)return!1;Dr.cursor--}return!0}function X(r,i,e){if(Dr.cursor=Dr.limit-r,V()){var n=Dr.limit-Dr.cursor;if(!Dr.eq_s_b(1,i)&&(Dr.cursor=Dr.limit-n,!Dr.eq_s_b(1,e)))return!0;Dr.cursor=Dr.limit-r;var t=Dr.cursor;return Dr.insert(Dr.cursor,Dr.cursor,e),Dr.cursor=t,!1}return!0}function Y(){var r=Dr.limit-Dr.cursor;(Dr.eq_s_b(1,"d")||(Dr.cursor=Dr.limit-r,Dr.eq_s_b(1,"g")))&&X(r,"a","ı")&&X(r,"e","i")&&X(r,"o","u")&&X(r,"ö","ü")}function $(){for(var r,i=Dr.cursor,e=2;;){for(r=Dr.cursor;!Dr.in_grouping(Wr,97,305);){if(Dr.cursor>=Dr.limit)return Dr.cursor=r,!(e>0)&&(Dr.cursor=i,!0);Dr.cursor++}e--}}function rr(r,i,e){for(;!Dr.eq_s(i,e);){if(Dr.cursor>=Dr.limit)return!0;Dr.cursor++}return(tr=i)!=Dr.limit||(Dr.cursor=r,!1)}function ir(){var r=Dr.cursor;return!rr(r,2,"ad")||(Dr.cursor=r,!rr(r,5,"soyad"))}function er(){var r=Dr.cursor;return!ir()&&(Dr.limit_backward=r,Dr.cursor=Dr.limit,Y(),Dr.cursor=Dr.limit,U(),!0)}var nr,tr,ur=[new i("m",-1,-1),new i("n",-1,-1),new i("miz",-1,-1),new i("niz",-1,-1),new i("muz",-1,-1),new i("nuz",-1,-1),new i("müz",-1,-1),new i("nüz",-1,-1),new i("mız",-1,-1),new i("nız",-1,-1)],or=[new i("leri",-1,-1),new i("ları",-1,-1)],sr=[new i("ni",-1,-1),new i("nu",-1,-1),new i("nü",-1,-1),new i("nı",-1,-1)],cr=[new i("in",-1,-1),new i("un",-1,-1),new i("ün",-1,-1),new i("ın",-1,-1)],lr=[new i("a",-1,-1),new i("e",-1,-1)],ar=[new i("na",-1,-1),new i("ne",-1,-1)],mr=[new i("da",-1,-1),new i("ta",-1,-1),new i("de",-1,-1),new i("te",-1,-1)],dr=[new i("nda",-1,-1),new i("nde",-1,-1)],fr=[new i("dan",-1,-1),new i("tan",-1,-1),new i("den",-1,-1),new i("ten",-1,-1)],br=[new i("ndan",-1,-1),new i("nden",-1,-1)],wr=[new i("la",-1,-1),new i("le",-1,-1)],_r=[new i("ca",-1,-1),new i("ce",-1,-1)],kr=[new i("im",-1,-1),new i("um",-1,-1),new i("üm",-1,-1),new i("ım",-1,-1)],pr=[new 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i("dün",-1,-1),new i("tün",-1,-1),new i("dın",-1,-1),new i("tın",-1,-1),new i("du",-1,-1),new i("tu",-1,-1),new i("dü",-1,-1),new i("tü",-1,-1),new i("dı",-1,-1),new i("tı",-1,-1)],Pr=[new i("sa",-1,-1),new i("se",-1,-1),new i("sak",-1,-1),new i("sek",-1,-1),new i("sam",-1,-1),new i("sem",-1,-1),new i("san",-1,-1),new i("sen",-1,-1)],Fr=[new i("miş",-1,-1),new i("muş",-1,-1),new i("müş",-1,-1),new i("mış",-1,-1)],Sr=[new i("b",-1,1),new i("c",-1,2),new i("d",-1,3),new i("ğ",-1,4)],Wr=[17,65,16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,32,8,0,0,0,0,0,0,1],Lr=[1,16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,1],xr=[1,64,16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1],Ar=[17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,130],Er=[1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1],jr=[17],Tr=[65],Zr=[65],Br=[["a",xr,97,305],["e",Ar,101,252],["ı",Er,97,305],["i",jr,101,105],["o",Tr,111,117],["ö",Zr,246,252],["u",Tr,111,117]],Dr=new e;this.setCurrent=function(r){Dr.setCurrent(r)},this.getCurrent=function(){return Dr.getCurrent()},this.stem=function(){return!!($()&&(Dr.limit_backward=Dr.cursor,Dr.cursor=Dr.limit,J(),Dr.cursor=Dr.limit,nr&&(R(),Dr.cursor=Dr.limit_backward,er())))}};return function(r){return"function"==typeof r.update?r.update(function(r){return n.setCurrent(r),n.stem(),n.getCurrent()}):(n.setCurrent(r),n.stem(),n.getCurrent())}}(),r.Pipeline.registerFunction(r.tr.stemmer,"stemmer-tr"),r.tr.stopWordFilter=r.generateStopWordFilter("acaba altmış altı ama ancak arada aslında ayrıca bana bazı belki ben benden beni benim beri beş bile bin bir biri birkaç birkez birçok birşey birşeyi biz bizden bize bizi bizim bu buna bunda bundan bunlar bunları bunların bunu bunun burada böyle böylece da daha dahi de defa değil diye diğer doksan dokuz dolayı dolayısıyla dört edecek eden ederek edilecek ediliyor edilmesi ediyor elli en etmesi etti ettiği ettiğini eğer gibi göre halen hangi hatta hem henüz hep hepsi her herhangi herkesin hiç hiçbir iki ile ilgili ise itibaren itibariyle için işte kadar karşın katrilyon kendi kendilerine kendini kendisi kendisine kendisini kez ki kim kimden kime kimi kimse kırk milyar milyon mu mü mı nasıl ne neden nedenle nerde nerede nereye niye niçin o olan olarak oldu olduklarını olduğu olduğunu olmadı olmadığı olmak olması olmayan olmaz olsa olsun olup olur olursa oluyor on ona ondan onlar onlardan onları onların onu onun otuz oysa pek rağmen sadece sanki sekiz seksen sen senden seni senin siz sizden sizi sizin tarafından trilyon tüm var vardı ve veya ya yani yapacak yapmak yaptı yaptıkları yaptığı yaptığını yapılan yapılması yapıyor yedi yerine yetmiş yine yirmi yoksa yüz zaten çok çünkü öyle üzere üç şey şeyden şeyi şeyler şu şuna şunda şundan şunları şunu şöyle".split(" ")),r.Pipeline.registerFunction(r.tr.stopWordFilter,"stopWordFilter-tr")}});
\ No newline at end of file
diff --git a/assets/javascripts/lunr/min/lunr.vi.min.js b/assets/javascripts/lunr/min/lunr.vi.min.js
new file mode 100644
index 0000000..22aed28
--- /dev/null
+++ b/assets/javascripts/lunr/min/lunr.vi.min.js
@@ -0,0 +1 @@
+!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.vi=function(){this.pipeline.reset(),this.pipeline.add(e.vi.stopWordFilter,e.vi.trimmer)},e.vi.wordCharacters="[A-Za-ẓ̀͐́͑̉̃̓ÂâÊêÔôĂ-ăĐ-đƠ-ơƯ-ư]",e.vi.trimmer=e.trimmerSupport.generateTrimmer(e.vi.wordCharacters),e.Pipeline.registerFunction(e.vi.trimmer,"trimmer-vi"),e.vi.stopWordFilter=e.generateStopWordFilter("là cái nhưng mà".split(" "))}});
\ No newline at end of file
diff --git a/assets/javascripts/lunr/min/lunr.zh.min.js b/assets/javascripts/lunr/min/lunr.zh.min.js
new file mode 100644
index 0000000..7727bbe
--- /dev/null
+++ b/assets/javascripts/lunr/min/lunr.zh.min.js
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+!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r(require("nodejieba")):r()(e.lunr)}(this,function(e){return function(r,t){if(void 0===r)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===r.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");var i="2"==r.version[0];r.zh=function(){this.pipeline.reset(),this.pipeline.add(r.zh.trimmer,r.zh.stopWordFilter,r.zh.stemmer),i?this.tokenizer=r.zh.tokenizer:(r.tokenizer&&(r.tokenizer=r.zh.tokenizer),this.tokenizerFn&&(this.tokenizerFn=r.zh.tokenizer))},r.zh.tokenizer=function(n){if(!arguments.length||null==n||void 0==n)return[];if(Array.isArray(n))return n.map(function(e){return i?new r.Token(e.toLowerCase()):e.toLowerCase()});t&&e.load(t);var o=n.toString().trim().toLowerCase(),s=[];e.cut(o,!0).forEach(function(e){s=s.concat(e.split(" "))}),s=s.filter(function(e){return!!e});var u=0;return s.map(function(e,t){if(i){var n=o.indexOf(e,u),s={};return s.position=[n,e.length],s.index=t,u=n,new r.Token(e,s)}return e})},r.zh.wordCharacters="\\w一-龥",r.zh.trimmer=r.trimmerSupport.generateTrimmer(r.zh.wordCharacters),r.Pipeline.registerFunction(r.zh.trimmer,"trimmer-zh"),r.zh.stemmer=function(){return function(e){return e}}(),r.Pipeline.registerFunction(r.zh.stemmer,"stemmer-zh"),r.zh.stopWordFilter=r.generateStopWordFilter("的 一 不 在 人 有 是 为 以 于 上 他 而 后 之 来 及 了 因 下 可 到 由 这 与 也 此 但 并 个 其 已 无 小 我 们 起 最 再 今 去 好 只 又 或 很 亦 某 把 那 你 乃 它 吧 被 比 别 趁 当 从 到 得 打 凡 儿 尔 该 各 给 跟 和 何 还 即 几 既 看 据 距 靠 啦 了 另 么 每 们 嘛 拿 哪 那 您 凭 且 却 让 仍 啥 如 若 使 谁 虽 随 同 所 她 哇 嗡 往 哪 些 向 沿 哟 用 于 咱 则 怎 曾 至 致 着 诸 自".split(" ")),r.Pipeline.registerFunction(r.zh.stopWordFilter,"stopWordFilter-zh")}});
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diff --git a/assets/javascripts/lunr/tinyseg.js b/assets/javascripts/lunr/tinyseg.js
new file mode 100644
index 0000000..167fa6d
--- /dev/null
+++ b/assets/javascripts/lunr/tinyseg.js
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+/**
+ * export the module via AMD, CommonJS or as a browser global
+ * Export code from https://github.com/umdjs/umd/blob/master/returnExports.js
+ */
+;(function (root, factory) {
+    if (typeof define === 'function' && define.amd) {
+        // AMD. Register as an anonymous module.
+        define(factory)
+    } else if (typeof exports === 'object') {
+        /**
+         * Node. Does not work with strict CommonJS, but
+         * only CommonJS-like environments that support module.exports,
+         * like Node.
+         */
+        module.exports = factory()
+    } else {
+        // Browser globals (root is window)
+        factory()(root.lunr);
+    }
+}(this, function () {
+    /**
+     * Just return a value to define the module export.
+     * This example returns an object, but the module
+     * can return a function as the exported value.
+     */
+
+    return function(lunr) {
+        // TinySegmenter 0.1 -- Super compact Japanese tokenizer in Javascript
+        // (c) 2008 Taku Kudo <taku@chasen.org>
+        // TinySegmenter is freely distributable under the terms of a new BSD licence.
+        // For details, see http://chasen.org/~taku/software/TinySegmenter/LICENCE.txt
+
+        function TinySegmenter() {
+          var patterns = {
+            "[一二三四五六七八九十百千万億兆]":"M",
+            "[一-龠々〆ヵヶ]":"H",
+            "[ぁ-ん]":"I",
+            "[ァ-ヴーｱ-ﾝﾞｰ]":"K",
+            "[a-zA-Zａ-ｚＡ-Ｚ]":"A",
+            "[0-9０-９]":"N"
+          }
+          this.chartype_ = [];
+          for (var i in patterns) {
+            var regexp = new RegExp(i);
+            this.chartype_.push([regexp, patterns[i]]);
+          }
+
+          this.BIAS__ = -332
+          this.BC1__ = {"HH":6,"II":2461,"KH":406,"OH":-1378};
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+          this.BP2__ = {"BO":60,"OO":-1762};
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+          this.UW4__ = {",":3930,".":3508,"―":-4841,"、":3930,"。":3508,"〇":4999,"「":1895,"」":3798,"〓":-5156,"あ":4752,"い":-3435,"う":-640,"え":-2514,"お":2405,"か":530,"が":6006,"き":-4482,"ぎ":-3821,"く":-3788,"け":-4376,"げ":-4734,"こ":2255,"ご":1979,"さ":2864,"し":-843,"じ":-2506,"す":-731,"ず":1251,"せ":181,"そ":4091,"た":5034,"だ":5408,"ち":-3654,"っ":-5882,"つ":-1659,"て":3994,"で":7410,"と":4547,"な":5433,"に":6499,"ぬ":1853,"ね":1413,"の":7396,"は":8578,"ば":1940,"ひ":4249,"び":-4134,"ふ":1345,"へ":6665,"べ":-744,"ほ":1464,"ま":1051,"み":-2082,"む":-882,"め":-5046,"も":4169,"ゃ":-2666,"や":2795,"ょ":-1544,"よ":3351,"ら":-2922,"り":-9726,"る":-14896,"れ":-2613,"ろ":-4570,"わ":-1783,"を":13150,"ん":-2352,"カ":2145,"コ":1789,"セ":1287,"ッ":-724,"ト":-403,"メ":-1635,"ラ":-881,"リ":-541,"ル":-856,"ン":-3637,"・":-4371,"ー":-11870,"一":-2069,"中":2210,"予":782,"事":-190,"井":-1768,"人":1036,"以":544,"会":950,"体":-1286,"作":530,"側":4292,"先":601,"党":-2006,"共":-1212,"内":584,"円":788,"初":1347,"前":1623,"副":3879,"力":-302,"動":-740,"務":-2715,"化":776,"区":4517,"協":1013,"参":1555,"合":-1834,"和":-681,"員":-910,"器":-851,"回":1500,"国":-619,"園":-1200,"地":866,"場":-1410,"塁":-2094,"士":-1413,"多":1067,"大":571,"子":-4802,"学":-1397,"定":-1057,"寺":-809,"小":1910,"屋":-1328,"山":-1500,"島":-2056,"川":-2667,"市":2771,"年":374,"庁":-4556,"後":456,"性":553,"感":916,"所":-1566,"支":856,"改":787,"政":2182,"教":704,"文":522,"方":-856,"日":1798,"時":1829,"最":845,"月":-9066,"木":-485,"来":-442,"校":-360,"業":-1043,"氏":5388,"民":-2716,"気":-910,"沢":-939,"済":-543,"物":-735,"率":672,"球":-1267,"生":-1286,"産":-1101,"田":-2900,"町":1826,"的":2586,"目":922,"省":-3485,"県":2997,"空":-867,"立":-2112,"第":788,"米":2937,"系":786,"約":2171,"経":1146,"統":-1169,"総":940,"線":-994,"署":749,"者":2145,"能":-730,"般":-852,"行":-792,"規":792,"警":-1184,"議":-244,"谷":-1000,"賞":730,"車":-1481,"軍":1158,"輪":-1433,"込":-3370,"近":929,"道":-1291,"選":2596,"郎":-4866,"都":1192,"野":-1100,"銀":-2213,"長":357,"間":-2344,"院":-2297,"際":-2604,"電":-878,"領":-1659,"題":-792,"館":-1984,"首":1749,"高":2120,"｢":1895,"｣":3798,"･":-4371,"ｯ":-724,"ｰ":-11870,"ｶ":2145,"ｺ":1789,"ｾ":1287,"ﾄ":-403,"ﾒ":-1635,"ﾗ":-881,"ﾘ":-541,"ﾙ":-856,"ﾝ":-3637};
+          this.UW5__ = {",":465,".":-299,"1":-514,"E2":-32768,"]":-2762,"、":465,"。":-299,"「":363,"あ":1655,"い":331,"う":-503,"え":1199,"お":527,"か":647,"が":-421,"き":1624,"ぎ":1971,"く":312,"げ":-983,"さ":-1537,"し":-1371,"す":-852,"だ":-1186,"ち":1093,"っ":52,"つ":921,"て":-18,"で":-850,"と":-127,"ど":1682,"な":-787,"に":-1224,"の":-635,"は":-578,"べ":1001,"み":502,"め":865,"ゃ":3350,"ょ":854,"り":-208,"る":429,"れ":504,"わ":419,"を":-1264,"ん":327,"イ":241,"ル":451,"ン":-343,"中":-871,"京":722,"会":-1153,"党":-654,"務":3519,"区":-901,"告":848,"員":2104,"大":-1296,"学":-548,"定":1785,"嵐":-1304,"市":-2991,"席":921,"年":1763,"思":872,"所":-814,"挙":1618,"新":-1682,"日":218,"月":-4353,"査":932,"格":1356,"機":-1508,"氏":-1347,"田":240,"町":-3912,"的":-3149,"相":1319,"省":-1052,"県":-4003,"研":-997,"社":-278,"空":-813,"統":1955,"者":-2233,"表":663,"語":-1073,"議":1219,"選":-1018,"郎":-368,"長":786,"間":1191,"題":2368,"館":-689,"１":-514,"Ｅ２":-32768,"｢":363,"ｲ":241,"ﾙ":451,"ﾝ":-343};
+          this.UW6__ = {",":227,".":808,"1":-270,"E1":306,"、":227,"。":808,"あ":-307,"う":189,"か":241,"が":-73,"く":-121,"こ":-200,"じ":1782,"す":383,"た":-428,"っ":573,"て":-1014,"で":101,"と":-105,"な":-253,"に":-149,"の":-417,"は":-236,"も":-206,"り":187,"る":-135,"を":195,"ル":-673,"ン":-496,"一":-277,"中":201,"件":-800,"会":624,"前":302,"区":1792,"員":-1212,"委":798,"学":-960,"市":887,"広":-695,"後":535,"業":-697,"相":753,"社":-507,"福":974,"空":-822,"者":1811,"連":463,"郎":1082,"１":-270,"Ｅ１":306,"ﾙ":-673,"ﾝ":-496};
+          
+          return this;
+        }
+        TinySegmenter.prototype.ctype_ = function(str) {
+          for (var i in this.chartype_) {
+            if (str.match(this.chartype_[i][0])) {
+              return this.chartype_[i][1];
+            }
+          }
+          return "O";
+        }
+
+        TinySegmenter.prototype.ts_ = function(v) {
+          if (v) { return v; }
+          return 0;
+        }
+
+        TinySegmenter.prototype.segment = function(input) {
+          if (input == null || input == undefined || input == "") {
+            return [];
+          }
+          var result = [];
+          var seg = ["B3","B2","B1"];
+          var ctype = ["O","O","O"];
+          var o = input.split("");
+          for (i = 0; i < o.length; ++i) {
+            seg.push(o[i]);
+            ctype.push(this.ctype_(o[i]))
+          }
+          seg.push("E1");
+          seg.push("E2");
+          seg.push("E3");
+          ctype.push("O");
+          ctype.push("O");
+          ctype.push("O");
+          var word = seg[3];
+          var p1 = "U";
+          var p2 = "U";
+          var p3 = "U";
+          for (var i = 4; i < seg.length - 3; ++i) {
+            var score = this.BIAS__;
+            var w1 = seg[i-3];
+            var w2 = seg[i-2];
+            var w3 = seg[i-1];
+            var w4 = seg[i];
+            var w5 = seg[i+1];
+            var w6 = seg[i+2];
+            var c1 = ctype[i-3];
+            var c2 = ctype[i-2];
+            var c3 = ctype[i-1];
+            var c4 = ctype[i];
+            var c5 = ctype[i+1];
+            var c6 = ctype[i+2];
+            score += this.ts_(this.UP1__[p1]);
+            score += this.ts_(this.UP2__[p2]);
+            score += this.ts_(this.UP3__[p3]);
+            score += this.ts_(this.BP1__[p1 + p2]);
+            score += this.ts_(this.BP2__[p2 + p3]);
+            score += this.ts_(this.UW1__[w1]);
+            score += this.ts_(this.UW2__[w2]);
+            score += this.ts_(this.UW3__[w3]);
+            score += this.ts_(this.UW4__[w4]);
+            score += this.ts_(this.UW5__[w5]);
+            score += this.ts_(this.UW6__[w6]);
+            score += this.ts_(this.BW1__[w2 + w3]);
+            score += this.ts_(this.BW2__[w3 + w4]);
+            score += this.ts_(this.BW3__[w4 + w5]);
+            score += this.ts_(this.TW1__[w1 + w2 + w3]);
+            score += this.ts_(this.TW2__[w2 + w3 + w4]);
+            score += this.ts_(this.TW3__[w3 + w4 + w5]);
+            score += this.ts_(this.TW4__[w4 + w5 + w6]);
+            score += this.ts_(this.UC1__[c1]);
+            score += this.ts_(this.UC2__[c2]);
+            score += this.ts_(this.UC3__[c3]);
+            score += this.ts_(this.UC4__[c4]);
+            score += this.ts_(this.UC5__[c5]);
+            score += this.ts_(this.UC6__[c6]);
+            score += this.ts_(this.BC1__[c2 + c3]);
+            score += this.ts_(this.BC2__[c3 + c4]);
+            score += this.ts_(this.BC3__[c4 + c5]);
+            score += this.ts_(this.TC1__[c1 + c2 + c3]);
+            score += this.ts_(this.TC2__[c2 + c3 + c4]);
+            score += this.ts_(this.TC3__[c3 + c4 + c5]);
+            score += this.ts_(this.TC4__[c4 + c5 + c6]);
+        //  score += this.ts_(this.TC5__[c4 + c5 + c6]);    
+            score += this.ts_(this.UQ1__[p1 + c1]);
+            score += this.ts_(this.UQ2__[p2 + c2]);
+            score += this.ts_(this.UQ3__[p3 + c3]);
+            score += this.ts_(this.BQ1__[p2 + c2 + c3]);
+            score += this.ts_(this.BQ2__[p2 + c3 + c4]);
+            score += this.ts_(this.BQ3__[p3 + c2 + c3]);
+            score += this.ts_(this.BQ4__[p3 + c3 + c4]);
+            score += this.ts_(this.TQ1__[p2 + c1 + c2 + c3]);
+            score += this.ts_(this.TQ2__[p2 + c2 + c3 + c4]);
+            score += this.ts_(this.TQ3__[p3 + c1 + c2 + c3]);
+            score += this.ts_(this.TQ4__[p3 + c2 + c3 + c4]);
+            var p = "O";
+            if (score > 0) {
+              result.push(word);
+              word = "";
+              p = "B";
+            }
+            p1 = p2;
+            p2 = p3;
+            p3 = p;
+            word += seg[i];
+          }
+          result.push(word);
+
+          return result;
+        }
+
+        lunr.TinySegmenter = TinySegmenter;
+    };
+
+}));
\ No newline at end of file
diff --git a/assets/javascripts/lunr/wordcut.js b/assets/javascripts/lunr/wordcut.js
new file mode 100644
index 0000000..146f4b4
--- /dev/null
+++ b/assets/javascripts/lunr/wordcut.js
@@ -0,0 +1,6708 @@
+(function(f){if(typeof exports==="object"&&typeof module!=="undefined"){module.exports=f()}else if(typeof define==="function"&&define.amd){define([],f)}else{var g;if(typeof window!=="undefined"){g=window}else if(typeof global!=="undefined"){g=global}else if(typeof self!=="undefined"){g=self}else{g=this}(g.lunr || (g.lunr = {})).wordcut = f()}})(function(){var define,module,exports;return (function e(t,n,r){function s(o,u){if(!n[o]){if(!t[o]){var a=typeof require=="function"&&require;if(!u&&a)return a(o,!0);if(i)return i(o,!0);var f=new Error("Cannot find module '"+o+"'");throw f.code="MODULE_NOT_FOUND",f}var l=n[o]={exports:{}};t[o][0].call(l.exports,function(e){var n=t[o][1][e];return s(n?n:e)},l,l.exports,e,t,n,r)}return n[o].exports}var i=typeof require=="function"&&require;for(var o=0;o<r.length;o++)s(r[o]);return s})({1:[function(require,module,exports){
+var _ = require("underscore");
+
+var Acceptors = {
+  creators: null,
+  current: null,
+  tag: null,
+
+  init: function() {
+    this.creators = [];
+    this.current = [];
+    this.tag = {};
+  },
+
+  reset: function() {
+    this.current = [];
+    this.tag = {}
+  },
+
+  transit: function(ch) {
+    var self = this;
+
+    self.creators.forEach(function(creator) {
+      var acceptor = creator.createAcceptor(self.tag);
+      if (acceptor) 
+        self.current.push(acceptor);
+    });
+    
+    var _current = [];
+    self.tag = {};
+
+    for (var i = 0; i < self.current.length; i++) {
+      var _acceptor = self.current[i]
+        , acceptor = _acceptor.transit(ch);
+      
+      if (!acceptor.isError) {
+        _current.push(acceptor);
+        self.tag[acceptor.tag] = acceptor;
+      }
+    }
+    self.current = _current;
+
+  },
+
+  getFinalAcceptors: function() {    
+    return this.current.filter(function(acceptor) {
+      return acceptor.isFinal;
+    });
+  }
+};
+
+module.exports = function() {
+  var acceptors = _.clone(Acceptors);
+  acceptors.init();
+  return acceptors;
+};
+
+},{"underscore":25}],2:[function(require,module,exports){
+(function (__dirname){
+
+var LEFT = 0;
+var RIGHT = 1;
+var path = require("path");
+var glob = require("glob");
+
+var WordcutDict = {
+
+
+  init: function (dictPathFile, withDefault, words) {
+    withDefault = withDefault || false
+    defaultDict = path.normalize(__dirname + "/..") + "/data/tdict-*.txt";
+    this.dict=[]
+    var dictPathIsDefined = dictPathFile !== undefined
+    var dictPath = (withDefault || !dictPathIsDefined) ? [defaultDict]: [];
+    var dictPathFile = dictPathFile || defaultDict
+
+    if(dictPathIsDefined){
+      if (Array.isArray(dictPathFile)) {
+        dictPath.concat.apply(dictPath, dictPathFile);
+      } else {
+        dictPath.push(dictPathFile)
+      }
+    }
+
+    this.addFiles(dictPath, false)
+
+    if(words!==undefined){
+      this.addWords(words, false)
+    }
+    this.finalizeDict();
+  },
+
+  addWords: function(words, finalize){
+    finalize = finalize===undefined || finalize;
+    this.dict.push.apply(this.dict, words)
+    if(finalize){
+      this.finalizeDict();
+    }
+  },
+
+  finalizeDict: function(){
+    this.dict = this.sortuniq(this.dict);
+  },
+
+  addFiles: function(files, finalize){
+    finalize = finalize===undefined || finalize;
+    
+    for (var i = 0; i < 1; i++) {
+      words = "ก.ก.\nก.ก.น.\nก.ข.ค.\nก.ค.\nก.จ.\nก.ช.น.\nก.ฌ.\nก.ต.\nก.ต.ง.\nก.ต.ช.\nก.ตร.\nก.ท.\nก.น.ช.\nก.บช.\nก.บถ.\nก.ป.ส.\nก.พ.\nก.ม.\nก.ย.\nก.ร.\nก.ล.ต.\nก.ว.\nก.ศ.ว.\nก.ส.ท.\nก.ส.ธ.\nก.ส.อ.\nก.อ.\nกก.ตชด.\nกก.ตร.น.\nกก.ภ.จว.\nกก.รสช.\nกกบ.ขส.ทบ.\nกกล.รพน.\nกง.กห.\nกง.ทบ.\nกง.ทร.\nกซข.ป.\nกซม.ป.\nกทม.กรุงเทพมหานคร\nกบ.ทบ.\nกป.สป.\nกพ.ทบ.\nกพ.ทร.\nกพ.ทหาร\nกร.ทบ.\nกรป.กลาง\nกรอ.พอ.\nกศ.ด.\nกศ.บ.\nกศ.บป.\nกศ.ม.\nกษ.ด.\nกษ.บ.\nกษ.ม.\nกส.ด.\nกส.ทบ.\nกส.บ.\nกส.ม.\nกอ.ปค.\nกอ.รพน.\nกอ.รมน.\nกอ.รสต.\nข.ต.ว.\nขว.ทบ.\nขว.ทร.\nขว.ทหาร\nขส.ทบ.\nขส.ทร.\nขส.ทอ.\nค.ด.\nค.บ.\nค.พ.ศ.\nค.ม.\nค.ร.น.\nค.ร.ฟ.\nค.ร.ม.\nค.ศ.\nค.อ.ด.\nค.อ.บ.\nค.อ.ม.\nคศ.ด.\nคศ.บ.\nคศ.ม.\nง.ด.\nจ.จ.\nจ.จ.จ.\nจ.ช.\nจ.ต.\nจ.ท.\nจ.ป.ร.\nจ.ม.\nจ.ศ.\nจ.ส.ต.\nจ.ส.ท.\nจ.ส.อ.\nจ.อ.\nจ.อ.ร.\nจ.๑๘\nจก.ธน.\nจก.สน.\nช.ค.\nช.ค.บ.\nช.พ.ค.\nช.ส.\nช.ส.ค.\nฌ.ป.ค.\nฌ.ศ.ร.\nฌ.ส.อ.\nฐท.สห.\nด.ช.\nด.ญ.\nด.ต.\nด.ศ.ค.\nด.ศ.ร.\nดย.ทร.\nต.ก.\nต.ค.\nต.จ.\nต.จ.ว.\nต.ช.\nต.ต.\nต.บ.\nต.ม.\nต.ร.\nต.ศ.ร.\nต.ห.\nต.อ.\nต.อ.จ.\nตร.กม.\nตร.ซม.\nตร.ต.\nตร.ทล.\nตร.น.\nตร.ปม.\nตร.ภ.\nตร.ม.\nตร.รฟ.\nตร.ว.\nตร.ส.\nตร.สข.\nท.จ.\nท.จ.ว.\nท.ช.\nท.ญ.\nท.ด.\nท.ท.ท.\nท.ทบ.\nท.บ.\nท.พ.\nท.ม.\nท.ศ.\nทก.ด.\nทก.บ.\nทก.ม.\nทส.ปช.\nทส.รมว.กห.\nทุ.ส.นิ.ม.\nธ.ก.ส.\nธ.ค.\nธ.ญ\nธ.บ.\nน.ช.\nน.ญ.\nน.ด.\nน.ต.\nน.ท.\nน.น.\nน.บ.\nน.บ.ท.\nน.ป.ท.\nน.พ.\nน.ม.\nน.ร.\nน.ว.\nน.ศ.\nน.ส.\nน.ส.พ.\nน.ส.๓\nน.สพ.\nน.อ.\nนปพ.ภ.\nนศ.ด.\nนศ.บ.\nนศ.ม.\nบ.ก.\nบ.ข.ส.\nบ.ช.\nบ.ด.ท.\nบ.ตร.\nบ.ภ.\nบ.ม.\nบก.จร.\nบก.ตชด.\nบก.ตม.\nบก.ทล.\nบก.น.\nบก.ป.\nบก.ปค.\nบก.ปม.\nบก.ภ.เขต\nบก.รน.\nบก.รฟ.\nบก.ร้อย.ตชด.\nบก.ส.\nบกข.ป.\nบจพ.ป.\nบช.ก.\nบช.ด.\nบช.ตชด.\nบช.น.\nบช.บ.\nบช.ปส.\nบช.ภ.\nบช.ม.\nบชท.ป.\nบชน.ป.\nบชส.ป.\nบธ.ด.\nบธ.บ.\nบธ.ม.\nบนท.ป.\nบนอ.ป.\nบปช.ป.\nป.กท.\nป.กศ.\nป.กศ.สูง\nป.จ.\nป.จ.ว.\nป.ช.\nป.ธ.\nป.ป.\nป.ป.ก.\nป.ป.ช.\nป.ป.ป.\nป.ป.ร.\nป.ป.ส.\nป.พ.\nป.พ.พ.\nป.พย.\nป.ม.\nป.ม.ก.\nป.ม.ช.\nป.ม.ธ.\nป.ม.ศ.\nป.ม.อ.\nป.ร.ร.๔\nป.ร.ร.๕\nป.ร.ร.๖\nป.ล.\nป.ว.พ.\nป.วิ.อ.\nป.ส.ส.\nป.อ.\nป.อ.ร.ส.\nป.๑\nปม.วส.\nปอ.พ.\nผกก.ภ.\nผช.ผอ.\nผต.มท.\nผบ.ตร.\nผบ.ทบ.\nผบ.ทร.\nผบ.ทสส.\nผบ.ทอ.\nผบก.น.\nผบก.ป.\nผบก.ปค.\nผบก.ปม.\nผบก.ภ.\nผบช.ก.\nผบช.ตชด.\nผบช.น.\nผบช.ภ.\nผว.กทม.\nผอ.ปจ.\nพ.ก.ง.\nพ.กศ.\nพ.ข.ต.\nพ.ค.\nพ.ค.ช.\nพ.ค.ว.\nพ.ค.ศ.\nพ.จ.ต.\nพ.จ.ท.\nพ.จ.อ.\nพ.ช.\nพ.ช.ค.\nพ.ด.\nพ.ต.\nพ.ต.ต.\nพ.ต.ท.\nพ.ต.อ.\nพ.ต.อ.พิเศษ\nพ.ท.\nพ.บ.\nพ.ป.\nพ.ภ.ม.\nพ.ม.\nพ.ม.ช.\nพ.ย.\nพ.ร.ก.\nพ.ร.ฎ.\nพ.ร.ต.\nพ.ร.ธ.\nพ.ร.บ.\nพ.ศ.\nพ.ศ.บ.\nพ.ส.ร.\nพ.ส.ล.\nพ.อ.\nพ.อ.ต.\nพ.อ.ท.\nพ.อ.พิเศษ\nพ.อ.อ.\nพณ.ด.\nพณ.บ.\nพณ.ม.\nพธ.ด.\nพธ.บ.\nพธ.ม.\nพบ.ด.\nพบ.บ.\nพบ.ม.\nพย.ด.\nพย.บ.\nพย.ม.\nพล.จ.\nพล.ต.\nพล.ต.จ.\nพล.ต.ต.\nพล.ต.ท.\nพล.ต.อ.\nพล.ท.\nพล.ปตอ.\nพล.ม.\nพล.ม.๒\nพล.ร.จ.\nพล.ร.ต.\nพล.ร.ท.\nพล.ร.อ.\nพล.อ.\nพล.อ.จ.\nพล.อ.ต.\nพล.อ.ท.\nพล.อ.อ.\nพลา.ทร.\nพศ.ด.\nพศ.บ.\nพศ.ม.\nพอ.สว.\nภ.ง.ด.\nภ.ง.ด.๙\nภ.ด.\nภ.บ.\nภ.บ.ท.๕\nภ.ป.ร.\nภ.พ.\nภ.ม.\nภ.สถ.บ.\nม.ค.\nม.จ.\nม.ป.ท.\nม.ป.ป.\nม.ป.พ.\nม.ร.ว.\nม.ศ.\nม.อ.\nม.อ.ปัตตานี\nมิ.ย.\nมี.ค.\nยศ.ทบ.\nยศ.ทร.\nยศ.ทอ.\nร.ง.\nร.ด.\nร.ต.\nร.ต.ต.\nร.ต.ท.\nร.ต.อ.\nร.ท.\nร.น.\nร.บ.\nร.พ.\nร.ฟ.ล.\nร.ย.ล.\nร.ย.ส.ท.\nร.ล.\nร.ศ.\nร.ส.พ.\nร.อ.\nรป.ม.\nรร.จปร.\nรร.จอ.\nรร.ชท.\nรร.ตท.\nรร.นร.\nรร.นรต.\nรร.นอ.\nล.ญ.\nล.ว.\nลส.ชบ.\nว.ค.\nว.ฉ.\nว.ช.\nว.ด.ป.\nว.ป.ถ.\nวท.บ.\nศ.บ.\nศ.ป.ก.\nศ.ศ.ป.\nศฝร.ภ.\nศศ.บ.\nศษ.บ.\nศส.บ.\nส.ก.\nส.ก.ศ.ท.\nส.ค.\nส.ค.1\nส.ค.ร.\nส.ค.ส.\nส.ต.\nส.ต.ต.\nส.ต.ท.\nส.ต.อ.\nส.ท.\nส.ทร.\nส.ป.ช.\nส.ป.ส.ท.\nส.ป.อ.\nส.ร.\nส.ล.น.\nส.ว.\nส.ว.ท.\nส.ว.ส.ท.\nส.ส.\nส.ส.ท.\nส.ส.ร.\nส.ห.\nส.อ.\nสถ.บ.\nสนง.สสอ.\nสพ.ญ.\nสพ.บ.\nสว.จร.\nสว.ธร.\nสว.ส.\nสว.สป.\nสว.สส.\nสว.อก.\nสส.บ.\nสุ.จิ.ปุ.ลิ.\nห.ร.ม.\nอ.ก.ค.\nอ.ก.จ.\nอ.จ.\nอ.ช.พ.\nอ.ตร.\nอ.บ.\nอ.ส.ท.\nอ.ส.ม.ท.\nอ.ส.ย.\nอ.อ.ป.\nอส.รด.\nอุ.อา.ก.ส.\nฮ.จ.\nฮ.ท.\nฮ.ฝ.\nฮ.ล.\nฮ.ศ.\nเม.ย.\n\nกรีนิช\nกลันตัน\nกัลกัตตา\nกัวลาลัมเปอร์\nกัศมีร์\nกาฐมาณฑุ\nโกลกาตา\nควิเบก\nคอนเนตทิคัต\nคาบูล\nคุชราต\nคุนหมิง\nเคนตักกี\nเคนทักกี\nเคมบริดจ์\nแคชเมียร์\nแคนซัส\nแคนเบอร์รา\nแคโรไลนา\nแคลิฟอร์เนีย\nโคเปนเฮเกน\nโคลัมโบ\nโคโลราโด\nไครสต์เชิร์ช\nไคโร\nจาการ์ตา\nจำปาศักดิ์\nเจนไน\nเจนีวา\nเจ้อเจียง\nฉงชิ่ง\nเฉิงตู\nชานตง\nชิคาโก\nเชนไน\nเชอร์โนบิล\nซัปโปโร\nซานมารีโน\nซาบาห์\nซาราเยโว\nซาราวัก\nซิดนีย์\nซีอาน\nซีแอตเทิล\nซูริก\nซูริค\nเซเชลส์\nเซนได\nเซี่ยงไฮ้\nโซเฟีย\nโซล\nโซโลมอน\nไซ่ง่อน\nไซบีเรีย\nดัลลัส\nดาโคตา\nดานัง\nดีทรอยต์\nดูไบ\nเดนเวอร์\nเดลาแวร์\nเดียนเบียนฟู\nโดเวอร์\nโดฮา\nไดฟุกุ\nไดฟูกุ\nตรังกานู\nตริโปลี\nตูวาลู\nเตหะราน\nโตเกียว\nโตรอนโต\nทมิฬนาฑู\nทริโปลี\nทิเบต\nเทกซัส\nเท็กซัส\nเทนเนสซี\nเทลอาวีฟ\nแทสเมเนีย\nโทรอนโต\nไทเป\nธากา\nนางาซากิ\nนาริตะ\nนิวเจอร์ซีย์\nนิวเดลี\nนิวยอร์ก\nนิวยอร์ค\nนิวแฮมป์เชียร์\nเนบราสกา\nเนแบรสกา\nเนวาดา\nบรัสเซลส์\nบราซิเลีย\nบอมเบย์\nบอสตัน\nบังกาลอร์\nบังคาลอร์\nบูคาเรสต์\nบูดาเปสต์\nเบงกาซี\nเบนกาซี\nเบรุต\nเบลเกรด\nเบอร์ลิน\nแบกแดด\nปอยเปต\nปะลิส\nปะหัง\nปักกิ่ง\nปัญจาบ\nปัฏนา\nปารีส\nปีนัง\nเประ\nเปียงยาง\nพนมเปญ\nพระตะบอง\nพะโค\nพะสิม\nพาราณสี\nพิหารี\nเพนซิลวาเนีย\nเพนซิลเวเนีย\nฟรานซ์\nฟลอริดา\nฟิลาเดลเฟีย\nฟุกุชิมะ\nฟุกุชิมา\nฟุกุโอกะ\nฟูกูโอกะ\nแฟรงก์เฟิร์ต\nมอสโก\nมะนิลา\nมะละแหม่ง\nมัณฑะเลย์\nมัทราส\nมาดริด\nมิชิแกน\nมินนิโซตา\nมิยางิ\nมิลาน\nมิวนิก\nมิสซูรี\nมุมไบ\nเมน\nเมลเบิร์น\nเมาะตะมะ\nเมาะลำเลิง\nแมนจูเรีย\nแมนเชสเตอร์\nแมนฮัตตัน\nแมริแลนด์\nแมรีแลนด์\nแมสซาชูเซตส์\nยะไข่\nย่างกุ้ง\nยูทาห์\nยูนนาน\nเยรูซาเล็ม\nโยโกฮามา\nริยาด\nรีโอเดจาเนโร\nโรดไอแลนด์\nลอนดอน\nลอสแองเจลิส\nลาปาซ\nลาสเวกัส\nลิสบอน\nลุยเซียนา\nโลซาน\nโลซานน์\nวอชิงตัน\nวอร์ซอ\nวิสคอนซิน\nเวนิส\nเวลส์\nเวอร์จิเนีย\nเวอร์มอนต์\nเวียงจันทน์\nเวียนนา\nแวนคูเวอร์\nไวโอมิง\nสกอตแลนด์\nสก็อตแลนด์\nสตอกโฮล์ม\nสลังงอร์\nเสฉวน\nเสียมราฐ\nเสียมเรียบ\nหงสา\nหงสาวดี\nหนานไห่\nหลวงพระบาง\nหูเป่ย\nหูเป่ย์\nหูหนาน\nเหอเป่ย\nเหอเป่ย์\nเหอหนาน\nอชันตา\nอลาสกา\nอวันตี\nออริกอน\nออสโล\nอะแลสกา\nอัตตะปือ\nอัมมาน\nอัมสเตอร์ดัม\nอัสสัม\nอาบูดาบี\nอาร์คันซอ\nอินเดียนา\nอิบารากิ\nอิลลินอยส์\nอิสตันบูล\nอิสลามาบัด\nอุรุมชี\nอูลานบาตอร์\nเอดินบะระ\nเอเธนส์\nแอตแลนตา\nแอริโซนา\nแอลเจียร์\nโอคลาโฮมา\nโอค็อตสค์\nโอกินาวา\nโอซากา\nโอริสสา\nโอเรกอน\nโอไฮโอ\nไอดาโฮ\nไอโอวา\nฮอนโนลูลู\nฮานอย\nฮาเนดะ\nฮาราเร\nฮาวาย\nฮิโรชิมา\nฮุสตัน\nเฮลซิงกิ\n\nมกรา\nกุมภา\nมีนา\nเมษา\nพฤษภา\nมิถุนา\nกรกฎา\nสิงหา\nกันยา\nตุลา\nพฤศจิกา\nธันวา\nเอ\nบี\nซี\nดี\nอี\nเอฟ\nจี\nเอช\nไอ\nเจ\nเค\nแอล\nเอ็ม\nเอ็น\nโอ\nพี\nคิว\nอาร์\nเอส\nที\nยู\nวี\nดับเบิล\nดับบลิว\nเอ็กซ์\nเอ๊กซ์\nวาย\nแซด\nแอลฟา\nแอลฟ่า\nเบตา\nเบต้า\nแกมมา\nแกมม่า\nเดลตา\nเดลต้า\nโอเมกา\nโอเมก้า\nเมกะ\nกิกะ\nนาโน\nไมโคร\n\nกรรมาชน\nกรอบรูป\nกระดี๊กระด๊า\nกระบับ\nกราวนด์\nกรีน\nกรุ๊ป\nกฤษณ์\nกลาส\nก๊วน\nกษัตริยา\nกษัตริยาธิราช\nก่อนหน้า\nกะบับ\nกับดัก\nกัมมันตะ\nก๊าก\nก๋ากั่น\nกาญจน์\nกาญจนาภิเษก\nกามิกาเซ่\nการันตี\nกาหลิบ\nกิฟท์\nกิมจิ\nกีวี\nกึ๊ก\nกึ๋ย\nกุนซือ\nกุมภาพันธ์\nกู๋\nเกจิ\nเกมส์\nเกย์\nเกรด\nเกรย์\nเกสต์เฮาส์\nเก๊ะ\nเก๋ากี้\nเกิร์ล\nแกงค์\nแกรนด์\nแกสโซฮอล์\nแก๊สโซฮอล์\nโกเต็กซ์\nโกลด์\nโกะ\nโก๊ะ\nไกด์\nขั้นตอน\nเขวี้ยง\nคณาญาติ\nครัวซอง\nครัวซองต์\nคร่ำครวญ\nครีเอทีฟ\nครูเสด\nคลับ\nคลาสสิก\nคลิตอริส\nคลิป\nความหมาย\nควิก\nควีน\nคองเกรส\nคอนซูเมอร์\nคอนเซปต์\nคอนเซ็ปต์\nคอนโด\nคอนโดมิเนียม\nคอนเทนเนอร์\nคอนแทค\nคอนแท็ค\nคอนโทรล\nคอนเฟิร์ม\nคอปเตอร์\nคอมพ์\nคอมเพล็กซ์\nคอมมอนส์\nคอมเมนท์\nคอมเมนต์\nคอร์ป\nคอร์ปอเรชั่น\nคอร์รัปชัน\nคอร์รัปชั่น\nคอรัปชัน\nคอรัปชั่น\nคอร์ส\nคอลเล็กชั่น\nคอลัมน์\nคอลัมนิสต์\nคัตเอาต์\nคันคาก\nคันถธุระ\nคันธาระ\nคันยิ\nคัสตาร์ด\nคาราโอเกะ\nคีตกวี\nคีตปฏิภาณ\nคีตราชัน\nคาปูชิโน\nคามิคาเซ่\nคาเฟ่\nคาร์\nคาร์โก้\nคาราเมล\nคาแรกเตอร์\nคาแร็กเตอร์\nคาแรคเตอร์\nคาแร็คเตอร์\nคาวบอย\nคาสิโน\nคิกขุ\nคิวบิก\nคูลเลอร์\nเคบับ\nเครป\nเคลม\nเคลียร์\nเคลื่อนย้าย\nเคส\nเคอร์ฟิว\nแคชเชียร์\nแคทวอล์ค\nแคนดิเดต\nแคนตาลูป\nแคนยอน\nแคนู\nแคป\nแคมป์\nแคมปัส\nแคมเปญ\nแคร์\nแครกเกอร์\nแคร็กเกอร์\nแครอท\nแคสต์\nแคสติง\nแคสติ้ง\nโค้ก\nโค้ช\nโคโยตี\nโคโยตี้\nโครนา\nโคอะล่า\nโคอาลา\nโคอาล่า\nไคลแมกซ์\nไคลแม็กซ์\nงั้น\nง่าว\nงี้\nเง็ง\nโง่เขลา\nไง\nจตุคาม\nจ๊อกกี้\nจอหงวน\nจังโก้\nจัมโบ้\nจ๊าบ\nจารกรรม\nจารชน\nจิ๊ก\nจิ๊กโก๋\nจิ๊กซอว์\nจิตพิสัย\nจิตเภท\nจีดีพี\nจึ๊ก\nจุ๊ย\nจูน\nจูเนียร์\nเจ๊\nเจได\nเจ็ต\nเจล\nเจ๊าะแจ๊ะ\nเจี๊ยว\nแจ็กเก็ต\nแจ๊กเก็ต\nแจ็กพอต\nแจ็กพ็อต\nแจ๊กพอต\nแจ๊กพ็อต\nแจม\nแจ๊ส\nโจ๋\nฉลุย\nเฉิ่ม\nชนะเลิศ\nช็อค\nช็อต\nช็อป\nช็อปปิ้ง\nช็อปเปอร์\nชะโนด\nชัตเตอร์\nชัวร์\nชาร์จ\nชาร์ต\nชาร์ป\nชินบัญชร\nชิฟฟอน\nชีส\nชีอะห์\nเช็ก\nเช็งเม้ง\nเชฟ\nเชลียร์\nเชอร์รี่\nแชเชือน\nแช่แข็ง\nแชมป์\nแชมปิยอง\nแชมเปญ\nแชมเปี้ยน\nแชมพู\nโชว์รูม\nโชห่วย\nใช้งาน\nไชน่า\nซ้อ\nซอมบี้\nซะ\nซังเต\nซันตาคลอส\nซัพพลาย\nซัพพลายเออร์\nซัมเมอร์\nซากุระ\nซาดิสต์\nซาดิสม์\nซาตาน\nซานตาคลอส\nซาฟารี\nซาบะ\nซามูไร\nซาร์\nซาร์ดีน\nซาเล้ง\nซิง\nซิ่ง\nซิงเกิล\nซิตี\nซิตี้\nซินโดรม\nซิม\nซิ้ม\nซิมโฟนี\nซิมโฟนี่\nซิลเวอร์\nซี้\nซี้ซั้ว\nซีดาน\nซีน\nซีนีเพล็กซ์\nซีเนียร์\nซีร็อกซ์\nซีรีส์\nซีเรียส\nซีอีโอ\nซื่อบื้อ\nซุนหนี่\nซุปเปอร์\nซูชิ\nซูเปอร์\nซูม\nซูโม่\nซูเอี๋ย\nซูฮก\nเซ็กซ์\nเซ็กซี่\nเซ็กส์\nเซนเซอร์\nเซ็นเซอร์\nเซนเตอร์\nเซ็นเตอร์\nเซ็นทรัล\nเซนส์\nเซ่นไหว้\nเซฟตี้\nเซรามิก\nเซลส์\nเซลส์แมน\nเซอร์\nเซอร์ไพรส์\nเซอร์วิส\nเซาท์\nเซี้ยว\nแซ็ก\nแซกโซโฟน\nแซ็กโซโฟน\nแซนด์วิช\nแซมบ้า\nแซลมอน\nแซว\nโซเชียล\nโซน\nโซนี่\nโซลาร์\nโซโล\nโซโล่\nญาณทัสสนะ\nดยุก\nดยุค\nดร็อป\nดรัมเมเยอร์\nดรามา\nดราม่า\nดอกเตอร์\nด็อกเตอร์\nดัมพ์\nดั๊มพ์\nดาวน์\nดิกชันนารี\nดิสเครดิต\nดีกรี\nดีเจ\nดีไซน์\nดีไซน์เนอร์\nดีไซเนอร์\nดีเบต\nดีพาร์ตเมนต์\nดีพาร์ตเมนท์\nดีพาร์ทเมนต์\nดีพาร์ทเมนท์\nดีมานด์\nดีล\nดีลเลอร์\nดีเลย์\nเดชานุภาพ\nเดบิต\nเดโม\nเดย์\nเด้อ\nเดอะ\nเด๊ะ\nเดี้ยง\nเดี๊ยะ\nแดนซ์\nแดนเซอร์\nแดรี่\nโดนัท\nโดมิโน\nโดรายากิ\nไดเอ็ต\nตถตา\nตนเอง\nตรวจทาน\nตรวจสอบ\nตอกย้ำ\nต๊อง\nต่อยอด\nต่อรอง\nตะหงิด\nตังค์\nตันเถียน\nตัวตน\nตัวเอง\nตาปรือ\nต้าอ่วย\nติงต๊อง\nติ๋ม\nติ่มซำ\nติว\nติวเตอร์\nตี๋\nตื้บ\nตุ๊ก\nตุ๊กตุ๊ก\nตุ๊ด\nตุ๋ย\nตู้เซฟ\nเต๊ะ\nเตี๊ยม\nแตงกวา\nแตงโม\nแต๋ว\nโต๋เต๋\nโต๊ะจีน\nไตรมาส\nถ่ายทำ\nถูกต้อง\nทงคัตสึ\nทริป\nทรู\nทอม\nท็อป\nทอร์นาโด\nทอล์ค\nทักซิโด\nทันตกรรม\nทันตแพทยศาสตร์\nทับซ้อน\nทัวร์\nทัวร์นาเมนต์\nทัวร์นาเมนท์\nทัวริสต์\nทาเลนต์\nทาวน์\nทาวน์เฮาส์\nทำงาน\nทิป\nทิพยสมบัติ\nทิวลิป\nทีรามิสุ\nทีวี\nทูน่า\nเท็กซ์\nเทค\nเทคโน\nเทคโนแครต\nเทควันโด\nเทป\nเทรด\nเทรนด์\nเทรนเนอร์\nเทรลเลอร์\nเทรลเล่อร์\nเทเลกราฟ\nเทวบัญชา\nเทวบุตร\nเทวา\nเทวาธิราช\nเทโวโรหนะ\nเทอร์โบ\nเที่ยงคืน\nเที่ยงวัน\nเทียมทาน\nแทกติค\nแทคติค\nแทงกั๊ก\nแทงโก้\nโทมาฮอก\nโทมาฮอว์ก\nโทมาฮอว์ค\nโทร\nโทรโข่ง\nไทม์\nไทยแลนด์\nไทเฮา\nธรรมา\nธรรมาภิบาล\nธัมโม\nธีม\nธุรกรรม\nธุหร่ำ\nเธค\nนพมาศ\nนรีแพทย์\nน็อก\nน็อค\nน้องใหม่\nนอมินี\nนอร์ท\nน่ะ\nนางแบบ\nนาฏยศาลา\nนายแบบ\nนายพราน\nนินจา\nนิรันดร์\nนิว\nนิวส์\nนู้ด\nเนอะ\nเนิร์สเซอรี\nเนิร์สเซอรี่\nเนี้ยบ\nโนติส\nไนท์\nไนน์\nบรรพชน\nบร็อกโคลี\nบร็อคโคลี\nบรา\nบริกร\nบริวเวอรี่ส์\nบลอนด์\nบลูเบอร์รี\nบลูเบอร์รี่\nบ๊วย\nบอกซ์\nบ็อกซ์\nบ๊อกซ์\nบอดี้\nบอนด์\nบ๊อบ\nบอมบ์\nบ๋อย\nบอยคอต\nบอยคอตต์\nบอร์ด\nบังเกอร์\nบัตเตอร์\nบัลลาสต์\nบัส\nบาบูน\nบาร์บีคิว\nบาร์บี้\nบาลานซ์\nบิ๊ก\nบิล\nบึม\nบึ้ม\nบุญคุณ\nบุ๋น\nบุปผา\nบู๊\nบูชิโด\nบูติก\nบูติค\nบูม\nเบเกอรี่\nเบญจมบพิตร\nเบตาดีน\nเบนโตะ\nเบนโล\nเบบี้\nเบลอ\nเบอร์เกอร์\nเบอร์รี\nเบิร์ด\nเบิร์น\nแบ็กโฮ\nแบคโฮ\nแบด\nแบต\nแบนเนอร์\nแบรนด์\nแบล็ก\nแบล็ค\nไบโอ\nโบกี้\nโบตั๋น\nโบ้ย\nโบรกเกอร์\nโบรชัวร์\nโบว์\nโบว์ลิ่ง\nไบเบิล\nปฏิสัมพันธ์\nป๊อก\nปอดแหก\nป๊อป\nป๋อหลอ\nปักขคณนา\nปัจเจกชน\nปัจฉิมนิเทศ\nป๊า\nป๋า\nป่าไม้\nปาร์ตี้\nปาสกาล\nปาสคาล\nปาสเตอร์\nปิกอัพ\nปิ๊ง\nปิโตรเคมี\nปิยมิตร\nปึ้ก\nปูอัด\nเปโซ\nเป็นไง\nเปปเปอร์มินต์\nเปเปอร์\nเปราะบาง\nเป๊ะ\nเป่ายิงฉุบ\nเป่ายิ้งฉุบ\nเปียโน\nแป้ก\nแป๋ว\nแป๊ะเจี๊ยะ\nโปร\nโปรเจกต์\nโปรเจ็กต์\nโปรเจกเตอร์\nโปรเจ็กเตอร์\nโปรเจคท์\nโปรเจ็คท์\nโปรดักชั่น\nโปรดิวเซอร์\nโปรโมชั่น\nโปรโมต\nโปรโมเตอร์\nโปรโมท\nโปลิศ\nโปสเตอร์\nผลไม้\nผลักดัน\nผ้าห่ม\nผิดพลาด\nผู้นำ\nแผดเผา\nเฝอ\nพงษ์\nพริตตี้\nพรีเซนต์\nพรีเซ็นเตอร์\nพรีเมียม\nพรีเมียร์\nพฤหัส\nพล็อต\nพลาซ่า\nพลานุภาพ\nพ่อค้า\nพอเพียง\nพะเรอ\nพันธกิจ\nพันธุวิศวกรรม\nพาร์\nพาร์ตเนอร์\nพาร์ทเนอร์\nพาวเวอร์\nพาสเจอร์ไรส์\nพาสตา\nพาสต้า\nพาสปอร์ต\nพาเหรด\nพิซซ่า\nพีเรียด\nพุดดิ้ง\nพุทธภูมิ\nพุทธศตวรรษ\nพุทโธ\nพูล\nเพทนาการ\nเพนกวิน\nเพนตากอน\nเพรส\nเพรียวบาง\nเพลซ\nเพลท\nเพลย์บอย\nเพียบแปร้\nเพียว\nเพาเวอร์\nแพกเกจ\nแพ็ค\nแพตเทิร์น\nแพทเทิร์น\nแพทยสภา\nแพนงเชิญ\nแพนดา\nแพนด้า\nแพลน\nโพลล์\nโพลารอยด์\nโพสต์\nไพลิน\nฟยอร์ด\nฟรังก์\nฟรุต\nฟลอร์\nฟลุก\nฟลุค\nฟลุต\nฟลุท\nฟอยล์\nฟอร์ม\nฟันด์\nฟาวล์\nฟาสต์ฟู้ด\nฟินิกซ์\nฟิวเจอร์\nฟีด\nฟีเวอร์\nฟุตบาท\nเฟรช\nเฟรชชี่\nเฟรม\nเฟมินิสต์\nเฟส\nเฟอร์นิเจอร์\nเฟอร์รี่\nเฟิร์ม\nเฟี้ยวฟ้าว\nแฟกซ์\nแฟ็กซ์\nแฟนซี\nแฟนตาซี\nแฟ้บ\nแฟร์\nแฟรนไชส์\nแฟรี\nแฟรี่\nแฟลช\nแฟล็ต\nโฟน\nโฟม\nโฟล์ค\nไฟต์\nไฟแนนซ์\nไฟลต์\nไฟลท์\nภควัทคีตา\nภควัมบดี\nภควัมปติ\nภคันทลาพาธ\nภววิสัย\nภารตะ\nภูมิทัศน์\nม้ง\nมวลชน\nมยุราภิรมย์\nมลภาวะ\nมหภาค\nมหาอุปราชา\nมอคคา\nมอคค่า\nมอนสเตอร์\nม็อบ\nมอบตัว\nมอยส์เจอไรเซอร์\nมอลล์\nมะกัน\nมั้ง\nมัฟฟิน\nมั้ย\nม้านั่ง\nมาเฟีย\nมาม่า\nมายองเนส\nมายาคติ\nมาร์ก\nมาร์เก็ต\nมาร์เก็ตติ้ง\nมาร์ค\nมาร์จิน\nมาร์ช\nมาร์ต\nมาร์ท\nมาราธอน\nม้าหินอ่อน\nมินต์\nมินท์\nมินิ\nมิลค์\nมิวสิค\nมิสซัง\nมิสไซล์\nมิสเตอร์\nมือถือ\nมุมมอง\nเมคอัพ\nเมจิก\nเมจิค\nเมทัล\nเมเปิล\nเมาท์\nเมี่ยงคำ\nแมกกาซีน\nแม็กกาซีน\nแมคเคอเรล\nแม่ค้า\nแมชชีน\nแมชีน\nแมนชั่น\nแมมบ้า\nแมมโบ้\nโมจิ\nโมเดล\nโมเดิร์น\nโมเต็ล\nโมโนเรล\nโมหจริต\nไมค์\nไมเกรน\nยนตรกรรม\nยอมรับ\nยะเยือก\nยังไง\nยากูซ่า\nยาวี\nยิม\nยิว\nยุวทูต\nยูโทเปีย\nยูโร\nยูวี\nเยน\nเยลลี่\nเย้ว\nเยอบีรา\nเยอบีร่า\nเยอร์บีรา\nเยอร์บีร่า\nแยมโรล\nโยเกิร์ต\nโยโย่\nรวมมิตร\nร็อค\nร็อคเก็ต\nรองรับ\nรอมฎอน\nรอยัลตี้\nระโงก\nรันเวย์\nรัม\nรากหญ้า\nราชบัณฑิตยสถาน\nราชานุญาต\nราชานุสาวรีย์\nรามเทพ\nรามาธิบดี\nรามายณะ\nราเม็ง\nราเมน\nรายชื่อ\nราสเบอร์รี\nริกเตอร์\nริคเตอร์\nรีไซเคิล\nรีดไถ\nรีทัช\nรีเทิร์น\nรีไทร์\nรีแบรนด์\nรีพอร์ท\nรีโมต\nรีโมท\nรีวิว\nรีสอร์ต\nรีสอร์ท\nรีเสิร์ช\nรุมบ้า\nรุสโซ\nรูบิก\nรูบิค\nเรซิน\nเรซิ่น\nเรดิโอ\nเรต\nเรตติ้ง\nแรงใจ\nแรงดูด\nแรงผลัก\nแรลลี\nแรลลี่\nโรดแมป\nโรเนียว\nโรแมนติก\nโรแมนติค\nโรล\nโรลออน\nไรเฟิล\nล็อกเกอร์\nลอจิสติกส์\nล็อต\nล็อบบี้\nลอร์ด\nล้มเหลว\nละติน\nละอ่อน\nลาซานญ่า\nลาติน\nลาเต้\nลานีญา\nลามะ\nลิมิต\nลิมูซีน\nลิสต์\nลีก\nลีด\nลีดเดอร์\nลีเมอร์\nลีลาวดี\nลุค\nลูกชาย\nลูกสาว\nเลกเชอร์\nเลคเชอร์\nเลดี้\nเลสเบี้ยน\nเลิฟ\nแลนด์\nแล็บ\nโลโก้\nโลชั่น\nไลท์\nไลน์\nไลฟ์\nวนาราม\nวราราม\nวโรกาส\nว้อดก้า\nวอเตอร์\nวอฟเฟิล\nว้อย\nวอร์ม\nวอร์มอัพ\nวอร์รูม\nวอล์ก\nวอล์ค\nวอลซ์\nวอลนัต\nวอลนัท\nวอลล์\nว่ะ\nวันเวย์\nวัสสา\nวาซาบิ\nวาทกรรม\nวาทะ\nวานิลลา\nวานิลา\nวาฟเฟิล\nวาริชศาสตร์\nว้าว\nวัคค์\nวัจนะ\nวาไรตี้\nวิก\nวิดีโอ\nวิทย์\nวิน\nวิป\nวิปปิ้ง\nวิภัชภาค\nวิว\nวิลล์\nวิลเลจ\nวีเจ\nวีซ่า\nวีดิทัศน์\nวีน\nวีไอพี\nวืด\nเวณิกา\nเวเฟอร์\nเวสต์\nเวอร์\nเวิร์ก\nเวิร์กช็อป\nเวิร์ค\nเวิร์ลด์\nเวิลด์\nแวมไพร์\nไวกิ้ง\nไวเบรเตอร์\nไวอะกร้า\nไวอากร้า\nศากยบุตร\nศิรินทร์\nศิลปวัฒนธรรม\nศิลปากร\nศิวิไลซ์\nศึกษาศาสตร์\nสกรัม\nสกาย\nสกู๊ป\nสเกตช์\nสเก็ตช์\nสคริปต์\nสแควร์\nสงบสุข\nสจ๊วต\nสตรอเบอร์รี\nสตรอเบอรี\nสตรอว์เบอร์รี\nสตริง\nสต็อก\nสต๊อก\nสต็อค\nสต๊อค\nสตอรี\nสตาร์\nสตาร์ท\nสติกเกอร์\nสติ๊กเกอร์\nสตีล\nสตูดิโอ\nสเตชัน\nสเตชั่น\nสเตเดียม\nสเตนเลส\nสเต็ป\nสเตย์\nสเตริโอ\nสเตอริโอ\nสแตนดาร์ด\nสแตนเลส\nสโตน\nสโตร์\nสไตรค์\nสไตล์\nสถาปัตย์\nสไนเปอร์\nสปอต\nสป็อต\nสปอนเซอร์\nสปอร์ต\nสปา\nสปาย\nสปิริต\nสเปก\nสเปค\nสไปเดอร์\nสมณพราหมณ์\nสมาพันธ์\nสมิติเวช\nสโรชา\nสลัม\nสแล็ก\nสโลแกน\nสโลว์\nสไลด์\nสวีท\nสหรัฐ\nสหัชญาณ\nสหัสวรรษ\nสะกอม\nสะเด่า\nสะบึม\nสะบึมส์\nสะออน\nสังโฆ\nสะโหลสะเหล\nสันทนาการ\nสัมนา\nสามช่า\nสามแยก\nสารขัณฑ์\nสี่แยก\nสึนามิ\nสุนทรีย์\nสุริยยาตร\nสุริยยาตร์\nสุหนี่\nเสกสรรค์\nเสพติด\nเสือโคร่ง\nหงวน\nหน่อมแน้ม\nหมวย\nหมั่นโถว\nหม่านโถว\nหมายปอง\nหมิง\nหยวน\nหลวงตา\nหลวงปู่\nหลวงพี่\nหล่อฮังก้วย\nหลินจือ\nห่วย\nเห็นด้วย\nเหมย\nเห่ย\nเหี่ยวย่น\nแหม็บ\nแหวว\nโหงว\nโหงวเฮ้ง\nโหลน\nโหลยโท่ย\nไหง\nไหร่\nอพาร์ตเมนต์\nอพาร์ตเมนท์\nอพาร์ทเมนต์\nอพาร์ทเมนท์\nอมาตยาธิปไตย\nอยุติธรรม\nอริยสงฆ์\nอ่วม\nอวอร์ด\nออกแบบ\nออดิชั่น\nออดิทอเรียม\nออเดอร์\nออโต้\nออทิสติก\nอ่อนด้อย\nออฟ\nออยล์\nออร์แกน\nออร์แกนิก\nออร์แกนิค\nออร์เดอร์\nออรัล\nออสซี่\nอะ\nอัตลักษณ์\nอัตวิสัย\nอันเดอร์\nอันตรกิริยา\nอัลตรา\nอัลไซเมอร์\nอัลบัม\nอัลบั้ม\nอัลมอนด์\nอาข่า\nอาโนเนะ\nอาฟเตอร์\nอาร์ติสต์\nอาร์พีจี\nอาว์\nอาสวะ\nอิกัวนา\nอินดอร์\nอินดัสตรีส์\nอินเตอร์\nอิ่มแปร้\nอิมพีเรียล\nอิเล็กทริก\nอิเล็กทริค\nอิเลียด\nอิสรชน\nอิเหนา\nอิออน\nอีแต๋น\nอีโรติก\nอีเวนท์\nอีสต์\nอีสเตอร์\nอึ๊บ\nอึ้ม\nอึ๋ม\nอึมครึม\nอุด้ง\nอุตสาหการ\nอุเทน\nอุปการคุณ\nอุปทาน\nอุปนายก\nอุปนายิกา\nอุปสงค์\nอุปัทวเหตุ\nอุรังคธาตุ\nอูคูเลเล่\nอู้ฟู่\nเอ๋\nเอ็กซ์โป\nเอ็กซ์เพรส\nเอ็กโซเซต์\nเอ็กโซเซ่ต์\nเอเซีย\nเอ็นจีโอ\nเอ็นเตอร์เทน\nเอนทรานซ์\nเอ็นทรานซ์\nเอฟเฟ็กต์\nเอเยนต์\nเอลนีโญ\nเอสเปรสโซ\nเอสเพรสโซ\nเอ๋อ\nเอาต์\nเอาท์\nเอาท์ดอร์\nเอ๊าะ\nแอ็กชั่น\nแอ็คชั่น\nแอคทีฟ\nแอดมิชชั่น\nแอดมิสชัน\nแอนด์\nแอ๊บแบ๊ว\nแอปเปิล\nแอปเปิ้ล\nแอปพริคอท\nแอพพริคอท\nแอพริคอต\nแอร์\nแอโรบิก\nแอโรบิค\nแอลมอนด์\nแอสเตอร์\nโอเค\nโอเปอเรเตอร์\nโอเปร่า\nโอเพ่น\nโอ้ย\nโอยัวะ\nโอรสาธิราช\nโอเลี้ยง\nโอวัลติน\nโอเวอร์\nไอซ์\nไอซียู\nไอดอล\nไอเดีย\nไอติม\nฮวงจุ้ย\nฮ่องเต้\nฮองเฮา\nฮอต\nฮ็อต\nฮอตดอก\nฮ็อตด็อก\nฮันนีมูน\nฮัม\nฮัลโลวีน\nฮัลโหล\nฮากกา\nฮาร์ด\nฮาราคีรี\nฮาลาล\nฮาโลวีน\nฮิ\nฮิต\nฮิบรู\nฮิปโป\nฮิปฮอป\nฮีโร่\nฮูลาฮูป\nฮูล่าฮูป\nเฮฟวี\nเฮฟวี่\nเฮอร์ริเคน\nเฮีย\nแฮนด์\nแฮปปี้\nแฮมเบอร์เกอร์\nโฮป\nโฮม\nโฮลดิงส์\nโฮลวีต\nโฮสเตส\nไฮกุ\nไฮแจ็ค\nไฮโซ\nไฮเทค\nไฮบริด\nไฮเปอร์\nไฮไลต์\nไฮไลท์\nไฮเวย์\nไฮสคูล\nไฮเอนด์\n\nกรีซ\nกัมพูชา\nกัวเตมาลา\nกาตาร์\nกานา\nกาบอง\nกายอานา\nกินี\nเกรนาดีนส์\nเกรเนดา\nเกาหลี\nแกมเบีย\nโกตดิวัวร์\nคองโก\nคอโมโรส\nคอสตาริกา\nคาซัคสถาน\nคิตส์\nคิริบาตี\nคิริบาส\nคิวบา\nคีร์กีซสถาน\nคูเวต\nเคนยา\nเคปเวิร์ด\nเคย์แมน\nแคนาดา\nแคเมอรูน\nโครเอเชีย\nโคลอมเบีย\nจอร์เจีย\nจอร์แดน\nจาเมกา\nจิบูตี\nจีน\nชาด\nชิลี\nเช็ก\nซามัว\nซาอุ\nซิมบับเว\nซีเรีย\nซูดาน\nซูรินาเม\nเซนต์\nเซเนกัล\nเซอร์เบีย\nเซาตูเม\nเซียร์รา\nแซมเบีย\nโซมาเลีย\nโซเวียต\nไซปรัส\nญี่ปุ่น\nดารุสซาลาม\nเดนมาร์ก\nโดมินิกัน\nโดมินิกา\nตรินิแดด\nตองกา\nติมอร์\nตุรกี\nตูนิเซีย\nเติร์กเมนิสถาน\nโตโก\nโตเบโก\nไต้หวัน\nทาจิกิสถาน\nแทนซาเนีย\nนอร์เวย์\nนามิเบีย\nนาอูรู\nนิการากัว\nนิวซีแลนด์\nเนเธอร์แลนด์\nเนปาล\nเนวิส\nไนจีเรีย\nไนเจอร์\nบราซิล\nบริติช\nบริเตน\nบรูไน\nบอตสวานา\nบอสเนีย\nบังกลาเทศ\nบังคลาเทศ\nบัลแกเรีย\nบาร์บูดา\nบาร์เบโดส\nบาห์เรน\nบาฮามาส\nบิสเซา\nบุรุนดี\nบูร์กินาฟาโซ\nเบนิน\nเบลเยียม\nเบลารุส\nเบลีซ\nเบอร์มิวดา\nโบลิเวีย\nปรินซิปี\nปากีสถาน\nปานามา\nปาปัวนิวกินี\nปารากวัย\nปาเลสไตน์\nปาเลา\nเปรู\nเปอร์เซีย\nเปอร์โตริโก\nโปรตุเกส\nโปแลนด์\nฝรั่งเศส\nพม่า\nฟิจิ\nฟินแลนด์\nฟิลิปปินส์\nเฟรนช์\nภูฏาน\nภูฐาน\nมองโกเลีย\nมอนเตเนโกร\nมอนแทนา\nมอริเชียส\nมอริเตเนีย\nมอลโดวา\nมอลตา\nมัลดีฟส์\nมาเก๊า\nมาซิโดเนีย\nมาดากัสการ์\nมาร์แชลล์\nมาลาวี\nมาลี\nมาเลเซีย\nเม็กซิโก\nเมียนมาร์\nโมซัมบิก\nโมนาโก\nโมนาโค\nโมร็อกโก\nไมโครนีเซีย\nยูกันดา\nยูโกสลาเวีย\nยูเครน\nเยเมน\nเยอรมนี\nรวันดา\nรัสเซีย\nโรมาเนีย\nลักเซมเบิร์ก\nลัตเวีย\nลาว\nลิกเตนสไตน์\nลิทัวเนีย\nลิเบีย\nลีโอน\nลูเซีย\nเลโซโท\nเลบานอน\nเลสเต\nไลบีเรีย\nวาติกัน\nวานูอาตู\nวินเซนต์\nเวเนซุเอลา\nเวียดนาม\nศรีลังกา\nสเปน\nสโลวะเกีย\nสโลวัก\nสโลวีเนีย\nสวาซิแลนด์\nสวิตเซอร์แลนด์\nสวีเดน\nสหรัฐ\nสหราชอาณาจักร\nสิกขิม\nสิงคโปร์\nอเมริกา\nออสเตรเลีย\nออสเตรีย\nอันดอร์รา\nอัฟกานิสถาน\nอาเซอร์ไบจาน\nอาร์เจนตินา\nอาร์เมเนีย\nอาระเบีย\nอิเควทอเรียล\nอิตาลี\nอินเดีย\nอินโดนีเซีย\nอิรัก\nอิสราเอล\nอิหร่าน\nอียิปต์\nอุซเบกิสถาน\nอุรุกวัย\nเอกวาดอร์\nเอธิโอเปีย\nเอมิเรตส์\nเอริเทรีย\nเอลซัลวาดอร์\nเอสโตเนีย\nแองโกลา\nแอนติกา\nแอลจีเรีย\nแอลเบเนีย\nโอมาน\nไอซ์แลนด์\nไอร์แลนด์\nฮ่องกง\nฮอนดูรัส\nฮังการี\nเฮติ\nเฮอร์เซโกวีนา\n\nกระบี่\nกรุงเทพ\nกาญจนบุรี\nกาฬสินธุ์\nกำแพงเพชร\nขอนแก่น\nจันทบุรี\nฉะเชิงเทรา\nชลบุรี\nชัยนาท\nชัยภูมิ\nชุมพร\nเชียงราย\nเชียงใหม่\nตรัง\nตราด\nตาก\nนครนายก\nนครปฐม\nนครพนม\nนครราชสีมา\nนครศรีธรรมราช\nนครสวรรค์\nนนทบุรี\nนราธิวาส\nน่าน\nบึงกาฬ\nบุรีรัมย์\nปทุมธานี\nประจวบคีรีขันธ์\nปราจีนบุรี\nปัตตานี\nพะเยา\nพังงา\nพัทลุง\nพิจิตร\nพิษณุโลก\nเพชรบุรี\nเพชรบูรณ์\nแพร่\nภูเก็ต\nมหาสารคาม\nมุกดาหาร\nแม่ฮ่องสอน\nยโสธร\nยะลา\nร้อยเอ็ด\nระนอง\nระยอง\nราชบุรี\nลพบุรี\nลำปาง\nลำพูน\nเลย\nศรีสะเกษ\nสกลนคร\nสงขลา\nสตูล\nสมุทรปราการ\nสมุทรสงคราม\nสมุทรสาคร\nสระแก้ว\nสระบุรี\nสิงห์บุรี\nสุโขทัย\nสุพรรณบุรี\nสุราษฎร์\nสุราษฎร์ธานี\nสุรินทร์\nหนองคาย\nหนองบัวลำภู\nอยุธยา\nอ่างทอง\nอำนาจเจริญ\nอุดรธานี\nอุตรดิตถ์\nอุทัยธานี\nอุบลราชธานี\nกันทรลักษ์\nจตุจักร\nไชยา\nซีคอน\nดอนเมือง\nถลาง\nไทรโยค\nธนบุรี\nธัญบุรี\nบางกอก\nบางปะกง\nบางระจัน\nปะทิว\nปาย\nพญาไท\nพัฒน์พงษ์\nพัทยา\nพารากอน\nภูมิซรอล\nรัตนาธิเบศร์\nรังสิต\nลันตา\nลาดพร้าว\nวโรรส\nวิภาวดี\nสตึก\nสมุย\nสัตหีบ\nสิมิลัน\nสุขุมวิท\nสุไหง\nเสลภูมิ\nอังรีดูนังต์\nอ่างขาง\nอินทนนท์\nเอ็มโพเรียม\n\nคิวชู\nแคริบเบียน\nแคสเปียน\nดานูบ\nตะนาวศรี\nนอร์วีเจียน\nนิโคบาร์\nเนรัญชรา\nไนล์\nบอร์เนียว\nบอลติก\nเบงกอล\nปิง\nแปซิฟิก\nมะละกา\nมินดาเนา\nมิสซิสซิปปี\nเมดิเตอร์เรเนียน\nเมโสโปเตเมีย\nยมุนา\nยุโรป\nยูเรเชีย\nยูเรเซีย\nแยงซี\nแยงซีเกียง\nสแกนดิเนเวีย\nสะโตง\nสาละวิน\nสุมาตรา\nสุเอซ\nอะเมซอน\nอันดามัน\nอัลไต\nอาร์กติก\nอาหรับ\nอินโดจีน\nอิรวดี\nอิระวดี\nอีเจียน\nอุษาคเณย์\nอูราล\nเอเชีย\nเอเดรียติก\nเอเวอเรสต์\nแอตแลนติก\nแอนตาร์กติก\nแอนตาร์กติกา\nแอฟริกา\nโอลิมปัส\nไอโอเนียน\nฮวงโห\nฮอกไกโด\nฮอนชู\n\nกบิลพัสดุ์\nกุสินารา\nโกลิยะ\nโกสัมพี\nโคตรบอง\nโคตรบูรณ์\nตองอู\nทรอย\nทวารวดี\nทวาราวดี\nเทวทหะ\nไทรบุรี\nนาลันทา\nไบแซนไทน์\nปรัสเซีย\nปัลลวะ\nปาฏลีบุตร\nพุทธคยา\nมถุรา\nมายัน\nมิถิลา\nราชคฤห์\nล้านช้าง\nล้านนา\nลุมพินี\nวัชชี\nเวสาลี\nสารนาถ\nสาวัตถี\nหริภุญชัย\nอโยธยา\nออตโตมัน\nอังวะ\nอินทปัตถ์\nอุชเชนี\n\nกราฟิก\nกราฟิกส์\nกราฟิค\nกริด\nกิกะไบต์\nกูเกิล\nกูเกิ้ล\nเกตเวย์\nโกลบอล\nคลัสเตอร์\nคลาส\nคลิก\nคลิปอาร์ต\nคอนโซล\nคอนเทนต์\nคอมพิวติ้ง\nคอมไพล์\nคอมไพเลอร์\nคอมมูนิเคชั่น\nคอร์\nคีย์\nคีย์บอร์ด\nเครือข่าย\nเคอร์เซอร์\nเคอร์เนล\nแคช\nแคมฟรอก\nแคมฟร็อก\nแคร็ก\nโค้ด\nจาวา\nจีพีเอส\nชิป\nชิพ\nเชลล์\nแช็ต\nแชนเนล\nแชนแนล\nซ็อกเก็ต\nซอฟต์แวร์\nซอฟท์แวร์\nซอร์ส\nซัพพอร์ต\nซัพพอร์ท\nซีดี\nซีดีรอม\nซีเนอร์\nเซิร์ฟเวอร์\nโซลูชัน\nโซลูชั่น\nไซต์\nไซเบอร์\nทรานแซกชัน\nทรานแซกชั่น\nทรานแซ็กชัน\nทรานแซ็กชั่น\nทรานแซคชัน\nทรานแซคชั่น\nทรานแซ็คชัน\nทรานแซ็คชั่น\nทวิตเตอร์\nทวีต\nทัชแพด\nเทมเพลต\nเทอร์มินัล\nแท็ก\nแท็บ\nแทบเล็ต\nโทรจัน\nเน็ต\nเน็ตบุ๊ก\nเน็ตบุค\nเน็ตบุ๊ค\nเน็ตเวิร์ก\nเน็ตเวิร์ค\nโน้ตบุ๊ก\nโน้ตบุค\nโน้ตบุ๊ค\nดอส\nดาวน์เกรด\nดาวน์โหลด\nดิจิตอล\nดิจิทัล\nดีบั๊ก\nดีวีดี\nดีไวซ์\nเดเบียน\nเดลไฟ\nเดสก์ท็อป\nโดเมน\nไดรว์\nไดรเวอร์\nไดเรกทอรี\nไดโอด\nเทเลคอม\nบล็อกเกอร์\nบรอดแบนด์\nบราวเซอร์\nบลูทูท\nบลูทูธ\nบลูเรย์\nบั๊ก\nบัฟเฟอร์\nบิต\nบิท\nบูต\nเบราว์เซอร์\nแบนด์วิดท์\nไบต์\nไบนารี\nโปรแกรมเมอร์\nโปรเซส\nโปรเซสเซอร์\nโปรโตคอล\nพร็อกซี\nพอร์ต\nพอร์ท\nพาเนล\nพาร์ทิชัน\nพารามิเตอร์\nพาสเวิร์ด\nพิกเซล\nเพจ\nเพจเจอร์\nแพกเก็ต\nแพตช์\nแพลตฟอร์ม\nโพรเซส\nโพรเซสเซอร์\nโพรโทคอล\nไพธอน\nฟล็อปส์\nฟอนต์\nฟอร์แมต\nฟอร์เวิร์ด\nฟอรัม\nฟีเจอร์\nเฟซบุ๊ก\nเฟิร์มแวร์\nแฟล็ก\nโฟลเดอร์\nไฟร์ฟอกซ์\nไฟร์วอลล์\nไฟล์\nมอดูล\nมอนิเตอร์\nมัลติ\nมัลติทัช\nมัลติเพล็กซ์\nมัลแวร์\nมาสเตอร์\nมีเดีย\nเมนู\nเมมโมรี\nเมล\nเมาส์\nแมค\nโมดูล\nโมเด็ม\nโมบาย\nโมบายล์\nโมไบล์\nไมโครซอฟท์\nยูนิกซ์\nยูนิโคด\nยูนิโค้ด\nริงโทน\nรีเฟรช\nรีเลย์\nเราเตอร์\nเรียลไทม์\nลิงก์\nลินุกซ์\nลีนุกซ์\nลูป\nเลเยอร์\nแล็ปท็อป\nไลเซนส์\nไลบรารี\nวิกิ\nวิกิพีเดีย\nวินโดวส์\nวินโดว์ส\nเว็บ\nเวอร์ชวล\nเวอร์ชัน\nเวอร์ชั่น\nเวิร์กสเตชัน\nเวิร์กสเตชั่น\nเวิร์คสเตชัน\nเวิร์คสเตชั่น\nเวิร์ด\nเวิร์ม\nไวแมกซ์\nสกรีน\nสแกน\nสแกนเนอร์\nสแต็ก\nสนิฟเฟอร์\nสปายแวร์\nสเปซ\nสแปม\nสมาร์ท\nสล็อต\nเสิร์ช\nโหลด\nออนไลน์\nออปติก\nออปติคอล\nออปติคัล\nออฟไลน์\nออราเคิล\nอัพเกรด\nอัพเดต\nอัพโหลด\nอัปเกรด\nอัปเดต\nอัปโหลด\nอัลกอริทึม\nอาร์กิวเมนต์\nอินเตอร์เน็ต\nอินทิเกรเตอร์\nอินเทอร์เน็ต\nอินเทอร์เฟซ\nอินเทล\nอินพุต\nอินพุท\nอีเมล\nอีเมล์\nอูบุนตู\nอูบันตู\nเอนจิน\nเอ็นจิน\nเอาต์พุต\nเอาต์พุท\nเอาท์พุต\nเอาท์พุท\nแอนะล็อก\nแอนิเมชัน\nแอนิเมชั่น\nแอปพลิเคชัน\nแอปพลิเคชั่น\nแอพพลิเคชัน\nแอพพลิเคชั่น\nแอสเซมบลี\nแอสเซมเบลอร์\nโอเพน\nไอคอน\nไอซี\nไอพอด\nไอพ็อด\nไอแพด\nไอโฟน\nฮับ\nฮาร์ดดิสก์\nฮาร์ดแวร์\nแฮกเกอร์\nแฮ็กเกอร์\nแฮนด์เฮลด์\nโฮสต์\n\nกรีก\nกัณณาฑ\nกัศมีรี\nคันจิ\nคาตาคานะ\nคุชราตี\nคุรุมุขี\nซีริลลิก\nซูลู\nเซลติก\nเซิร์บ\nตากาล็อก\nเตลุคู\nเติร์ก\nทราวิฑ\nเบงกาลี\nปัญจาบี\nพินอิน\nมลยาฬัม\nมองโกล\nมาราฐี\nมาเลย์\nเม็กซิกัน\nแมนจู\nแมนดาริน\nไมถิลี\nเยอรมัน\nรัสเซียน\nสวาฮิลี\nสวิส\nสินธี\nอูรดู\nอัสสมี\nอารบิก\nอิตาเลียน\nอุยกูร์\nแอฟริกัน\nโอริยา\nไอริช\nฮันกึล\nฮินดี\nฮิรางานะ\nฮีบรู\n\nกรีนพีซ\nกรือเซะ\nกวนอิม\nกวนอู\nกัดดาฟี\nกัลยาณวัตร\nกัสสปะ\nกาลิเลโอ\nกินเนส\nกุมภกรรณ\nกูเตนเบิร์ก\nเกตส์\nเกษมณี\nโกณฑัญญะ\nขงเบ้ง\nคริสโตเฟอร์\nคองคอร์ด\nคอลเกต\nคานธี\nคาเบรียล\nคาร์ฟูร์\nคาร์สัน\nคาราบาว\nคาสิโอ\nคิริน\nคุนลุ้น\nคูโบต้า\nเครมลิน\nแคทรีนา\nโคตมะ\nโคตมี\nโคลัมบัส\nไคฟง\nไครสเลอร์\nง้อไบ๊\nจตุพร\nจ็อบส์\nจอห์น\nจิ้น\nจิม\nจิ๋ม\nจิว\nจุฬาภรณ์\nจุฬาลงกรณ์\nเจมส์\nแจ็กสัน\nโจเซฟ\nโจว\nชมัยมรุเชฐ\nชมัยมรุเชษฐ์\nชเวดากอง\nชาร์ลส์\nชินราช\nชินวัตร\nชุนชิว\nเช็ง\nเชตวัน\nเชฟรอน\nเชฟโรเลต\nเชลซี\nไชยานุชิต\nซ่ง\nซังฮี้\nซัดดัม\nซันซิล\nซัมซุง\nซัวเจ๋ง\nซินหัว\nซีซาร์\nซีแพค\nซูซาน\nซูซูกิ\nซูบารุ\nเซ็นทารา\nเซเวน\nเซเว่น\nโซฟิเทล\nโซยุซ\nโซยูซ\nณัฐวุฒิ\nดาร์ลี่\nดาวโจนส์\nดิสนีย์\nดีแทค\nดูปองท์\nเดโมแครต\nเดลล์\nเดลินิวส์\nเดวิด\nแดวู\nโดนัลด์\nโดราเอมอน\nโดเรมอน\nต๋อง\nตั๊กม้อ\nตากสิน\nตาเมือน\nตาลีบัน\nตูน\nเตมีย์\nโต๋\nโตชิบา\nโตโยต้า\nถังซัมจั๋ง\nถังซำจั๋ง\nทรพา\nทราเวล\nทรูมูฟ\nทีปังกร\nเทปโก\nเทพรัตน\nเทวทัต\nเทสโก้\nโทมัส\nไททานิก\nไททานิค\nไทยรัฐ\nธีออส\nนครินทรา\nนโปเลียน\nนพดล\nนราดูร\nนเรนทร\nนอสตราดามุส\nนาซา\nนาซ่า\nนาซี\nนาโต\nนาโต้\nนาลแก\nนิคอน\nนิโคลัส\nนิด้า\nนินเทนโด\nนิปปอน\nนิวตัน\nนิสสัน\nเนคเทค\nเนชั่น\nเนชันแนล\nเนชั่นแนล\nเนวิน\nเนสเล่\nเนสาด\nแนท\nแนสแดค\nโนเกีย\nโนเบล\nโนเวลล์\nโนโวเทล\nไนเม็กซ์\nบรอดเวย์\nบรัดเลย์\nบรู๊ซ\nบัลเมอร์\nบารัก\nบารัค\nบู๊ตึ๊ง\nเบญกาย\nเบนซ์\nเบ็นซ์\nเบนจามิน\nโบตัน\nไบโอเทค\nประชาธิปัตย์\nปวีณา\nปอเต็กตึ๊ง\nปอเต๊กตึ๊ง\nป่อเต็กตึ๊ง\nปัตตะโชติ\nปารุสก์\nปีเตอร์\nปูติน\nเป๊ปซี่\nเป้ย\nเปอร์โยต์\nเปาบุ้นจิ้น\nโปเกมอน\nโป๊ยก่าย\nพรหมทัต\nพลาโต\nพอลล่า\nพานาโซนิค\nพานาโซนิก\nพิทยานุกูล\nพิมพิสาร\nเพนแทกซ์\nเพลโต\nไพโอเนียร์\nฟรอยด์\nฟรังซิส\nฟรานซิส\nฟลอเรนซ์\nฟอร์ด\nฟิลิปส์\nฟูจิ\nแฟซ่า\nโฟร์โมสต์\nภูมิพล\nภูริทัต\nมงฟอร์ต\nมณโฑ\nมติชน\nมหิตลาธิเบศร\nมโหสถ\nมัจฉานุ\nมาร์กาเร็ต\nมาร์ติน\nมาสด้า\nมิตซูบิชิ\nมิราเคิล\nมุสโสลินี\nเม้ง\nเมจิ\nเมอร์ซีเดส\nเมอร์เซเดส\nแมกซ์เวลล์\nแมกไซไซ\nแมคอินทอช\nแมชีนเนอรี่\nโมคคัลลานะ\nโมโตโรลา\nโมโตโรล่า\nโมเนีย\nไมเคิล\nไมยราพณ์\nยโสธรา\nยะโฮวา\nยะโฮวาห์\nยามาฮ่า\nยาเวห์\nยาฮู\nยูนิเซฟ\nยูเนสโก\nยูไล\nเยโฮวาห์\nรอยเตอร์\nรอยัล\nรัชดา\nรัสปูติน\nราฟาเอล\nรามาวตาร\nราเมศวร\nราหุล\nริชาร์ด\nรีพับลิกัน\nรูนีย์\nเรนโบว์\nแรมโบ้\nโรตารี\nโรนัลโด\nโรนัลโด้\nโรบินสัน\nโรเบิร์ต\nล็อกซเล่ย์\nลิงคอล์น\nลิจฉวี\nลิไท\nลิไทย\nลินคอล์น\nลิเวอร์พูล\nเลโนโว\nเลียดก๊ก\nโลตัส\nวชิราลงกรณ์\nวลาดิเมียร์\nวอลล์สตรีท\nวาเลนไทน์\nวิกตอเรีย\nวิทยานุสรณ์\nวิทยายน\nวิมเบิลดัน\nวิลเลียม\nวีระ\nวุฒิชัย\nเวียดกง\nไวตามิลค์\nศกุนตลา\nศรีวิชัย\nศิริพงษ์\nศิริราช\nศุภชลาศัย\nสดกก๊อกธม\nสดายุ\nสตาลิน\nสตีฟ\nสแตนฟอร์ด\nสวรินทิรา\nสังกัจจายน์\nสาทิตย์\nสารีบุตร\nสิริกิติ์\nสิรินธร\nสิหิงค์\nสีวลี\nสีหนุ\nสีหมุนี\nสีหโมนี\nสุครีพ\nสุทโธทนะ\nสุเทพ\nสุนทราภรณ์\nสุนีย์\nสุรนารี\nสุรยุทธ์\nสุริยาสน์\nเส้าหลิน\nโสกราตีส\nโสภิต\nหนุมาน\nหลินฮุ่ย\nหลุยส์\nเห้งเจีย\nไหหม่า\nองคต\nองคุลิมาล\nอชาตศัตรู\nอดุลยเดช\nอพอลโล\nอริสโตเติล\nอริสมันต์\nอลิซาเบธ\nอ๋อม\nออร์คิด\nออสการ์\nอะพอลโล\nอับราฮัม\nอั้ม\nอัลกออิดะห์\nอัลคาเทล\nอัลจาซีราห์\nอัลเฟรด\nอัลเลาะห์\nอัสซุส\nอัสสชิ\nอัสสัมชัญ\nอาเซม\nอาเซ็ม\nอาเซียน\nอาฟต้า\nอาร์เซนอล\nอินทรชิต\nอินทราทิตย์\nอีซูซุ\nอีเลฟเวน\nอีเลฟเว่น\nอุณรุท\nอุบลรัตน์\nอุบาลี\nอุ๋ย\nเอกทัศน์\nเอเซอร์\nเอ็ดเวิร์ด\nเอดิสัน\nเอแบค\nเอลิซาเบธ\nเอสพลานาด\nเอสพลานาร์ด\nแอคคอร์\nแอคคอร์ด\nแองเจลิน่า\nแอตแลนติส\nแอน\nแอ๋ม\nแอมบาสซาเดอร์\nแอมบาสเดอร์\nแอมเวย์\nแอ๋ว\nโอดีสซีย์\nโอเดียน\nโอบามา\nโอรสาราม\nโอลิมเปีย\nโออิชิ\nไอน์สไตน์\nฮอนด้า\nฮอปกินส์\nฮอลลีวูด\nฮอลลีวู้ด\nฮานามิ\nฮามาส\nฮิตเลอร์\nฮิตาชิ\nฮุนเซน\nฮุนเซ็น\nฮุนได\nฮุสเซ็น\nเฮนรี\nเฮนรี่\nเฮเลน\nโฮจิมินห์\nโฮปเวลล์\nโฮเมอร์\n\nกลีเซอรีน\nกำทอน\nแกนีมีด\nครอส\nคริสตัล\nคลอโรพลาสต์\nคลอไรด์\nควอนตัม\nคอนดักเตอร์\nคอปเปอร์\nคอลลาเจน\nคอเลสเตอรอล\nคอสมิก\nคูลอมบ์\nเคอราติน\nแคโรทีน\nแคสสินี\nโครมาโทกราฟี\nไคโตซาน\nจีโนม\nจุลชีววิทยา\nชิคุนกุนยา\nซัลฟิวริก\nซัลเฟต\nซัลไฟด์\nซิงค์\nซิริอุส\nซิลิกา\nซิลิเกต\nซิลิโคน\nซีเทน\nซีเวิร์ต\nเซ็กเตอร์\nเซ็กเมนต์\nเซมิ\nโซนาร์\nไซบอร์ก\nดอปเปลอร์\nดอปเพลอร์\nไดนามิก\nไดนามิกส์\nไดนามิค\nไดนามิคส์\nไดออกไซด์\nทรานส์\nทามิฟลู\nเทฟลอน\nเทสโทสเตอโรน\nเทอร์โม\nแทนนิน\nไททัน\nไทฟอยด์\nไทรอยด์\nธาลัสซีเมีย\nเนกาตีฟ\nโนวา\nบอแรกซ์\nโบทอกซ์\nโบท็อกซ์\nไบโอติน\nปฏิยานุพันธ์\nโปรเจสเตอโรน\nพอลิเมอร์\nพันธุศาสตร์\nพัลซาร์\nพาร์กินสัน\nพาราเซตามอล\nพาราโบลา\nเพอร์ออกไซด์\nโพรเจสเทอโรน\nโพลาไรซ์\nโพลิเมอร์\nโพลีเอทิลีน\nไพรเมต\nฟลาโวนอยด์\nฟลูออเรสเซนซ์\nฟลูออไรด์\nฟอสซิล\nฟิชชัน\nฟิวชัน\nฟีโรโมน\nไฟเบอร์\nมอนอกไซด์\nมิราจ\nเมตริกซ์\nเมทริกซ์\nเมลานิน\nเมลามีน\nโมเมนตัม\nไมโตคอนเดรีย\nไมโทคอนเดรีย\nยีสต์\nยูริก\nยูเรีย\nรูมาตอยด์\nวีก้า\nเวกเตอร์\nเวก้า\nเวสิเคิล\nโวลต์\nสเกล\nสเกลาร์\nสเต็ม\nสเตียรอยด์\nสปีชีส์\nสเปิร์ม\nสัมพัทธภาพ\nสุริยจักรวาล\nออกเทน\nออโรรา\nออโรร่า\nอะคริลิก\nอะครีลิก\nอะซีติก\nอะซีโตน\nอะมิโน\nอะลูมินา\nอันโดรเมดา\nอัลคาไลน์\nอัลตราซาวด์\nอัลตราซาวนด์\nอัลลอยด์\nอินทิกรัล\nอินทิเกรต\nอีโบลา\nอีโบล่า\nเอ็กซ์โพเนนเชียล\nเอทานอล\nเอทิลีน\nเอนโทรปี\nเอสเตอร์\nเอสโตรเจน\nเอสโทรเจน\nแอนดรอยด์\nแอนแทร็กซ์\nแอมพลิจูด\nแอมโมเนียม\nแอลกอฮอลิซึม\nแอสพาร์แตม\nโอเซลทามิเวียร์\nฮับเบิล\nฮิวมัส\nไฮดรอกไซด์\nไฮดรอลิก\nไฮโดรลิก\nไฮเพอร์โบลา\n\nกงเต็ก\nกฎุมพี\nกฏ\nกฏหมาย\nกบฎ\nกราไฟต์\nก๊อปปี้\nกะทะ\nกังวาล\nกุฎฐัง\nกุฏุมพี\nฃวด\nคฑา\nคลินิค\nคลีนิก\nคลีนิค\nคาทอลิค\nคึ่นฉ่าย\nแคตตาล็อก\nโควต้า\nฅน\nจุมพฎ\nช็อคโกแลต\nแซ่ด\nดัทช์\nทนง\nทรมาณ\nทราก\nทะแยง\nทิฏฐิ\nบล็อค\nบ๊องแบ๊ว\nบัลเล่ต์\nเบรค\nแบงค์\nปรากฎ\nปัคคหะ\nปาฏิโมกข์\nปิติ\nปิรามิด\nโปรเตสแตนท์\nพนิช\nพยักเพยิด\nพริ้ว\nพลูโตเนียม\nพากษ์\nเฟิร์น\nยากี้\nเยภุยยสิกา\nรุสเซีย\nฤาษี\nล็อค\nล็อคเกอร์\nวันทยาหัตถ์\nวานิช\nวิญญาน\nวิศิษฐ์\nศรีษะ\nสเปกโทรสโคป\nสฤษฎ์\nสลิ่ม\nสัตตสดก\nสาราณียากร\nสุกี้\nสุกี้ยากี้\nสูญญากาศ\nหยอมแหยม\nหยอย\nหล่ะ\nอะดรีนาลีน\nอะหลั่ย\nอัตคัต\nอัฟริกา\nอานิสงค์\nอาฟริกา\nอิริยาบท\nอิเลคโทรนิคส์\nอีรุงตุงนัง\nอุตรายัน\nอุลตรา\nอุลตร้า\nโอกาศ\n\nกกหู\nกงการ\nกงจักร\nกฎเกณฑ์\nกฎบัตร\nกฎหมาย\nกฎหมู่\nกดขี่\nกดดัน\nก้นกบ\nก้นบึ้ง\nก้นปล่อง\nกนิษฐภคินี\nกนิษฐภาดา\nกรงเล็บ\nกรมการ\nกรมท่า\nกรมธรรม์\nกรมนา\nกรมวัง\nกรรมกร\nกรรมการ\nกรรมฐาน\nกรรมบถ\nกรรมพันธุ์\nกรรมวิธี\nกรรมสิทธิ์\nกระจกเงา\nกระจกตา\nกระจกนูน\nกระจกเว้า\nกระจอกชวา\nกระจอกเทศ\nกระจ้อยร่อย\nกระจับบก\nกระจับปิ้ง\nกระจับปี่\nกระโชกโฮกฮาก\nกระดานดำ\nกระดานหก\nกระดาษแก้ว\nกระดาษไข\nกระดาษทราย\nกระดาษสา\nกระดูกงู\nกระทาชาย\nกระเท่เร่\nกระบวนการ\nกระบองเพชร\nกระผีกริ้น\nกระยาทิพย์\nกระยาสารท\nกระยาหาร\nกระสอบทราย\nกระสุนปืน\nกระแสจิต\nกระแสน้ำ\nกระแสลม\nกรับคู่\nกรับพวง\nกรับเสภา\nกราดเกรี้ยว\nกราวรูด\nกริ่งเกรง\nกรีฑาสถาน\nกรีดกราย\nกรี๊ดกร๊าด\nกลไก\nกลบท\nกลเม็ด\nกลยุทธ์\nกลวิธี\nกลศาสตร์\nกลอักษร\nกลบเกลื่อน\nกลมกล่อม\nกลมกลืน\nกลมเกลียว\nกล้วยแขก\nกล้วยไม้\nกล่องเสียง\nกล่อมเกลา\nกล่อมเกลี้ยง\nกลัดกลุ้ม\nกลัดมัน\nกลั่นกรอง\nกลั่นแกล้ง\nกลับกลอก\nกลางคน\nกลางคัน\nกลางค่ำ\nกลางคืน\nกลางแจ้ง\nกลางแปลง\nกลางวัน\nกลาดเกลื่อน\nกล่าวขวัญ\nกล่าวโทษ\nกล่าวหา\nกล้ำกราย\nกล้ำกลืน\nกลิ้งกลอก\nกลิ้งเกลือก\nกลิ่นอาย\nกลียุค\nกวดขัน\nกวัดแกว่ง\nกวัดไกว\nกวีนิพนธ์\nก่อกวน\nก่อการ\nก่อตั้ง\nก่อสร้าง\nก่อหวอด\nก้อร่อก้อติก\nกองกลาง\nกองเกิน\nกองโจร\nกองทัพ\nกองทุน\nกองพล\nกองพัน\nกองฟอน\nกองร้อย\nกองหนุน\nกอบโกย\nกะเกณฑ์\nกะบังลม\nกักกัน\nกักขัง\nกักตัว\nกักตุน\nกัณฑ์เทศน์\nกัดฟัน\nกันชน\nกันท่า\nกันสาด\nกันเอง\nกับแกล้ม\nกับข้าว\nกับระเบิด\nกากเพชร\nกากหมู\nกาชาด\nกาญจนาภิเษก\nก้านคอ\nกาฝาก\nก้ามกราม\nกามกิจ\nกามคุณ\nกามเทพ\nกามโรค\nก้ามปู\nกายกรรม\nกายบริหาร\nกายภาพ\nกายวิภาค\nกายสิทธิ์\nก่ายกอง\nการคลัง\nการเงิน\nการบ้าน\nการเปรียญ\nการเมือง\nการเรือน\nการละเล่น\nกาลกิริยา\nกาลเทศะ\nก้าวก่าย\nก้าวร้าว\nก้าวหน้า\nกาสาวพัสตร์\nกาฬพฤกษ์\nกาฬโรค\nกำปั้น\nกำมือ\nกำแพงขาว\nกำลังใจ\nกำลังม้า\nกี่งอำเภอ\nกิจกรรม\nกิจการ\nกิจวัตร\nกิจจะลักษณะ\nกิตติคุณ\nกิตติศัพท์\nกินขาด\nกินใจ\nกินดอง\nกินโต๊ะ\nกินแบ่ง\nกินเปล่า\nกินเลี้ยง\nกินเส้น\nกินแหนง\nกิโลกรัม\nกิโลเมตร\nกิโลลิตร\nกิโลเฮิรตซ์\nกีดกัน\nกีดกั้น\nกีดขวาง\nกึกก้อง\nกึกกัก\nกึกกือ\nกึ่งกลาง\nกุกกัก\nกุ๊กกิ๊ก\nกุ้งฝอย\nกุ้งมังกร\nกุ้งแห้ง\nกุ้งเต้น\nกุ้งยิง\nกุญแจผี\nกุญแจมือ\nกุญแจเลื่อน\nกุญแจเสียง\nกุลธิดา\nกุลบุตร\nกุลสตรี\nกู้ยืม\nเก้งก้าง\nเก็บกวาด\nเก็บเกี่ยว\nเก็บงำ\nเก็บตก\nเกรงกลัว\nเกรงใจ\nเกรงขาม\nเกรียงไกร\nเกรียมกรม\nเกรี้ยวกราด\nเกล็ดเลือด\nเกลี้ยกล่อม\nเกลี้ยงเกลา\nเกลือกกลั้ว\nเกลือกกลิ้ง\nเกลื่อนกล่น\nเกลื่อนกลาด\nเกศธาตุ\nเกษตรกร\nเกษตรกรรม\nเกษตรศาสตร์\nเกษมสันต์\nเกษียรสมุทร\nเก้อเขิน\nเกาะแกะ\nเกี่ยงงอน\nเกียจคร้าน\nเกียรติคุณ\nเกียรตินิยม\nเกียรติประวัติ\nเกียรติภูมิ\nเกียรติยศ\nเกียรติศักดิ์\nเกียรติมุข\nเกี่ยวข้อง\nเกี่ยวดอง\nเกี่ยวพัน\nเกี่ยวโยง\nเกี้ยวพาน\nเกี้ยวพาราสี\nแก่แดด\nแก้ขัด\nแก้ไข\nแก้ตัว\nแก้เผ็ด\nแก้ลำ\nแกงคั่ว\nแกงจืด\nแกงบวด\nแกงป่า\nแกงเผ็ด\nแกงส้ม\nแก่งแย่ง\nแก่นแก้ว\nแก่นสาร\nแก้วตา\nแก้วหู\nแกว่งกวัด\nแกว่งไกว\nแกะรอย\nโก้เก๋\nโกรธเกรี้ยว\nโกรธขึ้ง\nไก่เขี่ย\nไก่ชน\nไก่บ้าน\nไก่ป่า\nไก่ฟ้า\nไกล่เกลี่ย\nขจัดขจาย\nขนเพชร\nขนสัตว์\nขนหนู\nขนส่ง\nขนมจีน\nขบขัน\nขบวนการ\nข่มขี่\nข่มขู่\nข่มขืน\nข่มเหง\nขมหิน\nขมิ้นชัน\nขมุบขมิบ\nขยะแขยง\nขยักขย่อน\nขยักขย้อน\nขยับขยาย\nขยับเขยื้อน\nขวดโหล\nขวยเขิน\nขวัญใจ\nขวัญตา\nขวัญอ่อน\nขวากหนาม\nขวางโลก\nของ้าว\nขอสับ\nขอขมา\nขอทาน\nขอโทษ\nขอร้อง\nขออภัย\nข้อเขียน\nข้อความ\nข้อเท็จจริง\nของกลาง\nของขวัญ\nของแข็ง\nของชำ\nของลับ\nของเล่น\nของว่าง\nของเหลว\nของไหล\nของไหว้\nข้องใจ\nข้องแวะ\nขอบข่าย\nขอบเขต\nขอบคุณ\nขอบใจ\nขอบพระคุณ\nข้อมูล\nข้อแม้\nข้อหา\nข้อสังเกต\nขัดข้อง\nขัดขืน\nขัดเขิน\nขัดจังหวะ\nขัดดอก\nขัดแตะ\nขัดยอก\nขัดแย้ง\nขัดสมาธิ\nขันหมาก\nขันอาสา\nขับขี่\nขับเคี่ยว\nขั้วโลก\nขาจร\nขาประจำ\nข้าราชการ\nข้าศึก\nข้าหลวง\nขาดแคลน\nขาดใจ\nขาดตอน\nขาดตัว\nขาดทุน\nขาดเหลือ\nขายหน้า\nข่าวกรอง\nข่าวคราว\nข่าวล่า\nข่าวลือ\nข่าวสาร\nข้าวเกรียบ\nข้าวแกง\nข้าวของ\nข้าวจี่\nข้าวเจ้า\nข้าวซอย\nข้าวต้ม\nข้าวตอก\nข้าวตัง\nข้าวแตน\nข้าวทิพย์\nข้าวบิณฑ์\nข้าวเปลือก\nข้าวโพด\nข้าวฟ่าง\nข้าวเม่า\nข้าวสวย\nข้าวสาร\nข้าวเหนียว\nข้าวหมาก\nข้าวหลาม\nขี้เกียจ\nขี้ข้า\nขี้ครอก\nขี้คร้าน\nขี้คุก\nขี้ไคล\nขี้เซา\nขีดขั้น\nขีดคร่อม\nขีดคั่น\nขีดฆ่า\nขี้ตา\nขี้ตืด\nขี้เถ้า\nขี้ทูด\nขี้ปะติ๋ว\nขี้ผึ้ง\nขี้มูก\nขี้ยา\nขี้แย\nขี้ริ้ว\nขี้เรื้อน\nขี้เล็บ\nขี้หู\nขี้หน้า\nขี้เหนียว\nขี้เหล็ก\nขี้เหร่\nขึงขัง\nขึงพืด\nขึ้งโกรธ\nขึ้นใจ\nขึ้นชื่อ\nขึ้นมือ\nขืนใจ\nขื่นขม\nขุดคุ้ย\nขุนทอง\nขุนนาง\nขุนพล\nขุนศึก\nขุนหลวง\nขูดรีด\nเข็มกลัด\nเข็มขัด\nเข้มข้น\nเข้มแข็ง\nเข้มงวด\nเข็มทิศ\nเข็มหมุด\nเข้าขา\nเข้าเค้า\nเข้าใจ\nเข้าชื่อ\nเข้าตัว\nเข้าถึง\nเข้าทรง\nเข้าท่า\nเข้าที\nเข้าเนื้อ\nเข้าเล่ม\nเขียวเสวย\nเขียวหวาน\nแขกเต้า\nแข็งกร้าว\nแข็งกล้า\nแข็งแกร่ง\nแข็งข้อ\nแข็งขัน\nแข่งขัน\nแข็งใจ\nแข็งตัว\nแข็งเมือง\nแข็งแรง\nแขวนลอย\nโขกสับ\nโขยกเขยก\nไขข้อ\nไขควง\nไขมัน\nไข่มุก\nไขว่ห้าง\nไขสันหลัง\nไขสือ\nไข่เค็ม\nไข่เยี่ยวม้า\nไข่หงส์\nไข่เหี้ย\nคงกระพัน\nคงตัว\nคงทน\nคงที่\nคชลักษณ์\nคชสาร\nคชสีห์\nคชราช\nคณิตศาสตร์\nคดเคี้ยว\nคติธรรม\nคติพจน์\nคนกลาง\nคนไข้\nคนใช้\nคนทรง\nคบไฟ\nคบเพลิง\nคบค้า\nคบคิด\nคบหา\nคมคาย\nครบครัน\nครบถ้วน\nครอบครอง\nครอบคลุม\nครอบครัว\nครอบงำ\nครอบจักรวาล\nคริสตกาล\nคริสตจักร\nคริสต์มาส\nคริสต์ศตวรรษ\nคริสต์ศักราช\nคริสตัง\nคริสเตียน\nครุกรรม\nครุภัณฑ์\nครุศาสตร์\nครุฑพ่าห์\nครุ่นคิด\nคลอเคลีย\nคล่องแคล่ว\nคล่องตัว\nคล่องมือ\nคลั่งไคล้\nคลาคล่ำ\nคลาไคล\nคลาดเคลื่อน\nคลาดแคล้ว\nคลี่คลาย\nคลึงเคล้น\nคลึงเคล้า\nคลื่นไส้\nคลื่นเหียน\nคลุกคลี\nคลุกคลาน\nคลุมเครือ\nคลุมโปง\nคลุ้มคลั่ง\nควงสว่าน\nควบคุม\nควบคู่\nควบแน่น\nควันหลง\nความคิด\nความหลัง\nความเห็น\nคอหอย\nคอห่าน\nคอแห้ง\nค่อนขอด\nค่อนแคะ\nค้อนควัก\nคั่งค้าง\nคั่งแค้น\nคัดค้าน\nคัดง้าง\nคัดท้าย\nคัดเลือก\nคันจาม\nคันฉ่อง\nคันฉาย\nคันชัก\nคันชั่ง\nคันไถ\nคันนา\nคันเร่ง\nคับขัน\nคับคั่ง\nคับแค้น\nคับแคบ\nคางทูม\nคางหมู\nค้างคืน\nค้างปี\nคาดคั้น\nคาดเชือก\nคาดโทษ\nคาดหมาย\nคานหาม\nคาบเกี่ยว\nคาบศิลา\nคาบสมุทร\nคำขาด\nคำนำ\nคิดค้น\nคืนดี\nคืนตัว\nคุกเข่า\nคุณค่า\nคุณชาย\nคุณธรรม\nคุณนาย\nคุณภาพ\nคุณลักษณะ\nคุณวุฒิ\nคุณศัพท์\nคุณสมบัติ\nคุณหญิง\nคุณากร\nคุณูปการ\nคุโณปการ\nคุมเชิง\nคุ้มกัน\nคุยเขื่อง\nคุยโต\nคุ้ยเขี่ย\nคุ้มครอง\nคู่กรณี\nคู่กัด\nคู่ขา\nคู่แข่ง\nคู่ครอง\nคู่ควร\nคู่คิด\nคู่คี่\nคู่ใจ\nคู่ชีพ\nคู่ชีวิต\nคู่บารมี\nคู่บุญ\nคู่ปรปักษ์\nคู่ปรับ\nคู่ผสม\nคู่มือ\nคู่รัก\nคู่ลำดับ\nคู่สาย\nคู่หมั้น\nคู่หู\nคู่อริ\nคู่อาฆาต\nเคมีภัณฑ์\nเคยตัว\nเคร่งขรึม\nเคร่งครัด\nเคร่งเครียด\nเครดิตฟองซิเอร์\nเครื่องกล\nเครื่องกัณฑ์\nเครื่องแกง\nเครื่องเขิน\nเครื่องครัว\nเครื่องเคียง\nเครื่องเงิน\nเครื่องจักร\nเครื่องเซ่น\nเครื่องดนตรี\nเครื่องต้น\nเครื่องทุ่นแรง\nเครื่องเทศ\nเครื่องใน\nเครื่องบิน\nเครื่องบูชา\nเครื่องแบบ\nเครื่องประดับ\nเครื่องปรุง\nเครื่องปรุงรส\nเครื่องมือ\nเครื่องยนต์\nเครื่องร่อน\nเครื่องราง\nเครื่องเรือน\nเครื่องล่าง\nเครื่องเล่น\nเครื่องสาย\nเครื่องสำอาง\nเครื่องสุกำศพ\nเครื่องหมาย\nเครือรัฐ\nเคลียคลอ\nเคลื่อนที่\nเคลื่อนไหว\nเคลือบแคลง\nเคลือบแฝง\nเคลือบฟัน\nเคว้งคว้าง\nเคหสถาน\nเค้าโครง\nเคียดแค้น\nเคี่ยวเข็ญ\nเคี้ยวเอื้อง\nเคืองขุ่น\nโคนม\nโคบาล\nโคมูตร\nโคมลอย\nโครงการ\nโครงเรื่อง\nโครงงาน\nโครงสร้าง\nโครมคราม\nโคลงเคลง\nฆ้องกระแต\nฆ้องชัย\nฆ้องวง\nฆ้องหุ่ย\nฆ้องเหม่ง\nฆ้องโหม่ง\nฆาตกร\nฆาตกรรม\nฆานประสาท\nงงงวย\nงงงัน\nงดเว้น\nงบดุล\nงบประมาณ\nงมโข่ง\nงมงาย\nง่วงงุน\nง่วงเหงา\nงอหาย\nง้องอน\nงอนง้อ\nงอมแงม\nงาช้าง\nง่าเงย\nงานการ\nง่ายดาย\nงึมงำ\nเงินเดือน\nเงินตรา\nเงินยวง\nเงียบกริบ\nเงียบเชียบ\nเงียบเหงา\nเงื่องหงอย\nเงื่อนไข\nเงื่อนงำ\nเงื่อนเวลา\nเงื้อมมือ\nแง่งอน\nจงใจ\nจงรัก\nจดจ่อ\nจดจำ\nจดหมาย\nจดหมายเหตุ\nจรจัด\nจรรยาบรรณ\nจริงจัง\nจริงใจ\nจอมขวัญ\nจอมใจ\nจอมทัพ\nจอมปลวก\nจอมพล\nจ๊ะเอ๋\nจักสาน\nจักรพรรดิ\nจักรภพ\nจักรยาน\nจักรยานยนต์\nจักรราศี\nจักรวรรดิ\nจักรวรรดินิยม\nจักรวาล\nจังหนับ\nจัดการ\nจัดจ้าน\nจัดเจน\nจัดแจง\nจัดตั้ง\nจัดสรร\nจับกุม\nจับจด\nจับเจ่า\nจ่าหน้า\nจาตุทสี\nจาตุมหาราช\nจาตุมหาราชิก\nจาตุมหาราชิกา\nจาตุรงคสันนิบาต\nจาตุรราชการ\nจานเชิง\nจานบิน\nจานผี\nจานเสียง\nจาบจ้วง\nจำเป็น\nจำพรรษา\nจำวัด\nจ้ำจี้จ้ำไช\nจำเลาะตา\nจิงโจ้น้ำ\nจิตใจ\nจิตตภาวนา\nจิตตัง\nจิตตานุปัสสนา\nจิตนิยม\nจิตบำบัด\nจิตแพทย์\nจิตวิสัย\nจิตรกร\nจิตรกรรม\nจิตรลดา\nจิตวิทยา\nจิตเวช\nจิตเวชศาสตร์\nจินตกวี\nจินตนา\nจินตนาการ\nจินตภาพ\nจุฑามณี\nจุฑามาศ\nจุฑารัตน์\nจุนเจือ\nจุ้นจ้าน\nจุลชีพ\nจุลชีวัน\nจุลชีวิน\nจุลทรรศน์\nจุลภาค\nจุลวรรค\nจุลศักราช\nจุลสาร\nจุลินทรีย์\nจุฬามณี\nจุฬาลักษณ์\nเจตคติ\nเจตจำนง\nเจตนารมณ์\nเจตภูต\nเจริญพร\nเจ้ากรม\nเจ้ากรรม\nเจ้าของ\nเจ้าขา\nเจ้าข้า\nเจ้าคณะ\nเจ้าค่ะ\nเจ้าจอม\nเจ้าชู้\nเจ้าตัว\nเจ้าถิ่น\nเจ้าท่า\nเจ้าที่\nเจ้าทุกข์\nเจ้านาย\nเจ้าเนื้อ\nเจ้าบ้าน\nเจ้าบ่าว\nเจ้าประคุณ\nเจ้าประคู้น\nเจ้าพนักงาน\nเจ้าพระคุณ\nเจ้าพระยา\nเจ้าพ่อ\nเจ้าพายุ\nเจ้าฟ้า\nเจ้าภาพ\nเจ้ามือ\nเจ้าแม่\nเจ้าเรือน\nเจ้าสังกัด\nเจ้าสัว\nเจ้าสาว\nเจ้าหน้าที่\nเจ้าหนี้\nเจ้าอาวาส\nเจาะจง\nเจือจาง\nเจือจาน\nเจือปน\nเจื้อยแจ้ว\nแจกจ่าย\nแจ่มแจ้ง\nแจ่มใส\nโจงกระเบน\nโจมตี\nโจรกรรม\nโจรสลัด\nใจความ\nใจคอ\nฉกฉวย\nฉกชิง\nฉลองได\nฉ้อฉล\nฉัตรมงคล\nฉันทลักษณ์\nฉายาลักษณ์\nฉิบหาย\nฉุกเฉิน\nฉุกละหุก\nฉุนเฉียว\nฉุปศาสตร์\nเฉไฉ\nเฉยเมย\nเฉาโฉด\nเฉิดฉัน\nเฉิดฉาย\nเฉิดฉิน\nเฉียบขาด\nเฉียบพลัน\nเฉียบแหลม\nเฉื่อยชา\nแฉะแบะ\nโฉดเฉา\nโฉมงาม\nโฉมฉาย\nโฉมเฉลา\nโฉมตรู\nโฉมยง\nโฉมศรี\nโฉมหน้า\nชดช้อย\nชดเชย\nชดใช้\nชนบท\nชนินทร์\nชนกกรรม\nชนมพรรษา\nชนมายุ\nชมเชย\nชมพูทวีป\nชมพูนท\nชมพูนุท\nชราธรรม\nชราภาพ\nชลจร\nชลธาร\nชลธี\nชลนัยน์\nชลนา\nชลเนตร\nชลประทาน\nชลมารค\nชลาธาร\nชลาลัย\nชลาศัย\nชลาสินธุ์\nชโลทร\nช่วงชิง\nช่วงใช้\nชวนชม\nชวนหัว\nช่วยเหลือ\nช่อฟ้า\nช่อม่วง\nชอกช้ำ\nช่องเขา\nช่องแคบ\nช่องไฟ\nช่องว่าง\nช้องนาง\nชอบกล\nชอบใจ\nชอบธรรม\nชอบพอ\nชักโครก\nชักเงา\nชักจูง\nชักชวน\nชักนำ\nชักเนื้อ\nชักพระ\nชักเย่อ\nชักใย\nชั่งใจ\nชังฆวิหาร\nชัดเจน\nชั้นเชิง\nชั่วคน\nชั่วคราว\nชั่วช้า\nชั่วโมง\nชั่วแล่น\nชาเย็น\nช้านาน\nช่างเครื่อง\nช่างฝีมือ\nช่างฟิต\nช่างไฟ\nช้างน้ำ\nช้างเผือก\nช้างพลาย\nช้างพัง\nช้างสาร\nช้างสีดอ\nชาติธรรม\nชาตินิยม\nชาติพันธุ์\nชาติพันธุ์วิทยา\nชาติภูมิ\nชานชาลา\nชายชาตรี\nชายคา\nชายฝั่ง\nชายทะเล\nชาวเล\nชาววัง\nช้ำใจ\nช้ำชอก\nชิงชัง\nชิงพลบ\nชินชา\nชินบุตร\nชิ้นเอก\nชิมลาง\nชีเปลือย\nชี้ขาด\nชี้แจง\nชี้นำ\nชี้แนะ\nชี้ฟ้า\nชีพจร\nชีพิตักษัย\nชื่นชม\nชื่นบาน\nชื่นมื่น\nชื่อย่อ\nชื่อรอง\nชื่อเล่น\nชื่อเสียง\nชุกชุม\nชุติมา\nชุบตัว\nชุบเลี้ยง\nชุมชน\nชุมทาง\nชุมสาย\nชุ่มใจ\nชุ่มชื่น\nชุ่มชื้น\nชุมนุมชน\nชูชีพ\nชูโรง\nชู้สาว\nเชยชม\nเชลยศักดิ์\nเชลยศึก\nเช่าซื้อ\nเช้าตรู่\nเช้ามืด\nเชิงกราน\nเชิงกล\nเชิงชั้น\nเชิงชาย\nเชิงซ้อน\nเชิงเดียว\nเชิงเดี่ยว\nเชิงตะกอน\nเชิงเทิน\nเชิงมุม\nเชิดชู\nเชิงอรรถ\nเชี่ยนหมาก\nเชี่ยวชาญ\nเชื่องช้า\nเชื่อใจ\nเชื่อถือ\nเชื่อฟัง\nเชื่อมือ\nเชื้อชาติ\nเชื้อเพลิง\nเชื้อไฟ\nเชื้อโรค\nเชื้อสาย\nเชื้อเชิญ\nเชื่องช้า\nเชือนแช\nเชื่อวัน\nแช่เย็น\nแช่อิ่ม\nแช่มช้อย\nแช่มชื่น\nโชกโชน\nโชติช่วง\nโชติรส\nใช้สอย\nซบเซา\nซมซาน\nซวนเซ\nซอกซอน\nซอกแซก\nซ่องสุม\nซ่องเสพ\nซ่องแซ่ง\nซ่อนรูป\nซ่อนเร้น\nซ่อนหา\nซ่อนกลิ่น\nซ่อนทราย\nซ่อมแซม\nซักค้าน\nซักซ้อม\nซักไซ้\nซักฟอก\nซักแห้ง\nซังกะตาย\nซังตาย\nซัดเซ\nซัดทอด\nซับซ้อน\nซับใน\nซับพระพักตร์\nซากศพ\nซ่านเซ็น\nซ้ำซ้อน\nซ้ำซาก\nซ้ำเติม\nซ้ำร้าย\nซี่โครง\nซีดเซียว\nซึมกะทือ\nซึมซาบ\nซึมเซา\nซึมทราบ\nซึมเศร้า\nซื่อตรง\nซื่อสัตย์\nซื้อขาย\nซุกซน\nซุกซ่อน\nซุบซิบ\nซู่ซ่า\nเซซัง\nเซ่อซ่า\nแซ่ซ้อง\nโซดาไฟ\nญาณทัสนะ\nญาณวิทยา\nญาณศาสตร์\nญาติกา\nฐานราก\nดกดื่น\nดงดิบ\nดลใจ\nดลบันดาล\nดวงแก้ว\nดวงใจ\nดวงเดือน\nดวงตรา\nดวงตา\nดวงสมร\nดอกจัน\nดอกจิก\nดอกบัว\nดอกเบี้ย\nดอกฟ้า\nดอกไม้\nดอกยาง\nดอกเล็บ\nดอกทอง\nดอกสร้อย\nดองยา\nดักคอ\nดักฟัง\nดังนั้น\nดังนี้\nดังหนึ่ง\nดั้งเดิม\nดัดจริต\nดัดแปลง\nดันทุรัง\nดับขันธ์\nดับจิต\nดับชีพ\nด่าทอ\nด่างทับทิม\nด่างพร้อย\nดาดฟ้า\nดาราศาสตร์\nดาลเดือด\nดาวกระจาย\nดาวเคราะห์\nดาวตก\nดาวเทียม\nดาวรุ่ง\nดาวเรือง\nดาวฤกษ์\nดาวหาง\nดาวเหนือ\nดาษดื่น\nดินขาว\nดินดาน\nดินดำ\nดินประสิว\nดินปืน\nดินระเบิด\nดินสอ\nดินสอพอง\nดิ้นรน\nดิบดี\nดีเกลือ\nดีใจ\nดีซ่าน\nดีดัก\nดีเดือด\nดีฝ่อ\nดีดดิ้น\nดึกดำบรรพ์\nดึกดื่น\nดึงดัน\nดึงดูด\nดื่มด่ำ\nดื้อด้าน\nดื้อดึง\nดื้อแพ่ง\nดื้อยา\nดื้อรั้น\nดุดัน\nดุเดือด\nดุร้าย\nดุลการค้า\nดุลพินิจ\nดุลภาค\nดุลยพินิจ\nดุลยภาพ\nดุษฎีนิพนธ์\nดุษฎีบัณฑิต\nดุษณีภาพ\nดูแคลน\nดูถูก\nดูดาย\nดูเบา\nดูแล\nดูหมิ่น\nดูเหมือน\nดูดดื่ม\nเด็ดขาด\nเด็ดดวง\nเด็ดเดี่ยว\nเดนตาย\nเดาสวด\nเดาสุ่ม\nเดินทาง\nเดินสะพัด\nเดินสาย\nเดินเหิน\nเดิมพัน\nเดียงสา\nเดียดฉันท์\nเดียวกัน\nเดียวดาย\nเดี๋ยวเดียว\nเดี๋ยวนี้\nเดือดดาล\nเดือดร้อน\nเดือนมืด\nเดือนหงาย\nแดดาล\nแดดิ้น\nแดกดัน\nโด่เด่\nโด่งดัง\nโดดเดี่ยว\nโดยสาร\nได้การ\nได้แก่\nได้ใจ\nได้ที\nได้ยิน\nได้เสีย\nตกเขียว\nตกค้าง\nตกใจ\nตกต่ำ\nตกแต่ง\nตกทอด\nตกฟาก\nตกมัน\nตกยาก\nตกลง\nตกหล่น\nต้นขั้ว\nต้นคิด\nต้นฉบับ\nต้นตอ\nต้นตำรับ\nต้นทุน\nต้นแบบ\nต้นเพลิง\nต้นมือ\nต้นไม้\nต้นร่าง\nต้นเรื่อง\nต้นสังกัด\nต้นหน\nต้นเหตุ\nตบตา\nตบแต่ง\nตบแผละ\nตบมือ\nต้มข่า\nต้มโคล้ง\nต้มยำ\nต้มส้ม\nตรมตรอม\nตรรกวิทยา\nตรรกศาสตร์\nตรวจการ\nตรวจการณ์\nตรวจตรา\nตระบัดสัตย์\nตรัสรู้\nตราตั้ง\nตราบาป\nตรายาง\nตราสาร\nตริตรอง\nตรีกฏุก\nตรีกาย\nตรีโกณ\nตรีโกณมิติ\nตรีคูณ\nตรีทูต\nตรีปิฎก\nตรีภพ\nตรีมูรติ\nตรึกตรอง\nตรึงตรา\nตรุษจีน\nตฤณชาติ\nตฤณมัย\nตลกบาตร\nตลบตะแลง\nตลบหลัง\nตลาดนัด\nตลาดน้ำ\nตลาดมืด\nตลาดสด\nต่อตี\nต่อเติม\nต่อว่า\nต่อสู้\nต่อกร\nต่อต้าน\nต่อแย้ง\nต้องการ\nต้องโทษ\nต้องหา\nต้อนรับ\nตอบโต้\nตอบแทน\nต่อยหอย\nตะพาบน้ำ\nตักตวง\nตักบาตร\nตั้งเข็ม\nตั้งไข่\nตั้งเค้า\nตั้งแง่\nตั้งใจ\nตั้งต้น\nตั้งแต่\nตั้งท้อง\nตัดขาด\nตัดใจ\nตัดเชือก\nตัดตอน\nตัดทอน\nตัดบท\nตัดพ้อ\nตัดรอน\nตัดสิน\nตับเต่า\nตับแลบ\nตับอ่อน\nตัวกลาง\nตัวการ\nตัวเก็ง\nตัวดี\nตัวตั้ง\nตัวเต็ง\nตัวถัง\nตัวแทน\nตัวประกอบ\nตัวประกัน\nตัวแปร\nตัวผู้\nตัวพิมพ์\nตัวเมีย\nตัวยืน\nตัวเลข\nตัวอย่าง\nตั๋วเงิน\nตั๋วแลกเงิน\nตากล้อง\nตาไก่\nตาข่าย\nตาชั่ง\nตาตุ่ม\nตาทวด\nตาปลา\nตาราง\nต่างหาก\nต้านทาน\nตามใจ\nตายใจ\nตายซาก\nตายด้าน\nตายตัว\nตายทั้งกลม\nตายห่า\nตายโหง\nตาลปัตร\nต่ำช้า\nต่ำต้อย\nตำส้ม\nติเตียน\nติณชาติ\nติดขัด\nติดใจ\nติดต่อ\nติดตั้ง\nติดตาม\nติดตื้น\nติดพัน\nติดลม\nติดอ่าง\nตีเกลียว\nตีขลุม\nตีความ\nตีคู่\nตีจาก\nตีตื้น\nตีแผ่\nตีรวน\nตีลังกา\nตีวง\nตีเสมอ\nตีนกา\nตีนคู้\nตีนจก\nตีนตะขาบ\nตีนผี\nตีนเหยียด\nตึกแถว\nตึกระฟ้า\nตึงเครียด\nตึงตัง\nตื้นตัน\nตื่นตัว\nตื่นตูม\nตื่นเต้น\nตุ๊ต๊ะ\nตุ้บตั้บ\nตุ้มหู\nตุลาการ\nตุลาคม\nตู้นิรภัย\nตูมตาม\nตู้เสบียง\nเตโชธาตุ\nเตร็ดเตร่\nเต้นรำ\nเตาแก๊ส\nเตาผิง\nเตาฟู่\nเตาไฟ\nเตารีด\nเตาสูบ\nเต่าทอง\nเต้ารับ\nเต้าส่วน\nเต้าเสียบ\nเต้าหู้ยี้\nแต่ละ\nแตกคอ\nแตกคอก\nแตกฉาน\nแตกดับ\nแตกตื่น\nแตกพาน\nแตกแยก\nแตกร้าว\nแตกหัก\nแต่งงาน\nแต่งตั้ง\nแต้มคู\nแต้มต่อ\nแตรงอน\nแตรเดี่ยว\nแตรฝรั่ง\nแตรฟันฟาร์\nแตรวง\nโต้ตอบ\nโต้เถียง\nโต้แย้ง\nโต๊ะหมู่\nโต๊ะอิหม่าม\nใต้ถุน\nไต้ก๋ง\nไต่คู้\nไต่เต้า\nไต่ถาม\nไต้ฝุ่น\nไตรจักร\nไตรจีวร\nไตรตรึงษ์\nไตรทวาร\nไตรปิฎก\nไตรเพท\nไตรภพ\nไตรภูมิ\nไตรภาคี\nไตรยางศ์\nไตรรงค์\nไตรรัตน์\nไตรลักษณ์\nไตรโลก\nไตรสรณคมน์\nไตรสิกขา\nไต่สวน\nถกเถียง\nถดถอย\nถนัดถนี่\nถนิมสร้อย\nถมถืด\nถมเถ\nถมไป\nถลากไถล\nถ้วนถี่\nถ้วยฟู\nถ่องแท้\nถอดถอน\nถ้อยคำ\nถ้อยแถลง\nถากถาง\nถ่านไฟฉาย\nถ่านหิน\nถามไถ่\nถ่ายทอด\nถ่ายทุกข์\nถ่ายเท\nถาวรวัตถุ\nถ้ำมอง\nถี่ถ้วน\nถึงใจ\nถูกใจ\nถูกชะตา\nเถรวาท\nเถ้าแก่\nเถ้าแก่เนี้ย\nแถมพก\nแถลงการณ์\nไถ่ถอน\nไถ่ถาม\nทดแทน\nทดรอง\nทดลอง\nทดสอบ\nทนทาน\nทนายความ\nทบทวน\nทแยงมุม\nทรงกลด\nทรงเครื่อง\nทรงเจ้า\nทรัพย์สิน\nทรามชม\nทรามเชย\nทรามวัย\nทรามสงวน\nทรามสวาท\nทรุดโทรม\nทฤษฎีบท\nท้วงติง\nท่วมท้น\nทวาทศ\nทวาทศมาส\nทวาบรยุค\nทวารบาล\nทวิบถ\nทวิบท\nทวิบาท\nทวิภาค\nทวิภาคี\nทวีคูณ\nทศกัณฐ์\nทศชาติ\nทศทิศ\nทศนิยม\nทศพร\nทศพล\nทศพิธราชธรรม\nทศมาส\nทศวรรษ\nท่อไอเสีย\nท้อถอย\nท้อแท้\nทองขาว\nทองคำ\nทองคำขาว\nทองคำเปลว\nทองเค\nทองแดง\nทองบรอนซ์\nทองม้วน\nทองย้อย\nทองสัมฤทธิ์\nทองหยอด\nทองหยิบ\nทองเหลือง\nทองเอก\nท่องเที่ยว\nท้องตรา\nท้องถิ่น\nท้องที่\nท้องน้อย\nท้องร่อง\nท้องเรื่อง\nทอดมัน\nทอดทิ้ง\nทอดน่อง\nทอดยอด\nทอดหุ่ย\nทอยกอง\nทะเบียนบ้าน\nทะเลทราย\nทะเลสาบ\nทะเลหลวง\nทักขิณาวัฏ\nทักท้วง\nทักทาย\nทักษิณาวรรต\nทักษิณาทาน\nทักษิณานุประทาน\nทั้งกลม\nทั้งคน\nทั้งดุ้น\nทั้งที\nทั้งนั้น\nทั้งนี้\nทั้งปวง\nทั้งผอง\nทั้งเพ\nทั้งมวล\nทั้งสิ้น\nทั้งหมด\nทั้งหลาย\nทัณฑ์บน\nทัดทาน\nทัดเทียม\nทันควัน\nทันใจ\nทันใด\nทันตา\nทันสมัย\nทันที\nทับถม\nทับทรวง\nทับศัพท์\nทั่วถึง\nทั่วไป\nท่าทาง\nท่าที\nท้าทาย\nทางการ\nทางข้าม\nทางด่วน\nทางเท้า\nทางโท\nทางใน\nทางผ่าน\nทางม้าลาย\nทางหลวง\nทางออก\nทางเอก\nทานกัณฑ์\nทานตะวัน\nท่านชาย\nทานบารมี\nท่านผู้หญิง\nท่านหญิง\nทาบทาม\nท้ายทอย\nทารุณกรรม\nทำคลอด\nทำใจ\nทำซ้ำ\nทำท่า\nทำที\nทำแท้ง\nทำโทษ\nทำบาป\nทำบุญ\nทำพิษ\nทำฟัน\nทำร้าย\nทำวัตร\nทำสาว\nทำเสน่ห์\nทำหมัน\nทำให้\nทิ้งขว้าง\nทิ้งทวน\nทิ้งท้าย\nทินกร\nทิพจักขุ\nทิพโสต\nทิพยจักษุ\nทิพยญาณ\nทิพยเนตร\nทิพยรส\nทิพากร\nทิ่มตำ\nทิ่มแทง\nทิวากร\nทิวากาล\nทิศทาง\nทีเด็ด\nทีท่า\nทีนี้\nทีหลัง\nทีฆนิกาย\nทีฆสระ\nที่ดิน\nที่นอน\nที่นั่ง\nที่ปรึกษา\nที่พึ่ง\nที่มั่น\nที่ราบ\nที่ว่าการ\nที่สุด\nที่หมาย\nที่ไหน\nทุกที\nทุกเมื่อ\nทุกข์สุข\nทุนทรัพย์\nทุนนิยม\nทุนรอน\nทุนสำรอง\nทุ่มเถียง\nทุ่มเท\nทูนหัว\nทูลกระหม่อม\nเทกระจาด\nเทครัว\nเทพเจ้า\nเทพดา\nเทพธิดา\nเทพนม\nเทพนิยม\nเทพนิยาย\nเทพบุตร\nเทพสังหรณ์\nเทศกาล\nเทศนาโวหาร\nเทศบัญญัติ\nเทศบาล\nเทศมนตรี\nเทห์ฟากฟ้า\nเท่ากับ\nเท่าใด\nเท่าตัว\nเท่าทัน\nเท่าทุน\nเท่าเทียม\nเท่านั้น\nเท่าไร\nเท้าช้าง\nเทิดทูน\nเที่ยงตรง\nเที่ยงแท้\nเที่ยงธรรม\nเทียนชนวน\nเทียนพรรษา\nเทียบเคียง\nเทียบเท่า\nเทือกเขา\nเทือกเถา\nแท็งก์น้ำ\nแท่นพิมพ์\nแท่นมณฑล\nแท่นหมึก\nแทรกซอน\nแทรกซ้อน\nแทรกซึม\nแทรกแซง\nแทะโลม\nไทยดำ\nไทยทาน\nไทยธรรม\nไทยน้อย\nไทยใหญ่\nธงชัย\nธงชาติ\nธงทิว\nธรณีวิทยา\nธรณีสงฆ์\nธรรมกาย\nธรรมการ\nธรรมเกษตร\nธรรมขันธ์\nธรรมคุณ\nธรรมจรรยา\nธรรมจริยา\nธรรมจักร\nธรรมจักษุ\nธรรมจาคะ\nธรรมจารี\nธรรมชาติ\nธรรมดา\nธรรมเนียม\nธรรมราชา\nธรรมศาสตร์\nธรรมสภา\nธรรมสังเวช\nธัญพืช\nธารพระกร\nธีรภาพ\nธีรราช\nนกเขา\nนกต่อ\nนกยูง\nนกรู้\nนกหวีด\nนครบาล\nนครรัฐ\nนงคราญ\nนงนุช\nนงพะงา\nนงเยาว์\nนงราม\nนงลักษณ์\nนบนอบ\nนพเก้า\nนพคุณ\nนพเคราะห์\nนพปฎล\nนพพล\nนพรัตน์\nนพศก\nนพศูล\nนมข้น\nนมผง\nนมไม้\nนมนาน\nนมหนู\nนมแมว\nนรีเวช\nนรีเวชวิทยา\nนวดฟั้น\nนวยนาด\nนวลระหง\nนวลลออ\nนวลละออง\nนวลจันทร์\nนอกครู\nนอกคอก\nนอกจาก\nนอกใจ\nนอกชาน\nนอกรีต\nนอกเหนือ\nนองเนือง\nนองเลือด\nนอนก้น\nนอนใจ\nนอบนบ\nนอนเล่น\nนอบน้อม\nน้อมนำ\nน้อยใจ\nน้อยหน้า\nนักการ\nนักการเมือง\nนักกีฬา\nนักข่าว\nนักท่องเที่ยว\nนักเทศน์\nนักโทษ\nนักธรรม\nนักบวช\nนักบิน\nนักบุญ\nนักปราชญ์\nนักพรต\nนักรบ\nนักเรียน\nนักเลง\nนักวิชาการ\nนักศึกษา\nนักสิทธิ์\nนักสืบ\nนักหนา\nนั่งเทียน\nนั่งร้าน\nนัดแนะ\nนัดหมาย\nนั่นแหละ\nนั่นเอง\nนับถือ\nนับประสา\nนัยน์ตา\nนาดำ\nนาปรัง\nนาปี\nนาสวน\nนาหว่าน\nนาคบาศ\nนาคปรก\nนาคราช\nนางกวัก\nนางกำนัล\nนางงาม\nนางใน\nนางบำเรอ\nนางแบบ\nนางพญา\nนางฟ้า\nนางไม้\nนางโลม\nนางสาว\nนางห้าม\nนางเอก\nนาฏกรรม\nนาฏดนตรี\nนาฏศิลป์\nนานนม\nน่านน้ำ\nน่านฟ้า\nนามกร\nนามธรรม\nนามไธย\nนามบัตร\nนามปากกา\nนามแฝง\nนามสกุล\nนามสงเคราะห์\nนามสมญา\nนายทะเบียน\nนายท่า\nนายท้าย\nนายทุน\nนายประกัน\nนายหน้า\nนายอำเภอ\nนารายณ์หัตถ์\nนารีผล\nนาวิกโยธิน\nนำจับ\nนำพา\nนำทาง\nนำร่อง\nนำสืบ\nนำแสดง\nน้ำกรด\nน้ำกาม\nน้ำเกลือ\nน้ำข้าว\nน้ำแข็ง\nน้ำแข็งไส\nน้ำแข็งแห้ง\nน้ำครำ\nน้ำคร่ำ\nน้ำค้าง\nน้ำค้างแข็ง\nน้ำคาวปลา\nน้ำคำ\nน้ำเค็ม\nน้ำเคย\nน้ำเงิน\nน้ำเงี้ยว\nน้ำจัณฑ์\nน้ำจิ้ม\nน้ำใจ\nน้ำเชื้อ\nน้ำเชื่อม\nน้ำซาวข้าว\nน้ำดอกไม้\nน้ำดี\nน้ำตก\nน้ำตา\nน้ำตาล\nน้ำท่า\nน้ำนม\nน้ำนวล\nน้ำบาดาล\nน้ำประสานทอง\nน้ำประปา\nน้ำปลา\nน้ำป่า\nน้ำผึ้ง\nน้ำพริก\nน้ำพริกเผา\nน้ำพี้\nน้ำพุ\nน้ำมนต์\nน้ำมนตร์\nน้ำมัน\nน้ำมือ\nน้ำมูก\nน้ำเมา\nน้ำย่อย\nน้ำยา\nน้ำรัก\nน้ำแร่\nน้ำลาย\nน้ำเลี้ยง\nน้ำสต๊อก\nน้ำส้ม\nน้ำส้มสายชู\nน้ำสังข์\nน้ำสาบาน\nน้ำเสียง\nน้ำหนวก\nน้ำหนอง\nน้ำหนัก\nน้ำหน้า\nน้ำหนึ่ง\nน้ำหมึก\nน้ำหอม\nน้ำเหลือง\nน้ำอบ\nน้ำอ้อย\nน้ำอัดลม\nนิ่งเฉย\nนิจศีล\nนิดเดียว\nนิดหน่อย\nนิติกร\nนิติกรรม\nนิติธรรม\nนิตินัย\nนิติบัญญัติ\nนิติบุคคล\nนิติภาวะ\nนิติวิทยาศาสตร์\nนิติเวช\nนิติเวชศาสตร์\nนิติศาสตร์\nนิเทศศาสตร์\nนิ่มนวล\nนิรุกติศาสตร์\nนิเวศวิทยา\nนิศากร\nนิศากาล\nนิศาชล\nนิศารัตน์\nนี่แน่ะ\nนี่แหละ\nนี่เอง\nนึกคิด\nนุงถุง\nนุ่งห่ม\nนุ่มนวล\nนุ่มนิ่ม\nเนตรนารี\nเนติบัณฑิต\nเนยเทียม\nเนยใส\nเนิ่นนาน\nเนิบนาบ\nเนื้อความ\nเนื้อคู่\nเนื้อเค็ม\nเนื้องอก\nเนื้อตัว\nเนื้อตาย\nเนื้อที่\nเนื้อแท้\nเนื้อเปื่อย\nเนื้อผ้า\nเนื้อเพลง\nเนื้อไม้\nเนื้อเยื่อ\nเนื้อร้อง\nเนื้อร้าย\nเนื้อเรื่อง\nเนื้อหา\nเนืองนอง\nเนืองนิตย์\nเนืองแน่น\nแน่ใจ\nแน่ชัด\nแน่แท้\nแน่นอน\nแน่นิ่ง\nแน่แน่ว\nแน่นแฟ้น\nแน่นหนา\nแนบเนียน\nแนบแน่น\nแนวคิด\nแนวทาง\nแนวโน้ม\nแนวป่า\nแนวรบ\nแนวร่วม\nแนวหน้า\nแนวหลัง\nแน่วแน่\nแนะนำ\nแนะแนว\nโน้มน้าว\nในหลวง\nบกพร่อง\nบงกช\nบงการ\nบดบัง\nบทกลอน\nบทกวี\nบทความ\nบทคัดย่อ\nบทเฉพาะกาล\nบทนำ\nบทบัญญัติ\nบทบาท\nบทประพันธ์\nบทเพลง\nบทร้อง\nบทเรียน\nบทลงโทษ\nบทสนทนา\nบทอัศจรรย์\nบทจร\nบทบงสุ์\nบทมาลย์\nบทรัช\nบทเรศ\nบทวลัญช์\nบนบาน\nบรมครู\nบรมธาตุ\nบรมบพิตร\nบรมวงศานุวงศ์\nบรมอัฐิ\nบรรณพิภพ\nบรรณศาลา\nบรรณาการ\nบรรณาธิการ\nบรรณานุกรม\nบรรณารักษ์\nบรรณารักษศาสตร์\nบรรดามี\nบรรดาศักดิ์\nบรรทัดฐาน\nบรรพบุรุษ\nบรรลัยกัลป์\nบรรลัยจักร\nบริคณห์สนธิ\nบวงสรวง\nบ่วงบาศ\nบ้วนพระโอษฐ์\nบ่อเกิด\nบอกกล่าว\nบอกบท\nบอกบุญ\nบอกใบ้\nบอกปัด\nบ้องกัญชา\nบ้องตื้น\nบ้องไฟ\nบ้องหู\nบอดสี\nบ่อนทำลาย\nบอบช้ำ\nบอบบาง\nบอบแบบ\nบังโกลน\nบังโคลน\nบังใบ\nบั้งไฟ\nบังคับการ\nบังคับบัญชา\nบัญชาการ\nบัณฑุกัมพล\nบัดดล\nบัดเดี๋ยว\nบัดนั้น\nบัดนี้\nบัดสีบัดเถลิง\nบัตรเครดิต\nบัตรพลี\nบัตรสนเท่ห์\nบัตรสินเชื่อ\nบั่นทอน\nบั้นท้าย\nบั้นปลาย\nบั้นพระองค์\nบั้นเอว\nบันไดลิง\nบันไดเลื่อน\nบันเทิงคดี\nบัวลอย\nบัวบก\nบ้าจี้\nบ้าดีเดือด\nบ้าน้ำลาย\nบ้าบิ่น\nบ้าระห่ำ\nบ้าเลือด\nบ้าหอบฟาง\nบากบั่น\nบากหน้า\nบางตา\nบางเบา\nบางที\nบาดเจ็บ\nบาดแผล\nบาดหมาง\nบาตรใหญ่\nบาทบงกช\nบาทบงสุ์\nบาทบริจาริกา\nบาทวิถี\nบานเกล็ด\nบานตะเกียง\nบานตะไท\nบานเบอะ\nบานปลาย\nบานแผละ\nบานพับ\nบ้านจัดสรร\nบ้านช่อง\nบ้านนอก\nบ้านพัก\nบ้านเมือง\nบ้านรับรอง\nบ้านเรือน\nบาปกรรม\nบายศรี\nบ่ายเบี่ยง\nบ่ายหน้า\nบ่าวไพร่\nบิดเบี้ยว\nบิดเบือน\nบิดพลิ้ว\nบี้แบน\nบีบคั้น\nบีบรัด\nบึ้งตึง\nบึ้งบูด\nบุกบั่น\nบุกเบิก\nบุกรุก\nบุคลิกภาพ\nบุคลิกลักษณะ\nบุญธรรม\nบุญนิธิ\nบุญฤทธิ์\nบุบสลาย\nบุ้ยใบ้\nบุรุษเพศ\nบุหงารำไป\nบู้บี้\nบูชายัญ\nบูดบึ้ง\nบูดเบี้ยว\nเบาความ\nเบาใจ\nเบาบาง\nเบาปัญญา\nเบามือ\nเบาแรง\nเบาสมอง\nเบาหวาน\nเบาโหวง\nเบ้าตา\nเบาะแส\nเบิกความ\nเบิกบาน\nเบี้ยล่าง\nเบี้ยเลี้ยง\nเบี้ยหวัด\nเบี่ยงบ่าย\nเบียดบัง\nเบียดเบียน\nเบียดเสียด\nเบื้องต้น\nเบื้องบน\nเบื้องหน้า\nเบื้องหลัง\nแบกะดิน\nแบเบาะ\nแบ่งเบา\nแบ่งปัน\nแบ่งแยก\nแบบฉบับ\nแบบแปลน\nแบบแผน\nแบบฝึกหัด\nแบบพิมพ์\nแบบสอบถาม\nแบบอย่าง\nแบะแฉะ\nแบะท่า\nโบแดง\nโบราณคดี\nโบราณวัตถุ\nโบราณสถาน\nใบขับขี่\nใบจอง\nใบตอง\nใบแทรก\nใบบอก\nใบบุญ\nใบเบิกทาง\nใบปลิว\nใบพัด\nใบโพ\nใบไม้\nใบระกา\nใบรับรอง\nใบลา\nใบเลี้ยง\nใบสั่ง\nใบสำคัญ\nใบสุทธิ\nใบเสร็จ\nใบหน้า\nใบอนุญาต\nใบระกา\nปกครอง\nปกคลุม\nปกป้อง\nปกปิด\nปฏิบัติการ\nปฏิบัติบูชา\nปฐพีวิทยา\nปฐมฌาน\nปฐมทัศน์\nปฐมเทศนา\nปฐมนิเทศ\nปฐมพยาบาล\nปฐมยาม\nปฐมฤกษ์\nปฐมวัย\nปฐมสมโพธิ\nปนเป\nป่นปี้\nปมเขื่อง\nปมเด่น\nปมด้อย\nปรนเปรอ\nปรบไก่\nปรบมือ\nปรสิตวิทยา\nประโปรย\nประพรม\nประกันชีวิต\nประกันภัย\nประจักษ์พยาน\nประจัญบาน\nประจันหน้า\nประจำการ\nประจำเดือน\nประจำเมือง\nประจำยาม\nประชดประชัน\nประชากร\nประชากรศาสตร์\nประชาคม\nประชาชน\nประชาราษฎร์\nประชาชาติ\nประชาชี\nประชาทัณฑ์\nประชาบาล\nประชาพิจารณ์\nประชาภิบาล\nประชามติ\nประชาสงเคราะห์\nประชาสัมพันธ์\nประดับประดา\nประดามี\nประดาน้ำ\nประเดี๋ยวเดียว\nประเดี๋ยวนี้\nประทับใจ\nประทุษร้าย\nประเทศราช\nประพาสต้น\nประเพณีนิยม\nประลัยกัลป์\nประวัติการณ์\nประวัติศาสตร์\nประสบการณ์\nประสบการณ์นิยม\nประสาทการ\nประสูติการ\nประสูติกาล\nประเส\nปรับทุกข์\nปรับโทษ\nปรับปรุง\nปรากฏการณ์\nปราดเปรียว\nปราดเปรื่อง\nปราบปราม\nปริญญาบัตร\nปรัยัติธรรม\nปรุโปร่ง\nปลงใจ\nปลงตก\nปลดทุกข์\nปลดปลง\nปลดปล่อย\nปลดเปลื้อง\nปลดระวาง\nปลดแอก\nปล้นสะดม\nปลอกกระสุน\nปลอกคอ\nปลอดโปร่ง\nปลอดภัย\nปลอมปน\nปลอมแปลง\nปลอบโยน\nปล่อยใจ\nปล่อยตัว\nปล่อยปละ\nปลั๊กไฟ\nปลากริม\nปลาเค็ม\nปลาจ่อม\nปลาเจ่า\nปลาแดก\nปลาตู้\nปลาทอง\nปลาร้า\nปลาส้ม\nปลาดาว\nปลาบิน\nปลาฝา\nปลาวาฬ\nปลาหมึก\nปลาบปลื้ม\nปลายข้าว\nปลายแถว\nปลายทาง\nปลิ้นปลอก\nปลิ้นปล้อน\nปลีกตัว\nปลีกย่อย\nปลุกใจ\nปลุกปล้ำ\nปลุกปั่น\nปลุกระดม\nปลุกเสก\nปลูกฝัง\nปลูกสร้าง\nปวดถ่วง\nปวดมวน\nปวดร้าว\nป่วนปั่น\nป่วยการ\nปอกลอก\nป้องกัน\nปักใจ\nปักดำ\nปักหลัก\nปัจเจกบุคคล\nปัจเจกพุทธะ\nปัจเจกโพธิ\nปัจฉิมชน\nปัจฉิมทิศ\nปัจฉิมภาค\nปัจฉิมยาม\nปัจฉิมลิขิต\nปัจฉิมวัย\nปัจฉิมวาจา\nปัญญาชน\nปัญญาวิมุติ\nปัญญาอ่อน\nปัดเป่า\nปันส่วน\nปั่นป่วน\nปั่นแปะ\nปั่นหัว\nปั้นจิ้ม\nปั้นเจ๋อ\nปั้นปึ่ง\nปั้นสิบ\nปั๊มน้ำมัน\nป่าช้า\nป่าชายเลน\nป่าดง\nป่าดงดิบ\nป่าดิบ\nป่าเถื่อน\nป่าเบญจพรรณ\nป่าละเมาะ\nปากกา\nปากขอ\nปากแข็ง\nปากคอ\nปากคำ\nปากคีบ\nปากจัด\nปากน้ำ\nปากเปล่า\nปากเสียง\nปานกลาง\nป่านนี้\nป้านลม\nป้ายสี\nป่าวร้อง\nปิดฉาก\nปิดบัง\nปิตุฆาต\nปิตุภูมิ\nปีมะโว้\nปีแสง\nปี่กลาง\nปี่ไฉน\nปี่ชวา\nปี่นอก\nปี่ใน\nปี่พาทย์\nปี่อ้อ\nปีกกา\nปีนเกลียว\nปีนป่าย\nปึกแผ่น\nปึงปัง\nปืนกล\nปืนครก\nปืนพก\nปืนยา\nปืนยาว\nปืนลม\nปืนเล็ก\nปืนเล็กยาว\nปืนสั้น\nปืนใหญ่\nปุบปับ\nปุ๊บปั๊บ\nปุ่มเปือก\nปุยฝ้าย\nปุ๋ยคอก\nปุ๋ยเคมี\nปุ๋ยวิทยาศาสตร์\nปุ๋ยหมัก\nปุ๋ยอินทรีย์\nปูจ๋า\nปูเสฉวน\nปู่เจ้า\nปู่ทวด\nปูนขาว\nปูนซีเมนต์\nปูนดิบ\nปูนแดง\nปูนปลาสเตอร์\nปูนปั้น\nเป็ดเทศ\nเป็ดน้ำ\nเป็นกลาง\nเป็นใจ\nเป็นต้น\nเป็นต่อ\nเป็นรอง\nเป็นไร\nเป็นลม\nเป็นห่วง\nเป็นอยู่\nเปรมปรีดิ์\nเปรอะเปื้อน\nเปรียบเทียบ\nเปรียบเปรย\nเปรี้ยวปาก\nเปรี้ยวหวาน\nเปรื่องปราด\nเปลญวน\nเปล่งปลั่ง\nเปล่าดาย\nเปล่าเปลี่ยว\nเปลี่ยนใจ\nเปลี่ยนตัว\nเปลี่ยนแปลง\nเปลี่ยนมือ\nเปลี่ยนหน้า\nเป๋อเหลอ\nเปะปะ\nเป่ากบ\nเป้านิ่ง\nเป้าหมาย\nเปิดฉาก\nเปิดเปิง\nเปิดโปง\nเปิดผนึก\nเปิดเผย\nเปียกปูน\nแป้งสาลี\nแป้งนวล\nแป้งเปียก\nแป้งมัน\nแป้งฝุ่น\nแป้งร่ำ\nแป้งสิงคโปร์\nแป้งหมี่\nแปดปน\nแปดเปื้อน\nแปรปรวน\nแปรผัน\nแปรพักตร์\nแปรรูป\nแปรอักษร\nแปลกปลอม\nแปะโป้ง\nโป้ปด\nโปร่งแสง\nโปร่งใส\nโปรดปราน\nโปรยทาน\nโปรยปราย\nโปโลน้ำ\nผกผัน\nผกากรอง\nผงขาว\nผงชูรส\nผงซักฟอก\nผงฟู\nผดุงครรภ์\nผมไฟ\nผลพลอยได้\nผลลัพธ์\nผลัดเปลี่ยน\nผลิตผล\nผลิตภัณฑ์\nผลุบโผล่\nผสมเทียม\nผสมผสาน\nผสมผเส\nผสมพันธุ์\nผสมโรง\nผสมเสร็จ\nผ่องแผ้ว\nผ่องใส\nผ่อนคลาย\nผ่อนชำระ\nผ่อนปรน\nผ่อนผัน\nผ่อนส่ง\nผอมโซ\nผอมแห้ง\nผักชี\nผักตบชวา\nผักบุ้ง\nผังเมือง\nผัดผ่อน\nผันแปร\nผันผวน\nผ่าตัด\nผ่าเผย\nผ่าหมาก\nผ่าเหล่า\nผ้าขนหนู\nผ้าขาวม้า\nผ้าขี้ริ้ว\nผ้าเช็ดตัว\nผ้าเช็ดปาก\nผ้าเช็ดมือ\nผ้าเช็ดหน้า\nผ้าดิบ\nผ้าต่วน\nผ้าไตร\nผ้าถุง\nผ้าแถบ\nผ้านวม\nผ้านุ่ง\nผ้าใบ\nผ้าป่า\nผ้าป่าน\nผ้าผ่อน\nผ้าพันคอ\nผ้าพันแผล\nผ้าแพร\nผ้าโพกหัว\nผ้ามัดหมี่\nผ้ายาง\nผ้าลูกไม้\nผ้าเหลือง\nผ้าอนามัย\nผ้าอ้อม\nผาดโผน\nผาติกรรม\nผิดหวัง\nผิวเผิน\nผิวพรรณ\nผิวหนัง\nผีกระสือ\nผีกระหัง\nผีกองกอย\nผีโขมด\nผีดิบ\nผีตองเหลือง\nผีถ้วยแก้ว\nผีแถน\nผีทะเล\nผีบุญ\nผีปอบ\nผีพุ่งไต้\nผีฟ้า\nผีเรือน\nผีสาง\nผีเสื้อ\nผีห่า\nผึ่งผาย\nผุดผ่อง\nผุดผาด\nผู้คน\nผู้คุม\nผู้จัดการ\nผู้ชาย\nผู้เชี่ยวชาญ\nผู้ดี\nผู้โดยสาร\nผู้ต้องขัง\nผู้ต้องหา\nผู้แทน\nผู้น้อย\nผู้บริโภค\nผู้บังคับบัญชา\nผู้ปกครอง\nผู้ประกอบการ\nผู้ป่วย\nผู้พิพากษา\nผู้เยาว์\nผู้ร้าย\nผู้วิเศษ\nผู้สื่อข่าว\nผู้เสียหาย\nผู้หญิง\nผู้ใหญ่\nผู้ใหญ่บ้าน\nผูกขวัญ\nผูกขาด\nผูกพัน\nผูกมัด\nเผชิญหน้า\nเผด็จการ\nเผด็จศึก\nเผยแผ่\nเผยแพร่\nเผละผละ\nเผ่าพันธุ์\nเผื่อแผ่\nแผงลอย\nแผนการ\nแผนงาน\nแผนที่\nแผนผัง\nแผนภาพ\nแผนภูมิ\nแผ่นดิน\nแผ่นเสียง\nแผ้วพาน\nโผงผาง\nฝนทอง\nฝอยทอง\nฝักแค\nฝักบัว\nฝักฝ่าย\nฝักใฝ่\nฝังใจ\nฝังหัว\nฝาชี\nฝาแฝด\nฝาละมี\nฝ่าพระบาท\nฝ่าฝืน\nฝ่าฟัน\nฝ้าฟาง\nฝากตัว\nฝากฝัง\nฝีดาษ\nฝีมะม่วง\nฝีจักร\nฝีเท้า\nฝีปาก\nฝีพาย\nฝีมือ\nฝีเย็บ\nฝึกงาน\nฝึกปรือ\nฝึกฝน\nฝึกสอน\nฝึกหัด\nฝืดเคือง\nใฝ่ฝัน\nพงพี\nพงศ์พันธุ์\nพญาโศก\nพญาไฟ\nพบปะ\nพบพาน\nพรสวรรค์\nพรมคด\nพรมแดน\nพรมมิ\nพรรคพวก\nพรรณราย\nพรวดพราด\nพรหมชาติ\nพรหมลิขิต\nพรหมโลก\nพรหมวิหาร\nพร้อมใจ\nพร้อมพรั่ง\nพร้อมเพรียง\nพร้อมมูล\nพร้อมสรรพ\nพร้อมหน้า\nพระครู\nพระคุณ\nพระเคราะห์\nพระเครื่อง\nพระเจ้า\nพระเจ้าอยู่หัว\nพระชายา\nพระทัย\nพระนาง\nพระนางเจ้า\nพระเป็นเจ้า\nพระผู้เป็นเจ้า\nพระพิมพ์\nพระพุทธเจ้า\nพระพุทธองค์\nพระภูมิ\nพระยา\nพระรอง\nพระสนม\nพระสนมเอก\nพระองค์\nพระองค์เจ้า\nพระเอก\nพรั่งพร้อม\nพรั่งพรู\nพรั่นพรึง\nพร่างพราว\nพรายน้ำ\nพรายแพรว\nพราวแพรว\nพร่ำพลอด\nพร่ำเพรื่อ\nพร่ำเพ้อ\nพริกไทย\nพริ้งพราย\nพริ้งเพรา\nพริ้งเพริศ\nพริบตา\nพริ้มพราย\nพริ้มเพรา\nพรุ่งนี้\nพฤติกรรม\nพฤติการณ์\nพฤตินัย\nพลการ\nพลขับ\nพลความ\nพลเมือง\nพลรบ\nพลร่ม\nพลเรือน\nพลโลก\nพลศึกษา\nพลบค่ำ\nพลอดรัก\nพลังงาน\nพลังเงียบ\nพลังจิต\nพลั้งปาก\nพลั้งเผลอ\nพลั้งพลาด\nพลัดถิ่น\nพลัดพราก\nพลาดท่า\nพลาดพลั้ง\nพลิกแพลง\nพลีกรรม\nพลุ่งพล่าน\nพวกพ้อง\nพวงมาลัย\nพวงมาลา\nพวงหรีด\nพวงคราม\nพวงชมพู\nพวงแสด\nพ่วงพี\nพวยน้ำ\nพวยพุ่ง\nพสกนิกร\nพหุคูณ\nพหุภาคี\nพหูพจน์\nพหูสูต\nพอควร\nพอใจ\nพอใช้\nพอใช้ได้\nพอดี\nพอตัว\nพอทำเนา\nพอประมาณ\nพอเพียง\nพอแรง\nพอสมควร\nพอเหมาะ\nพ่อขุน\nพ่อครัว\nพ่อตา\nพ่อบ้าน\nพ่อพันธุ์\nพ่อม่าย\nพ่อเมือง\nพ่อเลี้ยง\nพ่อสื่อ\nพอกพูน\nพ้องพาน\nพักผ่อน\nพักพิง\nพักฟื้น\nพักร้อน\nพักแรม\nพัดยศ\nพัดลม\nพันพัว\nพับฐาน\nพับเพียบ\nพัวพัน\nพาซื่อ\nพาดพิง\nพิณพาทย์\nพิธีกร\nพิธีกรรม\nพิธีการ\nพิธีรีตอง\nพิธีสาร\nพินัยกรรม\nพิมพ์เขียว\nพิมพ์ใจ\nพิมพ์ดีด\nพิษสง\nพี่น้อง\nพี่เบิ้ม\nพี่เลี้ยง\nพึงใจ\nพึงพอใจ\nพึ่งพา\nพึ่งพิง\nพืชพันธุ์\nพืชมงคล\nพื้นฐาน\nพื้นที่\nพื้นบ้าน\nพื้นเพ\nพื้นเมือง\nพื้นเสีย\nพุพอง\nพุทธกาล\nพุทธคุณ\nพุทธจักร\nพุทธเจดีย์\nพุทธฎีกา\nพุทธปฏิมา\nพุทธปฏิมากร\nพุทธมามกะ\nพุทธศักราช\nพุทธศาสนิกชน\nพุทธองค์\nพุทธชาด\nพุทธรักษา\nพุ่มพวง\nพุ่มไม้\nพู่กัน\nพูดจา\nเพ่งเล็ง\nเพดานบิน\nเพดานปาก\nเพริศพราย\nเพริศพริ้ง\nเพริศแพร้ว\nเพรียกพร้อง\nเพรียวลม\nเพลงเชิด\nเพลงยาว\nเพลิงกัลป์\nเพลินใจ\nเพลินตา\nเพลี่ยงพล้ำ\nเพ้อฝัน\nเพาะกาย\nเพาะชำ\nเพาะปลูก\nเพิกถอน\nเพิกเฉย\nเพิ่มเติม\nเพิ่มพูน\nเพียงตา\nเพียงพอ\nเพียบแประ\nเพียบพร้อม\nเพื่อนเกลอ\nเพื่อนตาย\nเพื่อนบ้าน\nเพื่อนฝูง\nเพื่อนยาก\nแพ้ท้อง\nแพร่หลาย\nแพร่งพราย\nแพรวพราว\nโพธิญาณ\nโพธิบัลลังก์\nโพธิสมภาร\nโพธิสัตว์\nโพ้นทะเล\nโพยภัย\nไพ่ตาย\nไพ่ป๊อก\nไพรวัน\nไพรสณฑ์\nไพรสัณฑ์\nไพร่พล\nไพร่ฟ้า\nไพร่สม\nไพร่ส่วย\nไพร่หลวง\nฟกช้ำ\nฟองเต้าหู้\nฟองน้ำ\nฟองมัน\nฟ้องกลับ\nฟ้องร้อง\nฟอนเฟะ\nฟักทอง\nฟัดเฟียด\nฟันดาบ\nฟันฝ่า\nฟันแท้\nฟันน้ำนม\nฟันปลา\nฟันฟาง\nฟันเฟือง\nฟันม้า\nฟันเลื่อย\nฟันหนู\nฟั่นเฝือ\nฟั่นเฟือน\nฟื้นตัว\nฟื้นฝอย\nฟื้นฟู\nฟุ้งซ่าน\nฟุ้งเฟ้อ\nฟุ้งเฟื่อง\nฟุตบอล\nฟูฟ่อง\nฟูเฟื่อง\nฟูมฟัก\nฟูมฟาย\nเฟะฟะ\nเฟื่องฟ้า\nเฟื่องฟุ้ง\nเฟื่องฟู\nไฟฉาย\nไฟแช็ก\nไฟธาตุ\nไฟฟ้า\nภัตกิจ\nภาคทัณฑ์\nภาคพื้น\nภาคเรียน\nภาคภูมิ\nภาพถ่าย\nภาพนิ่ง\nภาพประกอบ\nภาพพจน์\nภาพยนตร์\nภาพลวงตา\nภาพลักษณ์\nภายนอก\nภายใน\nภายหน้า\nภายหลัง\nภารกิจ\nภารธุระ\nภารโรง\nภารตวิทยา\nภาษาศาสตร์\nภาสกร\nภิญโญภาพ\nภินชาติ\nภูธร\nภูธเรศ\nภูบาล\nภูเบศ\nภูเบศวร์\nภูเขา\nภูเขาไฟ\nภูผา\nภูตคาม\nภูตบดี\nภูตรูป\nภูเตศวร\nภูมินทร์\nภูมิบาล\nภูมิประเทศ\nภูมิภาค\nภูมิรัฐศาสตร์\nภูมิลำเนา\nภูมิศาสตร์\nภูมิอากาศ\nภูมิธรรม\nภูมิปัญญา\nภูมิรู้\nภูมิใจ\nภูมิฐาน\nภูมิคุ้มกัน\nภูมิแพ้\nภูษาโยง\nเภทภัย\nเภสัชกร\nเภสัชกรรม\nเภสัชวิทยา\nเภสัชศาสตร์\nโภคทรัพย์\nโภคภัณฑ์\nโภชนากร\nโภชนาการ\nมกุฎราชกุมาร\nมงคลแฝด\nมงคลสูตร\nมงคลหัตถี\nมณเฑียรบาล\nมดดำ\nมดแดง\nมดเท็จ\nมดยอบ\nมดลูก\nมธุปายาส\nมธุรส\nมนเทียรบาล\nมนุษย์กบ\nมโนกรรม\nมโนคติ\nมโนทุจริต\nมโนธรรม\nมโนภาพ\nมโนมัย\nมโนรถ\nมโนรมย์\nมโนสุจริต\nมรรคนายก\nมรรคผล\nมฤคชาติ\nมฤคทายวัน\nมฤคราช\nมลทิน\nมลพิษ\nมลสาร\nมวกเหล็ก\nม้วนหน้า\nมวยไทย\nมวยปล้ำ\nมวยล้ม\nมวยวัด\nมวยสากล\nมวยหมู่\nมวลสาร\nมอคราม\nมอซอ\nมอหมึก\nมองเมียง\nมอบตัว\nมอบหมาย\nมอมเมา\nมะขามเทศ\nมะขามป้อม\nมะขามเปียก\nมะเขือเทศ\nมะเขือพวง\nมะพร้าวแก้ว\nมักคุ้น\nมักจี่\nมักง่าย\nมักน้อย\nมักมาก\nมักใหญ่\nมั่งคั่ง\nมั่งมี\nมัจจุราช\nมัชฌิมนิกาย\nมัชฌิมประเทศ\nมัชฌิมยาม\nมัชฌิมวัย\nมัดจำ\nมัดหมี่\nมัธยมกาล\nมัธยมศึกษา\nมันแกว\nมันเทศ\nมันฝรั่ง\nมันเปลว\nมันสมอง\nมั่นคง\nมั่นใจ\nมั่นหมาย\nมั่นเหมาะ\nมัวเมา\nมัวหมอง\nมั่วสุม\nม้าเทศ\nม้าน้ำ\nม้ามืด\nม้าเร็ว\nม้าล่อ\nม้าลาย\nมากมาย\nมาตรการ\nมาตรฐาน\nมาตราส่วน\nมาตุคาม\nมาตุฆาต\nมาตุภูมิ\nม่านตา\nม่านบังตา\nมายากร\nมายากล\nมายาการ\nมายาวี\nมารผจญ\nมารวิชัย\nมารสังคม\nมารหัวขน\nมาลาการ\nมิ่งขวัญ\nมิ่งมิตร\nมิจฉาจาร\nมิจฉาชีพ\nมิดชิด\nมิดเมี้ยน\nมิดหมี\nมิตรจิต\nมิตรภาพ\nมิตรสหาย\nมิน่า\nมีหน้า\nมีดโกน\nมีดดาบ\nมีดโต้\nมีดพก\nมีดพับ\nมีดสั้น\nมึนงง\nมึนชา\nมึนตึง\nมึนเมา\nมืดครึ้ม\nมืดมน\nมืดมัว\nมือจับ\nมือดี\nมือเติบ\nมือปืน\nมือเปล่า\nมือมืด\nมือสอง\nมือเสือ\nมือหนึ่ง\nมือใหม่\nมุกตลก\nมุขปาฐะ\nมุขมนตรี\nมุ่งมั่น\nมุ่งมาด\nมุ่งหน้า\nมุ่งหมาย\nมุ่งหวัง\nมุ้งลวด\nมุ้งสายบัว\nมุมก้ม\nมุมกลับ\nมุมเงย\nมุมฉาก\nมุมตรง\nมุมป้าน\nมุมมืด\nมุมแย้ง\nมุมสะท้อน\nมุมหักเห\nมุมแหลม\nมุสาวาท\nมูกเลือด\nมูกมัน\nมูกหลวง\nมูนดิน\nมูลฐาน\nมูลนาย\nมูลนิธิ\nมูลเหตุ\nมูลค่า\nมูลฝอย\nเม็ดเงิน\nเม็ดเลือด\nเม็ดโลหิต\nเม่นทะเล\nเมรุมาศ\nเมรุราช\nเมล์อากาศ\nเมาดิบ\nเมามัน\nเมามัว\nเมามาย\nเมินเฉย\nเมียน้อย\nเมียหลวง\nเมียงมอง\nเมี่ยงลาว\nเมี่ยงส้ม\nเมื่อกี้\nเมื่อตะกี้\nเมื่อใด\nเมื่อไร\nเมื่อไหร่\nเมื่อนั้น\nเมืองขึ้น\nเมืองท่า\nเมืองนอก\nเมืองหลวง\nเมื่อยขบ\nเมื่อยล้า\nแม่กอง\nแม่กุญแจ\nแม่คุณ\nแม่งาน\nแม่เจ้า\nแม่ชี\nแม่ทัพ\nแม่นม\nแม่น้ำ\nแม่บท\nแม่บ้าน\nแม่เบี้ย\nแม่พระ\nแม่พิมพ์\nแม่เพลง\nแม่มด\nแม่ม่าย\nแม่ไม้\nแม่ยก\nแม่ยาย\nแม่ร้าง\nแม่เรือน\nแม่แรง\nแม่เล้า\nแม่เลี้ยง\nแม่สี\nแม่สื่อ\nแม่เหล็ก\nแมงมุม\nแม่นยำ\nแมลงช้าง\nแมลงวัน\nแมลงปอ\nแมลงภู่\nแมลงเม่า\nแมวเซา\nแมวน้ำ\nแมวป่า\nแมวมอง\nไม้กลัด\nไม้กวาด\nไม้กางเขน\nไม้เกาหลัง\nไม้ขีดไฟ\nไม้จิ้มฟัน\nไม้เด็ด\nไม้ตาย\nไม้ตีพริก\nไม้ที\nไม้เท้า\nไม้บรรทัด\nไม้เมตร\nไม้ระแนง\nไม้เรียว\nไม้หมอน\nไม้อัด\nไม้จัตวา\nไม้ตรี\nไม้ไต่คู้\nไม้โท\nไม้ผัด\nไม้มลาย\nไม้ม้วน\nไม้ยมก\nไม้หน้า\nไม้หันอากาศ\nไม้เอก\nยกกลีบ\nยกครู\nยกเครื่อง\nยกเค้า\nยกทรง\nยกฟ้อง\nยกเมฆ\nยกยอ\nยกย่อง\nยกเลิก\nยกเว้น\nย่นย่อ\nยมทูต\nยมบาล\nยมราช\nยมโลก\nยวดยิ่ง\nยวดยาน\nยวนยี\nยวบยาบ\nย่อท้อ\nย่อส่วน\nย่อหน้า\nย่อหย่อน\nยอกย้อน\nยองใย\nย่องเบา\nย่องแย่ง\nยอดเยี่ยม\nยอดอก\nย้อนยอก\nย้อนรอย\nย้อนศร\nย้อนแสง\nย้อนหลัง\nยอบแยบ\nยอมความ\nย่อมเยา\nย่อยยับ\nยักยอก\nยักย้าย\nยักเยื้อง\nยัญกรรม\nยัญพิธี\nยัดเยียด\nยับเยิน\nยับยั้ง\nยั่วยวน\nยั่วยุ\nยั่วเย้า\nยากวาด\nยากันยุง\nยาเขียว\nยาใจ\nยาฉุน\nยาชา\nยาซัด\nยาดอง\nยาแดง\nยาถ่าย\nยาธาตุ\nยานัตถุ์\nยาเบื่อ\nยาโป๊\nยาแฝด\nยาพิษ\nยาระบาย\nยาสลบ\nยาสั่ง\nยาสีฟัน\nยาสูบ\nยาเส้น\nยาเสพติด\nยาหม่อง\nยาเหลือง\nย่าทวด\nย่านาง\nยากแค้น\nยากจน\nยากเย็น\nยากไร้\nยางนอก\nยางใน\nยางมะตอย\nยางมะตูม\nยางลบ\nยางสน\nยางอาย\nย่างกราย\nย่างเยื้อง\nย่างสด\nย่างสามขุม\nย่างเหยียบ\nยานเกราะ\nยานพาหนะ\nยานอวกาศ\nยานคาง\nยายทวด\nยาวเฟื้อย\nยาวยืด\nยาวเหยียด\nยำทวาย\nยำใหญ่\nยำเกรง\nยำเยง\nย่ำต๊อก\nย่ำยี\nย่ำแย่\nยิงเป้า\nยิ่งนัก\nยิ่งยวด\nยิ่งใหญ่\nยินดี\nยินยอม\nยินร้าย\nยิ้มกริ่ม\nยิ้มแฉ่ง\nยิ้มแต้\nยิ้มแป้น\nยิ้มเผล่\nยิ้มเยาะ\nยิ้มแย้ม\nยียวน\nยึดครอง\nยึดถือ\nยึดมั่น\nยึดเหนี่ยว\nยืดยาด\nยืดยาว\nยืดเยื้อ\nยืดหยุ่น\nยืดอก\nยืนกราน\nยืนต้น\nยืนพื้น\nยืนยง\nยืนยัน\nยืนหยัด\nยื้อยุด\nยุยง\nยุแยง\nยุแหย่\nยุคลบาท\nยุคเข็ญ\nยุคทอง\nยุคมืด\nยุ่งขิง\nยุ่งยาก\nยุ่งเหยิง\nยุติธรรม\nยุทธการ\nยุทธนาวี\nยุทธปัจจัย\nยุทธภัณฑ์\nยุทธภูมิ\nยุทธวิธี\nยุทธศาสตร์\nยุทธหัตถี\nยุทธนาการ\nยุทธนาธิการ\nยุบยับ\nยุบยิบ\nยุพราช\nยู่ยี่\nเย็นเจี๊ยบ\nเย็นใจ\nเย็นฉ่ำ\nเย็นเฉียบ\nเย็นชา\nเย็นชืด\nเย็นตา\nเย็นเยียบ\nเย็นเยือก\nเย็นวาบ\nเย็นวูบ\nเย็บกี่\nเย็บจักร\nเย็บด้าย\nเย้ยหยัน\nเย้าหยอก\nเยาะเย้ย\nเยี่ยมกราย\nเยี่ยมเยียน\nเยี่ยมเยือน\nเยี่ยมยอด\nเยื่อเคย\nเยื่อใย\nเยือกเย็น\nเยื้องกราย\nเยื้องยัก\nเยื้องย่าง\nแยกย้าย\nแยกแยะ\nแย่งชิง\nแยบคาย\nแยบยล\nแย้มพราย\nแย้มยิ้ม\nแย้มสรวล\nโยเย\nโย้เย้\nโยกโคลง\nโยกย้าย\nโยกโย้\nโยนกลอง\nใยหิน\nรกชัฏ\nรกร้าง\nรกเรี้ยว\nรกเรื้อ\nรกราก\nรงควัตถุ\nรชนีกร\nรถกระบะ\nรถเก๋ง\nรถเข็น\nรถแข่ง\nรถจักร\nรถจี๊ป\nรถตู้\nรถทัวร์\nรถบรรทุก\nรถพ่วง\nรถพยาบาล\nรถไฟ\nรถไฟฟ้า\nรถม้า\nรถเมล์\nรถยนต์\nรถราง\nรถลาก\nรถสปอร์ต\nรถสิบล้อ\nรบกวน\nรบรา\nรบเร้า\nรมดำ\nร่มเกล้า\nร่มชูชีพ\nร่มเย็น\nร่มรื่น\nร่วงโรย\nรวงผึ้ง\nรวงรัง\nรวดเร็ว\nรวนเร\nรวบยอด\nรวบรวม\nรวบรัด\nรวมพล\nรวมหัว\nร่วมใจ\nร่วมเพศ\nร่วมมือ\nร่วมรัก\nร่วมสมัย\nรวยริน\nรวยรื่น\nรสชาติ\nรสนิยม\nรองท้อง\nรองเท้า\nรองพื้น\nร่องน้ำ\nร่องรอย\nร้องขอ\nร้องทุกข์\nร้องเรียน\nร้องห่ม\nร้องไห้\nรองช้ำ\nรองทรง\nรอดชีวิต\nรอดตัว\nรอดตาย\nรอนแรม\nร่อนเร่\nร้อนใจ\nร้อนตัว\nร้อนรน\nร้อนรุ่ม\nร้อนวิชา\nร้อนอาสน์\nรอบจัด\nรอบเดือน\nรอบรู้\nรอยร้าว\nร่อยหรอ\nร้อยละ\nร้อยกรอง\nร้อยแก้ว\nร้อยหวาย\nระนาดทุ้ม\nระนาดเอก\nระเบิดขวด\nระเบิดมือ\nระเบียบการ\nรักใคร่\nรักษาการ\nรักษาการณ์\nรังไข่\nรังแตน\nรังนก\nรังผึ้ง\nรังเพลิง\nรังมด\nรังสรรค์\nรังสฤษฏ์\nรั้งรอ\nรังสีแพทย์\nรังสีวิทยา\nรัชกาล\nรัชทายาท\nรัชนีกร\nรัฐธรรมนูญ\nรัฐบาล\nรัฐบุรุษ\nรัฐประศาสน์\nรัฐประหาร\nรัฐพิธี\nรัฐมนตรี\nรัฐวิสาหกิจ\nรัฐศาสตร์\nรัฐสภา\nรัดกุม\nรัดเกล้า\nรัดตัว\nรัดประคด\nรัดรึง\nรัดรูป\nรัตติกาล\nรับขวัญ\nรับจ้าง\nรับช่วง\nรับใช้\nรับซื้อ\nรับทราบ\nรับประกัน\nรับประทาน\nรับปาก\nรับผิด\nรับผิดชอบ\nรับฟ้อง\nรับฟัง\nรับมือ\nรับรอง\nรับรู้\nรับสมัคร\nรับสั่ง\nรับหน้า\nรับเหมา\nรั่วไหล\nรามือ\nร่าเริง\nรากแก้ว\nรากขวัญ\nรากฐาน\nรากฟัน\nรากศัพท์\nรากเหง้า\nร่างกาย\nร่างแห\nร้างรา\nราชกรณียกิจ\nราชการ\nราชกิจ\nราชครู\nราชฐาน\nราชทัณฑ์\nราชทินนาม\nราชทูต\nราชธานี\nราชนาวี\nราชบัณฑิต\nราชบัลลังก์\nราชบาตร\nราชบุตร\nราชปะแตน\nราชภัฏ\nราชมัล\nราชยาน\nราชรถ\nราชลัญจกร\nราชเลขาธิการ\nราชเลขานุการ\nราชวงศ์\nราชวัติ\nราชสกุล\nราชสมบัติ\nราชสาส์น\nราชหัตถเลขา\nราชองครักษ์\nราชโองการ\nราชาคณะ\nราชาศัพท์\nราชินีนาถ\nร้านชำ\nร้านรวง\nราบคาบ\nราบรื่น\nราบเรียบ\nรายการ\nรายงาน\nรายจ่าย\nรายได้\nรายทาง\nรายรับ\nรายล้อม\nรายละเอียด\nรายวิชา\nร่ายยาว\nร่ายรำ\nร้ายกาจ\nร้ายแรง\nราวนม\nราวป่า\nร้าวฉาน\nร้าวราน\nรำพัด\nรำแพน\nรำวง\nร่ำไป\nร่ำร้อง\nร่ำเรียน\nร่ำไร\nร่ำลา\nร่ำไห้\nริเริ่ม\nริอ่าน\nริมฝีปาก\nริ้วรอย\nรีบร้อน\nรีบรุด\nรีบเร่ง\nรื่นรมย์\nรื่นเริง\nรื้อถอน\nรื้อฟื้น\nรุกฆาต\nรุกราน\nรุกล้ำ\nรุกไล่\nรุ่งขึ้น\nรุ่งแจ้ง\nรุ่งเช้า\nรุ่งเรือง\nรุ่งโรจน์\nรุ่งสว่าง\nรุ่งสาง\nรุ่งอรุณ\nรุจิเรข\nรุดหน้า\nรุนแรง\nรุมเร้า\nรุมล้อม\nรุ่มรวย\nรุ่มร้อน\nรุ่ยร่าย\nรู้แกว\nรู้ความ\nรู้คุณ\nรู้งาน\nรู้จัก\nรู้แจ้ง\nรู้ใจ\nรู้เชิง\nรู้ตัว\nรู้ทัน\nรู้เท่า\nรู้เรื่อง\nรู้สำนึก\nรู้สึก\nรู้เห็น\nรูปการณ์\nรูปโฉม\nรูปฌาน\nรูปถ่าย\nรูปทรง\nรูปธรรม\nรูปแบบ\nรูปพรรณ\nรูปพรหม\nรูปภพ\nรูปภาพ\nรูปร่าง\nรูปสมบัติ\nเร่ร่อน\nเร่งด่วน\nเร่งมือ\nเร่งรัด\nเร่งรีบ\nเร่งเร้า\nเร้นลับ\nเร่อร่า\nเร่าร้อน\nเราะราย\nเราะร้าย\nเริงใจ\nเริงรมย์\nเริดร้าง\nเริ่มต้น\nเริ่มแรก\nเรี่ยราด\nเรี่ยไร\nเรียกคืน\nเรียกตัว\nเรียกร้อง\nเรียกหา\nเรียบร้อย\nเรียงความ\nเรียงตัว\nเรียงเบอร์\nเรียงพิมพ์\nเรียงเม็ด\nเรียงราย\nเรียนรู้\nเรียบร้อย\nเรียบเรียง\nเรียบวุธ\nเรี่ยมเร้\nเรี่ยวแรง\nเรือกลไฟ\nเรือกอและ\nเรือกำปั่น\nเรือจ้าง\nเรือดำน้ำ\nเรือโดยสาร\nเรือตรวจการณ์\nเรือตังเก\nเรือธง\nเรือนำร่อง\nเรือบด\nเรือบิน\nเรือใบ\nเรือประมง\nเรือพ่วง\nเรือพิฆาต\nเรือยนต์\nเรือยาว\nเรือโยง\nเรือรบ\nเรือลากจูง\nเรือสำปั้น\nเรือสำเภา\nเรือหลวง\nเรือหางยาว\nเรืออีโปง\nเรือเอี้ยมจุ๊น\nเรื้อรัง\nเรือกสวน\nเรืองนาม\nเรืองรอง\nเรืองแสง\nเรื่องราว\nเรื่องสั้น\nเรือนแก้ว\nเรือนจำ\nเรือนเบี้ย\nเรือนแพ\nเรือนหอ\nเรื่อยเจื้อย\nเรื่อยเฉื่อย\nเรื่อยเปื่อย\nแรเงา\nแรกนา\nแรกนาขวัญ\nแรงงาน\nแรงดึงดูด\nแรงเทียน\nแรงม้า\nแรงเหวี่ยง\nแรมรอน\nแรมรา\nแรมโรย\nโรคจิต\nโรงครัว\nโรงงาน\nโรงเจ\nโรงเตี๊ยม\nโรงทาน\nโรงนา\nโรงพยาบาล\nโรงพัก\nโรงพิมพ์\nโรงเรียน\nโรงเรือน\nโรงแรม\nโรงเลี้ยง\nโรงเลื่อย\nโรงสี\nโรงสีข้าว\nโรงอาหาร\nโรมรัน\nโรยรา\nฤชากร\nฤดูกาล\nลงขัน\nลงแขก\nลงคอ\nลงตัว\nลงท้าย\nลงทุน\nลงโทษ\nลงพุง\nลงมือ\nลงรัก\nลงรอย\nลงแรง\nลงโรง\nลงเอย\nลดตัว\nลดละ\nลดเลี้ยว\nลดหย่อน\nลดหลั่น\nลนลาน\nล้นพ้น\nล้นหลาม\nล้นเหลือ\nลบล้าง\nลบเลือน\nลบหลู่\nลมกรด\nลมค้า\nลมงวง\nลมแดด\nลมทะเล\nลมบก\nลมบน\nลมบ้าหมู\nลมปราณ\nลมปาก\nลมพิษ\nลมว่าว\nลมเสีย\nลมหนาว\nลมหายใจ\nล่มจม\nล่มสลาย\nล้มละลาย\nล้มลุก\nล้มเลิก\nล่วงเกิน\nล่วงรู้\nล่วงละเมิด\nล่วงลับ\nล่วงล้ำ\nล่วงเลย\nล่วงหน้า\nลวดลาย\nลวดสปริง\nลวดหนาม\nล้วนแล้ว\nลหุโทษ\nล่อลวง\nล่อหลอก\nล่อแหลม\nล้อต๊อก\nล้อเลื่อน\nล้อเล่น\nล้อเลียน\nล้อหลอก\nลองเชิง\nลองดี\nลองภูมิ\nล่องหน\nลอดช่อง\nล่อนจ้อน\nลอบกัด\nล้อมวง\nลอยแก้ว\nลอยชาย\nลอยตัว\nลอยนวล\nลอยแพ\nลอยลำ\nละทิ้ง\nละเลย\nละเว้น\nละครนอก\nละครใน\nละครเพลง\nละครร้อง\nละครรำ\nละครลิง\nละครสัตว์\nละเอียดอ่อน\nลักไก่\nลักพา\nลักเพศ\nลักยิ้ม\nลักลอบ\nลักลั่น\nลักหลับ\nลัดเลาะ\nลับตา\nลับแล\nลับหลัง\nลาออก\nล่าช้า\nล่าทัพ\nล้าสมัย\nล้าหลัง\nลากข้าง\nล้างบาง\nล้างผลาญ\nลาดเขา\nลาดตระเวน\nลาดเท\nลาดยาง\nลานบิน\nลาภปาก\nลาภลอย\nลามปาม\nลามเลีย\nลายคราม\nลายเซ็น\nลายแทง\nลายน้ำ\nลายพร้อย\nลายมือ\nลายลักษณ์\nลายเส้น\nลำกล้อง\nลำแข้ง\nลำธาร\nลำแสง\nลำไส้\nลำตัด\nลำนำ\nล่ำสัน\nล้ำยุค\nล้ำสมัย\nล้ำลึก\nล้ำเลิศ\nล้ำเส้น\nล้ำหน้า\nลิงจุ่น\nลิงลม\nลิงโลด\nลิดรอน\nลิ้นไก่\nลิ้นชัก\nลิ้นปี่\nลิ้นควาย\nลิ้นงูเห่า\nลิ้นหมา\nลิบลับ\nลิบลิ่ว\nลิ่มเลือด\nลี้ภัย\nลี้ลับ\nลึกซึ้ง\nลึกลับ\nลึกล้ำ\nลืมตน\nลืมต้น\nลืมตัว\nลืมตา\nลืมเลือน\nลือชา\nลือชื่อ\nลือลั่น\nลุล่วง\nลุกลน\nลุกลาม\nลุกลี้ลุกลน\nลุกฮือ\nลุ่มน้ำ\nลุ่มลึก\nลุ่มหลง\nลุ่ทาง\nลูกกรง\nลูกกรอก\nลูกกรุง\nลูกกลอน\nลูกกลิ้ง\nลูกกวาด\nลูกกะจ๊อก\nลูกกุญแจ\nลูกเกด\nลูกแก้ว\nลูกขนไก่\nลูกข่าง\nลูกขุน\nลูกเขย\nลูกครึ่ง\nลูกคลื่น\nลูกความ\nลูกคอ\nลูกค้า\nลูกคิด\nลูกคู่\nลูกจ้าง\nลูกช้าง\nลูกชิด\nลูกชิ้น\nลูกชุบ\nลูกซอง\nลูกโซ่\nลูกดอก\nลูกดิ่ง\nลูกตะกั่ว\nลูกตุ้ม\nลูกเต้า\nลูกเต๋า\nลูกถ้วย\nลูกทุ่ง\nลูกเธอ\nลูกน้อง\nลูกน้ำ\nลูกนิมิต\nลูกบอล\nลูกบ้าน\nลูกบาศก์\nลูกบิด\nลูกเบี้ยว\nลูกประคบ\nลูกประคำ\nลูกปัด\nลูกปืน\nลูกโป่ง\nลูกผสม\nลูกผู้ชาย\nลูกผู้หญิง\nลูกพรรค\nลูกพี่\nลูกฟูก\nลูกไฟ\nลูกมือ\nลูกโม่\nลูกไม้\nลูกยาเธอ\nลูกรอก\nลูกรัง\nลูกเรือ\nลูกล้อ\nลูกลอย\nลูกเล่น\nลูกเลี้ยง\nลูกโลก\nลูกวัด\nลูกศร\nลูกศิษย์\nลูกสมุน\nลูกสะใภ้\nลูกสูบ\nลูกเสือ\nลูกหนัง\nลูกหนี้\nลูกหนู\nลูกหมาก\nลูกหลง\nลูกหลาน\nลูกหาบ\nลูกหิน\nลูกเห็บ\nลูกเหม็น\nลูกแหง่\nลูกอม\nลูกหม้อ\nลูบคม\nลูบคลำ\nลูบไล้\nเล็กน้อย\nเลขคณิต\nเลขผา\nเลขหมาย\nเล็ดลอด\nเล่นงาน\nเล่นแง่\nเล่นชู้\nเล่นตัว\nเล่นลิ้น\nเล่นหัว\nเลนส์นูน\nเลนส์เว้า\nเล็บครุฑ\nเลยเถิด\nเลศนัย\nเล่ห์กล\nเล่ห์เหลี่ยม\nเลอโฉม\nเลอมาน\nเลอเลิศ\nเลอสรวง\nเล่อล่า\nเลอะเลือน\nเล่าเรียน\nเล่าลือ\nเลาะลัด\nเลิกรา\nเลิกร้าง\nเลิกล้ม\nเลิศเลอ\nเลี้ยงชีพ\nเลี้ยงดู\nเลี้ยงต้อย\nเลียบเคียง\nเลี้ยวลด\nเลือกตั้ง\nเลือกเฟ้น\nเลือกสรร\nเลื่องลือ\nเลือดกำเดา\nเลือดเนื้อ\nเลือดฝาด\nเลือดเย็น\nเลือดร้อน\nเลือดหมู\nเลือดอุ่น\nเลือนราง\nเลื่อนเปื้อน\nเลื่อนลอย\nเลื่อมพราย\nเลื่อมใส\nเลื่อยฉลุ\nเลื่อยลันดา\nเลื่อยวงเดือน\nเลื้อยคลาน\nแลเหลียว\nแลกเปลี่ยน\nแล้วกัน\nและเล็ม\nโล่งใจ\nโล่งโถง\nโล่งอก\nโลดเต้น\nโลดโผน\nโลดลิ่ว\nโลดแล่น\nไล่ที่\nไล่เบี้ย\nไล่เลี่ย\nไล่เลียง\nไล่หลัง\nไล่ออก\nวกวน\nวงกบ\nวงกลม\nวงการ\nวงแขน\nวงเงิน\nวงจร\nวงนอก\nวงใน\nวงรี\nวงเล็บ\nวงเวียน\nวงแหวน\nวงศ์วาน\nวจีกรรม\nวจีเภท\nวจีภาค\nวนเวียน\nวอดวาย\nว็อบแว็บ\nวังวน\nวังหน้า\nวังหลวง\nวังหลัง\nวัดราษฎร์\nวัดวา\nวัดหลวง\nวัดผล\nวัดพื้น\nวัตถุนิยม\nวัตถุประสงค์\nวัตรปฏิบัติ\nวันโกน\nวันพระ\nวันเพ็ญ\nวัยรุ่น\nวัยวุฒิ\nว่ากล่าว\nว่าจ้าง\nว่าด้วย\nว่าที่\nวางก้าม\nวางใจ\nวางตัว\nวางตา\nวางโต\nวางท่า\nวางมวย\nวางมาด\nวางมือ\nวางวาย\nว่างเปล่า\nว่างเว้น\nวาดเขียน\nว่านเครือ\nวาบหวาม\nวายชนม์\nวายปราณ\nวายวาง\nวายวอด\nวายร้าย\nวายุภักษ์\nวาววับ\nวาววาม\nวาวแวว\nวาวแสง\nวิกฤตการณ์\nวิกฤติการณ์\nวิกฤตกาล\nวิกฤติกาล\nวิกลจริต\nวิงเวียน\nวิ่งเต้น\nวิ่งผลัด\nวิ่งรอก\nวิ่งราว\nวิจิตรศิลป์\nวิชาการ\nวิชาชีพ\nวิชาธร\nวิญญูชน\nวิดพื้น\nวิตกจริต\nวิถีทาง\nวิทยากร\nวิทยากล\nวิทยาการ\nวิทยาเขต\nวิทยาทาน\nวิทยาธร\nวิทยานิพนธ์\nวิทยาศาสตร์\nวิเทศสัมพันธ์\nวิธีการ\nวินัยธร\nวินัยปิฎก\nวินาศกรรม\nวินาศภัย\nวินาศสันตะโร\nวิภัชพยากรณ์\nวิภัชวาที\nวิไลวรรณ\nวิสัญญีแพทย์\nวิสัญญีภาพ\nวิสัญญีวิทยา\nวุฒิบัตร\nวุฒิสภา\nวุฒิสมาชิก\nวุ่นวาย\nวุ้นเส้น\nวูบวาบ\nเวจกุฎี\nเวจมรรค\nเวชกรรม\nเวชภัณฑ์\nเวชศาสตร์\nเวทมนตร์\nเวนคืน\nเวรกรรม\nเวฬุการ\nเวฬุวัน\nเว้าวอน\nเวิ้งว้าง\nเวียงวัง\nเวียนเทียน\nแว้งกัด\nแวดล้อม\nแวดวง\nแว่นขยาย\nแว่นแคว้น\nแว่นตา\nแวบวับ\nแววตา\nแวววาม\nแวววาว\nแวะเวียน\nโวยวาย\nไวไฟ\nไว้ใจ\nไว้ชื่อ\nไว้ตัว\nไว้ทุกข์\nไว้ลาย\nไว้หน้า\nไว้อาลัย\nศนิวาร\nศอกกลับ\nศอกกำ\nศอกกำมา\nศักดิ์ศรี\nศักดิ์สิทธิ์\nศารทวิษุวัติ\nศาลแขวง\nศาลจังหวัด\nศาลชั้นต้น\nศาลฎีกา\nศาลเตี้ย\nศาลทหาร\nศาลปกครอง\nศาลพระภูมิ\nศาลเพียงตา\nศาลแพ่ง\nศาลรัฐธรรมนูญ\nศาลแรงงาน\nศาลล้มละลาย\nศาลโลก\nศาลสูง\nศาลสูงสุด\nศาลอาญา\nศาลอุทธรณ์\nศาลากลาง\nศาลาดิน\nศาลาราย\nศาลาวัด\nศิลาฤกษ์\nศิลาแลง\nศิษย์เก่า\nศิษย์เอก\nศีลจุ่ม\nศีลธรรม\nศีลวัต\nศีลอด\nศูนย์กลาง\nศูนย์การค้า\nศูนย์ถ่วง\nศูนย์สูตร\nศูนย์หน้า\nเศร้าใจ\nเศร้าโศก\nเศร้าสร้อย\nเศร้าสลด\nเศร้าหมอง\nเศวตฉัตร\nเศษเกิน\nเศษซ้อน\nเศษวรรค\nเศษส่วน\nเศษเหล็ก\nโศกนาฏกรรม\nโศกศัลย์\nโศกเศร้า\nโศกสลด\nสกลโลก\nส่งเดช\nส่งท้าย\nส่งเสริม\nส่งเสีย\nส่งเสียง\nสงบเงียบ\nสงบเสงี่ยม\nสง่างาม\nสง่าราศี\nสดชื่น\nสดใส\nสตรีเพศ\nสติปัญญา\nสถลมารค\nสถานกงสุล\nสถานที่\nสถานทูต\nสถานการณ์\nสถานภาพ\nสถิติศาสตร์\nสนตะพาย\nสนใจ\nส้นตีน\nสนธิสัญญา\nสนนราคา\nสนับแข้ง\nสนับเพลา\nสนับมือ\nสนามบิน\nสนามเพลาะ\nสนิทสนม\nสนิมขุม\nสนิมสร้อย\nสนุกสนาน\nสบประมาท\nสบายใจ\nสภาพธรรม\nสมควร\nสมจริง\nสมใจ\nสมนัย\nสมน้ำหน้า\nสมประกอบ\nสมส่วน\nสมหวัง\nสมคบ\nสมทบ\nสมยอม\nสมรัก\nสมรู้\nสมสู่\nส้มฉุน\nส้มตำ\nส้มลิ้ม\nส้มกุ้ง\nส้มเช้า\nสมญานาม\nสมมติฐาน\nสมมุติฐาน\nสมมติเทพ\nสมรภูมิ\nสมัครใจ\nสมัยนิยม\nสมุทรศาสตร์\nสมุทรเสนา\nสยดสยอง\nสยองขวัญ\nสยามรัฐ\nสรรหา\nสรวมชีพ\nสรวลเส\nสร้อยเศร้า\nสร้างสรรค์\nสร้างเสริม\nสลดใจ\nสลบไสล\nสละสลวย\nสลาเหิน\nสลากภัต\nสวนครัว\nสวนป่า\nสวนสนุก\nสวนหย่อม\nส่วนกลาง\nส่วนเกิน\nส่วนตัว\nส่วนบุญ\nส่วนแบ่ง\nส่วนประกอบ\nส่วนพระองค์\nส่วนผสม\nส่วนรวม\nส่วนร่วม\nส่วนลด\nส่วนสัด\nสวมกอด\nสวมเขา\nสวมรอย\nสวยมภู\nสว่างไสว\nสวามิภักดิ์\nสวิงสวาย\nสสารนิยม\nส่อเสียด\nสอดคล้อง\nสอดแทรก\nสอดแนม\nสอบถาม\nสอบทาน\nสอบไล่\nสอบสวน\nส้อมเสียง\nสะสวย\nสะแกวัลย์\nสะแกแสง\nสะใจ\nสะเด็ดยาด\nสะเทือนใจ\nสะบัดช่อ\nสั่งสม\nสั่งสอน\nสั่งเสีย\nสังเกตการณ์\nสังคมนิยม\nสังคมวิทยา\nสังคมศาสตร์\nสังคมศึกษา\nสังคมสงเคราะห์\nสัญญาบัตร\nสัดส่วน\nสัตการ\nสัตบุรุษ\nสัตบริภัณฑ์\nสัตภัณฑ์\nสัตมหาสถาน\nสัตโลหะ\nสันเขา\nสันดอน\nสันหลัง\nสั่นเทา\nสั่นเทิ้ม\nสันติบาล\nสันติภาพ\nสันติวิธี\nสันติสุข\nสับเปลี่ยน\nสับสน\nสับหลีก\nสับหว่าง\nสัมมาคารวะ\nสัมมาชีพ\nส่าเหล้า\nสากกะเบือ\nสาทิสลักษณ์\nสาธุการ\nสาธุชน\nสาบเสือ\nสาปสรร\nสาปแช่ง\nสาปส่ง\nสามง่าม\nสามล้อ\nสามเหลี่ยม\nสามเวท\nสามัญชน\nสามัญสำนึก\nสายดิ่ง\nสายดิน\nสายตรวจ\nสายน้ำ\nสายบัว\nสายพาน\nสายฟ้า\nสายยาง\nสายยู\nสายใย\nสายรก\nสายรุ้ง\nสายล่อฟ้า\nสายลับ\nสายเลือด\nสายโลหิต\nสายวัด\nสายส่ง\nสายสวาท\nสายสะดือ\nสายสะพาย\nสายสัมพันธ์\nสายสิญจน์\nสายสืบ\nสายไหม\nสายอากาศ\nสายตา\nสายหยุด\nสารตรา\nสารประกอบ\nสารละลาย\nสารส้ม\nสารหนู\nสารทฤดู\nสาวใช้\nสาวน้อย\nสาวใหญ่\nสำนักงาน\nสำนักพิมพ์\nสำนักสงฆ์\nสำมะโนครัว\nสำเร็จรูป\nสิกขาบท\nสิงสถิต\nสิงสู่\nสิ่งก่อสร้าง\nสิ่งของ\nสิ่งปฏิกูล\nสิ่งพิมพ์\nสิ่งแวดล้อม\nสิ่งศักดิ์สิทธิ์\nสิทธิกร\nสิทธิ์ขาด\nสิทธิชัย\nสิทธิโชค\nสิทธิบัตร\nสินค้า\nสินจ้าง\nสินเชื่อ\nสินไถ่\nสินทรัพย์\nสินน้ำใจ\nสินบน\nสินแร่\nสินสมรส\nสินสอด\nสินไหม\nสิ้นเชิง\nสิ้นสุด\nสีผึ้ง\nสีลม\nสีชอล์ก\nสีถ่าน\nสีเทียน\nสีน้ำ\nสีน้ำมัน\nสีโปสเตอร์\nสีฝุ่น\nสี่เหลี่ยม\nสีหน้า\nสึกหรอ\nสืบทอด\nสืบค้น\nสืบสวน\nสืบสาว\nสืบเสาะ\nสื่อผสม\nสื่อมวลชน\nสื่อสาร\nสุกงอม\nสุกดิบ\nสุกปลั่ง\nสุกใส\nสุขนาฏกรรม\nสุขภัณฑ์\nสุขภาพ\nสุขลักษณะ\nสุขวิทยา\nสุขศาลา\nสุขศึกษา\nสุดท้าย\nสุตกวี\nสุนทรพจน์\nสุภาพชน\nสู่ขอ\nสู่รู้\nสู่สม\nสูงส่ง\nสูญเปล่า\nสูญสิ้น\nสูญเสีย\nสูญหาย\nเสสรวล\nเสแสร้ง\nเสกสรร\nเสถียรภาพ\nเส้นชัย\nเส้นตรง\nเส้นตาย\nเส้นทาง\nเส้นใย\nเส้นรุ้ง\nเส้นเลือด\nเส้นแวง\nเส้นสาย\nเส้นเสียง\nเส้นหมี่\nเส้นเอ็น\nเสบียงกรัง\nเสมอภาค\nเสมอหน้า\nเสมอเหมือน\nเสมียนตรา\nเสร็จสรรพ\nเสร็จสิ้น\nเสริมส่ง\nเสริมสร้าง\nเสริมสวย\nเสรีไทย\nเสรีธรรม\nเสรีนิยม\nเสรีภาพ\nเสาเข็ม\nเสาธง\nเสียใจ\nเสียเชิง\nเสียดาย\nเสียที\nเสียเที่ยว\nเสียเปรียบ\nเสียเปล่า\nเสียรู้\nเสียแรง\nเสียสละ\nเสียหลัก\nเสียหาย\nเสี่ยงทาย\nเสียดแทง\nเสียดแทรก\nเสียดสี\nเสี้ยนศึก\nเสี้ยนหนาม\nเสี้ยมสอน\nเสียวซ่าน\nเสียวไส้\nเสือดาว\nเสือดำ\nเสือปลา\nเสือป่า\nเสือไฟ\nเสื่อกก\nเสื่อกระจูด\nเสื่อน้ำมัน\nเสื่อลำแพน\nเสื้อกล้าม\nเสื้อกั๊ก\nเสื้อเกราะ\nเสื้อครุย\nเสื้อแสง\nเสื้อเมือง\nเสือกคลาน\nเสือกสน\nเสือกไส\nเสื่อมคลาย\nเสื่อมถอย\nเสื่อมทราม\nเสื่อมโทรม\nเสื่อมสลาย\nเสื่อมสูญ\nเสื่อมเสีย\nเสือหมอบ\nแสกหน้า\nแสดงออก\nแสเถา\nแสนกล\nแสนรู้\nแสร้งว่า\nใส่ความ\nใส่ไคล้\nใส่ใจ\nใส่ไฟ\nไส้กรอก\nไส้ไก่\nไส้ติ่ง\nไส้ศึก\nไส้อั่ว\nไส้เดือน\nไส้ตัน\nไสยเวท\nไสยศาสตร์\nหกล้ม\nหงส์หยก\nหงอนไก่\nหงอยก๋อย\nหงอยเหงา\nหงายท้อง\nหงายหลัง\nหงำเหงอะ\nหงำเหงือก\nหดหาย\nหดหู่\nหนทาง\nหนวกหู\nหน่วงเหนี่ยว\nหน่วยก้าน\nหน่อไม้\nหนองใน\nหนองแซง\nหนักข้อ\nหนักใจ\nหนักแน่น\nหนักหน่วง\nหนักหนา\nหนังกลับ\nหนังตะลุง\nหนังเรียด\nหนังสด\nหนังใหญ่\nหนังสือพิมพ์\nหนาแน่น\nหน้ากระดาน\nหน้ากาก\nหน้ากาฬ\nหน้าแข้ง\nหน้าจั่ว\nหน้าฉาน\nหน้าตัก\nหน้าตา\nหน้าต่าง\nหน้าท้อง\nหน้าทับ\nหน้าที่\nหน้าที่นั่ง\nหน้าบัน\nหน้าปัด\nหน้าผา\nหน้าผาก\nหน้าม้า\nหน้ามุข\nหน้าไม้\nหน้าเลือด\nหน้าอก\nหนามเตย\nหน่ายหนี\nหน่ายแหนง\nหนาวเหน็บ\nหนำใจ\nหนี้สิน\nหนี้ส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น\nกณิกนันต์\nกณิการ์\nกด\nกตเวทิตา\nกตเวที\nกตัญชลี\nกตัญญุตา\nกตัญญู\nกตาธิการ\nกตาภินิหาร\nกติกา\nกถา\nกถิกาจารย์\nกทลี\nกน\nก่น\nก้น\nกนก\nกนิษฐ์\nกนิษฐา\nกบ\nกบฏ\nกบดาน\nกบทู\nกบาล\nกบินทร์\nกบิล\nกบี่\nกบูร\nกเบนทร์\nกม\nก้ม\nกมณฑลาภิเษก\nกมณฑโลทก\nกมล\nกมลา\nกมลาศ\nกมลาสน์\nกมเลศ\nกมัณฑลุ\nกมุท\nกร\nกรกฎ\nกรกฎาคม\nกรกฏ\nกรง\nกรชกาย\nกรณฑ์\nกรณิการ์\nกรณี\nกรณีย์\nกรณียกิจ\nกรณียะ\nกรด\nกรน\nกรบ\nกรบูร\nกรพินธุ์\nกรม\nกรรกฎ\nกรรกศ\nกรรเกด\nกรรไกร\nกรรเจียก\nกรรชิง\nกรรเชียง\nกรรโชก\nกรรฐ์\nกรรฐา\nกรรณ\nกรรณา\nกรรณิกา\nกรรณิการ์\nกรรดิ\nกรรดิก\nกรรดึก\nกรรตุ\nกรรไตร\nกรรทบ\nกรรแทก\nกรรบิด\nกรรบูร\nกรรภิรมย์\nกรรม\nกรรม์\nกรรม์ภิรมย์\nกรรมชวาต\nกรรมัชวาต\nกรรมาชีพ\nกรรมาธิการ\nกรรมาร\nกรรษก\nกรรสะ\nกรรแสง\nกรวด\nกรวบ\nกรวม\nกร้วม\nกรวย\nกรวิก\nกรสาปน์\nกรสุทธิ์\nกรอ\nกร้อ\nกรอก\nกร็อกกร๋อย\nกรอกแกรก\nกรอง\nกรองกรอย\nกรอด\nกร่อน\nกรอบ\nกรอม\nกร่อย\nกระ\nกระกร\nกระกรุ่น\nกระกลับกลอก\nกระกี้\nกระเกรอก\nกระเกริก\nกระเกริ่น\nกระคน\nกระคาย\nกระงกกระเงิ่น\nกระง่องกระแง่ง\nกระง่อนกระแง่น\nกระเง้ากระงอด\nกระโงก\nกระจก\nกระจง\nกระจร\nกระจอก\nกระจองหง่อง\nกระจ๋องหง่อง\nกระจองอแง\nกระจ้อน\nกระจอนหู\nกระจ้อย\nกระจ๋อหวอ\nกระจะ\nกระจัก\nกระจัง\nกระจัด\nกระจับ\nกระจ่า\nกระจ่าง\nกระจาด\nกระจาน\nกระจาบ\nกระจาม\nกระจาย\nกระจาว\nกระจิก\nกระจิ๋ง\nกระจิด\nกระจิบ\nกระจิ๋ม\nกระจิริด\nกระจิ๋ว\nกระจี้\nกระจี๋\nกระจุก\nกระจุ๋งกระจิ๋ง\nกระจุบ\nกระจุ๊บ\nกระจุ๋มกระจิ๋ม\nกระจุย\nกระจู้\nกระจู๋กระจี๋\nกระจูด\nกระเจอะกระเจิง\nกระเจา\nกระเจ่า\nกระเจ้า\nกระเจาะ\nกระเจิง\nกระเจิดกระเจิง\nกระเจี้ยง\nกระเจี๊ยบ\nกระเจียว\nกระเจี๊ยว\nกระแจะ\nกระโจน\nกระโจม\nกระฉอก\nกระฉ่อน\nกระฉับกระเฉง\nกระฉิ่ง\nกระฉีก\nกระฉูด\nกระเฉก\nกระเฉด\nกระแฉก\nกระโฉกกระเฉก\nกระโฉม\nกระชดกระช้อย\nกระชอน\nกระชอมดอก\nกระช้อย\nกระชัง\nกระชั้น\nกระชับ\nกระชาก\nกระชาย\nกระชิง\nกระชิด\nกระชุ\nกระชุก\nกระชุ่มกระชวย\nกระเชอ\nกระเชา\nกระเช้า\nกระเชียง\nกระแชง\nกระแชะ\nกระโชก\nกระซ่องกระแซ่ง\nกระซับ\nกระซาบ\nกระซิก\nกระซิบ\nกระซี้\nกระซุง\nกระซุบกระซิบ\nกระซุ้ม\nกระซู่\nกระเซ็น\nกระเซอ\nกระเซอะกระเซอ\nกระเซอะกระเซิง\nกระเซ้า\nกระเซิง\nกระแซ\nกระแซะ\nกระโซกระเซ\nกระฎี\nกระฎุมพี\nกระดก\nกระด้ง\nกระดนโด่\nกระดวง\nกระดวน\nกระด้วมกระเดี้ยม\nกระดอ\nกระดอง\nกระดองหาย\nกระดอน\nกระดอม\nกระดักกระเดี้ย\nกระดังงัว\nกระดังงา\nกระดาก\nกระด้าง\nกระดางลาง\nกระดาด\nกระดาดขาว\nกระดาน\nกระดานพน\nกระดาษ\nกระดำกระด่าง\nกระดิก\nกระดิ่ง\nกระดิ้ง\nกระดิบ\nกระดี่\nกระดี้กระเดียม\nกระดึง\nกระดืบ\nกระดุ\nกระดุกกระดิก\nกระดุ้งกระดิ้ง\nกระดุบ\nกระดุบกระดิบ\nกระดุม\nกระดูก\nกระเดก\nกระเด้ง\nกระเด็น\nกระเด้า\nกระเดาะ\nกระเดิด\nกระเดี้ย\nกระเดียด\nกระเดือก\nกระเดื่อง\nกระแด็ก\nกระแด้ง\nกระแด้แร่\nกระแด่ว\nกระแดะ\nกระโดก\nกระโดง\nกระโดด\nกระโดน\nกระได\nกระตรับ\nกระตราก\nกระตรุด\nกระตรุม\nกระต้วมกระเตี้ยม\nกระต้อ\nกระต่องกระแต่ง\nกระต๊อบ\nกระต้อยตีวิด\nกระตัก\nกระตั้ว\nกระต่าย\nกระติก\nกระติ๊ด\nกระติบ\nกระตือรือร้น\nกระตุก\nกระตุ้งกระติ้ง\nกระตุ่น\nกระตุ้น\nกระตูบ\nกระเตง\nกระเต็น\nกระเตอะ\nกระเตาะ\nกระเตาะกระแตะ\nกระเตื้อง\nกระแต\nกระแตแต้แว้ด\nกระโตกกระตาก\nกระโตน\nกระถด\nกระถอบ\nกระถั่ว\nกระถาง\nกระถิก\nกระถิน\nกระเถิบ\nกระโถน\nกระทก\nกระทง\nกระทบ\nกระทรวง\nกระทอก\nกระท้อน\nกระท่อนกระแท่น\nกระท่อม\nกระท้อมกระแท้ม\nกระทะ\nกระทั่ง\nกระทั้น\nกระทา\nกระทาย\nกระทาสี\nกระทาหอง\nกระทำ\nกระทิกกระทวย\nกระทิง\nกระทึง\nกระทืบ\nกระทุ\nกระทุง\nกระทุ้ง\nกระทุ่ม\nกระทู้\nกระเท่\nกระเทียบ\nกระเทียม\nกระเทือน\nกระเทื้อม\nกระแทก\nกระแท่น\nกระแทะ\nกระไทชาย\nกระน่อง\nกระนั้น\nกระนี้\nกระแนะกระแหน\nกระโน้น\nกระไน\nกระบก\nกระบม\nกระบวน\nกระบวย\nกระบวร\nกระบอก\nกระบอง\nกระบะ\nกระบัด\nกระบั้วกระเบี้ย\nกระบ่า\nกระบ้า\nกระบาก\nกระบาย\nกระบาล\nกระบิ\nกระบิ้ง\nกระบิด\nกระบิล\nกระบี่\nกระบือ\nกระบุง\nกระบุ่มกระบ่าม\nกระบู้กระบี้\nกระบูน\nกระบูร\nกระเบง\nกระเบญ\nกระเบ็ดกระบวน\nกระเบน\nกระเบา\nกระเบิก\nกระเบียด\nกระเบียน\nกระเบื้อง\nกระแบก\nกระแบะ\nกระโบม\nกระปมกระปำ\nกระปมกระเปา\nกระปรอก\nกระปรอกว่าว\nกระปรี้กระเปร่า\nกระปอก\nกระป้อกระแป้\nกระป่อง\nกระป๋อง\nกระปอดกระแปด\nกระป๋อหลอ\nกระปั้วกระเปี้ย\nกระป่ำ\nกระปุก\nกระปุ๊กลุก\nกระปุ่ม\nกระปุ่มกระป่ำ\nกระปุ่มกระปิ่ม\nกระเป๋า\nกระเปาะ\nกระโปก\nกระโปรง\nกระผม\nกระผลีกระผลาม\nกระผาน\nกระผีก\nกระพรวน\nกระพริ้ม\nกระพอก\nกระพอง\nกระพ้อม\nกระพัก\nกระพัง\nกระพังเหิร\nกระพังโหม\nกระพัด\nกระพัตร\nกระพัน\nกระพั่น\nกระพา\nกระพาก\nกระพี้\nกระพือ\nกระพุ้ง\nกระพุ่ม\nกระเพาะ\nกระเพิง\nกระเพื่อม\nกระแพ้ง\nกระฟัดกระเฟียด\nกระฟูมกระฟาย\nกระมล\nกระมอบ\nกระมอมกระแมม\nกระมัง\nกระมัน\nกระมิดกระเมี้ยน\nกระมุท\nกระเมาะ\nกระย่อง\nกระย่องกระแย่ง\nกระย่อน\nกระย่อม\nกระยา\nกระยาง\nกระยาจก\nกระยาหงัน\nกระยิก\nกระยิ้มกระย่อง\nกระยึกกระยือ\nกระยืดกระยาด\nกระเย้อกระแหย่ง\nกระรอก\nกระเรียน\nกระโรกน้ำข้าว\nกระโรกใหญ่\nกระไร\nกระลด\nกระลบ\nกระลอก\nกระลอม\nกระละหล่ำ\nกระลัด\nกระลับ\nกระลัมพร\nกระลา\nกระลำ\nกระลำพัก\nกระลำพุก\nกระลิง\nกระลี\nกระลุมพาง\nกระลุมพุก\nกระลุมพู\nกระลูน\nกระลู่น์\nกระเล็น\nกระเลียด\nกระเลือก\nกระโลง\nกระวน\nกระวัด\nกระวาด\nกระวาน\nกระวาย\nกระวิน\nกระวี\nกระวีกระวาด\nกระวูดกระวาด\nกระเวน\nกระเวยกระวาย\nกระแวน\nกระโวยกระวาย\nกระษัย\nกระษาปณ์\nกระสง\nกระสบ\nกระสม\nกระสรวล\nกระสร้อย\nกระสวน\nกระสวย\nกระสอบ\nกระสะ\nกระสัง\nกระสัน\nกระสับกระส่าย\nกระสา\nกระสานติ์\nกระสาบ\nกระสาย\nกระสือ\nกระสุงกระสิง\nกระส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ทิง\nกากะเยีย\nกากี\nกาง\nก้าง\nกางเกง\nกางเขน\nก๊าซ\nกาซะลอง\nกาญจนา\nกาฐ\nกาด\nก๊าด\nกาน\nก่าน\nก้าน\nก๊าน\nกานดา\nกานต์\nกานน\nก้านพร้าว\nกานพลู\nกาน้า\nกาบ\nก้าบ\nกาบู\nกาพย์\nกาเฟอีน\nกาแฟ\nกาม\nก้าม\nกามารมณ์\nกามินี\nกาเมสุมิจฉาจาร\nกาย\nก่าย\nกาเยน\nการ\nการณ์\nการ์ด\nการ์ตูน\nการบูร\nการย์\nการวิก\nการเวก\nการะเกด\nการะบุหนิง\nการัณย์\nการันต์\nการางหัวขวาน\nการิตการก\nการิตวาจก\nการุญ\nการุณย์\nกาเรการ่อน\nกาล\nกาลกรรณี\nกาลกิณี\nกาลจักร\nกาลัญญุตา\nกาลัญญู\nกาลัด\nกาลานุกาล\nกาลิก\nกาลี\nกาแล\nกาแล็กซี\nกาแล็กโทส\nกาว\nก้าว\nกาววาว\nกาวาง\nกาแวน\nกาศิก\nกาษฐะ\nกาษา\nกาสร\nกาสะ\nกาสา\nกาสาร\nกาสาวะ\nกาสิโน\nกาหล\nกาหลง\nกาหลา\nกาเหว่า\nกาไหล่\nกาฬ\nกาฬาวก\nกาฮัง\nกำ\nก่ำ\nกำกวม\nกำกัด\nกำกับ\nก้ำกึ่ง\nกำกูน\nก้ำเกิน\nกำเกียง\nกำคูน\nกำจร\nกำจัด\nกำจาย\nกำชับ\nกำชำ\nกำซาบ\nกำซำ\nกำด้น\nกำดัด\nกำดาล\nกำเดา\nกำธร\nกำนล\nกำนัน\nกำนัล\nกำเนิด\nกำบัง\nก่ำบึ้ง\nกำเบ้อ\nกำปอ\nกำปั่น\nกำผลา\nกำพง\nกำพด\nกำพต\nกำพร้า\nกำพราก\nกำพวด\nกำพอง\nกำพืด\nกำพุด\nกำพู\nกำเพลิง\nกำแพง\nกำภู\nกำมลาศน์\nกำมเลศ\nกำมะถัน\nกำมะลอ\nกำมะหยี่\nกำมะหริด\nกำมังละการ\nกำมังวิลิต\nกำมัชพล\nกำยาน\nกำยำ\nกำรอ\nกำราบ\nกำราล\nกำเริบ\nกำไร\nกำลัง\nกำลุง\nกำเลา\nกำไล\nกำสรด\nกำสรวล\nกำหนด\nกำหนัด\nกำเหน็จ\nกำแหง\nกิก\nกิ๊ก\nกิ่ง\nกิ้งก่า\nกิ้งกือ\nกิ้งโครง\nกิจ\nกิจจะ\nกิจจา\nกิดาการ\nกิดาหยัน\nกิตติ\nกิตติมศักดิ์\nกิน\nกินนร\nกินปลี\nกินเปี้ยว\nกินริน\nกินรี\nกิ๊บ\nกิมตึ๋ง\nกิมิชาติ\nกิมิวิทยา\nกิโมโน\nกิโยตีน\nกิระ\nกิริณี\nกิรินท\nกิริเนศวร\nกิริยา\nกิเลน\nกิเลส\nกิโล\nกิโลมกะ\nกิ่ว\nกิ๋ว\nกี\nกี่\nกี้\nกี๊\nกี๋\nกีฏวิทยา\nกีด\nกีตาร์\nกีบ\nกีรติ\nกีฬา\nกึก\nกึง\nกึ่ง\nกึ๋น\nกุ\nกุก\nกุ๊ก\nกุกกุฏ\nกุกกุร\nกุกรรม\nกุ้ง\nกุงอน\nกุงาน\nกุโงก\nกุจี\nกุญแจ\nกุญชร\nกุฎ\nกุฎา\nกุฎี\nกุฎุมพี\nกุฏฐัง\nกุฏิ\nกุณฑ์\nกุณฑล\nกุณฑี\nกุณโฑ\nกุณาล\nกุณี\nกุด\nกุดัง\nกุดั่น\nกุดา\nกุทัณฑ์\nกุน\nกุ๊น\nกุนเชียง\nกุนที\nกุโนกามอ\nกุบ\nกุบกับ\nกุม\nกุ่ม\nกุมฝอย\nกุมภ์\nกุมภนิยา\nกุมภัณฑ์\nกุมภา\nกุมภิล\nกุมภีล์\nกุมเหง\nกุมาร\nกุมารา\nกุมารี\nกุมุท\nกุย\nกุ๊ย\nกุ๋ย\nกุยช่าย\nกุยเฮง\nกุรระ\nกุรุพินท์\nกุเรา\nกุล\nกุลา\nกุลาหล\nกุลี\nกุลีกุจอ\nกุเลา\nกุแล\nกุเวร\nกุศราช\nกุศล\nกุศโลบาย\nกุสุม\nกุสุมภ์\nกุสุมา\nกุสุมาลย์\nกุสุมิตลดาเวลลิตา\nกุหนี\nกุหนุง\nกุหร่า\nกุหล่า\nกุหลาบ\nกุแหละ\nกู\nกู่\nกู้\nกู๊ก\nกูฏ\nกูฏา\nกูณฑ์\nกูด\nกูบ\nกูปรี\nกูรมะ\nกูรมาวตาร\nเก\nเก้\nเก๊\nเก๋\nเกก\nเก๊ก\nเกกมะเหรก\nเก๊กฮวย\nเก้กัง\nเก็ง\nเก่ง\nเก้ง\nเก๋ง\nเกงกอย\nเก่งกาจ\nเกงเขง\nเก๋งเคง\nเก็จ\nเกจิอาจารย์\nเกชา\nเกณฑ์\nเกด\nเก็ด\nเกตุ\nเกน\nเก็บ\nเกม\nเกย\nเกยูร\nเกรง\nเกร็ง\nเกร็ด\nเกรน\nเกร่อ\nเกรอะ\nเกราะ\nเกริก\nเกริน\nเกริ่น\nเกรียก\nเกรียง\nเกรียด\nเกรียน\nเกรียบ\nเกรียม\nเกรียว\nเกรี้ยว\nเกเร\nเกล็ด\nเกลศ\nเกลอ\nเกลา\nเกล้า\nเกลาะ\nเกลี่ย\nเกลี้ย\nเกลียง\nเกลี้ยง\nเกลียด\nเกลียว\nเกลือ\nเกลื้อ\nเกลือก\nเกลื่อน\nเกลื้อน\nเกวัฏ\nเกวียน\nเกศ\nเกศว\nเกศวะ\nเกศา\nเกศินี\nเกศี\nเกษตร\nเกษม\nเกษียณ\nเกษียน\nเกษียร\nเกส\nเกสร\nเกสรี\nเกสา\nเกสี\nเก้อ\nเกอิชา\nเกะ\nเกะกะ\nเกา\nเก่า\nเก้า\nเก๋า\nเกาต์\nเกาทัณฑ์\nเกาบิล\nเกาลัด\nเกาลิน\nเกาไศย\nเกาหลี\nเกาเหลา\nเกาเหลียง\nเก้าอี้\nเกาะ\nเกิ้ง\nเกิด\nเกิน\nเกิบ\nเกีย\nเกียกกาย\nเกียง\nเกี่ยง\nเกี๋ยง\nเกียจ\nเกียด\nเกียน\nเกี้ยมไฉ่\nเกี้ยมอี๋\nเกียร์\nเกียรติ\nเกียรติ์\nเกี่ยว\nเกี้ยว\nเกี๊ยว\nเกี๊ยะ\nเกื้อ\nเกือก\nเกื้อกูล\nเกือบ\nแก\nแก่\nแก้\nแกง\nแก่ง\nแก้ง\nแก๊ง\nแกงได\nแกงแนง\nแกโดลิเนียม\nแกน\nแก่น\nแก๊ป\nแกม\nแก้ม\nแกมมา\nแกรก\nแกร่ง\nแกร็น\nแกรนิต\nแกรไฟต์\nแกร่ว\nแกระ\nแกล\nแกล้ง\nแกลน\nแกลบ\nแกล้ม\nแกลลอน\nแกลเลียม\nแกล้ว\nแกละ\nแกแล\nแกว\nแก้ว\nแกว่ง\nแก๊ส\nแกะ\nโก\nโก่\nโก้\nโก๋\nโกก\nโกกนุท\nโกกิลา\nโกโก้\nโกง\nโก่ง\nโกงกาง\nโก้งเก้ง\nโกงโก้\nโก้งโค้ง\nโกเชาว์\nโกญจนาท\nโกญจา\nโกฏิ\nโกฐ\nโกฐาส\nโกณะ\nโกดัง\nโกทัณฑ์\nโกน\nโก่น\nโก๋น\nโกมล\nโกมุท\nโกเมน\nโกเมศ\nโกย\nโกรก\nโกรกกราก\nโกรง\nโกร่ง\nโกร่งกร่าง\nโกรงเกรง\nโกร๋งเกร๋ง\nโกรญจ\nโกรต๋น\nโกรธ\nโกรธา\nโกร๋น\nโกรม\nโกรย\nโกรศ\nโกโรโกเต\nโกโรโกโรก\nโกโรโกโส\nโกลน\nโกลาหล\nโกไล\nโกวิท\nโกศ\nโกศล\nโกษม\nโกสน\nโกสัช\nโกสินทร์\nโกสีย์\nโกสุม\nโกไสย\nโกหก\nใกล้\nไก\nไก่\nไก๊\nไก๋\nไก่กอม\nไกพัล\nไกร\nไกรพ\nไกรลาส\nไกรศร\nไกรศรี\nไกรสร\nไกรสรี\nไกรสิทธิ\nไกล\nไกล่\nไกลาส\nไกว\nไกวัล\nขงจื๊อ\nขจร\nขจรจบ\nขจัด\nขจ่าง\nขจาย\nขจาว\nขจิต\nขจี\nขจุย\nขเจา\nขณะ\nขด\nขน\nข้น\nขนง\nขนด\nขนบ\nขนม\nขนอง\nขนอน\nขนอบ\nขนัด\nขนัน\nขนาก\nขนาง\nขนาด\nขนาน\nขนาบ\nขนาย\nขนำ\nขนิษฐ\nขนิษฐา\nขนุน\nขนุนนก\nขบ\nขบถ\nขบวน\nขบวร\nขม\nข่ม\nขมงโกรย\nขมวด\nขมวน\nขมอง\nขม่อม\nขมัง\nขมับ\nขมา\nขม้ำ\nขมิ้น\nขมิบ\nขมีขมัน\nขมึง\nขมึงทึง\nขมุ\nขมุกขมัว\nขมุบ\nขโมย\nขยด\nขยม\nขย่ม\nขยอก\nขยอง\nขย่อน\nขย้อน\nขยะ\nขยัก\nขยัน\nขยั้น\nขยับ\nขยาด\nขยาย\nขยำ\nขย้ำ\nขยิก\nขยิบ\nขยิ่ม\nขยี้\nขยุกขยิก\nขยุกขยุย\nขยุบ\nขยุบขยิบ\nขยุม\nขยุ้ม\nขยุย\nขรม\nขรรค์\nขรัว\nขริบ\nขรี\nขรึม\nขรุขระ\nขลบ\nขล้อ\nขลัง\nขลับ\nขลาด\nขลาย\nขลิบ\nขลุก\nขลุกขลัก\nขลุกขลิก\nขลุบ\nขลุม\nขลุ่ย\nขลู\nขลู่\nขวง\nข่วง\nขวด\nข่วน\nขวนขวาย\nขวบ\nขวย\nขวักไขว่\nขวัญ\nขวั้น\nขวับ\nขวับเขวียว\nขวา\nขวาก\nขวาง\nขว้าง\nขวาด\nขวาน\nขวายขวน\nขวาว\nขว้าว\nขวิด\nขอ\nข่อ\nข้อ\nของ\nข้อง\nขอด\nขอน\nข้อน\nขอบ\nขอม\nข่อย\nข้อย\nข่อยหยอง\nขะแจะ\nขะเน็ด\nขะมอมขะแมม\nขะมักเขม้น\nขะมุกขะมอม\nขะยิก\nขะยุก\nขะเย้อแขย่ง\nขัค\nขัง\nขังขอก\nขัช\nขัณฑสกร\nขัณฑสีมา\nขัด\nขัดมอน\nขัตติยมานะ\nขัน\nขั้น\nขันติ\nขันตี\nขันโตก\nขันที\nขันธ์\nขันธาวาร\nขับ\nขัว\nขั้ว\nขา\nข่า\nข้า\nขาก\nขาก๊วย\nขาง\nข่าง\nข้าง\nขาณุ\nขาด\nขาทนียะ\nขาน\nขาบ\nข้าพเจ้า\nขาม\nข่าม\nข้าม\nขาย\nข่าย\nขาล\nขาว\nข่าว\nข้าว\nข้าวอังกุลี\nขำ\nขิก\nขิง\nขิงแกลง\nขิงแครง\nขิด\nขิปสัทโท\nขิม\nขี่\nขี้\nขี้เข็บ\nขีณาสพ\nขีด\nขี้ตังนี\nขีปนาวุธ\nขี้ยอก\nขีระ\nขึง\nขึ้ง\nขึ้น\nขึ้นฉ่าย\nขืน\nขื่น\nขื่อ\nขุก\nขุด\nขุน\nขุ่น\nขุนเพ็ด\nขุม\nขุย\nขู่\nขูด\nเข\nเข้\nเขก\nเข็ง\nเข่ง\nเขจร\nเข็ญ\nเข็ด\nเขดา\nเขต\nเขน\nเข็น\nเข่น\nเขนง\nเขน็ด\nเขนย\nเขบ็จขบวน\nเขบ็ต\nเขม\nเข็ม\nเข้ม\nเข้มขาบ\nเขม็ง\nเขม็ดแขม่\nเขม่น\nเขม้น\nเขม้นขะมัก\nเขมร\nเขมา\nเขม่า\nเขมือบ\nเขย\nเขยก\nเขย่ง\nเขย้อแขย่ง\nเขย่า\nเขยิน\nเขยิบ\nเขยื้อน\nเขรอะ\nเขลง\nเขลอะ\nเขละ\nเขลา\nเขลาะ\nเขว\nเขษม\nเขฬะ\nเขะขะ\nเขา\nเข่า\nเข้า\nเขิง\nเขิน\nเขิบ\nเขี่ย\nเขียง\nเขียด\nเขียดตะปาด\nเขียน\nเขี่ยน\nเขียม\nเขียว\nเขี้ยว\nเขียะ\nเขือ\nเขือง\nเขื่อง\nเขื่อน\nเขือม\nแข\nแข้\nแขก\nแข็ง\nแข่ง\nแข้ง\nแขน\nแข่น\nแข้น\nแขนง\nแขม\nแขม็บ\nแขม่ว\nแขยง\nแขย่ง\nแขวก\nแขวง\nแขวน\nแขวะ\nโข\nโขก\nโขง\nโข่ง\nโขด\nโขดง\nโขน\nโขนง\nโขม\nโขมง\nโขมด\nโขยก\nโขยง\nโขย่ง\nโขยด\nโขลก\nโขลง\nโขลน\nโขษม\nไข\nไข่\nไข้\nไขว่\nไขว้\nคคนะ\nคคนัมพร\nคคนางค์\nคคนานต์\nคง\nคงคา\nคงไคย\nคช\nคชาชาติ\nคชาชีพ\nคชาธาร\nคชาภรณ์\nคณนา\nคณบดี\nคณะ\nคณาจารย์\nคณาธิการ\nคณาธิปไตย\nคณานับ\nคณิกา\nคณิต\nคเณศ\nคด\nคดี\nคติ\nคทา\nคน\nค้น\nคนทา\nคนทิสอ\nคนที\nคนโท\nคนธ์\nคันธ์\nคนธรรพ์\nคเนจร\nคบ\nคม\nคมน์\nคมนาการ\nคมนาคม\nคมิกภัต\nครก\nครบ\nครรชิต\nครรภ\nครรภ์\nครรลอง\nครรโลง\nครรไล\nครวญ\nครวี\nครหา\nครอก\nครอง\nครองแครง\nคร่อเงาะ\nคร่อเทียน\nครอบ\nคร่อม\nคระเมิม\nคระแลง\nคระไล\nคระแวง\nคระหน\nคระหวน\nคระหาย\nคระโหย\nครั่ง\nครั้ง\nครัดเคร่ง\nครัน\nครั่น\nครั้น\nครับ\nครัว\nครา\nคร่า\nคราก\nคราง\nคราญ\nคราด\nคร้าน\nคราบ\nคราม\nคร้าม\nครามครัน\nคราว\nคร่าว\nคราส\nครำ\nคร่ำ\nคร่ำเคร่ง\nคริปทอน\nคริสต์\nครีบ\nครีม\nครีษมายัน\nครึ\nครึกครื้น\nครึกโครม\nครึ่ง\nครึ่ด\nครึน\nครึ้ม\nครืด\nครืน\nครื้น\nครืนครั่น\nครื้นครั่น\nครื้นครึก\nครื้นเครง\nครือ\nครุ\nครุคระ\nครุฑ\nครุ่น\nครุมเครือ\nครุย\nครุวนา\nครู\nครู่\nครูด\nคฤโฆษ\nคฤนถ์\nคฤหบดี\nคฤหัสถ์\nคฤหาสน์\nคลวง\nคลอ\nคล้อ\nคลอก\nคลอง\nคล่อง\nคล้อง\nคลอด\nคลอน\nคล้อย\nคลอรีน\nคลอโรฟอร์ม\nคลอโรฟีลล์\nคละ\nคละคลุ้ง\nคลัก\nคลั่ก\nคลัง\nคลั่ง\nคลัตช์\nคลับคล้าย\nคลับคลา\nคลา\nคล้า\nคลางแคลง\nคลาด\nคลาน\nคลาย\nคล้าย\nคล้ายคลึง\nคล่าว\nคลำ\nคล่ำ\nคล้ำ\nคลิ้งโคลง\nคลิด\nคลินิก\nคลี\nคลี่\nคลึง\nคลื่น\nคลุก\nคลุ้ง\nคลุบ\nคลุม\nคลุ่ม\nคลุ้ม\nควง\nควณ\nควน\nควบ\nควย\nควร\nควัก\nควั่ก\nควั่งคว้าง\nควัน\nควั่น\nคว้า\nควาก\nคว้าง\nควาญ\nควาน\nคว้าน\nความ\nควาย\nคว่าว\nคว่ำ\nควินิน\nควิวคว่าง\nคหกรรม\nคหกรรมศาสตร์\nคหบดี\nคหัฐ\nคอ\nค้อ\nคอก\nค็อกคัส\nค็อกเทล\nคอเคซอยด์\nค่องอ้อย\nคอด\nคอแดง\nคอน\nค่อน\nค้อน\nคอนกรีต\nคอนเดนเซอร์\nคอนแวนต์\nคอนเสิร์ต\nคอม\nค่อม\nค้อม\nคอมพิวเตอร์\nคอมมานโด\nคอมมิวนิสต์\nคอย\nค่อย\nค้อย\nคอยล์\nคอร์ด\nคอแลน\nคอสติกโซดา\nคะ\nค่ะ\nคะไขว่\nคะค้อย\nคะคาน\nคะนน\nคะนอง\nคะน้า\nคะนึง\nคะเน\nคะเนงร้าย\nคะเน็ด\nคะแนน\nคะมำ\nคะยั้นคะยอ\nคะเยอ\nคัก\nคั่ก\nคัคนะ\nคัคนัมพร\nคัคนางค์\nคัคนานต์\nคั่ง\nคังไคย\nคัจฉ\nคัณฑมาลา\nคัณฑสูตร\nคัด\nคัดเค้า\nคัดมอน\nคัดเม็ง\nคัทลียา\nคัน\nคั่น\nคั้น\nคันถรจนาจารย์\nคันธกุฎี\nคันธมาทน์\nคันธารราษฎร์\nคับ\nคับค้อน\nคับคา\nคับแค\nคัพภ์\nคัมภีร์\nคัมภีรภาพ\nคัล\nคั่ว\nคา\nค่า\nค้า\nค่าคบ\nคาง\nค่าง\nค้าง\nคางคก\nค้างคาว\nคาด\nคาถา\nคาทอลิก\nคาน\nค้าน\nคาบ\nคาพยุต\nคาม\nคามวาสี\nคามณีย์\nคามภีร์\nคาย\nค่าย\nคาร์บอน\nคาร์บอเนต\nคาร์บอลิก\nคาร์บูเรเตอร์\nคาร์โบรันดัม\nคาร์โบไฮเดรต\nคารม\nคารวะ\nคาราเต้\nคาราวาน\nคาว\nค่าว\nค้าว\nคาวตอง\nคาวี\nคาวุต\nคาส\nคำ\nค่ำ\nค้ำ\nคำนวณ\nคำนวร\nคำนับ\nคำนัล\nคำนึง\nคำนูณ\nคำฝอย\nคำเพลิง\nคำรน\nคำรบ\nคำราม\nคำแสด\nคำแหง\nคำโอง\nคิก\nคิง\nคิด\nคิมหันต์\nคิรี\nคิลาน\nคิลานะ\nคิว\nคิ้ว\nคี่\nคีต\nคีบ\nคีม\nคีรี\nคีรีบูน\nคึก\nคึ่ก\nคึกคัก\nคืน\nคืบ\nคือ\nคุ\nคุก\nคุกกี้\nคุกคาม\nคุกพาทย์\nคุ้ง\nคุณ\nคุด\nคุดทะราด\nคุต\nคุตติ\nคุ่น\nคุ้น\nคุป\nคุปต์\nคุปติ\nคุม\nคุ่ม\nคุ้ม\nคุย\nคุ้ย\nคุยหฐาน\nคุยหประเทศ\nคุรุ\nคุลา\nคุลิก่า\nคุลีการ\nคุหา\nคู\nคู่\nคู้\nคูณ\nคูถ\nคูน\nคูปอง\nคูเรียม\nคูหา\nเค้ก\nเค้เก้\nเค้ง\nเคณฑะ\nเคด\nเค็ด\nเคน\nเค้น\nเคเบิล\nเค็ม\nเคมี\nเคย\nเครง\nเคร่ง\nเครงครา\nเครงครำ\nเครดิต\nเครน\nเครา\nเคร่า\nเคราหณี\nเคราะห์\nเครียด\nเครียว\nเครือ\nเครื่อง\nเคล้ง\nเคล็ด\nเคล้น\nเคล้า\nเคล่าคล่อง\nเคลิบเคลิ้ม\nเคลิ้ม\nเคลีย\nเคลื่อน\nเคลือบ\nเคว้ง\nเคหะ\nเคหา\nเคอะ\nเค้า\nเคาน์เตอร์\nเคารพ\nเคาะ\nเคาะแคะ\nเคียง\nเคียด\nเคียน\nเคียม\nเคี่ยม\nเคียร\nเคียว\nเคี่ยว\nเคี้ยว\nเคือง\nแค\nแค่\nแค้\nแคแล\nแคดเมียม\nแค็ตตาล็อก\nแคแตร\nแคโทด\nแคน\nแค่น\nแค้น\nแคบ\nแคบหมู\nแคปซูล\nแคม\nแคร่\nแครก\nแครง\nแคระ\nแคลคูลัส\nแคลง\nแคลเซียม\nแคลน\nแคล้ว\nแคล่วคล่อง\nแคลอรี\nแคลิฟอร์เนียม\nแคว\nแควก\nแคว้น\nแคแสด\nแคะ\nโค\nโคก\nโคเคน\nโค่ง\nโค้ง\nโคจร\nโคเซแคนต์\nโคไซน์\nโคตร\nโคแทนเจนต์\nโคธา\nโคน\nโค่น\nโคบอลต์\nโคปผกะ\nโคม\nโคม่า\nโครก\nโครกคราก\nโครง\nโคร่ง\nโคร่งคร่าง\nโครม\nโครเมียม\nโครโมโซม\nโคราช\nโครำ\nโคล\nโคลง\nโคลน\nโควตา\nโคออร์ดิเนต\nใคร\nใคร่\nใคร่ครวญ\nไค\nไค้\nไคร้\nไคร้เครือ\nไคล\nไคล้\nฆนะ\nฆราวาส\nฆ้อง\nฆ่า\nฆาต\nฆาน\nฆานินทรีย์\nเฆี่ยน\nโฆรวิส\nโฆษก\nโฆษณา\nโฆษะ\nโฆษ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nเฉ่ง\nเฉด\nเฉท\nเฉนียน\nเฉพาะ\nเฉย\nเฉลย\nเฉลว\nเฉลา\nเฉลิม\nเฉลี่ย\nเฉลียง\nเฉลี่ยง\nเฉลียบ\nเฉลียว\nเฉวียง\nเฉวียน\nเฉอะแฉะ\nเฉา\nเฉาก๊วย\nเฉาฮื้อ\nเฉาะ\nเฉิด\nเฉิบ\nเฉียง\nเฉียงพร้านางแอ\nเฉียด\nเฉียบ\nเฉียว\nเฉี่ยว\nเฉือน\nเฉื่อย\nแฉ\nแฉ่\nแฉก\nแฉง\nแฉ่ง\nแฉลบ\nแฉล้ม\nแฉะ\nโฉ\nโฉ่\nโฉเก\nโฉ่งฉ่าง\nโฉงเฉง\nโฉด\nโฉนด\nโฉบ\nโฉเบ๊\nโฉม\nโฉลก\nไฉน\nไฉไล\nชก\nชคัตตรัย\nชง\nชงโค\nชงฆ์\nชงฆา\nชงโลง\nชฎา\nชฎามังษี\nชฎามังสี\nชฎิล\nชด\nชน\nชนก\nชนนี\nชนม์\nชนวน\nชนะ\nชนัก\nชนา\nชนาง\nชนิด\nชเนตตี\nชบา\nชม\nชมดชม้อย\nชมนาด\nชมพู\nชมพู่\nชมรม\nชม้อย\nชม้าย\nชไม\nชยา\nชโย\nชรทึง\nชรริน\nชรอุ่ม\nชระล้ำ\nชระลุ\nชระอาบ\nชระเอม\nชรัด\nชรา\nชล\nชโลง\nชโลม\nช่วง\nชวด\nชวน\nชวย\nช่วย\nชวร\nชวลิต\nชวา\nชวาล\nชวาลา\nช่อ\nชอก\nช็อก\nช็อกโกเลต\nช็อกโกแลต\nชอง\nช่อง\nช้อง\nชองระอา\nชอน\nช่อน\nช้อน\nชอบ\nชอม\nช้อย\nชอล์ก\nชอ่ำ\nชอุ่ม\nชะ\nชะคราม\nชะงอก\nชะง่อน\nชะงัก\nชะงัด\nชะง้ำ\nชะงุ้ม\nชะเง้อ\nชะเงื้อม\nชะแง้\nชะโงก\nชะฉ่า\nชะช่อง\nชะชะ\nชะช้า\nชะโด\nชะตา\nชะต้า\nชะนี\nชะเนาะ\nชะเนียง\nชะพลู\nชะเพลิง\nชะมด\nชะมบ\nชะมวง\nชะมัง\nชะมัด\nชะแม่\nชะรอย\nชะลอ\nชะลอม\nชะล่า\nชะลาน\nชะลิน\nชะลูด\nชะเลง\nชะเลย\nชะแล็ก\nชะแลง\nชะวาก\nชะวาด\nชะเวิกชะวาก\nชะแวง\nชะอม\nชะอ้อน\nชะเอม\nชะโอน\nชัก\nชักคราม\nชักช้า\nชัง\nชั่ง\nชังคา\nชังฆ\nชัชวาล\nชัฏ\nชัด\nชัดช้า\nชัน\nชั้น\nชันกาด\nชันชี\nชันตุ\nชันนะตุ\nชันนุ\nชันโรง\nชันษา\nชันสูตร\nชัปนะ\nชัพ\nชัมพูนท\nชัย\nชัยพฤกษ์\nชัยภูมิ\nชัลลุกา\nชั่ว\nชั้ว\nชัวชม\nชา\nช้า\nชาคระ\nชาคริต\nชาคริยานุโยค\nช่าง\nช้าง\nช้าช่อน\nชาญ\nชาด\nชาดก\nชาต\nชาตบุษย์\nชาตรี\nชาตะ\nชาตา\nชาติ\nชาน\nชานุ\nช้าปี่\nชาปีไหน\nช้าแป้น\nช้าพลู\nชาม\nชามพูนท\nชามาดร\nชามาดา\nชามาตุ\nชาย\nชายา\nชาล\nชาลา\nชาลินี\nช้าเลือด\nชาว\nชาวี\nชำ\nช่ำ\nช้ำ\nชำงัด\nชำงาย\nช่ำชอง\nชำนะ\nชำนัญ\nชำนัน\nชำนาญ\nชำนิ\nชำเนียร\nชำมะนาด\nชำมะเลียง\nชำร่วย\nชำระ\nช้ำรั่ว\nชำรุด\nชำเรา\nชำเราะ\nชำแรก\nชำแระ\nชำเลือง\nชำแหละ\nชิ\nชิง\nชิ่ง\nชิงชัน\nชิงช้า\nชิงช้าชาลี\nชิงชี่\nชิงฮื้อ\nชิชะ\nชิชิ\nชิณณะ\nชิด\nชิเดนทรีย์\nชิต\nชิตินทรีย์\nชิน\nชิ้น\nชินโต\nชิโนรส\nชิม\nชิมแปนซี\nชิยา\nชิรณะ\nชิระ\nชิวหา\nชิสา\nชี\nชี่\nชี้\nชีปะขาว\nชีผะขาว\nชีผ้าขาว\nชีพ\nชีฟอง\nชีรณ\nชีระ\nชีวเคมี\nชีวประวัติ\nชีวภาพ\nชีววิทยา\nชีวะ\nชีวัน\nชีวา\nชีวาตม์\nชีวาลัย\nชีวิต\nชีวิตักษัย\nชีวิน\nชีวี\nชืด\nชื่น\nชื้น\nชื่อ\nชุก\nชุกชี\nชุ้ง\nชุณห\nชุด\nชุติ\nชุน\nชุบ\nชุม\nชุ่ม\nชุมนุม\nชุมพร\nชุมพา\nชุมแพรก\nชุมรุม\nชุมแสง\nชุมเห็ด\nชุ่ย\nชุลมุน\nชุลี\nชุษณะ\nชู\nชู้\nเช็ค\nเช้ง\nเช้งวับ\nเชงเลง\nเช็ด\nเชน\nเช่น\nเชย\nเชลง\nเชลย\nเชลแล็ก\nเชลียง\nเชวง\nเชษฐะ\nเชษฐา\nเชอ\nเช่า\nเช้า\nเชาว์\nเชาวน์\nเชิง\nเชิญ\nเชิด\nเชิ้ต\nเชียง\nเชี่ยน\nเชียบ\nเชี่ยม\nเชียร\nเชียร์\nเชียว\nเชี่ยว\nเชื่อ\nเชื้อ\nเชือก\nเชื่อง\nเชือด\nเชือน\nเชื่อม\nแช\nแช่\nแช่ง\nแชงมา\nแชบ๊วย\nแช่ม\nแชร์\nแชล่ม\nแชสซี\nแชะ\nโชก\nโชค\nโชงโลง\nโชดก\nโชดึก\nโชต\nโชตก\nโชติ\nโชติก\nโชน\nโชมโรม\nโชย\nโชยงการ\nโชยชาย\nโชยติส\nโชว์\nใช่\nใช้\nไช\nไชนะ\nไชย\nไชโย\nซก\nซ่ก\nซงดำ\nซ่งฮื้อ\nซด\nซน\nซ้น\nซบ\nซม\nซวดเซ\nซวน\nซวย\nซอ\nซอก\nซอง\nซ่อง\nซ้อง\nซองแมว\nซ้องแมว\nซอน\nซ่อน\nซ้อน\nซอม\nซ่อม\nซ้อม\nซอมซ่อ\nซอย\nซอส\nซัก\nซักส้าว\nซัง\nซั้ง\nซัด\nซับ\nซัลฟา\nซั้ว\nซา\nซ่า\nซาก\nซาง\nซ่าง\nซาด\nซาน\nซ่าน\nซาบซ่าน\nซาบซึ้ง\nซ่าโบะ\nซาแมเรียม\nซ้าย\nซาลาเปา\nซาว\nซ่าหริ่ม\nซำ\nซ้ำ\nซิ\nซี\nซิก\nซิกข์\nซิกซี้\nซิกแซ็ก\nซิการ์\nซิงโคนา\nซิ่น\nซินนามิก\nซินแส\nซิบ\nซิป\nซิฟิลิส\nซิลิคอน\nซิว\nซี่\nซีก\nซีเซียม\nซีด\nซี้ด\nซีนอน\nซีป่าย\nซีเมนต์\nซีเรียม\nซีลีเนียม\nซีอิ๊ว\nซึก\nซึง\nซึ่ง\nซึ้ง\nซึม\nซื่อ\nซื้อ\nซุก\nซุง\nซุน\nซุบ\nซุป\nซุ่ม\nซุ้ม\nซุ่มซ่าม\nซุย\nซู่\nซูโครส\nซูด\nซู้ด\nซูดซาด\nซูบ\nเซ\nเซ็ก\nเซแคนต์\nเซ็ง\nเซ่ง\nเซ้ง\nเซ็งแซ่\nเซต\nเซน\nเซ็น\nเซ่น\nเซนติกรัม\nเซนติเกรด\nเซนติเมตร\nเซนติลิตร\nเซปักตะกร้อ\nเซราะ\nเซรุ่ม\nเซลเซียส\nเซลล์\nเซลลูลอยด์\nเซลลูโลส\nเซ่อ\nเซอร์โคเนียม\nเซอะ\nเซา\nเซ้าซี้\nเซาะ\nเซิง\nเซิ้ง\nเซียน\nเซียบ\nเซียมซี\nเซียว\nเซี่ยว\nเซี่ยวกาง\nเซื่อง\nแซ\nแซ่\nแซ็กคาริน\nแซง\nแซงแซว\nแซด\nแซบ\nแซม\nแซยิด\nแซ่ว\nแซะ\nโซ\nโซ่\nโซก\nโซ่ง\nโซงโขดง\nโซเซ\nโซดา\nโซเดียม\nโซม\nโซรม\nโซลา\nไซ\nไซ้\nไซเกิล\nไซโคลน\nไซน์\nไซยาไนด์\nไซร้\nไซเรน\nไซโล\nฌาน\nฌาปน\nฌาปนกิจ\nฌาปนสถาน\nเฌอ\nเฌอเอม\nญวน\nญัตติ\nญาณ\nญาติ\nญานาซะฮ์\nญิบ\nญี่ปุ่น\nเญยธรรม\nไญยธรรม\nฎีกา\nฐกัด\nฐากูร\nฐาน\nฐานะ\nฐานันดร\nฐานานุกรม\nฐานานุรูป\nฐานานุศักดิ์\nฐานียะ\nฐาปน\nฐาปนา\nฐายี\nฐิต\nฐิติ\nฑาหก\nฑาหะ\nเฒ่า\nณรงค์\nเณร\nดก\nดง\nด้ง\nด้น\nดนโด่\nดนตรี\nดนัย\nดนุ\nดนู\nดบัสวิน\nดบัสวี\nดม\nดรงค์\nดรณี\nดรรชนี\nดราฟต์\nดรุณ\nดรุณี\nดล\nดวง\nด้วง\nดวด\nด่วน\nด้วน\nด้วย\nดอก\nดอง\nด่อง\nด้อง\nดองฉาย\nดองดึง\nดอด\nดอน\nด่อน\nดอม\nด้อม\nดอย\nด้อย\nดอลลาร์\nดะ\nดะโต๊ะ\nดะหมัง\nดัก\nดักดาน\nดักแด้\nดัง\nดั่ง\nดั้ง\nดัชนี\nดัด\nดัตช์\nดัน\nดั้น\nดับ\nดัมพ์\nดั้วเดี้ย\nดัสกร\nดา\nด่า\nดาก\nด่าง\nด้าง\nดาด\nดาน\nด่าน\nด้าน\nดาบ\nดาบส\nดาม\nด้าม\nด้ามจิ้ว\nดามพ์\nดาย\nด้าย\nดารกะ\nดารณี\nดารดาษ\nดาระ\nดารา\nดาล\nดาลัด\nดาว\nด่าว\nด้าว\nดาวดึงส์\nดาวบส\nดาษ\nดาษดา\nดำ\nด่ำ\nด้ำ\nดำกล\nดำเกิง\nดำแคง\nดำดง\nดำนาณ\nดำเนิน\nดำบล\nดำรง\nดำรวจ\nดำรัส\nดำริ\nดำรี\nดำรู\nดำฤษณา\nดำเลิง\nดิก\nดิ่ง\nดิฉัน\nดิฐ\nดิตถ์\nดิถี\nดิน\nดิ้น\nดิบ\nดิรัจฉาน\nดิลก\nดิ่ว\nดิ้ว\nดิ้วเดี้ยว\nดิษฐ์\nดิสโพรเซียม\nดี\nดีเซล\nดีด\nดีดีที\nดีบุก\nดีปลี\nดีเปรสชัน\nดีหมี\nดีหลี\nดึก\nดึง\nดึ่ง\nดึ่ม\nดื่น\nดื่ม\nดือ\nดื้อ\nดุ\nดุก\nดุกดิก\nดุกทะเล\nดุ้ง\nดุ้งดิ้ง\nดุจ\nดุด\nดุน\nดุ้น\nดุบ\nดุม\nดุ่ม\nดุ่ย\nดุรงค์\nดุริยะ\nดุริยางค์\nดุริยางคศาสตร์\nดุริยางคศิลป์\nดุล\nดุษฎี\nดุษณี\nดุษณีภาพ\nดุษิต\nดุสิต\nดุเหว่า\nดู\nดูกค่าง\nดูกร\nดูด\nดูรา\nดูแล\nเด\nเด่\nเดก\nเด็ก\nเดกซ์โทรส\nเดคากรัม\nเดคาเมตร\nเดคาลิตร\nเด้ง\nเด็จ\nเดช\nเดชน์\nเดชนะ\nเดชะ\nเดโช\nเดซิกรัม\nเดซิเมตร\nเดซิลิตร\nเด็ด\nเดน\nเด่น\nเดนมาร์ก\nเดรัจฉาน\nเด๋อ\nเด๋อด๋า\nเดา\nเด้า\nเดาะ\nเดิน\nเดิ่น\nเดิม\nเดียง\nเดียด\nเดียรดาษ\nเดียรถีย์\nเดียรัจฉาน\nเดียว\nเดี่ยว\nเดี๋ยว\nเดียะ\nเดื่อ\nเดือก\nเดื่อง\nเดือด\nเดือน\nเดือย\nแด\nแด่\nแดก\nแด็ก\nแดกงา\nแดกแด้\nแดง\nแดด\nแดน\nแด่น\nแด่ว\nแดะ\nแดะแด๋\nโด\nโด่\nโดกเดก\nโด่ง\nโดด\nโดน\nโดม\nโดมร\nโดย\nโดรณ\nใด\nได\nได้\nไดแซ็กคาไรด์\nไดนาโม\nไดนาไมต์\nไดโนเสาร์\nไดเรกตริกซ์\nตก\nต๊กโต\nตง\nต๋ง\nตงฉิน\nตงิด\nตงุ่น\nตด\nตติย\nตถาคต\nตน\nต้น\nตนัย\nตนุ\nตบ\nตบะ\nตปนียะ\nตม\nต้ม\nตมูก\nตยาคี\nตรง\nตรณี\nตรม\nตรรก\nตรรกะ\nตรลบ\nตรลอด\nตรลาด\nตรวจ\nตรวน\nตรอก\nตรอง\nตรอมใจ\nตรอมตรม\nตระ\nตระกล\nตระกวน\nตระกอง\nตระการ\nตระกูล\nตระคัร\nตระเตรียม\nตระนาว\nตระบก\nตระบอก\nตระบอง\nตระบัด\nตระบัน\nตระเบ็ง\nตระแบก\nตระแบง\nตระโบม\nตระพอง\nตระพัง\nตระลาการ\nตระวัน\nตระเว็ด\nตระเวน\nตระสัก\nตระหง่าน\nตระหนก\nตระหนัก\nตระหน่ำ\nตระหนี่\nตรัง\nตรังค์\nตรับ\nตรับฟัง\nตรัย\nตรัยตรึงศ์\nตรัส\nตรัสสา\nตรา\nตรากตรำ\nตราชู\nตราบ\nตราสัง\nตรำ\nตริ\nตริว\nตรี\nตรีปวาย\nตรีพิธพรรณ\nตรียัมปวาย\nตรึก\nตรึง\nตรุ\nตรุณ\nตรุณะ\nตรุษ\nตรู\nตรู่\nตฤณ\nตฤตีย\nตฤษณา\nตลก\nตลบ\nตลอด\nตลับ\nตลาด\nตลิ่ง\nตลึง\nตวง\nต่วน\nต้วมเตี้ยม\nตวัก\nตวัด\nตวาด\nตอ\nตอม่อ\nต่อ\nต้อ\nตอก\nต๊อก\nต๊อกต๋อย\nตอง\nต้อง\nตองกราย\nต้องเต\nตองแตก\nต่องแต่ง\nตองเหลือง\nตอด\nตอน\nต้อน\nตอบ\nตอเบา\nตอม\nต่อม\nต๋อม\nต่อย\nต้อย\nต้อยตริ่ง\nต้อยติ่ง\nต้อยตีวิด\nตอแย\nตอร์ปิโด\nต่อไส้\nตอแหล\nตะ\nตะกรน\nตะกร้อ\nตะกรัน\nตะกรับ\nตะกร้า\nตะกราม\nตะกรุด\nตะกรุม\nตะกรุมตะกราม\nตะกละ\nตะกลาม\nตะกวด\nตะกอ\nตะกอน\nตะกัง\nตะกั่ว\nตะกาง\nตะกาด\nตะกาย\nตะกาว\nตะกุกตะกัก\nตะกุย\nตะกู\nตะกูด\nตะเกียกตะกาย\nตะเกียง\nตะเกียบ\nตะแก\nตะแก่\nตะแกรง\nตะโก\nตะโก้\nตะโกก\nตะโกน\nตะโกรง\nตะโกรม\nตะไกร\nตะขบ\nตะขอ\nตะขาบ\nตะขิดตะขวง\nตะเข้\nตะเข็บ\nตะโขง\nตะคร้อ\nตะครอง\nตะครั่นตะครอ\nตะคร้ำ\nตะคริว\nตะคิว\nตะครุบ\nตะคอก\nตะคัน\nตะค้า\nตะคาก\nตะค้าน\nตะคุ่ม\nตะเครียว\nตะเคียว\nตะเคียน\nตะแคง\nตะไคร่\nตะไคร้\nตะเฆ่\nตะนอย\nตะนาว\nตะบม\nตะบอง\nตะบอย\nตะบัน\nตะบิ้ง\nตะบิด\nตะบิดตะบอย\nตะบี้ตะบัน\nตะบึง\nตะบูน\nตะเบ็ง\nตะเบ็งมาน\nตะเบ๊ะ\nตะแบก\nตะแบง\nตะโบม\nตะไบ\nตะปบ\nตะปลิง\nตะปิ้ง\nตะปุ่มตะป่ำ\nตะปู\nตะพง\nตะพด\nตะพอง\nตะพัก\nตะพัง\nตะพัด\nตะพั้น\nตะพาก\nตะพาน\nตะพาบ\nตะพาย\nตะพึด\nตะพึดตะพือ\nตะพุ่น\nตะเพรา\nตะเพิง\nตะเพิด\nตะเพียน\nตะโพก\nตะโพง\nตะโพน\nตะเภา\nตะใภ้\nตะม่อ\nตะมอย\nตะรังกะนู\nตะรังตังกวาง\nตะรังตังช้าง\nตะราง\nตะลอง\nตะลอน\nตะล่อม\nตะละ\nตะลาน\nตะลิงปลิง\nตะลิบ\nตะลีตะลาน\nตะลึง\nตะลึงพรึงเพริด\nตะลุง\nตะลุ่ม\nตะลุ่มนก\nตะลุมบอน\nตะลุ่มโปง\nตะลุมพอ\nตะลุมพุก\nตะลุย\nตะเลง\nตะแลงแกง\nตะไล\nตะวัน\nตะเวน\nตะหลิว\nตะหลุก\nตะหลุง\nตะแหง่ว\nตะแหมะแขะ\nตะโหงก\nตัก\nตักกะ\nตักเตือน\nตั๊กแตน\nตักษัย\nตัง\nตั่ง\nตั้ง\nตังเก\nตังฉ่าย\nตังเม\nตังวาย\nตังโอ๋\nตัจฉก\nตัจฉนี\nตัณฑุล\nตัณหา\nตัด\nตัน\nตันตระ\nตันติ\nตันหยง\nตับ\nตับปิ้ง\nตัว\nตั๋ว\nตัวจี๊ด\nตัวตืด\nตั้วโผ\nตั้วเหี่ย\nตา\nตาก\nตากวาง\nต่าง\nตาด\nตาน\nต่าน\nต้าน\nตานนกกด\nตานี\nตาบ\nตาม\nตามะแน\nตามิน\nตาย\nตาราไต\nตาล\nตาลุ\nต๋าว\nตาเสือ\nตาหนู\nตาฬ\nตำ\nต่ำ\nตำนาน\nตำบล\nตำแบ\nตำแย\nตำรวจ\nตำรับ\nตำรา\nตำรุ\nตำลึง\nตำเสา\nตำหนัก\nตำหนิ\nตำแหน่ง\nติ\nติก\nติ๊ก\nติกะ\nติกาหรัง\nติง\nติ่ง\nติ๋ง\nติ่งตั่ง\nติ๋งต่าง\nติงส\nติงสติ\nติณ\nติด\nติตติกะ\nติตติร\nติตถ\nติตถะ\nติถี\nติมิงคละ\nติรัจฉาน\nติลก\nติละ\nติ้ว\nตี\nตี่\nตีน\nตีบ\nตีรถะ\nตีระ\nตึ\nตึก\nตึ้ก\nตึ้กตั้ก\nตึง\nตึดตื๋อ\nตึ๊ดตื๋อ\nตืด\nตื่น\nตื้น\nตื้อ\nตื๊อ\nตื๋อ\nตุ\nตุ๊\nตุ๊กแก\nตุ๊กตา\nตุ๊กต่ำ\nตุกติก\nตุ๊กติ๊ก\nตุ๊กตุ่น\nตุ๊กตุ๋ย\nตุง\nตุ้งก่า\nตุ้งติ้ง\nตุ๊ดตู่\nตุน\nตุ่น\nตุ๋น\nตุนาหงัน\nตุบ\nตุ้บ\nตุ๊บป่อง\nตุปัดตุป่อง\nตุปัดตุเป๋\nตุ่ม\nตุ้ม\nตุ๋ม\nตุ้มกว้าว\nตุมกา\nตุ้มแซะ\nตุมตัง\nตุ้มเต๋น\nตุ้มปี่\nตุมพะ\nตุ่ย\nตุ้ย\nตุ๊ย\nตุ๋ยตุ่ย\nตุรคะ\nตุรงค์\nตุล\nตุลา\nตุหรัดตุเหร่\nตู\nตู่\nตู้\nตูก\nตูด\nตูบ\nตูม\nเต๊ก\nเต็ง\nเต่ง\nเตช\nเตโช\nเต้น\nเต็นท์\nเต็ม\nเตย\nเตร่\nเตร็ด\nเตรตา\nเตรียม\nเตรียมตรม\nเตละ\nเตลิด\nเตว็ด\nเต่อ\nเตอะ\nเตะ\nเตา\nเต่า\nเต้า\nเต๋า\nเต่าเกียด\nเต้าเจี้ยว\nเต้าทึง\nเต้าหู้\nเต้าฮวย\nเต๊าะ\nเตาะแตะ\nเติ่ง\nเติบ\nเติม\nเตี้ย\nเตียง\nเตียน\nเตียบ\nเตี๋ยม\nเตียรถ์\nเตียว\nเตี่ยว\nเตือน\nแต่\nแต้\nแตก\nแตง\nแต่ง\nแตงเม\nแต้จิ๋ว\nแตด\nแต๊ดแต๋\nแตน\nแต้ม\nแตร\nแตระ\nแต้ว\nแต้วแร้ว\nแต้วแล้ว\nแต่ว่า\nแต้แว้ด\nแตะ\nโต\nโต้\nโตก\nโต่ง\nโต้ง\nโตงเตง\nโตฎก\nโต๊ด\nโตน\nโตนด\nโต้โผ\nโตมร\nโตย\nโตรก\nโต๊ะ\nใต้\nไต\nไต่\nไต้\nไต๋\nไตร\nไตรกิศยา\nไตรดายุค\nไตร่ตรอง\nไตรย\nไต้หวัน\nถก\nถกล\nถงาด\nถด\nถนน\nถนอม\nถนัด\nถนัน\nถนำ\nถนิม\nถม\nถ่ม\nถมอ\nถมึงทึง\nถลก\nถลกบาตร\nถลน\nถล่ม\nถลอก\nถลัน\nถลา\nถลาก\nถลาย\nถลำ\nถลึงตา\nถลุง\nถ่วง\nถ้วน\nถ้วย\nถวัล\nถวัลย์\nถวาย\nถวิน\nถวิล\nถ่อ\nถ้อ\nถอก\nถอง\nถ่อง\nถ้อง\nถอด\nถอน\nถอบ\nถอบแถบ\nถ่อม\nถอย\nถ่อย\nถ้อย\nถะ\nถะถั่น\nถะมัดถะแมง\nถัก\nถัง\nถั่ง\nถัด\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นยา\nบิลเลียด\nบิวเรตต์\nบิสมัท\nบี้\nบีฑา\nบีตา\nบีบ\nบีเยศ\nบึก\nบึกบึน\nบึง\nบึ่ง\nบึ้ง\nบุ\nบุก\nบุคคล\nบุคลากร\nบุคลาธิษฐาน\nบุคลิก\nบุง\nบุ่ง\nบุ้ง\nบุ้งกี๋\nบุญ\nบุญญาธิการ\nบุญญานุภาพ\nบุญญาภินิหาร\nบุญญาภิสังขาร\nบุณฑริก\nบุณมี\nบุณย์\nบุตร\nบุตรี\nบุถุชน\nบุทคล\nบุนนาค\nบุบ\nบุบบิบ\nบุปผชาติ\nบุพกรรม\nบุพการี\nบุพกิจ\nบุพชาติ\nบุพทักษิณ\nบุพนิมิต\nบุพบท\nบุพพาจารย์\nบุพเพสันนิวาส\nบุพโพ\nบุ๋ม\nบุ่มบ่าม\nบุ้ย\nบุรณะ\nบุรพทิศ\nบุรพบท\nบุรพาจารย์\nบูรพาจารย์\nบุระ\nบุราณ\nบุรินทร์\nบุริมทิศ\nบุริมพรรษา\nบุริมสิทธิ\nบุรี\nบุรุษ\nบุโรทั่ง\nบุษกร\nบุษบก\nบุษบง\nบุษบัน\nบุษบา\nบุษบามินตรา\nบุษปราค\nบุษปะ\nบุษย์\nบุษย์น้ำทอง\nบุษยมาส\nบุษยะ\nบุษราคัม\nบุหงัน\nบุหงา\nบุหรง\nบุหรี่\nบุหลัน\nบู่\nบู้\nบูชนียสถาน\nบูชา\nบูชิต\nบูด\nบูดู\nบูร\nบูรณ์\nบูรณภาพ\nบูรณมี\nบูรณะ\nบูรณาการ\nบูรพ์\nบูรพะ\nบูรพา\nเบ้\nเบ่ง\nเบ๊จี๋\nเบญกานี\nเบญจกัลยาณี\nเบญจกามคุณ\nเบญจขันธ์\nเบญจดุริยางค์\nเบญจธรรม\nเบญจบรรพต\nเบญจพรรณ\nเบญจเพส\nเบญจมาศ\nเบญจรงค์\nเบญจศก\nเบญจศีล\nเบญจะ\nเบญจา\nเบญจางค์\nเบญจางคประดิษฐ์\nเบญญา\nเบญพาด\nเบ็ด\nเบ็ดเตล็ด\nเบ็ดเสร็จ\nเบน\nเบนซิน\nเบรก\nเบริลเลียม\nเบส\nเบ้อ\nเบอร์\nเบอร์คีเลียม\nเบ้อเร่อ\nเบ้อเร่อเท่อ\nเบ้อเริ่ม\nเบ้อเริ่มเทิ่ม\nเบอะ\nเบอะบะ\nเบะ\nเบา\nเบ้า\nเบาราณ\nเบาะ\nเบิก\nเบิ่ง\nเบี้ย\nเบี่ยง\nเบียด\nเบียน\nเบียร์\nเบี้ยว\nเบือ\nเบื่อ\nเบื้อ\nเบื้อง\nเบือน\nแบ\nแบ้\nแบก\nแบคทีเรีย\nแบ่ง\nแบงก์\nแบดมินตัน\nแบตเตอรี่\nแบน\nแบนโจ\nแบบ\nแบ็บ\nแบเรียม\nแบหลา\nแบะ\nโบ\nโบ้\nโบ๋\nโบก\nโบกขรณี\nโบกขรพรรษ\nโบชุก\nโบต\nโบนัส\nโบ๊เบ๊\nโบย\nโบรมีน\nโบรอน\nโบราณ\nโบสถ์\nใบ\nใบ้\nไบ่\nปก\nปกติ\nปกรณ์\nปกรณัม\nปกิณกะ\nปกีรณัม\nปโกฏิ\nปง\nป่ง\nปงช้าง\nปฎล\nปฏัก\nปฏิกรณ์\nปฏิกรรม\nปฏิการะ\nปฏิกิริยา\nปฏิกูล\nปฏิคม\nปฏิคหิต\nปฏิคาหก\nปฏิฆะ\nปฏิชีวนะ\nปฏิญญา\nปฏิญาณ\nปฏิทิน\nปฏิบถ\nปฏิบัติ\nปฏิปทา\nปฏิปักษ์\nปฏิปัน\nปฏิปุจฉาพยากรณ์\nปฏิปุจฉาวาที\nปฏิพัทธ์\nปฏิพากย์\nปฏิภาค\nปฏิภาณ\nปฏิมา\nปฏิมากร\nปฏิยุทธ์\nปฏิรพ\nปฏิรูป\nปฏิโลม\nปฏิวัติ\nปฏิวาต\nปฏิวาท\nปฏิเวธ\nปฏิสนธิ\nปฏิสวะ\nปฏิสังขรณ์\nปฏิสันถาร\nปฏิสัมภิทา\nปฏิเสธ\nปฐพี\nปฐม\nปฐวี\nปณต\nปณาม\nปณิธาน\nปณิธิ\nปณีต\nปด\nปดิวรัดา\nปติ\nปถพี\nปถมัง\nปถวี\nปทัฏฐาน\nปทัสถาน\nปทานุกรม\nปทีป\nปทุม\nปน\nป่น\nปนัดดา\nปปัญจะ\nปม\nปรนัย\nปรปักษ์\nปรโลก\nปรวาที\nปรก\nปรกติ\nปรง\nปรตยักษ์\nปรน\nปรนนิบัติ\nปรนิมมิตวสวัตดี\nปรบ\nปรปักษ์\nปรมัตถ์\nปรมาจารย์\nปรมาณู\nปรมาภิไธย\nปรมาภิเษก\nปรมินทร์\nบรเมนทร์\nปรเมศวร์\nปรเมษฐ์\nปรวด\nปรวนแปร\nปรศุ\nปรสิต\nปร๋อ\nปรองดอง\nปรอด\nปรอท\nปรอย\nประ\nประกบ\nประกฤต\nประกฤติ\nประกล\nประกวด\nประกวดประขัน\nประกอบ\nประกัน\nประกับ\nประกาย\nประกายพรึก\nประการ\nประกาศ\nประกาศนียบัตร\nประกาศิต\nประกำ\nประกิด\nประกิต\nประคด\nประคนธรรพ\nประคนธรรพ์\nประคบ\nประคบประหงม\nประคอง\nประคับประคอง\nประคัลภ์\nประคำ\nประคิ่น\nประคุณ\nประเคน\nประเคราะห์\nประโคน\nประโคนธรรพ\nประโคนธรรพ์\nประโคม\nประจง\nประจญ\nประจบ\nประจบประแจง\nประจวบ\nประจ๋อประแจ๋\nประจักษ์\nประจักษนิยม\nประจัญ\nประจัน\nประจาก\nประจาค\nประจาน\nประจำ\nประจิม\nประจิ้มประเจ๋อ\nประจุ\nประจุคมน์\nประจุบัน\nประเจก\nประเจิด\nประเจิดประเจ้อ\nประเจียด\nประแจ\nประชด\nประชน\nประชวม\nประชวร\nประชัน\nประชา\nประชาธิปไตย\nประชิด\nประชี\nประชุม\nประเชิญ\nประณต\nประณม\nประณาม\nประณิธาน\nประณิธิ\nประณีต\nประณุท\nประดง\nประดน\nประดวน\nประดอน\nประดอย\nประดัก\nประดักประเดิด\nประดัง\nประดับ\nประดา\nประดาก\nประดาป\nประดาษ\nประดิชญา\nประดิดประดอย\nประดิทิน\nประดิษฐ์\nประดิษฐกรรม\nประดิษฐาน\nประดุง\nประดุจ\nประดู่\nประเด\nประเด็น\nประเดิม\nประเดียง\nประเดี๋ยว\nประเดี๋ยวประด๋าว\nประแดง\nประแดะ\nประโดง\nประโดย\nประตง\nประตัก\nประตาป\nประติชญา\nประติญาณ\nประติทิน\nประติมากร\nประติมากรรม\nประติรพ\nประตู\nประถม\nประถมจินดา\nประทม\nประท้วง\nประทวน\nประทักษ์\nประทักษิณ\nประทัง\nประทัด\nประทับ\nประทาน\nประทาย\nประทาศี\nประทิน\nประทิ่น\nประทีป\nประทุฐ\nประทุน\nประทุษ\nประทุษฐ์\nประเทศ\nประเทา\nประเทียด\nประเทียบ\nประเทือง\nประธาน\nประธานาธิบดี\nประนม\nประนอ\nประนอม\nประนัง\nประนัปดา\nประนีประนอม\nประปราน\nประปราย\nประปา\nประเปรี้ยง\nประเปรียว\nประพจน์\nประพนธ์\nประพรม\nประพฤติ\nประพฤทธิ์\nประพัด\nประพัทธ์\nประพันธ์\nประพาต\nประพาส\nประพาสมหรณพ\nประพาฬ\nประพิณ\nประพิมพ์ประพาย\nประพุทธ์\nประเพณี\nประโพธ\nประไพ\nประไพร\nประภพ\nประภัสสร\nประภา\nประภาคาร\nประภาพ\nประภาษ\nประภาส\nประเภท\nประมง\nประมวล\nประมาณ\nประมาท\nประมุข\nประมุท\nประมูล\nประเมิน\nประโมง\nประโมทย์\nประยงค์\nประยุกต์\nประยุทธ์\nประยุร\nประยูร\nประโยค\nประโยชน์\nประโรหิต\nประลมพ์\nประลอง\nประลัย\nประลาต\nประลาย\nประลุ\nประเล่ห์\nประเล้าประโลม\nประโลม\nประวรรต\nประวรรตน์\nประวัติ\nประวาล\nประวาลปัทม์\nประวาส\nประวิง\nประวิช\nประวิตร\nประวิน\nประวีณ\nประเวณี\nประเวศ\nประเวศน์\nประศม\nประศาสน์\nประศุ\nประสก\nประสงค์\nประสบ\nประสพ\nประสม\nประสะ\nประสัก\nประสันนาการ\nประสัยห์\nประสา\nประสาท\nประสาธน์\nประสาน\nประสาร\nประสิทธิ์\nประสิทธิผล\nประสิทธิภาพ\nประสีประสา\nประสูต\nประสูติ\nประเสบัน\nประเสบันอากง\nประเสริฐ\nประหนึ่ง\nประหม่า\nประหยัด\nประหลาด\nประหล่ำ\nประหวัด\nประหวั่น\nประหัตประหาร\nประหาณ\nประหาร\nประเหล\nประเหส\nประไหมสุหรี\nประอบ\nประอร\nปรัก\nปรักปรำ\nปรักมะ\nปรัง\nปรัชญา\nปรัตถจริยา\nปรัตยุบัน\nปรัน\nปรับ\nปรัมปรา\nปรัศจิม\nปรัศนา\nปรัศนี\nปรัศว์\nปรัสสบท\nปร่า\nปรากฏ\nปรากรม\nปรากฤต\nปราการ\nปราง\nปรางค์\nปราจีน\nปราชญ์\nปราชญา\nปราชัย\nปราณ\nปราณี\nปราด\nปราน\nปรานี\nปราบ\nปราบดาภิเษก\nปราปต์\nปราม\nปรามาส\nปราโมช\nปราโมทย์\nปราย\nปรารถนา\nปรารภ\nปรารมภ์\nปราศ\nปราศจาก\nปราศรัย\nปราษณี\nปราษาณ\nปราสัย\nปราสาท\nปรำ\nปริ\nปริก\nปริกขาร\nปริกรรม\nปริกัป\nปริคณห์\nปริจาค\nปริจาริกา\nปริเฉท\nปริชน\nปริซึม\nปริญญา\nปริณาม\nปริณายก\nปริต\nปริตตะ\nปริตโตทก\nปริตร\nปริทรรศน์\nปริทัยหัคคี\nปริทัศน์\nปริเทพ\nปริเทวะ\nปรินิพพาน\nปริบ\nปริบท\nปริปันถ์\nปริพนธ์\nปริพัตร\nปริพันธ์\nปริพาชก\nปริภัณฑ์\nปริภาษ\nปริภูมิ\nปริโภค\nปริ่ม\nปริมณฑล\nปริมาณ\nปริมาตร\nปริยัติ\nปริยานุช\nปริยาย\nปริเยศ\nปริโยสาน\nปริวรรต\nปริวัตร\nปริวาร\nปริวาส\nปริวิตก\nปริเวณ\nปริศนา\nปริษัท\nปริสัญญู\nปริสุทธิ\nปริหาน\nปริหาร\nปรี่\nปรีชญา\nปรีชา\nปรี๊ด\nปรีดา\nปรีดิ\nปรีดิ์\nปรีดี\nปรีติ\nปรียะ\nปรียา\nปรึก\nปรึกษา\nปรึง\nปรือ\nปรื๋อ\nปรุ\nปรุง\nปรู\nปรู๋\nปรูด\nปรู๊ด\nปรู๊ดปร๊าด\nปรู๊ฟ\nปฤงคพ\nปฤจฉา\nปฤษฎางค์\nปฤษฐ\nปลก\nปลกเปลี้ย\nปลง\nปล่ง\nปลด\nปล้น\nปลวก\nปลอก\nปล่อง\nปล้อง\nปลอด\nปล้อน\nปลอบ\nปลอม\nปล่อย\nปละ\nปลัก\nปลั๊ก\nปลัง\nปลั่ง\nปลัด\nปลัดขิก\nปลา\nปลาต\nปลาบ\nปลาย\nปลาสเตอร์\nปลาสนาการ\nปล้ำ\nปลิง\nปลิด\nปลิ้น\nปลิโพธ\nปลิม\nปลิ่ม\nปลิว\nปลี\nปลีก\nปลื้ม\nปลุก\nปลูก\nปวกเปียก\nปวง\nป่วง\nปวด\nป่วน\nป้วน\nป้วนเปี้ยน\nป่วย\nปวัตน์\nปวารณา\nปวาล\nปวาส\nปวาฬ\nปวิช\nปวิตร\nปวิธ\nปวิเวก\nปวีณ\nปวุติ\nปเวณี\nปเวส\nปเวสน์\nปศุ\nปสันนะ\nปสันนาการ\nปสาท\nปสาน\nปสาสน์\nปสุ\nปสุต\nปสูติ\nปหังสนะ\nปหาน\nปหาร\nปหาส\nปอ\nป้อ\nป๋อ\nปอก\nปอง\nป่อง\nป้อง\nปอด\nปอน\nป้อน\nปอนด์\nปอเนาะ\nปอบ\nป้อแป้\nปอม\nป้อม\nป๋อม\nปอย\nป้อย\nป้อยอ\nปะ\nปะกน\nปะกัง\nปะการัง\nปะกำ\nปะขาว\nปะงาบ\nปะตาปา\nปะตาระกาหลา\nปะติดปะต่อ\nปะติยาน\nปะทะ\nปะทะปะทัง\nปะทุ\nปะทุน\nปะปน\nปะมง\nปะราลี\nปะรำ\nปะไร\nปะลอม\nปะเลง\nปะแล่ม\nปะโลง\nปะวะหล่ำ\nปะหงับ\nปะหนัน\nปะหัง\nปะเหลาะ\nปัก\nปักข์\nปักเป้า\nปักษ์\nปักษา\nปักษิน\nปักษี\nปัง\nปั๋ง\nปังสุ์\nปังสุกุล\nปัจจัตตะ\nปัจจันต์\nปัจจันตคาม\nปัจจันตชนบท\nปัจจันตประเทศ\nปัจจัย\nปัจจามิตร\nปัจจุคมน์\nปัจจุทธรณ์\nปัจจุบัน\nปัจจุสมัย\nปัจเจก\nปัจโจปการกิจ\nปัจฉา\nปัจฉิม\nปัจถรณ์\nปัจนึก\nปัจยาการ\nปัจเวกขณ์\nปัชชุน\nปัญจนที\nปัญจวัคคีย์\nปัญจก\nปัญจกะ\nปัญจมี\nปัญจวีสติ\nปัญญัติ\nปัญญา\nปัญหา\nปัฏ\nปัฏนะ\nปัฐยาวัต\nปัณฑรหัตถี\nปัณณะ\nปัณณาส\nปัณณาสก์\nปัณรสี\nปัณหิ\nปัด\nปัตคาด\nปัตตะ\nปัตตานึก\nปัตตานุโมทนา\nปัตตาเวีย\nปัตติ\nปัตติก\nปัตถร\nปัตถะ\nปัตนิ\nปัตนี\nปัตหล่า\nปัถพี\nปัถวี\nปัทม์\nปัทมะ\nปัทมาสน์\nปัน\nปั่น\nปั้น\nปั้นจั่น\nปันจุเหร็จ\nปั้นลม\nปั้นหยา\nปั้นเหน่ง\nปับ\nปั๊บ\nปัปผาสะ\nปัพพาชนียกรรม\nปัพภาระ\nปั๊ม\nปัยกา\nปัยยิกา\nปัวเปีย\nปัศจิม\nปัศตัน\nปัสสาวะ\nปัสสาสะ\nปา\nป่า\nป้า\nปาก\nปาง\nป้าง\nปาจรีย์\nปาจิตตีย์\nปาจีน\nปาฏลิ\nปาฏิบท\nปาฏิบุคลิก\nปาฏิโภค\nปาฏิหาริย์\nปาฐก\nปาฐกถา\nปาฐะ\nปาณกชาติ\nปาณะ\nปาณาติบาต\nปาณิ\nปาณี\nปาด\nปาติโมกข์\nปาตี\nปาเต๊ะ\nปาท่องโก๋\nปาทังกา\nปาทาน\nปาทุกา\nปาน\nป่าน\nป้าน\nปานะ\nปานียะ\nป้าบ\nป๊าบ\nปาพจน์\nปาม\nปาโมกข์\nป่าย\nป้าย\nปายาส\nปาร์เกต์\nปารมี\nปารเมศ\nปาราชิก\nปาริฉัตร\nปาริชาต\nปารุสกวัน\nปาล\nปาล์ม\nปาลิไลยก์\nปาลี\nปาว\nป่าว\nป๊าว\nปาวาร\nปาษาณ\nปาส\nปาสาณ\nปาสาทิกะ\nปาหนัน\nปาหี่\nปำ\nป้ำ\nป้ำเป๋อ\nปิกนิก\nปิ้ง\nปิงคละ\nปิงปอง\nปิฎก\nปิฏฐะ\nปิฐิ\nปิณฑะ\nปิด\nปิตตะ\nปิตา\nปิตามหัยกา\nปิตามหัยยิกา\nปิตุ\nปิตุจฉา\nปิตุภูมิ\nปิตุลา\nปิโตรเลียม\nปิ่น\nปิ่นแก้ว\nปิ่นโต\nปิปผลี\nปิ่ม\nปิ้ม\nปิยะ\nปิยังคุ\nปิโยรส\nปิลันธน์\nปิ๋ว\nปิศาจ\nปิหกะ\nปี\nปี่\nปี้\nปี๋\nปีก\nปีฐะ\nปี๊ด\nปีติ\nปีน\nปีบ\nปี๊บ\nปีศาจ\nปีฬกะ\nปึก\nปึง\nปึ่ง\nปึ๋ง\nปึ้ด\nปึมปื้อ\nปืน\nปื้น\nปือ\nปื้อ\nปื๋อ\nปุ\nปุ๊\nปุก\nปุกปุย\nปุคละ\nปุ้งกี๋\nปุงควะ\nปุงลิงค์\nปุงลึงค์\nปุจฉา\nปุฏะ\nปุณฑริก\nปุด\nปุตตะ\nปุถุชน\nปุนนาค\nปุนภพ\nปุนัพสุ\nปุบ\nปุ๊บ\nปุปผะ\nปุปะ\nปุพพะ\nปุ่ม\nปุ่มป่ำ\nปุ้ม\nปุ๋ม\nปุย\nปุ้ย\nปุ๋ย\nปุรณะ\nปุระ\nปุราณ\nปุราณะ\nปุริมพรรษา\nปุเรจาริก\nปุโรหิต\nปุลลิงค์\nปุลลึงค์\nปุลินท์\nปุโลปุเล\nปุษยะ\nปุสสะ\nปู\nปู่\nปูชกะ\nปูชนียบุคคล\nปูชนียวัตถุ\nปูชนียะ\nปูชา\nปูชิต\nปูด\nปูน\nปูม\nปู้ยี่ปู้ยำ\nปูระ\nปูลู\nเป้\nเป๋\nเปก\nเป๊ก\nเป่ง\nเป้ง\nเป๋ง\nเป็ด\nเปตพลี\nเปตอง\nเป็น\nเปยยาล\nเปร็ง\nเปรต\nเปรม\nเปรย\nเปรอ\nเปรอะ\nเปราะ\nเปรียง\nเปรี้ยง\nเปรี้ยงปร้าง\nเปรียญ\nเปรียบ\nเปรี่ยม\nเปรียว\nเปรี้ยว\nเปรียะ\nเปรี๊ยะ\nเปรื่อง\nเปรื้อย\nเปล\nเปล่ง\nเปลว\nเปลา\nเปล่า\nเปล้า\nเปลาะ\nเปลี้ย\nเปลี่ยน\nเปลี่ยม\nเปลี่ยว\nเปลือก\nเปลือง\nเปลื้อง\nเปลือย\nเปศะ\nเปศัส\nเปสการ\nเปสละ\nเปสุญวาท\nเป๋อ\nเป้อเย้อ\nเปอร์\nเปอร์เซ็นต์\nเปะ\nเปา\nเป่า\nเป้า\nเป๊า\nเป๋า\nเป๋าฮื้อ\nเปาะ\nเป๊าะ\nเปาะเปี๊ยะ\nเปาะแปะ\nเปิก\nเปิง\nเปิงมาง\nเปิด\nเปิ่น\nเปิบ\nเปิ๊บ\nเปีย\nเปียก\nเปี๊ยก\nเปี๊ยบ\nเปี่ยม\nเปี้ยว\nเปี๊ยว\nเปียะ\nเปี๊ยะ\nเปือก\nเปื้อน\nเปื่อย\nแป\nแป้ง\nแป๋ง\nแปด\nแป๊ด\nแปทู\nแป้น\nแป๊น\nแปบ\nแป๊บ\nแปม\nแปร\nแปร๋\nแปรก\nแปรง\nแปร่ง\nแปร๊ด\nแปร้น\nแปร๋น\nแปรปรวน\nแประ\nแปล\nแปล้\nแปลก\nแปลง\nแปลน\nแปลบ\nแปล๊บ\nแป้ว\nแป๊ว\nแปะ\nแป๊ะ\nแป๊ะซะ\nโป\nโป่\nโป้\nโป๊\nโปก\nโป๊ก\nโป๊กเกอร์\nโปกขรณี\nโปกขรพรรษ\nโปเก\nโปง\nโป่ง\nโป้ง\nโป่งข่าม\nโปงลาง\nโป่งวิด\nโปฐบท\nโปฐปทมาส\nโปดก\nโปตถกะ\nโปน\nโป๊ป\nโป๊ยเซียน\nโปรแกรม\nโปรง\nโปร่ง\nโปรด\nโปรตอน\nโปรตีน\nโปรเตสแตนต์\nโปรแทรกเตอร์\nโปรย\nโปล่ง\nโปลิโอ\nโปโล\nโปสก\nโปสต์การ์ด\nโปะ\nโป๊ะ\nโป๊ะจ้าย\nไป\nไป่\nไป๋\nไปยาล\nไปรษณีย์\nไปรษณียบัตร\nไปรษณียภัณฑ์\nไปรษณียากร\nไปล่\nไปศาจ\nผก\nผกา\nผกาย\nผคม\nผง\nผงก\nผงม\nผงะ\nผงาด\nผง่าน\nผจง\nผจญ\nผจัญ\nผจาน\nผชุม\nผณิน\nผณินทร\nผณิศวร\nผด\nผดุง\nผเดิน\nผทม\nผนวก\nผนวช\nผนัง\nผนิด\nผนึก\nผม\nผยอง\nผรณาปีติ\nผรสุ\nผริต\nผรุสวาท\nผล\nผลคุน\nผลคุนี\nผล็อย\nผละ\nผลัก\nผลัด\nผลับ\nผลัวะ\nผลา\nผลาญ\nผลานิสงส์\nผลาผล\nผลาหาร\nผลิ\nผลิกะ\nผลิต\nผลิน\nผลี\nผลีผลาม\nผลึก\nผลึ่ง\nผลือ\nผลุ\nผลุง\nผลุด\nผลุน\nผลุนผลัน\nผลุบ\nผลุบผลับ\nผลุย\nผลู\nผวน\nผวย\nผวา\nผสม\nผสาน\nผอง\nผ่อง\nผ่อน\nผอบ\nผอม\nผ็อย\nผอูน\nผะ\nผะดา\nผะสา\nผัก\nผัคคุณ\nผัคคุณี\nผัง\nผัด\nผัน\nผับ\nผัว\nผัวะ\nผัสสะ\nผัสสาหาร\nผา\nผ่า\nผ้า\nผาก\nผาง\nผ่าง\nผาณิต\nผาด\nผาติ\nผ่าน\nผาม\nผาย\nผ่ายผอม\nผาล\nผาลคุน\nผาลา\nผ่าว\nผาสุก\nผ้าฮาด\nผำ\nผ้ำ\nผิ\nผิง\nผิด\nผิตะ\nผิน\nผิว\nผี\nผี้ว์\nผึง\nผึ่ง\nผึ้ง\nผึ้งรวง\nผืน\nผื่น\nผุ\nผุด\nผุยผง\nผุสราคา\nผู้\nผูก\nเผ\nเผง\nเผชิญ\nเผ็ด\nเผด็จ\nเผดิม\nเผดียง\nเผ่น\nเผนิก\nเผย\nเผยอ\nเผยิบ\nเผยิบผยาบ\nเผล\nเผล่\nเผล็ด\nเผลอ\nเผลอไผล\nเผละ\nเผลาะ\nเผลาะแผละ\nเผลียง\nเผอเรอ\nเผอิญ\nเผอิล\nเผะ\nเผา\nเผ่า\nเผ้า\nเผาะ\nเผิ้ง\nเผิน\nเผียน\nเผือ\nเผื่อ\nเผือก\nเผือด\nเผือน\nเผื่อน\nแผ่\nแผก\nแผง\nแผด\nแผน\nแผ่น\nแผนก\nแผล\nแผลง\nแผล็บ\nแผล็ว\nแผละ\nแผ่ว\nแผ้ว\nโผ\nโผง\nโผฏฐัพพะ\nโผน\nโผเผ\nโผย\nโผล่\nโผลกเผลก\nโผละ\nโผอน\nโผะ\nไผ\nไผ่\nไผท\nฝน\nฝรั่ง\nฝรั่งเศส\nฝ่อ\nฝอย\nฝัก\nฝัง\nฝั่ง\nฝัด\nฝัน\nฝา\nฝ่า\nฝ้า\nฝาก\nฝาง\nฝาด\nฝาน\nฝาย\nฝ่าย\nฝ้าย\nฝิ่น\nฝี\nฝี่\nฝีก\nฝึก\nฝืด\nฝืน\nฝุ่น\nฝูง\nเฝ้า\nเฝือ\nเฝือก\nเฝือง\nเฝื่อน\nแฝก\nแฝง\nแฝด\nใฝ่\nไฝ\nพก\nพกุล\nพง\nพงศ์\nพงศกร\nพงศธร\nพงศา\nพงศาวดาร\nพจน์\nพจนา\nพจนานุกรม\nพจนารถ\nพจนีย์\nพจมาน\nพจี\nพชระ\nพญา\nพญาลอ\nพณิช\nพณิชย์\nพดด้วง\nพธู\nพนันดร\nพนาดร\nพนาดอน\nพนาราม\nพนาลัย\nพนาลี\nพนาวาส\nพนาเวศ\nพนาศรม\nพนาสณฑ์\nพนาสัณฑ์\nพเนจร\nพ่น\nพ้น\nพนม\nพนอง\nพนอม\nพนัก\nพนักงาน\nพนัง\nพนัน\nพนัส\nพนา\nพนาย\nพนิดา\nพนิต\nพบ\nพม่า\nพยนต์\nพยศ\nพยัก\nพยักพเยิด\nพยัคฆ์\nพยัคฆา\nพยัคฆิน\nพยัคฆี\nพยัชน์\nพยัญชนะ\nพยัต\nพยับ\nพยากรณ์\nพยาฆร์\nพยางค์\nพยาธิ\nพยาน\nพยาบาท\nพยาบาล\nพยาม\nพยามะ\nพยายาม\nพยุ\nพยุง\nพยุหยาตรา\nพยุหโยธา\nพยุหเสนา\nพยุหะ\nพยู่ห์\nพเยีย\nพร\nพรต\nพรม\nพรรค\nพรรค์\nพรรคานต์\nพรรณ\nพรรณนา\nพรรดึก\nพรรลาย\nพรรษ\nพรรษา\nพรรเอิญ\nพรวด\nพรวน\nพรหม\nพรหมจรรย์\nพรหมจาริณี\nพรหมจารี\nพรหมา\nพรหมาสตร์\nพรหมินทร์\nพรอก\nพร่อง\nพร้อง\nพรอด\nพร้อม\nพร้อมพรัก\nพร่อย\nพร้อย\nพระ\nพระนอม\nพระนาด\nพระฮาม\nพรักพร้อม\nพรั่ง\nพรัด\nพรั่น\nพรับ\nพร่า\nพร้า\nพราก\nพราง\nพร่าง\nพราด\nพราน\nพราย\nพราว\nพราหมณ์\nพราหมณะ\nพราหมณี\nพราหมี\nพรำ\nพร่ำ\nพริก\nพริ้ง\nพริบ\nพริ้ม\nพรึง\nพรึน\nพรึบ\nพรึ่บ\nพรืด\nพรุ\nพรุ่ง\nพรุน\nพรู\nพรูด\nพฤกษ์\nพฤกษชาติ\nพฤกษเทวดา\nพฤกษราช\nพฤกษศาสตร์\nพฤกษา\nพฤฒ\nพฤฒา\nพฤฒาจารย์\nพฤฒิ\nพฤต\nพฤติ\nพฤทธ์\nพฤทธิ์\nพฤนต์\nพฤนท์\nพฤศจิก\nพฤศจิกายน\nพฤษภ\nพฤษภาคม\nพฤหัสบดี\nพล\nพละ\nพลากร\nพลาดิศัย\nพลาธิการ\nพลานามัย\nพลบ\nพลวก\nพลวง\nพลวัต\nพลศาสตร์\nพลอ\nพล้อ\nพลอง\nพลอด\nพลอน\nพลอมแพลม\nพลอย\nพล่อย\nพลั่ก\nพลัง\nพลั่ง\nพลั้ง\nพลัด\nพลัน\nพลับ\nพลับพลา\nพลับพลึง\nพลั่ว\nพล่า\nพลาง\nพลาญ\nพลาด\nพล่าน\nพลาม\nพล่าม\nพลาย\nพลาสติก\nพลาสมา\nพลาหก\nพลำ\nพล้ำ\nพลำภัง\nพลิก\nพลิพัท\nพลิ้ว\nพลี\nพลุ\nพลุก\nพลุ่ก\nพลุกพล่าน\nพลุ่ง\nพลุ้ย\nพลู\nพลูโต\nพลูโทเนียม\nพวก\nพวง\nพ่วง\nพวน\nพวย\nพสก\nพสุ\nพสุธา\nพสุสงกรานต์\nพหล\nพหุ\nพหุล\nพหู\nพอ\nพ่อ\nพ้อ\nพอก\nพอง\nพ้อง\nพอน\nพ้อม\nพอโลเนียม\nพะ\nพะงา\nพะงาบ\nพะจง\nพะทำมะรง\nพะนอ\nพะเน้าพะนอ\nพะเนิน\nพะเนียง\nพะแนง\nพะพาน\nพะพิง\nพะเพิง\nพะยอม\nพ่ะย่ะค่ะ\nพะยูง\nพะยูน\nพะเยิบ\nพะเยิบพะยาบ\nพะรุงพะรัง\nพะโล้\nพะไล\nพะวง\nพะวักพะวน\nพะวา\nพะว้าพะวัง\nพะอง\nพะอากพะอำ\nพะอืดพะอม\nพัก\nพักตร์\nพักตรา\nพักร\nพัง\nพังกา\nพังงา\nพังผืด\nพังพวย\nพังพอน\nพังพาน\nพังพาบ\nพังเพย\nพัช\nพัชนี\nพัชระ\nพัญจน์\nพัฒนะ\nพัฒนา\nพัฒนากร\nพัฒนาการ\nพัด\nพัดชา\nพัดดึงส์\nพัตร\nพัทธ์\nพัทธยา\nพัทธสีมา\nพัทร\nพัน\nพันตู\nพันทาง\nพันธ์\nพันธะ\nพันธกรณี\nพันธบัตร\nพันธมิตร\nพันธนะ\nพันธนาคาร\nพันธนาการ\nพันธุ์\nพันธุกรรม\nพันลึก\nพันลือ\nพันเลิศ\nพันเอิญ\nพับ\nพัลลภ\nพัลวัน\nพัว\nพัวะ\nพัศดี\nพัสดุ\nพัสตร์\nพัสถาน\nพา\nพาก\nพากเพียร\nพากย์\nพาง\nพ่าง\nพาชี\nพาณิช\nพาณิชย์\nพาณิชยกรรม\nพาณิชยการ\nพาณิชยศาสตร์\nพาณิชยศิลป์\nพาณินี\nพาณี\nพาณีนี\nพาด\nพาท\nพาทย์\nพาธ\nพาธา\nพาน\nพ่าน\nพานร\nพานรินทร์\nพาม\nพาย\nพ่าย\nพายม้า\nพายัพ\nพายุ\nพาร์เซก\nพารณ\nพารา\nพาราฟิน\nพาล\nพาลา\nพาลี\nพาลุก\nพาโล\nพาไล\nพาส\nพาสน์\nพาสนา\nพาสุกรี\nพ่าห์\nพาหนะ\nพาหะ\nพาหา\nพาหิรกะ\nพาหิระ\nพาหุ\nพาหุรัด\nพาหุสัจจะ\nพาเหียร\nพาฬ\nพำ\nพำนัก\nพำพึม\nพำลา\nพิกล\nพิกสิต\nพิกัด\nพิกัติ\nพิกัน\nพิการ\nพิกุล\nพิเคราะห์\nพิฆน์\nพิฆเนศ\nพิฆเนศวร\nพิฆาต\nพิง\nพิจัย\nพิจาร\nพิจารณ์\nพิจารณา\nพิจิก\nพิจิต\nพิจิตร\nพิชญ์\nพิชัย\nพิชาน\nพิชิต\nพิเชฐ\nพิเชียร\nพิฑูรย์\nพิณ\nพิดทูล\nพิดาน\nพิโดร\nพิตร\nพิถี\nพิถีพิถัน\nพิทย\nพิทย์\nพิทยา\nพิทยาคม\nพิทยาคาร\nพิทยาธร\nพิทยาลัย\nพิทักษ์\nพิทูร\nพิเทศ\nพิธาน\nพิธี\nพิธุ\nพินทุ\nพินอบพิเทา\nพินัย\nพินาศ\nพินิจ\nพินิต\nพินิศ\nพินิศจัย\nพิเนต\nพิบัติ\nพิบุล\nพิบูล\nพิปริต\nพิปลาส\nพิพรรธ\nพิพรรธน์\nพิพักพิพ่วน\nพิพัฒ\nพิพัฒน์\nพิพากษ์\nพิพากษา\nพิพาท\nพิพิธ\nพิพิธภัณฑ์\nพิพิธภัณฑสถาน\nพิภพ\nพิภัช\nพิภาค\nพิภูษณะ\nพิเภก\nพิมปะการัง\nพิมพ์\nพิมพการัง\nพิมพา\nพิมพาภรณ์\nพิมล\nพิมเสน\nพิมาน\nพิมุข\nพิโมกข์\nพิโมกษ์\nพิโยกพิเกน\nพิโยค\nพิรอด\nพิระ\nพิรากล\nพิราบ\nพิราม\nพิราลัย\nพิริยะ\nพิรี้พิไร\nพิรุณ\nพิรุธ\nพิรุฬห์\nพิเรนทร์\nพิเราะ\nพิโรธ\nพิไร\nพิลังกาสา\nพิลาป\nพิลาส\nพิลิปดา\nพิลึก\nพิลึกกึกกือ\nพิลึกพิลั่น\nพิโลน\nพิไล\nพิศ\nพิศวง\nพิศวาส\nพิศาล\nพิศุทธ์\nพิศุทธิ์\nพิเศษ\nพิษ\nพิษฐาน\nพิษนาศน์\nพิสดาร\nพิสมร\nพิสมัย\nพิสัง\nพิสัช\nพิสัย\nพิสิฐ\nพิสุทธิ์\nพิสูจน์\nพิหค\nพิหาร\nพิฬาร\nพี\nพี่\nพี้\nพีชคณิต\nพีระมิด\nพึง\nพึ่ง\nพึ่บ\nพึ่บพั่บ\nพึม\nพึมพำ\nพืช\nพืด\nพื้น\nพุ\nพุก\nพุกาม\nพุง\nพุ่ง\nพุงดอ\nพุฒ\nพุฒิ\nพุด\nพุดตาน\nพุทธ\nพุทธะ\nพุทธังกูร\nพุทธางกูร\nพุทธันดร\nพุทธาภิเษก\nพุทธาวาส\nพุทธิ\nพุทโธ่\nพุทรา\nพุธ\nพุ่ม\nพุมเรียง\nพุ้ย\nพู\nพูพอน\nพู่\nพูด\nพูน\nพู้น\nพู่ระหง\nเพ\nเพ็ก\nเพกา\nเพคะ\nเพ็ง\nเพ่ง\nเพ็จ\nเพชฉลูกรรม\nเพชฌฆาต\nเพชร\nเพชรดา\nเพชรปาณี\nเพชรฤกษ์\nเพชรายุธ\nเพชรกลับ\nเพชรสังฆาต\nเพชรหลีก\nเพชรหึง\nเพ็ญ\nเพฑูริย์\nเพณี\nเพ็ดทูล\nเพดาน\nเพท\nเพทนา\nเพทาย\nเพทุบาย\nเพโทบาย\nเพ่นพ่าน\nเพนียด\nเพไนย\nเพ้ย\nเพรง\nเพรซีโอดิเมียม\nเพรา\nเพราะ\nเพริด\nเพริศ\nเพรียก\nเพรียง\nเพรียบ\nเพรี้ยม\nเพรียว\nเพรื่อ\nเพรือง\nเพล\nเพลง\nเพล็ด\nเพล้โพล้\nเพลา\nเพลาะ\nเพลิง\nเพลิดเพลิน\nเพลิน\nเพลีย\nเพลี้ย\nเพลี่ยง\nเพศ\nเพส\nเพสลาด\nเพ่อ\nเพ้อ\nเพ้อเจ้อ\nเพอิญ\nเพะ\nเพา\nเพาะ\nเพิก\nเพิง\nเพิ่ง\nเพิดเพ้ย\nเพิ่ม\nเพี้ย\nเพียง\nเพี้ยง\nเพียงออ\nเพี้ยน\nเพียบ\nเพียร\nเพื่อ\nเพื่อน\nแพ\nแพ้\nแพง\nแพ่ง\nแพงพวย\nแพทย์\nแพทยศาสตร์\nแพน\nแพ่น\nแพนก\nแพนงเชิง\nแพร\nแพร่\nแพรก\nแพร่ง\nแพรว\nแพร้ว\nแพลง\nแพลทินัม\nแพล็บ\nแพลม\nแพลเลเดียม\nแพละ\nแพละโลม\nแพว\nแพ้ว\nแพศย์\nแพศยา\nแพะ\nโพ\nโพก\nโพกพาย\nโพง\nโพงพาง\nโพชฌงค์\nโพซิตรอน\nโพด\nโพทะเล\nโพแทสเซียม\nโพธ\nโพธิ\nโพธิ์\nโพน\nโพ้น\nโพนทะนา\nโพบาย\nโพย\nโพยก๊วน\nโพยม\nโพรก\nโพรง\nโพรโทแอกทิเนียม\nโพรมีเทียม\nโพระดก\nโพล่\nโพลง\nโพล่ง\nโพล้ง\nโพลน\nโพล้เพล้\nโพละ\nโพสพ\nไพ\nไพ่\nไพจิตร\nไพชน\nไพชยนต์\nไพฑูรย์\nไพที\nไพบูลย์\nไพพรรณ\nไพร\nไพร่\nไพรจิตร\nไพรชน\nไพรชยนต์\nไพรฑูรย์\nไพรที\nไพรบูลย์\nไพรเราะ\nไพรัช\nไพรำ\nไพริน\nไพรินทร์\nไพรี\nไพเราะ\nไพโรจน์\nไพล\nไพล่\nไพศาขะ\nไพศาล\nไพเศษ\nไพสพ\nไพสิฐ\nไพหาร\nฟก\nฟ้ง\nฟรักโทส\nฟรี\nฟลูออรีน\nฟ่อ\nฟ้อ\nฟอก\nฟอง\nฟ่อง\nฟ้อง\nฟอด\nฟอน\nฟ่อน\nฟ้อน\nฟ้อแฟ้\nฟอร์มาลดีไฮด์\nฟอร์มาลิน\nฟอสฟอรัส\nฟอสเฟต\nฟัก\nฟักฟุ้น\nฟัง\nฟังก์ชัน\nฟัด\nฟัน\nฟั่น\nฟั้น\nฟ้า\nฟาก\nฟาง\nฟ่าง\nฟาด\nฟาทอม\nฟาน\nฟ่าม\nฟาย\nฟาร์ม\nฟาสซิสต์\nฟิด\nฟิต\nฟิบ\nฟิล์ม\nฟิวส์\nฟิสิกส์\nฟี่\nฟี้\nฟืดฟาด\nฟืน\nฟื้น\nฟืม\nฟุ\nฟุ้ง\nฟุต\nฟุน\nฟุบ\nฟุ่บ\nฟุ่มเฟือย\nฟุลสแก๊ป\nฟู\nฟู่\nฟูก\nฟูด\nฟูม\nเฟ็ด\nเฟ้น\nเฟลด์สปาร์\nเฟ้อ\nเฟอร์เมียม\nเฟอะ\nเฟอะฟะ\nเฟะ\nเฟะฟะ\nเฟิน\nเฟี้ยม\nเฟี้ยว\nเฟือ\nเฟื้อ\nเฟือง\nเฟื่อง\nเฟื้อง\nเฟือน\nเฟือย\nเฟื้อย\nแฟ่\nแฟง\nแฟชั่น\nแฟน\nแฟบ\nแฟ้ม\nแฟรนเซียม\nแฟลกซ์\nแฟลต\nแฟะ\nโฟกัส\nไฟ\nภควดี\nภควัต\nภควันต์\nภควัม\nภควา\nภควาน\nภคะ\nภคันทลา\nภคินี\nภณะ\nภณิดา\nภพ\nภมร\nภมริน\nภมรี\nภมุกา\nภยันตราย\nภยาคติ\nภระ\nภรณี\nภรต\nภรรดร\nภรรดา\nภรรยา\nภระมร\nภระมรี\nภราดร\nภราดรภาพ\nภราดา\nภริยา\nภฤศ\nภวะ\nภวตัณหา\nภวนะ\nภวังค์\nภวังคจิต\nภักดี\nภักตะ\nภักติ\nภักษ์\nภักษา\nภักษาหาร\nภัค\nภัคน์\nภังคะ\nภังคี\nภัจ\nภัณฑ์\nภัณฑาคาร\nภัณฑาคาริก\nภัณฑารักษ์\nภัณฑนะ\nภัณฑู\nภัต\nภัตตาคาร\nภัตตาหาร\nภัตร\nภัทระ\nภัทรกัป\nภัพ\nภัย\nภัสดา\nภัสตรา\nภัสมะ\nภัสสร\nภา\nภาค\nภาคย์\nภาคยานุวัติ\nภาคินี\nภาคิไนย\nภาคี\nภาคียะ\nภาชนะ\nภาชี\nภาณ\nภาณวาร\nภาณกะ\nภาณี\nภาณุ\nภาดร\nภาดา\nภาตระ\nภาตา\nภาตุ\nภาติกะ\nภาติยะ\nภาพ\nภาพย์\nภาม\nภาย\nภาร\nภาระ\nภารดี\nภารต\nภารตี\nภารยทรัพย์\nภารยา\nภารา\nภาวนา\nภาวะ\nภาษ\nภาษณ์\nภาษา\nภาษิต\nภาษี\nภาส\nภาสน์\nภาสวร\nภาสา\nภาสุระ\nภิกขา\nภิกขาจาร\nภิกขุ\nภิกขุนี\nภิกษา\nภิกษาจาร\nภิกษาหาร\nภิกษุ\nภิกษุณี\nภิงคาร\nภิญโญ\nภิตติ\nภินท์\nภินทนาการ\nภิยโย\nภิรมย์\nภิรมย์สุรางค์\nภิษัช\nภิสัก\nภีตะ\nภีมะ\nภีรุ\nภุกต์\nภุขัน\nภุช\nภุชงค์\nภุต\nภุมมะ\nภุมรัตน์\nภุมวาร\nภุมรา\nภุมริน\nภุมรี\nภุมเรศ\nภู\nภู่\nภูต\nภูติ\nภูม\nภูมิ\nภูมี\nภูริ\nภูรี\nภูวดล\nภูวนาถ\nภูวเนตร\nภูวไนย\nภูษา\nภูษิต\nเภกะ\nเภตรา\nเภท\nเภทุบาย\nเภรี\nเภสัช\nโภค\nโภคะ\nโภคิน\nโภคี\nโภไคย\nโภไคศวรรย์\nโภช\nโภชย์\nโภชก\nโภชนะ\nโภชนา\nโภชนาหาร\nโภชนียะ\nไภริน\nไภรี\nไภษัชคุรุ\nไภษัชย์\nมกร\nมกราคม\nมกุฎ\nมคธ\nมฆวัน\nมฆะ\nมฆา\nม่ง\nมงกุฎ\nมงโกรย\nมงคล\nมงคลวาร\nมณฑ์\nมณฑก\nมณฑนะ\nมณฑป\nมณฑล\nมณฑา\nมณฑารพ\nมณฑิระ\nมณเฑียร\nมณี\nมด\nมตะ\nมตกภัต\nมติ\nมทนะ\nมทะ\nมธุ\nมธุกร\nมธุการี\nมธุลีห์\nมธุระ\nมธุรพจน์\nมน\nมนินทรีย์\nม่น\nมนต์\nมนตร์\nมนตรี\nมนท์\nมนทิราลัย\nมนเทียร\nมนสิการ\nมนัส\nมนัสวี\nมนินทรีย์\nมนิมนา\nมนิลา\nมนุญ\nมนุษย์\nมนุษยชาติ\nมนุษยธรรม\nมนุษย์มนา\nมนุษยโลก\nมนุษยศาสตร์\nมนุษยสัมพันธ์\nมนุสาร\nมนู\nมนูสาร\nมโน\nมโนช\nมโนชญ์\nมโนราห์\nมโนสาเร่\nมโนห์รา\nมมังการ\nมยุรฉัตร\nมยุระ\nมยุรา\nมยุรี\nมยุเรศ\nมยูร\nมรกต\nมรคา\nมรฑป\nมรณ์\nมรณะ\nมรณกรรม\nมรณบัตร\nมรณภัย\nมรณภาพ\nมรดก\nมรรค\nมรรคา\nมรรตัย\nมรรยาท\nมรรษ\nมรสุม\nมริจ\nมริยาท\nมรีจิ\nมรุต\nมฤค\nมฤคย์\nมฤคศิระ\nมฤคศิรมาส\nมฤคเศียร\nมฤคินทร์\nมฤเคนทร์\nมฤดก\nมฤต\nมฤตยู\nมฤทุ\nมล\nมละ\nมลัก\nมลังเมลือง\nมล้าง\nมลาย\nมลายู\nมวก\nม่วง\nมวน\nม่วน\nม้วน\nม้วนต้วน\nมวย\nม้วย\nมวล\nมหกรรม\nมหรณพ\nมหรรณพ\nมหรสพ\nมหัจฉริยะ\nมหัต\nมหัทธนะ\nมหันต์\nมหันตโทษ\nมหัพภาค\nมหัศจรรย์\nมหา\nมหากฐิน\nมหากาฬ\nมหาขันธกะ\nมหาจักร\nมหาชน\nมหาชัย\nมหาชาติ\nมหาโชตรัต\nมหาดไทย\nมหาดเล็ก\nมหาตมะ\nมหาไถ่\nมหาเทพ\nมหาเทพี\nมหาเทวี\nมหาธาตุ\nมหานิกาย\nมหานิล\nมหาบพิตร\nมหาบัณฑิต\nมหาพน\nมหาพรหม\nมหาภารตะ\nมหาภิเนษกรมณ์\nมหาภูต\nมหาเมฆ\nมหายาน\nมหายุค\nมหาราช\nมหาฤกษ์\nมหาละลวย\nมหาละลาย\nมหาวงศ์\nมหาวรรค\nมหาวิทยาลัย\nมหาศักราช\nมหาศาล\nมหาสงกรานต์\nมหาสดมภ์\nมหาสดำ\nมหาสมุทร\nมหาสาวก\nมหาหงส์\nมหาหิงคุ์\nมหาอำนาจ\nมหาอุจ\nมหาอุด\nมหาอุปรากร\nมหาอุปราช\nมหิ\nมหิดล\nมหิธร\nมหิป\nมหิงส์\nมหิทธิ\nมหินท์\nมหิมา\nมหิศร\nมหิศวร\nมหิษ\nมหิษี\nมหึมา\nมเหยงค์\nมเหศ\nมเหศวร\nมเหศักดิ์\nมเหสักข์\nมเหสิ\nมเหสี\nมเหาฬาร\nมโหรสพ\nมโหระทึก\nมโหรี\nมโหฬาร\nมไหศวรรย์\nมอ\nมอง\nมองโกลอยด์\nมองโกเลีย\nมองคร่อ\nมอญ\nมอด\nม่อต้อ\nมอเตอร์\nมอเตอร์ไซค์\nมอน\nม่อน\nมอบ\nมอม\nมอมแมม\nม่อย\nมอร์ฟีน\nมอระกู่\nมอลโทส\nม่อลอกม่อแลก\nม่อห้อม\nม่อฮ่อม\nมะ\nมะกรูด\nมะกล่ำ\nมะกอก\nมะก่อง\nมะกะโรนี\nมะกา\nมะเกลือ\nมะเกี๋ยง\nมะข่วง\nมะขวิด\nมะขาม\nมะเขือ\nมะแข่น\nมะคังแดง\nมะค่า\nมะคำไก่\nมะคำดีควาย\nมะงั่ว\nมะงุมมะงาหรา\nมะซัก\nมะซาง\nมะดัน\nมะดีหวี\nมะดูก\nมะเดหวี\nมะเดื่อ\nมะต้อง\nมะตะบะ\nมะตาด\nมะตาหะรี\nมะตึ่ง\nมะตื๋น\nมะตูม\nมะแตก\nมะโต\nมะนาว\nมะปราง\nมะปริง\nมะฝ่อ\nมะพร้าว\nมะพลับ\nมะพูด\nมะแพน\nมะแพร้ว\nมะเฟือง\nมะแฟน\nมะไฟ\nมะม่วง\nมะม่าว\nมะมี่\nมะมื่น\nมะมุด\nมะเมอ\nมะเมีย\nมะเมื่อย\nมะแม\nมะยง\nมะยม\nมะระ\nมะริด\nมะรืน\nมะรุม\nมะรุมมะตุ้ม\nมะเร็ง\nมะเรื่อง\nมะโรง\nมะลอกมะแลก\nมะละกอ\nมะลิ\nมะลื่น\nมะลืมดำ\nมะลุลี\nมะแว้ง\nมะสัง\nมะเส็ง\nมะหวด\nมะหะหมัด\nมะหาด\nมะหิ่ง\nมะเหงก\nมะอึก\nมะฮอกกานี\nมัก\nมักกะโรนี\nมักกะลีผล\nมักกะสัน\nมักขะ\nมั่กขั้ก\nมักขิกา\nมัค\nมัคคะ\nมัคคุเทศก์\nมัคนายก\nมัฆวาน\nมั่ง\nมังกง\nมังกร\nมังกุ\nมังคละ\nมังค่า\nมังคุด\nมังตาน\nมังสวิรัติ\nมังสะ\nมังสี\nมัจจะ\nมัจจุ\nมัจฉริยะ\nมัจฉรี\nมัจฉะ\nมัจฉา\nมัชชะ\nมัชวิรัติ\nมัชชาระ\nมัชฌันติกสมัย\nมัชฌิม\nมัชฌิมา\nมัญจา\nมัญชิษฐา\nมัญชุ\nมัญชุสา\nมัญชูสา\nมัญเชฏฐะ\nมัฏฐะ\nมัณฑนศิลป์\nมัณฑนา\nมัด\nมัตตะ\nมัตตัญญู\nมัตตา\nมัตติกา\nมัตถกะ\nมัตถลุงค์\nมัตสยะ\nมัตสยา\nมัตสระ\nมัตสริน\nมัททวะ\nมัทนะ\nมัทยะ\nมัธยฐาน\nมัธยม\nมัธยันห์\nมัธยัสถ์\nมัน\nมั่น\nมันตา\nมันถะ\nมันทิระ\nมันทิราลัย\nมับ\nมั้ม\nมัมมี่\nมัย\nมัลละ\nมัลลิกา\nมัว\nมัวซัว\nมั่ว\nมัศยา\nมัสตุ\nมัสตาร์ด\nมัสมั่น\nมัสยิด\nมัสรู่\nมัสลิน\nมัสสุ\nมา\nม้า\nมาก\nมาคสิระ\nมาฆบูชา\nมาฆะ\nม้าง\nมางสะ\nมาณพ\nมาณวิกา\nมาด\nมาดา\nมาตงค์\nมาตร\nมาตรา\nมาตฤ\nมาตังคะ\nมาตา\nมาตามหัยกะ\nมาตามหัยกา\nมาตามหัยยิกา\nมาติกะ\nมาติกา\nมาตุ\nมาตุจฉา\nมาตุรงค์\nมาตุเรศ\nมาตุละ\nมาตุลา\nมาตุลานี\nมาทะ\nมาธยมิก\nมาธยมิกะ\nมาธุระ\nมาธุสร\nมาธูระ\nมาน\nม่าน\nม้าน\nมานพ\nมานะ\nมานัต\nมานัส\nมานิต\nมานี\nมานุษ\nมานุษยวิทยา\nมาโนชญ์\nมาบ\nมาภา\nม้าม\nม่าย\nมายา\nมาร\nมาราธิราช\nมารค\nมารดร\nมารดา\nมารยา\nมารยาท\nมารศรี\nมารษา\nมาริต\nมารุต\nมาลย์\nมาลัย\nมาลา\nมาลาตี\nมาลาเรีย\nมาลินี\nมาลี\nมาลุต\nมาศ\nมาส\nมาสก\nมาห์\nม่าห์\nมาหิส\nม่าเหมี่ยว\nมาฬก\nมิ\nมิค\nมิคสัญญี\nมิ่ง\nมิจฉา\nมิด\nมิตร\nมิติ\nมิเตอร์\nมิถยา\nมิถุน\nมิถุนายน\nมิทธะ\nมินตรา\nมินตา\nมินหม้อ\nมิ่ม\nมิ้ม\nมิไย\nมิรันตี\nมิลลิกรัม\nมิลลิบาร์\nมิลลิเมตร\nมิลลิลิตร\nมิลักขะ\nมิลักขู\nมิส\nมิสกรี\nมิสกวัน\nมิสซา\nมี\nมี่\nมีด\nมีเทน\nมีน\nมีนาคม\nมี่สั้ว\nมึง\nมึน\nมืด\nมืน\nมื่น\nมือ\nมื้อ\nมุ\nมุก\nมุกดา\nมุกดาหาร\nมุกุระ\nมุข\nมุขเด็จ\nมุขยประโยค\nมุโขโลกนะ\nมุคคะ\nมุง\nมุ่ง\nมุ้ง\nมุจฉา\nมุจนะ\nมุจลินท์\nมุญจนะ\nมุญชะ\nมุฐิ\nมุณฑกะ\nมุณฑะ\nมุด\nมุตกิด\nมุตฆาต\nมุตตะ\nมุตตา\nมุตติ\nมุตะ\nมุติ\nมุททา\nมุทธชะ\nมุทธา\nมุทธาภิเษก\nมุทรา\nมุทริกา\nมุทะลุ\nมุทา\nมุทิกา\nมุทิงค์\nมุทิตา\nมุทุ\nมุทุตา\nมุ่น\nมุนิ\nมุนินทร์\nมุนี\nมุบ\nมุบมิบ\nมุม\nมุ้ม\nมุ่ย\nมุรธา\nมุรธาภิเษก\nมุสละ\nมุสลิม\nมุสา\nมุสิก\nมุหงิด\nมุหน่าย\nมุหุต\nมุฮัมมัด\nมูก\nมูเซอ\nมูตร\nมู่ทู่\nมูน\nมูมมาม\nมูรติ\nมูรธา\nมูรธาภิเษก\nมูล\nมูละ\nมูลา\nมูลิกากร\nมู่ลี่\nมู่เล่\nมูสัง\nมูสิก\nมูสิกะ\nมูสิกทันต์\nเม\nเม็ก\nเมกะเฮิรตซ์\nเมขลา\nเมฆ\nเมฆา\nเมฆินทร์\nเมฆี\nเม็ง\nเม็ด\nเมตตา\nเมตไตรย\nเมตร\nเมตริก\nเมตริกตัน\nเมถุน\nเมท\nเมโท\nเมทนี\nเมทินี\nเมทนีดล\nเมทานอล\nเมทิลแอลกอฮอล์\nเมธ\nเมธา\nเมธาวี\nเมธี\nเมน\nเม่น\nเม้น\nเมนเดลีเวียม\nเมนทอล\nเม้ม\nเมรัย\nเมริเดียน\nเมรุ\nเมล์\nเมล็ด\nเมลือง\nเมษ\nเมษายน\nเมห์\nเมหนะ\nเมหะ\nเมะ\nเมา\nเม่า\nเม้า\nเมารี\nเมาลี\nเมาฬี\nเมาะ\nเมิง\nเมิน\nเมิล\nเมีย\nเมียง\nเมี่ยง\nเมี้ยน\nเมือ\nเมื้อ\nเมื่อ\nเมือก\nเมือง\nเมือบ\nเมื่อย\nแม่\nแม้\nแมก\nแมกนีเซียม\nแมง\nแมงกะพรุน\nแมงกานิน\nแมงกานีส\nแมงคา\nแมงคาเรือง\nแมงช้าง\nแมงดา\nแมงลัก\nแม่ตะงาว\nแมน\nแม่น\nแม้น\nแมลง\nแมลบ\nแมว\nแม้ว\nแมะ\nโม\nโม่\nโม้\nโมก\nโมกข์\nโมกษะ\nโมฆกรรม\nโมฆสัญญา\nโมฆะ\nโมฆียกรรม\nโมฆียะ\nโมง\nโม่ง\nโมงครุ่ม\nโมทนา\nโมโนแซ็กคาไรด์\nโมไนย\nโมเม\nโมเมนต์\nโมเย\nโมรา\nโมรี\nโมเรส\nโมลิบดีนัม\nโมลี\nโมเลกุล\nโมเสก\nโมเสส\nโม่ห์\nโมหะ\nโมหันธ์\nโมหาคติ\nโมโห\nไม่\nไม้\nไมกา\nไมครอน\nไมโครกรัม\nไมโครฟิล์ม\nไมโครโฟน\nไมโครมิเตอร์\nไมโครเมตร\nไมโครลิตร\nไมโครเวฟ\nไมตรี\nไมยราบ\nไมล์\nยก\nยกกระบัตร\nยกนะ\nยง\nยงโย่\nยชุรเวท\nยติ\nยติภังค์\nยถากรรม\nยถาภูตญาณ\nย่น\nยนต์\nยนตร์\nยม\nยมก\nยมโดย\nยมนา\nยมล\nยมะ\nยรรยง\nยล\nยวง\nยวด\nยวน\nยวบ\nย้วย\nยวรยาตร\nยศ\nยโส\nยอ\nย่อ\nยอก\nย็อกแย็ก\nยอง\nย่อง\nย้อง\nยอด\nยอน\nย้อน\nยอบ\nยอม\nย่อม\nย้อม\nย่อย\nย้อย\nย้อแย้\nยะ\nย่ะ\nยะยอบ\nยะยับ\nยัก\nยักข์\nยักขินี\nยักษ์\nยักษา\nยักษิณี\nยักษี\nยัง\nยั้ง\nยั่งยืน\nยัชโญปวีต\nยัญ\nยัญญะ\nยัด\nยัติภังค์\nยัน\nยั่น\nยันต์\nยันตร\nยันตร์\nยันตรกรรม\nยั่นตะนี\nยับ\nยั่ว\nยั้ว\nยั้วเยี้ย\nยัวรยาตร\nยัวะ\nยัษฏิ\nยา\nย่า\nยาก\nยาคุ\nยาคู\nยาง\nย่าง\nยางพารา\nยาจก\nยาจนา\nยาไฉน\nยาด\nยาดา\nยาตร\nยาตรา\nยาน\nย่าน\nย่านพาโหม\nยานมาศ\nยานุมาศ\nยานี\nยาม\nย่าม\nยามะ\nยามักการ\nยามา\nยาย\nย้าย\nยายี\nยาว\nย้าว\nยาวกาลิก\nยาวชีวิก\nยาสูบ\nย่าหยา\nยาหยี\nยำ\nย่ำ\nย้ำ\nยำเยีย\nยิก\nยิง\nยิ่ง\nยิฏฐะ\nยิน\nยิบ\nยิบหยี\nยิปซัม\nยิปซี\nยิ้ม\nยิมนาสติก\nยิหวา\nยี\nยี่\nยี้\nยี่ก่า\nยี่เก\nยี่เข่ง\nยี่โถ\nยีน\nยี่โป้\nยี่ภู่\nยีราฟ\nยี่สก\nยี่สง\nยี่สน\nยี่สาน\nยี่สุ่น\nยี่หระ\nยี่หร่า\nยี่ห้อ\nยี่หุบ\nยึกยัก\nยึกยือ\nยึด\nยืด\nยืน\nยื่น\nยืม\nยื้อ\nยุ\nยุกกระบัตร\nยุกดิ\nยุกติ\nยุกติธรรม\nยุกต์\nยุค\nยุคนธร\nยุคล\nยุคันต์\nยุคันธร\nยุคุนธร\nยุง\nยุ่ง\nยุ้ง\nยุด\nยุต\nยุติ\nยุทธ\nยุทธ์\nยุทธนา\nยุทโธปกรณ์\nยุบ\nยุ่บ\nยุ่บยั่บ\nยุบล\nยุพดี\nยุพเรศ\nยุพา\nยุพาน\nยุพาพาล\nยุพาพิน\nยุ่มย่าม\nยุ่ย\nยุ้ย\nยุรยาตร\nยูรยาตร\nยุวชน\nยุวดี\nยุวราช\nยุวา\nยุวาน\nยู\nยู่\nยูง\nยูโด\nยูถะ\nยูถิกา\nยูริก\nยูเรนัส\nยูเรเนียม\nยูโรเพียม\nเย\nเย้\nเยง\nเยซู\nเย็ด\nเย็น\nเย็นตาโฟ\nเย็นเตาโฟ\nเย็บ\nเย้ย\nเยอ\nเย่อ\nเยอรมัน\nเยอว\nเย่อหยิ่ง\nเยอะ\nเยอะแยะ\nเยา\nเย้า\nเยาว์\nเยาวชน\nเยาวมาลย์\nเยาวยอด\nเยาวราช\nเยาวเรศ\nเยาวลักษณ์\nเยาวพา\nเยาวพาณี\nเยาวพาน\nเยาะ\nเยิง\nเยิน\nเยิ่น\nเยิ่นเย้อ\nเยินยอ\nเยิบ\nเยิบยาบ\nเยิ้ม\nเยีย\nเยี่ยง\nเยี่ยงอย่าง\nเยียงผา\nเยียดยัด\nเยียน\nเยียบ\nเยี่ยม\nเยียรบับ\nเยียรยง\nเยียว\nเยี่ยว\nเยียวยา\nเยือ\nเยื่อ\nเยื้อ\nเยือก\nเยือง\nเยื่อง\nเยื้อง\nเยือน\nเยื้อน\nแย่\nแย้\nแยก\nแยง\nแย่ง\nแย้ง\nแยงแย่\nแยงแย้\nแยบ\nแย็บ\nแยม\nแย้ม\nแยแส\nแยะ\nโย\nโย้\nโยก\nโยกเยก\nโยคาพจร\nโยคาวจร\nโยคเกณฑ์\nโยคยะ\nโยคะ\nโยคิน\nโยคี\nโยง\nโย่ง\nโย่งเย่ง\nโยงโย่\nโยชน์\nโยชนา\nโยถิกะ\nโยทะกา\nโยธวาทิต\nโยธา\nโยธิน\nโยน\nโยนก\nโยนิโส\nโยนี\nโยม\nโยโส\nใย\nไย\nไย่\nไยดี\nไยไพ\nรก\nรง\nรงค์\nรงควัตถุ\nรงรอง\nรจนา\nรจเรข\nรจเลข\nรจิต\nรชตะ\nรชนิ\nรชนี\nรชะ\nรณรงค์\nรด\nรดี\nรตนะ\nรตะ\nรติ\nรถ\nรน\nร่น\nรบ\nรบาญ\nรพี\nรม\nร่ม\nรมณี\nรมณีย์\nรมณียสถาน\nรมย์\nรมเยศ\nรยางค์\nรวก\nรวง\nร่วง\nรวด\nรวน\nรวนเร\nร่วน\nรวบ\nรวม\nร่วม\nรวย\nรวิ\nรวิวาร\nรวี\nรศนา\nรส\nรสนา\nรสสุคนธ์\nรสายนเวท\nรสิก\nรหัท\nรหัส\nรโห\nรโหฐาน\nรอ\nร่อ\nรอก\nรอง\nร่อง\nร้อง\nรองเง็ง\nร่องแร่ง\nรอด\nรอน\nร่อน\nร้อน\nรอบ\nรอบคอบ\nรอม\nรอมชอม\nรอมร่อ\nรอย\nร่อย\nร้อย\nร่อแร่\nระ\nระกะ\nระกา\nระกำ\nระเกะระกะ\nระคน\nระคาง\nระคาย\nระแคะ\nระฆัง\nระงม\nระงับ\nระแง้\nระโงกหิน\nระชวย\nระดม\nระดะ\nระดับ\nระดา\nระด่าว\nระดู\nระเด่น\nระเดียง\nระแด\nระตู\nระทก\nระทด\nระทม\nระทวย\nระทา\nระทึก\nระแทะ\nระนาด\nระนาบ\nระนาม\nระนาว\nระเนน\nระเนระนาด\nระเนียด\nระแนง\nระแนะ\nระบบ\nระบม\nระบอบ\nระบัด\nระบับ\nระบาด\nระบาย\nระบำ\nระบิล\nระบือ\nระบุ\nระเบง\nระเบ็ง\nระเบิด\nระเบียง\nระเบียน\nระเบียบ\nระแบบ\nระมัดระวัง\nระมาด\nระเมียร\nระย่อ\nระย่อม\nระยะ\nระยั้ง\nระยับ\nระย้า\nระยาบ\nระยำ\nระยิบระยับ\nระโยง\nระโยงระยาง\nระรวย\nระรอง\nระร่อน\nระรัว\nระราน\nระร่าย\nระริก\nระรี่\nระรึง\nระรื่น\nระรื้น\nระเร้ง\nระเริง\nระเรียง\nระเรื่อย\nระแรง\nระลวง\nระลอก\nระลึก\nระวัง\nระวาง\nระวาย\nระวิง\nระแวง\nระแวดระวัง\nระไว\nระส่ำระสาย\nระหกระเหิน\nระหง\nระหวย\nระหว่าง\nระหองระแหง\nระหัด\nระหาย\nระเห็จ\nระเหย\nระเหระหน\nระเหหน\nระเหิด\nระเหินระหก\nระแหง\nระโหย\nระอา\nระอิดระอา\nระอุ\nรัก\nรักข์\nรักขิต\nรักตะ\nรักบี้\nรักเร่\nรักแร้\nรักษ์\nรักษา\nรัง\nรั้ง\nรังเกียจ\nรังแก\nรังค์\nรังควาน\nรังแค\nรังรอง\nรังวัด\nรังสิ\nรังสี\nรังสิมันตุ์\nรังสิมา\nรัจฉา\nรัช\nรัชชูปการ\nรัชมังคลาภิเษก\nรัชชุ\nรัชฎาภิเษก\nรัชดาภิเษก\nรัชนะ\nรัชนี\nรัญจวน\nรัฏฐาภิปาลโนบาย\nรัฐ\nรัฐประศาสโนบาย\nรัฐประศาสนศาสตร์\nรัด\nรัต\nรัตกัมพล\nรัตมณี\nรัตคน\nรัตจันทน์\nรัตตัญญู\nรัตติ\nรัตน์\nรัตนะ\nรัตนโกสินทร์\nรัตนโกสินทรศก\nรัตนชาติ\nรัตนตรัย\nรัตนบัลลังก์\nรัตนวราภรณ์\nรัตนสิงหาสน์\nรัตนา\nรัตนากร\nรัตนาภรณ์\nรัตนาวลี\nรัตมา\nรัถ\nรัถยา\nรัทเทอร์ฟอร์เดียม\nรัน\nรั้น\nรันทด\nรันทวย\nรับ\nรัมณียสถาน\nรัมภา\nรัมมี่\nรัมย์\nรัย\nรัว\nรั่ว\nรั้ว\nรัศมิมัต\nรัศมิมาน\nรัศมี\nรัษฎากร\nรัสเซีย\nรัสสะ\nรัสสระ\nรา\nร่า\nร้า\nราก\nรากษส\nรากสาด\nราคะ\nราคจริต\nราคา\nราคิน\nราคี\nราง\nร่าง\nร้าง\nรางจืด\nรางชาง\nรางวัล\nราช\nราชกิจจานุเบกษา\nราชนิกุล\nราชวโรงการ\nราชญี\nราชดัด\nราชพฤกษ์\nราชมาณพ\nราชมาษ\nราชมาส\nราชย์\nราชสีห์\nราชะ\nราชัน\nราชันย์\nราชัย\nราชา\nราชาธิปไตย\nราชาธิราช\nราชาภิเษก\nราชายตนะ\nราชาวดี\nราชี\nราชินิกุล\nราชินีกุล\nราชินี\nราชินูปถัมภ์\nราชูปถัมภ์\nราชูปโภค\nราเชน\nราเชนทร์\nราเชนทรยาน\nราโชวาท\nราไชศวรรย์\nราญ\nราญรอน\nราด\nราต\nราตร\nราตรี\nราน\nร่าน\nร้าน\nราบ\nราพณ์\nราพณาสูร\nราม\nรามเกียรติ์\nรามสูร\nรามัญ\nรามา\nราย\nร่าย\nร้าย\nราว\nร้าว\nราวี\nราศี\nราษฎร\nราษฎร์\nราษตรี\nราษราตรี\nราหุ\nราหู\nรำ\nร่ำ\nรำคาญ\nรำงับ\nรำจวน\nรำบาญ\nรำพัน\nรำพาย\nรำพึง\nรำเพย\nรำไพ\nรำมะนา\nรำมะนาด\nรำมะร่อ\nร่ำรวย\nร่ำร่ำ\nรำไร\nรำลึก\nรำหัด\nรำหัส\nริ\nริก\nริดสีดวง\nริน\nริ้น\nริบ\nริบบิ้น\nริบรี่\nริบหรี่\nริปุ\nริปู\nริม\nริ้ว\nริษยา\nรี\nรี่\nรี้พล\nรีด\nรีดักชัน\nรีต\nรีเนียม\nรีบ\nรีม\nรีรอ\nรี้ริก\nรึง\nรึ้ง\nรื่น\nรื้น\nรื้อ\nรุ\nรุก\nรุกข์\nรุกขชาติ\nรุกขเทวดา\nรุกขมูล\nรุกขา\nรุกรุย\nรุ่ง\nรุ้ง\nรุงรัง\nรุ่งริ่ง\nรุจ\nรุจา\nรุจนะ\nรุจิ\nรุจี\nรุจิระ\nรุจิรา\nรุด\nรุต\nรุทธ์\nรุทระ\nรุธิร\nรุธิระ\nรุเธียร\nรุน\nรุ่น\nรุบรู่\nรุม\nรุ่ม\nรุ่มร่าม\nรุย\nรุ่ย\nรุรุ\nรุหะ\nรู\nรู่\nรู้\nรูจี\nรูด\nรูทีเนียม\nรูบิเดียม\nรูป\nรูปิยะ\nรูปี\nรูเล็ตต์\nเร่\nเรข\nเรขา\nเรขาคณิต\nเร็ง\nเร่ง\nเร้ง\nเรณุ\nเรณู\nเรดอน\nเรดาร์\nเรเดียม\nเร้น\nเรรวน\nเรไร\nเร็ว\nเร่ว\nเรวดี\nเรอ\nเร่อ\nเรา\nเร่า\nเร้า\nเราะ\nเริง\nเริด\nเริม\nเริ่ม\nเริ้ม\nเริศร้าง\nเรี่ย\nเรี้ย\nเรียก\nเรียง\nเรียด\nเรียน\nเรียบ\nเรียม\nเรี่ยม\nเรียว\nเรี่ยว\nเรี้ยวรก\nเรือ\nเรื่อ\nเรื้อ\nเรือก\nเรือง\nเรื่อง\nเรื้อง\nเรืองรอง\nเรือด\nเรือน\nเรื้อน\nเรื่อย\nแร\nแร่\nแรก\nแร็กเกต\nแรง\nแร่ง\nแร้ง\nแรด\nแร้นแค้น\nแรม\nแร้ว\nแระ\nโร\nโร่\nโรค\nโรคา\nโรคาพาธ\nโรง\nโรจ\nโรจน์\nโรเดียม\nโรตี\nโรท\nโรธ\nโรม\nโรมัน\nโรเมอร์\nโรย\nโรเร\nโรหิณี\nโรหิต\nไร\nไร่\nไร้\nไรย์\nฤกษ์\nฤกษณะ\nฤคเวท\nฤชา\nฤชุ\nฤณ\nฤดี\nฤดียา\nฤดู\nฤต\nฤติยา\nฤตุ\nฤทธา\nฤทธิ์\nฤทัย\nฤษภ\nฤษยา\nฤษี\nฤๅ\nฤๅดี\nฤๅทัย\nฤๅษี\nฤๅสาย\nลก\nล่ก\nลฆุ\nลง\nล่ง\nลงกา\nล้งเล้ง\nลด\nลดา\nลดาวัลย์\nลน\nล้น\nลบ\nลบอง\nลพ\nลพุช\nลม\nล่ม\nล้ม\nลมาด\nลรรลุง\nลลนา\nลลิต\nลวก\nลวง\nล่วง\nล้วง\nลวณะ\nลวด\nล้วน\nลวนลาม\nลวนะ\nล่วม\nลวะ\nลวิตร\nลหุ\nลหุกาบัติ\nล่อ\nล้อ\nลอก\nล็อก\nล็อกเกต\nลอกแลก\nลอการิทึม\nลอง\nล่อง\nลองกอง\nลองจิจูด\nลองไน\nลอด\nลอตเตอรี่\nลอน\nล่อน\nลอบ\nลอม\nล้อม\nลอมชอม\nลอมพอก\nลอย\nล่อย\nล่อแล่\nลอว์เรนเซียม\nลออ\nละ\nล่ะ\nละคร\nละติจูด\nละบม\nละบอง\nละบือ\nละเบ็ง\nละโบม\nละม่อม\nละมั่ง\nละมาน\nละม้าย\nละมุ\nละมุด\nละมุน\nละเมอ\nละเมาะ\nละเมิด\nละเมียด\nละแมะ\nละโมก\nละโมบ\nละไม\nละลวย\nละลอก\nละล้า\nละล้าละลัง\nละลาน\nละลาบละล้วง\nละลาย\nละล้าว\nละล่ำละลัก\nละลิบ\nละลุม\nละเลง\nละเล้า\nละเลาะ\nละเลิง\nละเลียด\nละเลียบ\nละไล้\nละว้า\nละวาด\nละเวง\nละแวก\nละโว้\nละหมาด\nละห้อย\nละหาน\nละหาร\nละหุ่ง\nละเหย\nละเหี่ย\nละอง\nละออง\nละอาย\nละเอียด\nละแอน\nลัก\nลักขณะ\nลักขณา\nลักขะ\nลักขี\nลักจั่น\nลักปิดลักเปิด\nลักษณ์\nลักษณนาม\nลักษณะ\nลักษณาการ\nลักษมณ์\nลักษมาณา\nลักษมี\nลักษะ\nลัคคะ\nลัคน์\nลัคนา\nลัง\nลั่ง\nลังกา\nลังคี\nลังถึง\nลังลอง\nลังเล\nลังสาด\nลัชชา\nลัชชี\nลัญจ์\nลัญจกร\nลัญฉกร\nลัญฉน์\nลัฐิ\nลัฐิกา\nลัด\nลัดา\nลัทธ์\nลัทธิ\nลัน\nลั่น\nลันเต\nลันเตา\nลันไต\nลั่นทม\nลันโทม\nลับ\nลัพธ์\nลัพธิ\nลัภ\nลัภนะ\nลัภย์\nลัมพ์\nลัย\nลา\nล่า\nล้า\nลาก\nลาง\nล่าง\nล้าง\nลางลิง\nลางสาด\nลาช\nลาชะ\nลาชา\nลาญ\nลาด\nลาดเลา\nล้าต้า\nล่าเตียง\nลาน\nล่าน\nล้าน\nลาบ\nลาพอน\nลาภ\nลาม\nล่าม\nลามก\nลาย\nล้าย\nลายสือ\nลาลา\nลาว\nลาวัณย์\nลาวา\nลำ\nล่ำ\nล้ำ\nลำเข็ญ\nลำแข\nลำเค็ญ\nลำเจียก\nลำดวน\nลำดับ\nลำเนา\nลำบอง\nลำบาก\nลำปำ\nลำพวน\nลำพอง\nลำพัง\nลำพู\nลำเพ็ญ\nลำเพา\nลำแพน\nลำโพง\nลำไพ่\nลำภุขัน\nลำมะลอก\nลำยอง\nลำไย\nลำลอง\nล่ำลา\nลำลาบ\nลำลึก\nลำเลาะ\nลำเลิก\nลำเลียง\nลำเวียง\nลำเอียก\nลำเอียง\nลิ\nลิกขา\nลิกไนต์\nลิกู\nลิเก\nลิขนะ\nลิขสิทธิ์\nลิขิต\nลิง\nลิงค์\nลิด\nลิต\nลิตมัส\nลิตร\nลิเทียม\nลิ่น\nลิ้น\nลินจง\nลิ้นจี่\nลินลา\nลินสีด\nลิ่นฮื้อ\nลินิน\nลิบ\nลิปดา\nลิปสติก\nลิปิ\nลิฟต์\nลิเภา\nลิ่ม\nลิ้ม\nลิมป์\nลิมปนะ\nลิลิต\nลิว\nลิ่ว\nลิสง\nลี\nลี่\nลี้\nลีซอ\nลีบ\nลีลา\nลีลาศ\nลีฬหา\nลึก\nลึงค์\nลืด\nลื่น\nลื้น\nลืบ\nลืม\nลือ\nลื่อ\nลื้อ\nลุ\nลุก\nลุง\nลุ้ง\nลุ่น\nลุ้น\nลุพธ์\nลุ่ม\nลุมพี\nลุมพู\nลุย\nลุ่ย\nลุ้ย\nลู่\nลูก\nลูกระมาศ\nลูกเอ็น\nลูขะ\nลูทีเชียม\nลูบ\nเลก\nเล็ก\nเลข\nเลขา\nเลขาธิการ\nเลขานุการ\nเล็ง\nเล้ง\nเล่งฮื้อ\nเลเซอร์\nเลฑฑุ\nเลณฑุ\nเลณะ\nเล็ด\nเลน\nเล็น\nเล่น\nเลนส์\nเล็บ\nเลบง\nเลปกร\nเลปน์\nเลเป\nเลเพ\nเล็ม\nเล่ม\nเลย\nเลว\nเลวง\nเลวูโลส\nเลศ\nเลษฏุ\nเล่ห์\nเล่ห์กระเท่ห์\nเลหลัง\nเลหะ\nเลอ\nเล่อ\nเลอะ\nเลอะเทอะ\nเละ\nเละเทะ\nเลา\nเล่า\nเล้า\nเลากัย\nเล้าโลม\nเลาะ\nเลิก\nเลิ่กลั่ก\nเลิง\nเลิ้ง\nเลินเล่อ\nเลิศ\nเลีย\nเลียง\nเลี่ยง\nเลี้ยง\nเลียงผา\nเลียงฝ้าย\nเลียงมัน\nเลียน\nเลี่ยน\nเลียนไฟ\nเลียบ\nเลี่ยม\nเลียว\nเลี้ยว\nเลือก\nเลือง\nเลื่อง\nเลือด\nเลือน\nเลื่อน\nเลื่อม\nเลื่อย\nเลื้อย\nเลื่อยล้า\nแล\nแล่\nแล้\nแลก\nแล็กเกอร์\nแล็กโทส\nแลง\nแล่ง\nแล้ง\nแลน\nแล่น\nแลนทานัม\nแลบ\nแล้ว\nและ\nโล่\nโล้\nโลก\nโลกเชษฐ์\nโลกธรรม\nโลกธาตุ\nโลกนาถ\nโลกบาล\nโลกย์\nโลกัย\nโลกวัชชะ\nโลกวิทู\nโลกัตถจริยา\nโลกันตร์\nโลกา\nโลกาธิบดี\nโลกาธิปไตย\nโลกานุวัตร\nโลกาภิวัตน์\nโลกามิส\nโลกายัต\nโลกาวินาศ\nโลกิยะ\nโลกีย์\nโลกียวัตร\nโลกียวิสัย\nโลกียสุข\nโลกุตระ\nโลกุตรธรรม\nโลกุตรภูมิ\nโลง\nโล่ง\nโล้ง\nโล่งโจ้ง\nโล่งโต้ง\nโล้งโต้ง\nโลจนะ\nโลณะ\nโลด\nโล่ติ๊น\nโลโต\nโลท\nโลน\nโล้น\nโลภ\nโลม\nโลมเล้า\nโลมะ\nโลมา\nโลลุป\nโลเล\nโลโล\nโลโล้\nโลหะ\nโลหกุมภี\nโลหัช\nโลหิต\nไล่\nไล้\nไลย\nไลลา\nไล่เลี่ย\nฦๅ\nฦๅชา\nฦๅสาย\nวก\nวง\nวงก์\nวงกต\nวงศ์\nวงศกร\nวงศา\nวงษ์\nวจนะ\nวจี\nวชิระ\nวชิรปาณี\nวชิรหัตถ์\nวชิราวุธ\nวฏะ\nวฏาการ\nวณิช\nวณิชชา\nวณิชย์\nวณิชยา\nวณิพก\nวดี\nวทนะ\nวทัญญุตา\nวทัญญู\nวธุกา\nวธู\nวน\nวนศาสตร์\nวนสณฑ์\nวนสัณฑ์\nวนอุทยาน\nวนัส\nวนัสบดี\nวนา\nวนาดร\nวนาดอน\nวนานต์\nวนาลัย\nวนาลี\nวนาวาส\nวนาศรม\nวนาสณฑ์\nวนาสัณฑ์\nวนิดา\nวนิพก\nวเนจร\nวโนทยาน\nวยัคฆ์\nวยากรณ์\nวรดนู\nวรทาน\nวรมหาวิหาร\nวรงค์\nวรณะ\nวรรค\nวรรคย์\nวรรช\nวรรชย์\nวรรณะ\nวรรณกรรม\nวรรณคดี\nวรรณยุกต์\nวรรณยุต\nวรรณศิลป์\nวรรณนา\nวรรณพฤติ\nวรรณึก\nวรรธกะ\nวรรธนะ\nวรรษ\nวรรษา\nวรวิหาร\nวรัญญู\nวรางคณา\nวรางคนา\nวราห์\nวราหะ\nวรุณ\nวโรดม\nวฤก\nวลัช\nวลัญช์\nวลัญชน์\nวลัย\nวลาหก\nวลี\nวศค\nวศะ\nวศิน\nวสนะ\nวสภะ\nวสละ\nวสวัดดี\nวสวัตตี\nวสะ\nวสันต์\nวสันตดิลก\nวสันตฤดู\nวสันตวิษุวัต\nวสา\nวสี\nวสุ\nวสุธา\nวสุนธรา\nวสุมดี\nวหะ\nวอ\nวอก\nวอกแวก\nว่องไว\nวอด\nวอน\nว่อน\nว็อบ\nวอมแวม\nวอลเลย์บอล\nวอแว\nวะ\nวัก\nวักกะ\nวัคคีย์\nวัคคุ\nวัคซีน\nวัง\nวังก์\nวังชา\nวังเวง\nวังศะ\nวังสะ\nวัจจะ\nวัจกุฎี\nวัจฉละ\nวัจน์\nวัช\nวัชชะ\nวัชพืช\nวัชฌ์\nวัชระ\nวัชรปาณี\nวัชรยาน\nวัชรอาสน์\nวัชราสน์\nวัชรินทร์\nวัชรี\nวัชเรนทร์\nวัฏ\nวัฏฏะ\nวัฏจักร\nวัฏทุกข์\nวัฏสงสาร\nวัฏกะ\nวัฏฏิ\nวัฒกะ\nวัฒกี\nวัฒนธรรม\nวัฒนะ\nวัฒนา\nวัณ\nวัณโรค\nวัณฏ์\nวัณณะ\nวัณนา\nวัด\nวัต\nวัตต์\nวัตตา\nวัตถ์\nวัตถาภรณ์\nวัตถาลังการ\nวัตถุ\nวัตนะ\nวัตร\nวัตสดร\nวัตสะ\nวัติ\nวัทน์\nวัน\nวันต์\nวันทนา\nวันทนาการ\nวันทนีย์\nวันทยหัตถ์\nวันทยาวุธ\nวันทา\nวันทิ\nวับ\nวับวาบ\nวับวาม\nวับแวบ\nวับแวม\nวัปปะ\nวัมมิกะ\nวัย\nวัลก์\nวัลคุ\nวัลย์\nวัลลภ\nวัลลี\nวัว\nวัสสะ\nวัสโสทก\nวัสดุ\nวัสตร์\nวัสน์\nวัสนะ\nวัสสานะ\nวัสสานฤดู\nวา\nว่า\nว้า\nว้าเหว่\nวาก\nว้าก\nวากยสัมพันธ์\nวากยะ\nวาง\nว่าง\nว้าง\nวาจก\nวาจา\nวาจาไปยะ\nวาจาล\nวาชเปยะ\nวาณิช\nวาณิชกะ\nวาณิชย์\nวาณี\nวาด\nวาต\nวาตะ\nวาตภัย\nวาท\nวาทศาสตร์\nวาทศิลป์\nวาทกะ\nวาทนะ\nวาทย์\nวาทยกร\nวาทิต\nวาทิน\nวาที\nวาน\nวานซืน\nว่าน\nวานร\nวานรินทร์\nวาเนเดียม\nวาบ\nวาปี\nวาม\nวามน\nวามนาวตาร\nวามะ\nวาย\nว่าย\nว้าย\nวายะ\nวาโย\nวายามะ\nวายุ\nวายุกูล\nวาร\nวาระ\nวารสาร\nวารสารศาสตร์\nวาริ\nวารี\nวาริช\nวารีช\nวาริท\nวาริธร\nวารุณ\nวารุณี\nวาล\nวาลวีชนี\nวาล์ว\nวาลิกา\nวาลุกา\nวาว\nว่าว\nว้าว่อน\nว้าวุ่น\nวาสนะ\nวาสนา\nวาสพ\nวาสะ\nวาสิน\nวาสี\nวาสุกรี\nวาสุกี\nวาสุเทพ\nวาหนะ\nวาหะ\nวาหินี\nวาฬ\nวิกขัมภ์\nวิกขัมภนะ\nวิกเขป\nวิกรม\nวิกรัย\nวิกรานต์\nวิกฤต\nวิกฤติ\nวิกล\nวิกสิต\nวิกัต\nวิกัติ\nวิกัติการก\nวิกัป\nวิกัย\nวิการ\nวิกาล\nวิกาลโภชน์\nวิคหะ\nวิเคราะห์\nวิฆเนศ\nวิฆเนศวร\nวิฆาต\nวิง\nวิ่ง\nวิ่งเปี้ยว\nวิงวอน\nวิจฉิกะ\nวิจล\nวิจักขณ์\nวิจักษ์\nวิจักษณ์\nวิจัย\nวิจาร\nวิจารณ์\nวิจารณญาณ\nวิจิ\nวิจิกิจฉา\nวิจิต\nวิจิตร\nวิจิน\nวิจุณ\nวิจุรณ\nวิชชา\nวิชชุ\nวิชชุดา\nวิชชุตา\nวิชชุลดา\nวิชญะ\nวิชน\nวิชนี\nวิชย\nวิชัย\nวิชา\nวิชานนะ\nวิชิต\nวิเชียร\nวิญญัตติ\nวิญญาณ\nวิญญาณกทรัพย์\nวิญญู\nวิฑูรย์\nวิด\nวิตก\nวิตถาร\nวิตามิน\nวิถี\nวิทธะ\nวิทยฐานะ\nวิทยา\nวิทยาคม\nวิทยาคาร\nวิทยาลัย\nวิทยุ\nวิทยุต\nวิทวัส\nวิทัตถิ\nวิทัศน์\nวิทารณ์\nวิทิต\nวิทู\nวิทูร\nวิเทศ\nวิเทโศบาย\nวิธ\nวิธวา\nวิธาน\nวิธี\nวิธุระ\nวิธู\nวิธูปนะ\nวิ่น\nวินตกะ\nวินัย\nวินาที\nวินายก\nวินาศ\nวินิจ\nวินิจฉัย\nวินิต\nวินิบาต\nวินิปาติก\nวิเนต\nวิบัติ\nวิบาก\nวิบุล\nวิบุลย์\nวิบูล\nวิบูลย์\nวิปการ\nวิปฏิสาร\nวิปโยค\nวิประโยค\nวิปริต\nวิปลาส\nวิปวาส\nวิปักษ์\nวิปัสสก\nวิปัสสนา\nวิปัสสนายานิก\nวิพากษ์\nวิพิธทัศนา\nวิพุธ\nวิภว\nวิภวตัณหา\nวิภังค์\nวิภัช\nวิภัตติ\nวิภา\nวิภาค\nวิภาช\nวิภาดา\nวิภาวี\nวิภาษ\nวิภาส\nวิภู\nวิภูษณะ\nวิภูษา\nวิภูษิต\nวิมน\nวิมล\nวิมลัก\nวิมังสา\nวิมัติ\nวิมาน\nวิมุข\nวิมุต\nวิมุตติ\nวิเมลือง\nวิโมกข์\nวิโยค\nวิระ\nวิรงรอง\nวิรังรอง\nวิรัช\nวิรัต\nวิรัติ\nวิราคะ\nวิราม\nวิริยภาพ\nวิริยะ\nวิรุธ\nวิรุฬห์\nวิรุฬหก\nวิรูป\nวิรูปักษ์\nวิเรนทร์\nวิโรจ\nวิโรจน์\nวิโรฒ\nวิโรธ\nวิลันดา\nวิลัย\nวิลาด\nวิลาศ\nวิลาป\nวิลาวัณย์\nวิลาส\nวิลาสินี\nวิลิปดา\nวิลิศมาหรา\nวิเลป\nวิเลปนะ\nวิโลกนะ\nวิโลม\nวิไล\nวิไลวรรณ\nวิวรณ์\nวิวรรธน์\nวิวัฏ\nวิวัฒน์\nวิวัฒนาการ\nวิวัน\nวิวาท\nวิวาห์\nวิวาหมงคล\nวิวาหะ\nวิวิต\nวิวิธ\nวิเวก\nวิศรุต\nวิศว\nวิศวกร\nวิศวกรรม\nวิศวกรรมศาสตร์\nวิศัลย์\nวิศาขบูชา\nวิศาขา\nวิศาล\nวิศิษฏ์\nวิศุทธ์\nวิศุทธิ์\nวิเศษ\nวิเศษณ์\nวิษณุ\nวิษณุกรรม\nวิษธร\nวิษัย\nวิษาณ\nวิษุวัต\nวิสกี้\nวิสรรชนีย์\nวิสฤต\nวิสสุกรรม\nวิสัชนา\nวิสัญญี\nวิสัย\nวิสัยทัศน์\nวิสาขบูชา\nวิสาขะ\nวิสาขา\nวิสามัญ\nวิสามานยนาม\nวิสาร\nวิสารทะ\nวิสาล\nวิสาสะ\nวิสาหกิจ\nวิสิฐ\nวิสุงคามสีมา\nวิสุทธ์\nวิสุทธิ์\nวิสูตร\nวิเสท\nวิหค\nวิหลั่น\nวิหาร\nวิหิงสา\nวิเหสา\nวิฬาร\nวิฬาร์\nวี\nวีจิ\nวีชนี\nวีณา\nวี้ด\nวีรกรรม\nวีรชน\nวีรบุรุษ\nวีรสตรี\nวี่วัน\nวี่แวว\nวีสะ\nวุ้ง\nวุฐิ\nวุฒ\nวุฒิ\nวุด\nวุ่น\nวุ้น\nวุบ\nวุ้ย\nวุลแฟรม\nวู้\nวูดวาด\nวูบ\nวู่วาม\nเว้\nเวค\nเวคิน\nเวคี\nเวจ\nเวช\nเวชยันต์\nเวฐน์\nเวณิ\nเวณิก\nเวณุ\nเวตน์\nเวตร\nเวตาล\nเวท\nเวทคู\nเวทนา\nเวทย์\nเวทัลละ\nเวทางค์\nเวทางคศาสตร์\nเวทานต์\nเวทานตะ\nเวทิ\nเวที\nเวธะ\nเวน\nเว้น\nเวนไตย\nเวไนย\nเวมัต\nเว้ย\nเวยยากรณะ\nเวร\nเวรมณี\nเวรี\nเวโรจน์\nเวลา\nเวเลนซี\nเวศม์\nเวศย์\nเวศยา\nเวสน์\nเวสภู\nเวสม์\nเวสวัณ\nเวสสะ\nเวสสันดร\nเวสสุกรรม\nเวสสุวัณ\nเวสารัช\nเวสิยา\nเวหน\nเวหะ\nเวหา\nเวหาส\nเวฬุ\nเวฬุริยะ\nเว่อ\nเว้า\nเวิก\nเวิ้ง\nเวี่ย\nเวียง\nเวียด\nเวียดนาม\nเวียน\nเวียร\nเวี่ยว\nแว้\nแวง\nแว้ง\nแวด\nแว้ด\nแวน\nแว่น\nแวนดา\nแวบ\nแว็บ\nแวม\nแว็ม\nแวว\nแว่ว\nแวะ\nโว\nโว่\nโวการ\nโว่ง\nโวทาน\nโวย\nโว้ย\nโว้เว้\nโวหาร\nไว\nไว้\nไวกูณฐ์\nไวฑูรย์\nไวทย์\nไวน์\nไวพจน์\nไวยากรณ์\nไวยาวัจกร\nไวยาวัจมัย\nไวรัส\nไววรรณ\nไวษณพ\nไวโอลิน\nศก\nศกุน\nศกุนต์\nศกุนิ\nศกุนี\nศจี\nศตะ\nศตภิษัช\nศตวรรษ\nศตพรรษ\nศตกะ\nศนิ\nศพ\nศมนะ\nศมะ\nศยาม\nศยามล\nศร\nศรายุธ\nศราวรณ์\nศรรกรา\nศรวณะ\nศรวณีย์\nศรวิษฐา\nศรัณย์\nศรัณยู\nศรัท\nศรัทธา\nศรัย\nศราทธ์\nศราทธพรต\nศราพก\nศราวก\nศราวณะ\nศรี\nศรีตรัง\nศรุติ\nศฤคาล\nศฤงค์\nศฤงคาร\nศฤงคาริน\nศฤงคารี\nศลิษฏ์\nศลิษา\nศวะ\nศวัส\nศวา\nศวาน\nศศะ\nศศธร\nศศพินทุ์\nศศลักษณ์\nศศิ\nศศิน\nศศี\nศศิขัณฑ์\nศศิธร\nศศิมณฑล\nศศิวิมล\nศอ\nศอก\nศักดา\nศักดิ\nศักดิ์\nศักดินา\nศักติ\nศักย\nศักยภาพ\nศักย์\nศักยะ\nศักร\nศักรินทร์\nศักเรนทร์\nศักราช\nศังกร\nศัตรู\nศันสนะ\nศันสนีย์\nศัพท์\nศัยยา\nศัล\nศัลย์\nศัลยกรรม\nศัลยแพทย์\nศัลยศาสตร์\nศัสดร\nศัสตร\nศัสตรศาสตร์\nศัสตรา\nศัสตราวุธ\nศากตะ\nศากย\nศากยะ\nศากยพุทธ\nศากยมุนี\nศาฎก\nศาณ\nศานต์\nศานติ\nศาป\nศารท\nศารทูล\nศาริกา\nศาล\nศาลา\nศาศวัต\nศาสดา\nศาสตร์\nศาสตรา\nศาสตราจารย์\nศาสนา\nศาสนกิจ\nศาสนจักร\nศาสนธรรม\nศาสนบุคคล\nศาสนพิธี\nศาสนวัตถุ\nศาสนศาสตร์\nศาสนสถาน\nศาสนสมบัติ\nศาสนิกชน\nศาสนีย์\nศาสนูปถัมภก\nศาสน์\nศิกษก\nศิการ\nศิขร\nศิขริน\nศิขรี\nศิขัณฑ์\nศิคาล\nศิงขร\nศิงขริน\nศิตะ\nศิถี\nศิพิระ\nศิระ\nศิรประภา\nศิราภรณ์\nศิโรรัตน์\nศิโรเวฐน์\nศิรา\nศิรามพุช\nศิโรราบ\nศิลป\nศิลป์\nศิลปะ\nศิลปกร\nศิลปกรรม\nศิลปกิจ\nศิลปวัตถุ\nศิลปวิทยา\nศิลปศาสตร์\nศิลปศึกษา\nศิลปหัตถกรรม\nศิลปิน\nศิลปี\nศิลา\nศิวะ\nศิวโมกข์\nศิวลึงค์\nศิวเวท\nศิวาลัย\nศิศีระ\nศิษฎิ\nศิษฏ์\nศิษย์\nศิษยานุศิษย์\nศีขร\nศีต\nศีตกาล\nศีรษะ\nศีล\nศึก\nศึกษา\nศึกษาธิการ\nศึกษานิเทศก์\nศุกร์\nศุกรวรรณ\nศุกรวาร\nศุกระ\nศุกล\nศุกลปักษ์\nศุจิ\nศุทธะ\nศุทธิ\nศุนะ\nศุนิ\nศุภกร\nศุภเคราะห์\nศุภนิมิต\nศุภมัสดุ\nศุภมาตรา\nศุภมาส\nศุภอักษร\nศุภางค์\nศูกร\nศุลกากร\nศุลการักษ์\nศุลี\nศุษิร\nศูทร\nศูนย์\nศูนยวาท\nศูละ\nศูลิน\nเศรณี\nเศรษฐ\nเศรษฐ์\nเศรษฐกิจ\nเศรษฐศาสตร์\nเศรษฐี\nเศร้า\nเศลษ\nเศวต\nเศวตร\nเศวตัมพร\nเศษ\nเศาจ\nเศาร์\nเศารยะ\nเศิก\nเศียร\nโศก\nโศกา\nโศกาดูร\nโศกาลัย\nโศกี\nโศจิ\nโศธนะ\nโศภน\nโศภะ\nโศภา\nโศภิต\nโศภิน\nโศภิษฐ์\nโศภี\nโศรดา\nโศรตร\nโศลก\nไศล\nไศวะ\nษมา\nษัฏ\nษัฑ\nษัณ\nษัษ\nษัษฐะ\nษัษฐี\nโษฑศัน\nสก\nสกวาที\nสกฏะ\nสกทาคามิผล\nสกิทาคามิผล\nสกทาคามิมรรค\nสกิทาคามิมรรค\nสกทาคามี\nสกิทาคามี\nสกนธ์\nสกปรก\nสกรณีย์\nสกรรจ์\nสกรรมกริยา\nสกล\nสกลมหาสังฆปริณายก\nสกัด\nสกา\nสกาว\nสกี\nสกุณ\nสกุณา\nสกุณี\nสกุน\nสกุนต์\nสกุล\nสเกต\nสแกนเดียม\nสขะ\nสง\nส่ง\nสงกร\nสงกรานต์\nสงกา\nสงค์\nสงคร\nสงคราม\nสงเคราะห์\nสงฆ์\nสงบ\nสงวน\nส่งสการ\nสงสัย\nสงสาร\nสงสารวัฏ\nสงัด\nสง่า\nสฐะ\nสณฑ์\nสด\nสดมภ์\nสดับ\nสดับปกรณ์\nสดำ\nสดุดี\nสตะ\nสตน\nสตภิสชะ\nสตรอนเชียม\nสตริกนิน\nสตรี\nสตัฟฟ์\nสตัมภ์\nสตางค์\nสติ\nสติปัฏฐาน\nสตี\nสตู\nสตูป\nสเต๊ก\nสถบดี\nสถล\nสถวีระ\nสถาน\nสถานะ\nสถานี\nสถาบัน\nสถาปนา\nสถาปนิก\nสถาปัตยกรรม\nสถาปัตยกรรมศาสตร์\nสถาปัตยเรขา\nสถาปัตยเวท\nสถาพร\nสถาวร\nสถิต\nสถิตยศาสตร์\nสถิติ\nสถิร\nสถีรวาท\nสถุล\nสถูป\nสทิง\nสทึง\nสทุม\nสธนะ\nสาธุสะ\nสน\nส้น\nสนทนา\nสนทรรศ\nสนทรรศน์\nสนเทศ\nสนเท่ห์\nสนธยา\nสนธิ\nสนน\nสนม\nสนวน\nสนอง\nสนอบ\nสนอม\nสนะ\nสนัด\nสนั่น\nสนับ\nสนับทึบ\nสนับสนุน\nสนาน\nสนาม\nสนายุ\nสนิกะ\nสนิท\nสนิธ\nสนิม\nสนุก\nสนุกเกอร์\nสนุข\nสนุต\nสนุ่น\nสบ\nสบง\nสบถ\nสบัน\nสบาย\nสบู่\nสไบ\nสปริง\nสปอร์\nสปาเกตตี\nสเปกตรัม\nสเปกโทรสโกป\nสไปริลลัม\nสพาบ\nสภา\nสภาพ\nสภาวการณ์\nสภาวะ\nสม\nสมการ\nสมจารี\nสมดุล\nสมมูล\nส้ม\nสมญา\nสมณะ\nสมณบริขาร\nสมณศักดิ์\nสมณสารูป\nสมเด็จ\nสมถะ\nสมถยานิก\nสมถวิปัสสนา\nสมนาคุณ\nสมบัติ\nสมบุกสมบัน\nสมบูรณ์\nสมบูรณาญาสิทธิราชย์\nสมประดี\nสมปฤดี\nสมปฤๅดี\nส้มป่อย\nสมปัก\nสมผุส\nสมพง\nสมพงศ์\nสมพล\nสมพัตสร\nสมพาส\nสมเพช\nสมโพธน์\nสมโพธิ\nสมภพ\nสมภาร\nสมโภค\nสมโภช\nสมมต\nสมมติ\nสมมุติ\nสมมาตร\nส้มมือ\nสมโมท\nสมโยค\nสมร\nสมรด\nสมรรถ\nสมรรถนะ\nสมรรถภาพ\nสมรส\nสมฤดี\nสมฤติ\nสมวายะ\nสมเสร็จ\nสมอ\nสมอง\nสมะ\nสมัคร\nสมังคี\nสมัช\nสมัชชา\nสมัญญา\nสมัต\nสมัน\nสมันต์\nสมัย\nสมา\nสมาคม\nสมาจาร\nสมาชิก\nสมาทาน\nสมาธิ\nสมาน\nสมานฉันท์\nสมาบัติ\nสมาพันธรัฐ\nสมาส\nสม่ำเสมอ\nสมิง\nสมิต\nสมิติ\nสมิทธ์\nสมิทธิ\nสมี\nสมุก\nสมุจจัย\nสมุจเฉท\nสมุฏฐาน\nสมุด\nสมุทร\nสมุทรโคดม\nสมุทัย\nสมุน\nสมุนไพร\nสมุลแว้ง\nสมุห\nสมุห์\nสมุหกลาโหม\nสมุหเทศาภิบาล\nสมุหนาม\nสมุหนายก\nสโมธาน\nสโมสร\nสยด\nสยนะ\nสยบ\nสยมพร\nสยมภู\nสยอง\nสยอน\nสยัมวรา\nสยาม\nสยามานุสติ\nสยามินทร์\nสยาย\nสยิว\nสยิ้ว\nสยุมพร\nสยุมภู\nสร\nสรง\nสร่ง\nสรณะ\nสรณคมน์\nสรณาคมน์\nสรณตรัย\nสรตะ\nสรทะ\nสรนุก\nสรเนาะ\nสรไน\nสรเพชญ\nสรภะ\nสรภัญญะ\nสรภู\nสรม\nสรร\nสรรค์\nสรรพ\nสรรพคุณ\nสรรพนาม\nสรรพสามิต\nสรรพัชญ\nสรรพากร\nสรรพางค์\nสรรเพชญ\nสรรเพชุดา\nสรรเสริญ\nสรลอน\nสรเลข\nสรวง\nสรวม\nสรวล\nสรเสริญ\nสร้อย\nสระ\nสระกอ\nสระท้อน\nสระพรั่ง\nสระอาด\nสรั่ง\nสรัสวดี\nสร่าง\nสร้าง\nสราญ\nสรี้\nสรีระ\nสรีรกิจ\nสรีรธาตุ\nสรีรวิทยา\nสรีรศาสตร์\nสรีรังคาร\nสรีรางคาร\nสรุป\nสโรช\nสโรชะ\nสฤก\nสฤต\nสฤษฎิ\nสฤษฎี\nสฤษฏ์\nสฤษดิ์\nสลด\nสลบ\nสลวน\nสลวย\nสลอด\nสลอน\nสลอย\nสละ\nสลัก\nสลัด\nสลัดได\nสลับ\nสลัว\nสลา\nสลาก\nสลาง\nสล้าง\nสลาด\nสลาตัน\nสลาบ\nสลาย\nสลิด\nสลิล\nสลึก\nสลึง\nสลุต\nสลุบ\nสลุมพร\nสแลง\nสวการย์\nสวภาพ\nสวราชย์\nสวก\nส้วง\nสวด\nสวน\nสวนะ\nสวนาการ\nส่วน\nสวนิต\nสวบ\nสวม\nส้วม\nสวย\nส่วย\nส้วย\nสวยม\nสวรรค\nสวรรค์\nสวรรคต\nสวรรคาลัย\nสวรรยา\nสวระ\nสวะ\nสวัสดิ\nสวัสดิ์\nสวัสดิการ\nสวัสดิภาพ\nสวัสดิมงคล\nสวัสดี\nสวัสติ\nสวาตี\nสวัสติกะ\nสวา\nสวาปาม\nสวาคตะ\nสวาง\nสว่าง\nสวาด\nสวาดิ\nสวาท\nสว่าน\nสว้าน\nสวาบ\nสวามิ\nสวามี\nสวามินี\nสวาย\nสวาสดิ์\nสวาหะ\nสวิง\nสวิญญาณกทรัพย์\nสวิตช์\nสสาร\nสสุระ\nสสุรี\nสหกรณ์\nสหการ\nสหจร\nสหชาต\nสหชาติ\nสหธรรม\nสหธรรมิก\nสหประชาชาติ\nสหพันธ์\nสหพันธรัฐ\nสหภาพ\nสหศึกษา\nสหัช\nสหัมบดี\nสหัส\nสหัสสะ\nสหัสธารา\nสหัสนัยน์\nสหัสเนตร\nสหัสรังสี\nสหัสา\nสหาย\nสอ\nส่อ\nสอง\nส่อง\nส้อง\nสอด\nสอน\nส่อน\nสอบ\nสอพลอ\nส้อม\nสอย\nสะ\nสะกด\nสะกอ\nสะกาง\nสะการะ\nสะกิด\nสะกิดสะเกา\nสะเก็ด\nสะแก\nสะคร้อ\nสะคราญ\nสะค้าน\nสะเงาะสะแงะ\nสะดม\nสะดวก\nสะดิ้ง\nสะดึง\nสะดือ\nสะดุ้ง\nสะดุด\nสะเด็ด\nสะเดา\nสะเดาะ\nสะตอ\nสะตาหมัน\nสะตึ\nสะตือ\nสะตุ\nสะเต๊ะ\nสะโตก\nสะทก\nสะท้อน\nสะท้าน\nสะทึก\nสะเทิน\nสะเทิ้น\nสะเทือน\nสะเทื้อน\nสะบะ\nสะบัก\nสะบักสะบอม\nสะบัด\nสะบัดสะบิ้ง\nสะบั้น\nสะบันงา\nสะบ้า\nสะบู\nสะแบง\nสะเปะสะปะ\nสะพรั่ง\nสะพรึงกลัว\nสะพรึบ\nสะพรึ่บ\nสะพัก\nสะพัง\nสะพัด\nสะพั้น\nสะพาน\nสะพาย\nสะเพร่า\nสะโพก\nสะเภา\nสะใภ้\nสะโมง\nสะระตะ\nสะระแหน่\nสะลาง\nสะลาบ\nสะลึมสะลือ\nสะวี้ดสะว้าด\nสะสม\nสะสวย\nสะสาง\nสะเหล่อ\nสะอาง\nสะอาด\nสะอ้าน\nสะอิ้ง\nสะอิดสะเอียน\nสะอึก\nสะอื้น\nสะเอ้ง\nสะเอว\nสะเออะ\nสะโอดสะอง\nสะไอ\nสัก\nสักกะ\nสักยะ\nสักกัจจะ\nสักกายทิฐิ\nสักการ\nสักการะ\nสักขี\nสักวา\nสักหลาด\nสัค\nสัคคะ\nสั่ง\nสังกร\nสังกรณี\nสังกรประโยค\nสังกะตัง\nสังกะวัง\nสังกะวาด\nสังกะสี\nสังกัด\nสังกัปปะ\nสังกา\nสังการ\nสังกาศ\nสังกิเลส\nสังเกต\nสังข์\nสังขกร\nสังขตธรรม\nสังขตะ\nสังขยา\nสังขลิก\nสังขลิกา\nสังขาร\nสังขารา\nสังเขป\nสังค์\nสังคญาติ\nสังคม\nสังคหะ\nสังคัง\nสังคายนา\nสังคายนาย\nสังคีต\nสังคีติ\nสังเค็ด\nสังเคราะห์\nสังฆกรรม\nสังฆการี\nสังฆเถระ\nสังฆทาน\nสังฆนายก\nสังฆปาโมกข์\nสังฆภัต\nสังฆเภท\nสังฆมณฑล\nสังฆมนตรี\nสังฆราช\nสังฆสภา\nสังฆาณัติ\nสังฆาฏิ\nสังฆาทิเสส\nสังฆาธิการ\nสังฆานุสติ\nสังฆาวาส\nสังยุตนิกาย\nสังโยค\nสังโยชน์\nสังวร\nสังวัจฉระ\nสังวัธยาย\nสังวาล\nสังวาส\nสังเวคะ\nสังเวช\nสังเวชนียสถาน\nสังเวย\nสังเวียน\nสังสกฤต\nสังสการ\nสังสนทนา\nสั่งสนทนา\nสังสรรค์\nสังสารวัฏ\nสังสิทธิ\nสังสุทธ์\nสังสุทธิ\nสังหร\nสังหรณ์\nสังหาร\nสังหาริมทรัพย์\nสังหาริมะ\nสังหิต\nสัจ\nสัจกิริยา\nสัจจะ\nสัจญาณ\nสัจธรรม\nสัจนิยม\nสัจพจน์\nสัชฌะ\nสัชฌุ\nสัญจร\nสัญเจตนา\nสัญชาตญาณ\nสัญชาติ\nสัญฌา\nสัญญา\nสัญญาณ\nสัญญี\nสัญโญชน์\nสัญนิยม\nสัญประกาศ\nสัญลักษณ์\nสัฐิ\nสัณฐาน\nสัณฐิติ\nสัณฑ์\nสัณห์\nสัด\nสัดจอง\nสัต\nสัตตะ\nสัตตาหกรณียะ\nสัตตาหกาลิก\nสัตมวาร\nสัตสดก\nสัตตบงกช\nสัตตบรรณ\nสัตตบุษย์\nสัตตู\nสัตถันดร\nสัตถา\nสัตถิ\nสัตถุ\nสัตถุศาสนา\nสัตบรรณ\nสัตย์\nสัตยพรต\nสัตยวาที\nสัตยาเคราะห์\nสัตยาธิษฐาน\nสัตยาบัน\nสัตว์\nสัตวชาติ\nสัตวบาล\nสัตวแพทย์\nสัตววิทยา\nสัตวา\nสัทธรรม\nสัทธา\nสัทธาจริต\nสัทธาธิกะ\nสัทธินทรีย์\nสัทธิงวิหาริก\nสัทธิวิหาริก\nสัทวิทยา\nสัทศาสตร์\nสัทอักษร\nสัน\nสั่น\nสั้น\nสันดาน\nสันดาป\nสันโดษ\nสันต์\nสันตติ\nสันตะปาปา\nสันตะวา\nสันติ\nสันตุฏฐี\nสันถวไมตรี\nสันถวะ\nสันถัต\nสันถาร\nสันทนะ\nสันทะ\nสันทัด\nสันทัสนะ\nสันทาน\nสันทิฐิก\nสันทิส\nสันเทหะ\nสันธาน\nสันนิธิ\nสันนิบาต\nสันนิวาส\nสันนิษฐาน\nสันสกฤต\nสับ\nสับปลับ\nสับปลี้\nสับปะรด\nสัปคับ\nสัปดาห์\nสัปดาหะ\nสัปดน\nสัปตศก\nสัปทน\nสัปปะ\nสัปปิ\nสัปปุริส\nสัปปุรุษ\nสัประยุทธ์\nสัปหงก\nสัปเหร่อ\nสัพ\nสัพพะ\nสัพพัญญู\nสัพเพเหระ\nสัพยอก\nสัมบูรณ์\nสัมปชัญญะ\nสัมปทา\nสัมปทาน\nสัมปยุต\nสัมปโยค\nสัมประสิทธิ์\nสัมประหาร\nสัมปรายภพ\nสัมปรายิกภพ\nสัมปัตติ\nสัมผัปลาป\nสัมผัปลาปะ\nสัมผัส\nสัมพล\nสัมพหุลา\nสัมพัจฉรฉินท์\nสัมพัตสร\nสัมพัทธ์\nสัมพันธ์\nสัมพันธน์\nสัมพันธภาพ\nสัมพันธมิตร\nสัมพันธไมตรี\nสัมพาหะ\nสัมพุทธ\nสัมพุทธะ\nสัมโพธิ\nสัมภวะ\nสัมภเวสี\nสัมภัต\nสัมภัตตะ\nสัมภาระ\nสัมภาษณ์\nสัมโภคกาย\nสัมมนา\nสัมมัปธาน\nสัมมา\nสัมโมทนียกถา\nสัมฤทธิ\nสัมฤทธิ์\nสัมฤทธิศก\nสัยน์\nสัลเลข\nสัสดี\nสัสตทิฐิ\nสัสสะ\nสัสสุ\nสัสสู\nสา\nส่า\nสาก\nสากรรจ์\nสากล\nสากัจฉา\nสากัลย์\nสากิยะ\nสาเก\nสาขา\nสาคร\nสาคเรศ\nสาคู\nสาง\nส้าง\nสาชล\nสาฎก\nสาฏิก\nสาณี\nสาด\nสาไถย\nสาทร\nสาทิส\nสาทุ\nสาโท\nสาธก\nสาธยะ\nสาธยาย\nสาธารณะ\nสาธารณชน\nสาธารณประโยชน์\nสาธารณภัย\nสาธารณรัฐ\nสาธารณสถาน\nสาธารณสมบัติ\nสาธารณสุข\nสาธารณูปการ\nสาธารณูปโภค\nสาธารณ์\nสาธิต\nสาธุ\nสาน\nส่าน\nสานุ\nสานู\nสานุศิษย์\nสาบ\nสาบสูญ\nสาบาน\nสาป\nสาปไตย\nสาม\nสามชุก\nสามเณร\nสามเณรี\nสามนต์\nสามนตราช\nสามยทรัพย์\nสามล\nสามหาว\nสามะ\nสามัคคี\nสามัญ\nสามัตถิยะ\nสามานย์\nสามานยนาม\nสามารถ\nสามิต\nสามินี\nสามิภักดิ์\nสามี\nสามีจิกรรม\nสาย\nส่าย\nส้าย\nสายชู\nสายัณห์\nสายาห์\nสาร\nสารคดี\nสารธรรม\nสารนิเทศ\nสารบบ\nสารบรรณ\nสารบัญ\nสารบาญ\nสารบาญชี\nสารประโยชน์\nสารสนเทศ\nสารทุกข์\nสารถี\nสารท\nสารพัด\nสารพัน\nสารพางค์\nสารภาพ\nสารภี\nสารวัตร\nสาระ\nสาระแน\nสาระพา\nสาระยำ\nสาระวารี\nสาระสะมา\nสารัตถประโยชน์\nสารัตถศึกษา\nสารัตถะ\nสารัทธ์\nสารัมภ์\nสาราณียกร\nสาราณียธรรม\nสาราณียะ\nสารานุกรม\nสารีริกธาตุ\nสารูป\nสาโรช\nสาละ\nสาละวน\nสาลิ\nสาลิกา\nสาลินี\nสาลี\nสาลี่\nสาลู\nสาโลหิต\nสาว\nสาวก\nสาวิกา\nสาวิตร\nสาวิตรี\nสาสน\nสาสน์\nสาส์น\nสาสนา\nสาสม\nสาหร่าย\nสาหรี\nส่าหรี\nสาหัส\nสาเหตุ\nสาแหรก\nสำ\nสำส่อน\nส่ำ\nสำคัญ\nสำซ่าง\nสำแดง\nสำทับ\nสำนวน\nสำนอง\nสำนัก\nสำนาน\nสำนึก\nสำนึง\nสำเนา\nสำเนียง\nสำบัด\nสำปะลอ\nสำปะหลัง\nสำปั้น\nสำปันนี\nสำเภา\nสำมะงา\nสำมะโน\nสำมะลอ\nสำมะเลเทเมา\nสำมะหา\nสำรด\nสำรวจ\nสำรวม\nสำรวย\nสำรวล\nสำรอก\nสำรอง\nสำรับ\nสำราก\nสำราญ\nสำริด\nสำเร็จ\nสำเรา\nสำเริง\nสำโรง\nสำลัก\nสำลาน\nสำลี\nสำแลง\nสำหรวด\nสำหรับ\nสำหา\nสำเหนียก\nสำเหร่\nสำออย\nสำอาง\nสิ\nสิกข์\nสิข\nสิกขมานา\nสิกขา\nสิขร\nสิขรี\nสิขเรศ\nสิขา\nสิขานล\nสิขี\nสิคาล\nสิง\nสิ่ง\nสิงขร\nสิงค์\nสิงคลิ้ง\nสิงคลี\nสิงคาร\nสิงคาล\nสิงคี\nสิงโต\nสิงห์\nสิงหนาท\nสิงหบัญชร\nสิงหรา\nสิงหราช\nสิงหาคม\nสิงหาสน์\nสิงหล\nสิญจน์\nสิตะ\nสิตางศุ์\nสิถิล\nสิทธ์\nสิทธัตถะ\nสิทธา\nสิทธาจารย์\nสิทธารถ\nสิทธิ\nสิทธิ์\nสิทธิการิยะ\nสิธยะ\nสิน\nสิ้น\nสินเทา\nสินธพ\nสินธุ\nสินธุ์\nสินธุระ\nสินธู\nสินเธาว์\nสินาด\nสินิทธ์\nสินี\nสิเนรุ\nสิเนหก\nสิเนหะ\nสิเนหา\nสิเน่หา\nสิบ\nสิปปะ\nสิมพลี\nสิระ\nสิโรดม\nสิโรตม์\nสิริ\nสิรี\nสิลา\nสิว\nสิ่ว\nสิวะ\nสิวาลัย\nสิวิกา\nสี\nสี่\nสี้\nสีกา\nสีกุน\nสีข้าง\nสีด\nสีดอ\nสีดา\nสีตลรัศมี\nสีตโลทก\nสีโตทก\nสีทันดร\nสีมันต์\nสีมา\nสีละมัน\nสีวิกา\nสีสอ\nสีสะ\nสีสา\nสีสุก\nสีเสียด\nสีห์\nสีหนาท\nสีหบัญชร\nสีหราช\nสีหไสยา\nสีหไสยาสน์\nสีหะ\nสึก\nสึง\nสืบ\nสื่อ\nสุ\nสุก\nสุกข์\nสุกร\nสุกรม\nสุกำศพ\nสุกียากี้\nสุข\nสุขา\nสุขาภิบาล\nสุขารมณ์\nสุขาวดี\nสุขิน\nสุขี\nสุขุม\nสุขุมาล\nสุโข\nสุคต\nสุคติ\nสุคนธ\nสุคนธ์\nสุคนธชาติ\nสุคนธรส\nสุคันธ์\nสุคันธรส\nสุงกะ\nสุงกากร\nสุงสิง\nสุงสุมาร\nสุจริต\nสุจหนี่\nสุจิ\nสุจิต\nสุจิตร\nสุชน\nสุชัมบดี\nสุชา\nสุชาดา\nสุญ\nสุญญากาศ\nสุญตา\nสุญนิยม\nสุณ\nสุณิสา\nสุด\nสุดา\nสุต\nสุตตนิบาต\nสุตตะ\nสุตตันตปิฎก\nสุตตันตะ\nสุติ\nสุทธ\nสุทธ์\nสุทธาวาส\nสุทธิ\nสุทรรศน์\nสุทัศน์\nสุธา\nสุธาโภชน์\nสุธารส\nสุธาสินี\nสุธาสี\nสุธี\nสุนทร\nสุนทรี\nสุนทรียภาพ\nสุนทรียศาสตร์\nสุนทรียะ\nสุนัข\nสุนันท์\nสุโนก\nสุบดี\nสุบรรณ\nสุบิน\nสุปรีดิ์\nสุปรีย์\nสุปาณี\nสุพพัต\nสุพรรณ\nสุพรรณบัฏ\nสุพรรณภาชน์\nสุพรรณราช\nสุพรรณศรี\nสุพรรณถัน\nสุพรรณิการ์\nสุภร\nสุภัค\nสุภา\nสุภาพ\nสุภาษิต\nสุม\nสุ่ม\nสุมทุม\nสุมน\nสุมนะ\nสุมนัส\nสุมนา\nสุ้มเสียง\nสุมะ\nสุมาลี\nสุเมธ\nสุเมรุ\nสุรคต\nสุรเชษฐ์\nสุรบดี\nสุรภาพ\nสุรโลก\nสุรสีหนาท\nสุรเสียง\nสุรงค์\nสุรังค์\nสุรภี\nสุรัติ\nสุรัสวดี\nสุรา\nสุรางค์จำเรียง\nสุรางคนา\nสุรางคนางค์\nสุรารักษ์\nสุราลัย\nสุรินทร์\nสุรินทราหู\nสุริยะ\nสุริยกันต์\nสุริยกานต์\nสุริยการ\nสุริยกาล\nสุริยคติ\nสุริยคราส\nสุริยมณฑล\nสุริยวงศ์\nสุริยง\nสุริยา\nสุริเยนทร์\nสุริเยศ\nสุริโย\nสุริยน\nสุริยัน\nสุริยุปราคา\nสุรีย์\nสุรุ่ยสุร่าย\nสุลต่าน\nสุวคนธ์\nสุวภาพ\nสุวรรณ\nสุวรรณภูมิ\nสุวะ\nสุวาน\nสุวินัย\nสุวิมล\nสุษิระ\nสุสาน\nสุหนัต\nสุหร่ง\nสุหร่าย\nสุหฤท\nสุหัท\nสุเหร่า\nสู\nสู่\nสู้\nสูง\nสูจิ\nสูจิบัตร\nสูญ\nสูด\nสูต\nสูตร\nสูติ\nสูติกรรม\nสูตินรีเวช\nสูติบัตร\nสูติแพทย์\nสูติศาสตร์\nสูท\nสูทกรรม\nสูทศาสตร์\nสูบ\nสูปะ\nสูร\nสูรย์\nสูรยกานต์\nสูริ\nสูสี\nเส\nเสก\nเสกขบุคคล\nเสกขะ\nเสขบุคคล\nเสขะ\nเส็ง\nเส้ง\nเส็งเคร็ง\nเสงี่ยม\nเสฏฐี\nเสณี\nเสด\nเสด็จ\nเสตุ\nเสถียร\nเสทะ\nเสโท\nเสน\nเส้น\nเสนง\nเสน่ง\nเสน่ห์\nเสนหา\nเสน่หา\nเสนอ\nเสนะ\nเสนา\nเสนาธิการ\nเสนาบดี\nเสน่า\nเสนากุฎ\nเสนางค์\nเสนางคนิกร\nเสนานี\nเสนาสนะ\nเสนาะ\nเสนี\nเสนีย์\nเสนียะ\nเสนียด\nเสบย\nเสบียง\nเสพ\nเสพย์\nเสเพล\nเสภา\nเสม็ด\nเสมหะ\nเสมอ\nเสมา\nเสมียน\nเสมือน\nเสย\nเสร็จ\nเสริด\nเสริม\nเสรี\nเสลด\nเสลบรรพต\nเสลา\nเสลี่ยง\nเสลือกสลน\nเสโล\nเสวก\nเสวกามาตย์\nเสวนะ\nเสวนา\nเสวย\nเสวียน\nเสสรวง\nเสสรวล\nเสา\nเส้า\nเสาร์\nเสารภย์\nเสารี\nเสาวคนธ์\nเสาวธาร\nเสาวภา\nเสาวภาคย์\nเสาวภาพ\nเสาวรภย์\nเสาวรส\nเสาวลักษณ์\nเสาวณิต\nเสาวนะ\nเสาวนา\nเสาวนีย์\nเสาหฤท\nเสาะ\nเสาะแสะ\nเสิร์จ\nเสิร์ฟ\nเสีย\nเสียง\nเสี่ยง\nเสียด\nเสี้ยน\nเสียบ\nเสียม\nเสี่ยม\nเสี้ยม\nเสียว\nเสี่ยว\nเสี้ยว\nเสือ\nเสื่อ\nเสื้อ\nเสือก\nเสื่อม\nแส\nแส่\nแส้\nแสก\nแสง\nแสด\nแสดง\nแสตมป์\nแสน\nแสนย์\nแสนยากร\nแสนยานุภาพ\nแสบ\nแสม\nแสยก\nแสยง\nแสยะ\nแสรก\nแสร้ง\nแสลง\nแสล้ม\nแสวง\nแสะ\nโสก\nโสกโดก\nโสกันต์\nโสโครก\nโสณฑ์\nโสณิ\nโสณี\nโสด\nโสดก\nโสดม\nโสดา\nโสดาบัน\nโสดาปัตติผล\nโสดาปัตติมรรค\nโสต\nโสตทัศนวัสดุ\nโสตทัศนอุปกรณ์\nโสตทัศนูปกรณ์\nโสตินทรีย์\nโสตถิ\nโสทก\nโสทร\nโสธก\nโสธนะ\nโสน\nโสภณ\nโสภา\nโสภี\nโสภิณี\nโสเภณี\nโสม\nโสมนัส\nโสมม\nโสมย์\nโสร่ง\nโสรจ\nโสรวาร\nโสโร\nโสวรรณ\nโสหุ้ย\nโสฬส\nใส\nใส่\nไส\nไส้\nไสย\nไสยา\nไสยาสน์\nไสร้\nไสว\nหก\nหกคะเมน\nหง\nหงก\nหงส์\nหงสบาท\nหงสรถ\nหงอ\nหงอก\nหง่อง\nหงองแหงง\nหงอด\nหงอน\nหง่อม\nหงอย\nหง่อย\nหงัก\nหงับ\nหง่าง\nหงาย\nหง่าว\nหงำ\nหงิก\nหงิง\nหงิม\nหงึก\nหงุงหงิง\nหงุดหงิด\nหงุบ\nหงุ่ย\nหญ้า\nหญ้าฝรั่น\nหญ้ายายเภา\nหญิง\nหญิบ\nหด\nหตะ\nหทัย\nหน\nหนวก\nหน่วง\nหนวด\nหน่วย\nหน่วยกิต\nหนอ\nหน่อ\nหนอก\nหนอง\nหนอน\nหนอนตายหยาก\nหน่อย\nหน็อยแน่\nหนัก\nหนัง\nหนังสติ๊ก\nหนังสือ\nหนั่น\nหนับ\nหนา\nหน้า\nหน่าง\nหนาด\nหนาน\nหนาม\nหน่าย\nหนาว\nหนำ\nหนำเลี้ยบ\nหนี\nหนี้\nหนีบ\nหนึก\nหนึ่ง\nหนึบ\nหนืด\nหนุ\nหนุน\nหนุบ\nหนุ่ม\nหนุ่ย\nหนู\nห่ม\nหมก\nหมด\nหม่น\nหมวก\nหมวด\nหมวน\nหมอ\nหม่อ\nหม้อ\nหมอก\nหมอง\nหม่อง\nหมอน\nหม่อน\nหมอบ\nหม่อม\nหมอย\nหม้อห้อม\nหมัก\nหมักหมม\nหมัด\nหมัน\nหมั่น\nหมั้น\nหมับ\nหมา\nหม่า\nหมาก\nหมากฮอส\nหมาง\nหมาด\nหมามุ่ย\nหมามุ้ย\nหมาย\nหม้าย\nหมาร่า\nหม่ำ\nหม้ำ\nหมิ่น\nหมี\nหมี่\nหมึก\nหมืน\nหมื่น\nหมุด\nหมุน\nหมุบ\nหมุบหมับ\nหมุบหมิบ\nหมุ่ย\nหมุยขาว\nหมู\nหมู่\nหมูหริ่ง\nหยก\nหย่ง\nหยด\nหยวก\nหยวบ\nหยอก\nหยอกเอิน\nหย็อกหย็อย\nหยอง\nหย็อง\nหย่อง\nหย็องกรอด\nหย็องแหย็ง\nหยอด\nหยอน\nหย่อน\nหย่อม\nหย็อมแหย็ม\nหย็อย\nหย่อย\nหยัก\nหยักไย่\nหยักเหยา\nหยัง\nหยั่ง\nหยังหยัง\nหยัด\nหยัน\nหยับ\nหยั่วเมือง\nหย่า\nหยากเยื่อ\nหยากไย่\nหยาด\nหยาบ\nหยาม\nหยาว\nหย้าว\nหยำเป\nหยำเหยอะ\nหยำแหยะ\nหยิก\nหยิ่ง\nหยิบ\nหยิม\nหยี\nหยี่\nหยุกหยิก\nหยุด\nหยุ่น\nหยุบ\nหยุมหยิม\nหยูกยา\nหโยดม\nหรคุณ\nหรณะ\nหรดาล\nหรดี\nหรรษ์\nหรรษา\nหรอ\nหรอก\nหร็อมแหร็ม\nหรอย\nหระ\nหรับ\nหรา\nหริ\nหริ่ง\nหริณะ\nหริต\nหริตกี\nหรีตกี\nหรี่\nหรีด\nหรือ\nหรุบ\nหรุบรู่\nหรุบหรู่\nหรุ่ม\nหรู\nหรูหรา\nหฤทัย\nหฤทย์\nหฤษฎ์\nหฤษฎี\nหฤหรรษ์\nหฤโหด\nหลง\nหลงใหล\nหลงจู๊\nหลด\nหลน\nหล่น\nหลบ\nหล่ม\nหลวง\nหลวม\nหลอ\nหล่อ\nหลอก\nหลอด\nหลอน\nหล็อน\nหล่อน\nหลอม\nหละ\nหละหลวม\nหลัก\nหลัง\nหลั่ง\nหลัด\nหลั่น\nหลับ\nหลัว\nหลา\nหล้า\nหลาก\nหลาน\nหลาบ\nหลาม\nหลาย\nหลาว\nหลิ่ง\nหลิท\nหลิน\nหลิม\nหลิว\nหลิ่ว\nหลี\nหลีก\nหลีโก\nหลีบ\nหลีฮื้อ\nหลืบ\nหลุกหลิก\nหลุด\nหลุน\nหลุบ\nหลุม\nหลุมพอ\nหลุมพี\nหลู่\nหวง\nห่วง\nห้วง\nหวด\nหวน\nห้วน\nหวย\nห้วย\nหวอ\nหวอด\nหวะ\nหวัง\nหวัด\nหวั่น\nหวันยิหวา\nหวัว\nหวัวร่อ\nหวัวเราะ\nหวา\nหว่า\nหว้า\nหวาก\nหว่าง\nหวาด\nหวาน\nหว่าน\nหวาม\nหวาย\nหวำ\nหวิด\nหวิว\nหวี\nหวี่\nหวีด\nหวือ\nหวุดหวิด\nหวูด\nหอ\nห่อ\nห้อ\nหอก\nหอง\nห้อง\nหอน\nห่อน\nหอบ\nหอม\nห้อม\nหอย\nห้อย\nหะ\nหะยี\nหะหาย\nหัก\nหัจญ์\nหัจญี\nหัช\nหัฏฐะ\nหัด\nหัต\nหัตถ์\nหัตถกรรม\nหัตถการ\nหัตถกิจ\nหัตถบาส\nหัตถพันธ์\nหัตถาภรณ์\nหัตถศาสตร์\nหัตถศิลป์\nหัตถศึกษา\nหัตถาจารย์\nหัตถินี\nหัตถี\nหัน\nหั่น\nหั้น\nหันตรา\nหับ\nหัย\nหัว\nหัวร่อ\nหัวเราะ\nหัส\nหัสดิน\nหัสดี\nหัสต์\nหัสตะ\nหา\nห่า\nห้า\nหาก\nหาง\nห่าง\nห้าง\nหาญ\nหาด\nห่าน\nหาบ\nหาม\nห่าม\nห้าม\nหาย\nหายใจ\nหายนะ\nหาร\nหารือ\nหาว\nห้าว\nหาสะ\nหำ\nห้ำ\nหิ้ง\nหิงคุ\nหิงสา\nหิงห้อย\nหิ่งห้อย\nหิ่งหาย\nหิด\nหิต\nหิตานุหิตประโยชน์\nหิน\nหิมพาน\nหิมพานต์\nหิมวัต\nหิมวันต์\nหิมวา\nหิมะ\nหิมาลัย\nหิรัญ\nหิรัญญิการ์\nหิรัญบัฏ\nหิรัณย์\nหิรัณยรัศมี\nหิริ\nหิว\nหิ้ว\nหี\nหีด\nหีนยาน\nหีบ\nหึ\nหึง\nหึ่ง\nหึงสา\nหืด\nหืน\nหื่น\nหือ\nหื้อ\nหุง\nหุน\nหุ่น\nหุ้น\nหุนหัน\nหุบ\nหุ้ม\nหุยฮา\nหู\nหู่\nหูก\nหูด\nเห\nเห่\nเหง\nเหง่ง\nเหงา\nเหง้า\nเหงื่อ\nเหงือก\nเห็จ\nเห็ด\nเหติ\nเหตุ\nเห็น\nเหน่ง\nเหนงนายพราน\nเหน็ดเหนื่อย\nเหน็บ\nเหน่อ\nเห็นอ้ม\nเหนอะ\nเหนอะหนะ\nเหน้า\nเหนาะ\nเหนียง\nเหนี่ยง\nเหนี่ยน\nเหนียม\nเหนียว\nเหนี่ยว\nเหนือ\nเหนื่อย\nเห็บ\nเหม\nเหม่\nเหม็ง\nเหม่ง\nเหม็น\nเหมวดี\nเหม่อ\nเหมันต์\nเหมันตฤดู\nเหมา\nเหมายัน\nเหมาะ\nเหมียว\nเหมี่ยว\nเหมือง\nเหมือด\nเหมือน\nเหมื่อย\nเหย\nเหยง\nเหย่อย\nเหยา\nเหย่า\nเหย้า\nเหยาะ\nเหยาะแหยะ\nเหยิง\nเหยิบ\nเหยียด\nเหยียบ\nเหยี่ยว\nเหยื่อ\nเหยือก\nเหรอ\nเหรอะ\nเหรัญญิก\nเหรา\nเหราะ\nเหรียญ\nเหล่\nเหล็ก\nเหลน\nเหลว\nเหลอ\nเหลา\nเหล่า\nเหล้า\nเหลาะแหละ\nเหลิง\nเหลิงเจิ้ง\nเหลียน\nเหลี่ยม\nเหลียว\nเหลือ\nเหลือก\nเหลือง\nเหลือบ\nเหลือม\nเหลื่อม\nเหว\nเหว่\nเหวง\nเหวย\nเหวอะ\nเหวอะหวะ\nเหวี่ยง\nเห่อ\nเหอะ\nเหะ\nเหะหะ\nเหา\nเห่า\nเหาะ\nเหิน\nเหิม\nเหี้ย\nเหียง\nเหียน\nเหี้ยน\nเหี้ยม\nเหี่ยว\nเหื่อ\nเหือด\nแห\nแห่\nแห้\nแหก\nแหง\nแหง่\nแห่ง\nแห้ง\nแหงแก๋\nแหง่ง\nแหงน\nแหน\nแห้น\nแหนง\nแหนบ\nแหนม\nแหบ\nแหม\nแหม่\nแหม่ม\nแหมะ\nแหย\nแหย่\nแหยง\nแหย่ง\nแหยม\nแหย็ม\nแหยะ\nแหล่\nแหลก\nแหล่ง\nแหลน\nแหลม\nแหละ\nแหว\nแห้ว\nแหวก\nแหว่ง\nแหวด\nแหวน\nแหวะ\nแหะ\nโห่\nโหง\nโหด\nโหน\nโหนก\nโหน่ง\nโหม\nโหม่ง\nโหมด\nโหย\nโหยกเหยก\nโหยง\nโหย่ง\nโหร\nโหรง\nโหรงเหรง\nโหรดาจารย์\nโหระพา\nโหรา\nโหราจารย์\nโหราศาสตร์\nโหล\nโหล่\nโหลงโจ้ง\nโหว\nโหว่\nโหว้\nโหวกเหวก\nโหวง\nโหวด\nโหวต\nให้\nใหญ่\nใหม่\nไห\nไห่\nไห้\nไหน\nไหม\nไหม้\nไหรณย์\nไหล\nไหล่\nไหว\nไหว้\nไหหลำ\nอก\nอกตเวทิตา\nอกตเวที\nอกตัญญุตา\nอกตัญญู\nอกนิษฐ์\nอกรณีย์\nอกรรมกริยา\nอกัปปิยวัตถุ\nอกัปปิยะ\nอกุศล\nอคติ\nอคาธ\nอโฆษะ\nองก์\nองค์\nองคชาต\nองคมนตรี\nองครักษ์\nองคาพยพ\nองคุลี\nองศ์\nองศา\nองอาจ\nองุ่น\nอจลา\nอจินตา\nอจินไตย\nอจิระ\nอเจลก\nอเจละ\nอชะ\nอชิน\nอชินี\nอชิระ\nอฏวี\nอณิ\nอณู\nอโณทัย\nอด\nอดิถี\nอดิเทพ\nอดิเรก\nอดิศร\nอดิศวร\nอดิศัย\nอดีต\nอดุล\nอดุลย์\nอติ\nอติชาต\nอติมานะ\nอติราช\nอติเรก\nอติสาร\nอถรรพเวท\nอาถรรพเวท\nอทระ\nอทินนาทาน\nอธรรม\nอธิ\nอธิกมาส\nอธิกรณ์\nอธิกวาร\nอธิกสุรทิน\nอธิการ\nอธิคม\nอธิฏฐาน\nอธิบดี\nอธิบาย\nอธิป\nอธิปไตย\nอธิมาตร\nอธิมุตติ\nอธิโมกข์\nอธิราช\nอธิวาส\nอธิวาสนะ\nอธิศีล\nอธิษฐาน\nอธึก\nอ้น\nอนงค์\nอนงคณะ\nอนงคเลขา\nอนธการ\nอนนต์\nอนยะ\nอนรรฆ\nอนรรถ\nอนล\nอนวัช\nอนัญ\nอนัตตา\nอนันต์\nอนันตริยกรรม\nอนัม\nอนาคต\nอนาคามิผล\nอนาคามิมรรค\nอนาคามี\nอนาจาร\nอนาถ\nอนาถา\nอนาทร\nอนาธิปไตย\nอนามัย\nอนามิกา\nอนารยชน\nอนารยธรรม\nอนารยะ\nอนาลัย\nอนำ\nอนิจ\nอนิจจัง\nอนิจจา\nอนิฏฐารมณ์\nอนิยต\nอนิยม\nอนิล\nอนิวรรต\nอนิวรรตน์\nอนีกะ\nอนีจะ\nอนึก\nอนึ่ง\nอนุ\nอนุกร\nอนุกรม\nอนุกรรมการ\nอนุกระเบียด\nอนุกาชาด\nอนุการ\nอนุกูล\nอนุคามิก\nอนุเคราะห์\nอนุจร\nอนุช\nอนุชน\nอนุชา\nอนุชาต\nอนุชิต\nอนุญาต\nอนุญาโตตุลาการ\nอนุตร\nอนุเถระ\nอนุทิน\nอนุบท\nอนุบาล\nอนุประโยค\nอนุปริญญา\nอนุปสัมบัน\nอนุปัสนา\nอนุพงศ์\nอนุพัทธ์\nอนุพันธ์\nอนุโพธ\nอนุภรรยา\nอนุภริยา\nอนุภาค\nอนุภาษ\nอนุมัติ\nอนุมาตรา\nอนุมาน\nอนุมูล\nอนุโมทนา\nอนุโยค\nอนุรักษ์\nอนุรักษนิยม\nอนุราช\nอนุราธ\nอนุราธะ\nอนุราธา\nอนุรูป\nอนุโลม\nอนุวงศ์\nอนุวรรตน์\nอนุวัต\nอนุวัตน์\nอนุวัตร\nอนุวัติ\nอนุวาต\nอนุศาสก\nอนุศาสน์\nอนุศาสนาจารย์\nอนุศิษฏ์\nอนุสติ\nอนุสนธิ\nอนุสร\nอนุสรณ์\nอนุสัญญา\nอนุสัย\nอนุสาวรีย์\nอนุสาสนี\nอเนก\nอเนกคุณ\nอเนกรรถประโยค\nอเนจอนาถ\nอโนชา\nอโนดาต\nอบ\nอบเชย\nอบาย\nอปจายนธรรม\nอปจายนมัย\nอปมงคล\nอปยศ\nอประไมย\nอปรัณณชาติ\nอปรา\nอปราชัย\nอปราชิต\nอปริมาณ\nอปลักษณ์\nอปโลกน์\nอปวาท\nอเปหิ\nอพพะ\nอพยพ\nอภัพ\nอภัย\nอภิ\nอภิฆาต\nอภิชฌา\nอภิชน\nอภิชนาธิปไตย\nอภิชัย\nอภิชาต\nอภิชิต\nอภิญญา\nอภิญญาณ\nอภิธรรม\nอภิธาน\nอภิไธย\nอภินันท์\nอภินันทนาการ\nอภินัย\nอภินิหาร\nอภิเนษกรมณ์\nอภิบาล\nอภิปรัชญา\nอภิปราย\nอภิมหาอำนาจ\nอภิมานะ\nอภิมุข\nอภิรดี\nอภิรติ\nอภิรมย์\nอภิรักษ์\nอภิราม\nอภิรุต\nอภิรุม\nอภิรูป\nอภิลักขิต\nอภิลักขิตสมัย\nอภิเลปน์\nอภิวันท์\nอภิวาท\nอภิวาทน์\nอภิเษก\nอภิสมโพธิ\nอภิสมัย\nอภิสมาจาร\nอภิสัมโพธิ\nอภิสัมโพธิญาณ\nอภิสิต\nอภิสิทธิ์\nอภูตะ\nอม\nอมพะนำ\nอ้ม\nอมตธรรม\nอมตบท\nอมตะ\nอมนุษย์\nอมร\nอมรา\nอมราวดี\nอมรินทร์\nอมเรนทร์\nอมเรศ\nอมฤต\nอมัตร\nอมาตย์\nอมาวสี\nอมาวสุ\nอมาวาสี\nอมิตร\nอเมริกัน\nอย่า\nอยาก\nอย่าง\nอยุทธ์\nอยู่\nอร\nอรชร\nอรชุน\nอรดี\nอรติ\nอรทัย\nอรไท\nอรนุช\nอรพินท์\nอรพิม\nอรรค\nอรรฆ\nอรรฆย์\nอรรจน์\nอรรณพ\nอรรถ\nอรรถกถา\nอรรถกถาจารย์\nอรรถาธิบาย\nอรรธ\nอรสุม\nอรหะ\nอรหัง\nอรหัต\nอรหัตผล\nอรหัตมรรค\nอรหัน\nอรหันต์\nอรหันตฆาต\nอร่อย\nอรัญ\nอรัญญิก\nอรัญวาส\nอรัญวาสี\nอรัณย์\nอราดี\nอร่าม\nอริ\nอรินทร์\nอริน\nอริยกะ\nอริยทรัพย์\nอริยบุคคล\nอริยผล\nอริยมรรค\nอริยสั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nอำพล\nอำพะนำ\nอำพัน\nอำไพ\nอำเภอ\nอำมร\nอำมฤคโชค\nอำมฤต\nอำมหิต\nอำมาตย์\nอำมาตยาธิปไตย\nอำยวน\nอำรุง\nอำลา\nอำอวม\nอ้ำอึ้ง\nอิก\nอิง\nอิงค์\nอิงอร\nอิจฉา\nอิฉัน\nอิชยา\nอิฏฐารมณ์\nอิฐ\nอิฐผล\nอิด\nอิตถี\nอิตเทรียม\nอิตเทอร์เบียม\nอิติวุตตกะ\nอิทธิ\nอิน\nอินซูลิน\nอินเดีย\nอินเดียนแดง\nอินเดียม\nอินท์\nอินทขีล\nอินทนิล\nอินทผลัม\nอินทร์\nอินทรธนู\nอินทรวงศ์\nอินทรวิเชียร\nอินทรศักดิ์\nอินทราณี\nอินทราภิเษก\nอินทรายุธ\nอินทรี\nอินทรีย์\nอินทรียสังวร\nอินทีวร\nอินทุ\nอินฟราเรด\nอินัง\nอิ่ม\nอิมัลชัน\nอิริเดียม\nอิริยา\nอิริยาบถ\nอิรุพเพท\nอิเล็กตรอน\nอิเล็กทรอนิกส์\nอิเล็กโทน\nอิศร\nอิศวร\nอิษฏ์\nอิษฏี\nอิส\nอิสตรี\nอิสัตรี\nอิสรภาพ\nอิสระ\nอิสริยยศ\nอิสริยะ\nอิสริยาภรณ์\nอิสลาม\nอิสสา\nอิสิ\nอิสี\nอิหม่าม\nอิหลักอิเหลื่อ\nอี\nอี่\nอี้\nอี๊\nอี๋\nอี๋อ๋อ\nอีก\nอีก้อ\nอีก๋อย\nอีโก้ง\nอีจู้\nอี๊ด\nอีเต้อ\nอีโต้\nอีทุบ\nอีเทอร์\nอีนุงตุงนัง\nอีนูน\nอีโน\nอีแปะ\nอีโปง\nอีเพา\nอีมู\nอีรม\nอีลุ้ม\nอีลุ่ยฉุยแฉก\nอีเลิ้ง\nอีศ\nอีศวร\nอีส\nอีสาน\nอีสุกอีใส\nอีหรอบ\nอีหลักอีเหลื่อ\nอีหลี\nอีหลุกขลุกขลัก\nอีหลุกขลุกขลุ่ย\nอีเห็น\nอีเหน็บ\nอีเหนียว\nอีเหละเขละขละ\nอีเหละเขะขะ\nอีโหน่อีเหน่\nอีโหลกโขลกเขลก\nอีแอ่น\nอึ\nอึก\nอึ้ก\nอึ๊ก\nอึกทึก\nอึกอัก\nอึง\nอึ่ง\nอึ้ง\nอึด\nอึดตะปือ\nอึ้ดทึ่ด\nอึน\nอืด\nอื่น\nอื้น\nอือ\nอื้อ\nอื้อฮือ\nอุ\nอุก\nอุกกา\nอุกกาบาต\nอุกฤษฏ์\nอุกลาบาต\nอุค\nอุคระ\nอุคหนิมิต\nอุโฆษ\nอุ้ง\nอุจ\nอุจจาระ\nอุจฉุ\nอุจเฉท\nอุจเฉททิฐิ\nอุจาด\nอุชุ\nอุฏฐาการ\nอุณหภูมิ\nอุณหะ\nอุณหาหาร\nอุณหิส\nอุณา\nอุณาโลม\nอุด\nอุดเตา\nอุดม\nอุดมการณ์\nอุดมคติ\nอุดมศึกษา\nอุดร\nอุดหนุน\nอุตดม\nอุตตมะ\nอุตมภาพ\nอุตมางค์\nอุตดร\nอุตรกุรุทวีป\nอุตตรายัน\nอุตรนิกาย\nอุตรผลคุนี\nอุตตรผลคุนี\nอุตรภัทรบท\nอุตตรภัทรบท\nอุตตรภัททะ\nอุตราภิมุข\nอุตราวรรต\nอุตราวัฏ\nอุตราษาฒ\nอุตตราสาฬหะ\nอุตราสงค์\nอุตตานภาพ\nอุตพิด\nอุตรา\nอุตริ\nอุตริมนุสธรรม\nอุตลุด\nอุตส่าห์\nอุตสาหกรรม\nอุตสาหะ\nอุตุ\nอุตุนิยม\nอุตุนิยมวิทยา\nอุทก\nอุทกธาร\nอุทกธารา\nอุทกภัย\nอุทกวิทยา\nอุทกศาสตร์\nอุทธรณ์\nอุทธัจ\nอุทยาน\nอุทร\nอุทริยะ\nอุทลุม\nอุทัช\nอุทัย\nอุทาน\nอุทาร\nอุทาหรณ์\nอุทิศ\nอุทุมพร\nอุเทศ\nอุเทสิกเจดีย์\nอุธัจ\nอุ่น\nอุบ\nอุบล\nอุบะ\nอุบ๊ะ\nอุบัติ\nอุบาท\nอุบาทว์\nอุบาย\nอุบาสก\nอุบาสิกา\nอุเบกขา\nอุโบสถ\nอุปกรณ์\nอุปกรม\nอุปการ\nอุปการะ\nอุปการี\nอุปกิเลส\nอุปจาร\nอุปถัมภ์\nอุปถัมภก\nอุปทม\nอุปทูต\nอุปเทศ\nอุปเท่ห์\nอุปธิ\nอุปนัย\nอุปนิกขิต\nอุปนิษัท\nอุปนิสัย\nอุปบัติ\nอุปปาติกะ\nอุปพัทธ์\nอุปพันธ์\nอุปโภค\nอุปมา\nอุปมาน\nอุปไมย\nอุปยุวราช\nอุปรากร\nอุปราคา\nอุปราช\nอุปริ\nอุปริมปริยาย\nอุปโลกน์\nอุปเวท\nอุปสมบท\nอุปสมบัน\nอุปสัมบัน\nอุปสรรค\nอุปสัมปทา\nอุปฮาด\nอุปัชฌาย์\nอุปัชฌายวัตร\nอุปัชฌายะ\nอุปัฏฐาก\nอุปัฏฐานะ\nอุปัทวะ\nอุปัทวันตราย\nอุปาทาน\nอุปาหนา\nอุภัย\nอุ้ม\nอุมงค์\nอุโมงค์\nอุย\nอุ่ย\nอุ้ย\nอุ๊ย\nอุยยาน\nอุยยาม\nอุรณะ\nอุรพี\nอุระ\nอุรังอุตัง\nอุรัจฉัท\nอุรัจฉทะ\nอุรา\nอุรุ\nอุไร\nอุลกมณี\nอุลโลจ\nอุลามก\nอุลิด\nอุโลก\nอุแว้\nอุษณกร\nอุษณกาล\nอุษณรัศมี\nอุษณรุจี\nอุษณาการ\nอุษณีษ์\nอุษมะ\nอุษมัน\nอุษา\nอุษาโยค\nอุสภ\nอุสส่าห์\nอุสสาหะ\nอุสา\nอุสุ\nอุสุภ\nอุสุภราช\nอุสุม\nอุหรับ\nอุหลบ\nอุเหม่\nอุฬาร\nอู\nอู่\nอู้\nอูฐ\nอูด\nอูม\nอูย\nอูรุ\nอู๋อี๋\nเอ\nเอ้\nเอ๊\nเอก\nเอกเขนก\nเอกซเรย์\nเอกรรถประโยค\nเอกัคตา\nเอกังสพยากรณ์\nเอกังสวาที\nเอกา\nเอ้กา\nเอกาธิปไตย\nเอเคอร์\nเอง\nเอ็ง\nเอ๋ง\nเอ็ด\nเอ็ดตะโร\nเอดส์\nเอตทัคคะ\nเอ้เต\nเอทิล\nเอน\nเอ็น\nเอนไซม์\nเอ็นดู\nเอ็นอ่อน\nเอม\nเอ็มบริโอ\nเอย\nเอ่ย\nเอ๊ย\nเอ๋ย\nเอร็ดอร่อย\nเอราวัณ\nเอว\nเอ๊ว\nเอวัง\nเอฬกะ\nเอฬา\nเออ\nเอ่อ\nเออร์เบียม\nเอ้อระเหย\nเอ้อเร้อ\nเอ้อเฮอ\nเอ๊ะ\nเอะใจ\nเอะอะ\nเอะอะมะเทิ่ง\nเอา\nเอาทาร\nเอาทารย์\nเอารส\nเอาฬาร\nเอาะลาย\nเอิก\nเอิกเกริก\nเอิ้น\nเอิบ\nเอียง\nเอี้ยง\nเอียด\nเอี๊ยด\nเอียน\nเอี่ยน\nเอี่ยม\nเอี๊ยม\nเอี้ยมจุ๊น\nเอี้ยมเฟี้ยม\nเอี่ยว\nเอี้ยว\nเอื้อ\nเอื๊อก\nเอื้อง\nเอือด\nเอือน\nเอื้อน\nเอือม\nเอื้อม\nเอื่อย\nเอื้อย\nแอ\nแอ้\nแอ๋\nแอก\nแอกทิเนียม\nแอ่ง\nแอ้งแม้ง\nแอด\nแอ้ด\nแอ๊ด\nแอ่น\nแอนติเจน\nแอนติบอดี\nแอนติอิเล็กตรอน\nแอโนด\nแอบ\nแอม\nแอ้ม\nแอมแปร์\nแอมมิเตอร์\nแอมโมเนีย\nแอร่ม\nแอลกอฮอล์\nแอลฟา\nแอว\nแอ่ว\nแอ้วแซ่ว\nแอสทาทีน\nแอสไพริน\nแอสฟัลต์\nแอหนัง\nแออัด\nโอ\nโอ่\nโอ้\nโอ๋\nโอก\nโอ้ก\nโอ้กอ้าก\nโอ๊ก\nโอกาส\nโอฆชล\nโอฆสงสาร\nโอฆะ\nโอ่ง\nโองการ\nโองโขดง\nโอชะ\nโอชา\nโอโซน\nโอฐ\nโอด\nโอ๊ต\nโอตตัปปะ\nโอทนะ\nโอน\nโอบ\nโอปปาติกะ\nโอภา\nโอภาส\nโอม\nโอย\nโอ๊ย\nโอรส\nโอละพ่อ\nโอลิมปิก\nโอวาท\nโอษฐ์\nโอษฐชะ\nโอษฐภัย\nโอสถ\nโอห์ม\nโอหัง\nโอฬาร\nโอฬาริก\nโอฬารึก\nโอ้เอ้\nโอเอซิส\nโอ้โฮ\nไอ\nไอ้\nไอโซโทป\nไอน์สไตเนียม\nไอยรา\nไอยเรศ\nไอราพต\nไอราวัณ\nไอราวัต\nไอศกรีม\nไอศวรรย์\nไอศุริยสมบัติ\nไอศูรย์\nไอออน\nไอโอดีน\nฮกเกี้ยน\nฮด\nฮวงซุ้ย\nฮวน\nฮ้วนหมู\nฮวบ\nฮ่อ\nฮ้อ\nฮอกกี้\nฮอด\nฮ่อม\nฮ่อยจ๊อ\nฮอร์โมน\nฮอลแลนด์\nฮอลันดา\nฮะ\nฮะเบส\nฮะเรีย\nฮัก\nฮังเล\nฮัจญ์\nฮัจญะฮ์\nฮัจญี\nฮั่น\nฮั้ว\nฮา\nฮ้า\nฮ่างหลวง\nฮาจญ์\nฮาม\nฮาเร็ม\nฮาห์เนียม\nฮิจเราะห์\nฮินดู\nฮิปโปโปเตมัส\nฮิสทีเรีย\nฮีเลียม\nฮึ\nฮึก\nฮึด\nฮึดฮัด\nฮึ่ม\nฮึย\nฮึ่ย\nฮืดฮาด\nฮือ\nฮื่อ\nฮื้อ\nฮื้อฉี่\nฮุด\nฮุบ\nฮุยเลฮุย\nฮู้\nฮูก\nฮูม\nเฮ\nเฮฮา\nเฮกตาร์\nเฮกโตกรัม\nเฮกโตเมตร\nเฮกโตลิตร\nเฮง\nเฮ็ด\nเฮโมโกลบิน\nเฮย\nเฮ่ย\nเฮ้ย\nเฮโรอีน\nเฮลิคอปเตอร์\nเฮโล\nเฮละโล\nเฮ้ว\nเฮอ\nเฮ่อ\nเฮ้อ\nเฮอริเคน\nเฮิรตซ์\nเฮี้ยน\nเฮี้ยบ\nเฮี้ยว\nเฮือก\nเฮือน\nแฮ\nแฮ่\nแฮก\nแฮ่กึ๊น\nแฮนด์บอล\nแฮฟเนียม\nแฮม\nแฮะ\nโฮ\nโฮก\nโฮ่ง\nโฮ้ง\nโฮเต็ล\nโฮลเมียม\nโฮะ\nไฮ้\nไฮโกรมิเตอร์\nไฮดรา\nไฮโดร\nไฮโดรคาร์บอน\nไฮโดรเจน\nไฮโดรมิเตอร์\nไฮไฟ\nไฮโล\nไฮฮี\n"
+              .split(/[\r\n]+/)
+              .filter(function (w) {
+                return w.length > 1;
+              })
+      this.addWords(words, false)
+    }
+    if(finalize){
+      this.finalizeDict();
+    }
+  },
+
+  dictSeek: function (l, r, ch, strOffset, pos) {
+    var ans = null;
+    while (l <= r) {
+      var m = Math.floor((l + r) / 2),
+        dict_item = this.dict[m],
+        len = dict_item.length;
+      if (len <= strOffset) {
+        l = m + 1;
+      } else {
+        var ch_ = dict_item[strOffset];
+        if (ch_ < ch) {
+          l = m + 1;
+        } else if (ch_ > ch) {
+          r = m - 1;
+        } else {
+          ans = m;
+          if (pos == LEFT) {
+            r = m - 1;
+          } else {
+            l = m + 1;
+          }
+        }
+      }
+    }
+    return ans;
+  },
+
+  isFinal: function (acceptor) {
+    return this.dict[acceptor.l].length == acceptor.strOffset;
+  },
+
+  createAcceptor: function () {
+    return {
+      l: 0,
+      r: this.dict.length - 1,
+      strOffset: 0,
+      isFinal: false,
+      dict: this,
+      transit: function (ch) {
+        return this.dict.transit(this, ch);
+      },
+      isError: false,
+      tag: "DICT",
+      w: 1,
+      type: "DICT"
+    };
+  },
+
+  transit: function (acceptor, ch) {
+    var l = this.dictSeek(acceptor.l,
+      acceptor.r,
+      ch,
+      acceptor.strOffset,
+      LEFT);
+    if (l !== null) {
+      var r = this.dictSeek(l,
+        acceptor.r,
+        ch,
+        acceptor.strOffset,
+        RIGHT);
+      acceptor.l = l;
+      acceptor.r = r;
+      acceptor.strOffset++;
+      acceptor.isFinal = this.isFinal(acceptor);
+    } else {
+      acceptor.isError = true;
+    }
+    return acceptor;
+  },
+
+  sortuniq: function(a){
+    return a.sort().filter(function(item, pos, arr){
+      return !pos || item != arr[pos - 1];
+    })
+  },
+
+  flatten: function(a){
+    //[[1,2],[3]] -> [1,2,3]
+    return [].concat.apply([], a);
+  }
+};
+module.exports = WordcutDict;
+
+}).call(this,"/dist/tmp")
+},{"glob":16,"path":22}],3:[function(require,module,exports){
+var WordRule = {
+  createAcceptor: function(tag) {
+    if (tag["WORD_RULE"])
+      return null;
+
+    return {strOffset: 0,
+            isFinal: false,
+            transit: function(ch) {
+              var lch = ch.toLowerCase();
+              if (lch >= "a" && lch <= "z") {
+                this.isFinal = true;
+                this.strOffset++;
+              } else {
+                this.isError = true;
+              }
+              return this;
+            },
+            isError: false,
+            tag: "WORD_RULE",
+            type: "WORD_RULE",
+            w: 1};
+  }
+};
+
+var NumberRule = {
+  createAcceptor: function(tag) {
+    if (tag["NUMBER_RULE"])
+      return null;
+
+    return {strOffset: 0,
+            isFinal: false,
+            transit: function(ch) {
+              if (ch >= "0" && ch <= "9") {
+                this.isFinal = true;
+                this.strOffset++;
+              } else {
+                this.isError = true;
+              }
+              return this;
+            },
+            isError: false,
+            tag: "NUMBER_RULE",
+            type: "NUMBER_RULE",
+            w: 1};
+  }
+};
+
+var SpaceRule = {
+  tag: "SPACE_RULE",
+  createAcceptor: function(tag) {
+
+    if (tag["SPACE_RULE"])
+      return null;
+
+    return {strOffset: 0,
+            isFinal: false,
+            transit: function(ch) {
+              if (ch == " " || ch == "\t" || ch == "\r" || ch == "\n" ||
+                  ch == "\u00A0" || ch=="\u2003"//nbsp and emsp
+                 ) {
+                this.isFinal = true;
+                this.strOffset++;
+              } else {
+                this.isError = true;
+              }
+              return this;
+            },
+            isError: false,
+            tag: SpaceRule.tag,
+            w: 1,
+            type: "SPACE_RULE"};
+  }
+}
+
+var SingleSymbolRule = {
+  tag: "SINSYM",
+  createAcceptor: function(tag) {
+    return {strOffset: 0,
+            isFinal: false,
+            transit: function(ch) {
+              if (this.strOffset == 0 && ch.match(/^[\@\(\)\/\,\-\."`]$/)) {
+                this.isFinal = true;
+                this.strOffset++;
+              } else {
+                this.isError = true;
+              }
+              return this;
+            },
+            isError: false,
+            tag: "SINSYM",
+            w: 1,
+            type: "SINSYM"};
+  }
+}
+
+
+var LatinRules = [WordRule, SpaceRule, SingleSymbolRule, NumberRule];
+
+module.exports = LatinRules;
+
+},{}],4:[function(require,module,exports){
+var _ = require("underscore")
+  , WordcutCore = require("./wordcut_core");
+var PathInfoBuilder = {
+
+  /*
+    buildByPartAcceptors: function(path, acceptors, i) {
+    var 
+    var genInfos = partAcceptors.reduce(function(genInfos, acceptor) {
+      
+    }, []);
+    
+    return genInfos;
+  } 
+  */
+
+  buildByAcceptors: function(path, finalAcceptors, i) {
+    var self = this;
+    var infos = finalAcceptors.map(function(acceptor) {
+      var p = i - acceptor.strOffset + 1
+        , _info = path[p];            
+      
+      var info = {p: p, 
+                  mw: _info.mw + (acceptor.mw === undefined ? 0 : acceptor.mw),
+                  w: acceptor.w + _info.w,
+                  unk: (acceptor.unk ? acceptor.unk : 0) + _info.unk, 
+                  type: acceptor.type};
+
+      if (acceptor.type == "PART") {
+        for(var j = p + 1; j <= i; j++) {
+          path[j].merge = p;
+        }
+        info.merge = p;
+      }
+
+      return info;
+    });
+    return infos.filter(function(info) { return info; });
+  },
+  
+  fallback: function(path, leftBoundary, text, i) {
+    var _info = path[leftBoundary];
+    if (text[i].match(/[\u0E48-\u0E4E]/)) {
+      if (leftBoundary != 0) 
+        leftBoundary = path[leftBoundary].p;
+      return {p: leftBoundary,
+              mw: 0,
+              w: 1 + _info.w,
+              unk: 1 + _info.unk,
+              type: "UNK"};      
+/*    } else if(leftBoundary > 0 && path[leftBoundary].type !== "UNK") {
+      leftBoundary = path[leftBoundary].p;
+      return {p: leftBoundary,
+              w: 1 + _info.w,
+              unk: 1 + _info.unk,
+              type: "UNK"};            */
+    } else {      
+      return {p: leftBoundary,
+              mw: _info.mw,
+              w: 1 + _info.w,
+              unk: 1 + _info.unk,
+              type: "UNK"};
+    }
+  },
+  
+  build: function(path, finalAcceptors, i, leftBoundary, text) {
+    var basicPathInfos = this.buildByAcceptors(path, finalAcceptors, i);
+    if (basicPathInfos.length > 0) {
+      return basicPathInfos;
+    } else {
+      return [this.fallback(path, leftBoundary, text, i)];
+    }
+  }
+};
+
+module.exports = function() {
+  return _.clone(PathInfoBuilder);
+}
+
+},{"./wordcut_core":8,"underscore":25}],5:[function(require,module,exports){
+var _ = require("underscore");
+
+
+var PathSelector = {
+  selectPath: function(paths) {
+    var path = paths.reduce(function(selectedPath, path) {
+      if (selectedPath == null) {        
+        return path;
+      } else {
+        if (path.unk < selectedPath.unk) 
+          return path;        
+        if (path.unk == selectedPath.unk) {
+          if (path.mw < selectedPath.mw)
+            return path
+          if (path.mw == selectedPath.mw) {
+            if (path.w < selectedPath.w) 
+              return path;
+          }
+        }
+        return selectedPath;
+      }
+    }, null);
+    return path;
+  },
+  
+  createPath: function() {
+    return [{p:null, w:0, unk:0, type: "INIT", mw:0}];
+  }
+};
+
+module.exports = function() {
+  return _.clone(PathSelector);
+};
+
+},{"underscore":25}],6:[function(require,module,exports){
+function isMatch(pat, offset, ch) {
+  if (pat.length <= offset)
+    return false;
+  var _ch = pat[offset];
+  return _ch == ch ||
+         (_ch.match(/[กข]/) && ch.match(/[ก-ฮ]/)) ||
+         (_ch.match(/[มบ]/) && ch.match(/[ก-ฮ]/)) ||
+         (_ch.match(/\u0E49/) && ch.match(/[\u0E48-\u0E4B]/));
+}
+
+var Rule0 = {
+  pat: "เหก็ม",
+  createAcceptor: function(tag) {
+    return {strOffset: 0,
+            isFinal: false,
+            transit: function(ch) {
+              if (isMatch(Rule0.pat, this.strOffset,ch)) {                 
+                this.isFinal = (this.strOffset + 1 == Rule0.pat.length);                
+                this.strOffset++;
+              } else {              
+                this.isError = true;             
+              }
+              return this;
+            },
+            isError: false,
+            tag: "THAI_RULE",
+            type: "THAI_RULE", 
+            w: 1};                        
+  }
+};
+
+var PartRule = {
+  createAcceptor: function(tag) {
+    return {strOffset: 0,
+            patterns: [
+              "แก", "เก", "ก้", "กก์", "กา", "กี", "กิ", "กืก"  
+            ],
+            isFinal: false,
+            transit: function(ch) {
+              var offset = this.strOffset;
+              this.patterns = this.patterns.filter(function(pat) {
+                return isMatch(pat, offset, ch);
+              });
+              
+              if (this.patterns.length > 0) {
+                var len = 1 + offset;
+                this.isFinal = this.patterns.some(function(pat) {
+                  return pat.length == len; 
+                });
+                this.strOffset++;
+              } else {              
+                this.isError = true;             
+              }
+              return this;
+            },
+            isError: false,
+            tag: "PART",
+            type: "PART", 
+            unk: 1,
+            w: 1};                        
+  }
+};
+
+var ThaiRules = [Rule0, PartRule];
+
+module.exports = ThaiRules;
+
+},{}],7:[function(require,module,exports){
+var sys = require("sys")
+  , WordcutDict = require("./dict")
+  , WordcutCore = require("./wordcut_core")
+  , PathInfoBuilder = require("./path_info_builder")
+  , PathSelector = require("./path_selector")
+  , Acceptors = require("./acceptors")
+  , latinRules = require("./latin_rules")
+  , thaiRules = require("./thai_rules")
+  , _ = require("underscore");
+
+
+var Wordcut = Object.create(WordcutCore);
+Wordcut.defaultPathInfoBuilder = PathInfoBuilder;
+Wordcut.defaultPathSelector = PathSelector;
+Wordcut.defaultAcceptors = Acceptors;
+Wordcut.defaultLatinRules = latinRules;
+Wordcut.defaultThaiRules = thaiRules;
+Wordcut.defaultDict = WordcutDict;
+
+
+Wordcut.initNoDict = function(dict_path) {
+  var self = this;
+  self.pathInfoBuilder = new self.defaultPathInfoBuilder;
+  self.pathSelector = new self.defaultPathSelector;
+  self.acceptors = new self.defaultAcceptors;
+  self.defaultLatinRules.forEach(function(rule) {
+    self.acceptors.creators.push(rule);
+  });
+  self.defaultThaiRules.forEach(function(rule) {
+    self.acceptors.creators.push(rule);
+  });
+};
+
+Wordcut.init = function(dict_path, withDefault, additionalWords) {
+  withDefault = withDefault || false;
+  this.initNoDict();
+  var dict = _.clone(this.defaultDict);
+  dict.init(dict_path, withDefault, additionalWords);
+  this.acceptors.creators.push(dict);
+};
+
+module.exports = Wordcut;
+
+},{"./acceptors":1,"./dict":2,"./latin_rules":3,"./path_info_builder":4,"./path_selector":5,"./thai_rules":6,"./wordcut_core":8,"sys":28,"underscore":25}],8:[function(require,module,exports){
+var WordcutCore = {
+
+  buildPath: function(text) {
+    var self = this
+      , path = self.pathSelector.createPath()
+      , leftBoundary = 0;
+    self.acceptors.reset();
+    for (var i = 0; i < text.length; i++) {
+      var ch = text[i];
+      self.acceptors.transit(ch);
+
+      var possiblePathInfos = self
+        .pathInfoBuilder
+        .build(path,
+               self.acceptors.getFinalAcceptors(),
+               i,
+               leftBoundary,
+               text);
+      var selectedPath = self.pathSelector.selectPath(possiblePathInfos)
+
+      path.push(selectedPath);
+      if (selectedPath.type !== "UNK") {
+        leftBoundary = i;
+      }
+    }
+    return path;
+  },
+
+  pathToRanges: function(path) {
+    var e = path.length - 1
+     , ranges = [];
+
+    while (e > 0) {
+      var info = path[e]
+       , s = info.p;
+
+      if (info.merge !== undefined && ranges.length > 0) {
+        var r = ranges[ranges.length - 1];
+        r.s = info.merge;
+        s = r.s;
+      } else {
+        ranges.push({s:s, e:e});
+      }
+      e = s;
+    }
+    return ranges.reverse();
+  },
+
+  rangesToText: function(text, ranges, delimiter) {
+    return ranges.map(function(r) {
+      return text.substring(r.s, r.e);
+    }).join(delimiter);
+  },
+
+  cut: function(text, delimiter) {
+    var path = this.buildPath(text)
+      , ranges = this.pathToRanges(path);
+    return this
+      .rangesToText(text, ranges,
+                    (delimiter === undefined ? "|" : delimiter));
+  },
+
+  cutIntoRanges: function(text, noText) {
+    var path = this.buildPath(text)
+      , ranges = this.pathToRanges(path);
+
+    if (!noText) {
+      ranges.forEach(function(r) {
+        r.text = text.substring(r.s, r.e);
+      });
+    }
+    return ranges;
+  },
+
+  cutIntoArray: function(text) {
+    var path = this.buildPath(text)
+      , ranges = this.pathToRanges(path);
+    
+    return ranges.map(function(r) {
+      return text.substring(r.s, r.e)
+    });
+  }
+};
+
+module.exports = WordcutCore;
+
+},{}],9:[function(require,module,exports){
+// http://wiki.commonjs.org/wiki/Unit_Testing/1.0
+//
+// THIS IS NOT TESTED NOR LIKELY TO WORK OUTSIDE V8!
+//
+// Originally from narwhal.js (http://narwhaljs.org)
+// Copyright (c) 2009 Thomas Robinson <280north.com>
+//
+// Permission is hereby granted, free of charge, to any person obtaining a copy
+// of this software and associated documentation files (the 'Software'), to
+// deal in the Software without restriction, including without limitation the
+// rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+// sell copies of the Software, and to permit persons to whom the Software is
+// furnished to do so, subject to the following conditions:
+//
+// The above copyright notice and this permission notice shall be included in
+// all copies or substantial portions of the Software.
+//
+// THE SOFTWARE IS PROVIDED 'AS IS', WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+// AUTHORS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN
+// ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
+// WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+// when used in node, this will actually load the util module we depend on
+// versus loading the builtin util module as happens otherwise
+// this is a bug in node module loading as far as I am concerned
+var util = require('util/');
+
+var pSlice = Array.prototype.slice;
+var hasOwn = Object.prototype.hasOwnProperty;
+
+// 1. The assert module provides functions that throw
+// AssertionError's when particular conditions are not met. The
+// assert module must conform to the following interface.
+
+var assert = module.exports = ok;
+
+// 2. The AssertionError is defined in assert.
+// new assert.AssertionError({ message: message,
+//                             actual: actual,
+//                             expected: expected })
+
+assert.AssertionError = function AssertionError(options) {
+  this.name = 'AssertionError';
+  this.actual = options.actual;
+  this.expected = options.expected;
+  this.operator = options.operator;
+  if (options.message) {
+    this.message = options.message;
+    this.generatedMessage = false;
+  } else {
+    this.message = getMessage(this);
+    this.generatedMessage = true;
+  }
+  var stackStartFunction = options.stackStartFunction || fail;
+
+  if (Error.captureStackTrace) {
+    Error.captureStackTrace(this, stackStartFunction);
+  }
+  else {
+    // non v8 browsers so we can have a stacktrace
+    var err = new Error();
+    if (err.stack) {
+      var out = err.stack;
+
+      // try to strip useless frames
+      var fn_name = stackStartFunction.name;
+      var idx = out.indexOf('\n' + fn_name);
+      if (idx >= 0) {
+        // once we have located the function frame
+        // we need to strip out everything before it (and its line)
+        var next_line = out.indexOf('\n', idx + 1);
+        out = out.substring(next_line + 1);
+      }
+
+      this.stack = out;
+    }
+  }
+};
+
+// assert.AssertionError instanceof Error
+util.inherits(assert.AssertionError, Error);
+
+function replacer(key, value) {
+  if (util.isUndefined(value)) {
+    return '' + value;
+  }
+  if (util.isNumber(value) && !isFinite(value)) {
+    return value.toString();
+  }
+  if (util.isFunction(value) || util.isRegExp(value)) {
+    return value.toString();
+  }
+  return value;
+}
+
+function truncate(s, n) {
+  if (util.isString(s)) {
+    return s.length < n ? s : s.slice(0, n);
+  } else {
+    return s;
+  }
+}
+
+function getMessage(self) {
+  return truncate(JSON.stringify(self.actual, replacer), 128) + ' ' +
+         self.operator + ' ' +
+         truncate(JSON.stringify(self.expected, replacer), 128);
+}
+
+// At present only the three keys mentioned above are used and
+// understood by the spec. Implementations or sub modules can pass
+// other keys to the AssertionError's constructor - they will be
+// ignored.
+
+// 3. All of the following functions must throw an AssertionError
+// when a corresponding condition is not met, with a message that
+// may be undefined if not provided.  All assertion methods provide
+// both the actual and expected values to the assertion error for
+// display purposes.
+
+function fail(actual, expected, message, operator, stackStartFunction) {
+  throw new assert.AssertionError({
+    message: message,
+    actual: actual,
+    expected: expected,
+    operator: operator,
+    stackStartFunction: stackStartFunction
+  });
+}
+
+// EXTENSION! allows for well behaved errors defined elsewhere.
+assert.fail = fail;
+
+// 4. Pure assertion tests whether a value is truthy, as determined
+// by !!guard.
+// assert.ok(guard, message_opt);
+// This statement is equivalent to assert.equal(true, !!guard,
+// message_opt);. To test strictly for the value true, use
+// assert.strictEqual(true, guard, message_opt);.
+
+function ok(value, message) {
+  if (!value) fail(value, true, message, '==', assert.ok);
+}
+assert.ok = ok;
+
+// 5. The equality assertion tests shallow, coercive equality with
+// ==.
+// assert.equal(actual, expected, message_opt);
+
+assert.equal = function equal(actual, expected, message) {
+  if (actual != expected) fail(actual, expected, message, '==', assert.equal);
+};
+
+// 6. The non-equality assertion tests for whether two objects are not equal
+// with != assert.notEqual(actual, expected, message_opt);
+
+assert.notEqual = function notEqual(actual, expected, message) {
+  if (actual == expected) {
+    fail(actual, expected, message, '!=', assert.notEqual);
+  }
+};
+
+// 7. The equivalence assertion tests a deep equality relation.
+// assert.deepEqual(actual, expected, message_opt);
+
+assert.deepEqual = function deepEqual(actual, expected, message) {
+  if (!_deepEqual(actual, expected)) {
+    fail(actual, expected, message, 'deepEqual', assert.deepEqual);
+  }
+};
+
+function _deepEqual(actual, expected) {
+  // 7.1. All identical values are equivalent, as determined by ===.
+  if (actual === expected) {
+    return true;
+
+  } else if (util.isBuffer(actual) && util.isBuffer(expected)) {
+    if (actual.length != expected.length) return false;
+
+    for (var i = 0; i < actual.length; i++) {
+      if (actual[i] !== expected[i]) return false;
+    }
+
+    return true;
+
+  // 7.2. If the expected value is a Date object, the actual value is
+  // equivalent if it is also a Date object that refers to the same time.
+  } else if (util.isDate(actual) && util.isDate(expected)) {
+    return actual.getTime() === expected.getTime();
+
+  // 7.3 If the expected value is a RegExp object, the actual value is
+  // equivalent if it is also a RegExp object with the same source and
+  // properties (`global`, `multiline`, `lastIndex`, `ignoreCase`).
+  } else if (util.isRegExp(actual) && util.isRegExp(expected)) {
+    return actual.source === expected.source &&
+           actual.global === expected.global &&
+           actual.multiline === expected.multiline &&
+           actual.lastIndex === expected.lastIndex &&
+           actual.ignoreCase === expected.ignoreCase;
+
+  // 7.4. Other pairs that do not both pass typeof value == 'object',
+  // equivalence is determined by ==.
+  } else if (!util.isObject(actual) && !util.isObject(expected)) {
+    return actual == expected;
+
+  // 7.5 For all other Object pairs, including Array objects, equivalence is
+  // determined by having the same number of owned properties (as verified
+  // with Object.prototype.hasOwnProperty.call), the same set of keys
+  // (although not necessarily the same order), equivalent values for every
+  // corresponding key, and an identical 'prototype' property. Note: this
+  // accounts for both named and indexed properties on Arrays.
+  } else {
+    return objEquiv(actual, expected);
+  }
+}
+
+function isArguments(object) {
+  return Object.prototype.toString.call(object) == '[object Arguments]';
+}
+
+function objEquiv(a, b) {
+  if (util.isNullOrUndefined(a) || util.isNullOrUndefined(b))
+    return false;
+  // an identical 'prototype' property.
+  if (a.prototype !== b.prototype) return false;
+  // if one is a primitive, the other must be same
+  if (util.isPrimitive(a) || util.isPrimitive(b)) {
+    return a === b;
+  }
+  var aIsArgs = isArguments(a),
+      bIsArgs = isArguments(b);
+  if ((aIsArgs && !bIsArgs) || (!aIsArgs && bIsArgs))
+    return false;
+  if (aIsArgs) {
+    a = pSlice.call(a);
+    b = pSlice.call(b);
+    return _deepEqual(a, b);
+  }
+  var ka = objectKeys(a),
+      kb = objectKeys(b),
+      key, i;
+  // having the same number of owned properties (keys incorporates
+  // hasOwnProperty)
+  if (ka.length != kb.length)
+    return false;
+  //the same set of keys (although not necessarily the same order),
+  ka.sort();
+  kb.sort();
+  //~~~cheap key test
+  for (i = ka.length - 1; i >= 0; i--) {
+    if (ka[i] != kb[i])
+      return false;
+  }
+  //equivalent values for every corresponding key, and
+  //~~~possibly expensive deep test
+  for (i = ka.length - 1; i >= 0; i--) {
+    key = ka[i];
+    if (!_deepEqual(a[key], b[key])) return false;
+  }
+  return true;
+}
+
+// 8. The non-equivalence assertion tests for any deep inequality.
+// assert.notDeepEqual(actual, expected, message_opt);
+
+assert.notDeepEqual = function notDeepEqual(actual, expected, message) {
+  if (_deepEqual(actual, expected)) {
+    fail(actual, expected, message, 'notDeepEqual', assert.notDeepEqual);
+  }
+};
+
+// 9. The strict equality assertion tests strict equality, as determined by ===.
+// assert.strictEqual(actual, expected, message_opt);
+
+assert.strictEqual = function strictEqual(actual, expected, message) {
+  if (actual !== expected) {
+    fail(actual, expected, message, '===', assert.strictEqual);
+  }
+};
+
+// 10. The strict non-equality assertion tests for strict inequality, as
+// determined by !==.  assert.notStrictEqual(actual, expected, message_opt);
+
+assert.notStrictEqual = function notStrictEqual(actual, expected, message) {
+  if (actual === expected) {
+    fail(actual, expected, message, '!==', assert.notStrictEqual);
+  }
+};
+
+function expectedException(actual, expected) {
+  if (!actual || !expected) {
+    return false;
+  }
+
+  if (Object.prototype.toString.call(expected) == '[object RegExp]') {
+    return expected.test(actual);
+  } else if (actual instanceof expected) {
+    return true;
+  } else if (expected.call({}, actual) === true) {
+    return true;
+  }
+
+  return false;
+}
+
+function _throws(shouldThrow, block, expected, message) {
+  var actual;
+
+  if (util.isString(expected)) {
+    message = expected;
+    expected = null;
+  }
+
+  try {
+    block();
+  } catch (e) {
+    actual = e;
+  }
+
+  message = (expected && expected.name ? ' (' + expected.name + ').' : '.') +
+            (message ? ' ' + message : '.');
+
+  if (shouldThrow && !actual) {
+    fail(actual, expected, 'Missing expected exception' + message);
+  }
+
+  if (!shouldThrow && expectedException(actual, expected)) {
+    fail(actual, expected, 'Got unwanted exception' + message);
+  }
+
+  if ((shouldThrow && actual && expected &&
+      !expectedException(actual, expected)) || (!shouldThrow && actual)) {
+    throw actual;
+  }
+}
+
+// 11. Expected to throw an error:
+// assert.throws(block, Error_opt, message_opt);
+
+assert.throws = function(block, /*optional*/error, /*optional*/message) {
+  _throws.apply(this, [true].concat(pSlice.call(arguments)));
+};
+
+// EXTENSION! This is annoying to write outside this module.
+assert.doesNotThrow = function(block, /*optional*/message) {
+  _throws.apply(this, [false].concat(pSlice.call(arguments)));
+};
+
+assert.ifError = function(err) { if (err) {throw err;}};
+
+var objectKeys = Object.keys || function (obj) {
+  var keys = [];
+  for (var key in obj) {
+    if (hasOwn.call(obj, key)) keys.push(key);
+  }
+  return keys;
+};
+
+},{"util/":28}],10:[function(require,module,exports){
+'use strict';
+module.exports = balanced;
+function balanced(a, b, str) {
+  if (a instanceof RegExp) a = maybeMatch(a, str);
+  if (b instanceof RegExp) b = maybeMatch(b, str);
+
+  var r = range(a, b, str);
+
+  return r && {
+    start: r[0],
+    end: r[1],
+    pre: str.slice(0, r[0]),
+    body: str.slice(r[0] + a.length, r[1]),
+    post: str.slice(r[1] + b.length)
+  };
+}
+
+function maybeMatch(reg, str) {
+  var m = str.match(reg);
+  return m ? m[0] : null;
+}
+
+balanced.range = range;
+function range(a, b, str) {
+  var begs, beg, left, right, result;
+  var ai = str.indexOf(a);
+  var bi = str.indexOf(b, ai + 1);
+  var i = ai;
+
+  if (ai >= 0 && bi > 0) {
+    begs = [];
+    left = str.length;
+
+    while (i >= 0 && !result) {
+      if (i == ai) {
+        begs.push(i);
+        ai = str.indexOf(a, i + 1);
+      } else if (begs.length == 1) {
+        result = [ begs.pop(), bi ];
+      } else {
+        beg = begs.pop();
+        if (beg < left) {
+          left = beg;
+          right = bi;
+        }
+
+        bi = str.indexOf(b, i + 1);
+      }
+
+      i = ai < bi && ai >= 0 ? ai : bi;
+    }
+
+    if (begs.length) {
+      result = [ left, right ];
+    }
+  }
+
+  return result;
+}
+
+},{}],11:[function(require,module,exports){
+var concatMap = require('concat-map');
+var balanced = require('balanced-match');
+
+module.exports = expandTop;
+
+var escSlash = '\0SLASH'+Math.random()+'\0';
+var escOpen = '\0OPEN'+Math.random()+'\0';
+var escClose = '\0CLOSE'+Math.random()+'\0';
+var escComma = '\0COMMA'+Math.random()+'\0';
+var escPeriod = '\0PERIOD'+Math.random()+'\0';
+
+function numeric(str) {
+  return parseInt(str, 10) == str
+    ? parseInt(str, 10)
+    : str.charCodeAt(0);
+}
+
+function escapeBraces(str) {
+  return str.split('\\\\').join(escSlash)
+            .split('\\{').join(escOpen)
+            .split('\\}').join(escClose)
+            .split('\\,').join(escComma)
+            .split('\\.').join(escPeriod);
+}
+
+function unescapeBraces(str) {
+  return str.split(escSlash).join('\\')
+            .split(escOpen).join('{')
+            .split(escClose).join('}')
+            .split(escComma).join(',')
+            .split(escPeriod).join('.');
+}
+
+
+// Basically just str.split(","), but handling cases
+// where we have nested braced sections, which should be
+// treated as individual members, like {a,{b,c},d}
+function parseCommaParts(str) {
+  if (!str)
+    return [''];
+
+  var parts = [];
+  var m = balanced('{', '}', str);
+
+  if (!m)
+    return str.split(',');
+
+  var pre = m.pre;
+  var body = m.body;
+  var post = m.post;
+  var p = pre.split(',');
+
+  p[p.length-1] += '{' + body + '}';
+  var postParts = parseCommaParts(post);
+  if (post.length) {
+    p[p.length-1] += postParts.shift();
+    p.push.apply(p, postParts);
+  }
+
+  parts.push.apply(parts, p);
+
+  return parts;
+}
+
+function expandTop(str) {
+  if (!str)
+    return [];
+
+  // I don't know why Bash 4.3 does this, but it does.
+  // Anything starting with {} will have the first two bytes preserved
+  // but *only* at the top level, so {},a}b will not expand to anything,
+  // but a{},b}c will be expanded to [a}c,abc].
+  // One could argue that this is a bug in Bash, but since the goal of
+  // this module is to match Bash's rules, we escape a leading {}
+  if (str.substr(0, 2) === '{}') {
+    str = '\\{\\}' + str.substr(2);
+  }
+
+  return expand(escapeBraces(str), true).map(unescapeBraces);
+}
+
+function identity(e) {
+  return e;
+}
+
+function embrace(str) {
+  return '{' + str + '}';
+}
+function isPadded(el) {
+  return /^-?0\d/.test(el);
+}
+
+function lte(i, y) {
+  return i <= y;
+}
+function gte(i, y) {
+  return i >= y;
+}
+
+function expand(str, isTop) {
+  var expansions = [];
+
+  var m = balanced('{', '}', str);
+  if (!m || /\$$/.test(m.pre)) return [str];
+
+  var isNumericSequence = /^-?\d+\.\.-?\d+(?:\.\.-?\d+)?$/.test(m.body);
+  var isAlphaSequence = /^[a-zA-Z]\.\.[a-zA-Z](?:\.\.-?\d+)?$/.test(m.body);
+  var isSequence = isNumericSequence || isAlphaSequence;
+  var isOptions = m.body.indexOf(',') >= 0;
+  if (!isSequence && !isOptions) {
+    // {a},b}
+    if (m.post.match(/,.*\}/)) {
+      str = m.pre + '{' + m.body + escClose + m.post;
+      return expand(str);
+    }
+    return [str];
+  }
+
+  var n;
+  if (isSequence) {
+    n = m.body.split(/\.\./);
+  } else {
+    n = parseCommaParts(m.body);
+    if (n.length === 1) {
+      // x{{a,b}}y ==> x{a}y x{b}y
+      n = expand(n[0], false).map(embrace);
+      if (n.length === 1) {
+        var post = m.post.length
+          ? expand(m.post, false)
+          : [''];
+        return post.map(function(p) {
+          return m.pre + n[0] + p;
+        });
+      }
+    }
+  }
+
+  // at this point, n is the parts, and we know it's not a comma set
+  // with a single entry.
+
+  // no need to expand pre, since it is guaranteed to be free of brace-sets
+  var pre = m.pre;
+  var post = m.post.length
+    ? expand(m.post, false)
+    : [''];
+
+  var N;
+
+  if (isSequence) {
+    var x = numeric(n[0]);
+    var y = numeric(n[1]);
+    var width = Math.max(n[0].length, n[1].length)
+    var incr = n.length == 3
+      ? Math.abs(numeric(n[2]))
+      : 1;
+    var test = lte;
+    var reverse = y < x;
+    if (reverse) {
+      incr *= -1;
+      test = gte;
+    }
+    var pad = n.some(isPadded);
+
+    N = [];
+
+    for (var i = x; test(i, y); i += incr) {
+      var c;
+      if (isAlphaSequence) {
+        c = String.fromCharCode(i);
+        if (c === '\\')
+          c = '';
+      } else {
+        c = String(i);
+        if (pad) {
+          var need = width - c.length;
+          if (need > 0) {
+            var z = new Array(need + 1).join('0');
+            if (i < 0)
+              c = '-' + z + c.slice(1);
+            else
+              c = z + c;
+          }
+        }
+      }
+      N.push(c);
+    }
+  } else {
+    N = concatMap(n, function(el) { return expand(el, false) });
+  }
+
+  for (var j = 0; j < N.length; j++) {
+    for (var k = 0; k < post.length; k++) {
+      var expansion = pre + N[j] + post[k];
+      if (!isTop || isSequence || expansion)
+        expansions.push(expansion);
+    }
+  }
+
+  return expansions;
+}
+
+
+},{"balanced-match":10,"concat-map":13}],12:[function(require,module,exports){
+
+},{}],13:[function(require,module,exports){
+module.exports = function (xs, fn) {
+    var res = [];
+    for (var i = 0; i < xs.length; i++) {
+        var x = fn(xs[i], i);
+        if (isArray(x)) res.push.apply(res, x);
+        else res.push(x);
+    }
+    return res;
+};
+
+var isArray = Array.isArray || function (xs) {
+    return Object.prototype.toString.call(xs) === '[object Array]';
+};
+
+},{}],14:[function(require,module,exports){
+// Copyright Joyent, Inc. and other Node contributors.
+//
+// Permission is hereby granted, free of charge, to any person obtaining a
+// copy of this software and associated documentation files (the
+// "Software"), to deal in the Software without restriction, including
+// without limitation the rights to use, copy, modify, merge, publish,
+// distribute, sublicense, and/or sell copies of the Software, and to permit
+// persons to whom the Software is furnished to do so, subject to the
+// following conditions:
+//
+// The above copyright notice and this permission notice shall be included
+// in all copies or substantial portions of the Software.
+//
+// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
+// OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+// MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN
+// NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
+// DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR
+// OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE
+// USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+function EventEmitter() {
+  this._events = this._events || {};
+  this._maxListeners = this._maxListeners || undefined;
+}
+module.exports = EventEmitter;
+
+// Backwards-compat with node 0.10.x
+EventEmitter.EventEmitter = EventEmitter;
+
+EventEmitter.prototype._events = undefined;
+EventEmitter.prototype._maxListeners = undefined;
+
+// By default EventEmitters will print a warning if more than 10 listeners are
+// added to it. This is a useful default which helps finding memory leaks.
+EventEmitter.defaultMaxListeners = 10;
+
+// Obviously not all Emitters should be limited to 10. This function allows
+// that to be increased. Set to zero for unlimited.
+EventEmitter.prototype.setMaxListeners = function(n) {
+  if (!isNumber(n) || n < 0 || isNaN(n))
+    throw TypeError('n must be a positive number');
+  this._maxListeners = n;
+  return this;
+};
+
+EventEmitter.prototype.emit = function(type) {
+  var er, handler, len, args, i, listeners;
+
+  if (!this._events)
+    this._events = {};
+
+  // If there is no 'error' event listener then throw.
+  if (type === 'error') {
+    if (!this._events.error ||
+        (isObject(this._events.error) && !this._events.error.length)) {
+      er = arguments[1];
+      if (er instanceof Error) {
+        throw er; // Unhandled 'error' event
+      }
+      throw TypeError('Uncaught, unspecified "error" event.');
+    }
+  }
+
+  handler = this._events[type];
+
+  if (isUndefined(handler))
+    return false;
+
+  if (isFunction(handler)) {
+    switch (arguments.length) {
+      // fast cases
+      case 1:
+        handler.call(this);
+        break;
+      case 2:
+        handler.call(this, arguments[1]);
+        break;
+      case 3:
+        handler.call(this, arguments[1], arguments[2]);
+        break;
+      // slower
+      default:
+        len = arguments.length;
+        args = new Array(len - 1);
+        for (i = 1; i < len; i++)
+          args[i - 1] = arguments[i];
+        handler.apply(this, args);
+    }
+  } else if (isObject(handler)) {
+    len = arguments.length;
+    args = new Array(len - 1);
+    for (i = 1; i < len; i++)
+      args[i - 1] = arguments[i];
+
+    listeners = handler.slice();
+    len = listeners.length;
+    for (i = 0; i < len; i++)
+      listeners[i].apply(this, args);
+  }
+
+  return true;
+};
+
+EventEmitter.prototype.addListener = function(type, listener) {
+  var m;
+
+  if (!isFunction(listener))
+    throw TypeError('listener must be a function');
+
+  if (!this._events)
+    this._events = {};
+
+  // To avoid recursion in the case that type === "newListener"! Before
+  // adding it to the listeners, first emit "newListener".
+  if (this._events.newListener)
+    this.emit('newListener', type,
+              isFunction(listener.listener) ?
+              listener.listener : listener);
+
+  if (!this._events[type])
+    // Optimize the case of one listener. Don't need the extra array object.
+    this._events[type] = listener;
+  else if (isObject(this._events[type]))
+    // If we've already got an array, just append.
+    this._events[type].push(listener);
+  else
+    // Adding the second element, need to change to array.
+    this._events[type] = [this._events[type], listener];
+
+  // Check for listener leak
+  if (isObject(this._events[type]) && !this._events[type].warned) {
+    var m;
+    if (!isUndefined(this._maxListeners)) {
+      m = this._maxListeners;
+    } else {
+      m = EventEmitter.defaultMaxListeners;
+    }
+
+    if (m && m > 0 && this._events[type].length > m) {
+      this._events[type].warned = true;
+      console.error('(node) warning: possible EventEmitter memory ' +
+                    'leak detected. %d listeners added. ' +
+                    'Use emitter.setMaxListeners() to increase limit.',
+                    this._events[type].length);
+      if (typeof console.trace === 'function') {
+        // not supported in IE 10
+        console.trace();
+      }
+    }
+  }
+
+  return this;
+};
+
+EventEmitter.prototype.on = EventEmitter.prototype.addListener;
+
+EventEmitter.prototype.once = function(type, listener) {
+  if (!isFunction(listener))
+    throw TypeError('listener must be a function');
+
+  var fired = false;
+
+  function g() {
+    this.removeListener(type, g);
+
+    if (!fired) {
+      fired = true;
+      listener.apply(this, arguments);
+    }
+  }
+
+  g.listener = listener;
+  this.on(type, g);
+
+  return this;
+};
+
+// emits a 'removeListener' event iff the listener was removed
+EventEmitter.prototype.removeListener = function(type, listener) {
+  var list, position, length, i;
+
+  if (!isFunction(listener))
+    throw TypeError('listener must be a function');
+
+  if (!this._events || !this._events[type])
+    return this;
+
+  list = this._events[type];
+  length = list.length;
+  position = -1;
+
+  if (list === listener ||
+      (isFunction(list.listener) && list.listener === listener)) {
+    delete this._events[type];
+    if (this._events.removeListener)
+      this.emit('removeListener', type, listener);
+
+  } else if (isObject(list)) {
+    for (i = length; i-- > 0;) {
+      if (list[i] === listener ||
+          (list[i].listener && list[i].listener === listener)) {
+        position = i;
+        break;
+      }
+    }
+
+    if (position < 0)
+      return this;
+
+    if (list.length === 1) {
+      list.length = 0;
+      delete this._events[type];
+    } else {
+      list.splice(position, 1);
+    }
+
+    if (this._events.removeListener)
+      this.emit('removeListener', type, listener);
+  }
+
+  return this;
+};
+
+EventEmitter.prototype.removeAllListeners = function(type) {
+  var key, listeners;
+
+  if (!this._events)
+    return this;
+
+  // not listening for removeListener, no need to emit
+  if (!this._events.removeListener) {
+    if (arguments.length === 0)
+      this._events = {};
+    else if (this._events[type])
+      delete this._events[type];
+    return this;
+  }
+
+  // emit removeListener for all listeners on all events
+  if (arguments.length === 0) {
+    for (key in this._events) {
+      if (key === 'removeListener') continue;
+      this.removeAllListeners(key);
+    }
+    this.removeAllListeners('removeListener');
+    this._events = {};
+    return this;
+  }
+
+  listeners = this._events[type];
+
+  if (isFunction(listeners)) {
+    this.removeListener(type, listeners);
+  } else {
+    // LIFO order
+    while (listeners.length)
+      this.removeListener(type, listeners[listeners.length - 1]);
+  }
+  delete this._events[type];
+
+  return this;
+};
+
+EventEmitter.prototype.listeners = function(type) {
+  var ret;
+  if (!this._events || !this._events[type])
+    ret = [];
+  else if (isFunction(this._events[type]))
+    ret = [this._events[type]];
+  else
+    ret = this._events[type].slice();
+  return ret;
+};
+
+EventEmitter.listenerCount = function(emitter, type) {
+  var ret;
+  if (!emitter._events || !emitter._events[type])
+    ret = 0;
+  else if (isFunction(emitter._events[type]))
+    ret = 1;
+  else
+    ret = emitter._events[type].length;
+  return ret;
+};
+
+function isFunction(arg) {
+  return typeof arg === 'function';
+}
+
+function isNumber(arg) {
+  return typeof arg === 'number';
+}
+
+function isObject(arg) {
+  return typeof arg === 'object' && arg !== null;
+}
+
+function isUndefined(arg) {
+  return arg === void 0;
+}
+
+},{}],15:[function(require,module,exports){
+(function (process){
+exports.alphasort = alphasort
+exports.alphasorti = alphasorti
+exports.setopts = setopts
+exports.ownProp = ownProp
+exports.makeAbs = makeAbs
+exports.finish = finish
+exports.mark = mark
+exports.isIgnored = isIgnored
+exports.childrenIgnored = childrenIgnored
+
+function ownProp (obj, field) {
+  return Object.prototype.hasOwnProperty.call(obj, field)
+}
+
+var path = require("path")
+var minimatch = require("minimatch")
+var isAbsolute = require("path-is-absolute")
+var Minimatch = minimatch.Minimatch
+
+function alphasorti (a, b) {
+  return a.toLowerCase().localeCompare(b.toLowerCase())
+}
+
+function alphasort (a, b) {
+  return a.localeCompare(b)
+}
+
+function setupIgnores (self, options) {
+  self.ignore = options.ignore || []
+
+  if (!Array.isArray(self.ignore))
+    self.ignore = [self.ignore]
+
+  if (self.ignore.length) {
+    self.ignore = self.ignore.map(ignoreMap)
+  }
+}
+
+function ignoreMap (pattern) {
+  var gmatcher = null
+  if (pattern.slice(-3) === '/**') {
+    var gpattern = pattern.replace(/(\/\*\*)+$/, '')
+    gmatcher = new Minimatch(gpattern)
+  }
+
+  return {
+    matcher: new Minimatch(pattern),
+    gmatcher: gmatcher
+  }
+}
+
+function setopts (self, pattern, options) {
+  if (!options)
+    options = {}
+
+  // base-matching: just use globstar for that.
+  if (options.matchBase && -1 === pattern.indexOf("/")) {
+    if (options.noglobstar) {
+      throw new Error("base matching requires globstar")
+    }
+    pattern = "**/" + pattern
+  }
+
+  self.silent = !!options.silent
+  self.pattern = pattern
+  self.strict = options.strict !== false
+  self.realpath = !!options.realpath
+  self.realpathCache = options.realpathCache || Object.create(null)
+  self.follow = !!options.follow
+  self.dot = !!options.dot
+  self.mark = !!options.mark
+  self.nodir = !!options.nodir
+  if (self.nodir)
+    self.mark = true
+  self.sync = !!options.sync
+  self.nounique = !!options.nounique
+  self.nonull = !!options.nonull
+  self.nosort = !!options.nosort
+  self.nocase = !!options.nocase
+  self.stat = !!options.stat
+  self.noprocess = !!options.noprocess
+
+  self.maxLength = options.maxLength || Infinity
+  self.cache = options.cache || Object.create(null)
+  self.statCache = options.statCache || Object.create(null)
+  self.symlinks = options.symlinks || Object.create(null)
+
+  setupIgnores(self, options)
+
+  self.changedCwd = false
+  var cwd = process.cwd()
+  if (!ownProp(options, "cwd"))
+    self.cwd = cwd
+  else {
+    self.cwd = options.cwd
+    self.changedCwd = path.resolve(options.cwd) !== cwd
+  }
+
+  self.root = options.root || path.resolve(self.cwd, "/")
+  self.root = path.resolve(self.root)
+  if (process.platform === "win32")
+    self.root = self.root.replace(/\\/g, "/")
+
+  self.nomount = !!options.nomount
+
+  // disable comments and negation unless the user explicitly
+  // passes in false as the option.
+  options.nonegate = options.nonegate === false ? false : true
+  options.nocomment = options.nocomment === false ? false : true
+  deprecationWarning(options)
+
+  self.minimatch = new Minimatch(pattern, options)
+  self.options = self.minimatch.options
+}
+
+// TODO(isaacs): remove entirely in v6
+// exported to reset in tests
+exports.deprecationWarned
+function deprecationWarning(options) {
+  if (!options.nonegate || !options.nocomment) {
+    if (process.noDeprecation !== true && !exports.deprecationWarned) {
+      var msg = 'glob WARNING: comments and negation will be disabled in v6'
+      if (process.throwDeprecation)
+        throw new Error(msg)
+      else if (process.traceDeprecation)
+        console.trace(msg)
+      else
+        console.error(msg)
+
+      exports.deprecationWarned = true
+    }
+  }
+}
+
+function finish (self) {
+  var nou = self.nounique
+  var all = nou ? [] : Object.create(null)
+
+  for (var i = 0, l = self.matches.length; i < l; i ++) {
+    var matches = self.matches[i]
+    if (!matches || Object.keys(matches).length === 0) {
+      if (self.nonull) {
+        // do like the shell, and spit out the literal glob
+        var literal = self.minimatch.globSet[i]
+        if (nou)
+          all.push(literal)
+        else
+          all[literal] = true
+      }
+    } else {
+      // had matches
+      var m = Object.keys(matches)
+      if (nou)
+        all.push.apply(all, m)
+      else
+        m.forEach(function (m) {
+          all[m] = true
+        })
+    }
+  }
+
+  if (!nou)
+    all = Object.keys(all)
+
+  if (!self.nosort)
+    all = all.sort(self.nocase ? alphasorti : alphasort)
+
+  // at *some* point we statted all of these
+  if (self.mark) {
+    for (var i = 0; i < all.length; i++) {
+      all[i] = self._mark(all[i])
+    }
+    if (self.nodir) {
+      all = all.filter(function (e) {
+        return !(/\/$/.test(e))
+      })
+    }
+  }
+
+  if (self.ignore.length)
+    all = all.filter(function(m) {
+      return !isIgnored(self, m)
+    })
+
+  self.found = all
+}
+
+function mark (self, p) {
+  var abs = makeAbs(self, p)
+  var c = self.cache[abs]
+  var m = p
+  if (c) {
+    var isDir = c === 'DIR' || Array.isArray(c)
+    var slash = p.slice(-1) === '/'
+
+    if (isDir && !slash)
+      m += '/'
+    else if (!isDir && slash)
+      m = m.slice(0, -1)
+
+    if (m !== p) {
+      var mabs = makeAbs(self, m)
+      self.statCache[mabs] = self.statCache[abs]
+      self.cache[mabs] = self.cache[abs]
+    }
+  }
+
+  return m
+}
+
+// lotta situps...
+function makeAbs (self, f) {
+  var abs = f
+  if (f.charAt(0) === '/') {
+    abs = path.join(self.root, f)
+  } else if (isAbsolute(f) || f === '') {
+    abs = f
+  } else if (self.changedCwd) {
+    abs = path.resolve(self.cwd, f)
+  } else {
+    abs = path.resolve(f)
+  }
+  return abs
+}
+
+
+// Return true, if pattern ends with globstar '**', for the accompanying parent directory.
+// Ex:- If node_modules/** is the pattern, add 'node_modules' to ignore list along with it's contents
+function isIgnored (self, path) {
+  if (!self.ignore.length)
+    return false
+
+  return self.ignore.some(function(item) {
+    return item.matcher.match(path) || !!(item.gmatcher && item.gmatcher.match(path))
+  })
+}
+
+function childrenIgnored (self, path) {
+  if (!self.ignore.length)
+    return false
+
+  return self.ignore.some(function(item) {
+    return !!(item.gmatcher && item.gmatcher.match(path))
+  })
+}
+
+}).call(this,require('_process'))
+},{"_process":24,"minimatch":20,"path":22,"path-is-absolute":23}],16:[function(require,module,exports){
+(function (process){
+// Approach:
+//
+// 1. Get the minimatch set
+// 2. For each pattern in the set, PROCESS(pattern, false)
+// 3. Store matches per-set, then uniq them
+//
+// PROCESS(pattern, inGlobStar)
+// Get the first [n] items from pattern that are all strings
+// Join these together.  This is PREFIX.
+//   If there is no more remaining, then stat(PREFIX) and
+//   add to matches if it succeeds.  END.
+//
+// If inGlobStar and PREFIX is symlink and points to dir
+//   set ENTRIES = []
+// else readdir(PREFIX) as ENTRIES
+//   If fail, END
+//
+// with ENTRIES
+//   If pattern[n] is GLOBSTAR
+//     // handle the case where the globstar match is empty
+//     // by pruning it out, and testing the resulting pattern
+//     PROCESS(pattern[0..n] + pattern[n+1 .. $], false)
+//     // handle other cases.
+//     for ENTRY in ENTRIES (not dotfiles)
+//       // attach globstar + tail onto the entry
+//       // Mark that this entry is a globstar match
+//       PROCESS(pattern[0..n] + ENTRY + pattern[n .. $], true)
+//
+//   else // not globstar
+//     for ENTRY in ENTRIES (not dotfiles, unless pattern[n] is dot)
+//       Test ENTRY against pattern[n]
+//       If fails, continue
+//       If passes, PROCESS(pattern[0..n] + item + pattern[n+1 .. $])
+//
+// Caveat:
+//   Cache all stats and readdirs results to minimize syscall.  Since all
+//   we ever care about is existence and directory-ness, we can just keep
+//   `true` for files, and [children,...] for directories, or `false` for
+//   things that don't exist.
+
+module.exports = glob
+
+var fs = require('fs')
+var minimatch = require('minimatch')
+var Minimatch = minimatch.Minimatch
+var inherits = require('inherits')
+var EE = require('events').EventEmitter
+var path = require('path')
+var assert = require('assert')
+var isAbsolute = require('path-is-absolute')
+var globSync = require('./sync.js')
+var common = require('./common.js')
+var alphasort = common.alphasort
+var alphasorti = common.alphasorti
+var setopts = common.setopts
+var ownProp = common.ownProp
+var inflight = require('inflight')
+var util = require('util')
+var childrenIgnored = common.childrenIgnored
+var isIgnored = common.isIgnored
+
+var once = require('once')
+
+function glob (pattern, options, cb) {
+  if (typeof options === 'function') cb = options, options = {}
+  if (!options) options = {}
+
+  if (options.sync) {
+    if (cb)
+      throw new TypeError('callback provided to sync glob')
+    return globSync(pattern, options)
+  }
+
+  return new Glob(pattern, options, cb)
+}
+
+glob.sync = globSync
+var GlobSync = glob.GlobSync = globSync.GlobSync
+
+// old api surface
+glob.glob = glob
+
+glob.hasMagic = function (pattern, options_) {
+  var options = util._extend({}, options_)
+  options.noprocess = true
+
+  var g = new Glob(pattern, options)
+  var set = g.minimatch.set
+  if (set.length > 1)
+    return true
+
+  for (var j = 0; j < set[0].length; j++) {
+    if (typeof set[0][j] !== 'string')
+      return true
+  }
+
+  return false
+}
+
+glob.Glob = Glob
+inherits(Glob, EE)
+function Glob (pattern, options, cb) {
+  if (typeof options === 'function') {
+    cb = options
+    options = null
+  }
+
+  if (options && options.sync) {
+    if (cb)
+      throw new TypeError('callback provided to sync glob')
+    return new GlobSync(pattern, options)
+  }
+
+  if (!(this instanceof Glob))
+    return new Glob(pattern, options, cb)
+
+  setopts(this, pattern, options)
+  this._didRealPath = false
+
+  // process each pattern in the minimatch set
+  var n = this.minimatch.set.length
+
+  // The matches are stored as {<filename>: true,...} so that
+  // duplicates are automagically pruned.
+  // Later, we do an Object.keys() on these.
+  // Keep them as a list so we can fill in when nonull is set.
+  this.matches = new Array(n)
+
+  if (typeof cb === 'function') {
+    cb = once(cb)
+    this.on('error', cb)
+    this.on('end', function (matches) {
+      cb(null, matches)
+    })
+  }
+
+  var self = this
+  var n = this.minimatch.set.length
+  this._processing = 0
+  this.matches = new Array(n)
+
+  this._emitQueue = []
+  this._processQueue = []
+  this.paused = false
+
+  if (this.noprocess)
+    return this
+
+  if (n === 0)
+    return done()
+
+  for (var i = 0; i < n; i ++) {
+    this._process(this.minimatch.set[i], i, false, done)
+  }
+
+  function done () {
+    --self._processing
+    if (self._processing <= 0)
+      self._finish()
+  }
+}
+
+Glob.prototype._finish = function () {
+  assert(this instanceof Glob)
+  if (this.aborted)
+    return
+
+  if (this.realpath && !this._didRealpath)
+    return this._realpath()
+
+  common.finish(this)
+  this.emit('end', this.found)
+}
+
+Glob.prototype._realpath = function () {
+  if (this._didRealpath)
+    return
+
+  this._didRealpath = true
+
+  var n = this.matches.length
+  if (n === 0)
+    return this._finish()
+
+  var self = this
+  for (var i = 0; i < this.matches.length; i++)
+    this._realpathSet(i, next)
+
+  function next () {
+    if (--n === 0)
+      self._finish()
+  }
+}
+
+Glob.prototype._realpathSet = function (index, cb) {
+  var matchset = this.matches[index]
+  if (!matchset)
+    return cb()
+
+  var found = Object.keys(matchset)
+  var self = this
+  var n = found.length
+
+  if (n === 0)
+    return cb()
+
+  var set = this.matches[index] = Object.create(null)
+  found.forEach(function (p, i) {
+    // If there's a problem with the stat, then it means that
+    // one or more of the links in the realpath couldn't be
+    // resolved.  just return the abs value in that case.
+    p = self._makeAbs(p)
+    fs.realpath(p, self.realpathCache, function (er, real) {
+      if (!er)
+        set[real] = true
+      else if (er.syscall === 'stat')
+        set[p] = true
+      else
+        self.emit('error', er) // srsly wtf right here
+
+      if (--n === 0) {
+        self.matches[index] = set
+        cb()
+      }
+    })
+  })
+}
+
+Glob.prototype._mark = function (p) {
+  return common.mark(this, p)
+}
+
+Glob.prototype._makeAbs = function (f) {
+  return common.makeAbs(this, f)
+}
+
+Glob.prototype.abort = function () {
+  this.aborted = true
+  this.emit('abort')
+}
+
+Glob.prototype.pause = function () {
+  if (!this.paused) {
+    this.paused = true
+    this.emit('pause')
+  }
+}
+
+Glob.prototype.resume = function () {
+  if (this.paused) {
+    this.emit('resume')
+    this.paused = false
+    if (this._emitQueue.length) {
+      var eq = this._emitQueue.slice(0)
+      this._emitQueue.length = 0
+      for (var i = 0; i < eq.length; i ++) {
+        var e = eq[i]
+        this._emitMatch(e[0], e[1])
+      }
+    }
+    if (this._processQueue.length) {
+      var pq = this._processQueue.slice(0)
+      this._processQueue.length = 0
+      for (var i = 0; i < pq.length; i ++) {
+        var p = pq[i]
+        this._processing--
+        this._process(p[0], p[1], p[2], p[3])
+      }
+    }
+  }
+}
+
+Glob.prototype._process = function (pattern, index, inGlobStar, cb) {
+  assert(this instanceof Glob)
+  assert(typeof cb === 'function')
+
+  if (this.aborted)
+    return
+
+  this._processing++
+  if (this.paused) {
+    this._processQueue.push([pattern, index, inGlobStar, cb])
+    return
+  }
+
+  //console.error('PROCESS %d', this._processing, pattern)
+
+  // Get the first [n] parts of pattern that are all strings.
+  var n = 0
+  while (typeof pattern[n] === 'string') {
+    n ++
+  }
+  // now n is the index of the first one that is *not* a string.
+
+  // see if there's anything else
+  var prefix
+  switch (n) {
+    // if not, then this is rather simple
+    case pattern.length:
+      this._processSimple(pattern.join('/'), index, cb)
+      return
+
+    case 0:
+      // pattern *starts* with some non-trivial item.
+      // going to readdir(cwd), but not include the prefix in matches.
+      prefix = null
+      break
+
+    default:
+      // pattern has some string bits in the front.
+      // whatever it starts with, whether that's 'absolute' like /foo/bar,
+      // or 'relative' like '../baz'
+      prefix = pattern.slice(0, n).join('/')
+      break
+  }
+
+  var remain = pattern.slice(n)
+
+  // get the list of entries.
+  var read
+  if (prefix === null)
+    read = '.'
+  else if (isAbsolute(prefix) || isAbsolute(pattern.join('/'))) {
+    if (!prefix || !isAbsolute(prefix))
+      prefix = '/' + prefix
+    read = prefix
+  } else
+    read = prefix
+
+  var abs = this._makeAbs(read)
+
+  //if ignored, skip _processing
+  if (childrenIgnored(this, read))
+    return cb()
+
+  var isGlobStar = remain[0] === minimatch.GLOBSTAR
+  if (isGlobStar)
+    this._processGlobStar(prefix, read, abs, remain, index, inGlobStar, cb)
+  else
+    this._processReaddir(prefix, read, abs, remain, index, inGlobStar, cb)
+}
+
+Glob.prototype._processReaddir = function (prefix, read, abs, remain, index, inGlobStar, cb) {
+  var self = this
+  this._readdir(abs, inGlobStar, function (er, entries) {
+    return self._processReaddir2(prefix, read, abs, remain, index, inGlobStar, entries, cb)
+  })
+}
+
+Glob.prototype._processReaddir2 = function (prefix, read, abs, remain, index, inGlobStar, entries, cb) {
+
+  // if the abs isn't a dir, then nothing can match!
+  if (!entries)
+    return cb()
+
+  // It will only match dot entries if it starts with a dot, or if
+  // dot is set.  Stuff like @(.foo|.bar) isn't allowed.
+  var pn = remain[0]
+  var negate = !!this.minimatch.negate
+  var rawGlob = pn._glob
+  var dotOk = this.dot || rawGlob.charAt(0) === '.'
+
+  var matchedEntries = []
+  for (var i = 0; i < entries.length; i++) {
+    var e = entries[i]
+    if (e.charAt(0) !== '.' || dotOk) {
+      var m
+      if (negate && !prefix) {
+        m = !e.match(pn)
+      } else {
+        m = e.match(pn)
+      }
+      if (m)
+        matchedEntries.push(e)
+    }
+  }
+
+  //console.error('prd2', prefix, entries, remain[0]._glob, matchedEntries)
+
+  var len = matchedEntries.length
+  // If there are no matched entries, then nothing matches.
+  if (len === 0)
+    return cb()
+
+  // if this is the last remaining pattern bit, then no need for
+  // an additional stat *unless* the user has specified mark or
+  // stat explicitly.  We know they exist, since readdir returned
+  // them.
+
+  if (remain.length === 1 && !this.mark && !this.stat) {
+    if (!this.matches[index])
+      this.matches[index] = Object.create(null)
+
+    for (var i = 0; i < len; i ++) {
+      var e = matchedEntries[i]
+      if (prefix) {
+        if (prefix !== '/')
+          e = prefix + '/' + e
+        else
+          e = prefix + e
+      }
+
+      if (e.charAt(0) === '/' && !this.nomount) {
+        e = path.join(this.root, e)
+      }
+      this._emitMatch(index, e)
+    }
+    // This was the last one, and no stats were needed
+    return cb()
+  }
+
+  // now test all matched entries as stand-ins for that part
+  // of the pattern.
+  remain.shift()
+  for (var i = 0; i < len; i ++) {
+    var e = matchedEntries[i]
+    var newPattern
+    if (prefix) {
+      if (prefix !== '/')
+        e = prefix + '/' + e
+      else
+        e = prefix + e
+    }
+    this._process([e].concat(remain), index, inGlobStar, cb)
+  }
+  cb()
+}
+
+Glob.prototype._emitMatch = function (index, e) {
+  if (this.aborted)
+    return
+
+  if (this.matches[index][e])
+    return
+
+  if (isIgnored(this, e))
+    return
+
+  if (this.paused) {
+    this._emitQueue.push([index, e])
+    return
+  }
+
+  var abs = this._makeAbs(e)
+
+  if (this.nodir) {
+    var c = this.cache[abs]
+    if (c === 'DIR' || Array.isArray(c))
+      return
+  }
+
+  if (this.mark)
+    e = this._mark(e)
+
+  this.matches[index][e] = true
+
+  var st = this.statCache[abs]
+  if (st)
+    this.emit('stat', e, st)
+
+  this.emit('match', e)
+}
+
+Glob.prototype._readdirInGlobStar = function (abs, cb) {
+  if (this.aborted)
+    return
+
+  // follow all symlinked directories forever
+  // just proceed as if this is a non-globstar situation
+  if (this.follow)
+    return this._readdir(abs, false, cb)
+
+  var lstatkey = 'lstat\0' + abs
+  var self = this
+  var lstatcb = inflight(lstatkey, lstatcb_)
+
+  if (lstatcb)
+    fs.lstat(abs, lstatcb)
+
+  function lstatcb_ (er, lstat) {
+    if (er)
+      return cb()
+
+    var isSym = lstat.isSymbolicLink()
+    self.symlinks[abs] = isSym
+
+    // If it's not a symlink or a dir, then it's definitely a regular file.
+    // don't bother doing a readdir in that case.
+    if (!isSym && !lstat.isDirectory()) {
+      self.cache[abs] = 'FILE'
+      cb()
+    } else
+      self._readdir(abs, false, cb)
+  }
+}
+
+Glob.prototype._readdir = function (abs, inGlobStar, cb) {
+  if (this.aborted)
+    return
+
+  cb = inflight('readdir\0'+abs+'\0'+inGlobStar, cb)
+  if (!cb)
+    return
+
+  //console.error('RD %j %j', +inGlobStar, abs)
+  if (inGlobStar && !ownProp(this.symlinks, abs))
+    return this._readdirInGlobStar(abs, cb)
+
+  if (ownProp(this.cache, abs)) {
+    var c = this.cache[abs]
+    if (!c || c === 'FILE')
+      return cb()
+
+    if (Array.isArray(c))
+      return cb(null, c)
+  }
+
+  var self = this
+  fs.readdir(abs, readdirCb(this, abs, cb))
+}
+
+function readdirCb (self, abs, cb) {
+  return function (er, entries) {
+    if (er)
+      self._readdirError(abs, er, cb)
+    else
+      self._readdirEntries(abs, entries, cb)
+  }
+}
+
+Glob.prototype._readdirEntries = function (abs, entries, cb) {
+  if (this.aborted)
+    return
+
+  // if we haven't asked to stat everything, then just
+  // assume that everything in there exists, so we can avoid
+  // having to stat it a second time.
+  if (!this.mark && !this.stat) {
+    for (var i = 0; i < entries.length; i ++) {
+      var e = entries[i]
+      if (abs === '/')
+        e = abs + e
+      else
+        e = abs + '/' + e
+      this.cache[e] = true
+    }
+  }
+
+  this.cache[abs] = entries
+  return cb(null, entries)
+}
+
+Glob.prototype._readdirError = function (f, er, cb) {
+  if (this.aborted)
+    return
+
+  // handle errors, and cache the information
+  switch (er.code) {
+    case 'ENOTSUP': // https://github.com/isaacs/node-glob/issues/205
+    case 'ENOTDIR': // totally normal. means it *does* exist.
+      this.cache[this._makeAbs(f)] = 'FILE'
+      break
+
+    case 'ENOENT': // not terribly unusual
+    case 'ELOOP':
+    case 'ENAMETOOLONG':
+    case 'UNKNOWN':
+      this.cache[this._makeAbs(f)] = false
+      break
+
+    default: // some unusual error.  Treat as failure.
+      this.cache[this._makeAbs(f)] = false
+      if (this.strict) {
+        this.emit('error', er)
+        // If the error is handled, then we abort
+        // if not, we threw out of here
+        this.abort()
+      }
+      if (!this.silent)
+        console.error('glob error', er)
+      break
+  }
+
+  return cb()
+}
+
+Glob.prototype._processGlobStar = function (prefix, read, abs, remain, index, inGlobStar, cb) {
+  var self = this
+  this._readdir(abs, inGlobStar, function (er, entries) {
+    self._processGlobStar2(prefix, read, abs, remain, index, inGlobStar, entries, cb)
+  })
+}
+
+
+Glob.prototype._processGlobStar2 = function (prefix, read, abs, remain, index, inGlobStar, entries, cb) {
+  //console.error('pgs2', prefix, remain[0], entries)
+
+  // no entries means not a dir, so it can never have matches
+  // foo.txt/** doesn't match foo.txt
+  if (!entries)
+    return cb()
+
+  // test without the globstar, and with every child both below
+  // and replacing the globstar.
+  var remainWithoutGlobStar = remain.slice(1)
+  var gspref = prefix ? [ prefix ] : []
+  var noGlobStar = gspref.concat(remainWithoutGlobStar)
+
+  // the noGlobStar pattern exits the inGlobStar state
+  this._process(noGlobStar, index, false, cb)
+
+  var isSym = this.symlinks[abs]
+  var len = entries.length
+
+  // If it's a symlink, and we're in a globstar, then stop
+  if (isSym && inGlobStar)
+    return cb()
+
+  for (var i = 0; i < len; i++) {
+    var e = entries[i]
+    if (e.charAt(0) === '.' && !this.dot)
+      continue
+
+    // these two cases enter the inGlobStar state
+    var instead = gspref.concat(entries[i], remainWithoutGlobStar)
+    this._process(instead, index, true, cb)
+
+    var below = gspref.concat(entries[i], remain)
+    this._process(below, index, true, cb)
+  }
+
+  cb()
+}
+
+Glob.prototype._processSimple = function (prefix, index, cb) {
+  // XXX review this.  Shouldn't it be doing the mounting etc
+  // before doing stat?  kinda weird?
+  var self = this
+  this._stat(prefix, function (er, exists) {
+    self._processSimple2(prefix, index, er, exists, cb)
+  })
+}
+Glob.prototype._processSimple2 = function (prefix, index, er, exists, cb) {
+
+  //console.error('ps2', prefix, exists)
+
+  if (!this.matches[index])
+    this.matches[index] = Object.create(null)
+
+  // If it doesn't exist, then just mark the lack of results
+  if (!exists)
+    return cb()
+
+  if (prefix && isAbsolute(prefix) && !this.nomount) {
+    var trail = /[\/\\]$/.test(prefix)
+    if (prefix.charAt(0) === '/') {
+      prefix = path.join(this.root, prefix)
+    } else {
+      prefix = path.resolve(this.root, prefix)
+      if (trail)
+        prefix += '/'
+    }
+  }
+
+  if (process.platform === 'win32')
+    prefix = prefix.replace(/\\/g, '/')
+
+  // Mark this as a match
+  this._emitMatch(index, prefix)
+  cb()
+}
+
+// Returns either 'DIR', 'FILE', or false
+Glob.prototype._stat = function (f, cb) {
+  var abs = this._makeAbs(f)
+  var needDir = f.slice(-1) === '/'
+
+  if (f.length > this.maxLength)
+    return cb()
+
+  if (!this.stat && ownProp(this.cache, abs)) {
+    var c = this.cache[abs]
+
+    if (Array.isArray(c))
+      c = 'DIR'
+
+    // It exists, but maybe not how we need it
+    if (!needDir || c === 'DIR')
+      return cb(null, c)
+
+    if (needDir && c === 'FILE')
+      return cb()
+
+    // otherwise we have to stat, because maybe c=true
+    // if we know it exists, but not what it is.
+  }
+
+  var exists
+  var stat = this.statCache[abs]
+  if (stat !== undefined) {
+    if (stat === false)
+      return cb(null, stat)
+    else {
+      var type = stat.isDirectory() ? 'DIR' : 'FILE'
+      if (needDir && type === 'FILE')
+        return cb()
+      else
+        return cb(null, type, stat)
+    }
+  }
+
+  var self = this
+  var statcb = inflight('stat\0' + abs, lstatcb_)
+  if (statcb)
+    fs.lstat(abs, statcb)
+
+  function lstatcb_ (er, lstat) {
+    if (lstat && lstat.isSymbolicLink()) {
+      // If it's a symlink, then treat it as the target, unless
+      // the target does not exist, then treat it as a file.
+      return fs.stat(abs, function (er, stat) {
+        if (er)
+          self._stat2(f, abs, null, lstat, cb)
+        else
+          self._stat2(f, abs, er, stat, cb)
+      })
+    } else {
+      self._stat2(f, abs, er, lstat, cb)
+    }
+  }
+}
+
+Glob.prototype._stat2 = function (f, abs, er, stat, cb) {
+  if (er) {
+    this.statCache[abs] = false
+    return cb()
+  }
+
+  var needDir = f.slice(-1) === '/'
+  this.statCache[abs] = stat
+
+  if (abs.slice(-1) === '/' && !stat.isDirectory())
+    return cb(null, false, stat)
+
+  var c = stat.isDirectory() ? 'DIR' : 'FILE'
+  this.cache[abs] = this.cache[abs] || c
+
+  if (needDir && c !== 'DIR')
+    return cb()
+
+  return cb(null, c, stat)
+}
+
+}).call(this,require('_process'))
+},{"./common.js":15,"./sync.js":17,"_process":24,"assert":9,"events":14,"fs":12,"inflight":18,"inherits":19,"minimatch":20,"once":21,"path":22,"path-is-absolute":23,"util":28}],17:[function(require,module,exports){
+(function (process){
+module.exports = globSync
+globSync.GlobSync = GlobSync
+
+var fs = require('fs')
+var minimatch = require('minimatch')
+var Minimatch = minimatch.Minimatch
+var Glob = require('./glob.js').Glob
+var util = require('util')
+var path = require('path')
+var assert = require('assert')
+var isAbsolute = require('path-is-absolute')
+var common = require('./common.js')
+var alphasort = common.alphasort
+var alphasorti = common.alphasorti
+var setopts = common.setopts
+var ownProp = common.ownProp
+var childrenIgnored = common.childrenIgnored
+
+function globSync (pattern, options) {
+  if (typeof options === 'function' || arguments.length === 3)
+    throw new TypeError('callback provided to sync glob\n'+
+                        'See: https://github.com/isaacs/node-glob/issues/167')
+
+  return new GlobSync(pattern, options).found
+}
+
+function GlobSync (pattern, options) {
+  if (!pattern)
+    throw new Error('must provide pattern')
+
+  if (typeof options === 'function' || arguments.length === 3)
+    throw new TypeError('callback provided to sync glob\n'+
+                        'See: https://github.com/isaacs/node-glob/issues/167')
+
+  if (!(this instanceof GlobSync))
+    return new GlobSync(pattern, options)
+
+  setopts(this, pattern, options)
+
+  if (this.noprocess)
+    return this
+
+  var n = this.minimatch.set.length
+  this.matches = new Array(n)
+  for (var i = 0; i < n; i ++) {
+    this._process(this.minimatch.set[i], i, false)
+  }
+  this._finish()
+}
+
+GlobSync.prototype._finish = function () {
+  assert(this instanceof GlobSync)
+  if (this.realpath) {
+    var self = this
+    this.matches.forEach(function (matchset, index) {
+      var set = self.matches[index] = Object.create(null)
+      for (var p in matchset) {
+        try {
+          p = self._makeAbs(p)
+          var real = fs.realpathSync(p, self.realpathCache)
+          set[real] = true
+        } catch (er) {
+          if (er.syscall === 'stat')
+            set[self._makeAbs(p)] = true
+          else
+            throw er
+        }
+      }
+    })
+  }
+  common.finish(this)
+}
+
+
+GlobSync.prototype._process = function (pattern, index, inGlobStar) {
+  assert(this instanceof GlobSync)
+
+  // Get the first [n] parts of pattern that are all strings.
+  var n = 0
+  while (typeof pattern[n] === 'string') {
+    n ++
+  }
+  // now n is the index of the first one that is *not* a string.
+
+  // See if there's anything else
+  var prefix
+  switch (n) {
+    // if not, then this is rather simple
+    case pattern.length:
+      this._processSimple(pattern.join('/'), index)
+      return
+
+    case 0:
+      // pattern *starts* with some non-trivial item.
+      // going to readdir(cwd), but not include the prefix in matches.
+      prefix = null
+      break
+
+    default:
+      // pattern has some string bits in the front.
+      // whatever it starts with, whether that's 'absolute' like /foo/bar,
+      // or 'relative' like '../baz'
+      prefix = pattern.slice(0, n).join('/')
+      break
+  }
+
+  var remain = pattern.slice(n)
+
+  // get the list of entries.
+  var read
+  if (prefix === null)
+    read = '.'
+  else if (isAbsolute(prefix) || isAbsolute(pattern.join('/'))) {
+    if (!prefix || !isAbsolute(prefix))
+      prefix = '/' + prefix
+    read = prefix
+  } else
+    read = prefix
+
+  var abs = this._makeAbs(read)
+
+  //if ignored, skip processing
+  if (childrenIgnored(this, read))
+    return
+
+  var isGlobStar = remain[0] === minimatch.GLOBSTAR
+  if (isGlobStar)
+    this._processGlobStar(prefix, read, abs, remain, index, inGlobStar)
+  else
+    this._processReaddir(prefix, read, abs, remain, index, inGlobStar)
+}
+
+
+GlobSync.prototype._processReaddir = function (prefix, read, abs, remain, index, inGlobStar) {
+  var entries = this._readdir(abs, inGlobStar)
+
+  // if the abs isn't a dir, then nothing can match!
+  if (!entries)
+    return
+
+  // It will only match dot entries if it starts with a dot, or if
+  // dot is set.  Stuff like @(.foo|.bar) isn't allowed.
+  var pn = remain[0]
+  var negate = !!this.minimatch.negate
+  var rawGlob = pn._glob
+  var dotOk = this.dot || rawGlob.charAt(0) === '.'
+
+  var matchedEntries = []
+  for (var i = 0; i < entries.length; i++) {
+    var e = entries[i]
+    if (e.charAt(0) !== '.' || dotOk) {
+      var m
+      if (negate && !prefix) {
+        m = !e.match(pn)
+      } else {
+        m = e.match(pn)
+      }
+      if (m)
+        matchedEntries.push(e)
+    }
+  }
+
+  var len = matchedEntries.length
+  // If there are no matched entries, then nothing matches.
+  if (len === 0)
+    return
+
+  // if this is the last remaining pattern bit, then no need for
+  // an additional stat *unless* the user has specified mark or
+  // stat explicitly.  We know they exist, since readdir returned
+  // them.
+
+  if (remain.length === 1 && !this.mark && !this.stat) {
+    if (!this.matches[index])
+      this.matches[index] = Object.create(null)
+
+    for (var i = 0; i < len; i ++) {
+      var e = matchedEntries[i]
+      if (prefix) {
+        if (prefix.slice(-1) !== '/')
+          e = prefix + '/' + e
+        else
+          e = prefix + e
+      }
+
+      if (e.charAt(0) === '/' && !this.nomount) {
+        e = path.join(this.root, e)
+      }
+      this.matches[index][e] = true
+    }
+    // This was the last one, and no stats were needed
+    return
+  }
+
+  // now test all matched entries as stand-ins for that part
+  // of the pattern.
+  remain.shift()
+  for (var i = 0; i < len; i ++) {
+    var e = matchedEntries[i]
+    var newPattern
+    if (prefix)
+      newPattern = [prefix, e]
+    else
+      newPattern = [e]
+    this._process(newPattern.concat(remain), index, inGlobStar)
+  }
+}
+
+
+GlobSync.prototype._emitMatch = function (index, e) {
+  var abs = this._makeAbs(e)
+  if (this.mark)
+    e = this._mark(e)
+
+  if (this.matches[index][e])
+    return
+
+  if (this.nodir) {
+    var c = this.cache[this._makeAbs(e)]
+    if (c === 'DIR' || Array.isArray(c))
+      return
+  }
+
+  this.matches[index][e] = true
+  if (this.stat)
+    this._stat(e)
+}
+
+
+GlobSync.prototype._readdirInGlobStar = function (abs) {
+  // follow all symlinked directories forever
+  // just proceed as if this is a non-globstar situation
+  if (this.follow)
+    return this._readdir(abs, false)
+
+  var entries
+  var lstat
+  var stat
+  try {
+    lstat = fs.lstatSync(abs)
+  } catch (er) {
+    // lstat failed, doesn't exist
+    return null
+  }
+
+  var isSym = lstat.isSymbolicLink()
+  this.symlinks[abs] = isSym
+
+  // If it's not a symlink or a dir, then it's definitely a regular file.
+  // don't bother doing a readdir in that case.
+  if (!isSym && !lstat.isDirectory())
+    this.cache[abs] = 'FILE'
+  else
+    entries = this._readdir(abs, false)
+
+  return entries
+}
+
+GlobSync.prototype._readdir = function (abs, inGlobStar) {
+  var entries
+
+  if (inGlobStar && !ownProp(this.symlinks, abs))
+    return this._readdirInGlobStar(abs)
+
+  if (ownProp(this.cache, abs)) {
+    var c = this.cache[abs]
+    if (!c || c === 'FILE')
+      return null
+
+    if (Array.isArray(c))
+      return c
+  }
+
+  try {
+    return this._readdirEntries(abs, fs.readdirSync(abs))
+  } catch (er) {
+    this._readdirError(abs, er)
+    return null
+  }
+}
+
+GlobSync.prototype._readdirEntries = function (abs, entries) {
+  // if we haven't asked to stat everything, then just
+  // assume that everything in there exists, so we can avoid
+  // having to stat it a second time.
+  if (!this.mark && !this.stat) {
+    for (var i = 0; i < entries.length; i ++) {
+      var e = entries[i]
+      if (abs === '/')
+        e = abs + e
+      else
+        e = abs + '/' + e
+      this.cache[e] = true
+    }
+  }
+
+  this.cache[abs] = entries
+
+  // mark and cache dir-ness
+  return entries
+}
+
+GlobSync.prototype._readdirError = function (f, er) {
+  // handle errors, and cache the information
+  switch (er.code) {
+    case 'ENOTSUP': // https://github.com/isaacs/node-glob/issues/205
+    case 'ENOTDIR': // totally normal. means it *does* exist.
+      this.cache[this._makeAbs(f)] = 'FILE'
+      break
+
+    case 'ENOENT': // not terribly unusual
+    case 'ELOOP':
+    case 'ENAMETOOLONG':
+    case 'UNKNOWN':
+      this.cache[this._makeAbs(f)] = false
+      break
+
+    default: // some unusual error.  Treat as failure.
+      this.cache[this._makeAbs(f)] = false
+      if (this.strict)
+        throw er
+      if (!this.silent)
+        console.error('glob error', er)
+      break
+  }
+}
+
+GlobSync.prototype._processGlobStar = function (prefix, read, abs, remain, index, inGlobStar) {
+
+  var entries = this._readdir(abs, inGlobStar)
+
+  // no entries means not a dir, so it can never have matches
+  // foo.txt/** doesn't match foo.txt
+  if (!entries)
+    return
+
+  // test without the globstar, and with every child both below
+  // and replacing the globstar.
+  var remainWithoutGlobStar = remain.slice(1)
+  var gspref = prefix ? [ prefix ] : []
+  var noGlobStar = gspref.concat(remainWithoutGlobStar)
+
+  // the noGlobStar pattern exits the inGlobStar state
+  this._process(noGlobStar, index, false)
+
+  var len = entries.length
+  var isSym = this.symlinks[abs]
+
+  // If it's a symlink, and we're in a globstar, then stop
+  if (isSym && inGlobStar)
+    return
+
+  for (var i = 0; i < len; i++) {
+    var e = entries[i]
+    if (e.charAt(0) === '.' && !this.dot)
+      continue
+
+    // these two cases enter the inGlobStar state
+    var instead = gspref.concat(entries[i], remainWithoutGlobStar)
+    this._process(instead, index, true)
+
+    var below = gspref.concat(entries[i], remain)
+    this._process(below, index, true)
+  }
+}
+
+GlobSync.prototype._processSimple = function (prefix, index) {
+  // XXX review this.  Shouldn't it be doing the mounting etc
+  // before doing stat?  kinda weird?
+  var exists = this._stat(prefix)
+
+  if (!this.matches[index])
+    this.matches[index] = Object.create(null)
+
+  // If it doesn't exist, then just mark the lack of results
+  if (!exists)
+    return
+
+  if (prefix && isAbsolute(prefix) && !this.nomount) {
+    var trail = /[\/\\]$/.test(prefix)
+    if (prefix.charAt(0) === '/') {
+      prefix = path.join(this.root, prefix)
+    } else {
+      prefix = path.resolve(this.root, prefix)
+      if (trail)
+        prefix += '/'
+    }
+  }
+
+  if (process.platform === 'win32')
+    prefix = prefix.replace(/\\/g, '/')
+
+  // Mark this as a match
+  this.matches[index][prefix] = true
+}
+
+// Returns either 'DIR', 'FILE', or false
+GlobSync.prototype._stat = function (f) {
+  var abs = this._makeAbs(f)
+  var needDir = f.slice(-1) === '/'
+
+  if (f.length > this.maxLength)
+    return false
+
+  if (!this.stat && ownProp(this.cache, abs)) {
+    var c = this.cache[abs]
+
+    if (Array.isArray(c))
+      c = 'DIR'
+
+    // It exists, but maybe not how we need it
+    if (!needDir || c === 'DIR')
+      return c
+
+    if (needDir && c === 'FILE')
+      return false
+
+    // otherwise we have to stat, because maybe c=true
+    // if we know it exists, but not what it is.
+  }
+
+  var exists
+  var stat = this.statCache[abs]
+  if (!stat) {
+    var lstat
+    try {
+      lstat = fs.lstatSync(abs)
+    } catch (er) {
+      return false
+    }
+
+    if (lstat.isSymbolicLink()) {
+      try {
+        stat = fs.statSync(abs)
+      } catch (er) {
+        stat = lstat
+      }
+    } else {
+      stat = lstat
+    }
+  }
+
+  this.statCache[abs] = stat
+
+  var c = stat.isDirectory() ? 'DIR' : 'FILE'
+  this.cache[abs] = this.cache[abs] || c
+
+  if (needDir && c !== 'DIR')
+    return false
+
+  return c
+}
+
+GlobSync.prototype._mark = function (p) {
+  return common.mark(this, p)
+}
+
+GlobSync.prototype._makeAbs = function (f) {
+  return common.makeAbs(this, f)
+}
+
+}).call(this,require('_process'))
+},{"./common.js":15,"./glob.js":16,"_process":24,"assert":9,"fs":12,"minimatch":20,"path":22,"path-is-absolute":23,"util":28}],18:[function(require,module,exports){
+(function (process){
+var wrappy = require('wrappy')
+var reqs = Object.create(null)
+var once = require('once')
+
+module.exports = wrappy(inflight)
+
+function inflight (key, cb) {
+  if (reqs[key]) {
+    reqs[key].push(cb)
+    return null
+  } else {
+    reqs[key] = [cb]
+    return makeres(key)
+  }
+}
+
+function makeres (key) {
+  return once(function RES () {
+    var cbs = reqs[key]
+    var len = cbs.length
+    var args = slice(arguments)
+
+    // XXX It's somewhat ambiguous whether a new callback added in this
+    // pass should be queued for later execution if something in the
+    // list of callbacks throws, or if it should just be discarded.
+    // However, it's such an edge case that it hardly matters, and either
+    // choice is likely as surprising as the other.
+    // As it happens, we do go ahead and schedule it for later execution.
+    try {
+      for (var i = 0; i < len; i++) {
+        cbs[i].apply(null, args)
+      }
+    } finally {
+      if (cbs.length > len) {
+        // added more in the interim.
+        // de-zalgo, just in case, but don't call again.
+        cbs.splice(0, len)
+        process.nextTick(function () {
+          RES.apply(null, args)
+        })
+      } else {
+        delete reqs[key]
+      }
+    }
+  })
+}
+
+function slice (args) {
+  var length = args.length
+  var array = []
+
+  for (var i = 0; i < length; i++) array[i] = args[i]
+  return array
+}
+
+}).call(this,require('_process'))
+},{"_process":24,"once":21,"wrappy":29}],19:[function(require,module,exports){
+if (typeof Object.create === 'function') {
+  // implementation from standard node.js 'util' module
+  module.exports = function inherits(ctor, superCtor) {
+    ctor.super_ = superCtor
+    ctor.prototype = Object.create(superCtor.prototype, {
+      constructor: {
+        value: ctor,
+        enumerable: false,
+        writable: true,
+        configurable: true
+      }
+    });
+  };
+} else {
+  // old school shim for old browsers
+  module.exports = function inherits(ctor, superCtor) {
+    ctor.super_ = superCtor
+    var TempCtor = function () {}
+    TempCtor.prototype = superCtor.prototype
+    ctor.prototype = new TempCtor()
+    ctor.prototype.constructor = ctor
+  }
+}
+
+},{}],20:[function(require,module,exports){
+module.exports = minimatch
+minimatch.Minimatch = Minimatch
+
+var path = { sep: '/' }
+try {
+  path = require('path')
+} catch (er) {}
+
+var GLOBSTAR = minimatch.GLOBSTAR = Minimatch.GLOBSTAR = {}
+var expand = require('brace-expansion')
+
+var plTypes = {
+  '!': { open: '(?:(?!(?:', close: '))[^/]*?)'},
+  '?': { open: '(?:', close: ')?' },
+  '+': { open: '(?:', close: ')+' },
+  '*': { open: '(?:', close: ')*' },
+  '@': { open: '(?:', close: ')' }
+}
+
+// any single thing other than /
+// don't need to escape / when using new RegExp()
+var qmark = '[^/]'
+
+// * => any number of characters
+var star = qmark + '*?'
+
+// ** when dots are allowed.  Anything goes, except .. and .
+// not (^ or / followed by one or two dots followed by $ or /),
+// followed by anything, any number of times.
+var twoStarDot = '(?:(?!(?:\\\/|^)(?:\\.{1,2})($|\\\/)).)*?'
+
+// not a ^ or / followed by a dot,
+// followed by anything, any number of times.
+var twoStarNoDot = '(?:(?!(?:\\\/|^)\\.).)*?'
+
+// characters that need to be escaped in RegExp.
+var reSpecials = charSet('().*{}+?[]^$\\!')
+
+// "abc" -> { a:true, b:true, c:true }
+function charSet (s) {
+  return s.split('').reduce(function (set, c) {
+    set[c] = true
+    return set
+  }, {})
+}
+
+// normalizes slashes.
+var slashSplit = /\/+/
+
+minimatch.filter = filter
+function filter (pattern, options) {
+  options = options || {}
+  return function (p, i, list) {
+    return minimatch(p, pattern, options)
+  }
+}
+
+function ext (a, b) {
+  a = a || {}
+  b = b || {}
+  var t = {}
+  Object.keys(b).forEach(function (k) {
+    t[k] = b[k]
+  })
+  Object.keys(a).forEach(function (k) {
+    t[k] = a[k]
+  })
+  return t
+}
+
+minimatch.defaults = function (def) {
+  if (!def || !Object.keys(def).length) return minimatch
+
+  var orig = minimatch
+
+  var m = function minimatch (p, pattern, options) {
+    return orig.minimatch(p, pattern, ext(def, options))
+  }
+
+  m.Minimatch = function Minimatch (pattern, options) {
+    return new orig.Minimatch(pattern, ext(def, options))
+  }
+
+  return m
+}
+
+Minimatch.defaults = function (def) {
+  if (!def || !Object.keys(def).length) return Minimatch
+  return minimatch.defaults(def).Minimatch
+}
+
+function minimatch (p, pattern, options) {
+  if (typeof pattern !== 'string') {
+    throw new TypeError('glob pattern string required')
+  }
+
+  if (!options) options = {}
+
+  // shortcut: comments match nothing.
+  if (!options.nocomment && pattern.charAt(0) === '#') {
+    return false
+  }
+
+  // "" only matches ""
+  if (pattern.trim() === '') return p === ''
+
+  return new Minimatch(pattern, options).match(p)
+}
+
+function Minimatch (pattern, options) {
+  if (!(this instanceof Minimatch)) {
+    return new Minimatch(pattern, options)
+  }
+
+  if (typeof pattern !== 'string') {
+    throw new TypeError('glob pattern string required')
+  }
+
+  if (!options) options = {}
+  pattern = pattern.trim()
+
+  // windows support: need to use /, not \
+  if (path.sep !== '/') {
+    pattern = pattern.split(path.sep).join('/')
+  }
+
+  this.options = options
+  this.set = []
+  this.pattern = pattern
+  this.regexp = null
+  this.negate = false
+  this.comment = false
+  this.empty = false
+
+  // make the set of regexps etc.
+  this.make()
+}
+
+Minimatch.prototype.debug = function () {}
+
+Minimatch.prototype.make = make
+function make () {
+  // don't do it more than once.
+  if (this._made) return
+
+  var pattern = this.pattern
+  var options = this.options
+
+  // empty patterns and comments match nothing.
+  if (!options.nocomment && pattern.charAt(0) === '#') {
+    this.comment = true
+    return
+  }
+  if (!pattern) {
+    this.empty = true
+    return
+  }
+
+  // step 1: figure out negation, etc.
+  this.parseNegate()
+
+  // step 2: expand braces
+  var set = this.globSet = this.braceExpand()
+
+  if (options.debug) this.debug = console.error
+
+  this.debug(this.pattern, set)
+
+  // step 3: now we have a set, so turn each one into a series of path-portion
+  // matching patterns.
+  // These will be regexps, except in the case of "**", which is
+  // set to the GLOBSTAR object for globstar behavior,
+  // and will not contain any / characters
+  set = this.globParts = set.map(function (s) {
+    return s.split(slashSplit)
+  })
+
+  this.debug(this.pattern, set)
+
+  // glob --> regexps
+  set = set.map(function (s, si, set) {
+    return s.map(this.parse, this)
+  }, this)
+
+  this.debug(this.pattern, set)
+
+  // filter out everything that didn't compile properly.
+  set = set.filter(function (s) {
+    return s.indexOf(false) === -1
+  })
+
+  this.debug(this.pattern, set)
+
+  this.set = set
+}
+
+Minimatch.prototype.parseNegate = parseNegate
+function parseNegate () {
+  var pattern = this.pattern
+  var negate = false
+  var options = this.options
+  var negateOffset = 0
+
+  if (options.nonegate) return
+
+  for (var i = 0, l = pattern.length
+    ; i < l && pattern.charAt(i) === '!'
+    ; i++) {
+    negate = !negate
+    negateOffset++
+  }
+
+  if (negateOffset) this.pattern = pattern.substr(negateOffset)
+  this.negate = negate
+}
+
+// Brace expansion:
+// a{b,c}d -> abd acd
+// a{b,}c -> abc ac
+// a{0..3}d -> a0d a1d a2d a3d
+// a{b,c{d,e}f}g -> abg acdfg acefg
+// a{b,c}d{e,f}g -> abdeg acdeg abdeg abdfg
+//
+// Invalid sets are not expanded.
+// a{2..}b -> a{2..}b
+// a{b}c -> a{b}c
+minimatch.braceExpand = function (pattern, options) {
+  return braceExpand(pattern, options)
+}
+
+Minimatch.prototype.braceExpand = braceExpand
+
+function braceExpand (pattern, options) {
+  if (!options) {
+    if (this instanceof Minimatch) {
+      options = this.options
+    } else {
+      options = {}
+    }
+  }
+
+  pattern = typeof pattern === 'undefined'
+    ? this.pattern : pattern
+
+  if (typeof pattern === 'undefined') {
+    throw new TypeError('undefined pattern')
+  }
+
+  if (options.nobrace ||
+    !pattern.match(/\{.*\}/)) {
+    // shortcut. no need to expand.
+    return [pattern]
+  }
+
+  return expand(pattern)
+}
+
+// parse a component of the expanded set.
+// At this point, no pattern may contain "/" in it
+// so we're going to return a 2d array, where each entry is the full
+// pattern, split on '/', and then turned into a regular expression.
+// A regexp is made at the end which joins each array with an
+// escaped /, and another full one which joins each regexp with |.
+//
+// Following the lead of Bash 4.1, note that "**" only has special meaning
+// when it is the *only* thing in a path portion.  Otherwise, any series
+// of * is equivalent to a single *.  Globstar behavior is enabled by
+// default, and can be disabled by setting options.noglobstar.
+Minimatch.prototype.parse = parse
+var SUBPARSE = {}
+function parse (pattern, isSub) {
+  if (pattern.length > 1024 * 64) {
+    throw new TypeError('pattern is too long')
+  }
+
+  var options = this.options
+
+  // shortcuts
+  if (!options.noglobstar && pattern === '**') return GLOBSTAR
+  if (pattern === '') return ''
+
+  var re = ''
+  var hasMagic = !!options.nocase
+  var escaping = false
+  // ? => one single character
+  var patternListStack = []
+  var negativeLists = []
+  var stateChar
+  var inClass = false
+  var reClassStart = -1
+  var classStart = -1
+  // . and .. never match anything that doesn't start with .,
+  // even when options.dot is set.
+  var patternStart = pattern.charAt(0) === '.' ? '' // anything
+  // not (start or / followed by . or .. followed by / or end)
+  : options.dot ? '(?!(?:^|\\\/)\\.{1,2}(?:$|\\\/))'
+  : '(?!\\.)'
+  var self = this
+
+  function clearStateChar () {
+    if (stateChar) {
+      // we had some state-tracking character
+      // that wasn't consumed by this pass.
+      switch (stateChar) {
+        case '*':
+          re += star
+          hasMagic = true
+        break
+        case '?':
+          re += qmark
+          hasMagic = true
+        break
+        default:
+          re += '\\' + stateChar
+        break
+      }
+      self.debug('clearStateChar %j %j', stateChar, re)
+      stateChar = false
+    }
+  }
+
+  for (var i = 0, len = pattern.length, c
+    ; (i < len) && (c = pattern.charAt(i))
+    ; i++) {
+    this.debug('%s\t%s %s %j', pattern, i, re, c)
+
+    // skip over any that are escaped.
+    if (escaping && reSpecials[c]) {
+      re += '\\' + c
+      escaping = false
+      continue
+    }
+
+    switch (c) {
+      case '/':
+        // completely not allowed, even escaped.
+        // Should already be path-split by now.
+        return false
+
+      case '\\':
+        clearStateChar()
+        escaping = true
+      continue
+
+      // the various stateChar values
+      // for the "extglob" stuff.
+      case '?':
+      case '*':
+      case '+':
+      case '@':
+      case '!':
+        this.debug('%s\t%s %s %j <-- stateChar', pattern, i, re, c)
+
+        // all of those are literals inside a class, except that
+        // the glob [!a] means [^a] in regexp
+        if (inClass) {
+          this.debug('  in class')
+          if (c === '!' && i === classStart + 1) c = '^'
+          re += c
+          continue
+        }
+
+        // if we already have a stateChar, then it means
+        // that there was something like ** or +? in there.
+        // Handle the stateChar, then proceed with this one.
+        self.debug('call clearStateChar %j', stateChar)
+        clearStateChar()
+        stateChar = c
+        // if extglob is disabled, then +(asdf|foo) isn't a thing.
+        // just clear the statechar *now*, rather than even diving into
+        // the patternList stuff.
+        if (options.noext) clearStateChar()
+      continue
+
+      case '(':
+        if (inClass) {
+          re += '('
+          continue
+        }
+
+        if (!stateChar) {
+          re += '\\('
+          continue
+        }
+
+        patternListStack.push({
+          type: stateChar,
+          start: i - 1,
+          reStart: re.length,
+          open: plTypes[stateChar].open,
+          close: plTypes[stateChar].close
+        })
+        // negation is (?:(?!js)[^/]*)
+        re += stateChar === '!' ? '(?:(?!(?:' : '(?:'
+        this.debug('plType %j %j', stateChar, re)
+        stateChar = false
+      continue
+
+      case ')':
+        if (inClass || !patternListStack.length) {
+          re += '\\)'
+          continue
+        }
+
+        clearStateChar()
+        hasMagic = true
+        var pl = patternListStack.pop()
+        // negation is (?:(?!js)[^/]*)
+        // The others are (?:<pattern>)<type>
+        re += pl.close
+        if (pl.type === '!') {
+          negativeLists.push(pl)
+        }
+        pl.reEnd = re.length
+      continue
+
+      case '|':
+        if (inClass || !patternListStack.length || escaping) {
+          re += '\\|'
+          escaping = false
+          continue
+        }
+
+        clearStateChar()
+        re += '|'
+      continue
+
+      // these are mostly the same in regexp and glob
+      case '[':
+        // swallow any state-tracking char before the [
+        clearStateChar()
+
+        if (inClass) {
+          re += '\\' + c
+          continue
+        }
+
+        inClass = true
+        classStart = i
+        reClassStart = re.length
+        re += c
+      continue
+
+      case ']':
+        //  a right bracket shall lose its special
+        //  meaning and represent itself in
+        //  a bracket expression if it occurs
+        //  first in the list.  -- POSIX.2 2.8.3.2
+        if (i === classStart + 1 || !inClass) {
+          re += '\\' + c
+          escaping = false
+          continue
+        }
+
+        // handle the case where we left a class open.
+        // "[z-a]" is valid, equivalent to "\[z-a\]"
+        if (inClass) {
+          // split where the last [ was, make sure we don't have
+          // an invalid re. if so, re-walk the contents of the
+          // would-be class to re-translate any characters that
+          // were passed through as-is
+          // TODO: It would probably be faster to determine this
+          // without a try/catch and a new RegExp, but it's tricky
+          // to do safely.  For now, this is safe and works.
+          var cs = pattern.substring(classStart + 1, i)
+          try {
+            RegExp('[' + cs + ']')
+          } catch (er) {
+            // not a valid class!
+            var sp = this.parse(cs, SUBPARSE)
+            re = re.substr(0, reClassStart) + '\\[' + sp[0] + '\\]'
+            hasMagic = hasMagic || sp[1]
+            inClass = false
+            continue
+          }
+        }
+
+        // finish up the class.
+        hasMagic = true
+        inClass = false
+        re += c
+      continue
+
+      default:
+        // swallow any state char that wasn't consumed
+        clearStateChar()
+
+        if (escaping) {
+          // no need
+          escaping = false
+        } else if (reSpecials[c]
+          && !(c === '^' && inClass)) {
+          re += '\\'
+        }
+
+        re += c
+
+    } // switch
+  } // for
+
+  // handle the case where we left a class open.
+  // "[abc" is valid, equivalent to "\[abc"
+  if (inClass) {
+    // split where the last [ was, and escape it
+    // this is a huge pita.  We now have to re-walk
+    // the contents of the would-be class to re-translate
+    // any characters that were passed through as-is
+    cs = pattern.substr(classStart + 1)
+    sp = this.parse(cs, SUBPARSE)
+    re = re.substr(0, reClassStart) + '\\[' + sp[0]
+    hasMagic = hasMagic || sp[1]
+  }
+
+  // handle the case where we had a +( thing at the *end*
+  // of the pattern.
+  // each pattern list stack adds 3 chars, and we need to go through
+  // and escape any | chars that were passed through as-is for the regexp.
+  // Go through and escape them, taking care not to double-escape any
+  // | chars that were already escaped.
+  for (pl = patternListStack.pop(); pl; pl = patternListStack.pop()) {
+    var tail = re.slice(pl.reStart + pl.open.length)
+    this.debug('setting tail', re, pl)
+    // maybe some even number of \, then maybe 1 \, followed by a |
+    tail = tail.replace(/((?:\\{2}){0,64})(\\?)\|/g, function (_, $1, $2) {
+      if (!$2) {
+        // the | isn't already escaped, so escape it.
+        $2 = '\\'
+      }
+
+      // need to escape all those slashes *again*, without escaping the
+      // one that we need for escaping the | character.  As it works out,
+      // escaping an even number of slashes can be done by simply repeating
+      // it exactly after itself.  That's why this trick works.
+      //
+      // I am sorry that you have to see this.
+      return $1 + $1 + $2 + '|'
+    })
+
+    this.debug('tail=%j\n   %s', tail, tail, pl, re)
+    var t = pl.type === '*' ? star
+      : pl.type === '?' ? qmark
+      : '\\' + pl.type
+
+    hasMagic = true
+    re = re.slice(0, pl.reStart) + t + '\\(' + tail
+  }
+
+  // handle trailing things that only matter at the very end.
+  clearStateChar()
+  if (escaping) {
+    // trailing \\
+    re += '\\\\'
+  }
+
+  // only need to apply the nodot start if the re starts with
+  // something that could conceivably capture a dot
+  var addPatternStart = false
+  switch (re.charAt(0)) {
+    case '.':
+    case '[':
+    case '(': addPatternStart = true
+  }
+
+  // Hack to work around lack of negative lookbehind in JS
+  // A pattern like: *.!(x).!(y|z) needs to ensure that a name
+  // like 'a.xyz.yz' doesn't match.  So, the first negative
+  // lookahead, has to look ALL the way ahead, to the end of
+  // the pattern.
+  for (var n = negativeLists.length - 1; n > -1; n--) {
+    var nl = negativeLists[n]
+
+    var nlBefore = re.slice(0, nl.reStart)
+    var nlFirst = re.slice(nl.reStart, nl.reEnd - 8)
+    var nlLast = re.slice(nl.reEnd - 8, nl.reEnd)
+    var nlAfter = re.slice(nl.reEnd)
+
+    nlLast += nlAfter
+
+    // Handle nested stuff like *(*.js|!(*.json)), where open parens
+    // mean that we should *not* include the ) in the bit that is considered
+    // "after" the negated section.
+    var openParensBefore = nlBefore.split('(').length - 1
+    var cleanAfter = nlAfter
+    for (i = 0; i < openParensBefore; i++) {
+      cleanAfter = cleanAfter.replace(/\)[+*?]?/, '')
+    }
+    nlAfter = cleanAfter
+
+    var dollar = ''
+    if (nlAfter === '' && isSub !== SUBPARSE) {
+      dollar = '$'
+    }
+    var newRe = nlBefore + nlFirst + nlAfter + dollar + nlLast
+    re = newRe
+  }
+
+  // if the re is not "" at this point, then we need to make sure
+  // it doesn't match against an empty path part.
+  // Otherwise a/* will match a/, which it should not.
+  if (re !== '' && hasMagic) {
+    re = '(?=.)' + re
+  }
+
+  if (addPatternStart) {
+    re = patternStart + re
+  }
+
+  // parsing just a piece of a larger pattern.
+  if (isSub === SUBPARSE) {
+    return [re, hasMagic]
+  }
+
+  // skip the regexp for non-magical patterns
+  // unescape anything in it, though, so that it'll be
+  // an exact match against a file etc.
+  if (!hasMagic) {
+    return globUnescape(pattern)
+  }
+
+  var flags = options.nocase ? 'i' : ''
+  try {
+    var regExp = new RegExp('^' + re + '$', flags)
+  } catch (er) {
+    // If it was an invalid regular expression, then it can't match
+    // anything.  This trick looks for a character after the end of
+    // the string, which is of course impossible, except in multi-line
+    // mode, but it's not a /m regex.
+    return new RegExp('$.')
+  }
+
+  regExp._glob = pattern
+  regExp._src = re
+
+  return regExp
+}
+
+minimatch.makeRe = function (pattern, options) {
+  return new Minimatch(pattern, options || {}).makeRe()
+}
+
+Minimatch.prototype.makeRe = makeRe
+function makeRe () {
+  if (this.regexp || this.regexp === false) return this.regexp
+
+  // at this point, this.set is a 2d array of partial
+  // pattern strings, or "**".
+  //
+  // It's better to use .match().  This function shouldn't
+  // be used, really, but it's pretty convenient sometimes,
+  // when you just want to work with a regex.
+  var set = this.set
+
+  if (!set.length) {
+    this.regexp = false
+    return this.regexp
+  }
+  var options = this.options
+
+  var twoStar = options.noglobstar ? star
+    : options.dot ? twoStarDot
+    : twoStarNoDot
+  var flags = options.nocase ? 'i' : ''
+
+  var re = set.map(function (pattern) {
+    return pattern.map(function (p) {
+      return (p === GLOBSTAR) ? twoStar
+      : (typeof p === 'string') ? regExpEscape(p)
+      : p._src
+    }).join('\\\/')
+  }).join('|')
+
+  // must match entire pattern
+  // ending in a * or ** will make it less strict.
+  re = '^(?:' + re + ')$'
+
+  // can match anything, as long as it's not this.
+  if (this.negate) re = '^(?!' + re + ').*$'
+
+  try {
+    this.regexp = new RegExp(re, flags)
+  } catch (ex) {
+    this.regexp = false
+  }
+  return this.regexp
+}
+
+minimatch.match = function (list, pattern, options) {
+  options = options || {}
+  var mm = new Minimatch(pattern, options)
+  list = list.filter(function (f) {
+    return mm.match(f)
+  })
+  if (mm.options.nonull && !list.length) {
+    list.push(pattern)
+  }
+  return list
+}
+
+Minimatch.prototype.match = match
+function match (f, partial) {
+  this.debug('match', f, this.pattern)
+  // short-circuit in the case of busted things.
+  // comments, etc.
+  if (this.comment) return false
+  if (this.empty) return f === ''
+
+  if (f === '/' && partial) return true
+
+  var options = this.options
+
+  // windows: need to use /, not \
+  if (path.sep !== '/') {
+    f = f.split(path.sep).join('/')
+  }
+
+  // treat the test path as a set of pathparts.
+  f = f.split(slashSplit)
+  this.debug(this.pattern, 'split', f)
+
+  // just ONE of the pattern sets in this.set needs to match
+  // in order for it to be valid.  If negating, then just one
+  // match means that we have failed.
+  // Either way, return on the first hit.
+
+  var set = this.set
+  this.debug(this.pattern, 'set', set)
+
+  // Find the basename of the path by looking for the last non-empty segment
+  var filename
+  var i
+  for (i = f.length - 1; i >= 0; i--) {
+    filename = f[i]
+    if (filename) break
+  }
+
+  for (i = 0; i < set.length; i++) {
+    var pattern = set[i]
+    var file = f
+    if (options.matchBase && pattern.length === 1) {
+      file = [filename]
+    }
+    var hit = this.matchOne(file, pattern, partial)
+    if (hit) {
+      if (options.flipNegate) return true
+      return !this.negate
+    }
+  }
+
+  // didn't get any hits.  this is success if it's a negative
+  // pattern, failure otherwise.
+  if (options.flipNegate) return false
+  return this.negate
+}
+
+// set partial to true to test if, for example,
+// "/a/b" matches the start of "/*/b/*/d"
+// Partial means, if you run out of file before you run
+// out of pattern, then that's fine, as long as all
+// the parts match.
+Minimatch.prototype.matchOne = function (file, pattern, partial) {
+  var options = this.options
+
+  this.debug('matchOne',
+    { 'this': this, file: file, pattern: pattern })
+
+  this.debug('matchOne', file.length, pattern.length)
+
+  for (var fi = 0,
+      pi = 0,
+      fl = file.length,
+      pl = pattern.length
+      ; (fi < fl) && (pi < pl)
+      ; fi++, pi++) {
+    this.debug('matchOne loop')
+    var p = pattern[pi]
+    var f = file[fi]
+
+    this.debug(pattern, p, f)
+
+    // should be impossible.
+    // some invalid regexp stuff in the set.
+    if (p === false) return false
+
+    if (p === GLOBSTAR) {
+      this.debug('GLOBSTAR', [pattern, p, f])
+
+      // "**"
+      // a/**/b/**/c would match the following:
+      // a/b/x/y/z/c
+      // a/x/y/z/b/c
+      // a/b/x/b/x/c
+      // a/b/c
+      // To do this, take the rest of the pattern after
+      // the **, and see if it would match the file remainder.
+      // If so, return success.
+      // If not, the ** "swallows" a segment, and try again.
+      // This is recursively awful.
+      //
+      // a/**/b/**/c matching a/b/x/y/z/c
+      // - a matches a
+      // - doublestar
+      //   - matchOne(b/x/y/z/c, b/**/c)
+      //     - b matches b
+      //     - doublestar
+      //       - matchOne(x/y/z/c, c) -> no
+      //       - matchOne(y/z/c, c) -> no
+      //       - matchOne(z/c, c) -> no
+      //       - matchOne(c, c) yes, hit
+      var fr = fi
+      var pr = pi + 1
+      if (pr === pl) {
+        this.debug('** at the end')
+        // a ** at the end will just swallow the rest.
+        // We have found a match.
+        // however, it will not swallow /.x, unless
+        // options.dot is set.
+        // . and .. are *never* matched by **, for explosively
+        // exponential reasons.
+        for (; fi < fl; fi++) {
+          if (file[fi] === '.' || file[fi] === '..' ||
+            (!options.dot && file[fi].charAt(0) === '.')) return false
+        }
+        return true
+      }
+
+      // ok, let's see if we can swallow whatever we can.
+      while (fr < fl) {
+        var swallowee = file[fr]
+
+        this.debug('\nglobstar while', file, fr, pattern, pr, swallowee)
+
+        // XXX remove this slice.  Just pass the start index.
+        if (this.matchOne(file.slice(fr), pattern.slice(pr), partial)) {
+          this.debug('globstar found match!', fr, fl, swallowee)
+          // found a match.
+          return true
+        } else {
+          // can't swallow "." or ".." ever.
+          // can only swallow ".foo" when explicitly asked.
+          if (swallowee === '.' || swallowee === '..' ||
+            (!options.dot && swallowee.charAt(0) === '.')) {
+            this.debug('dot detected!', file, fr, pattern, pr)
+            break
+          }
+
+          // ** swallows a segment, and continue.
+          this.debug('globstar swallow a segment, and continue')
+          fr++
+        }
+      }
+
+      // no match was found.
+      // However, in partial mode, we can't say this is necessarily over.
+      // If there's more *pattern* left, then
+      if (partial) {
+        // ran out of file
+        this.debug('\n>>> no match, partial?', file, fr, pattern, pr)
+        if (fr === fl) return true
+      }
+      return false
+    }
+
+    // something other than **
+    // non-magic patterns just have to match exactly
+    // patterns with magic have been turned into regexps.
+    var hit
+    if (typeof p === 'string') {
+      if (options.nocase) {
+        hit = f.toLowerCase() === p.toLowerCase()
+      } else {
+        hit = f === p
+      }
+      this.debug('string match', p, f, hit)
+    } else {
+      hit = f.match(p)
+      this.debug('pattern match', p, f, hit)
+    }
+
+    if (!hit) return false
+  }
+
+  // Note: ending in / means that we'll get a final ""
+  // at the end of the pattern.  This can only match a
+  // corresponding "" at the end of the file.
+  // If the file ends in /, then it can only match a
+  // a pattern that ends in /, unless the pattern just
+  // doesn't have any more for it. But, a/b/ should *not*
+  // match "a/b/*", even though "" matches against the
+  // [^/]*? pattern, except in partial mode, where it might
+  // simply not be reached yet.
+  // However, a/b/ should still satisfy a/*
+
+  // now either we fell off the end of the pattern, or we're done.
+  if (fi === fl && pi === pl) {
+    // ran out of pattern and filename at the same time.
+    // an exact hit!
+    return true
+  } else if (fi === fl) {
+    // ran out of file, but still had pattern left.
+    // this is ok if we're doing the match as part of
+    // a glob fs traversal.
+    return partial
+  } else if (pi === pl) {
+    // ran out of pattern, still have file left.
+    // this is only acceptable if we're on the very last
+    // empty segment of a file with a trailing slash.
+    // a/* should match a/b/
+    var emptyFileEnd = (fi === fl - 1) && (file[fi] === '')
+    return emptyFileEnd
+  }
+
+  // should be unreachable.
+  throw new Error('wtf?')
+}
+
+// replace stuff like \* with *
+function globUnescape (s) {
+  return s.replace(/\\(.)/g, '$1')
+}
+
+function regExpEscape (s) {
+  return s.replace(/[-[\]{}()*+?.,\\^$|#\s]/g, '\\$&')
+}
+
+},{"brace-expansion":11,"path":22}],21:[function(require,module,exports){
+var wrappy = require('wrappy')
+module.exports = wrappy(once)
+module.exports.strict = wrappy(onceStrict)
+
+once.proto = once(function () {
+  Object.defineProperty(Function.prototype, 'once', {
+    value: function () {
+      return once(this)
+    },
+    configurable: true
+  })
+
+  Object.defineProperty(Function.prototype, 'onceStrict', {
+    value: function () {
+      return onceStrict(this)
+    },
+    configurable: true
+  })
+})
+
+function once (fn) {
+  var f = function () {
+    if (f.called) return f.value
+    f.called = true
+    return f.value = fn.apply(this, arguments)
+  }
+  f.called = false
+  return f
+}
+
+function onceStrict (fn) {
+  var f = function () {
+    if (f.called)
+      throw new Error(f.onceError)
+    f.called = true
+    return f.value = fn.apply(this, arguments)
+  }
+  var name = fn.name || 'Function wrapped with `once`'
+  f.onceError = name + " shouldn't be called more than once"
+  f.called = false
+  return f
+}
+
+},{"wrappy":29}],22:[function(require,module,exports){
+(function (process){
+// Copyright Joyent, Inc. and other Node contributors.
+//
+// Permission is hereby granted, free of charge, to any person obtaining a
+// copy of this software and associated documentation files (the
+// "Software"), to deal in the Software without restriction, including
+// without limitation the rights to use, copy, modify, merge, publish,
+// distribute, sublicense, and/or sell copies of the Software, and to permit
+// persons to whom the Software is furnished to do so, subject to the
+// following conditions:
+//
+// The above copyright notice and this permission notice shall be included
+// in all copies or substantial portions of the Software.
+//
+// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
+// OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+// MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN
+// NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
+// DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR
+// OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE
+// USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+// resolves . and .. elements in a path array with directory names there
+// must be no slashes, empty elements, or device names (c:\) in the array
+// (so also no leading and trailing slashes - it does not distinguish
+// relative and absolute paths)
+function normalizeArray(parts, allowAboveRoot) {
+  // if the path tries to go above the root, `up` ends up > 0
+  var up = 0;
+  for (var i = parts.length - 1; i >= 0; i--) {
+    var last = parts[i];
+    if (last === '.') {
+      parts.splice(i, 1);
+    } else if (last === '..') {
+      parts.splice(i, 1);
+      up++;
+    } else if (up) {
+      parts.splice(i, 1);
+      up--;
+    }
+  }
+
+  // if the path is allowed to go above the root, restore leading ..s
+  if (allowAboveRoot) {
+    for (; up--; up) {
+      parts.unshift('..');
+    }
+  }
+
+  return parts;
+}
+
+// Split a filename into [root, dir, basename, ext], unix version
+// 'root' is just a slash, or nothing.
+var splitPathRe =
+    /^(\/?|)([\s\S]*?)((?:\.{1,2}|[^\/]+?|)(\.[^.\/]*|))(?:[\/]*)$/;
+var splitPath = function(filename) {
+  return splitPathRe.exec(filename).slice(1);
+};
+
+// path.resolve([from ...], to)
+// posix version
+exports.resolve = function() {
+  var resolvedPath = '',
+      resolvedAbsolute = false;
+
+  for (var i = arguments.length - 1; i >= -1 && !resolvedAbsolute; i--) {
+    var path = (i >= 0) ? arguments[i] : process.cwd();
+
+    // Skip empty and invalid entries
+    if (typeof path !== 'string') {
+      throw new TypeError('Arguments to path.resolve must be strings');
+    } else if (!path) {
+      continue;
+    }
+
+    resolvedPath = path + '/' + resolvedPath;
+    resolvedAbsolute = path.charAt(0) === '/';
+  }
+
+  // At this point the path should be resolved to a full absolute path, but
+  // handle relative paths to be safe (might happen when process.cwd() fails)
+
+  // Normalize the path
+  resolvedPath = normalizeArray(filter(resolvedPath.split('/'), function(p) {
+    return !!p;
+  }), !resolvedAbsolute).join('/');
+
+  return ((resolvedAbsolute ? '/' : '') + resolvedPath) || '.';
+};
+
+// path.normalize(path)
+// posix version
+exports.normalize = function(path) {
+  var isAbsolute = exports.isAbsolute(path),
+      trailingSlash = substr(path, -1) === '/';
+
+  // Normalize the path
+  path = normalizeArray(filter(path.split('/'), function(p) {
+    return !!p;
+  }), !isAbsolute).join('/');
+
+  if (!path && !isAbsolute) {
+    path = '.';
+  }
+  if (path && trailingSlash) {
+    path += '/';
+  }
+
+  return (isAbsolute ? '/' : '') + path;
+};
+
+// posix version
+exports.isAbsolute = function(path) {
+  return path.charAt(0) === '/';
+};
+
+// posix version
+exports.join = function() {
+  var paths = Array.prototype.slice.call(arguments, 0);
+  return exports.normalize(filter(paths, function(p, index) {
+    if (typeof p !== 'string') {
+      throw new TypeError('Arguments to path.join must be strings');
+    }
+    return p;
+  }).join('/'));
+};
+
+
+// path.relative(from, to)
+// posix version
+exports.relative = function(from, to) {
+  from = exports.resolve(from).substr(1);
+  to = exports.resolve(to).substr(1);
+
+  function trim(arr) {
+    var start = 0;
+    for (; start < arr.length; start++) {
+      if (arr[start] !== '') break;
+    }
+
+    var end = arr.length - 1;
+    for (; end >= 0; end--) {
+      if (arr[end] !== '') break;
+    }
+
+    if (start > end) return [];
+    return arr.slice(start, end - start + 1);
+  }
+
+  var fromParts = trim(from.split('/'));
+  var toParts = trim(to.split('/'));
+
+  var length = Math.min(fromParts.length, toParts.length);
+  var samePartsLength = length;
+  for (var i = 0; i < length; i++) {
+    if (fromParts[i] !== toParts[i]) {
+      samePartsLength = i;
+      break;
+    }
+  }
+
+  var outputParts = [];
+  for (var i = samePartsLength; i < fromParts.length; i++) {
+    outputParts.push('..');
+  }
+
+  outputParts = outputParts.concat(toParts.slice(samePartsLength));
+
+  return outputParts.join('/');
+};
+
+exports.sep = '/';
+exports.delimiter = ':';
+
+exports.dirname = function(path) {
+  var result = splitPath(path),
+      root = result[0],
+      dir = result[1];
+
+  if (!root && !dir) {
+    // No dirname whatsoever
+    return '.';
+  }
+
+  if (dir) {
+    // It has a dirname, strip trailing slash
+    dir = dir.substr(0, dir.length - 1);
+  }
+
+  return root + dir;
+};
+
+
+exports.basename = function(path, ext) {
+  var f = splitPath(path)[2];
+  // TODO: make this comparison case-insensitive on windows?
+  if (ext && f.substr(-1 * ext.length) === ext) {
+    f = f.substr(0, f.length - ext.length);
+  }
+  return f;
+};
+
+
+exports.extname = function(path) {
+  return splitPath(path)[3];
+};
+
+function filter (xs, f) {
+    if (xs.filter) return xs.filter(f);
+    var res = [];
+    for (var i = 0; i < xs.length; i++) {
+        if (f(xs[i], i, xs)) res.push(xs[i]);
+    }
+    return res;
+}
+
+// String.prototype.substr - negative index don't work in IE8
+var substr = 'ab'.substr(-1) === 'b'
+    ? function (str, start, len) { return str.substr(start, len) }
+    : function (str, start, len) {
+        if (start < 0) start = str.length + start;
+        return str.substr(start, len);
+    }
+;
+
+}).call(this,require('_process'))
+},{"_process":24}],23:[function(require,module,exports){
+(function (process){
+'use strict';
+
+function posix(path) {
+	return path.charAt(0) === '/';
+}
+
+function win32(path) {
+	// https://github.com/nodejs/node/blob/b3fcc245fb25539909ef1d5eaa01dbf92e168633/lib/path.js#L56
+	var splitDeviceRe = /^([a-zA-Z]:|[\\\/]{2}[^\\\/]+[\\\/]+[^\\\/]+)?([\\\/])?([\s\S]*?)$/;
+	var result = splitDeviceRe.exec(path);
+	var device = result[1] || '';
+	var isUnc = Boolean(device && device.charAt(1) !== ':');
+
+	// UNC paths are always absolute
+	return Boolean(result[2] || isUnc);
+}
+
+module.exports = process.platform === 'win32' ? win32 : posix;
+module.exports.posix = posix;
+module.exports.win32 = win32;
+
+}).call(this,require('_process'))
+},{"_process":24}],24:[function(require,module,exports){
+// shim for using process in browser
+var process = module.exports = {};
+
+// cached from whatever global is present so that test runners that stub it
+// don't break things.  But we need to wrap it in a try catch in case it is
+// wrapped in strict mode code which doesn't define any globals.  It's inside a
+// function because try/catches deoptimize in certain engines.
+
+var cachedSetTimeout;
+var cachedClearTimeout;
+
+function defaultSetTimout() {
+    throw new Error('setTimeout has not been defined');
+}
+function defaultClearTimeout () {
+    throw new Error('clearTimeout has not been defined');
+}
+(function () {
+    try {
+        if (typeof setTimeout === 'function') {
+            cachedSetTimeout = setTimeout;
+        } else {
+            cachedSetTimeout = defaultSetTimout;
+        }
+    } catch (e) {
+        cachedSetTimeout = defaultSetTimout;
+    }
+    try {
+        if (typeof clearTimeout === 'function') {
+            cachedClearTimeout = clearTimeout;
+        } else {
+            cachedClearTimeout = defaultClearTimeout;
+        }
+    } catch (e) {
+        cachedClearTimeout = defaultClearTimeout;
+    }
+} ())
+function runTimeout(fun) {
+    if (cachedSetTimeout === setTimeout) {
+        //normal enviroments in sane situations
+        return setTimeout(fun, 0);
+    }
+    // if setTimeout wasn't available but was latter defined
+    if ((cachedSetTimeout === defaultSetTimout || !cachedSetTimeout) && setTimeout) {
+        cachedSetTimeout = setTimeout;
+        return setTimeout(fun, 0);
+    }
+    try {
+        // when when somebody has screwed with setTimeout but no I.E. maddness
+        return cachedSetTimeout(fun, 0);
+    } catch(e){
+        try {
+            // When we are in I.E. but the script has been evaled so I.E. doesn't trust the global object when called normally
+            return cachedSetTimeout.call(null, fun, 0);
+        } catch(e){
+            // same as above but when it's a version of I.E. that must have the global object for 'this', hopfully our context correct otherwise it will throw a global error
+            return cachedSetTimeout.call(this, fun, 0);
+        }
+    }
+
+
+}
+function runClearTimeout(marker) {
+    if (cachedClearTimeout === clearTimeout) {
+        //normal enviroments in sane situations
+        return clearTimeout(marker);
+    }
+    // if clearTimeout wasn't available but was latter defined
+    if ((cachedClearTimeout === defaultClearTimeout || !cachedClearTimeout) && clearTimeout) {
+        cachedClearTimeout = clearTimeout;
+        return clearTimeout(marker);
+    }
+    try {
+        // when when somebody has screwed with setTimeout but no I.E. maddness
+        return cachedClearTimeout(marker);
+    } catch (e){
+        try {
+            // When we are in I.E. but the script has been evaled so I.E. doesn't  trust the global object when called normally
+            return cachedClearTimeout.call(null, marker);
+        } catch (e){
+            // same as above but when it's a version of I.E. that must have the global object for 'this', hopfully our context correct otherwise it will throw a global error.
+            // Some versions of I.E. have different rules for clearTimeout vs setTimeout
+            return cachedClearTimeout.call(this, marker);
+        }
+    }
+
+
+
+}
+var queue = [];
+var draining = false;
+var currentQueue;
+var queueIndex = -1;
+
+function cleanUpNextTick() {
+    if (!draining || !currentQueue) {
+        return;
+    }
+    draining = false;
+    if (currentQueue.length) {
+        queue = currentQueue.concat(queue);
+    } else {
+        queueIndex = -1;
+    }
+    if (queue.length) {
+        drainQueue();
+    }
+}
+
+function drainQueue() {
+    if (draining) {
+        return;
+    }
+    var timeout = runTimeout(cleanUpNextTick);
+    draining = true;
+
+    var len = queue.length;
+    while(len) {
+        currentQueue = queue;
+        queue = [];
+        while (++queueIndex < len) {
+            if (currentQueue) {
+                currentQueue[queueIndex].run();
+            }
+        }
+        queueIndex = -1;
+        len = queue.length;
+    }
+    currentQueue = null;
+    draining = false;
+    runClearTimeout(timeout);
+}
+
+process.nextTick = function (fun) {
+    var args = new Array(arguments.length - 1);
+    if (arguments.length > 1) {
+        for (var i = 1; i < arguments.length; i++) {
+            args[i - 1] = arguments[i];
+        }
+    }
+    queue.push(new Item(fun, args));
+    if (queue.length === 1 && !draining) {
+        runTimeout(drainQueue);
+    }
+};
+
+// v8 likes predictible objects
+function Item(fun, array) {
+    this.fun = fun;
+    this.array = array;
+}
+Item.prototype.run = function () {
+    this.fun.apply(null, this.array);
+};
+process.title = 'browser';
+process.browser = true;
+process.env = {};
+process.argv = [];
+process.version = ''; // empty string to avoid regexp issues
+process.versions = {};
+
+function noop() {}
+
+process.on = noop;
+process.addListener = noop;
+process.once = noop;
+process.off = noop;
+process.removeListener = noop;
+process.removeAllListeners = noop;
+process.emit = noop;
+process.prependListener = noop;
+process.prependOnceListener = noop;
+
+process.listeners = function (name) { return [] }
+
+process.binding = function (name) {
+    throw new Error('process.binding is not supported');
+};
+
+process.cwd = function () { return '/' };
+process.chdir = function (dir) {
+    throw new Error('process.chdir is not supported');
+};
+process.umask = function() { return 0; };
+
+},{}],25:[function(require,module,exports){
+//     Underscore.js 1.8.3
+//     http://underscorejs.org
+//     (c) 2009-2015 Jeremy Ashkenas, DocumentCloud and Investigative Reporters & Editors
+//     Underscore may be freely distributed under the MIT license.
+
+(function() {
+
+  // Baseline setup
+  // --------------
+
+  // Establish the root object, `window` in the browser, or `exports` on the server.
+  var root = this;
+
+  // Save the previous value of the `_` variable.
+  var previousUnderscore = root._;
+
+  // Save bytes in the minified (but not gzipped) version:
+  var ArrayProto = Array.prototype, ObjProto = Object.prototype, FuncProto = Function.prototype;
+
+  // Create quick reference variables for speed access to core prototypes.
+  var
+    push             = ArrayProto.push,
+    slice            = ArrayProto.slice,
+    toString         = ObjProto.toString,
+    hasOwnProperty   = ObjProto.hasOwnProperty;
+
+  // All **ECMAScript 5** native function implementations that we hope to use
+  // are declared here.
+  var
+    nativeIsArray      = Array.isArray,
+    nativeKeys         = Object.keys,
+    nativeBind         = FuncProto.bind,
+    nativeCreate       = Object.create;
+
+  // Naked function reference for surrogate-prototype-swapping.
+  var Ctor = function(){};
+
+  // Create a safe reference to the Underscore object for use below.
+  var _ = function(obj) {
+    if (obj instanceof _) return obj;
+    if (!(this instanceof _)) return new _(obj);
+    this._wrapped = obj;
+  };
+
+  // Export the Underscore object for **Node.js**, with
+  // backwards-compatibility for the old `require()` API. If we're in
+  // the browser, add `_` as a global object.
+  if (typeof exports !== 'undefined') {
+    if (typeof module !== 'undefined' && module.exports) {
+      exports = module.exports = _;
+    }
+    exports._ = _;
+  } else {
+    root._ = _;
+  }
+
+  // Current version.
+  _.VERSION = '1.8.3';
+
+  // Internal function that returns an efficient (for current engines) version
+  // of the passed-in callback, to be repeatedly applied in other Underscore
+  // functions.
+  var optimizeCb = function(func, context, argCount) {
+    if (context === void 0) return func;
+    switch (argCount == null ? 3 : argCount) {
+      case 1: return function(value) {
+        return func.call(context, value);
+      };
+      case 2: return function(value, other) {
+        return func.call(context, value, other);
+      };
+      case 3: return function(value, index, collection) {
+        return func.call(context, value, index, collection);
+      };
+      case 4: return function(accumulator, value, index, collection) {
+        return func.call(context, accumulator, value, index, collection);
+      };
+    }
+    return function() {
+      return func.apply(context, arguments);
+    };
+  };
+
+  // A mostly-internal function to generate callbacks that can be applied
+  // to each element in a collection, returning the desired result — either
+  // identity, an arbitrary callback, a property matcher, or a property accessor.
+  var cb = function(value, context, argCount) {
+    if (value == null) return _.identity;
+    if (_.isFunction(value)) return optimizeCb(value, context, argCount);
+    if (_.isObject(value)) return _.matcher(value);
+    return _.property(value);
+  };
+  _.iteratee = function(value, context) {
+    return cb(value, context, Infinity);
+  };
+
+  // An internal function for creating assigner functions.
+  var createAssigner = function(keysFunc, undefinedOnly) {
+    return function(obj) {
+      var length = arguments.length;
+      if (length < 2 || obj == null) return obj;
+      for (var index = 1; index < length; index++) {
+        var source = arguments[index],
+            keys = keysFunc(source),
+            l = keys.length;
+        for (var i = 0; i < l; i++) {
+          var key = keys[i];
+          if (!undefinedOnly || obj[key] === void 0) obj[key] = source[key];
+        }
+      }
+      return obj;
+    };
+  };
+
+  // An internal function for creating a new object that inherits from another.
+  var baseCreate = function(prototype) {
+    if (!_.isObject(prototype)) return {};
+    if (nativeCreate) return nativeCreate(prototype);
+    Ctor.prototype = prototype;
+    var result = new Ctor;
+    Ctor.prototype = null;
+    return result;
+  };
+
+  var property = function(key) {
+    return function(obj) {
+      return obj == null ? void 0 : obj[key];
+    };
+  };
+
+  // Helper for collection methods to determine whether a collection
+  // should be iterated as an array or as an object
+  // Related: http://people.mozilla.org/~jorendorff/es6-draft.html#sec-tolength
+  // Avoids a very nasty iOS 8 JIT bug on ARM-64. #2094
+  var MAX_ARRAY_INDEX = Math.pow(2, 53) - 1;
+  var getLength = property('length');
+  var isArrayLike = function(collection) {
+    var length = getLength(collection);
+    return typeof length == 'number' && length >= 0 && length <= MAX_ARRAY_INDEX;
+  };
+
+  // Collection Functions
+  // --------------------
+
+  // The cornerstone, an `each` implementation, aka `forEach`.
+  // Handles raw objects in addition to array-likes. Treats all
+  // sparse array-likes as if they were dense.
+  _.each = _.forEach = function(obj, iteratee, context) {
+    iteratee = optimizeCb(iteratee, context);
+    var i, length;
+    if (isArrayLike(obj)) {
+      for (i = 0, length = obj.length; i < length; i++) {
+        iteratee(obj[i], i, obj);
+      }
+    } else {
+      var keys = _.keys(obj);
+      for (i = 0, length = keys.length; i < length; i++) {
+        iteratee(obj[keys[i]], keys[i], obj);
+      }
+    }
+    return obj;
+  };
+
+  // Return the results of applying the iteratee to each element.
+  _.map = _.collect = function(obj, iteratee, context) {
+    iteratee = cb(iteratee, context);
+    var keys = !isArrayLike(obj) && _.keys(obj),
+        length = (keys || obj).length,
+        results = Array(length);
+    for (var index = 0; index < length; index++) {
+      var currentKey = keys ? keys[index] : index;
+      results[index] = iteratee(obj[currentKey], currentKey, obj);
+    }
+    return results;
+  };
+
+  // Create a reducing function iterating left or right.
+  function createReduce(dir) {
+    // Optimized iterator function as using arguments.length
+    // in the main function will deoptimize the, see #1991.
+    function iterator(obj, iteratee, memo, keys, index, length) {
+      for (; index >= 0 && index < length; index += dir) {
+        var currentKey = keys ? keys[index] : index;
+        memo = iteratee(memo, obj[currentKey], currentKey, obj);
+      }
+      return memo;
+    }
+
+    return function(obj, iteratee, memo, context) {
+      iteratee = optimizeCb(iteratee, context, 4);
+      var keys = !isArrayLike(obj) && _.keys(obj),
+          length = (keys || obj).length,
+          index = dir > 0 ? 0 : length - 1;
+      // Determine the initial value if none is provided.
+      if (arguments.length < 3) {
+        memo = obj[keys ? keys[index] : index];
+        index += dir;
+      }
+      return iterator(obj, iteratee, memo, keys, index, length);
+    };
+  }
+
+  // **Reduce** builds up a single result from a list of values, aka `inject`,
+  // or `foldl`.
+  _.reduce = _.foldl = _.inject = createReduce(1);
+
+  // The right-associative version of reduce, also known as `foldr`.
+  _.reduceRight = _.foldr = createReduce(-1);
+
+  // Return the first value which passes a truth test. Aliased as `detect`.
+  _.find = _.detect = function(obj, predicate, context) {
+    var key;
+    if (isArrayLike(obj)) {
+      key = _.findIndex(obj, predicate, context);
+    } else {
+      key = _.findKey(obj, predicate, context);
+    }
+    if (key !== void 0 && key !== -1) return obj[key];
+  };
+
+  // Return all the elements that pass a truth test.
+  // Aliased as `select`.
+  _.filter = _.select = function(obj, predicate, context) {
+    var results = [];
+    predicate = cb(predicate, context);
+    _.each(obj, function(value, index, list) {
+      if (predicate(value, index, list)) results.push(value);
+    });
+    return results;
+  };
+
+  // Return all the elements for which a truth test fails.
+  _.reject = function(obj, predicate, context) {
+    return _.filter(obj, _.negate(cb(predicate)), context);
+  };
+
+  // Determine whether all of the elements match a truth test.
+  // Aliased as `all`.
+  _.every = _.all = function(obj, predicate, context) {
+    predicate = cb(predicate, context);
+    var keys = !isArrayLike(obj) && _.keys(obj),
+        length = (keys || obj).length;
+    for (var index = 0; index < length; index++) {
+      var currentKey = keys ? keys[index] : index;
+      if (!predicate(obj[currentKey], currentKey, obj)) return false;
+    }
+    return true;
+  };
+
+  // Determine if at least one element in the object matches a truth test.
+  // Aliased as `any`.
+  _.some = _.any = function(obj, predicate, context) {
+    predicate = cb(predicate, context);
+    var keys = !isArrayLike(obj) && _.keys(obj),
+        length = (keys || obj).length;
+    for (var index = 0; index < length; index++) {
+      var currentKey = keys ? keys[index] : index;
+      if (predicate(obj[currentKey], currentKey, obj)) return true;
+    }
+    return false;
+  };
+
+  // Determine if the array or object contains a given item (using `===`).
+  // Aliased as `includes` and `include`.
+  _.contains = _.includes = _.include = function(obj, item, fromIndex, guard) {
+    if (!isArrayLike(obj)) obj = _.values(obj);
+    if (typeof fromIndex != 'number' || guard) fromIndex = 0;
+    return _.indexOf(obj, item, fromIndex) >= 0;
+  };
+
+  // Invoke a method (with arguments) on every item in a collection.
+  _.invoke = function(obj, method) {
+    var args = slice.call(arguments, 2);
+    var isFunc = _.isFunction(method);
+    return _.map(obj, function(value) {
+      var func = isFunc ? method : value[method];
+      return func == null ? func : func.apply(value, args);
+    });
+  };
+
+  // Convenience version of a common use case of `map`: fetching a property.
+  _.pluck = function(obj, key) {
+    return _.map(obj, _.property(key));
+  };
+
+  // Convenience version of a common use case of `filter`: selecting only objects
+  // containing specific `key:value` pairs.
+  _.where = function(obj, attrs) {
+    return _.filter(obj, _.matcher(attrs));
+  };
+
+  // Convenience version of a common use case of `find`: getting the first object
+  // containing specific `key:value` pairs.
+  _.findWhere = function(obj, attrs) {
+    return _.find(obj, _.matcher(attrs));
+  };
+
+  // Return the maximum element (or element-based computation).
+  _.max = function(obj, iteratee, context) {
+    var result = -Infinity, lastComputed = -Infinity,
+        value, computed;
+    if (iteratee == null && obj != null) {
+      obj = isArrayLike(obj) ? obj : _.values(obj);
+      for (var i = 0, length = obj.length; i < length; i++) {
+        value = obj[i];
+        if (value > result) {
+          result = value;
+        }
+      }
+    } else {
+      iteratee = cb(iteratee, context);
+      _.each(obj, function(value, index, list) {
+        computed = iteratee(value, index, list);
+        if (computed > lastComputed || computed === -Infinity && result === -Infinity) {
+          result = value;
+          lastComputed = computed;
+        }
+      });
+    }
+    return result;
+  };
+
+  // Return the minimum element (or element-based computation).
+  _.min = function(obj, iteratee, context) {
+    var result = Infinity, lastComputed = Infinity,
+        value, computed;
+    if (iteratee == null && obj != null) {
+      obj = isArrayLike(obj) ? obj : _.values(obj);
+      for (var i = 0, length = obj.length; i < length; i++) {
+        value = obj[i];
+        if (value < result) {
+          result = value;
+        }
+      }
+    } else {
+      iteratee = cb(iteratee, context);
+      _.each(obj, function(value, index, list) {
+        computed = iteratee(value, index, list);
+        if (computed < lastComputed || computed === Infinity && result === Infinity) {
+          result = value;
+          lastComputed = computed;
+        }
+      });
+    }
+    return result;
+  };
+
+  // Shuffle a collection, using the modern version of the
+  // [Fisher-Yates shuffle](http://en.wikipedia.org/wiki/Fisher–Yates_shuffle).
+  _.shuffle = function(obj) {
+    var set = isArrayLike(obj) ? obj : _.values(obj);
+    var length = set.length;
+    var shuffled = Array(length);
+    for (var index = 0, rand; index < length; index++) {
+      rand = _.random(0, index);
+      if (rand !== index) shuffled[index] = shuffled[rand];
+      shuffled[rand] = set[index];
+    }
+    return shuffled;
+  };
+
+  // Sample **n** random values from a collection.
+  // If **n** is not specified, returns a single random element.
+  // The internal `guard` argument allows it to work with `map`.
+  _.sample = function(obj, n, guard) {
+    if (n == null || guard) {
+      if (!isArrayLike(obj)) obj = _.values(obj);
+      return obj[_.random(obj.length - 1)];
+    }
+    return _.shuffle(obj).slice(0, Math.max(0, n));
+  };
+
+  // Sort the object's values by a criterion produced by an iteratee.
+  _.sortBy = function(obj, iteratee, context) {
+    iteratee = cb(iteratee, context);
+    return _.pluck(_.map(obj, function(value, index, list) {
+      return {
+        value: value,
+        index: index,
+        criteria: iteratee(value, index, list)
+      };
+    }).sort(function(left, right) {
+      var a = left.criteria;
+      var b = right.criteria;
+      if (a !== b) {
+        if (a > b || a === void 0) return 1;
+        if (a < b || b === void 0) return -1;
+      }
+      return left.index - right.index;
+    }), 'value');
+  };
+
+  // An internal function used for aggregate "group by" operations.
+  var group = function(behavior) {
+    return function(obj, iteratee, context) {
+      var result = {};
+      iteratee = cb(iteratee, context);
+      _.each(obj, function(value, index) {
+        var key = iteratee(value, index, obj);
+        behavior(result, value, key);
+      });
+      return result;
+    };
+  };
+
+  // Groups the object's values by a criterion. Pass either a string attribute
+  // to group by, or a function that returns the criterion.
+  _.groupBy = group(function(result, value, key) {
+    if (_.has(result, key)) result[key].push(value); else result[key] = [value];
+  });
+
+  // Indexes the object's values by a criterion, similar to `groupBy`, but for
+  // when you know that your index values will be unique.
+  _.indexBy = group(function(result, value, key) {
+    result[key] = value;
+  });
+
+  // Counts instances of an object that group by a certain criterion. Pass
+  // either a string attribute to count by, or a function that returns the
+  // criterion.
+  _.countBy = group(function(result, value, key) {
+    if (_.has(result, key)) result[key]++; else result[key] = 1;
+  });
+
+  // Safely create a real, live array from anything iterable.
+  _.toArray = function(obj) {
+    if (!obj) return [];
+    if (_.isArray(obj)) return slice.call(obj);
+    if (isArrayLike(obj)) return _.map(obj, _.identity);
+    return _.values(obj);
+  };
+
+  // Return the number of elements in an object.
+  _.size = function(obj) {
+    if (obj == null) return 0;
+    return isArrayLike(obj) ? obj.length : _.keys(obj).length;
+  };
+
+  // Split a collection into two arrays: one whose elements all satisfy the given
+  // predicate, and one whose elements all do not satisfy the predicate.
+  _.partition = function(obj, predicate, context) {
+    predicate = cb(predicate, context);
+    var pass = [], fail = [];
+    _.each(obj, function(value, key, obj) {
+      (predicate(value, key, obj) ? pass : fail).push(value);
+    });
+    return [pass, fail];
+  };
+
+  // Array Functions
+  // ---------------
+
+  // Get the first element of an array. Passing **n** will return the first N
+  // values in the array. Aliased as `head` and `take`. The **guard** check
+  // allows it to work with `_.map`.
+  _.first = _.head = _.take = function(array, n, guard) {
+    if (array == null) return void 0;
+    if (n == null || guard) return array[0];
+    return _.initial(array, array.length - n);
+  };
+
+  // Returns everything but the last entry of the array. Especially useful on
+  // the arguments object. Passing **n** will return all the values in
+  // the array, excluding the last N.
+  _.initial = function(array, n, guard) {
+    return slice.call(array, 0, Math.max(0, array.length - (n == null || guard ? 1 : n)));
+  };
+
+  // Get the last element of an array. Passing **n** will return the last N
+  // values in the array.
+  _.last = function(array, n, guard) {
+    if (array == null) return void 0;
+    if (n == null || guard) return array[array.length - 1];
+    return _.rest(array, Math.max(0, array.length - n));
+  };
+
+  // Returns everything but the first entry of the array. Aliased as `tail` and `drop`.
+  // Especially useful on the arguments object. Passing an **n** will return
+  // the rest N values in the array.
+  _.rest = _.tail = _.drop = function(array, n, guard) {
+    return slice.call(array, n == null || guard ? 1 : n);
+  };
+
+  // Trim out all falsy values from an array.
+  _.compact = function(array) {
+    return _.filter(array, _.identity);
+  };
+
+  // Internal implementation of a recursive `flatten` function.
+  var flatten = function(input, shallow, strict, startIndex) {
+    var output = [], idx = 0;
+    for (var i = startIndex || 0, length = getLength(input); i < length; i++) {
+      var value = input[i];
+      if (isArrayLike(value) && (_.isArray(value) || _.isArguments(value))) {
+        //flatten current level of array or arguments object
+        if (!shallow) value = flatten(value, shallow, strict);
+        var j = 0, len = value.length;
+        output.length += len;
+        while (j < len) {
+          output[idx++] = value[j++];
+        }
+      } else if (!strict) {
+        output[idx++] = value;
+      }
+    }
+    return output;
+  };
+
+  // Flatten out an array, either recursively (by default), or just one level.
+  _.flatten = function(array, shallow) {
+    return flatten(array, shallow, false);
+  };
+
+  // Return a version of the array that does not contain the specified value(s).
+  _.without = function(array) {
+    return _.difference(array, slice.call(arguments, 1));
+  };
+
+  // Produce a duplicate-free version of the array. If the array has already
+  // been sorted, you have the option of using a faster algorithm.
+  // Aliased as `unique`.
+  _.uniq = _.unique = function(array, isSorted, iteratee, context) {
+    if (!_.isBoolean(isSorted)) {
+      context = iteratee;
+      iteratee = isSorted;
+      isSorted = false;
+    }
+    if (iteratee != null) iteratee = cb(iteratee, context);
+    var result = [];
+    var seen = [];
+    for (var i = 0, length = getLength(array); i < length; i++) {
+      var value = array[i],
+          computed = iteratee ? iteratee(value, i, array) : value;
+      if (isSorted) {
+        if (!i || seen !== computed) result.push(value);
+        seen = computed;
+      } else if (iteratee) {
+        if (!_.contains(seen, computed)) {
+          seen.push(computed);
+          result.push(value);
+        }
+      } else if (!_.contains(result, value)) {
+        result.push(value);
+      }
+    }
+    return result;
+  };
+
+  // Produce an array that contains the union: each distinct element from all of
+  // the passed-in arrays.
+  _.union = function() {
+    return _.uniq(flatten(arguments, true, true));
+  };
+
+  // Produce an array that contains every item shared between all the
+  // passed-in arrays.
+  _.intersection = function(array) {
+    var result = [];
+    var argsLength = arguments.length;
+    for (var i = 0, length = getLength(array); i < length; i++) {
+      var item = array[i];
+      if (_.contains(result, item)) continue;
+      for (var j = 1; j < argsLength; j++) {
+        if (!_.contains(arguments[j], item)) break;
+      }
+      if (j === argsLength) result.push(item);
+    }
+    return result;
+  };
+
+  // Take the difference between one array and a number of other arrays.
+  // Only the elements present in just the first array will remain.
+  _.difference = function(array) {
+    var rest = flatten(arguments, true, true, 1);
+    return _.filter(array, function(value){
+      return !_.contains(rest, value);
+    });
+  };
+
+  // Zip together multiple lists into a single array -- elements that share
+  // an index go together.
+  _.zip = function() {
+    return _.unzip(arguments);
+  };
+
+  // Complement of _.zip. Unzip accepts an array of arrays and groups
+  // each array's elements on shared indices
+  _.unzip = function(array) {
+    var length = array && _.max(array, getLength).length || 0;
+    var result = Array(length);
+
+    for (var index = 0; index < length; index++) {
+      result[index] = _.pluck(array, index);
+    }
+    return result;
+  };
+
+  // Converts lists into objects. Pass either a single array of `[key, value]`
+  // pairs, or two parallel arrays of the same length -- one of keys, and one of
+  // the corresponding values.
+  _.object = function(list, values) {
+    var result = {};
+    for (var i = 0, length = getLength(list); i < length; i++) {
+      if (values) {
+        result[list[i]] = values[i];
+      } else {
+        result[list[i][0]] = list[i][1];
+      }
+    }
+    return result;
+  };
+
+  // Generator function to create the findIndex and findLastIndex functions
+  function createPredicateIndexFinder(dir) {
+    return function(array, predicate, context) {
+      predicate = cb(predicate, context);
+      var length = getLength(array);
+      var index = dir > 0 ? 0 : length - 1;
+      for (; index >= 0 && index < length; index += dir) {
+        if (predicate(array[index], index, array)) return index;
+      }
+      return -1;
+    };
+  }
+
+  // Returns the first index on an array-like that passes a predicate test
+  _.findIndex = createPredicateIndexFinder(1);
+  _.findLastIndex = createPredicateIndexFinder(-1);
+
+  // Use a comparator function to figure out the smallest index at which
+  // an object should be inserted so as to maintain order. Uses binary search.
+  _.sortedIndex = function(array, obj, iteratee, context) {
+    iteratee = cb(iteratee, context, 1);
+    var value = iteratee(obj);
+    var low = 0, high = getLength(array);
+    while (low < high) {
+      var mid = Math.floor((low + high) / 2);
+      if (iteratee(array[mid]) < value) low = mid + 1; else high = mid;
+    }
+    return low;
+  };
+
+  // Generator function to create the indexOf and lastIndexOf functions
+  function createIndexFinder(dir, predicateFind, sortedIndex) {
+    return function(array, item, idx) {
+      var i = 0, length = getLength(array);
+      if (typeof idx == 'number') {
+        if (dir > 0) {
+            i = idx >= 0 ? idx : Math.max(idx + length, i);
+        } else {
+            length = idx >= 0 ? Math.min(idx + 1, length) : idx + length + 1;
+        }
+      } else if (sortedIndex && idx && length) {
+        idx = sortedIndex(array, item);
+        return array[idx] === item ? idx : -1;
+      }
+      if (item !== item) {
+        idx = predicateFind(slice.call(array, i, length), _.isNaN);
+        return idx >= 0 ? idx + i : -1;
+      }
+      for (idx = dir > 0 ? i : length - 1; idx >= 0 && idx < length; idx += dir) {
+        if (array[idx] === item) return idx;
+      }
+      return -1;
+    };
+  }
+
+  // Return the position of the first occurrence of an item in an array,
+  // or -1 if the item is not included in the array.
+  // If the array is large and already in sort order, pass `true`
+  // for **isSorted** to use binary search.
+  _.indexOf = createIndexFinder(1, _.findIndex, _.sortedIndex);
+  _.lastIndexOf = createIndexFinder(-1, _.findLastIndex);
+
+  // Generate an integer Array containing an arithmetic progression. A port of
+  // the native Python `range()` function. See
+  // [the Python documentation](http://docs.python.org/library/functions.html#range).
+  _.range = function(start, stop, step) {
+    if (stop == null) {
+      stop = start || 0;
+      start = 0;
+    }
+    step = step || 1;
+
+    var length = Math.max(Math.ceil((stop - start) / step), 0);
+    var range = Array(length);
+
+    for (var idx = 0; idx < length; idx++, start += step) {
+      range[idx] = start;
+    }
+
+    return range;
+  };
+
+  // Function (ahem) Functions
+  // ------------------
+
+  // Determines whether to execute a function as a constructor
+  // or a normal function with the provided arguments
+  var executeBound = function(sourceFunc, boundFunc, context, callingContext, args) {
+    if (!(callingContext instanceof boundFunc)) return sourceFunc.apply(context, args);
+    var self = baseCreate(sourceFunc.prototype);
+    var result = sourceFunc.apply(self, args);
+    if (_.isObject(result)) return result;
+    return self;
+  };
+
+  // Create a function bound to a given object (assigning `this`, and arguments,
+  // optionally). Delegates to **ECMAScript 5**'s native `Function.bind` if
+  // available.
+  _.bind = function(func, context) {
+    if (nativeBind && func.bind === nativeBind) return nativeBind.apply(func, slice.call(arguments, 1));
+    if (!_.isFunction(func)) throw new TypeError('Bind must be called on a function');
+    var args = slice.call(arguments, 2);
+    var bound = function() {
+      return executeBound(func, bound, context, this, args.concat(slice.call(arguments)));
+    };
+    return bound;
+  };
+
+  // Partially apply a function by creating a version that has had some of its
+  // arguments pre-filled, without changing its dynamic `this` context. _ acts
+  // as a placeholder, allowing any combination of arguments to be pre-filled.
+  _.partial = function(func) {
+    var boundArgs = slice.call(arguments, 1);
+    var bound = function() {
+      var position = 0, length = boundArgs.length;
+      var args = Array(length);
+      for (var i = 0; i < length; i++) {
+        args[i] = boundArgs[i] === _ ? arguments[position++] : boundArgs[i];
+      }
+      while (position < arguments.length) args.push(arguments[position++]);
+      return executeBound(func, bound, this, this, args);
+    };
+    return bound;
+  };
+
+  // Bind a number of an object's methods to that object. Remaining arguments
+  // are the method names to be bound. Useful for ensuring that all callbacks
+  // defined on an object belong to it.
+  _.bindAll = function(obj) {
+    var i, length = arguments.length, key;
+    if (length <= 1) throw new Error('bindAll must be passed function names');
+    for (i = 1; i < length; i++) {
+      key = arguments[i];
+      obj[key] = _.bind(obj[key], obj);
+    }
+    return obj;
+  };
+
+  // Memoize an expensive function by storing its results.
+  _.memoize = function(func, hasher) {
+    var memoize = function(key) {
+      var cache = memoize.cache;
+      var address = '' + (hasher ? hasher.apply(this, arguments) : key);
+      if (!_.has(cache, address)) cache[address] = func.apply(this, arguments);
+      return cache[address];
+    };
+    memoize.cache = {};
+    return memoize;
+  };
+
+  // Delays a function for the given number of milliseconds, and then calls
+  // it with the arguments supplied.
+  _.delay = function(func, wait) {
+    var args = slice.call(arguments, 2);
+    return setTimeout(function(){
+      return func.apply(null, args);
+    }, wait);
+  };
+
+  // Defers a function, scheduling it to run after the current call stack has
+  // cleared.
+  _.defer = _.partial(_.delay, _, 1);
+
+  // Returns a function, that, when invoked, will only be triggered at most once
+  // during a given window of time. Normally, the throttled function will run
+  // as much as it can, without ever going more than once per `wait` duration;
+  // but if you'd like to disable the execution on the leading edge, pass
+  // `{leading: false}`. To disable execution on the trailing edge, ditto.
+  _.throttle = function(func, wait, options) {
+    var context, args, result;
+    var timeout = null;
+    var previous = 0;
+    if (!options) options = {};
+    var later = function() {
+      previous = options.leading === false ? 0 : _.now();
+      timeout = null;
+      result = func.apply(context, args);
+      if (!timeout) context = args = null;
+    };
+    return function() {
+      var now = _.now();
+      if (!previous && options.leading === false) previous = now;
+      var remaining = wait - (now - previous);
+      context = this;
+      args = arguments;
+      if (remaining <= 0 || remaining > wait) {
+        if (timeout) {
+          clearTimeout(timeout);
+          timeout = null;
+        }
+        previous = now;
+        result = func.apply(context, args);
+        if (!timeout) context = args = null;
+      } else if (!timeout && options.trailing !== false) {
+        timeout = setTimeout(later, remaining);
+      }
+      return result;
+    };
+  };
+
+  // Returns a function, that, as long as it continues to be invoked, will not
+  // be triggered. The function will be called after it stops being called for
+  // N milliseconds. If `immediate` is passed, trigger the function on the
+  // leading edge, instead of the trailing.
+  _.debounce = function(func, wait, immediate) {
+    var timeout, args, context, timestamp, result;
+
+    var later = function() {
+      var last = _.now() - timestamp;
+
+      if (last < wait && last >= 0) {
+        timeout = setTimeout(later, wait - last);
+      } else {
+        timeout = null;
+        if (!immediate) {
+          result = func.apply(context, args);
+          if (!timeout) context = args = null;
+        }
+      }
+    };
+
+    return function() {
+      context = this;
+      args = arguments;
+      timestamp = _.now();
+      var callNow = immediate && !timeout;
+      if (!timeout) timeout = setTimeout(later, wait);
+      if (callNow) {
+        result = func.apply(context, args);
+        context = args = null;
+      }
+
+      return result;
+    };
+  };
+
+  // Returns the first function passed as an argument to the second,
+  // allowing you to adjust arguments, run code before and after, and
+  // conditionally execute the original function.
+  _.wrap = function(func, wrapper) {
+    return _.partial(wrapper, func);
+  };
+
+  // Returns a negated version of the passed-in predicate.
+  _.negate = function(predicate) {
+    return function() {
+      return !predicate.apply(this, arguments);
+    };
+  };
+
+  // Returns a function that is the composition of a list of functions, each
+  // consuming the return value of the function that follows.
+  _.compose = function() {
+    var args = arguments;
+    var start = args.length - 1;
+    return function() {
+      var i = start;
+      var result = args[start].apply(this, arguments);
+      while (i--) result = args[i].call(this, result);
+      return result;
+    };
+  };
+
+  // Returns a function that will only be executed on and after the Nth call.
+  _.after = function(times, func) {
+    return function() {
+      if (--times < 1) {
+        return func.apply(this, arguments);
+      }
+    };
+  };
+
+  // Returns a function that will only be executed up to (but not including) the Nth call.
+  _.before = function(times, func) {
+    var memo;
+    return function() {
+      if (--times > 0) {
+        memo = func.apply(this, arguments);
+      }
+      if (times <= 1) func = null;
+      return memo;
+    };
+  };
+
+  // Returns a function that will be executed at most one time, no matter how
+  // often you call it. Useful for lazy initialization.
+  _.once = _.partial(_.before, 2);
+
+  // Object Functions
+  // ----------------
+
+  // Keys in IE < 9 that won't be iterated by `for key in ...` and thus missed.
+  var hasEnumBug = !{toString: null}.propertyIsEnumerable('toString');
+  var nonEnumerableProps = ['valueOf', 'isPrototypeOf', 'toString',
+                      'propertyIsEnumerable', 'hasOwnProperty', 'toLocaleString'];
+
+  function collectNonEnumProps(obj, keys) {
+    var nonEnumIdx = nonEnumerableProps.length;
+    var constructor = obj.constructor;
+    var proto = (_.isFunction(constructor) && constructor.prototype) || ObjProto;
+
+    // Constructor is a special case.
+    var prop = 'constructor';
+    if (_.has(obj, prop) && !_.contains(keys, prop)) keys.push(prop);
+
+    while (nonEnumIdx--) {
+      prop = nonEnumerableProps[nonEnumIdx];
+      if (prop in obj && obj[prop] !== proto[prop] && !_.contains(keys, prop)) {
+        keys.push(prop);
+      }
+    }
+  }
+
+  // Retrieve the names of an object's own properties.
+  // Delegates to **ECMAScript 5**'s native `Object.keys`
+  _.keys = function(obj) {
+    if (!_.isObject(obj)) return [];
+    if (nativeKeys) return nativeKeys(obj);
+    var keys = [];
+    for (var key in obj) if (_.has(obj, key)) keys.push(key);
+    // Ahem, IE < 9.
+    if (hasEnumBug) collectNonEnumProps(obj, keys);
+    return keys;
+  };
+
+  // Retrieve all the property names of an object.
+  _.allKeys = function(obj) {
+    if (!_.isObject(obj)) return [];
+    var keys = [];
+    for (var key in obj) keys.push(key);
+    // Ahem, IE < 9.
+    if (hasEnumBug) collectNonEnumProps(obj, keys);
+    return keys;
+  };
+
+  // Retrieve the values of an object's properties.
+  _.values = function(obj) {
+    var keys = _.keys(obj);
+    var length = keys.length;
+    var values = Array(length);
+    for (var i = 0; i < length; i++) {
+      values[i] = obj[keys[i]];
+    }
+    return values;
+  };
+
+  // Returns the results of applying the iteratee to each element of the object
+  // In contrast to _.map it returns an object
+  _.mapObject = function(obj, iteratee, context) {
+    iteratee = cb(iteratee, context);
+    var keys =  _.keys(obj),
+          length = keys.length,
+          results = {},
+          currentKey;
+      for (var index = 0; index < length; index++) {
+        currentKey = keys[index];
+        results[currentKey] = iteratee(obj[currentKey], currentKey, obj);
+      }
+      return results;
+  };
+
+  // Convert an object into a list of `[key, value]` pairs.
+  _.pairs = function(obj) {
+    var keys = _.keys(obj);
+    var length = keys.length;
+    var pairs = Array(length);
+    for (var i = 0; i < length; i++) {
+      pairs[i] = [keys[i], obj[keys[i]]];
+    }
+    return pairs;
+  };
+
+  // Invert the keys and values of an object. The values must be serializable.
+  _.invert = function(obj) {
+    var result = {};
+    var keys = _.keys(obj);
+    for (var i = 0, length = keys.length; i < length; i++) {
+      result[obj[keys[i]]] = keys[i];
+    }
+    return result;
+  };
+
+  // Return a sorted list of the function names available on the object.
+  // Aliased as `methods`
+  _.functions = _.methods = function(obj) {
+    var names = [];
+    for (var key in obj) {
+      if (_.isFunction(obj[key])) names.push(key);
+    }
+    return names.sort();
+  };
+
+  // Extend a given object with all the properties in passed-in object(s).
+  _.extend = createAssigner(_.allKeys);
+
+  // Assigns a given object with all the own properties in the passed-in object(s)
+  // (https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Object/assign)
+  _.extendOwn = _.assign = createAssigner(_.keys);
+
+  // Returns the first key on an object that passes a predicate test
+  _.findKey = function(obj, predicate, context) {
+    predicate = cb(predicate, context);
+    var keys = _.keys(obj), key;
+    for (var i = 0, length = keys.length; i < length; i++) {
+      key = keys[i];
+      if (predicate(obj[key], key, obj)) return key;
+    }
+  };
+
+  // Return a copy of the object only containing the whitelisted properties.
+  _.pick = function(object, oiteratee, context) {
+    var result = {}, obj = object, iteratee, keys;
+    if (obj == null) return result;
+    if (_.isFunction(oiteratee)) {
+      keys = _.allKeys(obj);
+      iteratee = optimizeCb(oiteratee, context);
+    } else {
+      keys = flatten(arguments, false, false, 1);
+      iteratee = function(value, key, obj) { return key in obj; };
+      obj = Object(obj);
+    }
+    for (var i = 0, length = keys.length; i < length; i++) {
+      var key = keys[i];
+      var value = obj[key];
+      if (iteratee(value, key, obj)) result[key] = value;
+    }
+    return result;
+  };
+
+   // Return a copy of the object without the blacklisted properties.
+  _.omit = function(obj, iteratee, context) {
+    if (_.isFunction(iteratee)) {
+      iteratee = _.negate(iteratee);
+    } else {
+      var keys = _.map(flatten(arguments, false, false, 1), String);
+      iteratee = function(value, key) {
+        return !_.contains(keys, key);
+      };
+    }
+    return _.pick(obj, iteratee, context);
+  };
+
+  // Fill in a given object with default properties.
+  _.defaults = createAssigner(_.allKeys, true);
+
+  // Creates an object that inherits from the given prototype object.
+  // If additional properties are provided then they will be added to the
+  // created object.
+  _.create = function(prototype, props) {
+    var result = baseCreate(prototype);
+    if (props) _.extendOwn(result, props);
+    return result;
+  };
+
+  // Create a (shallow-cloned) duplicate of an object.
+  _.clone = function(obj) {
+    if (!_.isObject(obj)) return obj;
+    return _.isArray(obj) ? obj.slice() : _.extend({}, obj);
+  };
+
+  // Invokes interceptor with the obj, and then returns obj.
+  // The primary purpose of this method is to "tap into" a method chain, in
+  // order to perform operations on intermediate results within the chain.
+  _.tap = function(obj, interceptor) {
+    interceptor(obj);
+    return obj;
+  };
+
+  // Returns whether an object has a given set of `key:value` pairs.
+  _.isMatch = function(object, attrs) {
+    var keys = _.keys(attrs), length = keys.length;
+    if (object == null) return !length;
+    var obj = Object(object);
+    for (var i = 0; i < length; i++) {
+      var key = keys[i];
+      if (attrs[key] !== obj[key] || !(key in obj)) return false;
+    }
+    return true;
+  };
+
+
+  // Internal recursive comparison function for `isEqual`.
+  var eq = function(a, b, aStack, bStack) {
+    // Identical objects are equal. `0 === -0`, but they aren't identical.
+    // See the [Harmony `egal` proposal](http://wiki.ecmascript.org/doku.php?id=harmony:egal).
+    if (a === b) return a !== 0 || 1 / a === 1 / b;
+    // A strict comparison is necessary because `null == undefined`.
+    if (a == null || b == null) return a === b;
+    // Unwrap any wrapped objects.
+    if (a instanceof _) a = a._wrapped;
+    if (b instanceof _) b = b._wrapped;
+    // Compare `[[Class]]` names.
+    var className = toString.call(a);
+    if (className !== toString.call(b)) return false;
+    switch (className) {
+      // Strings, numbers, regular expressions, dates, and booleans are compared by value.
+      case '[object RegExp]':
+      // RegExps are coerced to strings for comparison (Note: '' + /a/i === '/a/i')
+      case '[object String]':
+        // Primitives and their corresponding object wrappers are equivalent; thus, `"5"` is
+        // equivalent to `new String("5")`.
+        return '' + a === '' + b;
+      case '[object Number]':
+        // `NaN`s are equivalent, but non-reflexive.
+        // Object(NaN) is equivalent to NaN
+        if (+a !== +a) return +b !== +b;
+        // An `egal` comparison is performed for other numeric values.
+        return +a === 0 ? 1 / +a === 1 / b : +a === +b;
+      case '[object Date]':
+      case '[object Boolean]':
+        // Coerce dates and booleans to numeric primitive values. Dates are compared by their
+        // millisecond representations. Note that invalid dates with millisecond representations
+        // of `NaN` are not equivalent.
+        return +a === +b;
+    }
+
+    var areArrays = className === '[object Array]';
+    if (!areArrays) {
+      if (typeof a != 'object' || typeof b != 'object') return false;
+
+      // Objects with different constructors are not equivalent, but `Object`s or `Array`s
+      // from different frames are.
+      var aCtor = a.constructor, bCtor = b.constructor;
+      if (aCtor !== bCtor && !(_.isFunction(aCtor) && aCtor instanceof aCtor &&
+                               _.isFunction(bCtor) && bCtor instanceof bCtor)
+                          && ('constructor' in a && 'constructor' in b)) {
+        return false;
+      }
+    }
+    // Assume equality for cyclic structures. The algorithm for detecting cyclic
+    // structures is adapted from ES 5.1 section 15.12.3, abstract operation `JO`.
+
+    // Initializing stack of traversed objects.
+    // It's done here since we only need them for objects and arrays comparison.
+    aStack = aStack || [];
+    bStack = bStack || [];
+    var length = aStack.length;
+    while (length--) {
+      // Linear search. Performance is inversely proportional to the number of
+      // unique nested structures.
+      if (aStack[length] === a) return bStack[length] === b;
+    }
+
+    // Add the first object to the stack of traversed objects.
+    aStack.push(a);
+    bStack.push(b);
+
+    // Recursively compare objects and arrays.
+    if (areArrays) {
+      // Compare array lengths to determine if a deep comparison is necessary.
+      length = a.length;
+      if (length !== b.length) return false;
+      // Deep compare the contents, ignoring non-numeric properties.
+      while (length--) {
+        if (!eq(a[length], b[length], aStack, bStack)) return false;
+      }
+    } else {
+      // Deep compare objects.
+      var keys = _.keys(a), key;
+      length = keys.length;
+      // Ensure that both objects contain the same number of properties before comparing deep equality.
+      if (_.keys(b).length !== length) return false;
+      while (length--) {
+        // Deep compare each member
+        key = keys[length];
+        if (!(_.has(b, key) && eq(a[key], b[key], aStack, bStack))) return false;
+      }
+    }
+    // Remove the first object from the stack of traversed objects.
+    aStack.pop();
+    bStack.pop();
+    return true;
+  };
+
+  // Perform a deep comparison to check if two objects are equal.
+  _.isEqual = function(a, b) {
+    return eq(a, b);
+  };
+
+  // Is a given array, string, or object empty?
+  // An "empty" object has no enumerable own-properties.
+  _.isEmpty = function(obj) {
+    if (obj == null) return true;
+    if (isArrayLike(obj) && (_.isArray(obj) || _.isString(obj) || _.isArguments(obj))) return obj.length === 0;
+    return _.keys(obj).length === 0;
+  };
+
+  // Is a given value a DOM element?
+  _.isElement = function(obj) {
+    return !!(obj && obj.nodeType === 1);
+  };
+
+  // Is a given value an array?
+  // Delegates to ECMA5's native Array.isArray
+  _.isArray = nativeIsArray || function(obj) {
+    return toString.call(obj) === '[object Array]';
+  };
+
+  // Is a given variable an object?
+  _.isObject = function(obj) {
+    var type = typeof obj;
+    return type === 'function' || type === 'object' && !!obj;
+  };
+
+  // Add some isType methods: isArguments, isFunction, isString, isNumber, isDate, isRegExp, isError.
+  _.each(['Arguments', 'Function', 'String', 'Number', 'Date', 'RegExp', 'Error'], function(name) {
+    _['is' + name] = function(obj) {
+      return toString.call(obj) === '[object ' + name + ']';
+    };
+  });
+
+  // Define a fallback version of the method in browsers (ahem, IE < 9), where
+  // there isn't any inspectable "Arguments" type.
+  if (!_.isArguments(arguments)) {
+    _.isArguments = function(obj) {
+      return _.has(obj, 'callee');
+    };
+  }
+
+  // Optimize `isFunction` if appropriate. Work around some typeof bugs in old v8,
+  // IE 11 (#1621), and in Safari 8 (#1929).
+  if (typeof /./ != 'function' && typeof Int8Array != 'object') {
+    _.isFunction = function(obj) {
+      return typeof obj == 'function' || false;
+    };
+  }
+
+  // Is a given object a finite number?
+  _.isFinite = function(obj) {
+    return isFinite(obj) && !isNaN(parseFloat(obj));
+  };
+
+  // Is the given value `NaN`? (NaN is the only number which does not equal itself).
+  _.isNaN = function(obj) {
+    return _.isNumber(obj) && obj !== +obj;
+  };
+
+  // Is a given value a boolean?
+  _.isBoolean = function(obj) {
+    return obj === true || obj === false || toString.call(obj) === '[object Boolean]';
+  };
+
+  // Is a given value equal to null?
+  _.isNull = function(obj) {
+    return obj === null;
+  };
+
+  // Is a given variable undefined?
+  _.isUndefined = function(obj) {
+    return obj === void 0;
+  };
+
+  // Shortcut function for checking if an object has a given property directly
+  // on itself (in other words, not on a prototype).
+  _.has = function(obj, key) {
+    return obj != null && hasOwnProperty.call(obj, key);
+  };
+
+  // Utility Functions
+  // -----------------
+
+  // Run Underscore.js in *noConflict* mode, returning the `_` variable to its
+  // previous owner. Returns a reference to the Underscore object.
+  _.noConflict = function() {
+    root._ = previousUnderscore;
+    return this;
+  };
+
+  // Keep the identity function around for default iteratees.
+  _.identity = function(value) {
+    return value;
+  };
+
+  // Predicate-generating functions. Often useful outside of Underscore.
+  _.constant = function(value) {
+    return function() {
+      return value;
+    };
+  };
+
+  _.noop = function(){};
+
+  _.property = property;
+
+  // Generates a function for a given object that returns a given property.
+  _.propertyOf = function(obj) {
+    return obj == null ? function(){} : function(key) {
+      return obj[key];
+    };
+  };
+
+  // Returns a predicate for checking whether an object has a given set of
+  // `key:value` pairs.
+  _.matcher = _.matches = function(attrs) {
+    attrs = _.extendOwn({}, attrs);
+    return function(obj) {
+      return _.isMatch(obj, attrs);
+    };
+  };
+
+  // Run a function **n** times.
+  _.times = function(n, iteratee, context) {
+    var accum = Array(Math.max(0, n));
+    iteratee = optimizeCb(iteratee, context, 1);
+    for (var i = 0; i < n; i++) accum[i] = iteratee(i);
+    return accum;
+  };
+
+  // Return a random integer between min and max (inclusive).
+  _.random = function(min, max) {
+    if (max == null) {
+      max = min;
+      min = 0;
+    }
+    return min + Math.floor(Math.random() * (max - min + 1));
+  };
+
+  // A (possibly faster) way to get the current timestamp as an integer.
+  _.now = Date.now || function() {
+    return new Date().getTime();
+  };
+
+   // List of HTML entities for escaping.
+  var escapeMap = {
+    '&': '&amp;',
+    '<': '&lt;',
+    '>': '&gt;',
+    '"': '&quot;',
+    "'": '&#x27;',
+    '`': '&#x60;'
+  };
+  var unescapeMap = _.invert(escapeMap);
+
+  // Functions for escaping and unescaping strings to/from HTML interpolation.
+  var createEscaper = function(map) {
+    var escaper = function(match) {
+      return map[match];
+    };
+    // Regexes for identifying a key that needs to be escaped
+    var source = '(?:' + _.keys(map).join('|') + ')';
+    var testRegexp = RegExp(source);
+    var replaceRegexp = RegExp(source, 'g');
+    return function(string) {
+      string = string == null ? '' : '' + string;
+      return testRegexp.test(string) ? string.replace(replaceRegexp, escaper) : string;
+    };
+  };
+  _.escape = createEscaper(escapeMap);
+  _.unescape = createEscaper(unescapeMap);
+
+  // If the value of the named `property` is a function then invoke it with the
+  // `object` as context; otherwise, return it.
+  _.result = function(object, property, fallback) {
+    var value = object == null ? void 0 : object[property];
+    if (value === void 0) {
+      value = fallback;
+    }
+    return _.isFunction(value) ? value.call(object) : value;
+  };
+
+  // Generate a unique integer id (unique within the entire client session).
+  // Useful for temporary DOM ids.
+  var idCounter = 0;
+  _.uniqueId = function(prefix) {
+    var id = ++idCounter + '';
+    return prefix ? prefix + id : id;
+  };
+
+  // By default, Underscore uses ERB-style template delimiters, change the
+  // following template settings to use alternative delimiters.
+  _.templateSettings = {
+    evaluate    : /<%([\s\S]+?)%>/g,
+    interpolate : /<%=([\s\S]+?)%>/g,
+    escape      : /<%-([\s\S]+?)%>/g
+  };
+
+  // When customizing `templateSettings`, if you don't want to define an
+  // interpolation, evaluation or escaping regex, we need one that is
+  // guaranteed not to match.
+  var noMatch = /(.)^/;
+
+  // Certain characters need to be escaped so that they can be put into a
+  // string literal.
+  var escapes = {
+    "'":      "'",
+    '\\':     '\\',
+    '\r':     'r',
+    '\n':     'n',
+    '\u2028': 'u2028',
+    '\u2029': 'u2029'
+  };
+
+  var escaper = /\\|'|\r|\n|\u2028|\u2029/g;
+
+  var escapeChar = function(match) {
+    return '\\' + escapes[match];
+  };
+
+  // JavaScript micro-templating, similar to John Resig's implementation.
+  // Underscore templating handles arbitrary delimiters, preserves whitespace,
+  // and correctly escapes quotes within interpolated code.
+  // NB: `oldSettings` only exists for backwards compatibility.
+  _.template = function(text, settings, oldSettings) {
+    if (!settings && oldSettings) settings = oldSettings;
+    settings = _.defaults({}, settings, _.templateSettings);
+
+    // Combine delimiters into one regular expression via alternation.
+    var matcher = RegExp([
+      (settings.escape || noMatch).source,
+      (settings.interpolate || noMatch).source,
+      (settings.evaluate || noMatch).source
+    ].join('|') + '|$', 'g');
+
+    // Compile the template source, escaping string literals appropriately.
+    var index = 0;
+    var source = "__p+='";
+    text.replace(matcher, function(match, escape, interpolate, evaluate, offset) {
+      source += text.slice(index, offset).replace(escaper, escapeChar);
+      index = offset + match.length;
+
+      if (escape) {
+        source += "'+\n((__t=(" + escape + "))==null?'':_.escape(__t))+\n'";
+      } else if (interpolate) {
+        source += "'+\n((__t=(" + interpolate + "))==null?'':__t)+\n'";
+      } else if (evaluate) {
+        source += "';\n" + evaluate + "\n__p+='";
+      }
+
+      // Adobe VMs need the match returned to produce the correct offest.
+      return match;
+    });
+    source += "';\n";
+
+    // If a variable is not specified, place data values in local scope.
+    if (!settings.variable) source = 'with(obj||{}){\n' + source + '}\n';
+
+    source = "var __t,__p='',__j=Array.prototype.join," +
+      "print=function(){__p+=__j.call(arguments,'');};\n" +
+      source + 'return __p;\n';
+
+    try {
+      var render = new Function(settings.variable || 'obj', '_', source);
+    } catch (e) {
+      e.source = source;
+      throw e;
+    }
+
+    var template = function(data) {
+      return render.call(this, data, _);
+    };
+
+    // Provide the compiled source as a convenience for precompilation.
+    var argument = settings.variable || 'obj';
+    template.source = 'function(' + argument + '){\n' + source + '}';
+
+    return template;
+  };
+
+  // Add a "chain" function. Start chaining a wrapped Underscore object.
+  _.chain = function(obj) {
+    var instance = _(obj);
+    instance._chain = true;
+    return instance;
+  };
+
+  // OOP
+  // ---------------
+  // If Underscore is called as a function, it returns a wrapped object that
+  // can be used OO-style. This wrapper holds altered versions of all the
+  // underscore functions. Wrapped objects may be chained.
+
+  // Helper function to continue chaining intermediate results.
+  var result = function(instance, obj) {
+    return instance._chain ? _(obj).chain() : obj;
+  };
+
+  // Add your own custom functions to the Underscore object.
+  _.mixin = function(obj) {
+    _.each(_.functions(obj), function(name) {
+      var func = _[name] = obj[name];
+      _.prototype[name] = function() {
+        var args = [this._wrapped];
+        push.apply(args, arguments);
+        return result(this, func.apply(_, args));
+      };
+    });
+  };
+
+  // Add all of the Underscore functions to the wrapper object.
+  _.mixin(_);
+
+  // Add all mutator Array functions to the wrapper.
+  _.each(['pop', 'push', 'reverse', 'shift', 'sort', 'splice', 'unshift'], function(name) {
+    var method = ArrayProto[name];
+    _.prototype[name] = function() {
+      var obj = this._wrapped;
+      method.apply(obj, arguments);
+      if ((name === 'shift' || name === 'splice') && obj.length === 0) delete obj[0];
+      return result(this, obj);
+    };
+  });
+
+  // Add all accessor Array functions to the wrapper.
+  _.each(['concat', 'join', 'slice'], function(name) {
+    var method = ArrayProto[name];
+    _.prototype[name] = function() {
+      return result(this, method.apply(this._wrapped, arguments));
+    };
+  });
+
+  // Extracts the result from a wrapped and chained object.
+  _.prototype.value = function() {
+    return this._wrapped;
+  };
+
+  // Provide unwrapping proxy for some methods used in engine operations
+  // such as arithmetic and JSON stringification.
+  _.prototype.valueOf = _.prototype.toJSON = _.prototype.value;
+
+  _.prototype.toString = function() {
+    return '' + this._wrapped;
+  };
+
+  // AMD registration happens at the end for compatibility with AMD loaders
+  // that may not enforce next-turn semantics on modules. Even though general
+  // practice for AMD registration is to be anonymous, underscore registers
+  // as a named module because, like jQuery, it is a base library that is
+  // popular enough to be bundled in a third party lib, but not be part of
+  // an AMD load request. Those cases could generate an error when an
+  // anonymous define() is called outside of a loader request.
+  if (typeof define === 'function' && define.amd) {
+    define('underscore', [], function() {
+      return _;
+    });
+  }
+}.call(this));
+
+},{}],26:[function(require,module,exports){
+arguments[4][19][0].apply(exports,arguments)
+},{"dup":19}],27:[function(require,module,exports){
+module.exports = function isBuffer(arg) {
+  return arg && typeof arg === 'object'
+    && typeof arg.copy === 'function'
+    && typeof arg.fill === 'function'
+    && typeof arg.readUInt8 === 'function';
+}
+},{}],28:[function(require,module,exports){
+(function (process,global){
+// Copyright Joyent, Inc. and other Node contributors.
+//
+// Permission is hereby granted, free of charge, to any person obtaining a
+// copy of this software and associated documentation files (the
+// "Software"), to deal in the Software without restriction, including
+// without limitation the rights to use, copy, modify, merge, publish,
+// distribute, sublicense, and/or sell copies of the Software, and to permit
+// persons to whom the Software is furnished to do so, subject to the
+// following conditions:
+//
+// The above copyright notice and this permission notice shall be included
+// in all copies or substantial portions of the Software.
+//
+// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
+// OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+// MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN
+// NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
+// DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR
+// OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE
+// USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+var formatRegExp = /%[sdj%]/g;
+exports.format = function(f) {
+  if (!isString(f)) {
+    var objects = [];
+    for (var i = 0; i < arguments.length; i++) {
+      objects.push(inspect(arguments[i]));
+    }
+    return objects.join(' ');
+  }
+
+  var i = 1;
+  var args = arguments;
+  var len = args.length;
+  var str = String(f).replace(formatRegExp, function(x) {
+    if (x === '%%') return '%';
+    if (i >= len) return x;
+    switch (x) {
+      case '%s': return String(args[i++]);
+      case '%d': return Number(args[i++]);
+      case '%j':
+        try {
+          return JSON.stringify(args[i++]);
+        } catch (_) {
+          return '[Circular]';
+        }
+      default:
+        return x;
+    }
+  });
+  for (var x = args[i]; i < len; x = args[++i]) {
+    if (isNull(x) || !isObject(x)) {
+      str += ' ' + x;
+    } else {
+      str += ' ' + inspect(x);
+    }
+  }
+  return str;
+};
+
+
+// Mark that a method should not be used.
+// Returns a modified function which warns once by default.
+// If --no-deprecation is set, then it is a no-op.
+exports.deprecate = function(fn, msg) {
+  // Allow for deprecating things in the process of starting up.
+  if (isUndefined(global.process)) {
+    return function() {
+      return exports.deprecate(fn, msg).apply(this, arguments);
+    };
+  }
+
+  if (process.noDeprecation === true) {
+    return fn;
+  }
+
+  var warned = false;
+  function deprecated() {
+    if (!warned) {
+      if (process.throwDeprecation) {
+        throw new Error(msg);
+      } else if (process.traceDeprecation) {
+        console.trace(msg);
+      } else {
+        console.error(msg);
+      }
+      warned = true;
+    }
+    return fn.apply(this, arguments);
+  }
+
+  return deprecated;
+};
+
+
+var debugs = {};
+var debugEnviron;
+exports.debuglog = function(set) {
+  if (isUndefined(debugEnviron))
+    debugEnviron = process.env.NODE_DEBUG || '';
+  set = set.toUpperCase();
+  if (!debugs[set]) {
+    if (new RegExp('\\b' + set + '\\b', 'i').test(debugEnviron)) {
+      var pid = process.pid;
+      debugs[set] = function() {
+        var msg = exports.format.apply(exports, arguments);
+        console.error('%s %d: %s', set, pid, msg);
+      };
+    } else {
+      debugs[set] = function() {};
+    }
+  }
+  return debugs[set];
+};
+
+
+/**
+ * Echos the value of a value. Trys to print the value out
+ * in the best way possible given the different types.
+ *
+ * @param {Object} obj The object to print out.
+ * @param {Object} opts Optional options object that alters the output.
+ */
+/* legacy: obj, showHidden, depth, colors*/
+function inspect(obj, opts) {
+  // default options
+  var ctx = {
+    seen: [],
+    stylize: stylizeNoColor
+  };
+  // legacy...
+  if (arguments.length >= 3) ctx.depth = arguments[2];
+  if (arguments.length >= 4) ctx.colors = arguments[3];
+  if (isBoolean(opts)) {
+    // legacy...
+    ctx.showHidden = opts;
+  } else if (opts) {
+    // got an "options" object
+    exports._extend(ctx, opts);
+  }
+  // set default options
+  if (isUndefined(ctx.showHidden)) ctx.showHidden = false;
+  if (isUndefined(ctx.depth)) ctx.depth = 2;
+  if (isUndefined(ctx.colors)) ctx.colors = false;
+  if (isUndefined(ctx.customInspect)) ctx.customInspect = true;
+  if (ctx.colors) ctx.stylize = stylizeWithColor;
+  return formatValue(ctx, obj, ctx.depth);
+}
+exports.inspect = inspect;
+
+
+// http://en.wikipedia.org/wiki/ANSI_escape_code#graphics
+inspect.colors = {
+  'bold' : [1, 22],
+  'italic' : [3, 23],
+  'underline' : [4, 24],
+  'inverse' : [7, 27],
+  'white' : [37, 39],
+  'grey' : [90, 39],
+  'black' : [30, 39],
+  'blue' : [34, 39],
+  'cyan' : [36, 39],
+  'green' : [32, 39],
+  'magenta' : [35, 39],
+  'red' : [31, 39],
+  'yellow' : [33, 39]
+};
+
+// Don't use 'blue' not visible on cmd.exe
+inspect.styles = {
+  'special': 'cyan',
+  'number': 'yellow',
+  'boolean': 'yellow',
+  'undefined': 'grey',
+  'null': 'bold',
+  'string': 'green',
+  'date': 'magenta',
+  // "name": intentionally not styling
+  'regexp': 'red'
+};
+
+
+function stylizeWithColor(str, styleType) {
+  var style = inspect.styles[styleType];
+
+  if (style) {
+    return '\u001b[' + inspect.colors[style][0] + 'm' + str +
+           '\u001b[' + inspect.colors[style][1] + 'm';
+  } else {
+    return str;
+  }
+}
+
+
+function stylizeNoColor(str, styleType) {
+  return str;
+}
+
+
+function arrayToHash(array) {
+  var hash = {};
+
+  array.forEach(function(val, idx) {
+    hash[val] = true;
+  });
+
+  return hash;
+}
+
+
+function formatValue(ctx, value, recurseTimes) {
+  // Provide a hook for user-specified inspect functions.
+  // Check that value is an object with an inspect function on it
+  if (ctx.customInspect &&
+      value &&
+      isFunction(value.inspect) &&
+      // Filter out the util module, it's inspect function is special
+      value.inspect !== exports.inspect &&
+      // Also filter out any prototype objects using the circular check.
+      !(value.constructor && value.constructor.prototype === value)) {
+    var ret = value.inspect(recurseTimes, ctx);
+    if (!isString(ret)) {
+      ret = formatValue(ctx, ret, recurseTimes);
+    }
+    return ret;
+  }
+
+  // Primitive types cannot have properties
+  var primitive = formatPrimitive(ctx, value);
+  if (primitive) {
+    return primitive;
+  }
+
+  // Look up the keys of the object.
+  var keys = Object.keys(value);
+  var visibleKeys = arrayToHash(keys);
+
+  if (ctx.showHidden) {
+    keys = Object.getOwnPropertyNames(value);
+  }
+
+  // IE doesn't make error fields non-enumerable
+  // http://msdn.microsoft.com/en-us/library/ie/dww52sbt(v=vs.94).aspx
+  if (isError(value)
+      && (keys.indexOf('message') >= 0 || keys.indexOf('description') >= 0)) {
+    return formatError(value);
+  }
+
+  // Some type of object without properties can be shortcutted.
+  if (keys.length === 0) {
+    if (isFunction(value)) {
+      var name = value.name ? ': ' + value.name : '';
+      return ctx.stylize('[Function' + name + ']', 'special');
+    }
+    if (isRegExp(value)) {
+      return ctx.stylize(RegExp.prototype.toString.call(value), 'regexp');
+    }
+    if (isDate(value)) {
+      return ctx.stylize(Date.prototype.toString.call(value), 'date');
+    }
+    if (isError(value)) {
+      return formatError(value);
+    }
+  }
+
+  var base = '', array = false, braces = ['{', '}'];
+
+  // Make Array say that they are Array
+  if (isArray(value)) {
+    array = true;
+    braces = ['[', ']'];
+  }
+
+  // Make functions say that they are functions
+  if (isFunction(value)) {
+    var n = value.name ? ': ' + value.name : '';
+    base = ' [Function' + n + ']';
+  }
+
+  // Make RegExps say that they are RegExps
+  if (isRegExp(value)) {
+    base = ' ' + RegExp.prototype.toString.call(value);
+  }
+
+  // Make dates with properties first say the date
+  if (isDate(value)) {
+    base = ' ' + Date.prototype.toUTCString.call(value);
+  }
+
+  // Make error with message first say the error
+  if (isError(value)) {
+    base = ' ' + formatError(value);
+  }
+
+  if (keys.length === 0 && (!array || value.length == 0)) {
+    return braces[0] + base + braces[1];
+  }
+
+  if (recurseTimes < 0) {
+    if (isRegExp(value)) {
+      return ctx.stylize(RegExp.prototype.toString.call(value), 'regexp');
+    } else {
+      return ctx.stylize('[Object]', 'special');
+    }
+  }
+
+  ctx.seen.push(value);
+
+  var output;
+  if (array) {
+    output = formatArray(ctx, value, recurseTimes, visibleKeys, keys);
+  } else {
+    output = keys.map(function(key) {
+      return formatProperty(ctx, value, recurseTimes, visibleKeys, key, array);
+    });
+  }
+
+  ctx.seen.pop();
+
+  return reduceToSingleString(output, base, braces);
+}
+
+
+function formatPrimitive(ctx, value) {
+  if (isUndefined(value))
+    return ctx.stylize('undefined', 'undefined');
+  if (isString(value)) {
+    var simple = '\'' + JSON.stringify(value).replace(/^"|"$/g, '')
+                                             .replace(/'/g, "\\'")
+                                             .replace(/\\"/g, '"') + '\'';
+    return ctx.stylize(simple, 'string');
+  }
+  if (isNumber(value))
+    return ctx.stylize('' + value, 'number');
+  if (isBoolean(value))
+    return ctx.stylize('' + value, 'boolean');
+  // For some reason typeof null is "object", so special case here.
+  if (isNull(value))
+    return ctx.stylize('null', 'null');
+}
+
+
+function formatError(value) {
+  return '[' + Error.prototype.toString.call(value) + ']';
+}
+
+
+function formatArray(ctx, value, recurseTimes, visibleKeys, keys) {
+  var output = [];
+  for (var i = 0, l = value.length; i < l; ++i) {
+    if (hasOwnProperty(value, String(i))) {
+      output.push(formatProperty(ctx, value, recurseTimes, visibleKeys,
+          String(i), true));
+    } else {
+      output.push('');
+    }
+  }
+  keys.forEach(function(key) {
+    if (!key.match(/^\d+$/)) {
+      output.push(formatProperty(ctx, value, recurseTimes, visibleKeys,
+          key, true));
+    }
+  });
+  return output;
+}
+
+
+function formatProperty(ctx, value, recurseTimes, visibleKeys, key, array) {
+  var name, str, desc;
+  desc = Object.getOwnPropertyDescriptor(value, key) || { value: value[key] };
+  if (desc.get) {
+    if (desc.set) {
+      str = ctx.stylize('[Getter/Setter]', 'special');
+    } else {
+      str = ctx.stylize('[Getter]', 'special');
+    }
+  } else {
+    if (desc.set) {
+      str = ctx.stylize('[Setter]', 'special');
+    }
+  }
+  if (!hasOwnProperty(visibleKeys, key)) {
+    name = '[' + key + ']';
+  }
+  if (!str) {
+    if (ctx.seen.indexOf(desc.value) < 0) {
+      if (isNull(recurseTimes)) {
+        str = formatValue(ctx, desc.value, null);
+      } else {
+        str = formatValue(ctx, desc.value, recurseTimes - 1);
+      }
+      if (str.indexOf('\n') > -1) {
+        if (array) {
+          str = str.split('\n').map(function(line) {
+            return '  ' + line;
+          }).join('\n').substr(2);
+        } else {
+          str = '\n' + str.split('\n').map(function(line) {
+            return '   ' + line;
+          }).join('\n');
+        }
+      }
+    } else {
+      str = ctx.stylize('[Circular]', 'special');
+    }
+  }
+  if (isUndefined(name)) {
+    if (array && key.match(/^\d+$/)) {
+      return str;
+    }
+    name = JSON.stringify('' + key);
+    if (name.match(/^"([a-zA-Z_][a-zA-Z_0-9]*)"$/)) {
+      name = name.substr(1, name.length - 2);
+      name = ctx.stylize(name, 'name');
+    } else {
+      name = name.replace(/'/g, "\\'")
+                 .replace(/\\"/g, '"')
+                 .replace(/(^"|"$)/g, "'");
+      name = ctx.stylize(name, 'string');
+    }
+  }
+
+  return name + ': ' + str;
+}
+
+
+function reduceToSingleString(output, base, braces) {
+  var numLinesEst = 0;
+  var length = output.reduce(function(prev, cur) {
+    numLinesEst++;
+    if (cur.indexOf('\n') >= 0) numLinesEst++;
+    return prev + cur.replace(/\u001b\[\d\d?m/g, '').length + 1;
+  }, 0);
+
+  if (length > 60) {
+    return braces[0] +
+           (base === '' ? '' : base + '\n ') +
+           ' ' +
+           output.join(',\n  ') +
+           ' ' +
+           braces[1];
+  }
+
+  return braces[0] + base + ' ' + output.join(', ') + ' ' + braces[1];
+}
+
+
+// NOTE: These type checking functions intentionally don't use `instanceof`
+// because it is fragile and can be easily faked with `Object.create()`.
+function isArray(ar) {
+  return Array.isArray(ar);
+}
+exports.isArray = isArray;
+
+function isBoolean(arg) {
+  return typeof arg === 'boolean';
+}
+exports.isBoolean = isBoolean;
+
+function isNull(arg) {
+  return arg === null;
+}
+exports.isNull = isNull;
+
+function isNullOrUndefined(arg) {
+  return arg == null;
+}
+exports.isNullOrUndefined = isNullOrUndefined;
+
+function isNumber(arg) {
+  return typeof arg === 'number';
+}
+exports.isNumber = isNumber;
+
+function isString(arg) {
+  return typeof arg === 'string';
+}
+exports.isString = isString;
+
+function isSymbol(arg) {
+  return typeof arg === 'symbol';
+}
+exports.isSymbol = isSymbol;
+
+function isUndefined(arg) {
+  return arg === void 0;
+}
+exports.isUndefined = isUndefined;
+
+function isRegExp(re) {
+  return isObject(re) && objectToString(re) === '[object RegExp]';
+}
+exports.isRegExp = isRegExp;
+
+function isObject(arg) {
+  return typeof arg === 'object' && arg !== null;
+}
+exports.isObject = isObject;
+
+function isDate(d) {
+  return isObject(d) && objectToString(d) === '[object Date]';
+}
+exports.isDate = isDate;
+
+function isError(e) {
+  return isObject(e) &&
+      (objectToString(e) === '[object Error]' || e instanceof Error);
+}
+exports.isError = isError;
+
+function isFunction(arg) {
+  return typeof arg === 'function';
+}
+exports.isFunction = isFunction;
+
+function isPrimitive(arg) {
+  return arg === null ||
+         typeof arg === 'boolean' ||
+         typeof arg === 'number' ||
+         typeof arg === 'string' ||
+         typeof arg === 'symbol' ||  // ES6 symbol
+         typeof arg === 'undefined';
+}
+exports.isPrimitive = isPrimitive;
+
+exports.isBuffer = require('./support/isBuffer');
+
+function objectToString(o) {
+  return Object.prototype.toString.call(o);
+}
+
+
+function pad(n) {
+  return n < 10 ? '0' + n.toString(10) : n.toString(10);
+}
+
+
+var months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep',
+              'Oct', 'Nov', 'Dec'];
+
+// 26 Feb 16:19:34
+function timestamp() {
+  var d = new Date();
+  var time = [pad(d.getHours()),
+              pad(d.getMinutes()),
+              pad(d.getSeconds())].join(':');
+  return [d.getDate(), months[d.getMonth()], time].join(' ');
+}
+
+
+// log is just a thin wrapper to console.log that prepends a timestamp
+exports.log = function() {
+  console.log('%s - %s', timestamp(), exports.format.apply(exports, arguments));
+};
+
+
+/**
+ * Inherit the prototype methods from one constructor into another.
+ *
+ * The Function.prototype.inherits from lang.js rewritten as a standalone
+ * function (not on Function.prototype). NOTE: If this file is to be loaded
+ * during bootstrapping this function needs to be rewritten using some native
+ * functions as prototype setup using normal JavaScript does not work as
+ * expected during bootstrapping (see mirror.js in r114903).
+ *
+ * @param {function} ctor Constructor function which needs to inherit the
+ *     prototype.
+ * @param {function} superCtor Constructor function to inherit prototype from.
+ */
+exports.inherits = require('inherits');
+
+exports._extend = function(origin, add) {
+  // Don't do anything if add isn't an object
+  if (!add || !isObject(add)) return origin;
+
+  var keys = Object.keys(add);
+  var i = keys.length;
+  while (i--) {
+    origin[keys[i]] = add[keys[i]];
+  }
+  return origin;
+};
+
+function hasOwnProperty(obj, prop) {
+  return Object.prototype.hasOwnProperty.call(obj, prop);
+}
+
+}).call(this,require('_process'),typeof global !== "undefined" ? global : typeof self !== "undefined" ? self : typeof window !== "undefined" ? window : {})
+},{"./support/isBuffer":27,"_process":24,"inherits":26}],29:[function(require,module,exports){
+// Returns a wrapper function that returns a wrapped callback
+// The wrapper function should do some stuff, and return a
+// presumably different callback function.
+// This makes sure that own properties are retained, so that
+// decorations and such are not lost along the way.
+module.exports = wrappy
+function wrappy (fn, cb) {
+  if (fn && cb) return wrappy(fn)(cb)
+
+  if (typeof fn !== 'function')
+    throw new TypeError('need wrapper function')
+
+  Object.keys(fn).forEach(function (k) {
+    wrapper[k] = fn[k]
+  })
+
+  return wrapper
+
+  function wrapper() {
+    var args = new Array(arguments.length)
+    for (var i = 0; i < args.length; i++) {
+      args[i] = arguments[i]
+    }
+    var ret = fn.apply(this, args)
+    var cb = args[args.length-1]
+    if (typeof ret === 'function' && ret !== cb) {
+      Object.keys(cb).forEach(function (k) {
+        ret[k] = cb[k]
+      })
+    }
+    return ret
+  }
+}
+
+},{}]},{},[7])(7)
+});
\ No newline at end of file
diff --git a/assets/javascripts/workers/search.ecf98df9.min.js b/assets/javascripts/workers/search.ecf98df9.min.js
new file mode 100644
index 0000000..f8c7399
--- /dev/null
+++ b/assets/javascripts/workers/search.ecf98df9.min.js
@@ -0,0 +1,48 @@
+"use strict";(()=>{var ge=Object.create;var W=Object.defineProperty,ye=Object.defineProperties,me=Object.getOwnPropertyDescriptor,ve=Object.getOwnPropertyDescriptors,xe=Object.getOwnPropertyNames,G=Object.getOwnPropertySymbols,Se=Object.getPrototypeOf,X=Object.prototype.hasOwnProperty,Qe=Object.prototype.propertyIsEnumerable;var J=(t,e,r)=>e in t?W(t,e,{enumerable:!0,configurable:!0,writable:!0,value:r}):t[e]=r,M=(t,e)=>{for(var r in e||(e={}))X.call(e,r)&&J(t,r,e[r]);if(G)for(var r of G(e))Qe.call(e,r)&&J(t,r,e[r]);return t},Z=(t,e)=>ye(t,ve(e));var K=(t,e)=>()=>(e||t((e={exports:{}}).exports,e),e.exports);var be=(t,e,r,n)=>{if(e&&typeof e=="object"||typeof e=="function")for(let i of xe(e))!X.call(t,i)&&i!==r&&W(t,i,{get:()=>e[i],enumerable:!(n=me(e,i))||n.enumerable});return t};var H=(t,e,r)=>(r=t!=null?ge(Se(t)):{},be(e||!t||!t.__esModule?W(r,"default",{value:t,enumerable:!0}):r,t));var z=(t,e,r)=>new Promise((n,i)=>{var s=u=>{try{a(r.next(u))}catch(c){i(c)}},o=u=>{try{a(r.throw(u))}catch(c){i(c)}},a=u=>u.done?n(u.value):Promise.resolve(u.value).then(s,o);a((r=r.apply(t,e)).next())});var re=K((ee,te)=>{/**
+ * lunr - http://lunrjs.com - A bit like Solr, but much smaller and not as bright - 2.3.9
+ * Copyright (C) 2020 Oliver Nightingale
+ * @license MIT
+ */(function(){var t=function(e){var r=new t.Builder;return r.pipeline.add(t.trimmer,t.stopWordFilter,t.stemmer),r.searchPipeline.add(t.stemmer),e.call(r,r),r.build()};t.version="2.3.9";/*!
+ * lunr.utils
+ * Copyright (C) 2020 Oliver Nightingale
+ */t.utils={},t.utils.warn=function(e){return function(r){e.console&&console.warn&&console.warn(r)}}(this),t.utils.asString=function(e){return e==null?"":e.toString()},t.utils.clone=function(e){if(e==null)return e;for(var r=Object.create(null),n=Object.keys(e),i=0;i<n.length;i++){var s=n[i],o=e[s];if(Array.isArray(o)){r[s]=o.slice();continue}if(typeof o=="string"||typeof o=="number"||typeof o=="boolean"){r[s]=o;continue}throw new TypeError("clone is not deep and does not support nested objects")}return r},t.FieldRef=function(e,r,n){this.docRef=e,this.fieldName=r,this._stringValue=n},t.FieldRef.joiner="/",t.FieldRef.fromString=function(e){var r=e.indexOf(t.FieldRef.joiner);if(r===-1)throw"malformed field ref string";var n=e.slice(0,r),i=e.slice(r+1);return new t.FieldRef(i,n,e)},t.FieldRef.prototype.toString=function(){return this._stringValue==null&&(this._stringValue=this.fieldName+t.FieldRef.joiner+this.docRef),this._stringValue};/*!
+ * lunr.Set
+ * Copyright (C) 2020 Oliver Nightingale
+ */t.Set=function(e){if(this.elements=Object.create(null),e){this.length=e.length;for(var r=0;r<this.length;r++)this.elements[e[r]]=!0}else this.length=0},t.Set.complete={intersect:function(e){return e},union:function(){return this},contains:function(){return!0}},t.Set.empty={intersect:function(){return this},union:function(e){return e},contains:function(){return!1}},t.Set.prototype.contains=function(e){return!!this.elements[e]},t.Set.prototype.intersect=function(e){var r,n,i,s=[];if(e===t.Set.complete)return this;if(e===t.Set.empty)return e;this.length<e.length?(r=this,n=e):(r=e,n=this),i=Object.keys(r.elements);for(var o=0;o<i.length;o++){var a=i[o];a in n.elements&&s.push(a)}return new t.Set(s)},t.Set.prototype.union=function(e){return e===t.Set.complete?t.Set.complete:e===t.Set.empty?this:new t.Set(Object.keys(this.elements).concat(Object.keys(e.elements)))},t.idf=function(e,r){var n=0;for(var i in e)i!="_index"&&(n+=Object.keys(e[i]).length);var s=(r-n+.5)/(n+.5);return Math.log(1+Math.abs(s))},t.Token=function(e,r){this.str=e||"",this.metadata=r||{}},t.Token.prototype.toString=function(){return this.str},t.Token.prototype.update=function(e){return this.str=e(this.str,this.metadata),this},t.Token.prototype.clone=function(e){return e=e||function(r){return r},new t.Token(e(this.str,this.metadata),this.metadata)};/*!
+ * lunr.tokenizer
+ * Copyright (C) 2020 Oliver Nightingale
+ */t.tokenizer=function(e,r){if(e==null||e==null)return[];if(Array.isArray(e))return e.map(function(y){return new t.Token(t.utils.asString(y).toLowerCase(),t.utils.clone(r))});for(var n=e.toString().toLowerCase(),i=n.length,s=[],o=0,a=0;o<=i;o++){var u=n.charAt(o),c=o-a;if(u.match(t.tokenizer.separator)||o==i){if(c>0){var h=t.utils.clone(r)||{};h.position=[a,c],h.index=s.length,s.push(new t.Token(n.slice(a,o),h))}a=o+1}}return s},t.tokenizer.separator=/[\s\-]+/;/*!
+ * lunr.Pipeline
+ * Copyright (C) 2020 Oliver Nightingale
+ */t.Pipeline=function(){this._stack=[]},t.Pipeline.registeredFunctions=Object.create(null),t.Pipeline.registerFunction=function(e,r){r in this.registeredFunctions&&t.utils.warn("Overwriting existing registered function: "+r),e.label=r,t.Pipeline.registeredFunctions[e.label]=e},t.Pipeline.warnIfFunctionNotRegistered=function(e){var r=e.label&&e.label in this.registeredFunctions;r||t.utils.warn(`Function is not registered with pipeline. This may cause problems when serialising the index.
+`,e)},t.Pipeline.load=function(e){var r=new t.Pipeline;return e.forEach(function(n){var i=t.Pipeline.registeredFunctions[n];if(i)r.add(i);else throw new Error("Cannot load unregistered function: "+n)}),r},t.Pipeline.prototype.add=function(){var e=Array.prototype.slice.call(arguments);e.forEach(function(r){t.Pipeline.warnIfFunctionNotRegistered(r),this._stack.push(r)},this)},t.Pipeline.prototype.after=function(e,r){t.Pipeline.warnIfFunctionNotRegistered(r);var n=this._stack.indexOf(e);if(n==-1)throw new Error("Cannot find existingFn");n=n+1,this._stack.splice(n,0,r)},t.Pipeline.prototype.before=function(e,r){t.Pipeline.warnIfFunctionNotRegistered(r);var n=this._stack.indexOf(e);if(n==-1)throw new Error("Cannot find existingFn");this._stack.splice(n,0,r)},t.Pipeline.prototype.remove=function(e){var r=this._stack.indexOf(e);r!=-1&&this._stack.splice(r,1)},t.Pipeline.prototype.run=function(e){for(var r=this._stack.length,n=0;n<r;n++){for(var i=this._stack[n],s=[],o=0;o<e.length;o++){var a=i(e[o],o,e);if(!(a==null||a===""))if(Array.isArray(a))for(var u=0;u<a.length;u++)s.push(a[u]);else s.push(a)}e=s}return e},t.Pipeline.prototype.runString=function(e,r){var n=new t.Token(e,r);return this.run([n]).map(function(i){return i.toString()})},t.Pipeline.prototype.reset=function(){this._stack=[]},t.Pipeline.prototype.toJSON=function(){return this._stack.map(function(e){return t.Pipeline.warnIfFunctionNotRegistered(e),e.label})};/*!
+ * lunr.Vector
+ * Copyright (C) 2020 Oliver Nightingale
+ */t.Vector=function(e){this._magnitude=0,this.elements=e||[]},t.Vector.prototype.positionForIndex=function(e){if(this.elements.length==0)return 0;for(var r=0,n=this.elements.length/2,i=n-r,s=Math.floor(i/2),o=this.elements[s*2];i>1&&(o<e&&(r=s),o>e&&(n=s),o!=e);)i=n-r,s=r+Math.floor(i/2),o=this.elements[s*2];if(o==e||o>e)return s*2;if(o<e)return(s+1)*2},t.Vector.prototype.insert=function(e,r){this.upsert(e,r,function(){throw"duplicate index"})},t.Vector.prototype.upsert=function(e,r,n){this._magnitude=0;var i=this.positionForIndex(e);this.elements[i]==e?this.elements[i+1]=n(this.elements[i+1],r):this.elements.splice(i,0,e,r)},t.Vector.prototype.magnitude=function(){if(this._magnitude)return this._magnitude;for(var e=0,r=this.elements.length,n=1;n<r;n+=2){var i=this.elements[n];e+=i*i}return this._magnitude=Math.sqrt(e)},t.Vector.prototype.dot=function(e){for(var r=0,n=this.elements,i=e.elements,s=n.length,o=i.length,a=0,u=0,c=0,h=0;c<s&&h<o;)a=n[c],u=i[h],a<u?c+=2:a>u?h+=2:a==u&&(r+=n[c+1]*i[h+1],c+=2,h+=2);return r},t.Vector.prototype.similarity=function(e){return this.dot(e)/this.magnitude()||0},t.Vector.prototype.toArray=function(){for(var e=new Array(this.elements.length/2),r=1,n=0;r<this.elements.length;r+=2,n++)e[n]=this.elements[r];return e},t.Vector.prototype.toJSON=function(){return this.elements};/*!
+ * lunr.stemmer
+ * Copyright (C) 2020 Oliver Nightingale
+ * Includes code from - http://tartarus.org/~martin/PorterStemmer/js.txt
+ */t.stemmer=function(){var e={ational:"ate",tional:"tion",enci:"ence",anci:"ance",izer:"ize",bli:"ble",alli:"al",entli:"ent",eli:"e",ousli:"ous",ization:"ize",ation:"ate",ator:"ate",alism:"al",iveness:"ive",fulness:"ful",ousness:"ous",aliti:"al",iviti:"ive",biliti:"ble",logi:"log"},r={icate:"ic",ative:"",alize:"al",iciti:"ic",ical:"ic",ful:"",ness:""},n="[^aeiou]",i="[aeiouy]",s=n+"[^aeiouy]*",o=i+"[aeiou]*",a="^("+s+")?"+o+s,u="^("+s+")?"+o+s+"("+o+")?$",c="^("+s+")?"+o+s+o+s,h="^("+s+")?"+i,y=new RegExp(a),g=new RegExp(c),b=new RegExp(u),m=new RegExp(h),Q=/^(.+?)(ss|i)es$/,p=/^(.+?)([^s])s$/,d=/^(.+?)eed$/,w=/^(.+?)(ed|ing)$/,L=/.$/,k=/(at|bl|iz)$/,I=new RegExp("([^aeiouylsz])\\1$"),j=new RegExp("^"+s+i+"[^aeiouwxy]$"),F=/^(.+?[^aeiou])y$/,A=/^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/,N=/^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/,C=/^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/,$=/^(.+?)(s|t)(ion)$/,T=/^(.+?)e$/,V=/ll$/,B=new RegExp("^"+s+i+"[^aeiouwxy]$"),D=function(l){var v,P,E,f,x,R,_;if(l.length<3)return l;if(E=l.substr(0,1),E=="y"&&(l=E.toUpperCase()+l.substr(1)),f=Q,x=p,f.test(l)?l=l.replace(f,"$1$2"):x.test(l)&&(l=l.replace(x,"$1$2")),f=d,x=w,f.test(l)){var S=f.exec(l);f=y,f.test(S[1])&&(f=L,l=l.replace(f,""))}else if(x.test(l)){var S=x.exec(l);v=S[1],x=m,x.test(v)&&(l=v,x=k,R=I,_=j,x.test(l)?l=l+"e":R.test(l)?(f=L,l=l.replace(f,"")):_.test(l)&&(l=l+"e"))}if(f=F,f.test(l)){var S=f.exec(l);v=S[1],l=v+"i"}if(f=A,f.test(l)){var S=f.exec(l);v=S[1],P=S[2],f=y,f.test(v)&&(l=v+e[P])}if(f=N,f.test(l)){var S=f.exec(l);v=S[1],P=S[2],f=y,f.test(v)&&(l=v+r[P])}if(f=C,x=$,f.test(l)){var S=f.exec(l);v=S[1],f=g,f.test(v)&&(l=v)}else if(x.test(l)){var S=x.exec(l);v=S[1]+S[2],x=g,x.test(v)&&(l=v)}if(f=T,f.test(l)){var S=f.exec(l);v=S[1],f=g,x=b,R=B,(f.test(v)||x.test(v)&&!R.test(v))&&(l=v)}return f=V,x=g,f.test(l)&&x.test(l)&&(f=L,l=l.replace(f,"")),E=="y"&&(l=E.toLowerCase()+l.substr(1)),l};return function(O){return O.update(D)}}(),t.Pipeline.registerFunction(t.stemmer,"stemmer");/*!
+ * lunr.stopWordFilter
+ * Copyright (C) 2020 Oliver Nightingale
+ */t.generateStopWordFilter=function(e){var r=e.reduce(function(n,i){return n[i]=i,n},{});return function(n){if(n&&r[n.toString()]!==n.toString())return n}},t.stopWordFilter=t.generateStopWordFilter(["a","able","about","across","after","all","almost","also","am","among","an","and","any","are","as","at","be","because","been","but","by","can","cannot","could","dear","did","do","does","either","else","ever","every","for","from","get","got","had","has","have","he","her","hers","him","his","how","however","i","if","in","into","is","it","its","just","least","let","like","likely","may","me","might","most","must","my","neither","no","nor","not","of","off","often","on","only","or","other","our","own","rather","said","say","says","she","should","since","so","some","than","that","the","their","them","then","there","these","they","this","tis","to","too","twas","us","wants","was","we","were","what","when","where","which","while","who","whom","why","will","with","would","yet","you","your"]),t.Pipeline.registerFunction(t.stopWordFilter,"stopWordFilter");/*!
+ * lunr.trimmer
+ * Copyright (C) 2020 Oliver Nightingale
+ */t.trimmer=function(e){return e.update(function(r){return r.replace(/^\W+/,"").replace(/\W+$/,"")})},t.Pipeline.registerFunction(t.trimmer,"trimmer");/*!
+ * lunr.TokenSet
+ * Copyright (C) 2020 Oliver Nightingale
+ */t.TokenSet=function(){this.final=!1,this.edges={},this.id=t.TokenSet._nextId,t.TokenSet._nextId+=1},t.TokenSet._nextId=1,t.TokenSet.fromArray=function(e){for(var r=new t.TokenSet.Builder,n=0,i=e.length;n<i;n++)r.insert(e[n]);return r.finish(),r.root},t.TokenSet.fromClause=function(e){return"editDistance"in e?t.TokenSet.fromFuzzyString(e.term,e.editDistance):t.TokenSet.fromString(e.term)},t.TokenSet.fromFuzzyString=function(e,r){for(var n=new t.TokenSet,i=[{node:n,editsRemaining:r,str:e}];i.length;){var s=i.pop();if(s.str.length>0){var o=s.str.charAt(0),a;o in s.node.edges?a=s.node.edges[o]:(a=new t.TokenSet,s.node.edges[o]=a),s.str.length==1&&(a.final=!0),i.push({node:a,editsRemaining:s.editsRemaining,str:s.str.slice(1)})}if(s.editsRemaining!=0){if("*"in s.node.edges)var u=s.node.edges["*"];else{var u=new t.TokenSet;s.node.edges["*"]=u}if(s.str.length==0&&(u.final=!0),i.push({node:u,editsRemaining:s.editsRemaining-1,str:s.str}),s.str.length>1&&i.push({node:s.node,editsRemaining:s.editsRemaining-1,str:s.str.slice(1)}),s.str.length==1&&(s.node.final=!0),s.str.length>=1){if("*"in s.node.edges)var c=s.node.edges["*"];else{var c=new t.TokenSet;s.node.edges["*"]=c}s.str.length==1&&(c.final=!0),i.push({node:c,editsRemaining:s.editsRemaining-1,str:s.str.slice(1)})}if(s.str.length>1){var h=s.str.charAt(0),y=s.str.charAt(1),g;y in s.node.edges?g=s.node.edges[y]:(g=new t.TokenSet,s.node.edges[y]=g),s.str.length==1&&(g.final=!0),i.push({node:g,editsRemaining:s.editsRemaining-1,str:h+s.str.slice(2)})}}}return n},t.TokenSet.fromString=function(e){for(var r=new t.TokenSet,n=r,i=0,s=e.length;i<s;i++){var o=e[i],a=i==s-1;if(o=="*")r.edges[o]=r,r.final=a;else{var u=new t.TokenSet;u.final=a,r.edges[o]=u,r=u}}return n},t.TokenSet.prototype.toArray=function(){for(var e=[],r=[{prefix:"",node:this}];r.length;){var n=r.pop(),i=Object.keys(n.node.edges),s=i.length;n.node.final&&(n.prefix.charAt(0),e.push(n.prefix));for(var o=0;o<s;o++){var a=i[o];r.push({prefix:n.prefix.concat(a),node:n.node.edges[a]})}}return e},t.TokenSet.prototype.toString=function(){if(this._str)return this._str;for(var e=this.final?"1":"0",r=Object.keys(this.edges).sort(),n=r.length,i=0;i<n;i++){var s=r[i],o=this.edges[s];e=e+s+o.id}return e},t.TokenSet.prototype.intersect=function(e){for(var r=new t.TokenSet,n=void 0,i=[{qNode:e,output:r,node:this}];i.length;){n=i.pop();for(var s=Object.keys(n.qNode.edges),o=s.length,a=Object.keys(n.node.edges),u=a.length,c=0;c<o;c++)for(var h=s[c],y=0;y<u;y++){var g=a[y];if(g==h||h=="*"){var b=n.node.edges[g],m=n.qNode.edges[h],Q=b.final&&m.final,p=void 0;g in n.output.edges?(p=n.output.edges[g],p.final=p.final||Q):(p=new t.TokenSet,p.final=Q,n.output.edges[g]=p),i.push({qNode:m,output:p,node:b})}}}return r},t.TokenSet.Builder=function(){this.previousWord="",this.root=new t.TokenSet,this.uncheckedNodes=[],this.minimizedNodes={}},t.TokenSet.Builder.prototype.insert=function(e){var r,n=0;if(e<this.previousWord)throw new Error("Out of order word insertion");for(var i=0;i<e.length&&i<this.previousWord.length&&e[i]==this.previousWord[i];i++)n++;this.minimize(n),this.uncheckedNodes.length==0?r=this.root:r=this.uncheckedNodes[this.uncheckedNodes.length-1].child;for(var i=n;i<e.length;i++){var s=new t.TokenSet,o=e[i];r.edges[o]=s,this.uncheckedNodes.push({parent:r,char:o,child:s}),r=s}r.final=!0,this.previousWord=e},t.TokenSet.Builder.prototype.finish=function(){this.minimize(0)},t.TokenSet.Builder.prototype.minimize=function(e){for(var r=this.uncheckedNodes.length-1;r>=e;r--){var n=this.uncheckedNodes[r],i=n.child.toString();i in this.minimizedNodes?n.parent.edges[n.char]=this.minimizedNodes[i]:(n.child._str=i,this.minimizedNodes[i]=n.child),this.uncheckedNodes.pop()}};/*!
+ * lunr.Index
+ * Copyright (C) 2020 Oliver Nightingale
+ */t.Index=function(e){this.invertedIndex=e.invertedIndex,this.fieldVectors=e.fieldVectors,this.tokenSet=e.tokenSet,this.fields=e.fields,this.pipeline=e.pipeline},t.Index.prototype.search=function(e){return this.query(function(r){var n=new t.QueryParser(e,r);n.parse()})},t.Index.prototype.query=function(e){for(var r=new t.Query(this.fields),n=Object.create(null),i=Object.create(null),s=Object.create(null),o=Object.create(null),a=Object.create(null),u=0;u<this.fields.length;u++)i[this.fields[u]]=new t.Vector;e.call(r,r);for(var u=0;u<r.clauses.length;u++){var c=r.clauses[u],h=null,y=t.Set.empty;c.usePipeline?h=this.pipeline.runString(c.term,{fields:c.fields}):h=[c.term];for(var g=0;g<h.length;g++){var b=h[g];c.term=b;var m=t.TokenSet.fromClause(c),Q=this.tokenSet.intersect(m).toArray();if(Q.length===0&&c.presence===t.Query.presence.REQUIRED){for(var p=0;p<c.fields.length;p++){var d=c.fields[p];o[d]=t.Set.empty}break}for(var w=0;w<Q.length;w++)for(var L=Q[w],k=this.invertedIndex[L],I=k._index,p=0;p<c.fields.length;p++){var d=c.fields[p],j=k[d],F=Object.keys(j),A=L+"/"+d,N=new t.Set(F);if(c.presence==t.Query.presence.REQUIRED&&(y=y.union(N),o[d]===void 0&&(o[d]=t.Set.complete)),c.presence==t.Query.presence.PROHIBITED){a[d]===void 0&&(a[d]=t.Set.empty),a[d]=a[d].union(N);continue}if(i[d].upsert(I,c.boost,function(pe,de){return pe+de}),!s[A]){for(var C=0;C<F.length;C++){var $=F[C],T=new t.FieldRef($,d),V=j[$],B;(B=n[T])===void 0?n[T]=new t.MatchData(L,d,V):B.add(L,d,V)}s[A]=!0}}}if(c.presence===t.Query.presence.REQUIRED)for(var p=0;p<c.fields.length;p++){var d=c.fields[p];o[d]=o[d].intersect(y)}}for(var D=t.Set.complete,O=t.Set.empty,u=0;u<this.fields.length;u++){var d=this.fields[u];o[d]&&(D=D.intersect(o[d])),a[d]&&(O=O.union(a[d]))}var l=Object.keys(n),v=[],P=Object.create(null);if(r.isNegated()){l=Object.keys(this.fieldVectors);for(var u=0;u<l.length;u++){var T=l[u],E=t.FieldRef.fromString(T);n[T]=new t.MatchData}}for(var u=0;u<l.length;u++){var E=t.FieldRef.fromString(l[u]),f=E.docRef;if(!!D.contains(f)&&!O.contains(f)){var x=this.fieldVectors[E],R=i[E.fieldName].similarity(x),_;if((_=P[f])!==void 0)_.score+=R,_.matchData.combine(n[E]);else{var S={ref:f,score:R,matchData:n[E]};P[f]=S,v.push(S)}}}return v.sort(function(he,fe){return fe.score-he.score})},t.Index.prototype.toJSON=function(){var e=Object.keys(this.invertedIndex).sort().map(function(n){return[n,this.invertedIndex[n]]},this),r=Object.keys(this.fieldVectors).map(function(n){return[n,this.fieldVectors[n].toJSON()]},this);return{version:t.version,fields:this.fields,fieldVectors:r,invertedIndex:e,pipeline:this.pipeline.toJSON()}},t.Index.load=function(e){var r={},n={},i=e.fieldVectors,s=Object.create(null),o=e.invertedIndex,a=new t.TokenSet.Builder,u=t.Pipeline.load(e.pipeline);e.version!=t.version&&t.utils.warn("Version mismatch when loading serialised index. Current version of lunr '"+t.version+"' does not match serialized index '"+e.version+"'");for(var c=0;c<i.length;c++){var h=i[c],y=h[0],g=h[1];n[y]=new t.Vector(g)}for(var c=0;c<o.length;c++){var h=o[c],b=h[0],m=h[1];a.insert(b),s[b]=m}return a.finish(),r.fields=e.fields,r.fieldVectors=n,r.invertedIndex=s,r.tokenSet=a.root,r.pipeline=u,new t.Index(r)};/*!
+ * lunr.Builder
+ * Copyright (C) 2020 Oliver Nightingale
+ */t.Builder=function(){this._ref="id",this._fields=Object.create(null),this._documents=Object.create(null),this.invertedIndex=Object.create(null),this.fieldTermFrequencies={},this.fieldLengths={},this.tokenizer=t.tokenizer,this.pipeline=new t.Pipeline,this.searchPipeline=new t.Pipeline,this.documentCount=0,this._b=.75,this._k1=1.2,this.termIndex=0,this.metadataWhitelist=[]},t.Builder.prototype.ref=function(e){this._ref=e},t.Builder.prototype.field=function(e,r){if(/\//.test(e))throw new RangeError("Field '"+e+"' contains illegal character '/'");this._fields[e]=r||{}},t.Builder.prototype.b=function(e){e<0?this._b=0:e>1?this._b=1:this._b=e},t.Builder.prototype.k1=function(e){this._k1=e},t.Builder.prototype.add=function(e,r){var n=e[this._ref],i=Object.keys(this._fields);this._documents[n]=r||{},this.documentCount+=1;for(var s=0;s<i.length;s++){var o=i[s],a=this._fields[o].extractor,u=a?a(e):e[o],c=this.tokenizer(u,{fields:[o]}),h=this.pipeline.run(c),y=new t.FieldRef(n,o),g=Object.create(null);this.fieldTermFrequencies[y]=g,this.fieldLengths[y]=0,this.fieldLengths[y]+=h.length;for(var b=0;b<h.length;b++){var m=h[b];if(g[m]==null&&(g[m]=0),g[m]+=1,this.invertedIndex[m]==null){var Q=Object.create(null);Q._index=this.termIndex,this.termIndex+=1;for(var p=0;p<i.length;p++)Q[i[p]]=Object.create(null);this.invertedIndex[m]=Q}this.invertedIndex[m][o][n]==null&&(this.invertedIndex[m][o][n]=Object.create(null));for(var d=0;d<this.metadataWhitelist.length;d++){var w=this.metadataWhitelist[d],L=m.metadata[w];this.invertedIndex[m][o][n][w]==null&&(this.invertedIndex[m][o][n][w]=[]),this.invertedIndex[m][o][n][w].push(L)}}}},t.Builder.prototype.calculateAverageFieldLengths=function(){for(var e=Object.keys(this.fieldLengths),r=e.length,n={},i={},s=0;s<r;s++){var o=t.FieldRef.fromString(e[s]),a=o.fieldName;i[a]||(i[a]=0),i[a]+=1,n[a]||(n[a]=0),n[a]+=this.fieldLengths[o]}for(var u=Object.keys(this._fields),s=0;s<u.length;s++){var c=u[s];n[c]=n[c]/i[c]}this.averageFieldLength=n},t.Builder.prototype.createFieldVectors=function(){for(var e={},r=Object.keys(this.fieldTermFrequencies),n=r.length,i=Object.create(null),s=0;s<n;s++){for(var o=t.FieldRef.fromString(r[s]),a=o.fieldName,u=this.fieldLengths[o],c=new t.Vector,h=this.fieldTermFrequencies[o],y=Object.keys(h),g=y.length,b=this._fields[a].boost||1,m=this._documents[o.docRef].boost||1,Q=0;Q<g;Q++){var p=y[Q],d=h[p],w=this.invertedIndex[p]._index,L,k,I;i[p]===void 0?(L=t.idf(this.invertedIndex[p],this.documentCount),i[p]=L):L=i[p],k=L*((this._k1+1)*d)/(this._k1*(1-this._b+this._b*(u/this.averageFieldLength[a]))+d),k*=b,k*=m,I=Math.round(k*1e3)/1e3,c.insert(w,I)}e[o]=c}this.fieldVectors=e},t.Builder.prototype.createTokenSet=function(){this.tokenSet=t.TokenSet.fromArray(Object.keys(this.invertedIndex).sort())},t.Builder.prototype.build=function(){return this.calculateAverageFieldLengths(),this.createFieldVectors(),this.createTokenSet(),new t.Index({invertedIndex:this.invertedIndex,fieldVectors:this.fieldVectors,tokenSet:this.tokenSet,fields:Object.keys(this._fields),pipeline:this.searchPipeline})},t.Builder.prototype.use=function(e){var r=Array.prototype.slice.call(arguments,1);r.unshift(this),e.apply(this,r)},t.MatchData=function(e,r,n){for(var i=Object.create(null),s=Object.keys(n||{}),o=0;o<s.length;o++){var a=s[o];i[a]=n[a].slice()}this.metadata=Object.create(null),e!==void 0&&(this.metadata[e]=Object.create(null),this.metadata[e][r]=i)},t.MatchData.prototype.combine=function(e){for(var r=Object.keys(e.metadata),n=0;n<r.length;n++){var i=r[n],s=Object.keys(e.metadata[i]);this.metadata[i]==null&&(this.metadata[i]=Object.create(null));for(var o=0;o<s.length;o++){var a=s[o],u=Object.keys(e.metadata[i][a]);this.metadata[i][a]==null&&(this.metadata[i][a]=Object.create(null));for(var c=0;c<u.length;c++){var h=u[c];this.metadata[i][a][h]==null?this.metadata[i][a][h]=e.metadata[i][a][h]:this.metadata[i][a][h]=this.metadata[i][a][h].concat(e.metadata[i][a][h])}}}},t.MatchData.prototype.add=function(e,r,n){if(!(e in this.metadata)){this.metadata[e]=Object.create(null),this.metadata[e][r]=n;return}if(!(r in this.metadata[e])){this.metadata[e][r]=n;return}for(var i=Object.keys(n),s=0;s<i.length;s++){var o=i[s];o in this.metadata[e][r]?this.metadata[e][r][o]=this.metadata[e][r][o].concat(n[o]):this.metadata[e][r][o]=n[o]}},t.Query=function(e){this.clauses=[],this.allFields=e},t.Query.wildcard=new String("*"),t.Query.wildcard.NONE=0,t.Query.wildcard.LEADING=1,t.Query.wildcard.TRAILING=2,t.Query.presence={OPTIONAL:1,REQUIRED:2,PROHIBITED:3},t.Query.prototype.clause=function(e){return"fields"in e||(e.fields=this.allFields),"boost"in e||(e.boost=1),"usePipeline"in e||(e.usePipeline=!0),"wildcard"in e||(e.wildcard=t.Query.wildcard.NONE),e.wildcard&t.Query.wildcard.LEADING&&e.term.charAt(0)!=t.Query.wildcard&&(e.term="*"+e.term),e.wildcard&t.Query.wildcard.TRAILING&&e.term.slice(-1)!=t.Query.wildcard&&(e.term=""+e.term+"*"),"presence"in e||(e.presence=t.Query.presence.OPTIONAL),this.clauses.push(e),this},t.Query.prototype.isNegated=function(){for(var e=0;e<this.clauses.length;e++)if(this.clauses[e].presence!=t.Query.presence.PROHIBITED)return!1;return!0},t.Query.prototype.term=function(e,r){if(Array.isArray(e))return e.forEach(function(i){this.term(i,t.utils.clone(r))},this),this;var n=r||{};return n.term=e.toString(),this.clause(n),this},t.QueryParseError=function(e,r,n){this.name="QueryParseError",this.message=e,this.start=r,this.end=n},t.QueryParseError.prototype=new Error,t.QueryLexer=function(e){this.lexemes=[],this.str=e,this.length=e.length,this.pos=0,this.start=0,this.escapeCharPositions=[]},t.QueryLexer.prototype.run=function(){for(var e=t.QueryLexer.lexText;e;)e=e(this)},t.QueryLexer.prototype.sliceString=function(){for(var e=[],r=this.start,n=this.pos,i=0;i<this.escapeCharPositions.length;i++)n=this.escapeCharPositions[i],e.push(this.str.slice(r,n)),r=n+1;return e.push(this.str.slice(r,this.pos)),this.escapeCharPositions.length=0,e.join("")},t.QueryLexer.prototype.emit=function(e){this.lexemes.push({type:e,str:this.sliceString(),start:this.start,end:this.pos}),this.start=this.pos},t.QueryLexer.prototype.escapeCharacter=function(){this.escapeCharPositions.push(this.pos-1),this.pos+=1},t.QueryLexer.prototype.next=function(){if(this.pos>=this.length)return t.QueryLexer.EOS;var e=this.str.charAt(this.pos);return this.pos+=1,e},t.QueryLexer.prototype.width=function(){return this.pos-this.start},t.QueryLexer.prototype.ignore=function(){this.start==this.pos&&(this.pos+=1),this.start=this.pos},t.QueryLexer.prototype.backup=function(){this.pos-=1},t.QueryLexer.prototype.acceptDigitRun=function(){var e,r;do e=this.next(),r=e.charCodeAt(0);while(r>47&&r<58);e!=t.QueryLexer.EOS&&this.backup()},t.QueryLexer.prototype.more=function(){return this.pos<this.length},t.QueryLexer.EOS="EOS",t.QueryLexer.FIELD="FIELD",t.QueryLexer.TERM="TERM",t.QueryLexer.EDIT_DISTANCE="EDIT_DISTANCE",t.QueryLexer.BOOST="BOOST",t.QueryLexer.PRESENCE="PRESENCE",t.QueryLexer.lexField=function(e){return e.backup(),e.emit(t.QueryLexer.FIELD),e.ignore(),t.QueryLexer.lexText},t.QueryLexer.lexTerm=function(e){if(e.width()>1&&(e.backup(),e.emit(t.QueryLexer.TERM)),e.ignore(),e.more())return t.QueryLexer.lexText},t.QueryLexer.lexEditDistance=function(e){return e.ignore(),e.acceptDigitRun(),e.emit(t.QueryLexer.EDIT_DISTANCE),t.QueryLexer.lexText},t.QueryLexer.lexBoost=function(e){return e.ignore(),e.acceptDigitRun(),e.emit(t.QueryLexer.BOOST),t.QueryLexer.lexText},t.QueryLexer.lexEOS=function(e){e.width()>0&&e.emit(t.QueryLexer.TERM)},t.QueryLexer.termSeparator=t.tokenizer.separator,t.QueryLexer.lexText=function(e){for(;;){var r=e.next();if(r==t.QueryLexer.EOS)return t.QueryLexer.lexEOS;if(r.charCodeAt(0)==92){e.escapeCharacter();continue}if(r==":")return t.QueryLexer.lexField;if(r=="~")return e.backup(),e.width()>0&&e.emit(t.QueryLexer.TERM),t.QueryLexer.lexEditDistance;if(r=="^")return e.backup(),e.width()>0&&e.emit(t.QueryLexer.TERM),t.QueryLexer.lexBoost;if(r=="+"&&e.width()===1||r=="-"&&e.width()===1)return e.emit(t.QueryLexer.PRESENCE),t.QueryLexer.lexText;if(r.match(t.QueryLexer.termSeparator))return t.QueryLexer.lexTerm}},t.QueryParser=function(e,r){this.lexer=new t.QueryLexer(e),this.query=r,this.currentClause={},this.lexemeIdx=0},t.QueryParser.prototype.parse=function(){this.lexer.run(),this.lexemes=this.lexer.lexemes;for(var e=t.QueryParser.parseClause;e;)e=e(this);return this.query},t.QueryParser.prototype.peekLexeme=function(){return this.lexemes[this.lexemeIdx]},t.QueryParser.prototype.consumeLexeme=function(){var e=this.peekLexeme();return this.lexemeIdx+=1,e},t.QueryParser.prototype.nextClause=function(){var e=this.currentClause;this.query.clause(e),this.currentClause={}},t.QueryParser.parseClause=function(e){var r=e.peekLexeme();if(r!=null)switch(r.type){case t.QueryLexer.PRESENCE:return t.QueryParser.parsePresence;case t.QueryLexer.FIELD:return t.QueryParser.parseField;case t.QueryLexer.TERM:return t.QueryParser.parseTerm;default:var n="expected either a field or a term, found "+r.type;throw r.str.length>=1&&(n+=" with value '"+r.str+"'"),new t.QueryParseError(n,r.start,r.end)}},t.QueryParser.parsePresence=function(e){var r=e.consumeLexeme();if(r!=null){switch(r.str){case"-":e.currentClause.presence=t.Query.presence.PROHIBITED;break;case"+":e.currentClause.presence=t.Query.presence.REQUIRED;break;default:var n="unrecognised presence operator'"+r.str+"'";throw new t.QueryParseError(n,r.start,r.end)}var i=e.peekLexeme();if(i==null){var n="expecting term or field, found nothing";throw new t.QueryParseError(n,r.start,r.end)}switch(i.type){case t.QueryLexer.FIELD:return t.QueryParser.parseField;case t.QueryLexer.TERM:return t.QueryParser.parseTerm;default:var n="expecting term or field, found '"+i.type+"'";throw new t.QueryParseError(n,i.start,i.end)}}},t.QueryParser.parseField=function(e){var r=e.consumeLexeme();if(r!=null){if(e.query.allFields.indexOf(r.str)==-1){var n=e.query.allFields.map(function(o){return"'"+o+"'"}).join(", "),i="unrecognised field '"+r.str+"', possible fields: "+n;throw new t.QueryParseError(i,r.start,r.end)}e.currentClause.fields=[r.str];var s=e.peekLexeme();if(s==null){var i="expecting term, found nothing";throw new t.QueryParseError(i,r.start,r.end)}switch(s.type){case t.QueryLexer.TERM:return t.QueryParser.parseTerm;default:var i="expecting term, found '"+s.type+"'";throw new t.QueryParseError(i,s.start,s.end)}}},t.QueryParser.parseTerm=function(e){var r=e.consumeLexeme();if(r!=null){e.currentClause.term=r.str.toLowerCase(),r.str.indexOf("*")!=-1&&(e.currentClause.usePipeline=!1);var n=e.peekLexeme();if(n==null){e.nextClause();return}switch(n.type){case t.QueryLexer.TERM:return e.nextClause(),t.QueryParser.parseTerm;case t.QueryLexer.FIELD:return e.nextClause(),t.QueryParser.parseField;case t.QueryLexer.EDIT_DISTANCE:return t.QueryParser.parseEditDistance;case t.QueryLexer.BOOST:return t.QueryParser.parseBoost;case t.QueryLexer.PRESENCE:return e.nextClause(),t.QueryParser.parsePresence;default:var i="Unexpected lexeme type '"+n.type+"'";throw new t.QueryParseError(i,n.start,n.end)}}},t.QueryParser.parseEditDistance=function(e){var r=e.consumeLexeme();if(r!=null){var n=parseInt(r.str,10);if(isNaN(n)){var i="edit distance must be numeric";throw new t.QueryParseError(i,r.start,r.end)}e.currentClause.editDistance=n;var s=e.peekLexeme();if(s==null){e.nextClause();return}switch(s.type){case t.QueryLexer.TERM:return e.nextClause(),t.QueryParser.parseTerm;case t.QueryLexer.FIELD:return e.nextClause(),t.QueryParser.parseField;case t.QueryLexer.EDIT_DISTANCE:return t.QueryParser.parseEditDistance;case t.QueryLexer.BOOST:return t.QueryParser.parseBoost;case t.QueryLexer.PRESENCE:return e.nextClause(),t.QueryParser.parsePresence;default:var i="Unexpected lexeme type '"+s.type+"'";throw new t.QueryParseError(i,s.start,s.end)}}},t.QueryParser.parseBoost=function(e){var r=e.consumeLexeme();if(r!=null){var n=parseInt(r.str,10);if(isNaN(n)){var i="boost must be numeric";throw new t.QueryParseError(i,r.start,r.end)}e.currentClause.boost=n;var s=e.peekLexeme();if(s==null){e.nextClause();return}switch(s.type){case t.QueryLexer.TERM:return e.nextClause(),t.QueryParser.parseTerm;case t.QueryLexer.FIELD:return e.nextClause(),t.QueryParser.parseField;case t.QueryLexer.EDIT_DISTANCE:return t.QueryParser.parseEditDistance;case t.QueryLexer.BOOST:return t.QueryParser.parseBoost;case t.QueryLexer.PRESENCE:return e.nextClause(),t.QueryParser.parsePresence;default:var i="Unexpected lexeme type '"+s.type+"'";throw new t.QueryParseError(i,s.start,s.end)}}},function(e,r){typeof define=="function"&&define.amd?define(r):typeof ee=="object"?te.exports=r():e.lunr=r()}(this,function(){return t})})()});var q=K((Re,ne)=>{"use strict";/*!
+ * escape-html
+ * Copyright(c) 2012-2013 TJ Holowaychuk
+ * Copyright(c) 2015 Andreas Lubbe
+ * Copyright(c) 2015 Tiancheng "Timothy" Gu
+ * MIT Licensed
+ */var Le=/["'&<>]/;ne.exports=we;function we(t){var e=""+t,r=Le.exec(e);if(!r)return e;var n,i="",s=0,o=0;for(s=r.index;s<e.length;s++){switch(e.charCodeAt(s)){case 34:n="&quot;";break;case 38:n="&amp;";break;case 39:n="&#39;";break;case 60:n="&lt;";break;case 62:n="&gt;";break;default:continue}o!==s&&(i+=e.substring(o,s)),o=s+1,i+=n}return o!==s?i+e.substring(o,s):i}});var le=H(re());Object.entries||(Object.entries=function(t){let e=[];for(let r of Object.keys(t))e.push([r,t[r]]);return e});Object.values||(Object.values=function(t){let e=[];for(let r of Object.keys(t))e.push(t[r]);return e});typeof Element!="undefined"&&(Element.prototype.scrollTo||(Element.prototype.scrollTo=function(t,e){typeof t=="object"?(this.scrollLeft=t.left,this.scrollTop=t.top):(this.scrollLeft=t,this.scrollTop=e)}),Element.prototype.replaceWith||(Element.prototype.replaceWith=function(...t){let e=this.parentNode;if(e){t.length===0&&e.removeChild(this);for(let r=t.length-1;r>=0;r--){let n=t[r];typeof n!="object"?n=document.createTextNode(n):n.parentNode&&n.parentNode.removeChild(n),r?e.insertBefore(this.previousSibling,n):e.replaceChild(n,this)}}}));var ie=H(q());function se(t){let e=new Map,r=new Set;for(let n of t){let[i,s]=n.location.split("#"),o=n.location,a=n.title,u=n.tags,c=(0,ie.default)(n.text).replace(/\s+(?=[,.:;!?])/g,"").replace(/\s+/g," ");if(s){let h=e.get(i);r.has(h)?e.set(o,{location:o,title:a,text:c,parent:h}):(h.title=n.title,h.text=c,r.add(h))}else e.set(o,M({location:o,title:a,text:c},u&&{tags:u}))}return e}var oe=H(q());function ae(t,e){let r=new RegExp(t.separator,"img"),n=(i,s,o)=>`${s}<mark data-md-highlight>${o}</mark>`;return i=>{i=i.replace(/[\s*+\-:~^]+/g," ").trim();let s=new RegExp(`(^|${t.separator})(${i.replace(/[|\\{}()[\]^$+*?.-]/g,"\\$&").replace(r,"|")})`,"img");return o=>(e?(0,oe.default)(o):o).replace(s,n).replace(/<\/mark>(\s+)<mark[^>]*>/img,"$1")}}function ue(t){let e=new lunr.Query(["title","text"]);return new lunr.QueryParser(t,e).parse(),e.clauses}function ce(t,e){var i;let r=new Set(t),n={};for(let s=0;s<e.length;s++)for(let o of r)e[s].startsWith(o.term)&&(n[o.term]=!0,r.delete(o));for(let s of r)(i=lunr.stopWordFilter)!=null&&i.call(lunr,s.term)&&(n[s.term]=!1);return n}function Ee(t,e){let[r,n]=[new Set(t),new Set(e)];return[...new Set([...r].filter(i=>!n.has(i)))]}var U=class{constructor({config:e,docs:r,options:n}){this.options=n,this.documents=se(r),this.highlight=ae(e,!1),lunr.tokenizer.separator=new RegExp(e.separator),this.index=lunr(function(){e.lang.length===1&&e.lang[0]!=="en"?this.use(lunr[e.lang[0]]):e.lang.length>1&&this.use(lunr.multiLanguage(...e.lang));let i=Ee(["trimmer","stopWordFilter","stemmer"],n.pipeline);for(let s of e.lang.map(o=>o==="en"?lunr:lunr[o]))for(let o of i)this.pipeline.remove(s[o]),this.searchPipeline.remove(s[o]);this.ref("location"),this.field("title",{boost:1e3}),this.field("text"),this.field("tags",{boost:1e6,extractor:s=>{let{tags:o=[]}=s;return o.reduce((a,u)=>[...a,...lunr.tokenizer(u)],[])}});for(let s of r)this.add(s,{boost:s.boost})})}search(e){if(e)try{let r=this.highlight(e),n=ue(e).filter(o=>o.presence!==lunr.Query.presence.PROHIBITED),i=this.index.search(`${e}*`).reduce((o,{ref:a,score:u,matchData:c})=>{let h=this.documents.get(a);if(typeof h!="undefined"){let{location:y,title:g,text:b,tags:m,parent:Q}=h,p=ce(n,Object.keys(c.metadata)),d=+!Q+ +Object.values(p).every(w=>w);o.push(Z(M({location:y,title:r(g),text:r(b)},m&&{tags:m.map(r)}),{score:u*(1+d),terms:p}))}return o},[]).sort((o,a)=>a.score-o.score).reduce((o,a)=>{let u=this.documents.get(a.location);if(typeof u!="undefined"){let c="parent"in u?u.parent.location:u.location;o.set(c,[...o.get(c)||[],a])}return o},new Map),s;if(this.options.suggestions){let o=this.index.query(a=>{for(let u of n)a.term(u.term,{fields:["title"],presence:lunr.Query.presence.REQUIRED,wildcard:lunr.Query.wildcard.TRAILING})});s=o.length?Object.keys(o[0].matchData.metadata):[]}return M({items:[...i.values()]},typeof s!="undefined"&&{suggestions:s})}catch(r){console.warn(`Invalid query: ${e} \u2013 see https://bit.ly/2s3ChXG`)}return{items:[]}}};var Y;function ke(t){return z(this,null,function*(){let e="../lunr";if(typeof parent!="undefined"&&"IFrameWorker"in parent){let n=document.querySelector("script[src]"),[i]=n.src.split("/worker");e=e.replace("..",i)}let r=[];for(let n of t.lang){switch(n){case"ja":r.push(`${e}/tinyseg.js`);break;case"hi":case"th":r.push(`${e}/wordcut.js`);break}n!=="en"&&r.push(`${e}/min/lunr.${n}.min.js`)}t.lang.length>1&&r.push(`${e}/min/lunr.multi.min.js`),r.length&&(yield importScripts(`${e}/min/lunr.stemmer.support.min.js`,...r))})}function Te(t){return z(this,null,function*(){switch(t.type){case 0:return yield ke(t.data.config),Y=new U(t.data),{type:1};case 2:return{type:3,data:Y?Y.search(t.data):{items:[]}};default:throw new TypeError("Invalid message type")}})}self.lunr=le.default;addEventListener("message",t=>z(void 0,null,function*(){postMessage(yield Te(t.data))}));})();
+//# sourceMappingURL=search.ecf98df9.min.js.map
+
diff --git a/assets/javascripts/workers/search.ecf98df9.min.js.map b/assets/javascripts/workers/search.ecf98df9.min.js.map
new file mode 100644
index 0000000..eada3e6
--- /dev/null
+++ b/assets/javascripts/workers/search.ecf98df9.min.js.map
@@ -0,0 +1,8 @@
+{
+  "version": 3,
+  "sources": ["node_modules/lunr/lunr.js", "node_modules/escape-html/index.js", "src/assets/javascripts/integrations/search/worker/main/index.ts", "src/assets/javascripts/polyfills/index.ts", "src/assets/javascripts/integrations/search/document/index.ts", "src/assets/javascripts/integrations/search/highlighter/index.ts", "src/assets/javascripts/integrations/search/query/_/index.ts", "src/assets/javascripts/integrations/search/_/index.ts"],
+  "sourceRoot": "../../../..",
+  "sourcesContent": ["/**\n * lunr - http://lunrjs.com - A bit like Solr, but much smaller and not as bright - 2.3.9\n * Copyright (C) 2020 Oliver Nightingale\n * @license MIT\n */\n\n;(function(){\n\n/**\n * A convenience function for configuring and constructing\n * a new lunr Index.\n *\n * A lunr.Builder instance is created and the pipeline setup\n * with a trimmer, stop word filter and stemmer.\n *\n * This builder object is yielded to the configuration function\n * that is passed as a parameter, allowing the list of fields\n * and other builder parameters to be customised.\n *\n * All documents _must_ be added within the passed config function.\n *\n * @example\n * var idx = lunr(function () {\n *   this.field('title')\n *   this.field('body')\n *   this.ref('id')\n *\n *   documents.forEach(function (doc) {\n *     this.add(doc)\n *   }, this)\n * })\n *\n * @see {@link lunr.Builder}\n * @see {@link lunr.Pipeline}\n * @see {@link lunr.trimmer}\n * @see {@link lunr.stopWordFilter}\n * @see {@link lunr.stemmer}\n * @namespace {function} lunr\n */\nvar lunr = function (config) {\n  var builder = new lunr.Builder\n\n  builder.pipeline.add(\n    lunr.trimmer,\n    lunr.stopWordFilter,\n    lunr.stemmer\n  )\n\n  builder.searchPipeline.add(\n    lunr.stemmer\n  )\n\n  config.call(builder, builder)\n  return builder.build()\n}\n\nlunr.version = \"2.3.9\"\n/*!\n * lunr.utils\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * A namespace containing utils for the rest of the lunr library\n * @namespace lunr.utils\n */\nlunr.utils = {}\n\n/**\n * Print a warning message to the console.\n *\n * @param {String} message The message to be printed.\n * @memberOf lunr.utils\n * @function\n */\nlunr.utils.warn = (function (global) {\n  /* eslint-disable no-console */\n  return function (message) {\n    if (global.console && console.warn) {\n      console.warn(message)\n    }\n  }\n  /* eslint-enable no-console */\n})(this)\n\n/**\n * Convert an object to a string.\n *\n * In the case of `null` and `undefined` the function returns\n * the empty string, in all other cases the result of calling\n * `toString` on the passed object is returned.\n *\n * @param {Any} obj The object to convert to a string.\n * @return {String} string representation of the passed object.\n * @memberOf lunr.utils\n */\nlunr.utils.asString = function (obj) {\n  if (obj === void 0 || obj === null) {\n    return \"\"\n  } else {\n    return obj.toString()\n  }\n}\n\n/**\n * Clones an object.\n *\n * Will create a copy of an existing object such that any mutations\n * on the copy cannot affect the original.\n *\n * Only shallow objects are supported, passing a nested object to this\n * function will cause a TypeError.\n *\n * Objects with primitives, and arrays of primitives are supported.\n *\n * @param {Object} obj The object to clone.\n * @return {Object} a clone of the passed object.\n * @throws {TypeError} when a nested object is passed.\n * @memberOf Utils\n */\nlunr.utils.clone = function (obj) {\n  if (obj === null || obj === undefined) {\n    return obj\n  }\n\n  var clone = Object.create(null),\n      keys = Object.keys(obj)\n\n  for (var i = 0; i < keys.length; i++) {\n    var key = keys[i],\n        val = obj[key]\n\n    if (Array.isArray(val)) {\n      clone[key] = val.slice()\n      continue\n    }\n\n    if (typeof val === 'string' ||\n        typeof val === 'number' ||\n        typeof val === 'boolean') {\n      clone[key] = val\n      continue\n    }\n\n    throw new TypeError(\"clone is not deep and does not support nested objects\")\n  }\n\n  return clone\n}\nlunr.FieldRef = function (docRef, fieldName, stringValue) {\n  this.docRef = docRef\n  this.fieldName = fieldName\n  this._stringValue = stringValue\n}\n\nlunr.FieldRef.joiner = \"/\"\n\nlunr.FieldRef.fromString = function (s) {\n  var n = s.indexOf(lunr.FieldRef.joiner)\n\n  if (n === -1) {\n    throw \"malformed field ref string\"\n  }\n\n  var fieldRef = s.slice(0, n),\n      docRef = s.slice(n + 1)\n\n  return new lunr.FieldRef (docRef, fieldRef, s)\n}\n\nlunr.FieldRef.prototype.toString = function () {\n  if (this._stringValue == undefined) {\n    this._stringValue = this.fieldName + lunr.FieldRef.joiner + this.docRef\n  }\n\n  return this._stringValue\n}\n/*!\n * lunr.Set\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * A lunr set.\n *\n * @constructor\n */\nlunr.Set = function (elements) {\n  this.elements = Object.create(null)\n\n  if (elements) {\n    this.length = elements.length\n\n    for (var i = 0; i < this.length; i++) {\n      this.elements[elements[i]] = true\n    }\n  } else {\n    this.length = 0\n  }\n}\n\n/**\n * A complete set that contains all elements.\n *\n * @static\n * @readonly\n * @type {lunr.Set}\n */\nlunr.Set.complete = {\n  intersect: function (other) {\n    return other\n  },\n\n  union: function () {\n    return this\n  },\n\n  contains: function () {\n    return true\n  }\n}\n\n/**\n * An empty set that contains no elements.\n *\n * @static\n * @readonly\n * @type {lunr.Set}\n */\nlunr.Set.empty = {\n  intersect: function () {\n    return this\n  },\n\n  union: function (other) {\n    return other\n  },\n\n  contains: function () {\n    return false\n  }\n}\n\n/**\n * Returns true if this set contains the specified object.\n *\n * @param {object} object - Object whose presence in this set is to be tested.\n * @returns {boolean} - True if this set contains the specified object.\n */\nlunr.Set.prototype.contains = function (object) {\n  return !!this.elements[object]\n}\n\n/**\n * Returns a new set containing only the elements that are present in both\n * this set and the specified set.\n *\n * @param {lunr.Set} other - set to intersect with this set.\n * @returns {lunr.Set} a new set that is the intersection of this and the specified set.\n */\n\nlunr.Set.prototype.intersect = function (other) {\n  var a, b, elements, intersection = []\n\n  if (other === lunr.Set.complete) {\n    return this\n  }\n\n  if (other === lunr.Set.empty) {\n    return other\n  }\n\n  if (this.length < other.length) {\n    a = this\n    b = other\n  } else {\n    a = other\n    b = this\n  }\n\n  elements = Object.keys(a.elements)\n\n  for (var i = 0; i < elements.length; i++) {\n    var element = elements[i]\n    if (element in b.elements) {\n      intersection.push(element)\n    }\n  }\n\n  return new lunr.Set (intersection)\n}\n\n/**\n * Returns a new set combining the elements of this and the specified set.\n *\n * @param {lunr.Set} other - set to union with this set.\n * @return {lunr.Set} a new set that is the union of this and the specified set.\n */\n\nlunr.Set.prototype.union = function (other) {\n  if (other === lunr.Set.complete) {\n    return lunr.Set.complete\n  }\n\n  if (other === lunr.Set.empty) {\n    return this\n  }\n\n  return new lunr.Set(Object.keys(this.elements).concat(Object.keys(other.elements)))\n}\n/**\n * A function to calculate the inverse document frequency for\n * a posting. This is shared between the builder and the index\n *\n * @private\n * @param {object} posting - The posting for a given term\n * @param {number} documentCount - The total number of documents.\n */\nlunr.idf = function (posting, documentCount) {\n  var documentsWithTerm = 0\n\n  for (var fieldName in posting) {\n    if (fieldName == '_index') continue // Ignore the term index, its not a field\n    documentsWithTerm += Object.keys(posting[fieldName]).length\n  }\n\n  var x = (documentCount - documentsWithTerm + 0.5) / (documentsWithTerm + 0.5)\n\n  return Math.log(1 + Math.abs(x))\n}\n\n/**\n * A token wraps a string representation of a token\n * as it is passed through the text processing pipeline.\n *\n * @constructor\n * @param {string} [str=''] - The string token being wrapped.\n * @param {object} [metadata={}] - Metadata associated with this token.\n */\nlunr.Token = function (str, metadata) {\n  this.str = str || \"\"\n  this.metadata = metadata || {}\n}\n\n/**\n * Returns the token string that is being wrapped by this object.\n *\n * @returns {string}\n */\nlunr.Token.prototype.toString = function () {\n  return this.str\n}\n\n/**\n * A token update function is used when updating or optionally\n * when cloning a token.\n *\n * @callback lunr.Token~updateFunction\n * @param {string} str - The string representation of the token.\n * @param {Object} metadata - All metadata associated with this token.\n */\n\n/**\n * Applies the given function to the wrapped string token.\n *\n * @example\n * token.update(function (str, metadata) {\n *   return str.toUpperCase()\n * })\n *\n * @param {lunr.Token~updateFunction} fn - A function to apply to the token string.\n * @returns {lunr.Token}\n */\nlunr.Token.prototype.update = function (fn) {\n  this.str = fn(this.str, this.metadata)\n  return this\n}\n\n/**\n * Creates a clone of this token. Optionally a function can be\n * applied to the cloned token.\n *\n * @param {lunr.Token~updateFunction} [fn] - An optional function to apply to the cloned token.\n * @returns {lunr.Token}\n */\nlunr.Token.prototype.clone = function (fn) {\n  fn = fn || function (s) { return s }\n  return new lunr.Token (fn(this.str, this.metadata), this.metadata)\n}\n/*!\n * lunr.tokenizer\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * A function for splitting a string into tokens ready to be inserted into\n * the search index. Uses `lunr.tokenizer.separator` to split strings, change\n * the value of this property to change how strings are split into tokens.\n *\n * This tokenizer will convert its parameter to a string by calling `toString` and\n * then will split this string on the character in `lunr.tokenizer.separator`.\n * Arrays will have their elements converted to strings and wrapped in a lunr.Token.\n *\n * Optional metadata can be passed to the tokenizer, this metadata will be cloned and\n * added as metadata to every token that is created from the object to be tokenized.\n *\n * @static\n * @param {?(string|object|object[])} obj - The object to convert into tokens\n * @param {?object} metadata - Optional metadata to associate with every token\n * @returns {lunr.Token[]}\n * @see {@link lunr.Pipeline}\n */\nlunr.tokenizer = function (obj, metadata) {\n  if (obj == null || obj == undefined) {\n    return []\n  }\n\n  if (Array.isArray(obj)) {\n    return obj.map(function (t) {\n      return new lunr.Token(\n        lunr.utils.asString(t).toLowerCase(),\n        lunr.utils.clone(metadata)\n      )\n    })\n  }\n\n  var str = obj.toString().toLowerCase(),\n      len = str.length,\n      tokens = []\n\n  for (var sliceEnd = 0, sliceStart = 0; sliceEnd <= len; sliceEnd++) {\n    var char = str.charAt(sliceEnd),\n        sliceLength = sliceEnd - sliceStart\n\n    if ((char.match(lunr.tokenizer.separator) || sliceEnd == len)) {\n\n      if (sliceLength > 0) {\n        var tokenMetadata = lunr.utils.clone(metadata) || {}\n        tokenMetadata[\"position\"] = [sliceStart, sliceLength]\n        tokenMetadata[\"index\"] = tokens.length\n\n        tokens.push(\n          new lunr.Token (\n            str.slice(sliceStart, sliceEnd),\n            tokenMetadata\n          )\n        )\n      }\n\n      sliceStart = sliceEnd + 1\n    }\n\n  }\n\n  return tokens\n}\n\n/**\n * The separator used to split a string into tokens. Override this property to change the behaviour of\n * `lunr.tokenizer` behaviour when tokenizing strings. By default this splits on whitespace and hyphens.\n *\n * @static\n * @see lunr.tokenizer\n */\nlunr.tokenizer.separator = /[\\s\\-]+/\n/*!\n * lunr.Pipeline\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * lunr.Pipelines maintain an ordered list of functions to be applied to all\n * tokens in documents entering the search index and queries being ran against\n * the index.\n *\n * An instance of lunr.Index created with the lunr shortcut will contain a\n * pipeline with a stop word filter and an English language stemmer. Extra\n * functions can be added before or after either of these functions or these\n * default functions can be removed.\n *\n * When run the pipeline will call each function in turn, passing a token, the\n * index of that token in the original list of all tokens and finally a list of\n * all the original tokens.\n *\n * The output of functions in the pipeline will be passed to the next function\n * in the pipeline. To exclude a token from entering the index the function\n * should return undefined, the rest of the pipeline will not be called with\n * this token.\n *\n * For serialisation of pipelines to work, all functions used in an instance of\n * a pipeline should be registered with lunr.Pipeline. Registered functions can\n * then be loaded. If trying to load a serialised pipeline that uses functions\n * that are not registered an error will be thrown.\n *\n * If not planning on serialising the pipeline then registering pipeline functions\n * is not necessary.\n *\n * @constructor\n */\nlunr.Pipeline = function () {\n  this._stack = []\n}\n\nlunr.Pipeline.registeredFunctions = Object.create(null)\n\n/**\n * A pipeline function maps lunr.Token to lunr.Token. A lunr.Token contains the token\n * string as well as all known metadata. A pipeline function can mutate the token string\n * or mutate (or add) metadata for a given token.\n *\n * A pipeline function can indicate that the passed token should be discarded by returning\n * null, undefined or an empty string. This token will not be passed to any downstream pipeline\n * functions and will not be added to the index.\n *\n * Multiple tokens can be returned by returning an array of tokens. Each token will be passed\n * to any downstream pipeline functions and all will returned tokens will be added to the index.\n *\n * Any number of pipeline functions may be chained together using a lunr.Pipeline.\n *\n * @interface lunr.PipelineFunction\n * @param {lunr.Token} token - A token from the document being processed.\n * @param {number} i - The index of this token in the complete list of tokens for this document/field.\n * @param {lunr.Token[]} tokens - All tokens for this document/field.\n * @returns {(?lunr.Token|lunr.Token[])}\n */\n\n/**\n * Register a function with the pipeline.\n *\n * Functions that are used in the pipeline should be registered if the pipeline\n * needs to be serialised, or a serialised pipeline needs to be loaded.\n *\n * Registering a function does not add it to a pipeline, functions must still be\n * added to instances of the pipeline for them to be used when running a pipeline.\n *\n * @param {lunr.PipelineFunction} fn - The function to check for.\n * @param {String} label - The label to register this function with\n */\nlunr.Pipeline.registerFunction = function (fn, label) {\n  if (label in this.registeredFunctions) {\n    lunr.utils.warn('Overwriting existing registered function: ' + label)\n  }\n\n  fn.label = label\n  lunr.Pipeline.registeredFunctions[fn.label] = fn\n}\n\n/**\n * Warns if the function is not registered as a Pipeline function.\n *\n * @param {lunr.PipelineFunction} fn - The function to check for.\n * @private\n */\nlunr.Pipeline.warnIfFunctionNotRegistered = function (fn) {\n  var isRegistered = fn.label && (fn.label in this.registeredFunctions)\n\n  if (!isRegistered) {\n    lunr.utils.warn('Function is not registered with pipeline. This may cause problems when serialising the index.\\n', fn)\n  }\n}\n\n/**\n * Loads a previously serialised pipeline.\n *\n * All functions to be loaded must already be registered with lunr.Pipeline.\n * If any function from the serialised data has not been registered then an\n * error will be thrown.\n *\n * @param {Object} serialised - The serialised pipeline to load.\n * @returns {lunr.Pipeline}\n */\nlunr.Pipeline.load = function (serialised) {\n  var pipeline = new lunr.Pipeline\n\n  serialised.forEach(function (fnName) {\n    var fn = lunr.Pipeline.registeredFunctions[fnName]\n\n    if (fn) {\n      pipeline.add(fn)\n    } else {\n      throw new Error('Cannot load unregistered function: ' + fnName)\n    }\n  })\n\n  return pipeline\n}\n\n/**\n * Adds new functions to the end of the pipeline.\n *\n * Logs a warning if the function has not been registered.\n *\n * @param {lunr.PipelineFunction[]} functions - Any number of functions to add to the pipeline.\n */\nlunr.Pipeline.prototype.add = function () {\n  var fns = Array.prototype.slice.call(arguments)\n\n  fns.forEach(function (fn) {\n    lunr.Pipeline.warnIfFunctionNotRegistered(fn)\n    this._stack.push(fn)\n  }, this)\n}\n\n/**\n * Adds a single function after a function that already exists in the\n * pipeline.\n *\n * Logs a warning if the function has not been registered.\n *\n * @param {lunr.PipelineFunction} existingFn - A function that already exists in the pipeline.\n * @param {lunr.PipelineFunction} newFn - The new function to add to the pipeline.\n */\nlunr.Pipeline.prototype.after = function (existingFn, newFn) {\n  lunr.Pipeline.warnIfFunctionNotRegistered(newFn)\n\n  var pos = this._stack.indexOf(existingFn)\n  if (pos == -1) {\n    throw new Error('Cannot find existingFn')\n  }\n\n  pos = pos + 1\n  this._stack.splice(pos, 0, newFn)\n}\n\n/**\n * Adds a single function before a function that already exists in the\n * pipeline.\n *\n * Logs a warning if the function has not been registered.\n *\n * @param {lunr.PipelineFunction} existingFn - A function that already exists in the pipeline.\n * @param {lunr.PipelineFunction} newFn - The new function to add to the pipeline.\n */\nlunr.Pipeline.prototype.before = function (existingFn, newFn) {\n  lunr.Pipeline.warnIfFunctionNotRegistered(newFn)\n\n  var pos = this._stack.indexOf(existingFn)\n  if (pos == -1) {\n    throw new Error('Cannot find existingFn')\n  }\n\n  this._stack.splice(pos, 0, newFn)\n}\n\n/**\n * Removes a function from the pipeline.\n *\n * @param {lunr.PipelineFunction} fn The function to remove from the pipeline.\n */\nlunr.Pipeline.prototype.remove = function (fn) {\n  var pos = this._stack.indexOf(fn)\n  if (pos == -1) {\n    return\n  }\n\n  this._stack.splice(pos, 1)\n}\n\n/**\n * Runs the current list of functions that make up the pipeline against the\n * passed tokens.\n *\n * @param {Array} tokens The tokens to run through the pipeline.\n * @returns {Array}\n */\nlunr.Pipeline.prototype.run = function (tokens) {\n  var stackLength = this._stack.length\n\n  for (var i = 0; i < stackLength; i++) {\n    var fn = this._stack[i]\n    var memo = []\n\n    for (var j = 0; j < tokens.length; j++) {\n      var result = fn(tokens[j], j, tokens)\n\n      if (result === null || result === void 0 || result === '') continue\n\n      if (Array.isArray(result)) {\n        for (var k = 0; k < result.length; k++) {\n          memo.push(result[k])\n        }\n      } else {\n        memo.push(result)\n      }\n    }\n\n    tokens = memo\n  }\n\n  return tokens\n}\n\n/**\n * Convenience method for passing a string through a pipeline and getting\n * strings out. This method takes care of wrapping the passed string in a\n * token and mapping the resulting tokens back to strings.\n *\n * @param {string} str - The string to pass through the pipeline.\n * @param {?object} metadata - Optional metadata to associate with the token\n * passed to the pipeline.\n * @returns {string[]}\n */\nlunr.Pipeline.prototype.runString = function (str, metadata) {\n  var token = new lunr.Token (str, metadata)\n\n  return this.run([token]).map(function (t) {\n    return t.toString()\n  })\n}\n\n/**\n * Resets the pipeline by removing any existing processors.\n *\n */\nlunr.Pipeline.prototype.reset = function () {\n  this._stack = []\n}\n\n/**\n * Returns a representation of the pipeline ready for serialisation.\n *\n * Logs a warning if the function has not been registered.\n *\n * @returns {Array}\n */\nlunr.Pipeline.prototype.toJSON = function () {\n  return this._stack.map(function (fn) {\n    lunr.Pipeline.warnIfFunctionNotRegistered(fn)\n\n    return fn.label\n  })\n}\n/*!\n * lunr.Vector\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * A vector is used to construct the vector space of documents and queries. These\n * vectors support operations to determine the similarity between two documents or\n * a document and a query.\n *\n * Normally no parameters are required for initializing a vector, but in the case of\n * loading a previously dumped vector the raw elements can be provided to the constructor.\n *\n * For performance reasons vectors are implemented with a flat array, where an elements\n * index is immediately followed by its value. E.g. [index, value, index, value]. This\n * allows the underlying array to be as sparse as possible and still offer decent\n * performance when being used for vector calculations.\n *\n * @constructor\n * @param {Number[]} [elements] - The flat list of element index and element value pairs.\n */\nlunr.Vector = function (elements) {\n  this._magnitude = 0\n  this.elements = elements || []\n}\n\n\n/**\n * Calculates the position within the vector to insert a given index.\n *\n * This is used internally by insert and upsert. If there are duplicate indexes then\n * the position is returned as if the value for that index were to be updated, but it\n * is the callers responsibility to check whether there is a duplicate at that index\n *\n * @param {Number} insertIdx - The index at which the element should be inserted.\n * @returns {Number}\n */\nlunr.Vector.prototype.positionForIndex = function (index) {\n  // For an empty vector the tuple can be inserted at the beginning\n  if (this.elements.length == 0) {\n    return 0\n  }\n\n  var start = 0,\n      end = this.elements.length / 2,\n      sliceLength = end - start,\n      pivotPoint = Math.floor(sliceLength / 2),\n      pivotIndex = this.elements[pivotPoint * 2]\n\n  while (sliceLength > 1) {\n    if (pivotIndex < index) {\n      start = pivotPoint\n    }\n\n    if (pivotIndex > index) {\n      end = pivotPoint\n    }\n\n    if (pivotIndex == index) {\n      break\n    }\n\n    sliceLength = end - start\n    pivotPoint = start + Math.floor(sliceLength / 2)\n    pivotIndex = this.elements[pivotPoint * 2]\n  }\n\n  if (pivotIndex == index) {\n    return pivotPoint * 2\n  }\n\n  if (pivotIndex > index) {\n    return pivotPoint * 2\n  }\n\n  if (pivotIndex < index) {\n    return (pivotPoint + 1) * 2\n  }\n}\n\n/**\n * Inserts an element at an index within the vector.\n *\n * Does not allow duplicates, will throw an error if there is already an entry\n * for this index.\n *\n * @param {Number} insertIdx - The index at which the element should be inserted.\n * @param {Number} val - The value to be inserted into the vector.\n */\nlunr.Vector.prototype.insert = function (insertIdx, val) {\n  this.upsert(insertIdx, val, function () {\n    throw \"duplicate index\"\n  })\n}\n\n/**\n * Inserts or updates an existing index within the vector.\n *\n * @param {Number} insertIdx - The index at which the element should be inserted.\n * @param {Number} val - The value to be inserted into the vector.\n * @param {function} fn - A function that is called for updates, the existing value and the\n * requested value are passed as arguments\n */\nlunr.Vector.prototype.upsert = function (insertIdx, val, fn) {\n  this._magnitude = 0\n  var position = this.positionForIndex(insertIdx)\n\n  if (this.elements[position] == insertIdx) {\n    this.elements[position + 1] = fn(this.elements[position + 1], val)\n  } else {\n    this.elements.splice(position, 0, insertIdx, val)\n  }\n}\n\n/**\n * Calculates the magnitude of this vector.\n *\n * @returns {Number}\n */\nlunr.Vector.prototype.magnitude = function () {\n  if (this._magnitude) return this._magnitude\n\n  var sumOfSquares = 0,\n      elementsLength = this.elements.length\n\n  for (var i = 1; i < elementsLength; i += 2) {\n    var val = this.elements[i]\n    sumOfSquares += val * val\n  }\n\n  return this._magnitude = Math.sqrt(sumOfSquares)\n}\n\n/**\n * Calculates the dot product of this vector and another vector.\n *\n * @param {lunr.Vector} otherVector - The vector to compute the dot product with.\n * @returns {Number}\n */\nlunr.Vector.prototype.dot = function (otherVector) {\n  var dotProduct = 0,\n      a = this.elements, b = otherVector.elements,\n      aLen = a.length, bLen = b.length,\n      aVal = 0, bVal = 0,\n      i = 0, j = 0\n\n  while (i < aLen && j < bLen) {\n    aVal = a[i], bVal = b[j]\n    if (aVal < bVal) {\n      i += 2\n    } else if (aVal > bVal) {\n      j += 2\n    } else if (aVal == bVal) {\n      dotProduct += a[i + 1] * b[j + 1]\n      i += 2\n      j += 2\n    }\n  }\n\n  return dotProduct\n}\n\n/**\n * Calculates the similarity between this vector and another vector.\n *\n * @param {lunr.Vector} otherVector - The other vector to calculate the\n * similarity with.\n * @returns {Number}\n */\nlunr.Vector.prototype.similarity = function (otherVector) {\n  return this.dot(otherVector) / this.magnitude() || 0\n}\n\n/**\n * Converts the vector to an array of the elements within the vector.\n *\n * @returns {Number[]}\n */\nlunr.Vector.prototype.toArray = function () {\n  var output = new Array (this.elements.length / 2)\n\n  for (var i = 1, j = 0; i < this.elements.length; i += 2, j++) {\n    output[j] = this.elements[i]\n  }\n\n  return output\n}\n\n/**\n * A JSON serializable representation of the vector.\n *\n * @returns {Number[]}\n */\nlunr.Vector.prototype.toJSON = function () {\n  return this.elements\n}\n/* eslint-disable */\n/*!\n * lunr.stemmer\n * Copyright (C) 2020 Oliver Nightingale\n * Includes code from - http://tartarus.org/~martin/PorterStemmer/js.txt\n */\n\n/**\n * lunr.stemmer is an english language stemmer, this is a JavaScript\n * implementation of the PorterStemmer taken from http://tartarus.org/~martin\n *\n * @static\n * @implements {lunr.PipelineFunction}\n * @param {lunr.Token} token - The string to stem\n * @returns {lunr.Token}\n * @see {@link lunr.Pipeline}\n * @function\n */\nlunr.stemmer = (function(){\n  var step2list = {\n      \"ational\" : \"ate\",\n      \"tional\" : \"tion\",\n      \"enci\" : \"ence\",\n      \"anci\" : \"ance\",\n      \"izer\" : \"ize\",\n      \"bli\" : \"ble\",\n      \"alli\" : \"al\",\n      \"entli\" : \"ent\",\n      \"eli\" : \"e\",\n      \"ousli\" : \"ous\",\n      \"ization\" : \"ize\",\n      \"ation\" : \"ate\",\n      \"ator\" : \"ate\",\n      \"alism\" : \"al\",\n      \"iveness\" : \"ive\",\n      \"fulness\" : \"ful\",\n      \"ousness\" : \"ous\",\n      \"aliti\" : \"al\",\n      \"iviti\" : \"ive\",\n      \"biliti\" : \"ble\",\n      \"logi\" : \"log\"\n    },\n\n    step3list = {\n      \"icate\" : \"ic\",\n      \"ative\" : \"\",\n      \"alize\" : \"al\",\n      \"iciti\" : \"ic\",\n      \"ical\" : \"ic\",\n      \"ful\" : \"\",\n      \"ness\" : \"\"\n    },\n\n    c = \"[^aeiou]\",          // consonant\n    v = \"[aeiouy]\",          // vowel\n    C = c + \"[^aeiouy]*\",    // consonant sequence\n    V = v + \"[aeiou]*\",      // vowel sequence\n\n    mgr0 = \"^(\" + C + \")?\" + V + C,               // [C]VC... is m>0\n    meq1 = \"^(\" + C + \")?\" + V + C + \"(\" + V + \")?$\",  // [C]VC[V] is m=1\n    mgr1 = \"^(\" + C + \")?\" + V + C + V + C,       // [C]VCVC... is m>1\n    s_v = \"^(\" + C + \")?\" + v;                   // vowel in stem\n\n  var re_mgr0 = new RegExp(mgr0);\n  var re_mgr1 = new RegExp(mgr1);\n  var re_meq1 = new RegExp(meq1);\n  var re_s_v = new RegExp(s_v);\n\n  var re_1a = /^(.+?)(ss|i)es$/;\n  var re2_1a = /^(.+?)([^s])s$/;\n  var re_1b = /^(.+?)eed$/;\n  var re2_1b = /^(.+?)(ed|ing)$/;\n  var re_1b_2 = /.$/;\n  var re2_1b_2 = /(at|bl|iz)$/;\n  var re3_1b_2 = new RegExp(\"([^aeiouylsz])\\\\1$\");\n  var re4_1b_2 = new RegExp(\"^\" + C + v + \"[^aeiouwxy]$\");\n\n  var re_1c = /^(.+?[^aeiou])y$/;\n  var re_2 = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/;\n\n  var re_3 = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/;\n\n  var re_4 = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/;\n  var re2_4 = /^(.+?)(s|t)(ion)$/;\n\n  var re_5 = /^(.+?)e$/;\n  var re_5_1 = /ll$/;\n  var re3_5 = new RegExp(\"^\" + C + v + \"[^aeiouwxy]$\");\n\n  var porterStemmer = function porterStemmer(w) {\n    var stem,\n      suffix,\n      firstch,\n      re,\n      re2,\n      re3,\n      re4;\n\n    if (w.length < 3) { return w; }\n\n    firstch = w.substr(0,1);\n    if (firstch == \"y\") {\n      w = firstch.toUpperCase() + w.substr(1);\n    }\n\n    // Step 1a\n    re = re_1a\n    re2 = re2_1a;\n\n    if (re.test(w)) { w = w.replace(re,\"$1$2\"); }\n    else if (re2.test(w)) { w = w.replace(re2,\"$1$2\"); }\n\n    // Step 1b\n    re = re_1b;\n    re2 = re2_1b;\n    if (re.test(w)) {\n      var fp = re.exec(w);\n      re = re_mgr0;\n      if (re.test(fp[1])) {\n        re = re_1b_2;\n        w = w.replace(re,\"\");\n      }\n    } else if (re2.test(w)) {\n      var fp = re2.exec(w);\n      stem = fp[1];\n      re2 = re_s_v;\n      if (re2.test(stem)) {\n        w = stem;\n        re2 = re2_1b_2;\n        re3 = re3_1b_2;\n        re4 = re4_1b_2;\n        if (re2.test(w)) { w = w + \"e\"; }\n        else if (re3.test(w)) { re = re_1b_2; w = w.replace(re,\"\"); }\n        else if (re4.test(w)) { w = w + \"e\"; }\n      }\n    }\n\n    // Step 1c - replace suffix y or Y by i if preceded by a non-vowel which is not the first letter of the word (so cry -> cri, by -> by, say -> say)\n    re = re_1c;\n    if (re.test(w)) {\n      var fp = re.exec(w);\n      stem = fp[1];\n      w = stem + \"i\";\n    }\n\n    // Step 2\n    re = re_2;\n    if (re.test(w)) {\n      var fp = re.exec(w);\n      stem = fp[1];\n      suffix = fp[2];\n      re = re_mgr0;\n      if (re.test(stem)) {\n        w = stem + step2list[suffix];\n      }\n    }\n\n    // Step 3\n    re = re_3;\n    if (re.test(w)) {\n      var fp = re.exec(w);\n      stem = fp[1];\n      suffix = fp[2];\n      re = re_mgr0;\n      if (re.test(stem)) {\n        w = stem + step3list[suffix];\n      }\n    }\n\n    // Step 4\n    re = re_4;\n    re2 = re2_4;\n    if (re.test(w)) {\n      var fp = re.exec(w);\n      stem = fp[1];\n      re = re_mgr1;\n      if (re.test(stem)) {\n        w = stem;\n      }\n    } else if (re2.test(w)) {\n      var fp = re2.exec(w);\n      stem = fp[1] + fp[2];\n      re2 = re_mgr1;\n      if (re2.test(stem)) {\n        w = stem;\n      }\n    }\n\n    // Step 5\n    re = re_5;\n    if (re.test(w)) {\n      var fp = re.exec(w);\n      stem = fp[1];\n      re = re_mgr1;\n      re2 = re_meq1;\n      re3 = re3_5;\n      if (re.test(stem) || (re2.test(stem) && !(re3.test(stem)))) {\n        w = stem;\n      }\n    }\n\n    re = re_5_1;\n    re2 = re_mgr1;\n    if (re.test(w) && re2.test(w)) {\n      re = re_1b_2;\n      w = w.replace(re,\"\");\n    }\n\n    // and turn initial Y back to y\n\n    if (firstch == \"y\") {\n      w = firstch.toLowerCase() + w.substr(1);\n    }\n\n    return w;\n  };\n\n  return function (token) {\n    return token.update(porterStemmer);\n  }\n})();\n\nlunr.Pipeline.registerFunction(lunr.stemmer, 'stemmer')\n/*!\n * lunr.stopWordFilter\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * lunr.generateStopWordFilter builds a stopWordFilter function from the provided\n * list of stop words.\n *\n * The built in lunr.stopWordFilter is built using this generator and can be used\n * to generate custom stopWordFilters for applications or non English languages.\n *\n * @function\n * @param {Array} token The token to pass through the filter\n * @returns {lunr.PipelineFunction}\n * @see lunr.Pipeline\n * @see lunr.stopWordFilter\n */\nlunr.generateStopWordFilter = function (stopWords) {\n  var words = stopWords.reduce(function (memo, stopWord) {\n    memo[stopWord] = stopWord\n    return memo\n  }, {})\n\n  return function (token) {\n    if (token && words[token.toString()] !== token.toString()) return token\n  }\n}\n\n/**\n * lunr.stopWordFilter is an English language stop word list filter, any words\n * contained in the list will not be passed through the filter.\n *\n * This is intended to be used in the Pipeline. If the token does not pass the\n * filter then undefined will be returned.\n *\n * @function\n * @implements {lunr.PipelineFunction}\n * @params {lunr.Token} token - A token to check for being a stop word.\n * @returns {lunr.Token}\n * @see {@link lunr.Pipeline}\n */\nlunr.stopWordFilter = lunr.generateStopWordFilter([\n  'a',\n  'able',\n  'about',\n  'across',\n  'after',\n  'all',\n  'almost',\n  'also',\n  'am',\n  'among',\n  'an',\n  'and',\n  'any',\n  'are',\n  'as',\n  'at',\n  'be',\n  'because',\n  'been',\n  'but',\n  'by',\n  'can',\n  'cannot',\n  'could',\n  'dear',\n  'did',\n  'do',\n  'does',\n  'either',\n  'else',\n  'ever',\n  'every',\n  'for',\n  'from',\n  'get',\n  'got',\n  'had',\n  'has',\n  'have',\n  'he',\n  'her',\n  'hers',\n  'him',\n  'his',\n  'how',\n  'however',\n  'i',\n  'if',\n  'in',\n  'into',\n  'is',\n  'it',\n  'its',\n  'just',\n  'least',\n  'let',\n  'like',\n  'likely',\n  'may',\n  'me',\n  'might',\n  'most',\n  'must',\n  'my',\n  'neither',\n  'no',\n  'nor',\n  'not',\n  'of',\n  'off',\n  'often',\n  'on',\n  'only',\n  'or',\n  'other',\n  'our',\n  'own',\n  'rather',\n  'said',\n  'say',\n  'says',\n  'she',\n  'should',\n  'since',\n  'so',\n  'some',\n  'than',\n  'that',\n  'the',\n  'their',\n  'them',\n  'then',\n  'there',\n  'these',\n  'they',\n  'this',\n  'tis',\n  'to',\n  'too',\n  'twas',\n  'us',\n  'wants',\n  'was',\n  'we',\n  'were',\n  'what',\n  'when',\n  'where',\n  'which',\n  'while',\n  'who',\n  'whom',\n  'why',\n  'will',\n  'with',\n  'would',\n  'yet',\n  'you',\n  'your'\n])\n\nlunr.Pipeline.registerFunction(lunr.stopWordFilter, 'stopWordFilter')\n/*!\n * lunr.trimmer\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * lunr.trimmer is a pipeline function for trimming non word\n * characters from the beginning and end of tokens before they\n * enter the index.\n *\n * This implementation may not work correctly for non latin\n * characters and should either be removed or adapted for use\n * with languages with non-latin characters.\n *\n * @static\n * @implements {lunr.PipelineFunction}\n * @param {lunr.Token} token The token to pass through the filter\n * @returns {lunr.Token}\n * @see lunr.Pipeline\n */\nlunr.trimmer = function (token) {\n  return token.update(function (s) {\n    return s.replace(/^\\W+/, '').replace(/\\W+$/, '')\n  })\n}\n\nlunr.Pipeline.registerFunction(lunr.trimmer, 'trimmer')\n/*!\n * lunr.TokenSet\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * A token set is used to store the unique list of all tokens\n * within an index. Token sets are also used to represent an\n * incoming query to the index, this query token set and index\n * token set are then intersected to find which tokens to look\n * up in the inverted index.\n *\n * A token set can hold multiple tokens, as in the case of the\n * index token set, or it can hold a single token as in the\n * case of a simple query token set.\n *\n * Additionally token sets are used to perform wildcard matching.\n * Leading, contained and trailing wildcards are supported, and\n * from this edit distance matching can also be provided.\n *\n * Token sets are implemented as a minimal finite state automata,\n * where both common prefixes and suffixes are shared between tokens.\n * This helps to reduce the space used for storing the token set.\n *\n * @constructor\n */\nlunr.TokenSet = function () {\n  this.final = false\n  this.edges = {}\n  this.id = lunr.TokenSet._nextId\n  lunr.TokenSet._nextId += 1\n}\n\n/**\n * Keeps track of the next, auto increment, identifier to assign\n * to a new tokenSet.\n *\n * TokenSets require a unique identifier to be correctly minimised.\n *\n * @private\n */\nlunr.TokenSet._nextId = 1\n\n/**\n * Creates a TokenSet instance from the given sorted array of words.\n *\n * @param {String[]} arr - A sorted array of strings to create the set from.\n * @returns {lunr.TokenSet}\n * @throws Will throw an error if the input array is not sorted.\n */\nlunr.TokenSet.fromArray = function (arr) {\n  var builder = new lunr.TokenSet.Builder\n\n  for (var i = 0, len = arr.length; i < len; i++) {\n    builder.insert(arr[i])\n  }\n\n  builder.finish()\n  return builder.root\n}\n\n/**\n * Creates a token set from a query clause.\n *\n * @private\n * @param {Object} clause - A single clause from lunr.Query.\n * @param {string} clause.term - The query clause term.\n * @param {number} [clause.editDistance] - The optional edit distance for the term.\n * @returns {lunr.TokenSet}\n */\nlunr.TokenSet.fromClause = function (clause) {\n  if ('editDistance' in clause) {\n    return lunr.TokenSet.fromFuzzyString(clause.term, clause.editDistance)\n  } else {\n    return lunr.TokenSet.fromString(clause.term)\n  }\n}\n\n/**\n * Creates a token set representing a single string with a specified\n * edit distance.\n *\n * Insertions, deletions, substitutions and transpositions are each\n * treated as an edit distance of 1.\n *\n * Increasing the allowed edit distance will have a dramatic impact\n * on the performance of both creating and intersecting these TokenSets.\n * It is advised to keep the edit distance less than 3.\n *\n * @param {string} str - The string to create the token set from.\n * @param {number} editDistance - The allowed edit distance to match.\n * @returns {lunr.Vector}\n */\nlunr.TokenSet.fromFuzzyString = function (str, editDistance) {\n  var root = new lunr.TokenSet\n\n  var stack = [{\n    node: root,\n    editsRemaining: editDistance,\n    str: str\n  }]\n\n  while (stack.length) {\n    var frame = stack.pop()\n\n    // no edit\n    if (frame.str.length > 0) {\n      var char = frame.str.charAt(0),\n          noEditNode\n\n      if (char in frame.node.edges) {\n        noEditNode = frame.node.edges[char]\n      } else {\n        noEditNode = new lunr.TokenSet\n        frame.node.edges[char] = noEditNode\n      }\n\n      if (frame.str.length == 1) {\n        noEditNode.final = true\n      }\n\n      stack.push({\n        node: noEditNode,\n        editsRemaining: frame.editsRemaining,\n        str: frame.str.slice(1)\n      })\n    }\n\n    if (frame.editsRemaining == 0) {\n      continue\n    }\n\n    // insertion\n    if (\"*\" in frame.node.edges) {\n      var insertionNode = frame.node.edges[\"*\"]\n    } else {\n      var insertionNode = new lunr.TokenSet\n      frame.node.edges[\"*\"] = insertionNode\n    }\n\n    if (frame.str.length == 0) {\n      insertionNode.final = true\n    }\n\n    stack.push({\n      node: insertionNode,\n      editsRemaining: frame.editsRemaining - 1,\n      str: frame.str\n    })\n\n    // deletion\n    // can only do a deletion if we have enough edits remaining\n    // and if there are characters left to delete in the string\n    if (frame.str.length > 1) {\n      stack.push({\n        node: frame.node,\n        editsRemaining: frame.editsRemaining - 1,\n        str: frame.str.slice(1)\n      })\n    }\n\n    // deletion\n    // just removing the last character from the str\n    if (frame.str.length == 1) {\n      frame.node.final = true\n    }\n\n    // substitution\n    // can only do a substitution if we have enough edits remaining\n    // and if there are characters left to substitute\n    if (frame.str.length >= 1) {\n      if (\"*\" in frame.node.edges) {\n        var substitutionNode = frame.node.edges[\"*\"]\n      } else {\n        var substitutionNode = new lunr.TokenSet\n        frame.node.edges[\"*\"] = substitutionNode\n      }\n\n      if (frame.str.length == 1) {\n        substitutionNode.final = true\n      }\n\n      stack.push({\n        node: substitutionNode,\n        editsRemaining: frame.editsRemaining - 1,\n        str: frame.str.slice(1)\n      })\n    }\n\n    // transposition\n    // can only do a transposition if there are edits remaining\n    // and there are enough characters to transpose\n    if (frame.str.length > 1) {\n      var charA = frame.str.charAt(0),\n          charB = frame.str.charAt(1),\n          transposeNode\n\n      if (charB in frame.node.edges) {\n        transposeNode = frame.node.edges[charB]\n      } else {\n        transposeNode = new lunr.TokenSet\n        frame.node.edges[charB] = transposeNode\n      }\n\n      if (frame.str.length == 1) {\n        transposeNode.final = true\n      }\n\n      stack.push({\n        node: transposeNode,\n        editsRemaining: frame.editsRemaining - 1,\n        str: charA + frame.str.slice(2)\n      })\n    }\n  }\n\n  return root\n}\n\n/**\n * Creates a TokenSet from a string.\n *\n * The string may contain one or more wildcard characters (*)\n * that will allow wildcard matching when intersecting with\n * another TokenSet.\n *\n * @param {string} str - The string to create a TokenSet from.\n * @returns {lunr.TokenSet}\n */\nlunr.TokenSet.fromString = function (str) {\n  var node = new lunr.TokenSet,\n      root = node\n\n  /*\n   * Iterates through all characters within the passed string\n   * appending a node for each character.\n   *\n   * When a wildcard character is found then a self\n   * referencing edge is introduced to continually match\n   * any number of any characters.\n   */\n  for (var i = 0, len = str.length; i < len; i++) {\n    var char = str[i],\n        final = (i == len - 1)\n\n    if (char == \"*\") {\n      node.edges[char] = node\n      node.final = final\n\n    } else {\n      var next = new lunr.TokenSet\n      next.final = final\n\n      node.edges[char] = next\n      node = next\n    }\n  }\n\n  return root\n}\n\n/**\n * Converts this TokenSet into an array of strings\n * contained within the TokenSet.\n *\n * This is not intended to be used on a TokenSet that\n * contains wildcards, in these cases the results are\n * undefined and are likely to cause an infinite loop.\n *\n * @returns {string[]}\n */\nlunr.TokenSet.prototype.toArray = function () {\n  var words = []\n\n  var stack = [{\n    prefix: \"\",\n    node: this\n  }]\n\n  while (stack.length) {\n    var frame = stack.pop(),\n        edges = Object.keys(frame.node.edges),\n        len = edges.length\n\n    if (frame.node.final) {\n      /* In Safari, at this point the prefix is sometimes corrupted, see:\n       * https://github.com/olivernn/lunr.js/issues/279 Calling any\n       * String.prototype method forces Safari to \"cast\" this string to what\n       * it's supposed to be, fixing the bug. */\n      frame.prefix.charAt(0)\n      words.push(frame.prefix)\n    }\n\n    for (var i = 0; i < len; i++) {\n      var edge = edges[i]\n\n      stack.push({\n        prefix: frame.prefix.concat(edge),\n        node: frame.node.edges[edge]\n      })\n    }\n  }\n\n  return words\n}\n\n/**\n * Generates a string representation of a TokenSet.\n *\n * This is intended to allow TokenSets to be used as keys\n * in objects, largely to aid the construction and minimisation\n * of a TokenSet. As such it is not designed to be a human\n * friendly representation of the TokenSet.\n *\n * @returns {string}\n */\nlunr.TokenSet.prototype.toString = function () {\n  // NOTE: Using Object.keys here as this.edges is very likely\n  // to enter 'hash-mode' with many keys being added\n  //\n  // avoiding a for-in loop here as it leads to the function\n  // being de-optimised (at least in V8). From some simple\n  // benchmarks the performance is comparable, but allowing\n  // V8 to optimize may mean easy performance wins in the future.\n\n  if (this._str) {\n    return this._str\n  }\n\n  var str = this.final ? '1' : '0',\n      labels = Object.keys(this.edges).sort(),\n      len = labels.length\n\n  for (var i = 0; i < len; i++) {\n    var label = labels[i],\n        node = this.edges[label]\n\n    str = str + label + node.id\n  }\n\n  return str\n}\n\n/**\n * Returns a new TokenSet that is the intersection of\n * this TokenSet and the passed TokenSet.\n *\n * This intersection will take into account any wildcards\n * contained within the TokenSet.\n *\n * @param {lunr.TokenSet} b - An other TokenSet to intersect with.\n * @returns {lunr.TokenSet}\n */\nlunr.TokenSet.prototype.intersect = function (b) {\n  var output = new lunr.TokenSet,\n      frame = undefined\n\n  var stack = [{\n    qNode: b,\n    output: output,\n    node: this\n  }]\n\n  while (stack.length) {\n    frame = stack.pop()\n\n    // NOTE: As with the #toString method, we are using\n    // Object.keys and a for loop instead of a for-in loop\n    // as both of these objects enter 'hash' mode, causing\n    // the function to be de-optimised in V8\n    var qEdges = Object.keys(frame.qNode.edges),\n        qLen = qEdges.length,\n        nEdges = Object.keys(frame.node.edges),\n        nLen = nEdges.length\n\n    for (var q = 0; q < qLen; q++) {\n      var qEdge = qEdges[q]\n\n      for (var n = 0; n < nLen; n++) {\n        var nEdge = nEdges[n]\n\n        if (nEdge == qEdge || qEdge == '*') {\n          var node = frame.node.edges[nEdge],\n              qNode = frame.qNode.edges[qEdge],\n              final = node.final && qNode.final,\n              next = undefined\n\n          if (nEdge in frame.output.edges) {\n            // an edge already exists for this character\n            // no need to create a new node, just set the finality\n            // bit unless this node is already final\n            next = frame.output.edges[nEdge]\n            next.final = next.final || final\n\n          } else {\n            // no edge exists yet, must create one\n            // set the finality bit and insert it\n            // into the output\n            next = new lunr.TokenSet\n            next.final = final\n            frame.output.edges[nEdge] = next\n          }\n\n          stack.push({\n            qNode: qNode,\n            output: next,\n            node: node\n          })\n        }\n      }\n    }\n  }\n\n  return output\n}\nlunr.TokenSet.Builder = function () {\n  this.previousWord = \"\"\n  this.root = new lunr.TokenSet\n  this.uncheckedNodes = []\n  this.minimizedNodes = {}\n}\n\nlunr.TokenSet.Builder.prototype.insert = function (word) {\n  var node,\n      commonPrefix = 0\n\n  if (word < this.previousWord) {\n    throw new Error (\"Out of order word insertion\")\n  }\n\n  for (var i = 0; i < word.length && i < this.previousWord.length; i++) {\n    if (word[i] != this.previousWord[i]) break\n    commonPrefix++\n  }\n\n  this.minimize(commonPrefix)\n\n  if (this.uncheckedNodes.length == 0) {\n    node = this.root\n  } else {\n    node = this.uncheckedNodes[this.uncheckedNodes.length - 1].child\n  }\n\n  for (var i = commonPrefix; i < word.length; i++) {\n    var nextNode = new lunr.TokenSet,\n        char = word[i]\n\n    node.edges[char] = nextNode\n\n    this.uncheckedNodes.push({\n      parent: node,\n      char: char,\n      child: nextNode\n    })\n\n    node = nextNode\n  }\n\n  node.final = true\n  this.previousWord = word\n}\n\nlunr.TokenSet.Builder.prototype.finish = function () {\n  this.minimize(0)\n}\n\nlunr.TokenSet.Builder.prototype.minimize = function (downTo) {\n  for (var i = this.uncheckedNodes.length - 1; i >= downTo; i--) {\n    var node = this.uncheckedNodes[i],\n        childKey = node.child.toString()\n\n    if (childKey in this.minimizedNodes) {\n      node.parent.edges[node.char] = this.minimizedNodes[childKey]\n    } else {\n      // Cache the key for this node since\n      // we know it can't change anymore\n      node.child._str = childKey\n\n      this.minimizedNodes[childKey] = node.child\n    }\n\n    this.uncheckedNodes.pop()\n  }\n}\n/*!\n * lunr.Index\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * An index contains the built index of all documents and provides a query interface\n * to the index.\n *\n * Usually instances of lunr.Index will not be created using this constructor, instead\n * lunr.Builder should be used to construct new indexes, or lunr.Index.load should be\n * used to load previously built and serialized indexes.\n *\n * @constructor\n * @param {Object} attrs - The attributes of the built search index.\n * @param {Object} attrs.invertedIndex - An index of term/field to document reference.\n * @param {Object<string, lunr.Vector>} attrs.fieldVectors - Field vectors\n * @param {lunr.TokenSet} attrs.tokenSet - An set of all corpus tokens.\n * @param {string[]} attrs.fields - The names of indexed document fields.\n * @param {lunr.Pipeline} attrs.pipeline - The pipeline to use for search terms.\n */\nlunr.Index = function (attrs) {\n  this.invertedIndex = attrs.invertedIndex\n  this.fieldVectors = attrs.fieldVectors\n  this.tokenSet = attrs.tokenSet\n  this.fields = attrs.fields\n  this.pipeline = attrs.pipeline\n}\n\n/**\n * A result contains details of a document matching a search query.\n * @typedef {Object} lunr.Index~Result\n * @property {string} ref - The reference of the document this result represents.\n * @property {number} score - A number between 0 and 1 representing how similar this document is to the query.\n * @property {lunr.MatchData} matchData - Contains metadata about this match including which term(s) caused the match.\n */\n\n/**\n * Although lunr provides the ability to create queries using lunr.Query, it also provides a simple\n * query language which itself is parsed into an instance of lunr.Query.\n *\n * For programmatically building queries it is advised to directly use lunr.Query, the query language\n * is best used for human entered text rather than program generated text.\n *\n * At its simplest queries can just be a single term, e.g. `hello`, multiple terms are also supported\n * and will be combined with OR, e.g `hello world` will match documents that contain either 'hello'\n * or 'world', though those that contain both will rank higher in the results.\n *\n * Wildcards can be included in terms to match one or more unspecified characters, these wildcards can\n * be inserted anywhere within the term, and more than one wildcard can exist in a single term. Adding\n * wildcards will increase the number of documents that will be found but can also have a negative\n * impact on query performance, especially with wildcards at the beginning of a term.\n *\n * Terms can be restricted to specific fields, e.g. `title:hello`, only documents with the term\n * hello in the title field will match this query. Using a field not present in the index will lead\n * to an error being thrown.\n *\n * Modifiers can also be added to terms, lunr supports edit distance and boost modifiers on terms. A term\n * boost will make documents matching that term score higher, e.g. `foo^5`. Edit distance is also supported\n * to provide fuzzy matching, e.g. 'hello~2' will match documents with hello with an edit distance of 2.\n * Avoid large values for edit distance to improve query performance.\n *\n * Each term also supports a presence modifier. By default a term's presence in document is optional, however\n * this can be changed to either required or prohibited. For a term's presence to be required in a document the\n * term should be prefixed with a '+', e.g. `+foo bar` is a search for documents that must contain 'foo' and\n * optionally contain 'bar'. Conversely a leading '-' sets the terms presence to prohibited, i.e. it must not\n * appear in a document, e.g. `-foo bar` is a search for documents that do not contain 'foo' but may contain 'bar'.\n *\n * To escape special characters the backslash character '\\' can be used, this allows searches to include\n * characters that would normally be considered modifiers, e.g. `foo\\~2` will search for a term \"foo~2\" instead\n * of attempting to apply a boost of 2 to the search term \"foo\".\n *\n * @typedef {string} lunr.Index~QueryString\n * @example <caption>Simple single term query</caption>\n * hello\n * @example <caption>Multiple term query</caption>\n * hello world\n * @example <caption>term scoped to a field</caption>\n * title:hello\n * @example <caption>term with a boost of 10</caption>\n * hello^10\n * @example <caption>term with an edit distance of 2</caption>\n * hello~2\n * @example <caption>terms with presence modifiers</caption>\n * -foo +bar baz\n */\n\n/**\n * Performs a search against the index using lunr query syntax.\n *\n * Results will be returned sorted by their score, the most relevant results\n * will be returned first.  For details on how the score is calculated, please see\n * the {@link https://lunrjs.com/guides/searching.html#scoring|guide}.\n *\n * For more programmatic querying use lunr.Index#query.\n *\n * @param {lunr.Index~QueryString} queryString - A string containing a lunr query.\n * @throws {lunr.QueryParseError} If the passed query string cannot be parsed.\n * @returns {lunr.Index~Result[]}\n */\nlunr.Index.prototype.search = function (queryString) {\n  return this.query(function (query) {\n    var parser = new lunr.QueryParser(queryString, query)\n    parser.parse()\n  })\n}\n\n/**\n * A query builder callback provides a query object to be used to express\n * the query to perform on the index.\n *\n * @callback lunr.Index~queryBuilder\n * @param {lunr.Query} query - The query object to build up.\n * @this lunr.Query\n */\n\n/**\n * Performs a query against the index using the yielded lunr.Query object.\n *\n * If performing programmatic queries against the index, this method is preferred\n * over lunr.Index#search so as to avoid the additional query parsing overhead.\n *\n * A query object is yielded to the supplied function which should be used to\n * express the query to be run against the index.\n *\n * Note that although this function takes a callback parameter it is _not_ an\n * asynchronous operation, the callback is just yielded a query object to be\n * customized.\n *\n * @param {lunr.Index~queryBuilder} fn - A function that is used to build the query.\n * @returns {lunr.Index~Result[]}\n */\nlunr.Index.prototype.query = function (fn) {\n  // for each query clause\n  // * process terms\n  // * expand terms from token set\n  // * find matching documents and metadata\n  // * get document vectors\n  // * score documents\n\n  var query = new lunr.Query(this.fields),\n      matchingFields = Object.create(null),\n      queryVectors = Object.create(null),\n      termFieldCache = Object.create(null),\n      requiredMatches = Object.create(null),\n      prohibitedMatches = Object.create(null)\n\n  /*\n   * To support field level boosts a query vector is created per\n   * field. An empty vector is eagerly created to support negated\n   * queries.\n   */\n  for (var i = 0; i < this.fields.length; i++) {\n    queryVectors[this.fields[i]] = new lunr.Vector\n  }\n\n  fn.call(query, query)\n\n  for (var i = 0; i < query.clauses.length; i++) {\n    /*\n     * Unless the pipeline has been disabled for this term, which is\n     * the case for terms with wildcards, we need to pass the clause\n     * term through the search pipeline. A pipeline returns an array\n     * of processed terms. Pipeline functions may expand the passed\n     * term, which means we may end up performing multiple index lookups\n     * for a single query term.\n     */\n    var clause = query.clauses[i],\n        terms = null,\n        clauseMatches = lunr.Set.empty\n\n    if (clause.usePipeline) {\n      terms = this.pipeline.runString(clause.term, {\n        fields: clause.fields\n      })\n    } else {\n      terms = [clause.term]\n    }\n\n    for (var m = 0; m < terms.length; m++) {\n      var term = terms[m]\n\n      /*\n       * Each term returned from the pipeline needs to use the same query\n       * clause object, e.g. the same boost and or edit distance. The\n       * simplest way to do this is to re-use the clause object but mutate\n       * its term property.\n       */\n      clause.term = term\n\n      /*\n       * From the term in the clause we create a token set which will then\n       * be used to intersect the indexes token set to get a list of terms\n       * to lookup in the inverted index\n       */\n      var termTokenSet = lunr.TokenSet.fromClause(clause),\n          expandedTerms = this.tokenSet.intersect(termTokenSet).toArray()\n\n      /*\n       * If a term marked as required does not exist in the tokenSet it is\n       * impossible for the search to return any matches. We set all the field\n       * scoped required matches set to empty and stop examining any further\n       * clauses.\n       */\n      if (expandedTerms.length === 0 && clause.presence === lunr.Query.presence.REQUIRED) {\n        for (var k = 0; k < clause.fields.length; k++) {\n          var field = clause.fields[k]\n          requiredMatches[field] = lunr.Set.empty\n        }\n\n        break\n      }\n\n      for (var j = 0; j < expandedTerms.length; j++) {\n        /*\n         * For each term get the posting and termIndex, this is required for\n         * building the query vector.\n         */\n        var expandedTerm = expandedTerms[j],\n            posting = this.invertedIndex[expandedTerm],\n            termIndex = posting._index\n\n        for (var k = 0; k < clause.fields.length; k++) {\n          /*\n           * For each field that this query term is scoped by (by default\n           * all fields are in scope) we need to get all the document refs\n           * that have this term in that field.\n           *\n           * The posting is the entry in the invertedIndex for the matching\n           * term from above.\n           */\n          var field = clause.fields[k],\n              fieldPosting = posting[field],\n              matchingDocumentRefs = Object.keys(fieldPosting),\n              termField = expandedTerm + \"/\" + field,\n              matchingDocumentsSet = new lunr.Set(matchingDocumentRefs)\n\n          /*\n           * if the presence of this term is required ensure that the matching\n           * documents are added to the set of required matches for this clause.\n           *\n           */\n          if (clause.presence == lunr.Query.presence.REQUIRED) {\n            clauseMatches = clauseMatches.union(matchingDocumentsSet)\n\n            if (requiredMatches[field] === undefined) {\n              requiredMatches[field] = lunr.Set.complete\n            }\n          }\n\n          /*\n           * if the presence of this term is prohibited ensure that the matching\n           * documents are added to the set of prohibited matches for this field,\n           * creating that set if it does not yet exist.\n           */\n          if (clause.presence == lunr.Query.presence.PROHIBITED) {\n            if (prohibitedMatches[field] === undefined) {\n              prohibitedMatches[field] = lunr.Set.empty\n            }\n\n            prohibitedMatches[field] = prohibitedMatches[field].union(matchingDocumentsSet)\n\n            /*\n             * Prohibited matches should not be part of the query vector used for\n             * similarity scoring and no metadata should be extracted so we continue\n             * to the next field\n             */\n            continue\n          }\n\n          /*\n           * The query field vector is populated using the termIndex found for\n           * the term and a unit value with the appropriate boost applied.\n           * Using upsert because there could already be an entry in the vector\n           * for the term we are working with. In that case we just add the scores\n           * together.\n           */\n          queryVectors[field].upsert(termIndex, clause.boost, function (a, b) { return a + b })\n\n          /**\n           * If we've already seen this term, field combo then we've already collected\n           * the matching documents and metadata, no need to go through all that again\n           */\n          if (termFieldCache[termField]) {\n            continue\n          }\n\n          for (var l = 0; l < matchingDocumentRefs.length; l++) {\n            /*\n             * All metadata for this term/field/document triple\n             * are then extracted and collected into an instance\n             * of lunr.MatchData ready to be returned in the query\n             * results\n             */\n            var matchingDocumentRef = matchingDocumentRefs[l],\n                matchingFieldRef = new lunr.FieldRef (matchingDocumentRef, field),\n                metadata = fieldPosting[matchingDocumentRef],\n                fieldMatch\n\n            if ((fieldMatch = matchingFields[matchingFieldRef]) === undefined) {\n              matchingFields[matchingFieldRef] = new lunr.MatchData (expandedTerm, field, metadata)\n            } else {\n              fieldMatch.add(expandedTerm, field, metadata)\n            }\n\n          }\n\n          termFieldCache[termField] = true\n        }\n      }\n    }\n\n    /**\n     * If the presence was required we need to update the requiredMatches field sets.\n     * We do this after all fields for the term have collected their matches because\n     * the clause terms presence is required in _any_ of the fields not _all_ of the\n     * fields.\n     */\n    if (clause.presence === lunr.Query.presence.REQUIRED) {\n      for (var k = 0; k < clause.fields.length; k++) {\n        var field = clause.fields[k]\n        requiredMatches[field] = requiredMatches[field].intersect(clauseMatches)\n      }\n    }\n  }\n\n  /**\n   * Need to combine the field scoped required and prohibited\n   * matching documents into a global set of required and prohibited\n   * matches\n   */\n  var allRequiredMatches = lunr.Set.complete,\n      allProhibitedMatches = lunr.Set.empty\n\n  for (var i = 0; i < this.fields.length; i++) {\n    var field = this.fields[i]\n\n    if (requiredMatches[field]) {\n      allRequiredMatches = allRequiredMatches.intersect(requiredMatches[field])\n    }\n\n    if (prohibitedMatches[field]) {\n      allProhibitedMatches = allProhibitedMatches.union(prohibitedMatches[field])\n    }\n  }\n\n  var matchingFieldRefs = Object.keys(matchingFields),\n      results = [],\n      matches = Object.create(null)\n\n  /*\n   * If the query is negated (contains only prohibited terms)\n   * we need to get _all_ fieldRefs currently existing in the\n   * index. This is only done when we know that the query is\n   * entirely prohibited terms to avoid any cost of getting all\n   * fieldRefs unnecessarily.\n   *\n   * Additionally, blank MatchData must be created to correctly\n   * populate the results.\n   */\n  if (query.isNegated()) {\n    matchingFieldRefs = Object.keys(this.fieldVectors)\n\n    for (var i = 0; i < matchingFieldRefs.length; i++) {\n      var matchingFieldRef = matchingFieldRefs[i]\n      var fieldRef = lunr.FieldRef.fromString(matchingFieldRef)\n      matchingFields[matchingFieldRef] = new lunr.MatchData\n    }\n  }\n\n  for (var i = 0; i < matchingFieldRefs.length; i++) {\n    /*\n     * Currently we have document fields that match the query, but we\n     * need to return documents. The matchData and scores are combined\n     * from multiple fields belonging to the same document.\n     *\n     * Scores are calculated by field, using the query vectors created\n     * above, and combined into a final document score using addition.\n     */\n    var fieldRef = lunr.FieldRef.fromString(matchingFieldRefs[i]),\n        docRef = fieldRef.docRef\n\n    if (!allRequiredMatches.contains(docRef)) {\n      continue\n    }\n\n    if (allProhibitedMatches.contains(docRef)) {\n      continue\n    }\n\n    var fieldVector = this.fieldVectors[fieldRef],\n        score = queryVectors[fieldRef.fieldName].similarity(fieldVector),\n        docMatch\n\n    if ((docMatch = matches[docRef]) !== undefined) {\n      docMatch.score += score\n      docMatch.matchData.combine(matchingFields[fieldRef])\n    } else {\n      var match = {\n        ref: docRef,\n        score: score,\n        matchData: matchingFields[fieldRef]\n      }\n      matches[docRef] = match\n      results.push(match)\n    }\n  }\n\n  /*\n   * Sort the results objects by score, highest first.\n   */\n  return results.sort(function (a, b) {\n    return b.score - a.score\n  })\n}\n\n/**\n * Prepares the index for JSON serialization.\n *\n * The schema for this JSON blob will be described in a\n * separate JSON schema file.\n *\n * @returns {Object}\n */\nlunr.Index.prototype.toJSON = function () {\n  var invertedIndex = Object.keys(this.invertedIndex)\n    .sort()\n    .map(function (term) {\n      return [term, this.invertedIndex[term]]\n    }, this)\n\n  var fieldVectors = Object.keys(this.fieldVectors)\n    .map(function (ref) {\n      return [ref, this.fieldVectors[ref].toJSON()]\n    }, this)\n\n  return {\n    version: lunr.version,\n    fields: this.fields,\n    fieldVectors: fieldVectors,\n    invertedIndex: invertedIndex,\n    pipeline: this.pipeline.toJSON()\n  }\n}\n\n/**\n * Loads a previously serialized lunr.Index\n *\n * @param {Object} serializedIndex - A previously serialized lunr.Index\n * @returns {lunr.Index}\n */\nlunr.Index.load = function (serializedIndex) {\n  var attrs = {},\n      fieldVectors = {},\n      serializedVectors = serializedIndex.fieldVectors,\n      invertedIndex = Object.create(null),\n      serializedInvertedIndex = serializedIndex.invertedIndex,\n      tokenSetBuilder = new lunr.TokenSet.Builder,\n      pipeline = lunr.Pipeline.load(serializedIndex.pipeline)\n\n  if (serializedIndex.version != lunr.version) {\n    lunr.utils.warn(\"Version mismatch when loading serialised index. Current version of lunr '\" + lunr.version + \"' does not match serialized index '\" + serializedIndex.version + \"'\")\n  }\n\n  for (var i = 0; i < serializedVectors.length; i++) {\n    var tuple = serializedVectors[i],\n        ref = tuple[0],\n        elements = tuple[1]\n\n    fieldVectors[ref] = new lunr.Vector(elements)\n  }\n\n  for (var i = 0; i < serializedInvertedIndex.length; i++) {\n    var tuple = serializedInvertedIndex[i],\n        term = tuple[0],\n        posting = tuple[1]\n\n    tokenSetBuilder.insert(term)\n    invertedIndex[term] = posting\n  }\n\n  tokenSetBuilder.finish()\n\n  attrs.fields = serializedIndex.fields\n\n  attrs.fieldVectors = fieldVectors\n  attrs.invertedIndex = invertedIndex\n  attrs.tokenSet = tokenSetBuilder.root\n  attrs.pipeline = pipeline\n\n  return new lunr.Index(attrs)\n}\n/*!\n * lunr.Builder\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * lunr.Builder performs indexing on a set of documents and\n * returns instances of lunr.Index ready for querying.\n *\n * All configuration of the index is done via the builder, the\n * fields to index, the document reference, the text processing\n * pipeline and document scoring parameters are all set on the\n * builder before indexing.\n *\n * @constructor\n * @property {string} _ref - Internal reference to the document reference field.\n * @property {string[]} _fields - Internal reference to the document fields to index.\n * @property {object} invertedIndex - The inverted index maps terms to document fields.\n * @property {object} documentTermFrequencies - Keeps track of document term frequencies.\n * @property {object} documentLengths - Keeps track of the length of documents added to the index.\n * @property {lunr.tokenizer} tokenizer - Function for splitting strings into tokens for indexing.\n * @property {lunr.Pipeline} pipeline - The pipeline performs text processing on tokens before indexing.\n * @property {lunr.Pipeline} searchPipeline - A pipeline for processing search terms before querying the index.\n * @property {number} documentCount - Keeps track of the total number of documents indexed.\n * @property {number} _b - A parameter to control field length normalization, setting this to 0 disabled normalization, 1 fully normalizes field lengths, the default value is 0.75.\n * @property {number} _k1 - A parameter to control how quickly an increase in term frequency results in term frequency saturation, the default value is 1.2.\n * @property {number} termIndex - A counter incremented for each unique term, used to identify a terms position in the vector space.\n * @property {array} metadataWhitelist - A list of metadata keys that have been whitelisted for entry in the index.\n */\nlunr.Builder = function () {\n  this._ref = \"id\"\n  this._fields = Object.create(null)\n  this._documents = Object.create(null)\n  this.invertedIndex = Object.create(null)\n  this.fieldTermFrequencies = {}\n  this.fieldLengths = {}\n  this.tokenizer = lunr.tokenizer\n  this.pipeline = new lunr.Pipeline\n  this.searchPipeline = new lunr.Pipeline\n  this.documentCount = 0\n  this._b = 0.75\n  this._k1 = 1.2\n  this.termIndex = 0\n  this.metadataWhitelist = []\n}\n\n/**\n * Sets the document field used as the document reference. Every document must have this field.\n * The type of this field in the document should be a string, if it is not a string it will be\n * coerced into a string by calling toString.\n *\n * The default ref is 'id'.\n *\n * The ref should _not_ be changed during indexing, it should be set before any documents are\n * added to the index. Changing it during indexing can lead to inconsistent results.\n *\n * @param {string} ref - The name of the reference field in the document.\n */\nlunr.Builder.prototype.ref = function (ref) {\n  this._ref = ref\n}\n\n/**\n * A function that is used to extract a field from a document.\n *\n * Lunr expects a field to be at the top level of a document, if however the field\n * is deeply nested within a document an extractor function can be used to extract\n * the right field for indexing.\n *\n * @callback fieldExtractor\n * @param {object} doc - The document being added to the index.\n * @returns {?(string|object|object[])} obj - The object that will be indexed for this field.\n * @example <caption>Extracting a nested field</caption>\n * function (doc) { return doc.nested.field }\n */\n\n/**\n * Adds a field to the list of document fields that will be indexed. Every document being\n * indexed should have this field. Null values for this field in indexed documents will\n * not cause errors but will limit the chance of that document being retrieved by searches.\n *\n * All fields should be added before adding documents to the index. Adding fields after\n * a document has been indexed will have no effect on already indexed documents.\n *\n * Fields can be boosted at build time. This allows terms within that field to have more\n * importance when ranking search results. Use a field boost to specify that matches within\n * one field are more important than other fields.\n *\n * @param {string} fieldName - The name of a field to index in all documents.\n * @param {object} attributes - Optional attributes associated with this field.\n * @param {number} [attributes.boost=1] - Boost applied to all terms within this field.\n * @param {fieldExtractor} [attributes.extractor] - Function to extract a field from a document.\n * @throws {RangeError} fieldName cannot contain unsupported characters '/'\n */\nlunr.Builder.prototype.field = function (fieldName, attributes) {\n  if (/\\//.test(fieldName)) {\n    throw new RangeError (\"Field '\" + fieldName + \"' contains illegal character '/'\")\n  }\n\n  this._fields[fieldName] = attributes || {}\n}\n\n/**\n * A parameter to tune the amount of field length normalisation that is applied when\n * calculating relevance scores. A value of 0 will completely disable any normalisation\n * and a value of 1 will fully normalise field lengths. The default is 0.75. Values of b\n * will be clamped to the range 0 - 1.\n *\n * @param {number} number - The value to set for this tuning parameter.\n */\nlunr.Builder.prototype.b = function (number) {\n  if (number < 0) {\n    this._b = 0\n  } else if (number > 1) {\n    this._b = 1\n  } else {\n    this._b = number\n  }\n}\n\n/**\n * A parameter that controls the speed at which a rise in term frequency results in term\n * frequency saturation. The default value is 1.2. Setting this to a higher value will give\n * slower saturation levels, a lower value will result in quicker saturation.\n *\n * @param {number} number - The value to set for this tuning parameter.\n */\nlunr.Builder.prototype.k1 = function (number) {\n  this._k1 = number\n}\n\n/**\n * Adds a document to the index.\n *\n * Before adding fields to the index the index should have been fully setup, with the document\n * ref and all fields to index already having been specified.\n *\n * The document must have a field name as specified by the ref (by default this is 'id') and\n * it should have all fields defined for indexing, though null or undefined values will not\n * cause errors.\n *\n * Entire documents can be boosted at build time. Applying a boost to a document indicates that\n * this document should rank higher in search results than other documents.\n *\n * @param {object} doc - The document to add to the index.\n * @param {object} attributes - Optional attributes associated with this document.\n * @param {number} [attributes.boost=1] - Boost applied to all terms within this document.\n */\nlunr.Builder.prototype.add = function (doc, attributes) {\n  var docRef = doc[this._ref],\n      fields = Object.keys(this._fields)\n\n  this._documents[docRef] = attributes || {}\n  this.documentCount += 1\n\n  for (var i = 0; i < fields.length; i++) {\n    var fieldName = fields[i],\n        extractor = this._fields[fieldName].extractor,\n        field = extractor ? extractor(doc) : doc[fieldName],\n        tokens = this.tokenizer(field, {\n          fields: [fieldName]\n        }),\n        terms = this.pipeline.run(tokens),\n        fieldRef = new lunr.FieldRef (docRef, fieldName),\n        fieldTerms = Object.create(null)\n\n    this.fieldTermFrequencies[fieldRef] = fieldTerms\n    this.fieldLengths[fieldRef] = 0\n\n    // store the length of this field for this document\n    this.fieldLengths[fieldRef] += terms.length\n\n    // calculate term frequencies for this field\n    for (var j = 0; j < terms.length; j++) {\n      var term = terms[j]\n\n      if (fieldTerms[term] == undefined) {\n        fieldTerms[term] = 0\n      }\n\n      fieldTerms[term] += 1\n\n      // add to inverted index\n      // create an initial posting if one doesn't exist\n      if (this.invertedIndex[term] == undefined) {\n        var posting = Object.create(null)\n        posting[\"_index\"] = this.termIndex\n        this.termIndex += 1\n\n        for (var k = 0; k < fields.length; k++) {\n          posting[fields[k]] = Object.create(null)\n        }\n\n        this.invertedIndex[term] = posting\n      }\n\n      // add an entry for this term/fieldName/docRef to the invertedIndex\n      if (this.invertedIndex[term][fieldName][docRef] == undefined) {\n        this.invertedIndex[term][fieldName][docRef] = Object.create(null)\n      }\n\n      // store all whitelisted metadata about this token in the\n      // inverted index\n      for (var l = 0; l < this.metadataWhitelist.length; l++) {\n        var metadataKey = this.metadataWhitelist[l],\n            metadata = term.metadata[metadataKey]\n\n        if (this.invertedIndex[term][fieldName][docRef][metadataKey] == undefined) {\n          this.invertedIndex[term][fieldName][docRef][metadataKey] = []\n        }\n\n        this.invertedIndex[term][fieldName][docRef][metadataKey].push(metadata)\n      }\n    }\n\n  }\n}\n\n/**\n * Calculates the average document length for this index\n *\n * @private\n */\nlunr.Builder.prototype.calculateAverageFieldLengths = function () {\n\n  var fieldRefs = Object.keys(this.fieldLengths),\n      numberOfFields = fieldRefs.length,\n      accumulator = {},\n      documentsWithField = {}\n\n  for (var i = 0; i < numberOfFields; i++) {\n    var fieldRef = lunr.FieldRef.fromString(fieldRefs[i]),\n        field = fieldRef.fieldName\n\n    documentsWithField[field] || (documentsWithField[field] = 0)\n    documentsWithField[field] += 1\n\n    accumulator[field] || (accumulator[field] = 0)\n    accumulator[field] += this.fieldLengths[fieldRef]\n  }\n\n  var fields = Object.keys(this._fields)\n\n  for (var i = 0; i < fields.length; i++) {\n    var fieldName = fields[i]\n    accumulator[fieldName] = accumulator[fieldName] / documentsWithField[fieldName]\n  }\n\n  this.averageFieldLength = accumulator\n}\n\n/**\n * Builds a vector space model of every document using lunr.Vector\n *\n * @private\n */\nlunr.Builder.prototype.createFieldVectors = function () {\n  var fieldVectors = {},\n      fieldRefs = Object.keys(this.fieldTermFrequencies),\n      fieldRefsLength = fieldRefs.length,\n      termIdfCache = Object.create(null)\n\n  for (var i = 0; i < fieldRefsLength; i++) {\n    var fieldRef = lunr.FieldRef.fromString(fieldRefs[i]),\n        fieldName = fieldRef.fieldName,\n        fieldLength = this.fieldLengths[fieldRef],\n        fieldVector = new lunr.Vector,\n        termFrequencies = this.fieldTermFrequencies[fieldRef],\n        terms = Object.keys(termFrequencies),\n        termsLength = terms.length\n\n\n    var fieldBoost = this._fields[fieldName].boost || 1,\n        docBoost = this._documents[fieldRef.docRef].boost || 1\n\n    for (var j = 0; j < termsLength; j++) {\n      var term = terms[j],\n          tf = termFrequencies[term],\n          termIndex = this.invertedIndex[term]._index,\n          idf, score, scoreWithPrecision\n\n      if (termIdfCache[term] === undefined) {\n        idf = lunr.idf(this.invertedIndex[term], this.documentCount)\n        termIdfCache[term] = idf\n      } else {\n        idf = termIdfCache[term]\n      }\n\n      score = idf * ((this._k1 + 1) * tf) / (this._k1 * (1 - this._b + this._b * (fieldLength / this.averageFieldLength[fieldName])) + tf)\n      score *= fieldBoost\n      score *= docBoost\n      scoreWithPrecision = Math.round(score * 1000) / 1000\n      // Converts 1.23456789 to 1.234.\n      // Reducing the precision so that the vectors take up less\n      // space when serialised. Doing it now so that they behave\n      // the same before and after serialisation. Also, this is\n      // the fastest approach to reducing a number's precision in\n      // JavaScript.\n\n      fieldVector.insert(termIndex, scoreWithPrecision)\n    }\n\n    fieldVectors[fieldRef] = fieldVector\n  }\n\n  this.fieldVectors = fieldVectors\n}\n\n/**\n * Creates a token set of all tokens in the index using lunr.TokenSet\n *\n * @private\n */\nlunr.Builder.prototype.createTokenSet = function () {\n  this.tokenSet = lunr.TokenSet.fromArray(\n    Object.keys(this.invertedIndex).sort()\n  )\n}\n\n/**\n * Builds the index, creating an instance of lunr.Index.\n *\n * This completes the indexing process and should only be called\n * once all documents have been added to the index.\n *\n * @returns {lunr.Index}\n */\nlunr.Builder.prototype.build = function () {\n  this.calculateAverageFieldLengths()\n  this.createFieldVectors()\n  this.createTokenSet()\n\n  return new lunr.Index({\n    invertedIndex: this.invertedIndex,\n    fieldVectors: this.fieldVectors,\n    tokenSet: this.tokenSet,\n    fields: Object.keys(this._fields),\n    pipeline: this.searchPipeline\n  })\n}\n\n/**\n * Applies a plugin to the index builder.\n *\n * A plugin is a function that is called with the index builder as its context.\n * Plugins can be used to customise or extend the behaviour of the index\n * in some way. A plugin is just a function, that encapsulated the custom\n * behaviour that should be applied when building the index.\n *\n * The plugin function will be called with the index builder as its argument, additional\n * arguments can also be passed when calling use. The function will be called\n * with the index builder as its context.\n *\n * @param {Function} plugin The plugin to apply.\n */\nlunr.Builder.prototype.use = function (fn) {\n  var args = Array.prototype.slice.call(arguments, 1)\n  args.unshift(this)\n  fn.apply(this, args)\n}\n/**\n * Contains and collects metadata about a matching document.\n * A single instance of lunr.MatchData is returned as part of every\n * lunr.Index~Result.\n *\n * @constructor\n * @param {string} term - The term this match data is associated with\n * @param {string} field - The field in which the term was found\n * @param {object} metadata - The metadata recorded about this term in this field\n * @property {object} metadata - A cloned collection of metadata associated with this document.\n * @see {@link lunr.Index~Result}\n */\nlunr.MatchData = function (term, field, metadata) {\n  var clonedMetadata = Object.create(null),\n      metadataKeys = Object.keys(metadata || {})\n\n  // Cloning the metadata to prevent the original\n  // being mutated during match data combination.\n  // Metadata is kept in an array within the inverted\n  // index so cloning the data can be done with\n  // Array#slice\n  for (var i = 0; i < metadataKeys.length; i++) {\n    var key = metadataKeys[i]\n    clonedMetadata[key] = metadata[key].slice()\n  }\n\n  this.metadata = Object.create(null)\n\n  if (term !== undefined) {\n    this.metadata[term] = Object.create(null)\n    this.metadata[term][field] = clonedMetadata\n  }\n}\n\n/**\n * An instance of lunr.MatchData will be created for every term that matches a\n * document. However only one instance is required in a lunr.Index~Result. This\n * method combines metadata from another instance of lunr.MatchData with this\n * objects metadata.\n *\n * @param {lunr.MatchData} otherMatchData - Another instance of match data to merge with this one.\n * @see {@link lunr.Index~Result}\n */\nlunr.MatchData.prototype.combine = function (otherMatchData) {\n  var terms = Object.keys(otherMatchData.metadata)\n\n  for (var i = 0; i < terms.length; i++) {\n    var term = terms[i],\n        fields = Object.keys(otherMatchData.metadata[term])\n\n    if (this.metadata[term] == undefined) {\n      this.metadata[term] = Object.create(null)\n    }\n\n    for (var j = 0; j < fields.length; j++) {\n      var field = fields[j],\n          keys = Object.keys(otherMatchData.metadata[term][field])\n\n      if (this.metadata[term][field] == undefined) {\n        this.metadata[term][field] = Object.create(null)\n      }\n\n      for (var k = 0; k < keys.length; k++) {\n        var key = keys[k]\n\n        if (this.metadata[term][field][key] == undefined) {\n          this.metadata[term][field][key] = otherMatchData.metadata[term][field][key]\n        } else {\n          this.metadata[term][field][key] = this.metadata[term][field][key].concat(otherMatchData.metadata[term][field][key])\n        }\n\n      }\n    }\n  }\n}\n\n/**\n * Add metadata for a term/field pair to this instance of match data.\n *\n * @param {string} term - The term this match data is associated with\n * @param {string} field - The field in which the term was found\n * @param {object} metadata - The metadata recorded about this term in this field\n */\nlunr.MatchData.prototype.add = function (term, field, metadata) {\n  if (!(term in this.metadata)) {\n    this.metadata[term] = Object.create(null)\n    this.metadata[term][field] = metadata\n    return\n  }\n\n  if (!(field in this.metadata[term])) {\n    this.metadata[term][field] = metadata\n    return\n  }\n\n  var metadataKeys = Object.keys(metadata)\n\n  for (var i = 0; i < metadataKeys.length; i++) {\n    var key = metadataKeys[i]\n\n    if (key in this.metadata[term][field]) {\n      this.metadata[term][field][key] = this.metadata[term][field][key].concat(metadata[key])\n    } else {\n      this.metadata[term][field][key] = metadata[key]\n    }\n  }\n}\n/**\n * A lunr.Query provides a programmatic way of defining queries to be performed\n * against a {@link lunr.Index}.\n *\n * Prefer constructing a lunr.Query using the {@link lunr.Index#query} method\n * so the query object is pre-initialized with the right index fields.\n *\n * @constructor\n * @property {lunr.Query~Clause[]} clauses - An array of query clauses.\n * @property {string[]} allFields - An array of all available fields in a lunr.Index.\n */\nlunr.Query = function (allFields) {\n  this.clauses = []\n  this.allFields = allFields\n}\n\n/**\n * Constants for indicating what kind of automatic wildcard insertion will be used when constructing a query clause.\n *\n * This allows wildcards to be added to the beginning and end of a term without having to manually do any string\n * concatenation.\n *\n * The wildcard constants can be bitwise combined to select both leading and trailing wildcards.\n *\n * @constant\n * @default\n * @property {number} wildcard.NONE - The term will have no wildcards inserted, this is the default behaviour\n * @property {number} wildcard.LEADING - Prepend the term with a wildcard, unless a leading wildcard already exists\n * @property {number} wildcard.TRAILING - Append a wildcard to the term, unless a trailing wildcard already exists\n * @see lunr.Query~Clause\n * @see lunr.Query#clause\n * @see lunr.Query#term\n * @example <caption>query term with trailing wildcard</caption>\n * query.term('foo', { wildcard: lunr.Query.wildcard.TRAILING })\n * @example <caption>query term with leading and trailing wildcard</caption>\n * query.term('foo', {\n *   wildcard: lunr.Query.wildcard.LEADING | lunr.Query.wildcard.TRAILING\n * })\n */\n\nlunr.Query.wildcard = new String (\"*\")\nlunr.Query.wildcard.NONE = 0\nlunr.Query.wildcard.LEADING = 1\nlunr.Query.wildcard.TRAILING = 2\n\n/**\n * Constants for indicating what kind of presence a term must have in matching documents.\n *\n * @constant\n * @enum {number}\n * @see lunr.Query~Clause\n * @see lunr.Query#clause\n * @see lunr.Query#term\n * @example <caption>query term with required presence</caption>\n * query.term('foo', { presence: lunr.Query.presence.REQUIRED })\n */\nlunr.Query.presence = {\n  /**\n   * Term's presence in a document is optional, this is the default value.\n   */\n  OPTIONAL: 1,\n\n  /**\n   * Term's presence in a document is required, documents that do not contain\n   * this term will not be returned.\n   */\n  REQUIRED: 2,\n\n  /**\n   * Term's presence in a document is prohibited, documents that do contain\n   * this term will not be returned.\n   */\n  PROHIBITED: 3\n}\n\n/**\n * A single clause in a {@link lunr.Query} contains a term and details on how to\n * match that term against a {@link lunr.Index}.\n *\n * @typedef {Object} lunr.Query~Clause\n * @property {string[]} fields - The fields in an index this clause should be matched against.\n * @property {number} [boost=1] - Any boost that should be applied when matching this clause.\n * @property {number} [editDistance] - Whether the term should have fuzzy matching applied, and how fuzzy the match should be.\n * @property {boolean} [usePipeline] - Whether the term should be passed through the search pipeline.\n * @property {number} [wildcard=lunr.Query.wildcard.NONE] - Whether the term should have wildcards appended or prepended.\n * @property {number} [presence=lunr.Query.presence.OPTIONAL] - The terms presence in any matching documents.\n */\n\n/**\n * Adds a {@link lunr.Query~Clause} to this query.\n *\n * Unless the clause contains the fields to be matched all fields will be matched. In addition\n * a default boost of 1 is applied to the clause.\n *\n * @param {lunr.Query~Clause} clause - The clause to add to this query.\n * @see lunr.Query~Clause\n * @returns {lunr.Query}\n */\nlunr.Query.prototype.clause = function (clause) {\n  if (!('fields' in clause)) {\n    clause.fields = this.allFields\n  }\n\n  if (!('boost' in clause)) {\n    clause.boost = 1\n  }\n\n  if (!('usePipeline' in clause)) {\n    clause.usePipeline = true\n  }\n\n  if (!('wildcard' in clause)) {\n    clause.wildcard = lunr.Query.wildcard.NONE\n  }\n\n  if ((clause.wildcard & lunr.Query.wildcard.LEADING) && (clause.term.charAt(0) != lunr.Query.wildcard)) {\n    clause.term = \"*\" + clause.term\n  }\n\n  if ((clause.wildcard & lunr.Query.wildcard.TRAILING) && (clause.term.slice(-1) != lunr.Query.wildcard)) {\n    clause.term = \"\" + clause.term + \"*\"\n  }\n\n  if (!('presence' in clause)) {\n    clause.presence = lunr.Query.presence.OPTIONAL\n  }\n\n  this.clauses.push(clause)\n\n  return this\n}\n\n/**\n * A negated query is one in which every clause has a presence of\n * prohibited. These queries require some special processing to return\n * the expected results.\n *\n * @returns boolean\n */\nlunr.Query.prototype.isNegated = function () {\n  for (var i = 0; i < this.clauses.length; i++) {\n    if (this.clauses[i].presence != lunr.Query.presence.PROHIBITED) {\n      return false\n    }\n  }\n\n  return true\n}\n\n/**\n * Adds a term to the current query, under the covers this will create a {@link lunr.Query~Clause}\n * to the list of clauses that make up this query.\n *\n * The term is used as is, i.e. no tokenization will be performed by this method. Instead conversion\n * to a token or token-like string should be done before calling this method.\n *\n * The term will be converted to a string by calling `toString`. Multiple terms can be passed as an\n * array, each term in the array will share the same options.\n *\n * @param {object|object[]} term - The term(s) to add to the query.\n * @param {object} [options] - Any additional properties to add to the query clause.\n * @returns {lunr.Query}\n * @see lunr.Query#clause\n * @see lunr.Query~Clause\n * @example <caption>adding a single term to a query</caption>\n * query.term(\"foo\")\n * @example <caption>adding a single term to a query and specifying search fields, term boost and automatic trailing wildcard</caption>\n * query.term(\"foo\", {\n *   fields: [\"title\"],\n *   boost: 10,\n *   wildcard: lunr.Query.wildcard.TRAILING\n * })\n * @example <caption>using lunr.tokenizer to convert a string to tokens before using them as terms</caption>\n * query.term(lunr.tokenizer(\"foo bar\"))\n */\nlunr.Query.prototype.term = function (term, options) {\n  if (Array.isArray(term)) {\n    term.forEach(function (t) { this.term(t, lunr.utils.clone(options)) }, this)\n    return this\n  }\n\n  var clause = options || {}\n  clause.term = term.toString()\n\n  this.clause(clause)\n\n  return this\n}\nlunr.QueryParseError = function (message, start, end) {\n  this.name = \"QueryParseError\"\n  this.message = message\n  this.start = start\n  this.end = end\n}\n\nlunr.QueryParseError.prototype = new Error\nlunr.QueryLexer = function (str) {\n  this.lexemes = []\n  this.str = str\n  this.length = str.length\n  this.pos = 0\n  this.start = 0\n  this.escapeCharPositions = []\n}\n\nlunr.QueryLexer.prototype.run = function () {\n  var state = lunr.QueryLexer.lexText\n\n  while (state) {\n    state = state(this)\n  }\n}\n\nlunr.QueryLexer.prototype.sliceString = function () {\n  var subSlices = [],\n      sliceStart = this.start,\n      sliceEnd = this.pos\n\n  for (var i = 0; i < this.escapeCharPositions.length; i++) {\n    sliceEnd = this.escapeCharPositions[i]\n    subSlices.push(this.str.slice(sliceStart, sliceEnd))\n    sliceStart = sliceEnd + 1\n  }\n\n  subSlices.push(this.str.slice(sliceStart, this.pos))\n  this.escapeCharPositions.length = 0\n\n  return subSlices.join('')\n}\n\nlunr.QueryLexer.prototype.emit = function (type) {\n  this.lexemes.push({\n    type: type,\n    str: this.sliceString(),\n    start: this.start,\n    end: this.pos\n  })\n\n  this.start = this.pos\n}\n\nlunr.QueryLexer.prototype.escapeCharacter = function () {\n  this.escapeCharPositions.push(this.pos - 1)\n  this.pos += 1\n}\n\nlunr.QueryLexer.prototype.next = function () {\n  if (this.pos >= this.length) {\n    return lunr.QueryLexer.EOS\n  }\n\n  var char = this.str.charAt(this.pos)\n  this.pos += 1\n  return char\n}\n\nlunr.QueryLexer.prototype.width = function () {\n  return this.pos - this.start\n}\n\nlunr.QueryLexer.prototype.ignore = function () {\n  if (this.start == this.pos) {\n    this.pos += 1\n  }\n\n  this.start = this.pos\n}\n\nlunr.QueryLexer.prototype.backup = function () {\n  this.pos -= 1\n}\n\nlunr.QueryLexer.prototype.acceptDigitRun = function () {\n  var char, charCode\n\n  do {\n    char = this.next()\n    charCode = char.charCodeAt(0)\n  } while (charCode > 47 && charCode < 58)\n\n  if (char != lunr.QueryLexer.EOS) {\n    this.backup()\n  }\n}\n\nlunr.QueryLexer.prototype.more = function () {\n  return this.pos < this.length\n}\n\nlunr.QueryLexer.EOS = 'EOS'\nlunr.QueryLexer.FIELD = 'FIELD'\nlunr.QueryLexer.TERM = 'TERM'\nlunr.QueryLexer.EDIT_DISTANCE = 'EDIT_DISTANCE'\nlunr.QueryLexer.BOOST = 'BOOST'\nlunr.QueryLexer.PRESENCE = 'PRESENCE'\n\nlunr.QueryLexer.lexField = function (lexer) {\n  lexer.backup()\n  lexer.emit(lunr.QueryLexer.FIELD)\n  lexer.ignore()\n  return lunr.QueryLexer.lexText\n}\n\nlunr.QueryLexer.lexTerm = function (lexer) {\n  if (lexer.width() > 1) {\n    lexer.backup()\n    lexer.emit(lunr.QueryLexer.TERM)\n  }\n\n  lexer.ignore()\n\n  if (lexer.more()) {\n    return lunr.QueryLexer.lexText\n  }\n}\n\nlunr.QueryLexer.lexEditDistance = function (lexer) {\n  lexer.ignore()\n  lexer.acceptDigitRun()\n  lexer.emit(lunr.QueryLexer.EDIT_DISTANCE)\n  return lunr.QueryLexer.lexText\n}\n\nlunr.QueryLexer.lexBoost = function (lexer) {\n  lexer.ignore()\n  lexer.acceptDigitRun()\n  lexer.emit(lunr.QueryLexer.BOOST)\n  return lunr.QueryLexer.lexText\n}\n\nlunr.QueryLexer.lexEOS = function (lexer) {\n  if (lexer.width() > 0) {\n    lexer.emit(lunr.QueryLexer.TERM)\n  }\n}\n\n// This matches the separator used when tokenising fields\n// within a document. These should match otherwise it is\n// not possible to search for some tokens within a document.\n//\n// It is possible for the user to change the separator on the\n// tokenizer so it _might_ clash with any other of the special\n// characters already used within the search string, e.g. :.\n//\n// This means that it is possible to change the separator in\n// such a way that makes some words unsearchable using a search\n// string.\nlunr.QueryLexer.termSeparator = lunr.tokenizer.separator\n\nlunr.QueryLexer.lexText = function (lexer) {\n  while (true) {\n    var char = lexer.next()\n\n    if (char == lunr.QueryLexer.EOS) {\n      return lunr.QueryLexer.lexEOS\n    }\n\n    // Escape character is '\\'\n    if (char.charCodeAt(0) == 92) {\n      lexer.escapeCharacter()\n      continue\n    }\n\n    if (char == \":\") {\n      return lunr.QueryLexer.lexField\n    }\n\n    if (char == \"~\") {\n      lexer.backup()\n      if (lexer.width() > 0) {\n        lexer.emit(lunr.QueryLexer.TERM)\n      }\n      return lunr.QueryLexer.lexEditDistance\n    }\n\n    if (char == \"^\") {\n      lexer.backup()\n      if (lexer.width() > 0) {\n        lexer.emit(lunr.QueryLexer.TERM)\n      }\n      return lunr.QueryLexer.lexBoost\n    }\n\n    // \"+\" indicates term presence is required\n    // checking for length to ensure that only\n    // leading \"+\" are considered\n    if (char == \"+\" && lexer.width() === 1) {\n      lexer.emit(lunr.QueryLexer.PRESENCE)\n      return lunr.QueryLexer.lexText\n    }\n\n    // \"-\" indicates term presence is prohibited\n    // checking for length to ensure that only\n    // leading \"-\" are considered\n    if (char == \"-\" && lexer.width() === 1) {\n      lexer.emit(lunr.QueryLexer.PRESENCE)\n      return lunr.QueryLexer.lexText\n    }\n\n    if (char.match(lunr.QueryLexer.termSeparator)) {\n      return lunr.QueryLexer.lexTerm\n    }\n  }\n}\n\nlunr.QueryParser = function (str, query) {\n  this.lexer = new lunr.QueryLexer (str)\n  this.query = query\n  this.currentClause = {}\n  this.lexemeIdx = 0\n}\n\nlunr.QueryParser.prototype.parse = function () {\n  this.lexer.run()\n  this.lexemes = this.lexer.lexemes\n\n  var state = lunr.QueryParser.parseClause\n\n  while (state) {\n    state = state(this)\n  }\n\n  return this.query\n}\n\nlunr.QueryParser.prototype.peekLexeme = function () {\n  return this.lexemes[this.lexemeIdx]\n}\n\nlunr.QueryParser.prototype.consumeLexeme = function () {\n  var lexeme = this.peekLexeme()\n  this.lexemeIdx += 1\n  return lexeme\n}\n\nlunr.QueryParser.prototype.nextClause = function () {\n  var completedClause = this.currentClause\n  this.query.clause(completedClause)\n  this.currentClause = {}\n}\n\nlunr.QueryParser.parseClause = function (parser) {\n  var lexeme = parser.peekLexeme()\n\n  if (lexeme == undefined) {\n    return\n  }\n\n  switch (lexeme.type) {\n    case lunr.QueryLexer.PRESENCE:\n      return lunr.QueryParser.parsePresence\n    case lunr.QueryLexer.FIELD:\n      return lunr.QueryParser.parseField\n    case lunr.QueryLexer.TERM:\n      return lunr.QueryParser.parseTerm\n    default:\n      var errorMessage = \"expected either a field or a term, found \" + lexeme.type\n\n      if (lexeme.str.length >= 1) {\n        errorMessage += \" with value '\" + lexeme.str + \"'\"\n      }\n\n      throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n  }\n}\n\nlunr.QueryParser.parsePresence = function (parser) {\n  var lexeme = parser.consumeLexeme()\n\n  if (lexeme == undefined) {\n    return\n  }\n\n  switch (lexeme.str) {\n    case \"-\":\n      parser.currentClause.presence = lunr.Query.presence.PROHIBITED\n      break\n    case \"+\":\n      parser.currentClause.presence = lunr.Query.presence.REQUIRED\n      break\n    default:\n      var errorMessage = \"unrecognised presence operator'\" + lexeme.str + \"'\"\n      throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n  }\n\n  var nextLexeme = parser.peekLexeme()\n\n  if (nextLexeme == undefined) {\n    var errorMessage = \"expecting term or field, found nothing\"\n    throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n  }\n\n  switch (nextLexeme.type) {\n    case lunr.QueryLexer.FIELD:\n      return lunr.QueryParser.parseField\n    case lunr.QueryLexer.TERM:\n      return lunr.QueryParser.parseTerm\n    default:\n      var errorMessage = \"expecting term or field, found '\" + nextLexeme.type + \"'\"\n      throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end)\n  }\n}\n\nlunr.QueryParser.parseField = function (parser) {\n  var lexeme = parser.consumeLexeme()\n\n  if (lexeme == undefined) {\n    return\n  }\n\n  if (parser.query.allFields.indexOf(lexeme.str) == -1) {\n    var possibleFields = parser.query.allFields.map(function (f) { return \"'\" + f + \"'\" }).join(', '),\n        errorMessage = \"unrecognised field '\" + lexeme.str + \"', possible fields: \" + possibleFields\n\n    throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n  }\n\n  parser.currentClause.fields = [lexeme.str]\n\n  var nextLexeme = parser.peekLexeme()\n\n  if (nextLexeme == undefined) {\n    var errorMessage = \"expecting term, found nothing\"\n    throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n  }\n\n  switch (nextLexeme.type) {\n    case lunr.QueryLexer.TERM:\n      return lunr.QueryParser.parseTerm\n    default:\n      var errorMessage = \"expecting term, found '\" + nextLexeme.type + \"'\"\n      throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end)\n  }\n}\n\nlunr.QueryParser.parseTerm = function (parser) {\n  var lexeme = parser.consumeLexeme()\n\n  if (lexeme == undefined) {\n    return\n  }\n\n  parser.currentClause.term = lexeme.str.toLowerCase()\n\n  if (lexeme.str.indexOf(\"*\") != -1) {\n    parser.currentClause.usePipeline = false\n  }\n\n  var nextLexeme = parser.peekLexeme()\n\n  if (nextLexeme == undefined) {\n    parser.nextClause()\n    return\n  }\n\n  switch (nextLexeme.type) {\n    case lunr.QueryLexer.TERM:\n      parser.nextClause()\n      return lunr.QueryParser.parseTerm\n    case lunr.QueryLexer.FIELD:\n      parser.nextClause()\n      return lunr.QueryParser.parseField\n    case lunr.QueryLexer.EDIT_DISTANCE:\n      return lunr.QueryParser.parseEditDistance\n    case lunr.QueryLexer.BOOST:\n      return lunr.QueryParser.parseBoost\n    case lunr.QueryLexer.PRESENCE:\n      parser.nextClause()\n      return lunr.QueryParser.parsePresence\n    default:\n      var errorMessage = \"Unexpected lexeme type '\" + nextLexeme.type + \"'\"\n      throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end)\n  }\n}\n\nlunr.QueryParser.parseEditDistance = function (parser) {\n  var lexeme = parser.consumeLexeme()\n\n  if (lexeme == undefined) {\n    return\n  }\n\n  var editDistance = parseInt(lexeme.str, 10)\n\n  if (isNaN(editDistance)) {\n    var errorMessage = \"edit distance must be numeric\"\n    throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n  }\n\n  parser.currentClause.editDistance = editDistance\n\n  var nextLexeme = parser.peekLexeme()\n\n  if (nextLexeme == undefined) {\n    parser.nextClause()\n    return\n  }\n\n  switch (nextLexeme.type) {\n    case lunr.QueryLexer.TERM:\n      parser.nextClause()\n      return lunr.QueryParser.parseTerm\n    case lunr.QueryLexer.FIELD:\n      parser.nextClause()\n      return lunr.QueryParser.parseField\n    case lunr.QueryLexer.EDIT_DISTANCE:\n      return lunr.QueryParser.parseEditDistance\n    case lunr.QueryLexer.BOOST:\n      return lunr.QueryParser.parseBoost\n    case lunr.QueryLexer.PRESENCE:\n      parser.nextClause()\n      return lunr.QueryParser.parsePresence\n    default:\n      var errorMessage = \"Unexpected lexeme type '\" + nextLexeme.type + \"'\"\n      throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end)\n  }\n}\n\nlunr.QueryParser.parseBoost = function (parser) {\n  var lexeme = parser.consumeLexeme()\n\n  if (lexeme == undefined) {\n    return\n  }\n\n  var boost = parseInt(lexeme.str, 10)\n\n  if (isNaN(boost)) {\n    var errorMessage = \"boost must be numeric\"\n    throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n  }\n\n  parser.currentClause.boost = boost\n\n  var nextLexeme = parser.peekLexeme()\n\n  if (nextLexeme == undefined) {\n    parser.nextClause()\n    return\n  }\n\n  switch (nextLexeme.type) {\n    case lunr.QueryLexer.TERM:\n      parser.nextClause()\n      return lunr.QueryParser.parseTerm\n    case lunr.QueryLexer.FIELD:\n      parser.nextClause()\n      return lunr.QueryParser.parseField\n    case lunr.QueryLexer.EDIT_DISTANCE:\n      return lunr.QueryParser.parseEditDistance\n    case lunr.QueryLexer.BOOST:\n      return lunr.QueryParser.parseBoost\n    case lunr.QueryLexer.PRESENCE:\n      parser.nextClause()\n      return lunr.QueryParser.parsePresence\n    default:\n      var errorMessage = \"Unexpected lexeme type '\" + nextLexeme.type + \"'\"\n      throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end)\n  }\n}\n\n  /**\n   * export the module via AMD, CommonJS or as a browser global\n   * Export code from https://github.com/umdjs/umd/blob/master/returnExports.js\n   */\n  ;(function (root, factory) {\n    if (typeof define === 'function' && define.amd) {\n      // AMD. Register as an anonymous module.\n      define(factory)\n    } else if (typeof exports === 'object') {\n      /**\n       * Node. Does not work with strict CommonJS, but\n       * only CommonJS-like enviroments that support module.exports,\n       * like Node.\n       */\n      module.exports = factory()\n    } else {\n      // Browser globals (root is window)\n      root.lunr = factory()\n    }\n  }(this, function () {\n    /**\n     * Just return a value to define the module export.\n     * This example returns an object, but the module\n     * can return a function as the exported value.\n     */\n    return lunr\n  }))\n})();\n", "/*!\n * escape-html\n * Copyright(c) 2012-2013 TJ Holowaychuk\n * Copyright(c) 2015 Andreas Lubbe\n * Copyright(c) 2015 Tiancheng \"Timothy\" Gu\n * MIT Licensed\n */\n\n'use strict';\n\n/**\n * Module variables.\n * @private\n */\n\nvar matchHtmlRegExp = /[\"'&<>]/;\n\n/**\n * Module exports.\n * @public\n */\n\nmodule.exports = escapeHtml;\n\n/**\n * Escape special characters in the given string of html.\n *\n * @param  {string} string The string to escape for inserting into HTML\n * @return {string}\n * @public\n */\n\nfunction escapeHtml(string) {\n  var str = '' + string;\n  var match = matchHtmlRegExp.exec(str);\n\n  if (!match) {\n    return str;\n  }\n\n  var escape;\n  var html = '';\n  var index = 0;\n  var lastIndex = 0;\n\n  for (index = match.index; index < str.length; index++) {\n    switch (str.charCodeAt(index)) {\n      case 34: // \"\n        escape = '&quot;';\n        break;\n      case 38: // &\n        escape = '&amp;';\n        break;\n      case 39: // '\n        escape = '&#39;';\n        break;\n      case 60: // <\n        escape = '&lt;';\n        break;\n      case 62: // >\n        escape = '&gt;';\n        break;\n      default:\n        continue;\n    }\n\n    if (lastIndex !== index) {\n      html += str.substring(lastIndex, index);\n    }\n\n    lastIndex = index + 1;\n    html += escape;\n  }\n\n  return lastIndex !== index\n    ? html + str.substring(lastIndex, index)\n    : html;\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A RTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport lunr from \"lunr\"\n\nimport \"~/polyfills\"\n\nimport { Search, SearchIndexConfig } from \"../../_\"\nimport {\n  SearchMessage,\n  SearchMessageType\n} from \"../message\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Add support for usage with `iframe-worker` polyfill\n *\n * While `importScripts` is synchronous when executed inside of a web worker,\n * it's not possible to provide a synchronous polyfilled implementation. The\n * cool thing is that awaiting a non-Promise is a noop, so extending the type\n * definition to return a `Promise` shouldn't break anything.\n *\n * @see https://bit.ly/2PjDnXi - GitHub comment\n */\ndeclare global {\n  function importScripts(...urls: string[]): Promise<void> | void\n}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Search index\n */\nlet index: Search\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch (= import) multi-language support through `lunr-languages`\n *\n * This function automatically imports the stemmers necessary to process the\n * languages, which are defined through the search index configuration.\n *\n * If the worker runs inside of an `iframe` (when using `iframe-worker` as\n * a shim), the base URL for the stemmers to be loaded must be determined by\n * searching for the first `script` element with a `src` attribute, which will\n * contain the contents of this script.\n *\n * @param config - Search index configuration\n *\n * @returns Promise resolving with no result\n */\nasync function setupSearchLanguages(\n  config: SearchIndexConfig\n): Promise<void> {\n  let base = \"../lunr\"\n\n  /* Detect `iframe-worker` and fix base URL */\n  if (typeof parent !== \"undefined\" && \"IFrameWorker\" in parent) {\n    const worker = document.querySelector<HTMLScriptElement>(\"script[src]\")!\n    const [path] = worker.src.split(\"/worker\")\n\n    /* Prefix base with path */\n    base = base.replace(\"..\", path)\n  }\n\n  /* Add scripts for languages */\n  const scripts = []\n  for (const lang of config.lang) {\n    switch (lang) {\n\n      /* Add segmenter for Japanese */\n      case \"ja\":\n        scripts.push(`${base}/tinyseg.js`)\n        break\n\n      /* Add segmenter for Hindi and Thai */\n      case \"hi\":\n      case \"th\":\n        scripts.push(`${base}/wordcut.js`)\n        break\n    }\n\n    /* Add language support */\n    if (lang !== \"en\")\n      scripts.push(`${base}/min/lunr.${lang}.min.js`)\n  }\n\n  /* Add multi-language support */\n  if (config.lang.length > 1)\n    scripts.push(`${base}/min/lunr.multi.min.js`)\n\n  /* Load scripts synchronously */\n  if (scripts.length)\n    await importScripts(\n      `${base}/min/lunr.stemmer.support.min.js`,\n      ...scripts\n    )\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Message handler\n *\n * @param message - Source message\n *\n * @returns Target message\n */\nexport async function handler(\n  message: SearchMessage\n): Promise<SearchMessage> {\n  switch (message.type) {\n\n    /* Search setup message */\n    case SearchMessageType.SETUP:\n      await setupSearchLanguages(message.data.config)\n      index = new Search(message.data)\n      return {\n        type: SearchMessageType.READY\n      }\n\n    /* Search query message */\n    case SearchMessageType.QUERY:\n      return {\n        type: SearchMessageType.RESULT,\n        data: index ? index.search(message.data) : { items: [] }\n      }\n\n    /* All other messages */\n    default:\n      throw new TypeError(\"Invalid message type\")\n  }\n}\n\n/* ----------------------------------------------------------------------------\n * Worker\n * ------------------------------------------------------------------------- */\n\n/* @ts-expect-error - expose Lunr.js in global scope, or stemmers won't work */\nself.lunr = lunr\n\n/* Handle messages */\naddEventListener(\"message\", async ev => {\n  postMessage(await handler(ev.data))\n})\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Polyfills\n * ------------------------------------------------------------------------- */\n\n/* Polyfill `Object.entries` */\nif (!Object.entries)\n  Object.entries = function (obj: object) {\n    const data: [string, string][] = []\n    for (const key of Object.keys(obj))\n      // @ts-expect-error - ignore property access warning\n      data.push([key, obj[key]])\n\n    /* Return entries */\n    return data\n  }\n\n/* Polyfill `Object.values` */\nif (!Object.values)\n  Object.values = function (obj: object) {\n    const data: string[] = []\n    for (const key of Object.keys(obj))\n      // @ts-expect-error - ignore property access warning\n      data.push(obj[key])\n\n    /* Return values */\n    return data\n  }\n\n/* ------------------------------------------------------------------------- */\n\n/* Polyfills for `Element` */\nif (typeof Element !== \"undefined\") {\n\n  /* Polyfill `Element.scrollTo` */\n  if (!Element.prototype.scrollTo)\n    Element.prototype.scrollTo = function (\n      x?: ScrollToOptions | number, y?: number\n    ): void {\n      if (typeof x === \"object\") {\n        this.scrollLeft = x.left!\n        this.scrollTop = x.top!\n      } else {\n        this.scrollLeft = x!\n        this.scrollTop = y!\n      }\n    }\n\n  /* Polyfill `Element.replaceWith` */\n  if (!Element.prototype.replaceWith)\n    Element.prototype.replaceWith = function (\n      ...nodes: Array<string | Node>\n    ): void {\n      const parent = this.parentNode\n      if (parent) {\n        if (nodes.length === 0)\n          parent.removeChild(this)\n\n        /* Replace children and create text nodes */\n        for (let i = nodes.length - 1; i >= 0; i--) {\n          let node = nodes[i]\n          if (typeof node !== \"object\")\n            node = document.createTextNode(node)\n          else if (node.parentNode)\n            node.parentNode.removeChild(node)\n\n          /* Replace child or insert before previous sibling */\n          if (!i)\n            parent.replaceChild(node, this)\n          else\n            parent.insertBefore(this.previousSibling!, node)\n        }\n      }\n    }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport escapeHTML from \"escape-html\"\n\nimport { SearchIndexDocument } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search document\n */\nexport interface SearchDocument extends SearchIndexDocument {\n  parent?: SearchIndexDocument         /* Parent article */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Search document mapping\n */\nexport type SearchDocumentMap = Map<string, SearchDocument>\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Create a search document mapping\n *\n * @param docs - Search index documents\n *\n * @returns Search document map\n */\nexport function setupSearchDocumentMap(\n  docs: SearchIndexDocument[]\n): SearchDocumentMap {\n  const documents = new Map<string, SearchDocument>()\n  const parents   = new Set<SearchDocument>()\n  for (const doc of docs) {\n    const [path, hash] = doc.location.split(\"#\")\n\n    /* Extract location, title and tags */\n    const location = doc.location\n    const title    = doc.title\n    const tags     = doc.tags\n\n    /* Escape and cleanup text */\n    const text = escapeHTML(doc.text)\n      .replace(/\\s+(?=[,.:;!?])/g, \"\")\n      .replace(/\\s+/g, \" \")\n\n    /* Handle section */\n    if (hash) {\n      const parent = documents.get(path)!\n\n      /* Ignore first section, override article */\n      if (!parents.has(parent)) {\n        parent.title = doc.title\n        parent.text  = text\n\n        /* Remember that we processed the article */\n        parents.add(parent)\n\n      /* Add subsequent section */\n      } else {\n        documents.set(location, {\n          location,\n          title,\n          text,\n          parent\n        })\n      }\n\n    /* Add article */\n    } else {\n      documents.set(location, {\n        location,\n        title,\n        text,\n        ...tags && { tags }\n      })\n    }\n  }\n  return documents\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport escapeHTML from \"escape-html\"\n\nimport { SearchIndexConfig } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search highlight function\n *\n * @param value - Value\n *\n * @returns Highlighted value\n */\nexport type SearchHighlightFn = (value: string) => string\n\n/**\n * Search highlight factory function\n *\n * @param query - Query value\n *\n * @returns Search highlight function\n */\nexport type SearchHighlightFactoryFn = (query: string) => SearchHighlightFn\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Create a search highlighter\n *\n * @param config - Search index configuration\n * @param escape - Whether to escape HTML\n *\n * @returns Search highlight factory function\n */\nexport function setupSearchHighlighter(\n  config: SearchIndexConfig, escape: boolean\n): SearchHighlightFactoryFn {\n  const separator = new RegExp(config.separator, \"img\")\n  const highlight = (_: unknown, data: string, term: string) => {\n    return `${data}<mark data-md-highlight>${term}</mark>`\n  }\n\n  /* Return factory function */\n  return (query: string) => {\n    query = query\n      .replace(/[\\s*+\\-:~^]+/g, \" \")\n      .trim()\n\n    /* Create search term match expression */\n    const match = new RegExp(`(^|${config.separator})(${\n      query\n        .replace(/[|\\\\{}()[\\]^$+*?.-]/g, \"\\\\$&\")\n        .replace(separator, \"|\")\n    })`, \"img\")\n\n    /* Highlight string value */\n    return value => (\n      escape\n        ? escapeHTML(value)\n        : value\n      )\n        .replace(match, highlight)\n        .replace(/<\\/mark>(\\s+)<mark[^>]*>/img, \"$1\")\n  }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search query clause\n */\nexport interface SearchQueryClause {\n  presence: lunr.Query.presence        /* Clause presence */\n  term: string                         /* Clause term */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Search query terms\n */\nexport type SearchQueryTerms = Record<string, boolean>\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Parse a search query for analysis\n *\n * @param value - Query value\n *\n * @returns Search query clauses\n */\nexport function parseSearchQuery(\n  value: string\n): SearchQueryClause[] {\n  const query  = new (lunr as any).Query([\"title\", \"text\"])\n  const parser = new (lunr as any).QueryParser(value, query)\n\n  /* Parse and return query clauses */\n  parser.parse()\n  return query.clauses\n}\n\n/**\n * Analyze the search query clauses in regard to the search terms found\n *\n * @param query - Search query clauses\n * @param terms - Search terms\n *\n * @returns Search query terms\n */\nexport function getSearchQueryTerms(\n  query: SearchQueryClause[], terms: string[]\n): SearchQueryTerms {\n  const clauses = new Set<SearchQueryClause>(query)\n\n  /* Match query clauses against terms */\n  const result: SearchQueryTerms = {}\n  for (let t = 0; t < terms.length; t++)\n    for (const clause of clauses)\n      if (terms[t].startsWith(clause.term)) {\n        result[clause.term] = true\n        clauses.delete(clause)\n      }\n\n  /* Annotate unmatched non-stopword query clauses */\n  for (const clause of clauses)\n    if (lunr.stopWordFilter?.(clause.term as any))\n      result[clause.term] = false\n\n  /* Return query terms */\n  return result\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  SearchDocument,\n  SearchDocumentMap,\n  setupSearchDocumentMap\n} from \"../document\"\nimport {\n  SearchHighlightFactoryFn,\n  setupSearchHighlighter\n} from \"../highlighter\"\nimport { SearchOptions } from \"../options\"\nimport {\n  SearchQueryTerms,\n  getSearchQueryTerms,\n  parseSearchQuery\n} from \"../query\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search index configuration\n */\nexport interface SearchIndexConfig {\n  lang: string[]                       /* Search languages */\n  separator: string                    /* Search separator */\n}\n\n/**\n * Search index document\n */\nexport interface SearchIndexDocument {\n  location: string                     /* Document location */\n  title: string                        /* Document title */\n  text: string                         /* Document text */\n  tags?: string[]                      /* Document tags */\n  boost?: number                       /* Document boost */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Search index\n *\n * This interfaces describes the format of the `search_index.json` file which\n * is automatically built by the MkDocs search plugin.\n */\nexport interface SearchIndex {\n  config: SearchIndexConfig            /* Search index configuration */\n  docs: SearchIndexDocument[]          /* Search index documents */\n  options: SearchOptions               /* Search options */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Search metadata\n */\nexport interface SearchMetadata {\n  score: number                        /* Score (relevance) */\n  terms: SearchQueryTerms              /* Search query terms */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Search result document\n */\nexport type SearchResultDocument = SearchDocument & SearchMetadata\n\n/**\n * Search result item\n */\nexport type SearchResultItem = SearchResultDocument[]\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Search result\n */\nexport interface SearchResult {\n  items: SearchResultItem[]            /* Search result items */\n  suggestions?: string[]               /* Search suggestions */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Compute the difference of two lists of strings\n *\n * @param a - 1st list of strings\n * @param b - 2nd list of strings\n *\n * @returns Difference\n */\nfunction difference(a: string[], b: string[]): string[] {\n  const [x, y] = [new Set(a), new Set(b)]\n  return [\n    ...new Set([...x].filter(value => !y.has(value)))\n  ]\n}\n\n/* ----------------------------------------------------------------------------\n * Class\n * ------------------------------------------------------------------------- */\n\n/**\n * Search index\n */\nexport class Search {\n\n  /**\n   * Search document mapping\n   *\n   * A mapping of URLs (including hash fragments) to the actual articles and\n   * sections of the documentation. The search document mapping must be created\n   * regardless of whether the index was prebuilt or not, as Lunr.js itself\n   * only stores the actual index.\n   */\n  protected documents: SearchDocumentMap\n\n  /**\n   * Search highlight factory function\n   */\n  protected highlight: SearchHighlightFactoryFn\n\n  /**\n   * The underlying Lunr.js search index\n   */\n  protected index: lunr.Index\n\n  /**\n   * Search options\n   */\n  protected options: SearchOptions\n\n  /**\n   * Create the search integration\n   *\n   * @param data - Search index\n   */\n  public constructor({ config, docs, options }: SearchIndex) {\n    this.options = options\n\n    /* Set up document map and highlighter factory */\n    this.documents = setupSearchDocumentMap(docs)\n    this.highlight = setupSearchHighlighter(config, false)\n\n    /* Set separator for tokenizer */\n    lunr.tokenizer.separator = new RegExp(config.separator)\n\n    /* Create search index */\n    this.index = lunr(function () {\n\n      /* Set up multi-language support */\n      if (config.lang.length === 1 && config.lang[0] !== \"en\") {\n        this.use((lunr as any)[config.lang[0]])\n      } else if (config.lang.length > 1) {\n        this.use((lunr as any).multiLanguage(...config.lang))\n      }\n\n      /* Compute functions to be removed from the pipeline */\n      const fns = difference([\n        \"trimmer\", \"stopWordFilter\", \"stemmer\"\n      ], options.pipeline)\n\n      /* Remove functions from the pipeline for registered languages */\n      for (const lang of config.lang.map(language => (\n        language === \"en\" ? lunr : (lunr as any)[language]\n      ))) {\n        for (const fn of fns) {\n          this.pipeline.remove(lang[fn])\n          this.searchPipeline.remove(lang[fn])\n        }\n      }\n\n      /* Set up reference */\n      this.ref(\"location\")\n\n      /* Set up fields */\n      this.field(\"title\", { boost: 1e3 })\n      this.field(\"text\")\n      this.field(\"tags\", { boost: 1e6, extractor: doc => {\n        const { tags = [] } = doc as SearchDocument\n        return tags.reduce((list, tag) => [\n          ...list,\n          ...lunr.tokenizer(tag)\n        ], [] as lunr.Token[])\n      } })\n\n      /* Index documents */\n      for (const doc of docs)\n        this.add(doc, { boost: doc.boost })\n    })\n  }\n\n  /**\n   * Search for matching documents\n   *\n   * The search index which MkDocs provides is divided up into articles, which\n   * contain the whole content of the individual pages, and sections, which only\n   * contain the contents of the subsections obtained by breaking the individual\n   * pages up at `h1` ... `h6`. As there may be many sections on different pages\n   * with identical titles (for example within this very project, e.g. \"Usage\"\n   * or \"Installation\"), they need to be put into the context of the containing\n   * page. For this reason, section results are grouped within their respective\n   * articles which are the top-level results that are returned.\n   *\n   * @param query - Query value\n   *\n   * @returns Search results\n   */\n  public search(query: string): SearchResult {\n    if (query) {\n      try {\n        const highlight = this.highlight(query)\n\n        /* Parse query to extract clauses for analysis */\n        const clauses = parseSearchQuery(query)\n          .filter(clause => (\n            clause.presence !== lunr.Query.presence.PROHIBITED\n          ))\n\n        /* Perform search and post-process results */\n        const groups = this.index.search(`${query}*`)\n\n          /* Apply post-query boosts based on title and search query terms */\n          .reduce<SearchResultItem>((item, { ref, score, matchData }) => {\n            const document = this.documents.get(ref)\n            if (typeof document !== \"undefined\") {\n              const { location, title, text, tags, parent } = document\n\n              /* Compute and analyze search query terms */\n              const terms = getSearchQueryTerms(\n                clauses,\n                Object.keys(matchData.metadata)\n              )\n\n              /* Highlight title and text and apply post-query boosts */\n              const boost = +!parent + +Object.values(terms).every(t => t)\n              item.push({\n                location,\n                title: highlight(title),\n                text:  highlight(text),\n                ...tags && { tags: tags.map(highlight) },\n                score: score * (1 + boost),\n                terms\n              })\n            }\n            return item\n          }, [])\n\n          /* Sort search results again after applying boosts */\n          .sort((a, b) => b.score - a.score)\n\n          /* Group search results by page */\n          .reduce((items, result) => {\n            const document = this.documents.get(result.location)\n            if (typeof document !== \"undefined\") {\n              const ref = \"parent\" in document\n                ? document.parent!.location\n                : document.location\n              items.set(ref, [...items.get(ref) || [], result])\n            }\n            return items\n          }, new Map<string, SearchResultItem>())\n\n        /* Generate search suggestions, if desired */\n        let suggestions: string[] | undefined\n        if (this.options.suggestions) {\n          const titles = this.index.query(builder => {\n            for (const clause of clauses)\n              builder.term(clause.term, {\n                fields: [\"title\"],\n                presence: lunr.Query.presence.REQUIRED,\n                wildcard: lunr.Query.wildcard.TRAILING\n              })\n          })\n\n          /* Retrieve suggestions for best match */\n          suggestions = titles.length\n            ? Object.keys(titles[0].matchData.metadata)\n            : []\n        }\n\n        /* Return items and suggestions */\n        return {\n          items: [...groups.values()],\n          ...typeof suggestions !== \"undefined\" && { suggestions }\n        }\n\n      /* Log errors to console (for now) */\n      } catch {\n        console.warn(`Invalid query: ${query} \u2013 see https://bit.ly/2s3ChXG`)\n      }\n    }\n\n    /* Return nothing in case of error or empty query */\n    return { items: [] }\n  }\n}\n"],
+  "mappings": 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\ No newline at end of file
diff --git a/assets/stylesheets/main.69437709.min.css.map b/assets/stylesheets/main.69437709.min.css.map
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\ No newline at end of file
diff --git a/assets/stylesheets/palette.cbb835fc.min.css b/assets/stylesheets/palette.cbb835fc.min.css
new file mode 100644
index 0000000..30f9264
--- /dev/null
+++ b/assets/stylesheets/palette.cbb835fc.min.css
@@ -0,0 +1 @@
+@media screen{[data-md-color-scheme=slate]{--md-hue:232;--md-default-fg-color:hsla(var(--md-hue),75%,95%,1);--md-default-fg-color--light:hsla(var(--md-hue),75%,90%,0.62);--md-default-fg-color--lighter:hsla(var(--md-hue),75%,90%,0.32);--md-default-fg-color--lightest:hsla(var(--md-hue),75%,90%,0.12);--md-default-bg-color:hsla(var(--md-hue),15%,21%,1);--md-default-bg-color--light:hsla(var(--md-hue),15%,21%,0.54);--md-default-bg-color--lighter:hsla(var(--md-hue),15%,21%,0.26);--md-default-bg-color--lightest:hsla(var(--md-hue),15%,21%,0.07);--md-code-fg-color:hsla(var(--md-hue),18%,86%,1);--md-code-bg-color:hsla(var(--md-hue),15%,15%,1);--md-code-hl-color:rgba(66,135,255,.15);--md-code-hl-number-color:#e6695b;--md-code-hl-special-color:#f06090;--md-code-hl-function-color:#c973d9;--md-code-hl-constant-color:#9383e2;--md-code-hl-keyword-color:#6791e0;--md-code-hl-string-color:#2fb170;--md-code-hl-name-color:var(--md-code-fg-color);--md-code-hl-operator-color:var(--md-default-fg-color--light);--md-code-hl-punctuation-color:var(--md-default-fg-color--light);--md-code-hl-comment-color:var(--md-default-fg-color--light);--md-code-hl-generic-color:var(--md-default-fg-color--light);--md-code-hl-variable-color:var(--md-default-fg-color--light);--md-typeset-color:var(--md-default-fg-color);--md-typeset-a-color:var(--md-primary-fg-color);--md-typeset-mark-color:rgba(66,135,255,.3);--md-typeset-kbd-color:hsla(var(--md-hue),15%,94%,0.12);--md-typeset-kbd-accent-color:hsla(var(--md-hue),15%,94%,0.2);--md-typeset-kbd-border-color:hsla(var(--md-hue),15%,14%,1);--md-typeset-table-color:hsla(var(--md-hue),75%,95%,0.12);--md-admonition-fg-color:var(--md-default-fg-color);--md-admonition-bg-color:var(--md-default-bg-color);--md-footer-bg-color:hsla(var(--md-hue),15%,12%,0.87);--md-footer-bg-color--dark:hsla(var(--md-hue),15%,10%,1);--md-shadow-z1:0 0.2rem 0.5rem rgba(0,0,0,.2),0 0 0.05rem rgba(0,0,0,.1);--md-shadow-z2:0 0.2rem 0.5rem rgba(0,0,0,.3),0 0 0.05rem rgba(0,0,0,.25);--md-shadow-z3:0 0.2rem 0.5rem rgba(0,0,0,.4),0 0 0.05rem rgba(0,0,0,.35)}[data-md-color-scheme=slate] img[src$="#gh-light-mode-only"],[data-md-color-scheme=slate] img[src$="#only-light"]{display:none}[data-md-color-scheme=slate] img[src$="#gh-dark-mode-only"],[data-md-color-scheme=slate] 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+<h1 id="standard-data-formats-of-quakeflow">Standard Data Formats of QuakeFlow</h1>
+<ul>
+<li>Raw data: <ul>
+<li>Waveform (MSEED): <ul>
+<li>Year/Jday/Hour/Network.Station.Location.Channel.mseed</li>
+</ul>
+</li>
+<li>Station (xml):<ul>
+<li>Network.Station.xml</li>
+</ul>
+</li>
+<li>Events (CSV):<ul>
+<li>colums: time, latitude, longitude, depth_km, magnitude, event_id</li>
+</ul>
+</li>
+<li>Picks (CSV)<ul>
+<li>columns: station_id (network.station.location.channel) phase_time, phase_type, phase_score, event_id</li>
+</ul>
+</li>
+</ul>
+</li>
+<li>Phase picking:<ul>
+<li>Picks (CSV):<ul>
+<li>columns: station_id (network.station.location.channel) phase_time, phase_type, phase_score, phase_polarity</li>
+</ul>
+</li>
+</ul>
+</li>
+<li>Phase association:<ul>
+<li>Events (CSV):<ul>
+<li>colums: time, latitude, longitude, depth_km, magnitude, event_id</li>
+</ul>
+</li>
+<li>Picks (CSV):<ul>
+<li>columns: station_id (network.station.location.channel), phase_time, phase_type, phase_score, phase_polarity, event_id</li>
+</ul>
+</li>
+</ul>
+</li>
+<li>Earthquake location:<ul>
+<li>Events (CSV):<ul>
+<li>colums: time, latitude, longitude, depth_km, magnitude, event_id</li>
+</ul>
+</li>
+</ul>
+</li>
+<li>Earthquake relocation:<ul>
+<li>Events (CSV):<ul>
+<li>colums: time, latitude, longitude, depth_km, magnitude, event_id</li>
+</ul>
+</li>
+</ul>
+</li>
+<li>Focal mechanism:<ul>
+<li>Focal mechanism (CSV):<ul>
+<li>columns: strike1, dip1, rake1, strike2, dip2, rake2, event_id</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+
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+  <h1>Deepdenoiser</h1>
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diff --git a/earthquake_location/index.html b/earthquake_location/index.html
new file mode 100644
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+<nav class="md-nav md-nav--secondary" aria-label="Table of contents">
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+            <article class="md-content__inner md-typeset">
+              
+                
+  <a href="https://github.com/ai4eps/QuakeFlow/edit/master/docs/earthquake_location.md" title="Edit this page" class="md-content__button md-icon">
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
+  </a>
+
+
+
+  <h1>Earthquake location</h1>
+
+
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\ No newline at end of file
diff --git a/fastapi/index.html b/fastapi/index.html
new file mode 100644
index 0000000..678e8e7
--- /dev/null
+++ b/fastapi/index.html
@@ -0,0 +1,32405 @@
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+<!doctype html>
+<html lang="en" class="no-js">
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+      <meta name="viewport" content="width=device-width,initial-scale=1">
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+        <meta name="description" content="QuakeFlow: a Scalable Deep-learning-based Earthquake Monitoring Workflow with Cloud Computing">
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+      <link rel="stylesheet" href="../assets/stylesheets/main.69437709.min.css">
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+          Skip to content
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+            
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+      
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+  <a href="#start-fastapi" class="md-nav__link">
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+      if (content instanceof HTMLInputElement || content instanceof HTMLTextAreaElement) {
+        return copyText(content.value);
+      } else if (content instanceof HTMLAnchorElement && content.hasAttribute('href')) {
+        return copyText(content.href);
+      } else {
+        return copyNode(content);
+      }
+    }
+
+    function clicked(event) {
+      const button = event.currentTarget;
+
+      if (button instanceof HTMLElement) {
+        copy(button);
+      }
+    }
+
+    function keydown(event) {
+      if (event.key === ' ' || event.key === 'Enter') {
+        const button = event.currentTarget;
+
+        if (button instanceof HTMLElement) {
+          event.preventDefault();
+          copy(button);
+        }
+      }
+    }
+
+    function focused(event) {
+      event.currentTarget.addEventListener('keydown', keydown);
+    }
+
+    function blurred(event) {
+      event.currentTarget.removeEventListener('keydown', keydown);
+    }
+
+    class ClipboardCopyElement extends HTMLElement {
+      constructor() {
+        super();
+        this.addEventListener('click', clicked);
+        this.addEventListener('focus', focused);
+        this.addEventListener('blur', blurred);
+      }
+
+      connectedCallback() {
+        if (!this.hasAttribute('tabindex')) {
+          this.setAttribute('tabindex', '0');
+        }
+
+        if (!this.hasAttribute('role')) {
+          this.setAttribute('role', 'button');
+        }
+      }
+
+      get value() {
+        return this.getAttribute('value') || '';
+      }
+
+      set value(text) {
+        this.setAttribute('value', text);
+      }
+
+    }
+
+    if (!window.customElements.get('clipboard-copy')) {
+      window.ClipboardCopyElement = ClipboardCopyElement;
+      window.customElements.define('clipboard-copy', ClipboardCopyElement);
+    }
+
+    return ClipboardCopyElement;
+
+  }));
+</script>
+<script>
+      document.addEventListener('clipboard-copy', function(event) {
+        const notice = event.target.querySelector('.notice')
+        notice.hidden = false
+        setTimeout(function() {
+          notice.hidden = true
+        }, 1000)
+      })
+</script>
+
+
+
+
+<style type="text/css">
+    
+    pre { line-height: 125%; }
+td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+.highlight-ipynb .hll { background-color: var(--jp-cell-editor-active-background) }
+.highlight-ipynb { background: var(--jp-cell-editor-background); color: var(--jp-mirror-editor-variable-color) }
+.highlight-ipynb .c { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment */
+.highlight-ipynb .err { color: var(--jp-mirror-editor-error-color) } /* Error */
+.highlight-ipynb .k { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword */
+.highlight-ipynb .o { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator */
+.highlight-ipynb .p { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation */
+.highlight-ipynb .ch { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Hashbang */
+.highlight-ipynb .cm { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Multiline */
+.highlight-ipynb .cp { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Preproc */
+.highlight-ipynb .cpf { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.PreprocFile */
+.highlight-ipynb .c1 { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Single */
+.highlight-ipynb .cs { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Special */
+.highlight-ipynb .kc { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Constant */
+.highlight-ipynb .kd { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Declaration */
+.highlight-ipynb .kn { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Namespace */
+.highlight-ipynb .kp { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Pseudo */
+.highlight-ipynb .kr { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Reserved */
+.highlight-ipynb .kt { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Type */
+.highlight-ipynb .m { color: var(--jp-mirror-editor-number-color) } /* Literal.Number */
+.highlight-ipynb .s { color: var(--jp-mirror-editor-string-color) } /* Literal.String */
+.highlight-ipynb .ow { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator.Word */
+.highlight-ipynb .pm { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation.Marker */
+.highlight-ipynb .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */
+.highlight-ipynb .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */
+.highlight-ipynb .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */
+.highlight-ipynb .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */
+.highlight-ipynb .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */
+.highlight-ipynb .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */
+.highlight-ipynb .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */
+.highlight-ipynb .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */
+.highlight-ipynb .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */
+.highlight-ipynb .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */
+.highlight-ipynb .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */
+.highlight-ipynb .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */
+.highlight-ipynb .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */
+.highlight-ipynb .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */
+.highlight-ipynb .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */
+.highlight-ipynb .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */
+.highlight-ipynb .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */
+.highlight-ipynb .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */
+.highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */
+.highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */
+  </style>
+
+
+
+
+<style type="text/css">
+/*
+This file is taken from the built JupyterLab theme.css
+Found on share/nbconvert/templates/lab/static
+Some changes have been made and marked with CHANGE
+*/
+
+.jupyter-wrapper {
+    /* Elevation
+   *
+   * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here:
+   *
+   * https://github.com/material-components/material-components-web
+   * https://material-components-web.appspot.com/elevation.html
+   */
+
+    --jp-shadow-base-lightness: 0;
+    --jp-shadow-umbra-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.2
+    );
+    --jp-shadow-penumbra-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.14
+    );
+    --jp-shadow-ambient-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.12
+    );
+    --jp-elevation-z0: none;
+    --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color),
+        0px 1px 1px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 3px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color),
+        0px 2px 2px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 5px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color),
+        0px 4px 5px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 10px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color),
+        0px 6px 10px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 18px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color),
+        0px 8px 10px 1px var(--jp-shadow-penumbra-color),
+        0px 3px 14px 2px var(--jp-shadow-ambient-color);
+    --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color),
+        0px 12px 17px 2px var(--jp-shadow-penumbra-color),
+        0px 5px 22px 4px var(--jp-shadow-ambient-color);
+    --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color),
+        0px 16px 24px 2px var(--jp-shadow-penumbra-color),
+        0px 6px 30px 5px var(--jp-shadow-ambient-color);
+    --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color),
+        0px 20px 31px 3px var(--jp-shadow-penumbra-color),
+        0px 8px 38px 7px var(--jp-shadow-ambient-color);
+    --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color),
+        0px 24px 38px 3px var(--jp-shadow-penumbra-color),
+        0px 9px 46px 8px var(--jp-shadow-ambient-color);
+
+    /* Borders
+   *
+   * The following variables, specify the visual styling of borders in JupyterLab.
+   */
+
+    --jp-border-width: 1px;
+    --jp-border-color0: var(--md-grey-400);
+    --jp-border-color1: var(--md-grey-400);
+    --jp-border-color2: var(--md-grey-300);
+    --jp-border-color3: var(--md-grey-200);
+    --jp-border-radius: 2px;
+
+    /* UI Fonts
+   *
+   * The UI font CSS variables are used for the typography all of the JupyterLab
+   * user interface elements that are not directly user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-ui-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+    --jp-ui-font-scale-factor: 1.2;
+    --jp-ui-font-size0: 0.83333em;
+    --jp-ui-font-size1: 13px; /* Base font size */
+    --jp-ui-font-size2: 1.2em;
+    --jp-ui-font-size3: 1.44em;
+
+    --jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+
+    /*
+   * Use these font colors against the corresponding main layout colors.
+   * In a light theme, these go from dark to light.
+   */
+
+    /* Defaults use Material Design specification */
+    --jp-ui-font-color0: rgba(0, 0, 0, 1);
+    --jp-ui-font-color1: rgba(0, 0, 0, 0.87);
+    --jp-ui-font-color2: rgba(0, 0, 0, 0.54);
+    --jp-ui-font-color3: rgba(0, 0, 0, 0.38);
+
+    /*
+   * Use these against the brand/accent/warn/error colors.
+   * These will typically go from light to darker, in both a dark and light theme.
+   */
+
+    --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1);
+    --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1);
+    --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7);
+    --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5);
+
+    /* Content Fonts
+   *
+   * Content font variables are used for typography of user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-content-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+    --jp-content-line-height: 1.6;
+    --jp-content-font-scale-factor: 1.2;
+    --jp-content-font-size0: 0.83333em;
+    --jp-content-font-size1: 14px; /* Base font size */
+    --jp-content-font-size2: 1.2em;
+    --jp-content-font-size3: 1.44em;
+    --jp-content-font-size4: 1.728em;
+    --jp-content-font-size5: 2.0736em;
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-content-presentation-font-size1: 17px;
+
+    --jp-content-heading-line-height: 1;
+    --jp-content-heading-margin-top: 1.2em;
+    --jp-content-heading-margin-bottom: 0.8em;
+    --jp-content-heading-font-weight: 500;
+
+    /* Defaults use Material Design specification */
+    --jp-content-font-color0: rgba(0, 0, 0, 1);
+    --jp-content-font-color1: rgba(0, 0, 0, 0.87);
+    --jp-content-font-color2: rgba(0, 0, 0, 0.54);
+    --jp-content-font-color3: rgba(0, 0, 0, 0.38);
+
+    --jp-content-link-color: var(--md-blue-700);
+
+    --jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+
+    /*
+   * Code Fonts
+   *
+   * Code font variables are used for typography of code and other monospaces content.
+   */
+
+    --jp-code-font-size: 13px;
+    --jp-code-line-height: 1.3077; /* 17px for 13px base */
+    --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */
+    --jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono",
+        monospace;
+    --jp-code-font-family: var(--jp-code-font-family-default);
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-code-presentation-font-size: 16px;
+
+    /* may need to tweak cursor width if you change font size */
+    --jp-code-cursor-width0: 1.4px;
+    --jp-code-cursor-width1: 2px;
+    --jp-code-cursor-width2: 4px;
+
+    /* Layout
+   *
+   * The following are the main layout colors use in JupyterLab. In a light
+   * theme these would go from light to dark.
+   */
+
+    --jp-layout-color0: white;
+    --jp-layout-color1: white;
+    --jp-layout-color2: var(--md-grey-200);
+    --jp-layout-color3: var(--md-grey-400);
+    --jp-layout-color4: var(--md-grey-600);
+
+    /* Inverse Layout
+   *
+   * The following are the inverse layout colors use in JupyterLab. In a light
+   * theme these would go from dark to light.
+   */
+
+    --jp-inverse-layout-color0: #111111;
+    --jp-inverse-layout-color1: var(--md-grey-900);
+    --jp-inverse-layout-color2: var(--md-grey-800);
+    --jp-inverse-layout-color3: var(--md-grey-700);
+    --jp-inverse-layout-color4: var(--md-grey-600);
+
+    /* Brand/accent */
+
+    --jp-brand-color0: var(--md-blue-900);
+    --jp-brand-color1: var(--md-blue-700);
+    --jp-brand-color2: var(--md-blue-300);
+    --jp-brand-color3: var(--md-blue-100);
+    --jp-brand-color4: var(--md-blue-50);
+
+    --jp-accent-color0: var(--md-green-900);
+    --jp-accent-color1: var(--md-green-700);
+    --jp-accent-color2: var(--md-green-300);
+    --jp-accent-color3: var(--md-green-100);
+
+    /* State colors (warn, error, success, info) */
+
+    --jp-warn-color0: var(--md-orange-900);
+    --jp-warn-color1: var(--md-orange-700);
+    --jp-warn-color2: var(--md-orange-300);
+    --jp-warn-color3: var(--md-orange-100);
+
+    --jp-error-color0: var(--md-red-900);
+    --jp-error-color1: var(--md-red-700);
+    --jp-error-color2: var(--md-red-300);
+    --jp-error-color3: var(--md-red-100);
+
+    --jp-success-color0: var(--md-green-900);
+    --jp-success-color1: var(--md-green-700);
+    --jp-success-color2: var(--md-green-300);
+    --jp-success-color3: var(--md-green-100);
+
+    --jp-info-color0: var(--md-cyan-900);
+    --jp-info-color1: var(--md-cyan-700);
+    --jp-info-color2: var(--md-cyan-300);
+    --jp-info-color3: var(--md-cyan-100);
+
+    /* Cell specific styles */
+
+    --jp-cell-padding: 5px;
+
+    --jp-cell-collapser-width: 8px;
+    --jp-cell-collapser-min-height: 20px;
+    --jp-cell-collapser-not-active-hover-opacity: 0.6;
+
+    --jp-cell-editor-background: var(--md-grey-100);
+    --jp-cell-editor-border-color: var(--md-grey-300);
+    --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-cell-editor-active-background: var(--jp-layout-color0);
+    --jp-cell-editor-active-border-color: var(--jp-brand-color1);
+
+    --jp-cell-prompt-width: 64px;
+    --jp-cell-prompt-font-family: var(--jp-code-font-family-default);
+    --jp-cell-prompt-letter-spacing: 0px;
+    --jp-cell-prompt-opacity: 1;
+    --jp-cell-prompt-not-active-opacity: 0.5;
+    --jp-cell-prompt-not-active-font-color: var(--md-grey-700);
+    /* A custom blend of MD grey and blue 600
+   * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */
+    --jp-cell-inprompt-font-color: #307fc1;
+    /* A custom blend of MD grey and orange 600
+   * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */
+    --jp-cell-outprompt-font-color: #bf5b3d;
+
+    /* Notebook specific styles */
+
+    --jp-notebook-padding: 10px;
+    --jp-notebook-select-background: var(--jp-layout-color1);
+    --jp-notebook-multiselected-color: var(--md-blue-50);
+
+    /* The scroll padding is calculated to fill enough space at the bottom of the
+  notebook to show one single-line cell (with appropriate padding) at the top
+  when the notebook is scrolled all the way to the bottom. We also subtract one
+  pixel so that no scrollbar appears if we have just one single-line cell in the
+  notebook. This padding is to enable a 'scroll past end' feature in a notebook.
+  */
+    --jp-notebook-scroll-padding: calc(
+        100% - var(--jp-code-font-size) * var(--jp-code-line-height) -
+            var(--jp-code-padding) - var(--jp-cell-padding) - 1px
+    );
+
+    /* Rendermime styles */
+
+    --jp-rendermime-error-background: #fdd;
+    --jp-rendermime-table-row-background: var(--md-grey-100);
+    --jp-rendermime-table-row-hover-background: var(--md-light-blue-50);
+
+    /* Dialog specific styles */
+
+    --jp-dialog-background: rgba(0, 0, 0, 0.25);
+
+    /* Console specific styles */
+
+    --jp-console-padding: 10px;
+
+    /* Toolbar specific styles */
+
+    --jp-toolbar-border-color: var(--jp-border-color1);
+    --jp-toolbar-micro-height: 8px;
+    --jp-toolbar-background: var(--jp-layout-color1);
+    --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24);
+    --jp-toolbar-header-margin: 4px 4px 0px 4px;
+    --jp-toolbar-active-background: var(--md-grey-300);
+
+    /* Statusbar specific styles */
+
+    --jp-statusbar-height: 24px;
+
+    /* Input field styles */
+
+    --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-input-active-background: var(--jp-layout-color1);
+    --jp-input-hover-background: var(--jp-layout-color1);
+    --jp-input-background: var(--md-grey-100);
+    --jp-input-border-color: var(--jp-border-color1);
+    --jp-input-active-border-color: var(--jp-brand-color1);
+    --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3);
+
+    /* General editor styles */
+
+    --jp-editor-selected-background: #d9d9d9;
+    --jp-editor-selected-focused-background: #d7d4f0;
+    --jp-editor-cursor-color: var(--jp-ui-font-color0);
+
+    /* Code mirror specific styles */
+
+    --jp-mirror-editor-keyword-color: #008000;
+    --jp-mirror-editor-atom-color: #88f;
+    --jp-mirror-editor-number-color: #080;
+    --jp-mirror-editor-def-color: #00f;
+    --jp-mirror-editor-variable-color: var(--md-grey-900);
+    --jp-mirror-editor-variable-2-color: #05a;
+    --jp-mirror-editor-variable-3-color: #085;
+    --jp-mirror-editor-punctuation-color: #05a;
+    --jp-mirror-editor-property-color: #05a;
+    --jp-mirror-editor-operator-color: #aa22ff;
+    --jp-mirror-editor-comment-color: #408080;
+    --jp-mirror-editor-string-color: #ba2121;
+    --jp-mirror-editor-string-2-color: #708;
+    --jp-mirror-editor-meta-color: #aa22ff;
+    --jp-mirror-editor-qualifier-color: #555;
+    --jp-mirror-editor-builtin-color: #008000;
+    --jp-mirror-editor-bracket-color: #997;
+    --jp-mirror-editor-tag-color: #170;
+    --jp-mirror-editor-attribute-color: #00c;
+    --jp-mirror-editor-header-color: blue;
+    --jp-mirror-editor-quote-color: #090;
+    --jp-mirror-editor-link-color: #00c;
+    --jp-mirror-editor-error-color: #f00;
+    --jp-mirror-editor-hr-color: #999;
+
+    /* Vega extension styles */
+
+    --jp-vega-background: white;
+
+    /* Sidebar-related styles */
+
+    --jp-sidebar-min-width: 250px;
+
+    /* Search-related styles */
+
+    --jp-search-toggle-off-opacity: 0.5;
+    --jp-search-toggle-hover-opacity: 0.8;
+    --jp-search-toggle-on-opacity: 1;
+    --jp-search-selected-match-background-color: rgb(245, 200, 0);
+    --jp-search-selected-match-color: black;
+    --jp-search-unselected-match-background-color: var(
+        --jp-inverse-layout-color0
+    );
+    --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);
+
+    /* Icon colors that work well with light or dark backgrounds */
+    --jp-icon-contrast-color0: var(--md-purple-600);
+    --jp-icon-contrast-color1: var(--md-green-600);
+    --jp-icon-contrast-color2: var(--md-pink-600);
+    --jp-icon-contrast-color3: var(--md-blue-600);
+}
+
+[data-md-color-scheme="slate"] .jupyter-wrapper {
+    /* Elevation
+   *
+   * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here:
+   *
+   * https://github.com/material-components/material-components-web
+   * https://material-components-web.appspot.com/elevation.html
+   */
+
+    /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout
+   * colors used in the theme as well. */
+    --jp-shadow-base-lightness: 32;
+    --jp-shadow-umbra-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.2
+    );
+    --jp-shadow-penumbra-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.14
+    );
+    --jp-shadow-ambient-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.12
+    );
+    --jp-elevation-z0: none;
+    --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color),
+        0px 1px 1px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 3px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color),
+        0px 2px 2px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 5px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color),
+        0px 4px 5px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 10px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color),
+        0px 6px 10px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 18px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color),
+        0px 8px 10px 1px var(--jp-shadow-penumbra-color),
+        0px 3px 14px 2px var(--jp-shadow-ambient-color);
+    --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color),
+        0px 12px 17px 2px var(--jp-shadow-penumbra-color),
+        0px 5px 22px 4px var(--jp-shadow-ambient-color);
+    --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color),
+        0px 16px 24px 2px var(--jp-shadow-penumbra-color),
+        0px 6px 30px 5px var(--jp-shadow-ambient-color);
+    --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color),
+        0px 20px 31px 3px var(--jp-shadow-penumbra-color),
+        0px 8px 38px 7px var(--jp-shadow-ambient-color);
+    --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color),
+        0px 24px 38px 3px var(--jp-shadow-penumbra-color),
+        0px 9px 46px 8px var(--jp-shadow-ambient-color);
+
+    /* Borders
+   *
+   * The following variables, specify the visual styling of borders in JupyterLab.
+   */
+
+    --jp-border-width: 1px;
+    --jp-border-color0: var(--md-grey-700);
+    --jp-border-color1: var(--md-grey-700);
+    --jp-border-color2: var(--md-grey-800);
+    --jp-border-color3: var(--md-grey-900);
+    --jp-border-radius: 2px;
+
+    /* UI Fonts
+   *
+   * The UI font CSS variables are used for the typography all of the JupyterLab
+   * user interface elements that are not directly user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-ui-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+    --jp-ui-font-scale-factor: 1.2;
+    --jp-ui-font-size0: 0.83333em;
+    --jp-ui-font-size1: 13px; /* Base font size */
+    --jp-ui-font-size2: 1.2em;
+    --jp-ui-font-size3: 1.44em;
+
+    --jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+
+    /*
+   * Use these font colors against the corresponding main layout colors.
+   * In a light theme, these go from dark to light.
+   */
+
+    /* Defaults use Material Design specification */
+    --jp-ui-font-color0: rgba(255, 255, 255, 1);
+    --jp-ui-font-color1: rgba(255, 255, 255, 0.87);
+    --jp-ui-font-color2: rgba(255, 255, 255, 0.54);
+    --jp-ui-font-color3: rgba(255, 255, 255, 0.38);
+
+    /*
+   * Use these against the brand/accent/warn/error colors.
+   * These will typically go from light to darker, in both a dark and light theme.
+   */
+
+    --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1);
+    --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8);
+    --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5);
+    --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3);
+
+    /* Content Fonts
+   *
+   * Content font variables are used for typography of user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-content-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+    --jp-content-line-height: 1.6;
+    --jp-content-font-scale-factor: 1.2;
+    --jp-content-font-size0: 0.83333em;
+    --jp-content-font-size1: 14px; /* Base font size */
+    --jp-content-font-size2: 1.2em;
+    --jp-content-font-size3: 1.44em;
+    --jp-content-font-size4: 1.728em;
+    --jp-content-font-size5: 2.0736em;
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-content-presentation-font-size1: 17px;
+
+    --jp-content-heading-line-height: 1;
+    --jp-content-heading-margin-top: 1.2em;
+    --jp-content-heading-margin-bottom: 0.8em;
+    --jp-content-heading-font-weight: 500;
+
+    /* Defaults use Material Design specification */
+    --jp-content-font-color0: rgba(255, 255, 255, 1);
+    --jp-content-font-color1: rgba(255, 255, 255, 1);
+    --jp-content-font-color2: rgba(255, 255, 255, 0.7);
+    --jp-content-font-color3: rgba(255, 255, 255, 0.5);
+
+    --jp-content-link-color: var(--md-blue-300);
+
+    --jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+
+    /*
+   * Code Fonts
+   *
+   * Code font variables are used for typography of code and other monospaces content.
+   */
+
+    --jp-code-font-size: 13px;
+    --jp-code-line-height: 1.3077; /* 17px for 13px base */
+    --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */
+    --jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono",
+        monospace;
+    --jp-code-font-family: var(--jp-code-font-family-default);
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-code-presentation-font-size: 16px;
+
+    /* may need to tweak cursor width if you change font size */
+    --jp-code-cursor-width0: 1.4px;
+    --jp-code-cursor-width1: 2px;
+    --jp-code-cursor-width2: 4px;
+
+    /* Layout
+   *
+   * The following are the main layout colors use in JupyterLab. In a light
+   * theme these would go from light to dark.
+   */
+
+    --jp-layout-color0: #111111;
+    --jp-layout-color1: var(--md-grey-900);
+    --jp-layout-color2: var(--md-grey-800);
+    --jp-layout-color3: var(--md-grey-700);
+    --jp-layout-color4: var(--md-grey-600);
+
+    /* Inverse Layout
+   *
+   * The following are the inverse layout colors use in JupyterLab. In a light
+   * theme these would go from dark to light.
+   */
+
+    --jp-inverse-layout-color0: white;
+    --jp-inverse-layout-color1: white;
+    --jp-inverse-layout-color2: var(--md-grey-200);
+    --jp-inverse-layout-color3: var(--md-grey-400);
+    --jp-inverse-layout-color4: var(--md-grey-600);
+
+    /* Brand/accent */
+
+    --jp-brand-color0: var(--md-blue-700);
+    --jp-brand-color1: var(--md-blue-500);
+    --jp-brand-color2: var(--md-blue-300);
+    --jp-brand-color3: var(--md-blue-100);
+    --jp-brand-color4: var(--md-blue-50);
+
+    --jp-accent-color0: var(--md-green-700);
+    --jp-accent-color1: var(--md-green-500);
+    --jp-accent-color2: var(--md-green-300);
+    --jp-accent-color3: var(--md-green-100);
+
+    /* State colors (warn, error, success, info) */
+
+    --jp-warn-color0: var(--md-orange-700);
+    --jp-warn-color1: var(--md-orange-500);
+    --jp-warn-color2: var(--md-orange-300);
+    --jp-warn-color3: var(--md-orange-100);
+
+    --jp-error-color0: var(--md-red-700);
+    --jp-error-color1: var(--md-red-500);
+    --jp-error-color2: var(--md-red-300);
+    --jp-error-color3: var(--md-red-100);
+
+    --jp-success-color0: var(--md-green-700);
+    --jp-success-color1: var(--md-green-500);
+    --jp-success-color2: var(--md-green-300);
+    --jp-success-color3: var(--md-green-100);
+
+    --jp-info-color0: var(--md-cyan-700);
+    --jp-info-color1: var(--md-cyan-500);
+    --jp-info-color2: var(--md-cyan-300);
+    --jp-info-color3: var(--md-cyan-100);
+
+    /* Cell specific styles */
+
+    --jp-cell-padding: 5px;
+
+    --jp-cell-collapser-width: 8px;
+    --jp-cell-collapser-min-height: 20px;
+    --jp-cell-collapser-not-active-hover-opacity: 0.6;
+
+    --jp-cell-editor-background: var(--jp-layout-color1);
+    --jp-cell-editor-border-color: var(--md-grey-700);
+    --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-cell-editor-active-background: var(--jp-layout-color0);
+    --jp-cell-editor-active-border-color: var(--jp-brand-color1);
+
+    --jp-cell-prompt-width: 64px;
+    --jp-cell-prompt-font-family: var(--jp-code-font-family-default);
+    --jp-cell-prompt-letter-spacing: 0px;
+    --jp-cell-prompt-opacity: 1;
+    --jp-cell-prompt-not-active-opacity: 1;
+    --jp-cell-prompt-not-active-font-color: var(--md-grey-300);
+
+    /* A custom blend of MD grey and blue 600
+   * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */
+    --jp-cell-inprompt-font-color: #307fc1;
+    /* A custom blend of MD grey and orange 600
+   * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */
+    --jp-cell-outprompt-font-color: #bf5b3d;
+
+    /* Notebook specific styles */
+
+    --jp-notebook-padding: 10px;
+    --jp-notebook-select-background: var(--jp-layout-color1);
+    --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24);
+
+    /* The scroll padding is calculated to fill enough space at the bottom of the
+  notebook to show one single-line cell (with appropriate padding) at the top
+  when the notebook is scrolled all the way to the bottom. We also subtract one
+  pixel so that no scrollbar appears if we have just one single-line cell in the
+  notebook. This padding is to enable a 'scroll past end' feature in a notebook.
+  */
+    --jp-notebook-scroll-padding: calc(
+        100% - var(--jp-code-font-size) * var(--jp-code-line-height) -
+            var(--jp-code-padding) - var(--jp-cell-padding) - 1px
+    );
+
+    /* Rendermime styles */
+
+    --jp-rendermime-error-background: rgba(244, 67, 54, 0.28);
+    --jp-rendermime-table-row-background: var(--md-grey-900);
+    --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2);
+
+    /* Dialog specific styles */
+
+    --jp-dialog-background: rgba(0, 0, 0, 0.6);
+
+    /* Console specific styles */
+
+    --jp-console-padding: 10px;
+
+    /* Toolbar specific styles */
+
+    --jp-toolbar-border-color: var(--jp-border-color2);
+    --jp-toolbar-micro-height: 8px;
+    --jp-toolbar-background: var(--jp-layout-color1);
+    --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8);
+    --jp-toolbar-header-margin: 4px 4px 0px 4px;
+    --jp-toolbar-active-background: var(--jp-layout-color0);
+
+    /* Statusbar specific styles */
+
+    --jp-statusbar-height: 24px;
+
+    /* Input field styles */
+
+    --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-input-active-background: var(--jp-layout-color0);
+    --jp-input-hover-background: var(--jp-layout-color2);
+    --jp-input-background: var(--md-grey-800);
+    --jp-input-border-color: var(--jp-border-color1);
+    --jp-input-active-border-color: var(--jp-brand-color1);
+    --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3);
+
+    /* General editor styles */
+
+    --jp-editor-selected-background: var(--jp-layout-color2);
+    --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24);
+    --jp-editor-cursor-color: var(--jp-ui-font-color0);
+
+    /* Code mirror specific styles */
+
+    --jp-mirror-editor-keyword-color: var(--md-green-500);
+    --jp-mirror-editor-atom-color: var(--md-blue-300);
+    --jp-mirror-editor-number-color: var(--md-green-400);
+    --jp-mirror-editor-def-color: var(--md-blue-600);
+    --jp-mirror-editor-variable-color: var(--md-grey-300);
+    --jp-mirror-editor-variable-2-color: var(--md-blue-400);
+    --jp-mirror-editor-variable-3-color: var(--md-green-600);
+    --jp-mirror-editor-punctuation-color: var(--md-blue-400);
+    --jp-mirror-editor-property-color: var(--md-blue-400);
+    --jp-mirror-editor-operator-color: #aa22ff;
+    --jp-mirror-editor-comment-color: #408080;
+    --jp-mirror-editor-string-color: #ff7070;
+    --jp-mirror-editor-string-2-color: var(--md-purple-300);
+    --jp-mirror-editor-meta-color: #aa22ff;
+    --jp-mirror-editor-qualifier-color: #555;
+    --jp-mirror-editor-builtin-color: var(--md-green-600);
+    --jp-mirror-editor-bracket-color: #997;
+    --jp-mirror-editor-tag-color: var(--md-green-700);
+    --jp-mirror-editor-attribute-color: var(--md-blue-700);
+    --jp-mirror-editor-header-color: var(--md-blue-500);
+    --jp-mirror-editor-quote-color: var(--md-green-300);
+    --jp-mirror-editor-link-color: var(--md-blue-700);
+    --jp-mirror-editor-error-color: #f00;
+    --jp-mirror-editor-hr-color: #999;
+
+    /* Vega extension styles */
+
+    --jp-vega-background: var(--md-grey-400);
+
+    /* Sidebar-related styles */
+
+    --jp-sidebar-min-width: 250px;
+
+    /* Search-related styles */
+
+    --jp-search-toggle-off-opacity: 0.6;
+    --jp-search-toggle-hover-opacity: 0.8;
+    --jp-search-toggle-on-opacity: 1;
+    --jp-search-selected-match-background-color: rgb(255, 225, 0);
+    --jp-search-selected-match-color: black;
+    --jp-search-unselected-match-background-color: var(
+        --jp-inverse-layout-color0
+    );
+    --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);
+
+    /* scrollbar related styles. Supports every browser except Edge. */
+
+    /* colors based on JetBrain's Darcula theme */
+
+    --jp-scrollbar-background-color: #3f4244;
+    --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */
+
+    --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */
+
+    /* hacks for setting the thumb shape. These do nothing in Firefox */
+
+    --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */
+    --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */
+
+    /* Icon colors that work well with light or dark backgrounds */
+    --jp-icon-contrast-color0: var(--md-purple-600);
+    --jp-icon-contrast-color1: var(--md-green-600);
+    --jp-icon-contrast-color2: var(--md-pink-600);
+    --jp-icon-contrast-color3: var(--md-blue-600);
+}
+
+:root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: #7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: 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+Copyright 2015-present Palantir Technologies, Inc. All rights reserved.
+Licensed under the Apache License, Version 2.0.
+
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+
+Copyright 2017-present Palantir Technologies, Inc. All rights reserved.
+Licensed under the Apache License, Version 2.0.
+
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+
+/*# sourceMappingURL=mkdocs-jupyter.css.map*/</style>
+
+
+<!-- Load mathjax -->
+    <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.7/latest.js?config=TeX-AMS_CHTML-full,Safe"> </script>
+    <!-- MathJax configuration -->
+    <script type="text/x-mathjax-config">
+    init_mathjax = function() {
+        if (window.MathJax) {
+        // MathJax loaded
+            MathJax.Hub.Config({
+                TeX: {
+                    equationNumbers: {
+                    autoNumber: "AMS",
+                    useLabelIds: true
+                    }
+                },
+                tex2jax: {
+                    inlineMath: [ ['$','$'], ["\\(","\\)"] ],
+                    displayMath: [ ['$$','$$'], ["\\[","\\]"] ],
+                    processEscapes: true,
+                    processEnvironments: true
+                },
+                displayAlign: 'center',
+                CommonHTML: {
+                    linebreaks: {
+                    automatic: true
+                    }
+                }
+            });
+
+            MathJax.Hub.Queue(["Typeset", MathJax.Hub]);
+        }
+    }
+    init_mathjax();
+    </script>
+    <!-- End of mathjax configuration --><div class="jupyter-wrapper">
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="test-fastapi">Test FastAPI<a class="anchor-link" href="#test-fastapi">&#182;</a></h1>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="start-fastapi">Start FASTAPI<a class="anchor-link" href="#start-fastapi">&#182;</a></h2><div class="highlight"><pre><span></span>uvicorn<span class="w"> </span>--app-dir<span class="o">=</span>phasenet<span class="w"> </span>app:app<span class="w"> </span>--reload<span class="w"> </span>--port<span class="w"> </span><span class="m">8001</span>
+uvicorn<span class="w"> </span>--app-dir<span class="o">=</span>deepdenoiser<span class="w"> </span>app:app<span class="w"> </span>--reload<span class="w"> </span>--port<span class="w"> </span><span class="m">8003</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[1]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-1">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">os</span><span class="o">,</span> <span class="nn">sys</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">obspy</span>
+<span class="kn">import</span> <span class="nn">requests</span>
+<span class="n">sys</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">abspath</span><span class="p">(</span><span class="s2">&quot;../&quot;</span><span class="p">))</span>
+<span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">1000</span><span class="p">)</span>
+</pre></div>
+<div id="cell-1" class="clipboard-copy-txt">import os, sys
+import numpy as np
+import matplotlib.pyplot as plt
+import obspy
+import requests
+sys.path.insert(0, os.path.abspath("../"))
+np.random.seed(1000)</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[2]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-2">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
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+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">obspy</span>
+<span class="n">stream</span> <span class="o">=</span> <span class="n">obspy</span><span class="o">.</span><span class="n">read</span><span class="p">()</span>
+<span class="n">stream</span><span class="o">.</span><span class="n">plot</span><span class="p">();</span>
+</pre></div>
+<div id="cell-2" class="clipboard-copy-txt">import obspy
+stream = obspy.read()
+stream.plot();</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
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+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
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+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="c1">## Extract 3-component data</span>
+<span class="n">stream</span> <span class="o">=</span> <span class="n">stream</span><span class="o">.</span><span class="n">sort</span><span class="p">()</span>
+<span class="k">assert</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">stream</span><span class="p">)</span> <span class="o">==</span> <span class="mi">3</span><span class="p">)</span>
+<span class="n">data</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="k">for</span> <span class="n">trace</span> <span class="ow">in</span> <span class="n">stream</span><span class="p">:</span>
+    <span class="n">data</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">trace</span><span class="o">.</span><span class="n">data</span><span class="p">)</span>
+<span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">data</span><span class="p">)</span><span class="o">.</span><span class="n">T</span>
+<span class="k">assert</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="mi">3</span><span class="p">)</span>
+<span class="n">data_id</span> <span class="o">=</span> <span class="n">stream</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">get_id</span><span class="p">()[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+<span class="n">timestamp</span> <span class="o">=</span> <span class="n">stream</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">stats</span><span class="o">.</span><span class="n">starttime</span><span class="o">.</span><span class="n">datetime</span><span class="o">.</span><span class="n">strftime</span><span class="p">(</span><span class="s2">&quot;%Y-%m-</span><span class="si">%d</span><span class="s2">T%H:%M:%S.</span><span class="si">%f</span><span class="s2">&quot;</span><span class="p">)[:</span><span class="o">-</span><span class="mi">3</span><span class="p">]</span>
+
+<span class="c1">## Add some noise</span>
+<span class="n">noisy_data</span> <span class="o">=</span> <span class="n">data</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="o">*</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">data</span><span class="p">)</span><span class="o">/</span><span class="mi">5</span>
+</pre></div>
+<div id="cell-3" class="clipboard-copy-txt">## Extract 3-component data
+stream = stream.sort()
+assert(len(stream) == 3)
+data = []
+for trace in stream:
+    data.append(trace.data)
+data = np.array(data).T
+assert(data.shape[-1] == 3)
+data_id = stream[0].get_id()[:-1]
+timestamp = stream[0].stats.starttime.datetime.strftime("%Y-%m-%dT%H:%M:%S.%f")[:-3]
+
+## Add some noise
+noisy_data = data + np.random.randn(*data.shape)*np.max(data)/5</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
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+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="test-deepdenoiser">Test <a href="https://wayneweiqiang.github.io/DeepDenoiser">DeepDenoiser</a><a class="anchor-link" href="#test-deepdenoiser">&#182;</a></h2>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
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+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[4]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-4">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
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+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">req</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;id&quot;</span><span class="p">:</span> <span class="p">[</span><span class="n">data_id</span><span class="p">],</span>
+       <span class="s2">&quot;timestamp&quot;</span><span class="p">:</span> <span class="p">[</span><span class="n">timestamp</span><span class="p">],</span>
+       <span class="s2">&quot;vec&quot;</span><span class="p">:</span> <span class="p">[</span><span class="n">noisy_data</span><span class="o">.</span><span class="n">tolist</span><span class="p">()]}</span>
+
+<span class="n">DEEPDENOISER_API_URL</span> <span class="o">=</span> <span class="s2">&quot;http://127.0.0.1:8003&quot;</span>
+<span class="n">resp_deepdenoiser</span> <span class="o">=</span> <span class="n">requests</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">DEEPDENOISER_API_URL</span><span class="si">}</span><span class="s1">/predict&#39;</span><span class="p">,</span> <span class="n">json</span><span class="o">=</span><span class="n">req</span><span class="p">)</span>
+
+<span class="n">denoised_data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">resp_deepdenoiser</span><span class="o">.</span><span class="n">json</span><span class="p">()[</span><span class="s2">&quot;vec&quot;</span><span class="p">])</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span><span class="mi">4</span><span class="p">))</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">331</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">data</span><span class="p">[:,</span><span class="mi">0</span><span class="p">],</span> <span class="s1">&#39;k&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;E&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">&quot;Raw signal&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">332</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">noisy_data</span><span class="p">[:,</span><span class="mi">0</span><span class="p">],</span> <span class="s1">&#39;k&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;E&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">&quot;Nosiy signal&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">333</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">denoised_data</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="p">:,</span><span class="mi">0</span><span class="p">],</span> <span class="s1">&#39;k&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;E&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">&quot;Denoised signal&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">334</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">data</span><span class="p">[:,</span><span class="mi">1</span><span class="p">],</span> <span class="s1">&#39;k&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;N&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">335</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">noisy_data</span><span class="p">[:,</span><span class="mi">1</span><span class="p">],</span> <span class="s1">&#39;k&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;N&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">336</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">denoised_data</span><span class="p">[</span><span class="mi">0</span><span class="p">,:,</span><span class="mi">1</span><span class="p">],</span> <span class="s1">&#39;k&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;N&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">337</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">data</span><span class="p">[:,</span><span class="mi">2</span><span class="p">],</span> <span class="s1">&#39;k&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;Z&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">338</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">noisy_data</span><span class="p">[:,</span><span class="mi">2</span><span class="p">],</span> <span class="s1">&#39;k&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;Z&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">339</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">denoised_data</span><span class="p">[</span><span class="mi">0</span><span class="p">,:,</span><span class="mi">2</span><span class="p">],</span> <span class="s1">&#39;k&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;Z&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">();</span>
+</pre></div>
+<div id="cell-4" class="clipboard-copy-txt">req = {"id": [data_id],
+       "timestamp": [timestamp],
+       "vec": [noisy_data.tolist()]}
+
+DEEPDENOISER_API_URL = "http://127.0.0.1:8003"
+resp_deepdenoiser = requests.get(f'{DEEPDENOISER_API_URL}/predict', json=req)
+
+denoised_data = np.array(resp_deepdenoiser.json()["vec"])
+
+plt.figure(figsize=(10,4))
+plt.subplot(331)
+plt.plot(data[:,0], 'k', linewidth=0.5, label="E")
+plt.legend()
+plt.title("Raw signal")
+plt.subplot(332)
+plt.plot(noisy_data[:,0], 'k', linewidth=0.5, label="E")
+plt.title("Nosiy signal")
+plt.subplot(333)
+plt.plot(denoised_data[0, :,0], 'k', linewidth=0.5, label="E")
+plt.title("Denoised signal")
+plt.subplot(334)
+plt.plot(data[:,1], 'k', linewidth=0.5, label="N")
+plt.legend()
+plt.subplot(335)
+plt.plot(noisy_data[:,1], 'k', linewidth=0.5, label="N")
+plt.subplot(336)
+plt.plot(denoised_data[0,:,1], 'k', linewidth=0.5, label="N")
+plt.subplot(337)
+plt.plot(data[:,2], 'k', linewidth=0.5, label="Z")
+plt.legend()
+plt.subplot(338)
+plt.plot(noisy_data[:,2], 'k', linewidth=0.5, label="Z")
+plt.subplot(339)
+plt.plot(denoised_data[0,:,2], 'k', linewidth=0.5, label="Z")
+plt.tight_layout()
+plt.show();</div>
+    
+        </div>
+    </div>
+
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+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="test-phasenet">Test <a href="https://wayneweiqiang.github.io/PhaseNet">PhaseNet</a><a class="anchor-link" href="#test-phasenet">&#182;</a></h2>
+</div>
+</div>
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+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[5]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-5">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
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+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">PHASENET_API_URL</span> <span class="o">=</span> <span class="s2">&quot;http://127.0.0.1:8001&quot;</span>
+<span class="n">resp</span> <span class="o">=</span> <span class="n">requests</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">PHASENET_API_URL</span><span class="si">}</span><span class="s1">/predict&#39;</span><span class="p">,</span> <span class="n">json</span><span class="o">=</span><span class="n">req</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Picks&#39;</span><span class="p">,</span> <span class="n">resp</span><span class="o">.</span><span class="n">json</span><span class="p">())</span>
+</pre></div>
+<div id="cell-5" class="clipboard-copy-txt">PHASENET_API_URL = "http://127.0.0.1:8001"
+resp = requests.get(f'{PHASENET_API_URL}/predict', json=req)
+print('Picks', resp.json())</div>
+    
+        </div>
+    </div>
+
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+<pre>Picks [{&#39;id&#39;: &#39;BW.RJOB..EH&#39;, &#39;timestamp&#39;: &#39;2009-08-24T00:20:08.690&#39;, &#39;prob&#39;: 0.41293007135391235, &#39;amp&#39;: 3281.9875522716266, &#39;type&#39;: &#39;s&#39;}]
+</pre>
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+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">PHASENET_API_URL</span> <span class="o">=</span> <span class="s2">&quot;http://127.0.0.1:8001&quot;</span>
+<span class="n">resp</span> <span class="o">=</span> <span class="n">requests</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">PHASENET_API_URL</span><span class="si">}</span><span class="s1">/predict_prob&#39;</span><span class="p">,</span> <span class="n">json</span><span class="o">=</span><span class="n">req</span><span class="p">)</span>
+<span class="n">picks</span><span class="p">,</span> <span class="n">preds</span> <span class="o">=</span> <span class="n">resp</span><span class="o">.</span><span class="n">json</span><span class="p">()</span> 
+<span class="n">preds</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">preds</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Picks&#39;</span><span class="p">,</span> <span class="n">picks</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">211</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">noisy_data</span><span class="p">[:,</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="s1">&#39;k&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;Z&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">212</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">preds</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;P&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">preds</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;S&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">();</span>
+</pre></div>
+<div id="cell-6" class="clipboard-copy-txt">PHASENET_API_URL = "http://127.0.0.1:8001"
+resp = requests.get(f'{PHASENET_API_URL}/predict_prob', json=req)
+picks, preds = resp.json() 
+preds = np.array(preds)
+print('Picks', picks)
+
+plt.figure()
+plt.subplot(211)
+plt.plot(noisy_data[:,-1], 'k', label="Z")
+plt.subplot(212)
+plt.plot(preds[0, :, 0, 1], label="P")
+plt.plot(preds[0, :, 0, 2], label="S")
+plt.legend()
+plt.show();</div>
+    
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+<pre>Picks [{&#39;id&#39;: &#39;BW.RJOB..EH&#39;, &#39;timestamp&#39;: &#39;2009-08-24T00:20:08.690&#39;, &#39;prob&#39;: 0.41293007135391235, &#39;amp&#39;: 3281.9875522716266, &#39;type&#39;: &#39;s&#39;}]
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+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">PHASENET_API_URL</span> <span class="o">=</span> <span class="s2">&quot;http://127.0.0.1:8001&quot;</span>
+<span class="n">resp</span> <span class="o">=</span> <span class="n">requests</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">PHASENET_API_URL</span><span class="si">}</span><span class="s1">/predict&#39;</span><span class="p">,</span> <span class="n">json</span><span class="o">=</span><span class="n">resp_deepdenoiser</span><span class="o">.</span><span class="n">json</span><span class="p">())</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Picks&#39;</span><span class="p">,</span> <span class="n">resp</span><span class="o">.</span><span class="n">json</span><span class="p">())</span>
+</pre></div>
+<div id="cell-7" class="clipboard-copy-txt">PHASENET_API_URL = "http://127.0.0.1:8001"
+resp = requests.get(f'{PHASENET_API_URL}/predict', json=resp_deepdenoiser.json())
+print('Picks', resp.json())</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
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+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
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+<pre>Picks [{&#39;id&#39;: &#39;BW.RJOB..EH&#39;, &#39;timestamp&#39;: &#39;2009-08-24T00:20:07.030&#39;, &#39;prob&#39;: 0.763781726360321, &#39;amp&#39;: 1741.8168170560068, &#39;type&#39;: &#39;p&#39;}, {&#39;id&#39;: &#39;BW.RJOB..EH&#39;, &#39;timestamp&#39;: &#39;2009-08-24T00:20:08.610&#39;, &#39;prob&#39;: 0.5108724236488342, &#39;amp&#39;: 1741.8168170560068, &#39;type&#39;: &#39;s&#39;}]
+</pre>
+</div>
+</div>
+
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+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">PHASENET_API_URL</span> <span class="o">=</span> <span class="s2">&quot;http://127.0.0.1:8001&quot;</span>
+<span class="n">resp</span> <span class="o">=</span> <span class="n">requests</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">PHASENET_API_URL</span><span class="si">}</span><span class="s1">/predict_prob&#39;</span><span class="p">,</span> <span class="n">json</span><span class="o">=</span><span class="n">resp_deepdenoiser</span><span class="o">.</span><span class="n">json</span><span class="p">())</span>
+<span class="n">picks</span><span class="p">,</span> <span class="n">preds</span> <span class="o">=</span> <span class="n">resp</span><span class="o">.</span><span class="n">json</span><span class="p">()</span> 
+<span class="n">preds</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">preds</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Picks&#39;</span><span class="p">,</span> <span class="n">picks</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">211</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">denoised_data</span><span class="p">[</span><span class="mi">0</span><span class="p">,:,</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="s1">&#39;k&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;Z&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">212</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">preds</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;P&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">preds</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;S&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">();</span>
+</pre></div>
+<div id="cell-8" class="clipboard-copy-txt">PHASENET_API_URL = "http://127.0.0.1:8001"
+resp = requests.get(f'{PHASENET_API_URL}/predict_prob', json=resp_deepdenoiser.json())
+picks, preds = resp.json() 
+preds = np.array(preds)
+print('Picks', picks)
+
+plt.figure()
+plt.subplot(211)
+plt.plot(denoised_data[0,:,-1], 'k', label="Z")
+plt.subplot(212)
+plt.plot(preds[0, :, 0, 1], label="P")
+plt.plot(preds[0, :, 0, 2], label="S")
+plt.legend()
+plt.show();</div>
+    
+        </div>
+    </div>
+
+</div>
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+<pre>Picks [{&#39;id&#39;: &#39;BW.RJOB..EH&#39;, &#39;timestamp&#39;: &#39;2009-08-24T00:20:07.030&#39;, &#39;prob&#39;: 0.763781726360321, &#39;amp&#39;: 1741.8168170560068, &#39;type&#39;: &#39;p&#39;}, {&#39;id&#39;: &#39;BW.RJOB..EH&#39;, &#39;timestamp&#39;: &#39;2009-08-24T00:20:08.610&#39;, &#39;prob&#39;: 0.5108724236488342, &#39;amp&#39;: 1741.8168170560068, &#39;type&#39;: &#39;s&#39;}]
+</pre>
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diff --git a/gamma/index.html b/gamma/index.html
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+  <h1>Gamma</h1>
+
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+
+
+<h1 id="quick-readme-not-detailed">Quick readme, not detailed</h1>
+<ol>
+<li>Create a cluster on GCP with node autoscaling</li>
+</ol>
+<pre><code>gcloud container clusters create quakeflow-cluster --zone=&quot;us-west1-a&quot; --scopes=&quot;cloud-platform&quot; --image-type=&quot;ubuntu&quot;  --machine-type=&quot;n1-standard-2&quot; --num-nodes=2 --enable-autoscaling --min-nodes 1 --max-nodes 4
+</code></pre>
+<ol>
+<li>Switch to the correct context</li>
+</ol>
+<pre><code>gcloud container clusters get-credentials quakeflow-cluster
+</code></pre>
+<ol>
+<li>Deploy the services on the cluster</li>
+</ol>
+<pre><code>kubectl apply -f quakeflow-gcp.yaml 
+</code></pre>
+<ol>
+<li>Setup the APIs</li>
+</ol>
+<p>4.1 Add pods autoscaling</p>
+<pre><code>kubectl autoscale deployment phasenet-api --cpu-percent=80 --min=1 --max=10
+kubectl autoscale deployment gmma-api --cpu-percent=80 --min=1 --max=10
+</code></pre>
+<p>4.2 Expose API</p>
+<pre><code>kubectl expose deployment phasenet-api --type=LoadBalancer --name=phasenet-service
+kubectl expose deployment gmma-api --type=LoadBalancer --name=gmma-service
+kubectl expose deployment quakeflow-ui --type=LoadBalancer --name=quakeflow-ui
+</code></pre>
+<ol>
+<li>Install Kafka</li>
+</ol>
+<p>5.1 Install</p>
+<pre><code>helm install quakeflow-kafka bitnami/kafka   
+</code></pre>
+<p>5.2 Create topics</p>
+<pre><code>kubectl run --quiet=true -it --rm quakeflow-kafka-client --restart='Never' --image docker.io/bitnami/kafka:2.7.0-debian-10-r68 --restart=Never --command -- bash -c &quot;kafka-topics.sh --create --topic phasenet_picks --bootstrap-server my-kafka.default.svc.cluster.local:9092 &amp;&amp; kafka-topics.sh --create --topic gmma_events --bootstrap-server my-kafka.default.svc.cluster.local:9092 &amp;&amp; kafka-topics.sh --create --topic waveform_raw --bootstrap-server my-kafka.default.svc.cluster.local:9092&quot;
+</code></pre>
+<p>5.3 Check status</p>
+<pre><code>helm status quakeflow-kafka
+</code></pre>
+<ol>
+<li>Rollup restart deployments</li>
+</ol>
+<pre><code>kubectl rollout restart deployments   
+</code></pre>
+<ol>
+<li>Install Dashboard</li>
+</ol>
+<pre><code>kubectl apply -f https://raw.githubusercontent.com/kubernetes/dashboard/v2.0.0/aio/deploy/recommended.yaml
+</code></pre>
+<p>Run the following command and visit http://localhost:8001/api/v1/namespaces/kubernetes-dashboard/services/https:kubernetes-dashboard:/proxy/</p>
+<pre><code>kubectl proxy 
+</code></pre>
+<p>If you are asked to provide a token, get the token with the following command</p>
+<pre><code>gcloud config config-helper --format=json | jq -r '.credential.access_token'
+</code></pre>
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+  
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+        Standard Formats
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+</nav>
+                  </div>
+                </div>
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+            
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+  
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+    
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+  
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+      <span class="md-nav__icon md-icon"></span>
+      Table of contents
+    </label>
+    <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
+      
+        <li class="md-nav__item">
+  <a href="#overview" class="md-nav__link">
+    Overview
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#current-modules" class="md-nav__link">
+    Current Modules
+  </a>
+  
+    <nav class="md-nav" aria-label="Current Modules">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#models" class="md-nav__link">
+    Models
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#data-stream" class="md-nav__link">
+    Data stream
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#data-process" class="md-nav__link">
+    Data process
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+  
+</li>
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+</li>
+      
+        <li class="md-nav__item">
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+    Deployment
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+</li>
+      
+    </ul>
+  
+</nav>
+                  </div>
+                </div>
+              </div>
+            
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+          <div class="md-content" data-md-component="content">
+            <article class="md-content__inner md-typeset">
+              
+                
+  <a href="https://github.com/ai4eps/QuakeFlow/edit/master/docs/README.md" title="Edit this page" class="md-content__button md-icon">
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
+  </a>
+
+
+
+<!-- <img src="https://raw.githubusercontent.com/wayneweiqiang/QuakeFlow/master/docs/assets/logo.png"> -->
+
+<h1 id="quakeflow-a-scalable-machine-learning-based-earthquake-monitoring-workflow-with-cloud-computing"><a href="https://ai4eps.github.io/QuakeFlow/">QuakeFlow: A Scalable Machine-learning-based Earthquake Monitoring Workflow with Cloud Computing</a></h1>
+<p><a href="https://ai4eps.github.io/QuakeFlow/"><img alt="documentation" src="https://github.com/ai4eps/QuakeFlow/actions/workflows/docs.yml/badge.svg" /></a></p>
+<h2 id="overview">Overview</h2>
+<p><img alt="" src="https://raw.githubusercontent.com/ai4eps/QuakeFlow/master/docs/assets/quakeflow_diagram.png" /></p>
+<p>QuakeFlow is a scalable deep-learning-based earthquake monitoring system with cloud computing. 
+It applies the state-of-art deep learning/machine learning models for earthquake detection. 
+With auto-scaling enabled on Kubernetes, our system can balance computational loads with computational resources. </p>
+<!-- Checkout our Twitter Bot for realtime earthquake early warning at [@Quakeflow_Bot](https://twitter.com/QuakeFlow_bot). -->
+
+<h2 id="current-modules">Current Modules</h2>
+<h3 id="models">Models</h3>
+<ul>
+<li><a href="https://ai4eps.github.io/DeepDenoiser/">DeepDenoiser</a>: <a href="https://arxiv.org/abs/1811.02695">(paper)</a> <a href="https://ai4eps.github.io/DeepDenoiser/example_interactive/">(example)</a></li>
+<li><a href="https://ai4eps.github.io/PhaseNet/">PhaseNet</a>: <a href="https://arxiv.org/abs/1803.03211">(paper)</a> <a href="https://ai4eps.github.io/PhaseNet/example_interactive/">(example)</a></li>
+<li><a href="https://ai4eps.github.io/GaMMA/">GaMMA</a>: <a href="https://arxiv.org/abs/2109.09008">(paper)</a> <a href="https://ai4eps.github.io/GaMMA/example_interactive/">(example)</a></li>
+<li><a href="https://www.ldeo.columbia.edu/~felixw/hypoDD.html">HypoDD</a> <a href="https://pubs.geoscienceworld.org/ssa/bssa/article-abstract/90/6/1353/120565/A-Double-Difference-Earthquake-Location-Algorithm?redirectedFrom=fulltext">(paper)</a> <a href="https://github.com/ai4eps/QuakeFlow/blob/master/HypoDD/gamma2hypodd.py">(example)</a></li>
+<li>More models to be added. Contributions are highly welcomed!</li>
+</ul>
+<h3 id="data-stream">Data stream</h3>
+<ul>
+<li><a href="https://dash.gallery/Portal/">Plotly</a>: <a href="http://ui.quakeflow.com">ui.quakeflow.com</a></li>
+<li><a href="https://www.confluent.io/what-is-apache-kafka/">Kafka</a> </li>
+<li><a href="https://spark.apache.org/docs/latest/streaming-programming-guide.html">Spark Streaming</a></li>
+</ul>
+<h3 id="data-process">Data process</h3>
+<ul>
+<li><a href="https://colab.research.google.com/drive/19dC8-Vq0mv1Q9K-OS8VJf3xNEweKv4SN">Colab example</a></li>
+<li><a href="https://www.kubeflow.org/">Kubeflow</a>: <a href="https://ai4eps.github.io/QuakeFlow/kubeflow/workflow/">(example)</a></li>
+</ul>
+<p><img alt="" src="https://raw.githubusercontent.com/wayneweiqiang/QuakeFlow/master/docs/assets/quakeflow.gif" /></p>
+<h2 id="deployment">Deployment</h2>
+<p>QuakeFlow can be deployed on any cloud platforms with Kubernetes service.</p>
+<ul>
+<li>For google cloud platform (GCP), check out the <a href="gcp_readme/">GCP README</a>.</li>
+<li>For on-premise servers, check out the <a href="k8s_readme/">Kubernetes README</a>.</li>
+</ul>
+<!-- ## User-Facing Platform
+
+### Streamlit Web App
+
+<img src="https://i.imgur.com/xL696Yh.jpg" width="800px">
+
+
+### Twitter Bot
+
+<img src="https://i.imgur.com/50kVK4Q.png" width="400px"> -->
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diff --git a/k8s_readme/index.html b/k8s_readme/index.html
new file mode 100644
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+        Standard Formats
+      </a>
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+  
+  
+  
+    
+  
+  
+    <label class="md-nav__title" for="__toc">
+      <span class="md-nav__icon md-icon"></span>
+      Table of contents
+    </label>
+    <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
+      
+        <li class="md-nav__item">
+  <a href="#all-in-one-script" class="md-nav__link">
+    All-in-one script
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#prebuilt-kafka" class="md-nav__link">
+    Prebuilt Kafka
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#our-own-containers" class="md-nav__link">
+    Our own containers
+  </a>
+  
+</li>
+      
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+  
+</nav>
+                  </div>
+                </div>
+              </div>
+            
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+              
+                
+  <a href="https://github.com/ai4eps/QuakeFlow/edit/master/docs/k8s_readme.md" title="Edit this page" class="md-content__button md-icon">
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
+  </a>
+
+
+
+<h1 id="quick-readme-not-detailed">Quick readme, not detailed</h1>
+<h2 id="all-in-one-script">All-in-one script</h2>
+<p>You need to preinstall <a href="https://helm.sh/">helm</a>, <a href="https://kubernetes.io/docs/tasks/tools/">kubectl</a>, <a href="https://docs.docker.com/engine/install/">docker</a> and <a href="https://minikube.sigs.k8s.io/docs/start/">minikube</a> (or any other local Kubernetes framework)</p>
+<p>Then deploy everything with the following script!</p>
+<pre><code>$ git clone --recurse-submodules -j8 git@github.com:wayneweiqiang/QuakeFlow.git
+$ sh deploy_local.sh
+</code></pre>
+<h2 id="prebuilt-kafka">Prebuilt Kafka</h2>
+<ol>
+<li>Install</li>
+</ol>
+<pre><code>helm repo add bitnami https://charts.bitnami.com/bitnami
+helm install quakeflow-kafka bitnami/kafka   
+</code></pre>
+<ol>
+<li>Create topics</li>
+</ol>
+<pre><code>kubectl run --quiet=true -it --rm quakeflow-kafka-client --restart='Never' --image docker.io/bitnami/kafka:2.7.0-debian-10-r68 --restart=Never --command -- bash -c &quot;kafka-topics.sh --create --topic phasenet_picks --bootstrap-server quakeflow-kafka.default.svc.cluster.local:9092 &amp;&amp; kafka-topics.sh --create --topic gmma_events --bootstrap-server quakeflow-kafka.default.svc.cluster.local:9092 &amp;&amp; kafka-topics.sh --create --topic waveform_raw --bootstrap-server quakeflow-kafka.default.svc.cluster.local:9092&quot;
+</code></pre>
+<ol>
+<li>Check status</li>
+</ol>
+<pre><code>helm status quakeflow-kafka
+</code></pre>
+<h2 id="our-own-containers">Our own containers</h2>
+<ol>
+<li>Switch to minikube environment</li>
+</ol>
+<pre><code>eval $(minikube docker-env)     
+</code></pre>
+<p>1.1. Fix metrics-server for auto-scalling (Only for docker)
+https://stackoverflow.com/questions/54106725/docker-kubernetes-mac-autoscaler-unable-to-find-metrics</p>
+<pre><code>kubectl apply -f metrics-server.yaml
+</code></pre>
+<ol>
+<li>Build the docker images, see the docs for each container</li>
+</ol>
+<pre><code>docker build --tag quakeflow-spark:1.0 .
+...
+</code></pre>
+<ol>
+<li>Create everything</li>
+</ol>
+<pre><code>kubectl apply -f quakeflow-delpoyment.yaml     
+</code></pre>
+<p>3.1 Add autoscaling</p>
+<pre><code>kubectl autoscale deployment phasenet-api --cpu-percent=80 --min=1 --max=10
+kubectl autoscale deployment gmma-api --cpu-percent=80 --min=1 --max=10
+</code></pre>
+<p>3.2 Expose API</p>
+<pre><code>kubectl expose deployment phasenet-api --type=LoadBalancer --name=phasenet-service
+</code></pre>
+<ol>
+<li>Check the pods</li>
+</ol>
+<pre><code>kubectl get pods
+</code></pre>
+<ol>
+<li>Check the logs (an example)</li>
+</ol>
+<pre><code>kubectl logs quakeflow-spark-7699cd45d8-mvv6r
+</code></pre>
+<ol>
+<li>Delete a single deployment</li>
+</ol>
+<pre><code>kubectl delete deploy quakeflow-spark     
+</code></pre>
+<ol>
+<li>Delete everything</li>
+</ol>
+<pre><code>kubectl delete -f quakeflow-delpoyment.yaml   
+</code></pre>
+
+
+  
+
+
+
+              
+            </article>
+            
+          </div>
+        </div>
+        
+      </main>
+      
+        <footer class="md-footer">
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\ No newline at end of file
diff --git a/kubeflow/Dockerfile b/kubeflow/Dockerfile
new file mode 100644
index 0000000..98452f9
--- /dev/null
+++ b/kubeflow/Dockerfile
@@ -0,0 +1,24 @@
+# FROM continuumio/miniconda3
+
+FROM ubuntu:18.04
+
+ENV PATH="/root/miniconda3/bin:${PATH}"
+ARG PATH="/root/miniconda3/bin:${PATH}"
+RUN apt-get update && apt-get install -y wget && rm -rf /var/lib/apt/lists/*
+
+RUN wget \
+    https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh \
+    && mkdir /root/.conda \
+    && bash Miniconda3-latest-Linux-x86_64.sh -b \
+    && rm -f Miniconda3-latest-Linux-x86_64.sh 
+RUN conda --version
+
+# Setup env variables
+ENV PYTHONUNBUFFERED=1
+
+WORKDIR /app
+COPY env.yml /app
+RUN conda env update -f=env.yml -n base
+RUN pip install --no-cache-dir --upgrade -i https://pypi.anaconda.org/zhuwq0/simple gmma
+RUN conda clean --all 
+#SHELL ["conda", "run", "-n", "base", "/bin/bash", "-c"]
diff --git a/kubeflow/Stream/index.html b/kubeflow/Stream/index.html
new file mode 100644
index 0000000..e8e9b69
--- /dev/null
+++ b/kubeflow/Stream/index.html
@@ -0,0 +1,2239 @@
+
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+      
+        <meta name="description" content="QuakeFlow: a Scalable Deep-learning-based Earthquake Monitoring Workflow with Cloud Computing">
+      
+      
+        <meta name="author" content="Weiqiang Zhu">
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+.highlight-ipynb .p { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation */
+.highlight-ipynb .ch { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Hashbang */
+.highlight-ipynb .cm { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Multiline */
+.highlight-ipynb .cp { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Preproc */
+.highlight-ipynb .cpf { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.PreprocFile */
+.highlight-ipynb .c1 { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Single */
+.highlight-ipynb .cs { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Special */
+.highlight-ipynb .kc { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Constant */
+.highlight-ipynb .kd { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Declaration */
+.highlight-ipynb .kn { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Namespace */
+.highlight-ipynb .kp { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Pseudo */
+.highlight-ipynb .kr { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Reserved */
+.highlight-ipynb .kt { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Type */
+.highlight-ipynb .m { color: var(--jp-mirror-editor-number-color) } /* Literal.Number */
+.highlight-ipynb .s { color: var(--jp-mirror-editor-string-color) } /* Literal.String */
+.highlight-ipynb .ow { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator.Word */
+.highlight-ipynb .pm { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation.Marker */
+.highlight-ipynb .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */
+.highlight-ipynb .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */
+.highlight-ipynb .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */
+.highlight-ipynb .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */
+.highlight-ipynb .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */
+.highlight-ipynb .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */
+.highlight-ipynb .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */
+.highlight-ipynb .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */
+.highlight-ipynb .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */
+.highlight-ipynb .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */
+.highlight-ipynb .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */
+.highlight-ipynb .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */
+.highlight-ipynb .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */
+.highlight-ipynb .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */
+.highlight-ipynb .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */
+.highlight-ipynb .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */
+.highlight-ipynb .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */
+.highlight-ipynb .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */
+.highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */
+.highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */
+  </style>
+
+
+
+
+<style type="text/css">
+/*
+This file is taken from the built JupyterLab theme.css
+Found on share/nbconvert/templates/lab/static
+Some changes have been made and marked with CHANGE
+*/
+
+.jupyter-wrapper {
+    /* Elevation
+   *
+   * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here:
+   *
+   * https://github.com/material-components/material-components-web
+   * https://material-components-web.appspot.com/elevation.html
+   */
+
+    --jp-shadow-base-lightness: 0;
+    --jp-shadow-umbra-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.2
+    );
+    --jp-shadow-penumbra-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.14
+    );
+    --jp-shadow-ambient-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.12
+    );
+    --jp-elevation-z0: none;
+    --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color),
+        0px 1px 1px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 3px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color),
+        0px 2px 2px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 5px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color),
+        0px 4px 5px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 10px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color),
+        0px 6px 10px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 18px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color),
+        0px 8px 10px 1px var(--jp-shadow-penumbra-color),
+        0px 3px 14px 2px var(--jp-shadow-ambient-color);
+    --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color),
+        0px 12px 17px 2px var(--jp-shadow-penumbra-color),
+        0px 5px 22px 4px var(--jp-shadow-ambient-color);
+    --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color),
+        0px 16px 24px 2px var(--jp-shadow-penumbra-color),
+        0px 6px 30px 5px var(--jp-shadow-ambient-color);
+    --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color),
+        0px 20px 31px 3px var(--jp-shadow-penumbra-color),
+        0px 8px 38px 7px var(--jp-shadow-ambient-color);
+    --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color),
+        0px 24px 38px 3px var(--jp-shadow-penumbra-color),
+        0px 9px 46px 8px var(--jp-shadow-ambient-color);
+
+    /* Borders
+   *
+   * The following variables, specify the visual styling of borders in JupyterLab.
+   */
+
+    --jp-border-width: 1px;
+    --jp-border-color0: var(--md-grey-400);
+    --jp-border-color1: var(--md-grey-400);
+    --jp-border-color2: var(--md-grey-300);
+    --jp-border-color3: var(--md-grey-200);
+    --jp-border-radius: 2px;
+
+    /* UI Fonts
+   *
+   * The UI font CSS variables are used for the typography all of the JupyterLab
+   * user interface elements that are not directly user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-ui-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+    --jp-ui-font-scale-factor: 1.2;
+    --jp-ui-font-size0: 0.83333em;
+    --jp-ui-font-size1: 13px; /* Base font size */
+    --jp-ui-font-size2: 1.2em;
+    --jp-ui-font-size3: 1.44em;
+
+    --jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+
+    /*
+   * Use these font colors against the corresponding main layout colors.
+   * In a light theme, these go from dark to light.
+   */
+
+    /* Defaults use Material Design specification */
+    --jp-ui-font-color0: rgba(0, 0, 0, 1);
+    --jp-ui-font-color1: rgba(0, 0, 0, 0.87);
+    --jp-ui-font-color2: rgba(0, 0, 0, 0.54);
+    --jp-ui-font-color3: rgba(0, 0, 0, 0.38);
+
+    /*
+   * Use these against the brand/accent/warn/error colors.
+   * These will typically go from light to darker, in both a dark and light theme.
+   */
+
+    --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1);
+    --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1);
+    --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7);
+    --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5);
+
+    /* Content Fonts
+   *
+   * Content font variables are used for typography of user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-content-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+    --jp-content-line-height: 1.6;
+    --jp-content-font-scale-factor: 1.2;
+    --jp-content-font-size0: 0.83333em;
+    --jp-content-font-size1: 14px; /* Base font size */
+    --jp-content-font-size2: 1.2em;
+    --jp-content-font-size3: 1.44em;
+    --jp-content-font-size4: 1.728em;
+    --jp-content-font-size5: 2.0736em;
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-content-presentation-font-size1: 17px;
+
+    --jp-content-heading-line-height: 1;
+    --jp-content-heading-margin-top: 1.2em;
+    --jp-content-heading-margin-bottom: 0.8em;
+    --jp-content-heading-font-weight: 500;
+
+    /* Defaults use Material Design specification */
+    --jp-content-font-color0: rgba(0, 0, 0, 1);
+    --jp-content-font-color1: rgba(0, 0, 0, 0.87);
+    --jp-content-font-color2: rgba(0, 0, 0, 0.54);
+    --jp-content-font-color3: rgba(0, 0, 0, 0.38);
+
+    --jp-content-link-color: var(--md-blue-700);
+
+    --jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+
+    /*
+   * Code Fonts
+   *
+   * Code font variables are used for typography of code and other monospaces content.
+   */
+
+    --jp-code-font-size: 13px;
+    --jp-code-line-height: 1.3077; /* 17px for 13px base */
+    --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */
+    --jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono",
+        monospace;
+    --jp-code-font-family: var(--jp-code-font-family-default);
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-code-presentation-font-size: 16px;
+
+    /* may need to tweak cursor width if you change font size */
+    --jp-code-cursor-width0: 1.4px;
+    --jp-code-cursor-width1: 2px;
+    --jp-code-cursor-width2: 4px;
+
+    /* Layout
+   *
+   * The following are the main layout colors use in JupyterLab. In a light
+   * theme these would go from light to dark.
+   */
+
+    --jp-layout-color0: white;
+    --jp-layout-color1: white;
+    --jp-layout-color2: var(--md-grey-200);
+    --jp-layout-color3: var(--md-grey-400);
+    --jp-layout-color4: var(--md-grey-600);
+
+    /* Inverse Layout
+   *
+   * The following are the inverse layout colors use in JupyterLab. In a light
+   * theme these would go from dark to light.
+   */
+
+    --jp-inverse-layout-color0: #111111;
+    --jp-inverse-layout-color1: var(--md-grey-900);
+    --jp-inverse-layout-color2: var(--md-grey-800);
+    --jp-inverse-layout-color3: var(--md-grey-700);
+    --jp-inverse-layout-color4: var(--md-grey-600);
+
+    /* Brand/accent */
+
+    --jp-brand-color0: var(--md-blue-900);
+    --jp-brand-color1: var(--md-blue-700);
+    --jp-brand-color2: var(--md-blue-300);
+    --jp-brand-color3: var(--md-blue-100);
+    --jp-brand-color4: var(--md-blue-50);
+
+    --jp-accent-color0: var(--md-green-900);
+    --jp-accent-color1: var(--md-green-700);
+    --jp-accent-color2: var(--md-green-300);
+    --jp-accent-color3: var(--md-green-100);
+
+    /* State colors (warn, error, success, info) */
+
+    --jp-warn-color0: var(--md-orange-900);
+    --jp-warn-color1: var(--md-orange-700);
+    --jp-warn-color2: var(--md-orange-300);
+    --jp-warn-color3: var(--md-orange-100);
+
+    --jp-error-color0: var(--md-red-900);
+    --jp-error-color1: var(--md-red-700);
+    --jp-error-color2: var(--md-red-300);
+    --jp-error-color3: var(--md-red-100);
+
+    --jp-success-color0: var(--md-green-900);
+    --jp-success-color1: var(--md-green-700);
+    --jp-success-color2: var(--md-green-300);
+    --jp-success-color3: var(--md-green-100);
+
+    --jp-info-color0: var(--md-cyan-900);
+    --jp-info-color1: var(--md-cyan-700);
+    --jp-info-color2: var(--md-cyan-300);
+    --jp-info-color3: var(--md-cyan-100);
+
+    /* Cell specific styles */
+
+    --jp-cell-padding: 5px;
+
+    --jp-cell-collapser-width: 8px;
+    --jp-cell-collapser-min-height: 20px;
+    --jp-cell-collapser-not-active-hover-opacity: 0.6;
+
+    --jp-cell-editor-background: var(--md-grey-100);
+    --jp-cell-editor-border-color: var(--md-grey-300);
+    --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-cell-editor-active-background: var(--jp-layout-color0);
+    --jp-cell-editor-active-border-color: var(--jp-brand-color1);
+
+    --jp-cell-prompt-width: 64px;
+    --jp-cell-prompt-font-family: var(--jp-code-font-family-default);
+    --jp-cell-prompt-letter-spacing: 0px;
+    --jp-cell-prompt-opacity: 1;
+    --jp-cell-prompt-not-active-opacity: 0.5;
+    --jp-cell-prompt-not-active-font-color: var(--md-grey-700);
+    /* A custom blend of MD grey and blue 600
+   * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */
+    --jp-cell-inprompt-font-color: #307fc1;
+    /* A custom blend of MD grey and orange 600
+   * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */
+    --jp-cell-outprompt-font-color: #bf5b3d;
+
+    /* Notebook specific styles */
+
+    --jp-notebook-padding: 10px;
+    --jp-notebook-select-background: var(--jp-layout-color1);
+    --jp-notebook-multiselected-color: var(--md-blue-50);
+
+    /* The scroll padding is calculated to fill enough space at the bottom of the
+  notebook to show one single-line cell (with appropriate padding) at the top
+  when the notebook is scrolled all the way to the bottom. We also subtract one
+  pixel so that no scrollbar appears if we have just one single-line cell in the
+  notebook. This padding is to enable a 'scroll past end' feature in a notebook.
+  */
+    --jp-notebook-scroll-padding: calc(
+        100% - var(--jp-code-font-size) * var(--jp-code-line-height) -
+            var(--jp-code-padding) - var(--jp-cell-padding) - 1px
+    );
+
+    /* Rendermime styles */
+
+    --jp-rendermime-error-background: #fdd;
+    --jp-rendermime-table-row-background: var(--md-grey-100);
+    --jp-rendermime-table-row-hover-background: var(--md-light-blue-50);
+
+    /* Dialog specific styles */
+
+    --jp-dialog-background: rgba(0, 0, 0, 0.25);
+
+    /* Console specific styles */
+
+    --jp-console-padding: 10px;
+
+    /* Toolbar specific styles */
+
+    --jp-toolbar-border-color: var(--jp-border-color1);
+    --jp-toolbar-micro-height: 8px;
+    --jp-toolbar-background: var(--jp-layout-color1);
+    --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24);
+    --jp-toolbar-header-margin: 4px 4px 0px 4px;
+    --jp-toolbar-active-background: var(--md-grey-300);
+
+    /* Statusbar specific styles */
+
+    --jp-statusbar-height: 24px;
+
+    /* Input field styles */
+
+    --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-input-active-background: var(--jp-layout-color1);
+    --jp-input-hover-background: var(--jp-layout-color1);
+    --jp-input-background: var(--md-grey-100);
+    --jp-input-border-color: var(--jp-border-color1);
+    --jp-input-active-border-color: var(--jp-brand-color1);
+    --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3);
+
+    /* General editor styles */
+
+    --jp-editor-selected-background: #d9d9d9;
+    --jp-editor-selected-focused-background: #d7d4f0;
+    --jp-editor-cursor-color: var(--jp-ui-font-color0);
+
+    /* Code mirror specific styles */
+
+    --jp-mirror-editor-keyword-color: #008000;
+    --jp-mirror-editor-atom-color: #88f;
+    --jp-mirror-editor-number-color: #080;
+    --jp-mirror-editor-def-color: #00f;
+    --jp-mirror-editor-variable-color: var(--md-grey-900);
+    --jp-mirror-editor-variable-2-color: #05a;
+    --jp-mirror-editor-variable-3-color: #085;
+    --jp-mirror-editor-punctuation-color: #05a;
+    --jp-mirror-editor-property-color: #05a;
+    --jp-mirror-editor-operator-color: #aa22ff;
+    --jp-mirror-editor-comment-color: #408080;
+    --jp-mirror-editor-string-color: #ba2121;
+    --jp-mirror-editor-string-2-color: #708;
+    --jp-mirror-editor-meta-color: #aa22ff;
+    --jp-mirror-editor-qualifier-color: #555;
+    --jp-mirror-editor-builtin-color: #008000;
+    --jp-mirror-editor-bracket-color: #997;
+    --jp-mirror-editor-tag-color: #170;
+    --jp-mirror-editor-attribute-color: #00c;
+    --jp-mirror-editor-header-color: blue;
+    --jp-mirror-editor-quote-color: #090;
+    --jp-mirror-editor-link-color: #00c;
+    --jp-mirror-editor-error-color: #f00;
+    --jp-mirror-editor-hr-color: #999;
+
+    /* Vega extension styles */
+
+    --jp-vega-background: white;
+
+    /* Sidebar-related styles */
+
+    --jp-sidebar-min-width: 250px;
+
+    /* Search-related styles */
+
+    --jp-search-toggle-off-opacity: 0.5;
+    --jp-search-toggle-hover-opacity: 0.8;
+    --jp-search-toggle-on-opacity: 1;
+    --jp-search-selected-match-background-color: rgb(245, 200, 0);
+    --jp-search-selected-match-color: black;
+    --jp-search-unselected-match-background-color: var(
+        --jp-inverse-layout-color0
+    );
+    --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);
+
+    /* Icon colors that work well with light or dark backgrounds */
+    --jp-icon-contrast-color0: var(--md-purple-600);
+    --jp-icon-contrast-color1: var(--md-green-600);
+    --jp-icon-contrast-color2: var(--md-pink-600);
+    --jp-icon-contrast-color3: var(--md-blue-600);
+}
+
+[data-md-color-scheme="slate"] .jupyter-wrapper {
+    /* Elevation
+   *
+   * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here:
+   *
+   * https://github.com/material-components/material-components-web
+   * https://material-components-web.appspot.com/elevation.html
+   */
+
+    /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout
+   * colors used in the theme as well. */
+    --jp-shadow-base-lightness: 32;
+    --jp-shadow-umbra-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.2
+    );
+    --jp-shadow-penumbra-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.14
+    );
+    --jp-shadow-ambient-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.12
+    );
+    --jp-elevation-z0: none;
+    --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color),
+        0px 1px 1px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 3px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color),
+        0px 2px 2px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 5px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color),
+        0px 4px 5px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 10px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color),
+        0px 6px 10px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 18px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color),
+        0px 8px 10px 1px var(--jp-shadow-penumbra-color),
+        0px 3px 14px 2px var(--jp-shadow-ambient-color);
+    --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color),
+        0px 12px 17px 2px var(--jp-shadow-penumbra-color),
+        0px 5px 22px 4px var(--jp-shadow-ambient-color);
+    --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color),
+        0px 16px 24px 2px var(--jp-shadow-penumbra-color),
+        0px 6px 30px 5px var(--jp-shadow-ambient-color);
+    --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color),
+        0px 20px 31px 3px var(--jp-shadow-penumbra-color),
+        0px 8px 38px 7px var(--jp-shadow-ambient-color);
+    --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color),
+        0px 24px 38px 3px var(--jp-shadow-penumbra-color),
+        0px 9px 46px 8px var(--jp-shadow-ambient-color);
+
+    /* Borders
+   *
+   * The following variables, specify the visual styling of borders in JupyterLab.
+   */
+
+    --jp-border-width: 1px;
+    --jp-border-color0: var(--md-grey-700);
+    --jp-border-color1: var(--md-grey-700);
+    --jp-border-color2: var(--md-grey-800);
+    --jp-border-color3: var(--md-grey-900);
+    --jp-border-radius: 2px;
+
+    /* UI Fonts
+   *
+   * The UI font CSS variables are used for the typography all of the JupyterLab
+   * user interface elements that are not directly user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-ui-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+    --jp-ui-font-scale-factor: 1.2;
+    --jp-ui-font-size0: 0.83333em;
+    --jp-ui-font-size1: 13px; /* Base font size */
+    --jp-ui-font-size2: 1.2em;
+    --jp-ui-font-size3: 1.44em;
+
+    --jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+
+    /*
+   * Use these font colors against the corresponding main layout colors.
+   * In a light theme, these go from dark to light.
+   */
+
+    /* Defaults use Material Design specification */
+    --jp-ui-font-color0: rgba(255, 255, 255, 1);
+    --jp-ui-font-color1: rgba(255, 255, 255, 0.87);
+    --jp-ui-font-color2: rgba(255, 255, 255, 0.54);
+    --jp-ui-font-color3: rgba(255, 255, 255, 0.38);
+
+    /*
+   * Use these against the brand/accent/warn/error colors.
+   * These will typically go from light to darker, in both a dark and light theme.
+   */
+
+    --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1);
+    --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8);
+    --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5);
+    --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3);
+
+    /* Content Fonts
+   *
+   * Content font variables are used for typography of user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-content-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+    --jp-content-line-height: 1.6;
+    --jp-content-font-scale-factor: 1.2;
+    --jp-content-font-size0: 0.83333em;
+    --jp-content-font-size1: 14px; /* Base font size */
+    --jp-content-font-size2: 1.2em;
+    --jp-content-font-size3: 1.44em;
+    --jp-content-font-size4: 1.728em;
+    --jp-content-font-size5: 2.0736em;
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-content-presentation-font-size1: 17px;
+
+    --jp-content-heading-line-height: 1;
+    --jp-content-heading-margin-top: 1.2em;
+    --jp-content-heading-margin-bottom: 0.8em;
+    --jp-content-heading-font-weight: 500;
+
+    /* Defaults use Material Design specification */
+    --jp-content-font-color0: rgba(255, 255, 255, 1);
+    --jp-content-font-color1: rgba(255, 255, 255, 1);
+    --jp-content-font-color2: rgba(255, 255, 255, 0.7);
+    --jp-content-font-color3: rgba(255, 255, 255, 0.5);
+
+    --jp-content-link-color: var(--md-blue-300);
+
+    --jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+
+    /*
+   * Code Fonts
+   *
+   * Code font variables are used for typography of code and other monospaces content.
+   */
+
+    --jp-code-font-size: 13px;
+    --jp-code-line-height: 1.3077; /* 17px for 13px base */
+    --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */
+    --jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono",
+        monospace;
+    --jp-code-font-family: var(--jp-code-font-family-default);
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-code-presentation-font-size: 16px;
+
+    /* may need to tweak cursor width if you change font size */
+    --jp-code-cursor-width0: 1.4px;
+    --jp-code-cursor-width1: 2px;
+    --jp-code-cursor-width2: 4px;
+
+    /* Layout
+   *
+   * The following are the main layout colors use in JupyterLab. In a light
+   * theme these would go from light to dark.
+   */
+
+    --jp-layout-color0: #111111;
+    --jp-layout-color1: var(--md-grey-900);
+    --jp-layout-color2: var(--md-grey-800);
+    --jp-layout-color3: var(--md-grey-700);
+    --jp-layout-color4: var(--md-grey-600);
+
+    /* Inverse Layout
+   *
+   * The following are the inverse layout colors use in JupyterLab. In a light
+   * theme these would go from dark to light.
+   */
+
+    --jp-inverse-layout-color0: white;
+    --jp-inverse-layout-color1: white;
+    --jp-inverse-layout-color2: var(--md-grey-200);
+    --jp-inverse-layout-color3: var(--md-grey-400);
+    --jp-inverse-layout-color4: var(--md-grey-600);
+
+    /* Brand/accent */
+
+    --jp-brand-color0: var(--md-blue-700);
+    --jp-brand-color1: var(--md-blue-500);
+    --jp-brand-color2: var(--md-blue-300);
+    --jp-brand-color3: var(--md-blue-100);
+    --jp-brand-color4: var(--md-blue-50);
+
+    --jp-accent-color0: var(--md-green-700);
+    --jp-accent-color1: var(--md-green-500);
+    --jp-accent-color2: var(--md-green-300);
+    --jp-accent-color3: var(--md-green-100);
+
+    /* State colors (warn, error, success, info) */
+
+    --jp-warn-color0: var(--md-orange-700);
+    --jp-warn-color1: var(--md-orange-500);
+    --jp-warn-color2: var(--md-orange-300);
+    --jp-warn-color3: var(--md-orange-100);
+
+    --jp-error-color0: var(--md-red-700);
+    --jp-error-color1: var(--md-red-500);
+    --jp-error-color2: var(--md-red-300);
+    --jp-error-color3: var(--md-red-100);
+
+    --jp-success-color0: var(--md-green-700);
+    --jp-success-color1: var(--md-green-500);
+    --jp-success-color2: var(--md-green-300);
+    --jp-success-color3: var(--md-green-100);
+
+    --jp-info-color0: var(--md-cyan-700);
+    --jp-info-color1: var(--md-cyan-500);
+    --jp-info-color2: var(--md-cyan-300);
+    --jp-info-color3: var(--md-cyan-100);
+
+    /* Cell specific styles */
+
+    --jp-cell-padding: 5px;
+
+    --jp-cell-collapser-width: 8px;
+    --jp-cell-collapser-min-height: 20px;
+    --jp-cell-collapser-not-active-hover-opacity: 0.6;
+
+    --jp-cell-editor-background: var(--jp-layout-color1);
+    --jp-cell-editor-border-color: var(--md-grey-700);
+    --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-cell-editor-active-background: var(--jp-layout-color0);
+    --jp-cell-editor-active-border-color: var(--jp-brand-color1);
+
+    --jp-cell-prompt-width: 64px;
+    --jp-cell-prompt-font-family: var(--jp-code-font-family-default);
+    --jp-cell-prompt-letter-spacing: 0px;
+    --jp-cell-prompt-opacity: 1;
+    --jp-cell-prompt-not-active-opacity: 1;
+    --jp-cell-prompt-not-active-font-color: var(--md-grey-300);
+
+    /* A custom blend of MD grey and blue 600
+   * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */
+    --jp-cell-inprompt-font-color: #307fc1;
+    /* A custom blend of MD grey and orange 600
+   * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */
+    --jp-cell-outprompt-font-color: #bf5b3d;
+
+    /* Notebook specific styles */
+
+    --jp-notebook-padding: 10px;
+    --jp-notebook-select-background: var(--jp-layout-color1);
+    --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24);
+
+    /* The scroll padding is calculated to fill enough space at the bottom of the
+  notebook to show one single-line cell (with appropriate padding) at the top
+  when the notebook is scrolled all the way to the bottom. We also subtract one
+  pixel so that no scrollbar appears if we have just one single-line cell in the
+  notebook. This padding is to enable a 'scroll past end' feature in a notebook.
+  */
+    --jp-notebook-scroll-padding: calc(
+        100% - var(--jp-code-font-size) * var(--jp-code-line-height) -
+            var(--jp-code-padding) - var(--jp-cell-padding) - 1px
+    );
+
+    /* Rendermime styles */
+
+    --jp-rendermime-error-background: rgba(244, 67, 54, 0.28);
+    --jp-rendermime-table-row-background: var(--md-grey-900);
+    --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2);
+
+    /* Dialog specific styles */
+
+    --jp-dialog-background: rgba(0, 0, 0, 0.6);
+
+    /* Console specific styles */
+
+    --jp-console-padding: 10px;
+
+    /* Toolbar specific styles */
+
+    --jp-toolbar-border-color: var(--jp-border-color2);
+    --jp-toolbar-micro-height: 8px;
+    --jp-toolbar-background: var(--jp-layout-color1);
+    --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8);
+    --jp-toolbar-header-margin: 4px 4px 0px 4px;
+    --jp-toolbar-active-background: var(--jp-layout-color0);
+
+    /* Statusbar specific styles */
+
+    --jp-statusbar-height: 24px;
+
+    /* Input field styles */
+
+    --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-input-active-background: var(--jp-layout-color0);
+    --jp-input-hover-background: var(--jp-layout-color2);
+    --jp-input-background: var(--md-grey-800);
+    --jp-input-border-color: var(--jp-border-color1);
+    --jp-input-active-border-color: var(--jp-brand-color1);
+    --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3);
+
+    /* General editor styles */
+
+    --jp-editor-selected-background: var(--jp-layout-color2);
+    --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24);
+    --jp-editor-cursor-color: var(--jp-ui-font-color0);
+
+    /* Code mirror specific styles */
+
+    --jp-mirror-editor-keyword-color: var(--md-green-500);
+    --jp-mirror-editor-atom-color: var(--md-blue-300);
+    --jp-mirror-editor-number-color: var(--md-green-400);
+    --jp-mirror-editor-def-color: var(--md-blue-600);
+    --jp-mirror-editor-variable-color: var(--md-grey-300);
+    --jp-mirror-editor-variable-2-color: var(--md-blue-400);
+    --jp-mirror-editor-variable-3-color: var(--md-green-600);
+    --jp-mirror-editor-punctuation-color: var(--md-blue-400);
+    --jp-mirror-editor-property-color: var(--md-blue-400);
+    --jp-mirror-editor-operator-color: #aa22ff;
+    --jp-mirror-editor-comment-color: #408080;
+    --jp-mirror-editor-string-color: #ff7070;
+    --jp-mirror-editor-string-2-color: var(--md-purple-300);
+    --jp-mirror-editor-meta-color: #aa22ff;
+    --jp-mirror-editor-qualifier-color: #555;
+    --jp-mirror-editor-builtin-color: var(--md-green-600);
+    --jp-mirror-editor-bracket-color: #997;
+    --jp-mirror-editor-tag-color: var(--md-green-700);
+    --jp-mirror-editor-attribute-color: var(--md-blue-700);
+    --jp-mirror-editor-header-color: var(--md-blue-500);
+    --jp-mirror-editor-quote-color: var(--md-green-300);
+    --jp-mirror-editor-link-color: var(--md-blue-700);
+    --jp-mirror-editor-error-color: #f00;
+    --jp-mirror-editor-hr-color: #999;
+
+    /* Vega extension styles */
+
+    --jp-vega-background: var(--md-grey-400);
+
+    /* Sidebar-related styles */
+
+    --jp-sidebar-min-width: 250px;
+
+    /* Search-related styles */
+
+    --jp-search-toggle-off-opacity: 0.6;
+    --jp-search-toggle-hover-opacity: 0.8;
+    --jp-search-toggle-on-opacity: 1;
+    --jp-search-selected-match-background-color: rgb(255, 225, 0);
+    --jp-search-selected-match-color: black;
+    --jp-search-unselected-match-background-color: var(
+        --jp-inverse-layout-color0
+    );
+    --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);
+
+    /* scrollbar related styles. Supports every browser except Edge. */
+
+    /* colors based on JetBrain's Darcula theme */
+
+    --jp-scrollbar-background-color: #3f4244;
+    --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */
+
+    --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */
+
+    /* hacks for setting the thumb shape. These do nothing in Firefox */
+
+    --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */
+    --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */
+
+    /* Icon colors that work well with light or dark backgrounds */
+    --jp-icon-contrast-color0: var(--md-purple-600);
+    --jp-icon-contrast-color1: var(--md-green-600);
+    --jp-icon-contrast-color2: var(--md-pink-600);
+    --jp-icon-contrast-color3: var(--md-blue-600);
+}
+
+:root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: #7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: 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+    <!-- End of mathjax configuration --><div class="jupyter-wrapper">
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
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+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="0-pseudo-data-generator">0. Pseudo Data Generator<a class="anchor-link" href="#0-pseudo-data-generator">&#182;</a></h1><ul>
+<li>Output: (id, timestamp, data vec of 1s (100x3))</li>
+</ul>
+<h1 id="1-data-stream-data-preprocessing">1. Data Stream (? + Data Preprocessing)<a class="anchor-link" href="#1-data-stream-data-preprocessing">&#182;</a></h1><ul>
+<li><p>Input: (id, timestamp, data vec of 1s (100x3))</p>
+</li>
+<li><p>Process: Data buffer (Kafka)</p>
+</li>
+<li><p>Output: [(id, timestamp, data vec of 30s (3000x3)), (id, timestamp, data vec of 30s (3000x3)), ... ] for every second</p>
+</li>
+</ul>
+
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+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="2-phase-picking">2. Phase Picking<a class="anchor-link" href="#2-phase-picking">&#182;</a></h1><ul>
+<li><p>Preload: NN model of PhaseNet</p>
+</li>
+<li><p>Input: [(id, timestamp, data vec (3000x3)), (id, timestamp, data vec of (3000x3)), ... ]</p>
+</li>
+<li><p>Process: PhaseNet</p>
+</li>
+</ul>
+<p><!-- - Output: (id, timestamp, idx_p, prob_p, amp_p, idx_s, prob_s, amp_s) --></p>
+<ul>
+<li>Output: [(id, timestamp, idx(int), prob(float), amp(float), type(string)), ...]</li>
+</ul>
+
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
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+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="3-data-stream">3. Data Stream<a class="anchor-link" href="#3-data-stream">&#182;</a></h1><p><!-- - Input: (id, timestamp, idx_p, prob_p, amp_p, idx_s, prob_s, amp_s) of overlapping 30s --></p>
+<ul>
+<li><p>Input: [(id, timestamp, idx, prob, amp, type), ...] of overlapping 30s</p>
+</li>
+<li><p>Process: Data buffer (Kafka)</p>
+</li>
+</ul>
+<p><!-- - Output: (id, timestamp, idx_p, prob_p, amp_p, idx_s, prob_s, amp_s) with no overlapping and reprot the recent 10 s. --></p>
+<ul>
+<li>Output: [(id, timestamp, idx, prob, amp, type), ...] with no overlapping and reprot the recent 10 s. </li>
+</ul>
+
+</div>
+</div>
+</div>
+</div>
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+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="4-association">4. Association<a class="anchor-link" href="#4-association">&#182;</a></h1><ul>
+<li>Preload: csv file of seismic station information (id, latitude, longitude, elevation)</li>
+</ul>
+<p><!-- - Input: (id, timestamp, idx_p, prob_p, amp_p, idx_s, prob_s, amp_s) --></p>
+<ul>
+<li><p>Input: [(id, timestamp, idx, prob, amp, type), ...]</p>
+</li>
+<li><p>Process: GMMA</p>
+</li>
+<li><p>Output: [(timestamp, latitude, longtidue, depth, magnitude), ...] of earthquakes</p>
+</li>
+</ul>
+
+</div>
+</div>
+</div>
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+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
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+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="5-data-stream">5. Data Stream<a class="anchor-link" href="#5-data-stream">&#182;</a></h1><ul>
+<li><p>Input: [(timestamp, latitude, longtidue, depth, magnitude), ...]</p>
+</li>
+<li><p>Process: Data buffer (Kafka)</p>
+</li>
+<li><p>Output: [(timestamp, latitude, longtidue, depth, magnitude), ...]</p>
+</li>
+</ul>
+
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="6-visulization-and-notification">6. Visulization and Notification<a class="anchor-link" href="#6-visulization-and-notification">&#182;</a></h1><ul>
+<li><p>Input: [(timestamp, latitude, longtidue, depth, magnitude), ...] of earthquakes</p>
+</li>
+<li><p>WebUI + Twitter Bot</p>
+</li>
+</ul>
+
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="code">Code:<a class="anchor-link" href="#code">&#182;</a></h2>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="1-data">1. Data<a class="anchor-link" href="#1-data">&#182;</a></h2>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[6]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
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+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">obspy</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">import</span> <span class="nn">os</span>
+<span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">datetime</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">time</span>
+<span class="kn">import</span> <span class="nn">requests</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+</pre></div>
+<div id="cell-1" class="clipboard-copy-txt">import obspy
+import pandas as pd
+import os
+from datetime import datetime
+import numpy as np
+import time
+import requests
+import matplotlib.pyplot as plt</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[7]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
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+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="err">!</span><span class="n">pwd</span>
+</pre></div>
+<div id="cell-2" class="clipboard-copy-txt">!pwd</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
+<pre>/Users/weiqiang/Research/QuakeFlow
+</pre>
+</div>
+</div>
+
+</div>
+
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[8]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-3">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">waveform_path</span> <span class="o">=</span> <span class="s2">&quot;./data/waveforms&quot;</span>
+<span class="n">fnames</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">&quot;fname.csv&quot;</span><span class="p">)</span>
+<span class="n">stations</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">&quot;stations.csv&quot;</span><span class="p">,</span> <span class="n">sep</span><span class="o">=</span><span class="s2">&quot;</span><span class="se">\t</span><span class="s2">&quot;</span><span class="p">)</span>
+</pre></div>
+<div id="cell-3" class="clipboard-copy-txt">waveform_path = "./data/waveforms"
+fnames = pd.read_csv("fname.csv")
+stations = pd.read_csv("stations.csv", sep="\t")</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[9]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-4">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">normalize</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="p">(</span><span class="n">x</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">x</span><span class="p">))</span><span class="o">/</span><span class="n">np</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+<span class="n">calc_dt</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">t1</span><span class="p">,</span> <span class="n">t2</span><span class="p">:</span> <span class="p">(</span><span class="n">datetime</span><span class="o">.</span><span class="n">strptime</span><span class="p">(</span><span class="n">t1</span><span class="p">,</span> <span class="s2">&quot;%Y-%m-</span><span class="si">%d</span><span class="s2">T%H:%M:%S.</span><span class="si">%f</span><span class="s2">&quot;</span><span class="p">)</span> <span class="o">-</span> <span class="n">datetime</span><span class="o">.</span><span class="n">strptime</span><span class="p">(</span><span class="n">t2</span><span class="p">,</span> <span class="s2">&quot;%Y-%m-</span><span class="si">%d</span><span class="s2">T%H:%M:%S.</span><span class="si">%f</span><span class="s2">&quot;</span><span class="p">))</span><span class="o">.</span><span class="n">total_seconds</span><span class="p">()</span>
+<span class="k">def</span> <span class="nf">visulize</span><span class="p">(</span><span class="n">waveforms</span><span class="p">,</span> <span class="n">ids</span><span class="p">,</span> <span class="n">timestamps</span><span class="p">,</span> <span class="n">picks</span><span class="p">,</span> <span class="n">sampling_rate</span><span class="p">):</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">waveforms</span><span class="p">)):</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">normalize</span><span class="p">(</span><span class="n">waveforms</span><span class="p">[</span><span class="n">i</span><span class="p">][:,</span> <span class="mi">0</span><span class="p">])</span><span class="o">/</span><span class="mi">6</span> <span class="o">+</span> <span class="n">i</span><span class="p">,</span> <span class="s2">&quot;k&quot;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
+    
+    <span class="n">idx_dict</span> <span class="o">=</span> <span class="p">{</span><span class="n">k</span><span class="p">:</span> <span class="n">i</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">ids</span><span class="p">)}</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">picks</span><span class="p">)):</span>
+        <span class="k">if</span> <span class="n">picks</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="s2">&quot;type&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;p&quot;</span><span class="p">:</span>
+            <span class="n">color</span> <span class="o">=</span> <span class="s2">&quot;blue&quot;</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">color</span> <span class="o">=</span> <span class="s2">&quot;red&quot;</span>
+        <span class="n">idx</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">calc_dt</span><span class="p">(</span><span class="n">picks</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="s2">&quot;timestamp&quot;</span><span class="p">],</span> <span class="n">timestamps</span><span class="p">[</span><span class="n">idx_dict</span><span class="p">[</span><span class="n">picks</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="s2">&quot;id&quot;</span><span class="p">]]])</span> <span class="o">*</span> <span class="n">sampling_rate</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="n">idx</span><span class="p">,</span> <span class="n">idx</span><span class="p">],</span> <span class="p">[</span><span class="n">idx_dict</span><span class="p">[</span><span class="n">picks</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="s2">&quot;id&quot;</span><span class="p">]]</span><span class="o">-</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">idx_dict</span><span class="p">[</span><span class="n">picks</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="s2">&quot;id&quot;</span><span class="p">]]</span><span class="o">+</span><span class="mf">0.5</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="n">color</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+<div id="cell-4" class="clipboard-copy-txt">normalize = lambda x: (x - np.mean(x))/np.std(x)
+calc_dt = lambda t1, t2: (datetime.strptime(t1, "%Y-%m-%dT%H:%M:%S.%f") - datetime.strptime(t2, "%Y-%m-%dT%H:%M:%S.%f")).total_seconds()
+def visulize(waveforms, ids, timestamps, picks, sampling_rate):
+    plt.figure()
+    for i in range(len(waveforms)):
+        plt.plot(normalize(waveforms[i][:, 0])/6 + i, "k", linewidth=0.5)
+    
+    idx_dict = {k: i for i, k in enumerate(ids)}
+    for i in range(len(picks)):
+        if picks[i]["type"] == "p":
+            color = "blue"
+        else:
+            color = "red"
+        idx = int(calc_dt(picks[i]["timestamp"], timestamps[idx_dict[picks[i]["id"]]]) * sampling_rate)
+        plt.plot([idx, idx], [idx_dict[picks[i]["id"]]-0.5, idx_dict[picks[i]["id"]]+0.5], color=color)
+    plt.show()
+        </div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[11]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-5">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">window_size</span> <span class="o">=</span> <span class="mi">10000</span>
+<span class="n">sampling_rate</span> <span class="o">=</span> <span class="mi">100</span>
+<span class="n">dtype</span> <span class="o">=</span> <span class="s2">&quot;float32&quot;</span>
+<span class="n">timestamp</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="o">.</span><span class="n">strftime</span><span class="p">(</span><span class="s2">&quot;%Y-%m-</span><span class="si">%d</span><span class="s2">T%H:%M:%S.</span><span class="si">%f</span><span class="s2">&quot;</span><span class="p">)[:</span><span class="o">-</span><span class="mi">3</span><span class="p">]</span>
+
+<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">row</span> <span class="ow">in</span> <span class="n">fnames</span><span class="o">.</span><span class="n">iterrows</span><span class="p">():</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">row</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s2">&quot;fname&quot;</span><span class="p">])</span>
+    <span class="n">meta</span> <span class="o">=</span> <span class="n">obspy</span><span class="o">.</span><span class="n">read</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">waveform_path</span><span class="p">,</span> <span class="n">row</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s2">&quot;fname&quot;</span><span class="p">]))</span>
+    <span class="k">assert</span><span class="p">(</span><span class="n">meta</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">stats</span><span class="o">.</span><span class="n">sampling_rate</span> <span class="o">==</span> <span class="n">sampling_rate</span><span class="p">)</span>
+    <span class="n">meta</span> <span class="o">=</span> <span class="n">meta</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">fill_value</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+    <span class="n">start_time</span> <span class="o">=</span> <span class="nb">min</span><span class="p">([</span><span class="n">st</span><span class="o">.</span><span class="n">stats</span><span class="o">.</span><span class="n">starttime</span> <span class="k">for</span> <span class="n">st</span> <span class="ow">in</span> <span class="n">meta</span><span class="p">])</span>
+    <span class="n">end_time</span> <span class="o">=</span> <span class="nb">max</span><span class="p">([</span><span class="n">st</span><span class="o">.</span><span class="n">stats</span><span class="o">.</span><span class="n">endtime</span> <span class="k">for</span> <span class="n">st</span> <span class="ow">in</span> <span class="n">meta</span><span class="p">])</span>
+    <span class="n">meta</span> <span class="o">=</span> <span class="n">meta</span><span class="o">.</span><span class="n">trim</span><span class="p">(</span><span class="n">start_time</span><span class="p">,</span> <span class="n">end_time</span><span class="p">,</span> <span class="n">pad</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">fill_value</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+    <span class="n">num_station</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">stations</span><span class="p">)</span>
+    <span class="n">nt</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">meta</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">data</span><span class="p">)</span>
+    <span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">([</span><span class="n">num_station</span><span class="p">,</span> <span class="n">nt</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span>
+    <span class="n">station_id</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_station</span><span class="p">):</span>
+        <span class="n">sta</span> <span class="o">=</span> <span class="n">stations</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="s2">&quot;station&quot;</span><span class="p">]</span>
+        <span class="n">station_id</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">sta</span><span class="p">)</span>
+        <span class="n">comp</span> <span class="o">=</span> <span class="n">stations</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="s2">&quot;component&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;,&quot;</span><span class="p">)</span>
+        <span class="n">resp</span> <span class="o">=</span> <span class="n">stations</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="s2">&quot;response&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;,&quot;</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">comp</span><span class="p">)):</span>
+            <span class="n">data</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="p">:,</span> <span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">meta</span><span class="o">.</span><span class="n">select</span><span class="p">(</span><span class="nb">id</span><span class="o">=</span><span class="n">sta</span><span class="o">+</span><span class="n">comp</span><span class="p">[</span><span class="n">j</span><span class="p">])[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">dtype</span><span class="p">)</span> <span class="o">/</span> <span class="nb">float</span><span class="p">(</span><span class="n">resp</span><span class="p">[</span><span class="n">j</span><span class="p">])</span>
+    <span class="n">idx</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="k">while</span> <span class="n">idx</span> <span class="o">&lt;</span> <span class="n">nt</span><span class="p">:</span>
+        <span class="n">id_lst</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">timestamp_lst</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">vec</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_station</span><span class="p">):</span>
+            <span class="nb">print</span> <span class="p">(</span><span class="n">station_id</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">timestamp</span><span class="p">(</span><span class="n">start_time</span><span class="o">+</span><span class="n">idx</span><span class="o">/</span><span class="n">sampling_rate</span><span class="p">),</span> <span class="n">data</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">idx</span><span class="p">:</span><span class="n">idx</span><span class="o">+</span><span class="n">window_size</span><span class="p">,</span> <span class="p">:]</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+            <span class="n">id_lst</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">station_id</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+            <span class="n">timestamp_lst</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">timestamp</span><span class="p">(</span><span class="n">start_time</span><span class="o">+</span><span class="n">idx</span><span class="o">/</span><span class="n">sampling_rate</span><span class="p">))</span>
+            <span class="n">vec</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">idx</span><span class="p">:</span><span class="n">idx</span><span class="o">+</span><span class="n">window_size</span><span class="p">,</span> <span class="p">:])</span>
+        <span class="n">idx</span> <span class="o">+=</span> <span class="n">window_size</span>
+        <span class="n">time</span><span class="o">.</span><span class="n">sleep</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+
+        <span class="n">meta</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;id&quot;</span><span class="p">:</span> <span class="n">id_lst</span><span class="p">,</span> 
+                <span class="s2">&quot;timestamp&quot;</span><span class="p">:</span> <span class="n">timestamp_lst</span><span class="p">,</span> 
+                <span class="s2">&quot;vec&quot;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">vec</span><span class="p">)</span><span class="o">.</span><span class="n">tolist</span><span class="p">(),</span>
+                <span class="s2">&quot;dt&quot;</span><span class="p">:</span> <span class="mf">1.0</span><span class="o">/</span><span class="n">sampling_rate</span><span class="p">}</span>
+        <span class="n">resp</span> <span class="o">=</span> <span class="n">requests</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;http://localhost:8000/predict&quot;</span><span class="p">,</span> <span class="n">json</span><span class="o">=</span><span class="n">meta</span><span class="p">)</span>
+        <span class="nb">print</span><span class="p">(</span><span class="n">resp</span><span class="o">.</span><span class="n">json</span><span class="p">())</span>
+
+        <span class="n">visulize</span><span class="p">(</span><span class="n">vec</span><span class="p">,</span> <span class="n">id_lst</span><span class="p">,</span> <span class="n">timestamp_lst</span><span class="p">,</span> <span class="n">resp</span><span class="o">.</span><span class="n">json</span><span class="p">(),</span> <span class="n">sampling_rate</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">idx</span> <span class="o">&gt;=</span> <span class="mi">1</span><span class="o">*</span><span class="n">window_size</span><span class="p">:</span>
+            <span class="k">raise</span>
+</pre></div>
+<div id="cell-5" class="clipboard-copy-txt">window_size = 10000
+sampling_rate = 100
+dtype = "float32"
+timestamp = lambda x: x.strftime("%Y-%m-%dT%H:%M:%S.%f")[:-3]
+
+for i, row in fnames.iterrows():
+    print(row.loc["fname"])
+    meta = obspy.read(os.path.join(waveform_path, row.loc["fname"]))
+    assert(meta[0].stats.sampling_rate == sampling_rate)
+    meta = meta.merge(fill_value=0)
+    start_time = min([st.stats.starttime for st in meta])
+    end_time = max([st.stats.endtime for st in meta])
+    meta = meta.trim(start_time, end_time, pad=True, fill_value=0)
+    num_station = len(stations)
+    nt = len(meta[0].data)
+    data = np.zeros([num_station, nt, 3], dtype=dtype)
+    station_id = []
+    for i in range(num_station):
+        sta = stations.iloc[i]["station"]
+        station_id.append(sta)
+        comp = stations.iloc[i]["component"].split(",")
+        resp = stations.iloc[i]["response"].split(",")
+        for j in range(len(comp)):
+            data[i, :, j] = meta.select(id=sta+comp[j])[0].data.astype(dtype) / float(resp[j])
+    idx = 0
+    while idx < nt:
+        id_lst = []
+        timestamp_lst = []
+        vec = []
+        for i in range(num_station):
+            print (station_id[i], timestamp(start_time+idx/sampling_rate), data[i, idx:idx+window_size, :].shape)
+            id_lst.append(station_id[i])
+            timestamp_lst.append(timestamp(start_time+idx/sampling_rate))
+            vec.append(data[i, idx:idx+window_size, :])
+        idx += window_size
+        time.sleep(1)
+
+        meta = {"id": id_lst, 
+                "timestamp": timestamp_lst, 
+                "vec": np.array(vec).tolist(),
+                "dt": 1.0/sampling_rate}
+        resp = requests.get("http://localhost:8000/predict", json=meta)
+        print(resp.json())
+
+        visulize(vec, id_lst, timestamp_lst, resp.json(), sampling_rate)
+        if idx >= 1*window_size:
+            raise</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
+<pre>2020-10-01T01:00:00.mseed
+CI.BOM..HH 2020-10-01T00:59:59.998 (10000, 3)
+CI.COA..HH 2020-10-01T00:59:59.998 (10000, 3)
+CI.CRR..HH 2020-10-01T00:59:59.998 (10000, 3)
+CI.DRE..HH 2020-10-01T00:59:59.998 (10000, 3)
+CI.ERR..HH 2020-10-01T00:59:59.998 (10000, 3)
+CI.FRK..HH 2020-10-01T00:59:59.998 (10000, 3)
+CI.IMP..HH 2020-10-01T00:59:59.998 (10000, 3)
+CI.RXH..HH 2020-10-01T00:59:59.998 (10000, 3)
+CI.SAL..HH 2020-10-01T00:59:59.998 (10000, 3)
+CI.SGL..HH 2020-10-01T00:59:59.998 (10000, 3)
+CI.SLV..HH 2020-10-01T00:59:59.998 (10000, 3)
+CI.SNR..HH 2020-10-01T00:59:59.998 (10000, 3)
+CI.SWS..HH 2020-10-01T00:59:59.998 (10000, 3)
+CI.WES..HH 2020-10-01T00:59:59.998 (10000, 3)
+CI.WMD..HH 2020-10-01T00:59:59.998 (10000, 3)
+CI.YUH2..HH 2020-10-01T00:59:59.998 (10000, 3)
+[{&#39;id&#39;: &#39;CI.BOM..HH&#39;, &#39;timestamp&#39;: &#39;2020-10-01T01:00:21.668&#39;, &#39;prob&#39;: 0.6336886882781982, &#39;amp&#39;: 2.2203061491831466, &#39;type&#39;: &#39;p&#39;}, {&#39;id&#39;: &#39;CI.BOM..HH&#39;, &#39;timestamp&#39;: &#39;2020-10-01T01:00:22.398&#39;, &#39;prob&#39;: 0.6803838014602661, &#39;amp&#39;: 3.8069893228879987, &#39;type&#39;: &#39;p&#39;}, {&#39;id&#39;: &#39;CI.BOM..HH&#39;, &#39;timestamp&#39;: &#39;2020-10-01T01:00:27.658&#39;, &#39;prob&#39;: 0.8149189949035645, &#39;amp&#39;: 4.133871898080054, &#39;type&#39;: &#39;s&#39;}, {&#39;id&#39;: &#39;CI.COA..HH&#39;, &#39;timestamp&#39;: &#39;2020-10-01T01:00:19.588&#39;, &#39;prob&#39;: 0.4681239724159241, &#39;amp&#39;: 2.2111977446505655, &#39;type&#39;: &#39;p&#39;}, {&#39;id&#39;: &#39;CI.COA..HH&#39;, &#39;timestamp&#39;: &#39;2020-10-01T01:00:23.268&#39;, &#39;prob&#39;: 0.5819692015647888, &#39;amp&#39;: 6.1600568046490025, &#39;type&#39;: &#39;p&#39;}, {&#39;id&#39;: &#39;CI.COA..HH&#39;, &#39;timestamp&#39;: &#39;2020-10-01T01:00:30.058&#39;, &#39;prob&#39;: 0.7425823211669922, &#39;amp&#39;: 9.463716401102623, &#39;type&#39;: &#39;s&#39;}, {&#39;id&#39;: &#39;CI.CRR..HH&#39;, &#39;timestamp&#39;: &#39;2020-10-01T01:00:21.618&#39;, &#39;prob&#39;: 0.8768981099128723, &#39;amp&#39;: 5.341528397517869, &#39;type&#39;: &#39;p&#39;}, {&#39;id&#39;: &#39;CI.CRR..HH&#39;, &#39;timestamp&#39;: &#39;2020-10-01T01:00:27.018&#39;, &#39;prob&#39;: 0.6200856566429138, &#39;amp&#39;: 5.341528397517869, &#39;type&#39;: &#39;s&#39;}, {&#39;id&#39;: &#39;CI.DRE..HH&#39;, &#39;timestamp&#39;: &#39;2020-10-01T01:00:20.758&#39;, &#39;prob&#39;: 0.8471728563308716, &#39;amp&#39;: 3.7112379849499395, &#39;type&#39;: &#39;p&#39;}, {&#39;id&#39;: &#39;CI.ERR..HH&#39;, &#39;timestamp&#39;: &#39;2020-10-01T01:00:19.488&#39;, &#39;prob&#39;: 0.8610907196998596, &#39;amp&#39;: 6.153626479036863, &#39;type&#39;: &#39;p&#39;}, {&#39;id&#39;: &#39;CI.ERR..HH&#39;, 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
+"
+class="
+jp-needs-light-background
+"
+>
+</div>
+
+</div>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>
+<span class="ansi-red-fg">---------------------------------------------------------------------------</span>
+<span class="ansi-red-fg">RuntimeError</span>                              Traceback (most recent call last)
+<span class="ansi-green-fg">&lt;ipython-input-11-7131ec013b30&gt;</span> in <span class="ansi-cyan-fg">&lt;module&gt;</span>
+<span class="ansi-green-intense-fg ansi-bold">     45</span>         visulize<span class="ansi-blue-fg">(</span>vec<span class="ansi-blue-fg">,</span> id_lst<span class="ansi-blue-fg">,</span> timestamp_lst<span class="ansi-blue-fg">,</span> resp<span class="ansi-blue-fg">.</span>json<span class="ansi-blue-fg">(</span><span class="ansi-blue-fg">)</span><span class="ansi-blue-fg">,</span> sampling_rate<span class="ansi-blue-fg">)</span>
+<span class="ansi-green-intense-fg ansi-bold">     46</span>         <span class="ansi-green-fg">if</span> idx <span class="ansi-blue-fg">&gt;=</span> <span class="ansi-cyan-fg">1</span><span class="ansi-blue-fg">*</span>window_size<span class="ansi-blue-fg">:</span>
+<span class="ansi-green-fg">---&gt; 47</span><span class="ansi-red-fg">             </span><span class="ansi-green-fg">raise</span>
+
+<span class="ansi-red-fg">RuntimeError</span>: No active exception to reraise</pre>
+</div>
+</div>
+
+</div>
+
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[54]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-6">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">catalog</span> <span class="o">=</span> <span class="n">requests</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;http://localhost:8001/predict&quot;</span><span class="p">,</span> <span class="n">json</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;picks&quot;</span><span class="p">:</span> <span class="n">resp</span><span class="o">.</span><span class="n">json</span><span class="p">()})</span>
+</pre></div>
+<div id="cell-6" class="clipboard-copy-txt">catalog = requests.get("http://localhost:8001/predict", json={"picks": resp.json()})</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[55]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-7">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">catalog</span><span class="o">.</span><span class="n">json</span><span class="p">()</span>
+</pre></div>
+<div id="cell-7" class="clipboard-copy-txt">catalog.json()</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[55]:</div>
+
+
+
+
+<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
+<pre>[{&#39;time&#39;: &#39;2020-10-01T01:00:14.945&#39;,
+  &#39;location&#39;: [-1.0, 7.7168491279660305, 0.0],
+  &#39;magnitude&#39;: 8.027433018452191,
+  &#39;std&#39;: [[1.2615414108512575, -0.24888345954391966],
+   [-0.24888345954391966, 0.35544424087361315]]}]</pre>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[56]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-8">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">stations</span><span class="p">[</span><span class="s2">&quot;x(km)&quot;</span><span class="p">],</span> <span class="n">stations</span><span class="p">[</span><span class="s2">&quot;y(km)&quot;</span><span class="p">],</span> <span class="s2">&quot;k^&quot;</span><span class="p">)</span>
+<span class="k">for</span> <span class="n">event</span> <span class="ow">in</span> <span class="n">catalog</span><span class="o">.</span><span class="n">json</span><span class="p">():</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">event</span><span class="p">[</span><span class="s2">&quot;location&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">event</span><span class="p">[</span><span class="s2">&quot;location&quot;</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span> <span class="s2">&quot;r*&quot;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mi">16</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s2">&quot;scaled&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+<div id="cell-8" class="clipboard-copy-txt">plt.figure()
+plt.plot(stations["x(km)"], stations["y(km)"], "k^")
+for event in catalog.json():
+    plt.plot(event["location"][0], event["location"][1], "r*", markersize=16)
+plt.axis("scaled")
+plt.show()</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
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+
+<div class="jp-OutputArea jp-Cell-outputArea">
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+<div class="jp-OutputArea-child">
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+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
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+
+
+<div class="jp-RenderedImage jp-OutputArea-output ">
+<img 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+"
+class="
+jp-needs-light-background
+"
+>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[57]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-9">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">def</span> <span class="nf">webui</span><span class="p">(</span><span class="n">waveforms</span><span class="p">,</span> <span class="n">ids</span><span class="p">,</span> <span class="n">timestamps</span><span class="p">,</span> <span class="n">picks</span><span class="p">,</span> <span class="n">sampling_rate</span><span class="p">,</span> <span class="n">stations</span><span class="p">,</span> <span class="n">events</span><span class="p">):</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">311</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">waveforms</span><span class="p">)):</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">normalize</span><span class="p">(</span><span class="n">waveforms</span><span class="p">[</span><span class="n">i</span><span class="p">][:,</span> <span class="mi">0</span><span class="p">])</span><span class="o">/</span><span class="mi">6</span> <span class="o">+</span> <span class="n">i</span><span class="p">,</span> <span class="s2">&quot;k&quot;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">&quot;Waveforms&quot;</span><span class="p">)</span>
+    
+    <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">312</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">waveforms</span><span class="p">)):</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">normalize</span><span class="p">(</span><span class="n">waveforms</span><span class="p">[</span><span class="n">i</span><span class="p">][:,</span> <span class="mi">0</span><span class="p">])</span><span class="o">/</span><span class="mi">6</span> <span class="o">+</span> <span class="n">i</span><span class="p">,</span> <span class="s2">&quot;gray&quot;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
+        
+    <span class="n">idx_dict</span> <span class="o">=</span> <span class="p">{</span><span class="n">k</span><span class="p">:</span> <span class="n">i</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">ids</span><span class="p">)}</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">picks</span><span class="p">)):</span>
+        <span class="k">if</span> <span class="n">picks</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="s2">&quot;type&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;p&quot;</span><span class="p">:</span>
+            <span class="n">color</span> <span class="o">=</span> <span class="s2">&quot;blue&quot;</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">color</span> <span class="o">=</span> <span class="s2">&quot;red&quot;</span>
+        <span class="n">idx</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">calc_dt</span><span class="p">(</span><span class="n">picks</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="s2">&quot;timestamp&quot;</span><span class="p">],</span> <span class="n">timestamps</span><span class="p">[</span><span class="n">idx_dict</span><span class="p">[</span><span class="n">picks</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="s2">&quot;id&quot;</span><span class="p">]]])</span> <span class="o">*</span> <span class="n">sampling_rate</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="n">idx</span><span class="p">,</span> <span class="n">idx</span><span class="p">],</span> <span class="p">[</span><span class="n">idx_dict</span><span class="p">[</span><span class="n">picks</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="s2">&quot;id&quot;</span><span class="p">]]</span><span class="o">-</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">idx_dict</span><span class="p">[</span><span class="n">picks</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="s2">&quot;id&quot;</span><span class="p">]]</span><span class="o">+</span><span class="mf">0.5</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="n">color</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">&quot;Detected Phases&quot;</span><span class="p">)</span>
+        
+    <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">313</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">stations</span><span class="p">[</span><span class="s2">&quot;x(km)&quot;</span><span class="p">],</span> <span class="n">stations</span><span class="p">[</span><span class="s2">&quot;y(km)&quot;</span><span class="p">],</span> <span class="s2">&quot;k^&quot;</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">event</span> <span class="ow">in</span> <span class="n">catalog</span><span class="o">.</span><span class="n">json</span><span class="p">():</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">event</span><span class="p">[</span><span class="s2">&quot;location&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">event</span><span class="p">[</span><span class="s2">&quot;location&quot;</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span> <span class="n">s</span><span class="o">=</span><span class="n">event</span><span class="p">[</span><span class="s2">&quot;magnitude&quot;</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="s2">&quot;red&quot;</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s2">&quot;*&quot;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s2">&quot;scaled&quot;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">&quot;Detected Earthquakes&quot;</span><span class="p">)</span>
+    
+    <span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+<div id="cell-9" class="clipboard-copy-txt">def webui(waveforms, ids, timestamps, picks, sampling_rate, stations, events):
+    plt.figure(figsize=(6, 10))
+    plt.subplot(311)
+    for i in range(len(waveforms)):
+        plt.plot(normalize(waveforms[i][:, 0])/6 + i, "k", linewidth=0.5)
+    plt.title("Waveforms")
+    
+    plt.subplot(312)
+    for i in range(len(waveforms)):
+        plt.plot(normalize(waveforms[i][:, 0])/6 + i, "gray", linewidth=0.5, alpha=0.5)
+        
+    idx_dict = {k: i for i, k in enumerate(ids)}
+    for i in range(len(picks)):
+        if picks[i]["type"] == "p":
+            color = "blue"
+        else:
+            color = "red"
+        idx = int(calc_dt(picks[i]["timestamp"], timestamps[idx_dict[picks[i]["id"]]]) * sampling_rate)
+        plt.plot([idx, idx], [idx_dict[picks[i]["id"]]-0.5, idx_dict[picks[i]["id"]]+0.5], color=color)
+    plt.title("Detected Phases")
+        
+    plt.subplot(313)
+    plt.plot(stations["x(km)"], stations["y(km)"], "k^")
+    for event in catalog.json():
+        plt.scatter(event["location"][0], event["location"][1], s=event["magnitude"], c="red", marker="*")
+    plt.axis("scaled")
+    plt.title("Detected Earthquakes")
+    
+    plt.tight_layout()
+    plt.show()</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[58]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-10">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">webui</span><span class="p">(</span><span class="n">vec</span><span class="p">,</span> <span class="n">id_lst</span><span class="p">,</span> <span class="n">timestamp_lst</span><span class="p">,</span> <span class="n">resp</span><span class="o">.</span><span class="n">json</span><span class="p">(),</span> <span class="n">sampling_rate</span><span class="p">,</span> <span class="n">stations</span><span class="p">,</span> <span class="n">catalog</span><span class="o">.</span><span class="n">json</span><span class="p">())</span>
+</pre></div>
+<div id="cell-10" class="clipboard-copy-txt">webui(vec, id_lst, timestamp_lst, resp.json(), sampling_rate, stations, catalog.json())</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+
+
+<div class="jp-RenderedImage jp-OutputArea-output ">
+<img 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+    <a href="../.." title="QuakeFlow" class="md-header__button md-logo" aria-label="QuakeFlow" data-md-component="logo">
+      
+  
+  <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54Z"/></svg>
+
+    </a>
+    <label class="md-header__button md-icon" for="__drawer">
+      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M3 6h18v2H3V6m0 5h18v2H3v-2m0 5h18v2H3v-2Z"/></svg>
+    </label>
+    <div class="md-header__title" data-md-component="header-title">
+      <div class="md-header__ellipsis">
+        <div class="md-header__topic">
+          <span class="md-ellipsis">
+            QuakeFlow
+          </span>
+        </div>
+        <div class="md-header__topic" data-md-component="header-topic">
+          <span class="md-ellipsis">
+            
+              Training
+            
+          </span>
+        </div>
+      </div>
+    </div>
+    
+    
+    
+    
+      <div class="md-header__source">
+        <a href="https://github.com/ai4eps/QuakeFlow" title="Go to repository" class="md-source" data-md-component="source">
+  <div class="md-source__icon md-icon">
+    
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.1.2 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+  </div>
+  <div class="md-source__repository">
+    AI4EPS/QuakeFlow
+  </div>
+</a>
+      </div>
+    
+  </nav>
+  
+</header>
+    
+    <div class="md-container" data-md-component="container">
+      
+      
+        
+          
+        
+      
+      <main class="md-main" data-md-component="main">
+        <div class="md-main__inner md-grid">
+          
+            
+              
+              <div class="md-sidebar md-sidebar--primary" data-md-component="sidebar" data-md-type="navigation" >
+                <div class="md-sidebar__scrollwrap">
+                  <div class="md-sidebar__inner">
+                    
+
+
+<nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
+  <label class="md-nav__title" for="__drawer">
+    <a href="../.." title="QuakeFlow" class="md-nav__button md-logo" aria-label="QuakeFlow" data-md-component="logo">
+      
+  
+  <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54Z"/></svg>
+
+    </a>
+    QuakeFlow
+  </label>
+  
+    <div class="md-nav__source">
+      <a href="https://github.com/ai4eps/QuakeFlow" title="Go to repository" class="md-source" data-md-component="source">
+  <div class="md-source__icon md-icon">
+    
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.1.2 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+  </div>
+  <div class="md-source__repository">
+    AI4EPS/QuakeFlow
+  </div>
+</a>
+    </div>
+  
+  <ul class="md-nav__list" data-md-scrollfix>
+    
+      
+      
+      
+
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../.." class="md-nav__link">
+        Overview
+      </a>
+    </li>
+  
+
+    
+      
+      
+      
+
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../workflow/" class="md-nav__link">
+        Pipeline
+      </a>
+    </li>
+  
+
+    
+      
+      
+      
+
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../data_format/" class="md-nav__link">
+        Standard Formats
+      </a>
+    </li>
+  
+
+    
+  </ul>
+</nav>
+                  </div>
+                </div>
+              </div>
+            
+            
+              
+              <div class="md-sidebar md-sidebar--secondary" data-md-component="sidebar" data-md-type="toc" >
+                <div class="md-sidebar__scrollwrap">
+                  <div class="md-sidebar__inner">
+                    
+
+<nav class="md-nav md-nav--secondary" aria-label="Table of contents">
+  
+  
+  
+  
+</nav>
+                  </div>
+                </div>
+              </div>
+            
+          
+          <div class="md-content" data-md-component="content">
+            <article class="md-content__inner md-typeset">
+              
+                
+  <a href="https://github.com/ai4eps/QuakeFlow/edit/master/docs/kubeflow/Training.ipynb" title="Edit this page" class="md-content__button md-icon">
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
+  </a>
+
+
+
+  <h1>Training</h1>
+
+<script>
+(function (global, factory) {
+    typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() :
+    typeof define === 'function' && define.amd ? define(factory) :
+    (global = global || self, global.ClipboardCopyElement = factory());
+  }(this, function () { 'use strict';
+
+    function createNode(text) {
+      const node = document.createElement('pre');
+      node.style.width = '1px';
+      node.style.height = '1px';
+      node.style.position = 'fixed';
+      node.style.top = '5px';
+      node.textContent = text;
+      return node;
+    }
+
+    function copyNode(node) {
+      if ('clipboard' in navigator) {
+        // eslint-disable-next-line flowtype/no-flow-fix-me-comments
+        // $FlowFixMe Clipboard is not defined in Flow yet.
+        return navigator.clipboard.writeText(node.textContent);
+      }
+
+      const selection = getSelection();
+
+      if (selection == null) {
+        return Promise.reject(new Error());
+      }
+
+      selection.removeAllRanges();
+      const range = document.createRange();
+      range.selectNodeContents(node);
+      selection.addRange(range);
+      document.execCommand('copy');
+      selection.removeAllRanges();
+      return Promise.resolve();
+    }
+    function copyText(text) {
+      if ('clipboard' in navigator) {
+        // eslint-disable-next-line flowtype/no-flow-fix-me-comments
+        // $FlowFixMe Clipboard is not defined in Flow yet.
+        return navigator.clipboard.writeText(text);
+      }
+
+      const body = document.body;
+
+      if (!body) {
+        return Promise.reject(new Error());
+      }
+
+      const node = createNode(text);
+      body.appendChild(node);
+      copyNode(node);
+      body.removeChild(node);
+      return Promise.resolve();
+    }
+
+    function copy(button) {
+      const id = button.getAttribute('for');
+      const text = button.getAttribute('value');
+
+      function trigger() {
+        button.dispatchEvent(new CustomEvent('clipboard-copy', {
+          bubbles: true
+        }));
+      }
+
+      if (text) {
+        copyText(text).then(trigger);
+      } else if (id) {
+        const root = 'getRootNode' in Element.prototype ? button.getRootNode() : button.ownerDocument;
+        if (!(root instanceof Document || 'ShadowRoot' in window && root instanceof ShadowRoot)) return;
+        const node = root.getElementById(id);
+        if (node) copyTarget(node).then(trigger);
+      }
+    }
+
+    function copyTarget(content) {
+      if (content instanceof HTMLInputElement || content instanceof HTMLTextAreaElement) {
+        return copyText(content.value);
+      } else if (content instanceof HTMLAnchorElement && content.hasAttribute('href')) {
+        return copyText(content.href);
+      } else {
+        return copyNode(content);
+      }
+    }
+
+    function clicked(event) {
+      const button = event.currentTarget;
+
+      if (button instanceof HTMLElement) {
+        copy(button);
+      }
+    }
+
+    function keydown(event) {
+      if (event.key === ' ' || event.key === 'Enter') {
+        const button = event.currentTarget;
+
+        if (button instanceof HTMLElement) {
+          event.preventDefault();
+          copy(button);
+        }
+      }
+    }
+
+    function focused(event) {
+      event.currentTarget.addEventListener('keydown', keydown);
+    }
+
+    function blurred(event) {
+      event.currentTarget.removeEventListener('keydown', keydown);
+    }
+
+    class ClipboardCopyElement extends HTMLElement {
+      constructor() {
+        super();
+        this.addEventListener('click', clicked);
+        this.addEventListener('focus', focused);
+        this.addEventListener('blur', blurred);
+      }
+
+      connectedCallback() {
+        if (!this.hasAttribute('tabindex')) {
+          this.setAttribute('tabindex', '0');
+        }
+
+        if (!this.hasAttribute('role')) {
+          this.setAttribute('role', 'button');
+        }
+      }
+
+      get value() {
+        return this.getAttribute('value') || '';
+      }
+
+      set value(text) {
+        this.setAttribute('value', text);
+      }
+
+    }
+
+    if (!window.customElements.get('clipboard-copy')) {
+      window.ClipboardCopyElement = ClipboardCopyElement;
+      window.customElements.define('clipboard-copy', ClipboardCopyElement);
+    }
+
+    return ClipboardCopyElement;
+
+  }));
+</script>
+<script>
+      document.addEventListener('clipboard-copy', function(event) {
+        const notice = event.target.querySelector('.notice')
+        notice.hidden = false
+        setTimeout(function() {
+          notice.hidden = true
+        }, 1000)
+      })
+</script>
+
+
+
+
+<style type="text/css">
+    
+    pre { line-height: 125%; }
+td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+.highlight-ipynb .hll { background-color: var(--jp-cell-editor-active-background) }
+.highlight-ipynb { background: var(--jp-cell-editor-background); color: var(--jp-mirror-editor-variable-color) }
+.highlight-ipynb .c { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment */
+.highlight-ipynb .err { color: var(--jp-mirror-editor-error-color) } /* Error */
+.highlight-ipynb .k { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword */
+.highlight-ipynb .o { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator */
+.highlight-ipynb .p { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation */
+.highlight-ipynb .ch { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Hashbang */
+.highlight-ipynb .cm { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Multiline */
+.highlight-ipynb .cp { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Preproc */
+.highlight-ipynb .cpf { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.PreprocFile */
+.highlight-ipynb .c1 { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Single */
+.highlight-ipynb .cs { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Special */
+.highlight-ipynb .kc { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Constant */
+.highlight-ipynb .kd { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Declaration */
+.highlight-ipynb .kn { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Namespace */
+.highlight-ipynb .kp { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Pseudo */
+.highlight-ipynb .kr { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Reserved */
+.highlight-ipynb .kt { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Type */
+.highlight-ipynb .m { color: var(--jp-mirror-editor-number-color) } /* Literal.Number */
+.highlight-ipynb .s { color: var(--jp-mirror-editor-string-color) } /* Literal.String */
+.highlight-ipynb .ow { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator.Word */
+.highlight-ipynb .pm { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation.Marker */
+.highlight-ipynb .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */
+.highlight-ipynb .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */
+.highlight-ipynb .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */
+.highlight-ipynb .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */
+.highlight-ipynb .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */
+.highlight-ipynb .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */
+.highlight-ipynb .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */
+.highlight-ipynb .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */
+.highlight-ipynb .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */
+.highlight-ipynb .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */
+.highlight-ipynb .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */
+.highlight-ipynb .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */
+.highlight-ipynb .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */
+.highlight-ipynb .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */
+.highlight-ipynb .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */
+.highlight-ipynb .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */
+.highlight-ipynb .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */
+.highlight-ipynb .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */
+.highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */
+.highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */
+  </style>
+
+
+
+
+<style type="text/css">
+/*
+This file is taken from the built JupyterLab theme.css
+Found on share/nbconvert/templates/lab/static
+Some changes have been made and marked with CHANGE
+*/
+
+.jupyter-wrapper {
+    /* Elevation
+   *
+   * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here:
+   *
+   * https://github.com/material-components/material-components-web
+   * https://material-components-web.appspot.com/elevation.html
+   */
+
+    --jp-shadow-base-lightness: 0;
+    --jp-shadow-umbra-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.2
+    );
+    --jp-shadow-penumbra-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.14
+    );
+    --jp-shadow-ambient-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.12
+    );
+    --jp-elevation-z0: none;
+    --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color),
+        0px 1px 1px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 3px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color),
+        0px 2px 2px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 5px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color),
+        0px 4px 5px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 10px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color),
+        0px 6px 10px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 18px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color),
+        0px 8px 10px 1px var(--jp-shadow-penumbra-color),
+        0px 3px 14px 2px var(--jp-shadow-ambient-color);
+    --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color),
+        0px 12px 17px 2px var(--jp-shadow-penumbra-color),
+        0px 5px 22px 4px var(--jp-shadow-ambient-color);
+    --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color),
+        0px 16px 24px 2px var(--jp-shadow-penumbra-color),
+        0px 6px 30px 5px var(--jp-shadow-ambient-color);
+    --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color),
+        0px 20px 31px 3px var(--jp-shadow-penumbra-color),
+        0px 8px 38px 7px var(--jp-shadow-ambient-color);
+    --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color),
+        0px 24px 38px 3px var(--jp-shadow-penumbra-color),
+        0px 9px 46px 8px var(--jp-shadow-ambient-color);
+
+    /* Borders
+   *
+   * The following variables, specify the visual styling of borders in JupyterLab.
+   */
+
+    --jp-border-width: 1px;
+    --jp-border-color0: var(--md-grey-400);
+    --jp-border-color1: var(--md-grey-400);
+    --jp-border-color2: var(--md-grey-300);
+    --jp-border-color3: var(--md-grey-200);
+    --jp-border-radius: 2px;
+
+    /* UI Fonts
+   *
+   * The UI font CSS variables are used for the typography all of the JupyterLab
+   * user interface elements that are not directly user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-ui-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+    --jp-ui-font-scale-factor: 1.2;
+    --jp-ui-font-size0: 0.83333em;
+    --jp-ui-font-size1: 13px; /* Base font size */
+    --jp-ui-font-size2: 1.2em;
+    --jp-ui-font-size3: 1.44em;
+
+    --jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+
+    /*
+   * Use these font colors against the corresponding main layout colors.
+   * In a light theme, these go from dark to light.
+   */
+
+    /* Defaults use Material Design specification */
+    --jp-ui-font-color0: rgba(0, 0, 0, 1);
+    --jp-ui-font-color1: rgba(0, 0, 0, 0.87);
+    --jp-ui-font-color2: rgba(0, 0, 0, 0.54);
+    --jp-ui-font-color3: rgba(0, 0, 0, 0.38);
+
+    /*
+   * Use these against the brand/accent/warn/error colors.
+   * These will typically go from light to darker, in both a dark and light theme.
+   */
+
+    --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1);
+    --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1);
+    --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7);
+    --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5);
+
+    /* Content Fonts
+   *
+   * Content font variables are used for typography of user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-content-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+    --jp-content-line-height: 1.6;
+    --jp-content-font-scale-factor: 1.2;
+    --jp-content-font-size0: 0.83333em;
+    --jp-content-font-size1: 14px; /* Base font size */
+    --jp-content-font-size2: 1.2em;
+    --jp-content-font-size3: 1.44em;
+    --jp-content-font-size4: 1.728em;
+    --jp-content-font-size5: 2.0736em;
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-content-presentation-font-size1: 17px;
+
+    --jp-content-heading-line-height: 1;
+    --jp-content-heading-margin-top: 1.2em;
+    --jp-content-heading-margin-bottom: 0.8em;
+    --jp-content-heading-font-weight: 500;
+
+    /* Defaults use Material Design specification */
+    --jp-content-font-color0: rgba(0, 0, 0, 1);
+    --jp-content-font-color1: rgba(0, 0, 0, 0.87);
+    --jp-content-font-color2: rgba(0, 0, 0, 0.54);
+    --jp-content-font-color3: rgba(0, 0, 0, 0.38);
+
+    --jp-content-link-color: var(--md-blue-700);
+
+    --jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+
+    /*
+   * Code Fonts
+   *
+   * Code font variables are used for typography of code and other monospaces content.
+   */
+
+    --jp-code-font-size: 13px;
+    --jp-code-line-height: 1.3077; /* 17px for 13px base */
+    --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */
+    --jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono",
+        monospace;
+    --jp-code-font-family: var(--jp-code-font-family-default);
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-code-presentation-font-size: 16px;
+
+    /* may need to tweak cursor width if you change font size */
+    --jp-code-cursor-width0: 1.4px;
+    --jp-code-cursor-width1: 2px;
+    --jp-code-cursor-width2: 4px;
+
+    /* Layout
+   *
+   * The following are the main layout colors use in JupyterLab. In a light
+   * theme these would go from light to dark.
+   */
+
+    --jp-layout-color0: white;
+    --jp-layout-color1: white;
+    --jp-layout-color2: var(--md-grey-200);
+    --jp-layout-color3: var(--md-grey-400);
+    --jp-layout-color4: var(--md-grey-600);
+
+    /* Inverse Layout
+   *
+   * The following are the inverse layout colors use in JupyterLab. In a light
+   * theme these would go from dark to light.
+   */
+
+    --jp-inverse-layout-color0: #111111;
+    --jp-inverse-layout-color1: var(--md-grey-900);
+    --jp-inverse-layout-color2: var(--md-grey-800);
+    --jp-inverse-layout-color3: var(--md-grey-700);
+    --jp-inverse-layout-color4: var(--md-grey-600);
+
+    /* Brand/accent */
+
+    --jp-brand-color0: var(--md-blue-900);
+    --jp-brand-color1: var(--md-blue-700);
+    --jp-brand-color2: var(--md-blue-300);
+    --jp-brand-color3: var(--md-blue-100);
+    --jp-brand-color4: var(--md-blue-50);
+
+    --jp-accent-color0: var(--md-green-900);
+    --jp-accent-color1: var(--md-green-700);
+    --jp-accent-color2: var(--md-green-300);
+    --jp-accent-color3: var(--md-green-100);
+
+    /* State colors (warn, error, success, info) */
+
+    --jp-warn-color0: var(--md-orange-900);
+    --jp-warn-color1: var(--md-orange-700);
+    --jp-warn-color2: var(--md-orange-300);
+    --jp-warn-color3: var(--md-orange-100);
+
+    --jp-error-color0: var(--md-red-900);
+    --jp-error-color1: var(--md-red-700);
+    --jp-error-color2: var(--md-red-300);
+    --jp-error-color3: var(--md-red-100);
+
+    --jp-success-color0: var(--md-green-900);
+    --jp-success-color1: var(--md-green-700);
+    --jp-success-color2: var(--md-green-300);
+    --jp-success-color3: var(--md-green-100);
+
+    --jp-info-color0: var(--md-cyan-900);
+    --jp-info-color1: var(--md-cyan-700);
+    --jp-info-color2: var(--md-cyan-300);
+    --jp-info-color3: var(--md-cyan-100);
+
+    /* Cell specific styles */
+
+    --jp-cell-padding: 5px;
+
+    --jp-cell-collapser-width: 8px;
+    --jp-cell-collapser-min-height: 20px;
+    --jp-cell-collapser-not-active-hover-opacity: 0.6;
+
+    --jp-cell-editor-background: var(--md-grey-100);
+    --jp-cell-editor-border-color: var(--md-grey-300);
+    --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-cell-editor-active-background: var(--jp-layout-color0);
+    --jp-cell-editor-active-border-color: var(--jp-brand-color1);
+
+    --jp-cell-prompt-width: 64px;
+    --jp-cell-prompt-font-family: var(--jp-code-font-family-default);
+    --jp-cell-prompt-letter-spacing: 0px;
+    --jp-cell-prompt-opacity: 1;
+    --jp-cell-prompt-not-active-opacity: 0.5;
+    --jp-cell-prompt-not-active-font-color: var(--md-grey-700);
+    /* A custom blend of MD grey and blue 600
+   * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */
+    --jp-cell-inprompt-font-color: #307fc1;
+    /* A custom blend of MD grey and orange 600
+   * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */
+    --jp-cell-outprompt-font-color: #bf5b3d;
+
+    /* Notebook specific styles */
+
+    --jp-notebook-padding: 10px;
+    --jp-notebook-select-background: var(--jp-layout-color1);
+    --jp-notebook-multiselected-color: var(--md-blue-50);
+
+    /* The scroll padding is calculated to fill enough space at the bottom of the
+  notebook to show one single-line cell (with appropriate padding) at the top
+  when the notebook is scrolled all the way to the bottom. We also subtract one
+  pixel so that no scrollbar appears if we have just one single-line cell in the
+  notebook. This padding is to enable a 'scroll past end' feature in a notebook.
+  */
+    --jp-notebook-scroll-padding: calc(
+        100% - var(--jp-code-font-size) * var(--jp-code-line-height) -
+            var(--jp-code-padding) - var(--jp-cell-padding) - 1px
+    );
+
+    /* Rendermime styles */
+
+    --jp-rendermime-error-background: #fdd;
+    --jp-rendermime-table-row-background: var(--md-grey-100);
+    --jp-rendermime-table-row-hover-background: var(--md-light-blue-50);
+
+    /* Dialog specific styles */
+
+    --jp-dialog-background: rgba(0, 0, 0, 0.25);
+
+    /* Console specific styles */
+
+    --jp-console-padding: 10px;
+
+    /* Toolbar specific styles */
+
+    --jp-toolbar-border-color: var(--jp-border-color1);
+    --jp-toolbar-micro-height: 8px;
+    --jp-toolbar-background: var(--jp-layout-color1);
+    --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24);
+    --jp-toolbar-header-margin: 4px 4px 0px 4px;
+    --jp-toolbar-active-background: var(--md-grey-300);
+
+    /* Statusbar specific styles */
+
+    --jp-statusbar-height: 24px;
+
+    /* Input field styles */
+
+    --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-input-active-background: var(--jp-layout-color1);
+    --jp-input-hover-background: var(--jp-layout-color1);
+    --jp-input-background: var(--md-grey-100);
+    --jp-input-border-color: var(--jp-border-color1);
+    --jp-input-active-border-color: var(--jp-brand-color1);
+    --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3);
+
+    /* General editor styles */
+
+    --jp-editor-selected-background: #d9d9d9;
+    --jp-editor-selected-focused-background: #d7d4f0;
+    --jp-editor-cursor-color: var(--jp-ui-font-color0);
+
+    /* Code mirror specific styles */
+
+    --jp-mirror-editor-keyword-color: #008000;
+    --jp-mirror-editor-atom-color: #88f;
+    --jp-mirror-editor-number-color: #080;
+    --jp-mirror-editor-def-color: #00f;
+    --jp-mirror-editor-variable-color: var(--md-grey-900);
+    --jp-mirror-editor-variable-2-color: #05a;
+    --jp-mirror-editor-variable-3-color: #085;
+    --jp-mirror-editor-punctuation-color: #05a;
+    --jp-mirror-editor-property-color: #05a;
+    --jp-mirror-editor-operator-color: #aa22ff;
+    --jp-mirror-editor-comment-color: #408080;
+    --jp-mirror-editor-string-color: #ba2121;
+    --jp-mirror-editor-string-2-color: #708;
+    --jp-mirror-editor-meta-color: #aa22ff;
+    --jp-mirror-editor-qualifier-color: #555;
+    --jp-mirror-editor-builtin-color: #008000;
+    --jp-mirror-editor-bracket-color: #997;
+    --jp-mirror-editor-tag-color: #170;
+    --jp-mirror-editor-attribute-color: #00c;
+    --jp-mirror-editor-header-color: blue;
+    --jp-mirror-editor-quote-color: #090;
+    --jp-mirror-editor-link-color: #00c;
+    --jp-mirror-editor-error-color: #f00;
+    --jp-mirror-editor-hr-color: #999;
+
+    /* Vega extension styles */
+
+    --jp-vega-background: white;
+
+    /* Sidebar-related styles */
+
+    --jp-sidebar-min-width: 250px;
+
+    /* Search-related styles */
+
+    --jp-search-toggle-off-opacity: 0.5;
+    --jp-search-toggle-hover-opacity: 0.8;
+    --jp-search-toggle-on-opacity: 1;
+    --jp-search-selected-match-background-color: rgb(245, 200, 0);
+    --jp-search-selected-match-color: black;
+    --jp-search-unselected-match-background-color: var(
+        --jp-inverse-layout-color0
+    );
+    --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);
+
+    /* Icon colors that work well with light or dark backgrounds */
+    --jp-icon-contrast-color0: var(--md-purple-600);
+    --jp-icon-contrast-color1: var(--md-green-600);
+    --jp-icon-contrast-color2: var(--md-pink-600);
+    --jp-icon-contrast-color3: var(--md-blue-600);
+}
+
+[data-md-color-scheme="slate"] .jupyter-wrapper {
+    /* Elevation
+   *
+   * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here:
+   *
+   * https://github.com/material-components/material-components-web
+   * https://material-components-web.appspot.com/elevation.html
+   */
+
+    /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout
+   * colors used in the theme as well. */
+    --jp-shadow-base-lightness: 32;
+    --jp-shadow-umbra-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.2
+    );
+    --jp-shadow-penumbra-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.14
+    );
+    --jp-shadow-ambient-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.12
+    );
+    --jp-elevation-z0: none;
+    --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color),
+        0px 1px 1px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 3px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color),
+        0px 2px 2px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 5px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color),
+        0px 4px 5px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 10px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color),
+        0px 6px 10px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 18px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color),
+        0px 8px 10px 1px var(--jp-shadow-penumbra-color),
+        0px 3px 14px 2px var(--jp-shadow-ambient-color);
+    --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color),
+        0px 12px 17px 2px var(--jp-shadow-penumbra-color),
+        0px 5px 22px 4px var(--jp-shadow-ambient-color);
+    --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color),
+        0px 16px 24px 2px var(--jp-shadow-penumbra-color),
+        0px 6px 30px 5px var(--jp-shadow-ambient-color);
+    --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color),
+        0px 20px 31px 3px var(--jp-shadow-penumbra-color),
+        0px 8px 38px 7px var(--jp-shadow-ambient-color);
+    --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color),
+        0px 24px 38px 3px var(--jp-shadow-penumbra-color),
+        0px 9px 46px 8px var(--jp-shadow-ambient-color);
+
+    /* Borders
+   *
+   * The following variables, specify the visual styling of borders in JupyterLab.
+   */
+
+    --jp-border-width: 1px;
+    --jp-border-color0: var(--md-grey-700);
+    --jp-border-color1: var(--md-grey-700);
+    --jp-border-color2: var(--md-grey-800);
+    --jp-border-color3: var(--md-grey-900);
+    --jp-border-radius: 2px;
+
+    /* UI Fonts
+   *
+   * The UI font CSS variables are used for the typography all of the JupyterLab
+   * user interface elements that are not directly user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-ui-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+    --jp-ui-font-scale-factor: 1.2;
+    --jp-ui-font-size0: 0.83333em;
+    --jp-ui-font-size1: 13px; /* Base font size */
+    --jp-ui-font-size2: 1.2em;
+    --jp-ui-font-size3: 1.44em;
+
+    --jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+
+    /*
+   * Use these font colors against the corresponding main layout colors.
+   * In a light theme, these go from dark to light.
+   */
+
+    /* Defaults use Material Design specification */
+    --jp-ui-font-color0: rgba(255, 255, 255, 1);
+    --jp-ui-font-color1: rgba(255, 255, 255, 0.87);
+    --jp-ui-font-color2: rgba(255, 255, 255, 0.54);
+    --jp-ui-font-color3: rgba(255, 255, 255, 0.38);
+
+    /*
+   * Use these against the brand/accent/warn/error colors.
+   * These will typically go from light to darker, in both a dark and light theme.
+   */
+
+    --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1);
+    --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8);
+    --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5);
+    --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3);
+
+    /* Content Fonts
+   *
+   * Content font variables are used for typography of user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-content-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+    --jp-content-line-height: 1.6;
+    --jp-content-font-scale-factor: 1.2;
+    --jp-content-font-size0: 0.83333em;
+    --jp-content-font-size1: 14px; /* Base font size */
+    --jp-content-font-size2: 1.2em;
+    --jp-content-font-size3: 1.44em;
+    --jp-content-font-size4: 1.728em;
+    --jp-content-font-size5: 2.0736em;
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-content-presentation-font-size1: 17px;
+
+    --jp-content-heading-line-height: 1;
+    --jp-content-heading-margin-top: 1.2em;
+    --jp-content-heading-margin-bottom: 0.8em;
+    --jp-content-heading-font-weight: 500;
+
+    /* Defaults use Material Design specification */
+    --jp-content-font-color0: rgba(255, 255, 255, 1);
+    --jp-content-font-color1: rgba(255, 255, 255, 1);
+    --jp-content-font-color2: rgba(255, 255, 255, 0.7);
+    --jp-content-font-color3: rgba(255, 255, 255, 0.5);
+
+    --jp-content-link-color: var(--md-blue-300);
+
+    --jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+
+    /*
+   * Code Fonts
+   *
+   * Code font variables are used for typography of code and other monospaces content.
+   */
+
+    --jp-code-font-size: 13px;
+    --jp-code-line-height: 1.3077; /* 17px for 13px base */
+    --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */
+    --jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono",
+        monospace;
+    --jp-code-font-family: var(--jp-code-font-family-default);
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-code-presentation-font-size: 16px;
+
+    /* may need to tweak cursor width if you change font size */
+    --jp-code-cursor-width0: 1.4px;
+    --jp-code-cursor-width1: 2px;
+    --jp-code-cursor-width2: 4px;
+
+    /* Layout
+   *
+   * The following are the main layout colors use in JupyterLab. In a light
+   * theme these would go from light to dark.
+   */
+
+    --jp-layout-color0: #111111;
+    --jp-layout-color1: var(--md-grey-900);
+    --jp-layout-color2: var(--md-grey-800);
+    --jp-layout-color3: var(--md-grey-700);
+    --jp-layout-color4: var(--md-grey-600);
+
+    /* Inverse Layout
+   *
+   * The following are the inverse layout colors use in JupyterLab. In a light
+   * theme these would go from dark to light.
+   */
+
+    --jp-inverse-layout-color0: white;
+    --jp-inverse-layout-color1: white;
+    --jp-inverse-layout-color2: var(--md-grey-200);
+    --jp-inverse-layout-color3: var(--md-grey-400);
+    --jp-inverse-layout-color4: var(--md-grey-600);
+
+    /* Brand/accent */
+
+    --jp-brand-color0: var(--md-blue-700);
+    --jp-brand-color1: var(--md-blue-500);
+    --jp-brand-color2: var(--md-blue-300);
+    --jp-brand-color3: var(--md-blue-100);
+    --jp-brand-color4: var(--md-blue-50);
+
+    --jp-accent-color0: var(--md-green-700);
+    --jp-accent-color1: var(--md-green-500);
+    --jp-accent-color2: var(--md-green-300);
+    --jp-accent-color3: var(--md-green-100);
+
+    /* State colors (warn, error, success, info) */
+
+    --jp-warn-color0: var(--md-orange-700);
+    --jp-warn-color1: var(--md-orange-500);
+    --jp-warn-color2: var(--md-orange-300);
+    --jp-warn-color3: var(--md-orange-100);
+
+    --jp-error-color0: var(--md-red-700);
+    --jp-error-color1: var(--md-red-500);
+    --jp-error-color2: var(--md-red-300);
+    --jp-error-color3: var(--md-red-100);
+
+    --jp-success-color0: var(--md-green-700);
+    --jp-success-color1: var(--md-green-500);
+    --jp-success-color2: var(--md-green-300);
+    --jp-success-color3: var(--md-green-100);
+
+    --jp-info-color0: var(--md-cyan-700);
+    --jp-info-color1: var(--md-cyan-500);
+    --jp-info-color2: var(--md-cyan-300);
+    --jp-info-color3: var(--md-cyan-100);
+
+    /* Cell specific styles */
+
+    --jp-cell-padding: 5px;
+
+    --jp-cell-collapser-width: 8px;
+    --jp-cell-collapser-min-height: 20px;
+    --jp-cell-collapser-not-active-hover-opacity: 0.6;
+
+    --jp-cell-editor-background: var(--jp-layout-color1);
+    --jp-cell-editor-border-color: var(--md-grey-700);
+    --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-cell-editor-active-background: var(--jp-layout-color0);
+    --jp-cell-editor-active-border-color: var(--jp-brand-color1);
+
+    --jp-cell-prompt-width: 64px;
+    --jp-cell-prompt-font-family: var(--jp-code-font-family-default);
+    --jp-cell-prompt-letter-spacing: 0px;
+    --jp-cell-prompt-opacity: 1;
+    --jp-cell-prompt-not-active-opacity: 1;
+    --jp-cell-prompt-not-active-font-color: var(--md-grey-300);
+
+    /* A custom blend of MD grey and blue 600
+   * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */
+    --jp-cell-inprompt-font-color: #307fc1;
+    /* A custom blend of MD grey and orange 600
+   * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */
+    --jp-cell-outprompt-font-color: #bf5b3d;
+
+    /* Notebook specific styles */
+
+    --jp-notebook-padding: 10px;
+    --jp-notebook-select-background: var(--jp-layout-color1);
+    --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24);
+
+    /* The scroll padding is calculated to fill enough space at the bottom of the
+  notebook to show one single-line cell (with appropriate padding) at the top
+  when the notebook is scrolled all the way to the bottom. We also subtract one
+  pixel so that no scrollbar appears if we have just one single-line cell in the
+  notebook. This padding is to enable a 'scroll past end' feature in a notebook.
+  */
+    --jp-notebook-scroll-padding: calc(
+        100% - var(--jp-code-font-size) * var(--jp-code-line-height) -
+            var(--jp-code-padding) - var(--jp-cell-padding) - 1px
+    );
+
+    /* Rendermime styles */
+
+    --jp-rendermime-error-background: rgba(244, 67, 54, 0.28);
+    --jp-rendermime-table-row-background: var(--md-grey-900);
+    --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2);
+
+    /* Dialog specific styles */
+
+    --jp-dialog-background: rgba(0, 0, 0, 0.6);
+
+    /* Console specific styles */
+
+    --jp-console-padding: 10px;
+
+    /* Toolbar specific styles */
+
+    --jp-toolbar-border-color: var(--jp-border-color2);
+    --jp-toolbar-micro-height: 8px;
+    --jp-toolbar-background: var(--jp-layout-color1);
+    --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8);
+    --jp-toolbar-header-margin: 4px 4px 0px 4px;
+    --jp-toolbar-active-background: var(--jp-layout-color0);
+
+    /* Statusbar specific styles */
+
+    --jp-statusbar-height: 24px;
+
+    /* Input field styles */
+
+    --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-input-active-background: var(--jp-layout-color0);
+    --jp-input-hover-background: var(--jp-layout-color2);
+    --jp-input-background: var(--md-grey-800);
+    --jp-input-border-color: var(--jp-border-color1);
+    --jp-input-active-border-color: var(--jp-brand-color1);
+    --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3);
+
+    /* General editor styles */
+
+    --jp-editor-selected-background: var(--jp-layout-color2);
+    --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24);
+    --jp-editor-cursor-color: var(--jp-ui-font-color0);
+
+    /* Code mirror specific styles */
+
+    --jp-mirror-editor-keyword-color: var(--md-green-500);
+    --jp-mirror-editor-atom-color: var(--md-blue-300);
+    --jp-mirror-editor-number-color: var(--md-green-400);
+    --jp-mirror-editor-def-color: var(--md-blue-600);
+    --jp-mirror-editor-variable-color: var(--md-grey-300);
+    --jp-mirror-editor-variable-2-color: var(--md-blue-400);
+    --jp-mirror-editor-variable-3-color: var(--md-green-600);
+    --jp-mirror-editor-punctuation-color: var(--md-blue-400);
+    --jp-mirror-editor-property-color: var(--md-blue-400);
+    --jp-mirror-editor-operator-color: #aa22ff;
+    --jp-mirror-editor-comment-color: #408080;
+    --jp-mirror-editor-string-color: #ff7070;
+    --jp-mirror-editor-string-2-color: var(--md-purple-300);
+    --jp-mirror-editor-meta-color: #aa22ff;
+    --jp-mirror-editor-qualifier-color: #555;
+    --jp-mirror-editor-builtin-color: var(--md-green-600);
+    --jp-mirror-editor-bracket-color: #997;
+    --jp-mirror-editor-tag-color: var(--md-green-700);
+    --jp-mirror-editor-attribute-color: var(--md-blue-700);
+    --jp-mirror-editor-header-color: var(--md-blue-500);
+    --jp-mirror-editor-quote-color: var(--md-green-300);
+    --jp-mirror-editor-link-color: var(--md-blue-700);
+    --jp-mirror-editor-error-color: #f00;
+    --jp-mirror-editor-hr-color: #999;
+
+    /* Vega extension styles */
+
+    --jp-vega-background: var(--md-grey-400);
+
+    /* Sidebar-related styles */
+
+    --jp-sidebar-min-width: 250px;
+
+    /* Search-related styles */
+
+    --jp-search-toggle-off-opacity: 0.6;
+    --jp-search-toggle-hover-opacity: 0.8;
+    --jp-search-toggle-on-opacity: 1;
+    --jp-search-selected-match-background-color: rgb(255, 225, 0);
+    --jp-search-selected-match-color: black;
+    --jp-search-unselected-match-background-color: var(
+        --jp-inverse-layout-color0
+    );
+    --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);
+
+    /* scrollbar related styles. Supports every browser except Edge. */
+
+    /* colors based on JetBrain's Darcula theme */
+
+    --jp-scrollbar-background-color: #3f4244;
+    --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */
+
+    --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */
+
+    /* hacks for setting the thumb shape. These do nothing in Firefox */
+
+    --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */
+    --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */
+
+    /* Icon colors that work well with light or dark backgrounds */
+    --jp-icon-contrast-color0: var(--md-purple-600);
+    --jp-icon-contrast-color1: var(--md-green-600);
+    --jp-icon-contrast-color2: var(--md-pink-600);
+    --jp-icon-contrast-color3: var(--md-blue-600);
+}
+
+:root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: #7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: #ff5722;--md-deep-orange-600: #f4511e;--md-deep-orange-700: #e64a19;--md-deep-orange-800: #d84315;--md-deep-orange-900: #bf360c;--md-deep-orange-A100: #ff9e80;--md-deep-orange-A200: #ff6e40;--md-deep-orange-A400: #ff3d00;--md-deep-orange-A700: #dd2c00;--md-brown-50: #efebe9;--md-brown-100: #d7ccc8;--md-brown-200: #bcaaa4;--md-brown-300: #a1887f;--md-brown-400: #8d6e63;--md-brown-500: #795548;--md-brown-600: #6d4c41;--md-brown-700: #5d4037;--md-brown-800: #4e342e;--md-brown-900: #3e2723;--md-grey-50: #fafafa;--md-grey-100: #f5f5f5;--md-grey-200: #eeeeee;--md-grey-300: #e0e0e0;--md-grey-400: #bdbdbd;--md-grey-500: #9e9e9e;--md-grey-600: #757575;--md-grey-700: #616161;--md-grey-800: #424242;--md-grey-900: #212121;--md-blue-grey-50: #eceff1;--md-blue-grey-100: #cfd8dc;--md-blue-grey-200: #b0bec5;--md-blue-grey-300: #90a4ae;--md-blue-grey-400: #78909c;--md-blue-grey-500: #607d8b;--md-blue-grey-600: #546e7a;--md-blue-grey-700: #455a64;--md-blue-grey-800: #37474f;--md-blue-grey-900: #263238}.jupyter-wrapper{/*!
+
+Copyright 2015-present Palantir Technologies, Inc. All rights reserved.
+Licensed under the Apache License, Version 2.0.
+
+*//*!
+
+Copyright 2017-present Palantir Technologies, Inc. All rights reserved.
+Licensed under the Apache License, Version 2.0.
+
+*/}.jupyter-wrapper [data-jp-theme-scrollbars=true]{scrollbar-color:rgb(var(--jp-scrollbar-thumb-color)) var(--jp-scrollbar-background-color)}.jupyter-wrapper [data-jp-theme-scrollbars=true] .CodeMirror-hscrollbar,.jupyter-wrapper [data-jp-theme-scrollbars=true] .CodeMirror-vscrollbar{scrollbar-color:rgba(var(--jp-scrollbar-thumb-color), 0.5) rgba(0,0,0,0)}.jupyter-wrapper [data-jp-theme-scrollbars=true] ::-webkit-scrollbar,.jupyter-wrapper [data-jp-theme-scrollbars=true] ::-webkit-scrollbar-corner{background:var(--jp-scrollbar-background-color)}.jupyter-wrapper [data-jp-theme-scrollbars=true] ::-webkit-scrollbar-thumb{background:rgb(var(--jp-scrollbar-thumb-color));border:var(--jp-scrollbar-thumb-margin) solid rgba(0,0,0,0);background-clip:content-box;border-radius:var(--jp-scrollbar-thumb-radius)}.jupyter-wrapper [data-jp-theme-scrollbars=true] ::-webkit-scrollbar-track:horizontal{border-left:var(--jp-scrollbar-endpad) solid 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+
+/*# sourceMappingURL=mkdocs-jupyter.css.map*/</style>
+
+
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+    <!-- MathJax configuration -->
+    <script type="text/x-mathjax-config">
+    init_mathjax = function() {
+        if (window.MathJax) {
+        // MathJax loaded
+            MathJax.Hub.Config({
+                TeX: {
+                    equationNumbers: {
+                    autoNumber: "AMS",
+                    useLabelIds: true
+                    }
+                },
+                tex2jax: {
+                    inlineMath: [ ['$','$'], ["\\(","\\)"] ],
+                    displayMath: [ ['$$','$$'], ["\\[","\\]"] ],
+                    processEscapes: true,
+                    processEnvironments: true
+                },
+                displayAlign: 'center',
+                CommonHTML: {
+                    linebreaks: {
+                    automatic: true
+                    }
+                }
+            });
+
+            MathJax.Hub.Queue(["Typeset", MathJax.Hub]);
+        }
+    }
+    init_mathjax();
+    </script>
+    <!-- End of mathjax configuration --><div class="jupyter-wrapper"><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[16]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-1">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">kfp</span>
+<span class="kn">import</span> <span class="nn">kfp.dsl</span> <span class="k">as</span> <span class="nn">dsl</span>
+<span class="kn">import</span> <span class="nn">kfp.components</span> <span class="k">as</span> <span class="nn">comp</span>
+<span class="kn">from</span> <span class="nn">kfp.components</span> <span class="kn">import</span> <span class="n">InputPath</span><span class="p">,</span> <span class="n">OutputPath</span>
+<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">NamedTuple</span>
+</pre></div>
+<div id="cell-1" class="clipboard-copy-txt">import kfp
+import kfp.dsl as dsl
+import kfp.components as comp
+from kfp.components import InputPath, OutputPath
+from typing import NamedTuple</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[17]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-2">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
+<span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s2">&quot;GOOGLE_APPLICATION_CREDENTIALS&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;/Users/weiqiang/.dotbot/cloud/quakeflow_wayne.json&quot;</span>
+</pre></div>
+<div id="cell-2" class="clipboard-copy-txt">import os
+os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "/Users/weiqiang/.dotbot/cloud/quakeflow_wayne.json"</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
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+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[18]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
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+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">def</span> <span class="nf">download_dataset</span><span class="p">(</span><span class="n">root_path</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;/tmp&quot;</span><span class="p">):</span>
+<span class="c1"># data_path = &quot;./&quot;</span>
+<span class="c1"># if True:</span>
+    <span class="kn">from</span> <span class="nn">google.cloud</span> <span class="kn">import</span> <span class="n">storage</span>
+    <span class="kn">import</span> <span class="nn">os</span>
+    <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+    <span class="n">join_path</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">root_path</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span>
+    
+    <span class="c1">## download from gcp bucket</span>
+    <span class="n">bucket_name</span> <span class="o">=</span> <span class="s2">&quot;ncedc&quot;</span>
+    <span class="k">def</span> <span class="nf">download_blob</span><span class="p">(</span><span class="n">bucket_name</span><span class="p">,</span> <span class="n">source_blob_name</span><span class="p">,</span> <span class="n">destination_file_name</span><span class="p">):</span>
+
+        <span class="n">storage_client</span> <span class="o">=</span> <span class="n">storage</span><span class="o">.</span><span class="n">Client</span><span class="p">()</span>
+        <span class="n">bucket</span> <span class="o">=</span> <span class="n">storage_client</span><span class="o">.</span><span class="n">bucket</span><span class="p">(</span><span class="n">bucket_name</span><span class="p">)</span>
+        <span class="n">blob</span> <span class="o">=</span> <span class="n">bucket</span><span class="o">.</span><span class="n">blob</span><span class="p">(</span><span class="n">source_blob_name</span><span class="p">)</span>
+        <span class="n">blob</span><span class="o">.</span><span class="n">download_to_filename</span><span class="p">(</span><span class="n">destination_file_name</span><span class="p">)</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;download </span><span class="si">{</span><span class="n">source_blob_name</span><span class="si">}</span><span class="s2"> to </span><span class="si">{</span><span class="n">destination_file_name</span><span class="si">}</span><span class="s2">.&quot;</span><span class="p">)</span>
+        
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">join_path</span><span class="p">(</span><span class="s2">&quot;catalogs/&quot;</span><span class="p">)):</span>
+        <span class="n">os</span><span class="o">.</span><span class="n">mkdir</span><span class="p">(</span><span class="n">join_path</span><span class="p">(</span><span class="s2">&quot;catalogs/&quot;</span><span class="p">))</span>
+    <span class="n">download_blob</span><span class="p">(</span><span class="s2">&quot;ncedc&quot;</span><span class="p">,</span> <span class="s2">&quot;catalogs/combined_phases.csv&quot;</span><span class="p">,</span> <span class="n">join_path</span><span class="p">(</span><span class="s2">&quot;catalogs/combined_phases.csv&quot;</span><span class="p">))</span>
+    <span class="n">catalog</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">join_path</span><span class="p">(</span><span class="s2">&quot;catalogs/combined_phases.csv&quot;</span><span class="p">),</span> <span class="n">sep</span><span class="o">=</span><span class="s2">&quot;</span><span class="se">\t</span><span class="s2">&quot;</span><span class="p">)</span>
+    
+    <span class="n">index</span> <span class="o">=</span> <span class="n">catalog</span><span class="p">[</span><span class="s2">&quot;event_index&quot;</span><span class="p">]</span>
+    <span class="n">test</span> <span class="o">=</span> <span class="n">catalog</span><span class="p">[</span> <span class="n">index</span> <span class="o">&gt;=</span> <span class="p">(</span><span class="n">index</span><span class="o">.</span><span class="n">max</span><span class="p">()</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="p">]</span>
+    <span class="n">valid</span> <span class="o">=</span> <span class="n">catalog</span><span class="p">[</span> <span class="p">(</span><span class="n">index</span> <span class="o">&lt;</span> <span class="p">(</span><span class="n">index</span><span class="o">.</span><span class="n">max</span><span class="p">()</span><span class="o">-</span><span class="mi">1</span><span class="p">))</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">index</span> <span class="o">&gt;=</span> <span class="p">(</span><span class="n">index</span><span class="o">.</span><span class="n">max</span><span class="p">()</span><span class="o">-</span><span class="mi">2</span><span class="p">))</span> <span class="p">]</span>
+    <span class="n">train</span> <span class="o">=</span> <span class="n">catalog</span><span class="p">[</span> <span class="p">(</span><span class="n">index</span> <span class="o">&lt;</span> <span class="p">(</span><span class="n">index</span><span class="o">.</span><span class="n">max</span><span class="p">()</span><span class="o">-</span><span class="mi">2</span><span class="p">))</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">index</span> <span class="o">&gt;=</span> <span class="p">(</span><span class="n">index</span><span class="o">.</span><span class="n">max</span><span class="p">()</span><span class="o">-</span><span class="mi">10</span><span class="p">))</span> <span class="p">]</span>
+    
+    <span class="k">for</span> <span class="n">dataset</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;train&quot;</span><span class="p">,</span> <span class="s2">&quot;valid&quot;</span><span class="p">,</span> <span class="s2">&quot;test&quot;</span><span class="p">]:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Downloading </span><span class="si">{</span><span class="n">dataset</span><span class="si">}</span><span class="s2"> dataset...&quot;</span><span class="p">)</span>
+<span class="c1">#         eval(dataset).to_csv(join_path(f&quot;{dataset}.csv&quot;), sep=&quot;\t&quot;)</span>
+        <span class="nb">eval</span><span class="p">(</span><span class="n">dataset</span><span class="p">)[</span><span class="s2">&quot;itp&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">eval</span><span class="p">(</span><span class="n">dataset</span><span class="p">)[</span><span class="s2">&quot;p_idx&quot;</span><span class="p">]</span>
+        <span class="nb">eval</span><span class="p">(</span><span class="n">dataset</span><span class="p">)[</span><span class="s2">&quot;its&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">eval</span><span class="p">(</span><span class="n">dataset</span><span class="p">)[</span><span class="s2">&quot;s_idx&quot;</span><span class="p">]</span>
+        <span class="nb">eval</span><span class="p">(</span><span class="n">dataset</span><span class="p">)</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="n">join_path</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">dataset</span><span class="si">}</span><span class="s2">.csv&quot;</span><span class="p">),</span> <span class="n">sep</span><span class="o">=</span><span class="s2">&quot;</span><span class="se">\t</span><span class="s2">&quot;</span><span class="p">)</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Save </span><span class="si">{</span><span class="n">join_path</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">dataset</span><span class="si">}</span><span class="s1">.csv&#39;</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">join_path</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">dataset</span><span class="si">}</span><span class="s2">_data/&quot;</span><span class="p">)):</span>
+            <span class="n">os</span><span class="o">.</span><span class="n">mkdir</span><span class="p">(</span><span class="n">join_path</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">dataset</span><span class="si">}</span><span class="s2">_data/&quot;</span><span class="p">))</span>
+        <span class="n">num</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="nb">eval</span><span class="p">(</span><span class="n">dataset</span><span class="p">)[</span><span class="s2">&quot;fname&quot;</span><span class="p">])</span>
+        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">fname</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">eval</span><span class="p">(</span><span class="n">dataset</span><span class="p">)[</span><span class="s2">&quot;fname&quot;</span><span class="p">]):</span>
+            <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="si">}</span><span class="s2">/</span><span class="si">{</span><span class="n">num</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span>  <span class="n">end</span><span class="o">=</span><span class="s1">&#39; &#39;</span><span class="p">)</span>
+            <span class="n">download_blob</span><span class="p">(</span><span class="n">bucket_name</span><span class="p">,</span> <span class="sa">f</span><span class="s2">&quot;data/</span><span class="si">{</span><span class="n">fname</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">join_path</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">dataset</span><span class="si">}</span><span class="s2">_data/</span><span class="si">{</span><span class="n">fname</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">))</span>
+</pre></div>
+<div id="cell-3" class="clipboard-copy-txt">def download_dataset(root_path: str = "/tmp"):
+# data_path = "./"
+# if True:
+    from google.cloud import storage
+    import os
+    import pandas as pd
+    join_path = lambda x: os.path.join(root_path, x)
+    
+    ## download from gcp bucket
+    bucket_name = "ncedc"
+    def download_blob(bucket_name, source_blob_name, destination_file_name):
+
+        storage_client = storage.Client()
+        bucket = storage_client.bucket(bucket_name)
+        blob = bucket.blob(source_blob_name)
+        blob.download_to_filename(destination_file_name)
+        print(f"download {source_blob_name} to {destination_file_name}.")
+        
+    if not os.path.exists(join_path("catalogs/")):
+        os.mkdir(join_path("catalogs/"))
+    download_blob("ncedc", "catalogs/combined_phases.csv", join_path("catalogs/combined_phases.csv"))
+    catalog = pd.read_csv(join_path("catalogs/combined_phases.csv"), sep="\t")
+    
+    index = catalog["event_index"]
+    test = catalog[ index >= (index.max()-1) ]
+    valid = catalog[ (index < (index.max()-1)) & (index >= (index.max()-2)) ]
+    train = catalog[ (index < (index.max()-2)) & (index >= (index.max()-10)) ]
+    
+    for dataset in ["train", "valid", "test"]:
+        print(f"Downloading {dataset} dataset...")
+#         eval(dataset).to_csv(join_path(f"{dataset}.csv"), sep="\t")
+        eval(dataset)["itp"] = eval(dataset)["p_idx"]
+        eval(dataset)["its"] = eval(dataset)["s_idx"]
+        eval(dataset).to_csv(join_path(f"{dataset}.csv"), sep="\t")
+        print(f"Save {join_path(f'{dataset}.csv')}")
+        if not os.path.exists(join_path(f"{dataset}_data/")):
+            os.mkdir(join_path(f"{dataset}_data/"))
+        num = len(eval(dataset)["fname"])
+        for i, fname in enumerate(eval(dataset)["fname"]):
+            print(f"{i+1}/{num}",  end=' ')
+            download_blob(bucket_name, f"data/{fname}", join_path(f"{dataset}_data/{fname}"))
+    </div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[19]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-4">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># download_dataset(root_path = &quot;./&quot;)</span>
+</pre></div>
+<div id="cell-4" class="clipboard-copy-txt"># download_dataset(root_path = "./")</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[20]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-5">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">download_dataset_op</span> <span class="o">=</span> <span class="n">comp</span><span class="o">.</span><span class="n">func_to_container_op</span><span class="p">(</span><span class="n">download_dataset</span><span class="p">,</span> 
+                                                <span class="n">base_image</span><span class="o">=</span><span class="s1">&#39;python:3.7&#39;</span><span class="p">,</span>
+                                                <span class="n">packages_to_install</span><span class="o">=</span> <span class="p">[</span>
+                                                    <span class="s2">&quot;pandas&quot;</span><span class="p">,</span>
+                                                    <span class="s2">&quot;google-cloud-storage&quot;</span>
+                                                <span class="p">])</span>
+</pre></div>
+<div id="cell-5" class="clipboard-copy-txt">download_dataset_op = comp.func_to_container_op(download_dataset, 
+                                                base_image='python:3.7',
+                                                packages_to_install= [
+                                                    "pandas",
+                                                    "google-cloud-storage"
+                                                ])</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[21]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-6">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">def</span> <span class="nf">training_op</span><span class="p">(</span><span class="n">root_path</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> 
+                <span class="n">model_path</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
+                <span class="n">batch_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">2</span><span class="p">,</span>
+                <span class="n">train_path</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;train_data&quot;</span><span class="p">,</span> 
+                <span class="n">train_csv</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;train.csv&quot;</span><span class="p">,</span>
+                <span class="p">):</span>
+    
+    <span class="kn">import</span> <span class="nn">os</span>
+    <span class="n">join_path</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">root_path</span><span class="p">),</span> <span class="n">x</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">dsl</span><span class="o">.</span><span class="n">ContainerOp</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;PhaseNet training&#39;</span><span class="p">,</span>
+                           <span class="n">image</span><span class="o">=</span><span class="s2">&quot;zhuwq0/phasenet:latest&quot;</span><span class="p">,</span>
+<span class="c1">#                            command=[&#39;sh&#39;, &#39;-c&#39;],</span>
+<span class="c1">#                            arguments=[&quot;mkdir /tmp/model &amp;&amp; cp -r /opt/model/190703-214543 /tmp/model/190703-214543&quot;],</span>
+                           <span class="n">command</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;python&#39;</span><span class="p">],</span>
+                           <span class="n">arguments</span><span class="o">=</span><span class="p">[</span>
+                               <span class="s1">&#39;train.py&#39;</span><span class="p">,</span>
+                               <span class="s1">&#39;--epoch&#39;</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span>
+                               <span class="s1">&#39;--batch_size&#39;</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">,</span>
+                               <span class="s1">&#39;--train_dir&#39;</span><span class="p">,</span> <span class="n">join_path</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">train_path</span><span class="p">)),</span>
+                               <span class="s1">&#39;--train_list&#39;</span><span class="p">,</span> <span class="n">join_path</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">train_csv</span><span class="p">)),</span>
+                               <span class="s1">&#39;--model_dir&#39;</span><span class="p">,</span> <span class="n">join_path</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">model_path</span><span class="p">))</span>
+                               <span class="p">],</span>
+<span class="c1">#                            file_outputs = {&quot;model_path&quot;: model_path}</span>
+                           <span class="p">)</span>
+</pre></div>
+<div id="cell-6" class="clipboard-copy-txt">def training_op(root_path: str, 
+                model_path: str,
+                batch_size: int = 2,
+                train_path: str = "train_data", 
+                train_csv: str = "train.csv",
+                ):
+    
+    import os
+    join_path = lambda x: os.path.join(str(root_path), x)
+    return dsl.ContainerOp(name='PhaseNet training',
+                           image="zhuwq0/phasenet:latest",
+#                            command=['sh', '-c'],
+#                            arguments=["mkdir /tmp/model && cp -r /opt/model/190703-214543 /tmp/model/190703-214543"],
+                           command=['python'],
+                           arguments=[
+                               'train.py',
+                               '--epoch', 10,
+                               '--batch_size', batch_size,
+                               '--train_dir', join_path(str(train_path)),
+                               '--train_list', join_path(str(train_csv)),
+                               '--model_dir', join_path(str(model_path))
+                               ],
+#                            file_outputs = {"model_path": model_path}
+                           )</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[22]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-7">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># !python PhaseNet/phasenet/train.py --epoch=10 --batch_size=2 --model_dir=models --train_list=train.csv --train_dir=train_data</span>
+</pre></div>
+<div id="cell-7" class="clipboard-copy-txt"># !python PhaseNet/phasenet/train.py --epoch=10 --batch_size=2 --model_dir=models --train_list=train.csv --train_dir=train_data </div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[23]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-8">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">def</span> <span class="nf">test_op</span><span class="p">(</span><span class="n">root_path</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> 
+            <span class="n">model_path</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
+            <span class="n">batch_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">2</span><span class="p">,</span>
+            <span class="n">test_path</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;test_data&quot;</span><span class="p">,</span> 
+            <span class="n">test_csv</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;test.csv&quot;</span><span class="p">,</span>
+            <span class="n">result_path</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;results&quot;</span><span class="p">):</span>
+    
+    <span class="kn">import</span> <span class="nn">os</span>
+    <span class="n">join_path</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">root_path</span><span class="p">),</span> <span class="n">x</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">dsl</span><span class="o">.</span><span class="n">ContainerOp</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;PhaseNet test&#39;</span><span class="p">,</span>
+                           <span class="n">image</span><span class="o">=</span><span class="s2">&quot;zhuwq0/phasenet:latest&quot;</span><span class="p">,</span>
+<span class="c1">#                            command=[&#39;sh&#39;, &#39;-c&#39;],</span>
+<span class="c1">#                            arguments=[&quot;ls ./&quot;],</span>
+                           <span class="n">command</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;python&#39;</span><span class="p">],</span>
+                           <span class="n">arguments</span><span class="o">=</span><span class="p">[</span>
+                               <span class="s1">&#39;train.py&#39;</span><span class="p">,</span>
+                               <span class="s1">&#39;--mode&#39;</span><span class="p">,</span> <span class="s2">&quot;test&quot;</span><span class="p">,</span>
+                               <span class="s1">&#39;--model&#39;</span><span class="p">,</span> <span class="n">join_path</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">model_path</span><span class="p">)),</span>
+                               <span class="s1">&#39;--batch_size&#39;</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">,</span>
+                               <span class="s1">&#39;--test_dir&#39;</span><span class="p">,</span> <span class="n">join_path</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">test_path</span><span class="p">)),</span>
+                               <span class="s1">&#39;--test_list&#39;</span><span class="p">,</span> <span class="n">join_path</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">test_csv</span><span class="p">)),</span>
+                               <span class="s1">&#39;--result_dir&#39;</span><span class="p">,</span> <span class="n">join_path</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">result_path</span><span class="p">))</span>
+                               <span class="p">],</span>
+<span class="c1">#                            file_outputs = {&quot;result&quot;: f&quot;{result_path}/loss.log&quot;}</span>
+                           <span class="p">)</span>
+</pre></div>
+<div id="cell-8" class="clipboard-copy-txt">def test_op(root_path: str, 
+            model_path: str,
+            batch_size: int = 2,
+            test_path: str = "test_data", 
+            test_csv: str = "test.csv",
+            result_path: str = "results"):
+    
+    import os
+    join_path = lambda x: os.path.join(str(root_path), x)
+    return dsl.ContainerOp(name='PhaseNet test',
+                           image="zhuwq0/phasenet:latest",
+#                            command=['sh', '-c'],
+#                            arguments=["ls ./"],
+                           command=['python'],
+                           arguments=[
+                               'train.py',
+                               '--mode', "test",
+                               '--model', join_path(str(model_path)),
+                               '--batch_size', batch_size,
+                               '--test_dir', join_path(str(test_path)),
+                               '--test_list', join_path(str(test_csv)),
+                               '--result_dir', join_path(str(result_path))
+                               ],
+#                            file_outputs = {"result": f"{result_path}/loss.log"}
+                           )</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[24]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-9">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># !python PhaseNet/phasenet/train.py --mode=test --model=models --batch_size=2 --result_dir=results --test_list=test.csv --test_dir=test_data</span>
+</pre></div>
+<div id="cell-9" class="clipboard-copy-txt"># !python PhaseNet/phasenet/train.py --mode=test --model=models --batch_size=2 --result_dir=results --test_list=test.csv --test_dir=test_data</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[25]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-10">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">def</span> <span class="nf">upload_model</span><span class="p">(</span><span class="n">root_path</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
+                 <span class="n">local_path</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
+                 <span class="n">remote_path</span><span class="p">:</span><span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;phasenet&quot;</span><span class="p">,</span>
+                 <span class="n">bucket_name</span><span class="p">:</span><span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;models&quot;</span><span class="p">,</span>
+                 <span class="n">s3_url</span><span class="p">:</span><span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;localhost:9000&quot;</span><span class="p">,</span> 
+                 <span class="n">secure</span><span class="p">:</span><span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">):</span>
+    
+    <span class="kn">import</span> <span class="nn">os</span>
+    <span class="kn">from</span> <span class="nn">minio</span> <span class="kn">import</span> <span class="n">Minio</span>
+    <span class="n">minioClient</span> <span class="o">=</span> <span class="n">Minio</span><span class="p">(</span><span class="n">s3_url</span><span class="p">,</span>
+                  <span class="n">access_key</span><span class="o">=</span><span class="s1">&#39;minio&#39;</span><span class="p">,</span>
+                  <span class="n">secret_key</span><span class="o">=</span><span class="s1">&#39;minio123&#39;</span><span class="p">,</span>
+                  <span class="n">secure</span><span class="o">=</span><span class="n">secure</span><span class="p">)</span>
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">minioClient</span><span class="o">.</span><span class="n">bucket_exists</span><span class="p">(</span><span class="n">bucket_name</span><span class="p">):</span>
+        <span class="n">minioClient</span><span class="o">.</span><span class="n">make_bucket</span><span class="p">(</span><span class="n">bucket_name</span><span class="p">)</span>
+    
+    <span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="n">os</span><span class="o">.</span><span class="n">listdir</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">root_path</span><span class="p">,</span> <span class="n">local_path</span><span class="p">)):</span>
+        <span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isfile</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">root_path</span><span class="p">,</span> <span class="n">local_path</span><span class="p">,</span> <span class="n">f</span><span class="p">)):</span>
+            <span class="n">minioClient</span><span class="o">.</span><span class="n">fput_object</span><span class="p">(</span><span class="n">bucket_name</span><span class="p">,</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">remote_path</span><span class="p">,</span> <span class="n">f</span><span class="p">),</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">root_path</span><span class="p">,</span> <span class="n">local_path</span><span class="p">,</span> <span class="n">f</span><span class="p">))</span>
+            <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;upload </span><span class="si">{</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">root_path</span><span class="p">,</span><span class="w"> </span><span class="n">local_path</span><span class="p">,</span><span class="w"> </span><span class="n">f</span><span class="p">)</span><span class="si">}</span><span class="s2"> to </span><span class="si">{</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">remote_path</span><span class="p">,</span><span class="w"> </span><span class="n">f</span><span class="p">)</span><span class="si">}</span><span class="s2">.&quot;</span><span class="p">)</span>
+</pre></div>
+<div id="cell-10" class="clipboard-copy-txt">def upload_model(root_path: str,
+                 local_path: str,
+                 remote_path:str = "phasenet",
+                 bucket_name:str = "models",
+                 s3_url:str = "localhost:9000", 
+                 secure:bool = True):
+    
+    import os
+    from minio import Minio
+    minioClient = Minio(s3_url,
+                  access_key='minio',
+                  secret_key='minio123',
+                  secure=secure)
+    if not minioClient.bucket_exists(bucket_name):
+        minioClient.make_bucket(bucket_name)
+    
+    for f in os.listdir(os.path.join(root_path, local_path)):
+        if os.path.isfile(os.path.join(root_path, local_path, f)):
+            minioClient.fput_object(bucket_name, os.path.join(remote_path, f), os.path.join(root_path, local_path, f))
+            print(f"upload {os.path.join(root_path, local_path, f)} to {os.path.join(remote_path, f)}.")</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[26]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-11">
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+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># upload_model(&quot;./&quot;, &quot;PhaseNet/model/190703-214543&quot;, s3_url=&quot;localhost:9000&quot;, secure=False)</span>
+</pre></div>
+<div id="cell-11" class="clipboard-copy-txt"># upload_model("./", "PhaseNet/model/190703-214543", s3_url="localhost:9000", secure=False)</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[27]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-12">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">upload_model_op</span> <span class="o">=</span> <span class="n">comp</span><span class="o">.</span><span class="n">func_to_container_op</span><span class="p">(</span><span class="n">upload_model</span><span class="p">,</span> 
+                                            <span class="n">base_image</span><span class="o">=</span><span class="s1">&#39;python:3.7&#39;</span><span class="p">,</span>
+                                            <span class="n">packages_to_install</span><span class="o">=</span> <span class="p">[</span>
+                                                <span class="s2">&quot;pandas&quot;</span><span class="p">,</span>
+                                                <span class="s2">&quot;minio&quot;</span>
+                                            <span class="p">])</span>
+</pre></div>
+<div id="cell-12" class="clipboard-copy-txt">upload_model_op = comp.func_to_container_op(upload_model, 
+                                            base_image='python:3.7',
+                                            packages_to_install= [
+                                                "pandas",
+                                                "minio"
+                                            ])</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[31]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-13">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">def</span> <span class="nf">quakeflow_training</span><span class="p">(</span><span class="n">root_path</span><span class="p">:</span><span class="nb">str</span><span class="p">,</span> <span class="n">model_path</span><span class="p">:</span><span class="nb">str</span><span class="p">,</span> <span class="n">bucket_name</span><span class="p">:</span><span class="nb">str</span><span class="p">,</span> <span class="n">s3_url</span><span class="p">:</span><span class="nb">str</span><span class="p">,</span> <span class="n">secure</span><span class="p">:</span><span class="nb">bool</span><span class="p">):</span>
+    
+    <span class="n">vop_</span> <span class="o">=</span> <span class="n">dsl</span><span class="o">.</span><span class="n">VolumeOp</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;Create_volume&quot;</span><span class="p">,</span> <span class="n">resource_name</span><span class="o">=</span><span class="s2">&quot;data-volume&quot;</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="s2">&quot;10Gi&quot;</span><span class="p">,</span> <span class="n">modes</span><span class="o">=</span><span class="n">dsl</span><span class="o">.</span><span class="n">VOLUME_MODE_RWO</span><span class="p">)</span><span class="o">.</span><span class="n">set_display_name</span><span class="p">(</span><span class="s1">&#39;Persistent Volume&#39;</span><span class="p">)</span>
+    
+    <span class="n">download_</span> <span class="o">=</span> <span class="n">download_dataset_op</span><span class="p">(</span><span class="n">root_path</span><span class="p">)</span><span class="o">.</span><span class="n">add_pvolumes</span><span class="p">({</span><span class="n">root_path</span><span class="p">:</span> <span class="n">vop_</span><span class="o">.</span><span class="n">volume</span><span class="p">})</span><span class="o">.</span><span class="n">set_display_name</span><span class="p">(</span><span class="s1">&#39;Download Datasets&#39;</span><span class="p">)</span>
+    
+    <span class="n">train_</span> <span class="o">=</span> <span class="n">training_op</span><span class="p">(</span><span class="n">root_path</span><span class="p">,</span> <span class="n">model_path</span><span class="p">)</span><span class="o">.</span><span class="n">add_pvolumes</span><span class="p">({</span><span class="n">root_path</span><span class="p">:</span> <span class="n">download_</span><span class="o">.</span><span class="n">pvolume</span><span class="p">})</span><span class="o">.</span><span class="n">set_display_name</span><span class="p">(</span><span class="s1">&#39;Training&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">set_gpu_limit</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">train_</span><span class="o">.</span><span class="n">execution_options</span><span class="o">.</span><span class="n">caching_strategy</span><span class="o">.</span><span class="n">max_cache_staleness</span> <span class="o">=</span> <span class="s2">&quot;P0D&quot;</span>
+    
+    <span class="n">test_</span> <span class="o">=</span> <span class="n">test_op</span><span class="p">(</span><span class="n">root_path</span><span class="p">,</span> <span class="n">model_path</span><span class="o">=</span><span class="n">model_path</span><span class="p">)</span><span class="o">.</span><span class="n">add_pvolumes</span><span class="p">({</span><span class="n">root_path</span><span class="p">:</span> <span class="n">train_</span><span class="o">.</span><span class="n">pvolume</span><span class="p">})</span><span class="o">.</span><span class="n">set_display_name</span><span class="p">(</span><span class="s1">&#39;Inference&#39;</span><span class="p">)</span>
+<span class="c1">#     test_.execution_options.caching_strategy.max_cache_staleness = &quot;P0D&quot;</span>
+    
+<span class="c1">#     with dsl.Condition(test_.output &gt; 0.9):</span>
+    <span class="n">upload_</span> <span class="o">=</span> <span class="n">upload_model_op</span><span class="p">(</span><span class="n">root_path</span><span class="p">,</span> <span class="n">model_path</span><span class="p">,</span> <span class="n">bucket_name</span><span class="o">=</span><span class="n">bucket_name</span><span class="p">,</span> <span class="n">s3_url</span><span class="o">=</span><span class="n">s3_url</span><span class="p">,</span> <span class="n">secure</span><span class="o">=</span><span class="n">secure</span><span class="p">)</span><span class="o">.</span><span class="n">add_pvolumes</span><span class="p">({</span><span class="n">root_path</span><span class="p">:</span> <span class="n">test_</span><span class="o">.</span><span class="n">pvolume</span><span class="p">})</span><span class="o">.</span><span class="n">set_display_name</span><span class="p">(</span><span class="s1">&#39;Upload Model&#39;</span><span class="p">)</span>
+<span class="c1">#         upload_.execution_options.caching_strategy.max_cache_staleness = &quot;P0D&quot;</span>
+</pre></div>
+<div id="cell-13" class="clipboard-copy-txt">def quakeflow_training(root_path:str, model_path:str, bucket_name:str, s3_url:str, secure:bool):
+    
+    vop_ = dsl.VolumeOp(name="Create_volume", resource_name="data-volume", size="10Gi", modes=dsl.VOLUME_MODE_RWO).set_display_name('Persistent Volume')
+    
+    download_ = download_dataset_op(root_path).add_pvolumes({root_path: vop_.volume}).set_display_name('Download Datasets')
+    
+    train_ = training_op(root_path, model_path).add_pvolumes({root_path: download_.pvolume}).set_display_name('Training').set_gpu_limit(1)
+    train_.execution_options.caching_strategy.max_cache_staleness = "P0D"
+    
+    test_ = test_op(root_path, model_path=model_path).add_pvolumes({root_path: train_.pvolume}).set_display_name('Inference')
+#     test_.execution_options.caching_strategy.max_cache_staleness = "P0D"
+    
+#     with dsl.Condition(test_.output > 0.9):
+    upload_ = upload_model_op(root_path, model_path, bucket_name=bucket_name, s3_url=s3_url, secure=secure).add_pvolumes({root_path: test_.pvolume}).set_display_name('Upload Model')
+#         upload_.execution_options.caching_strategy.max_cache_staleness = "P0D"
+</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[32]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-14">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># client = kfp.Client(host=&#39;https://45ff9cb0ebef.ngrok.io&#39;)</span>
+<span class="n">client</span> <span class="o">=</span> <span class="n">kfp</span><span class="o">.</span><span class="n">Client</span><span class="p">(</span><span class="n">host</span><span class="o">=</span><span class="s2">&quot;5bbb5e6223b3b3e9-dot-us-west1.pipelines.googleusercontent.com&quot;</span><span class="p">)</span>
+<span class="c1"># client = kfp.Client(host=&#39;127.0.0.1:8080&#39;)</span>
+</pre></div>
+<div id="cell-14" class="clipboard-copy-txt"># client = kfp.Client(host='https://45ff9cb0ebef.ngrok.io')
+client = kfp.Client(host="5bbb5e6223b3b3e9-dot-us-west1.pipelines.googleusercontent.com")
+# client = kfp.Client(host='127.0.0.1:8080')</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[33]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-15">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
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+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">experiment_name</span> <span class="o">=</span> <span class="s1">&#39;QuakeFlow Training&#39;</span>
+<span class="n">pipeline_func</span> <span class="o">=</span> <span class="n">quakeflow_training</span>
+<span class="n">run_name</span> <span class="o">=</span> <span class="n">pipeline_func</span><span class="o">.</span><span class="vm">__name__</span> <span class="o">+</span> <span class="s1">&#39;_run&#39;</span>
+
+<span class="n">arguments</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;root_path&quot;</span><span class="p">:</span> <span class="s2">&quot;/tmp/&quot;</span><span class="p">,</span>
+             <span class="s2">&quot;model_path&quot;</span><span class="p">:</span> <span class="s2">&quot;models&quot;</span><span class="p">,</span>
+             <span class="s2">&quot;bucket_name&quot;</span><span class="p">:</span> <span class="s2">&quot;models&quot;</span><span class="p">,</span>
+<span class="c1">#              &quot;s3_url&quot;: &quot;localhost:9000&quot;,</span>
+<span class="c1">#              &quot;secure&quot;: False</span>
+             <span class="s2">&quot;s3_url&quot;</span><span class="p">:</span> <span class="s2">&quot;minio-service:9000&quot;</span><span class="p">,</span>
+             <span class="s2">&quot;secure&quot;</span><span class="p">:</span> <span class="kc">False</span>
+             <span class="p">}</span>
+
+<span class="c1"># Compile pipeline to generate compressed YAML definition of the pipeline.</span>
+<span class="n">kfp</span><span class="o">.</span><span class="n">compiler</span><span class="o">.</span><span class="n">Compiler</span><span class="p">()</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">pipeline_func</span><span class="p">,</span> <span class="s1">&#39;</span><span class="si">{}</span><span class="s1">.zip&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">experiment_name</span><span class="p">))</span>
+
+<span class="c1"># Submit pipeline directly from pipeline function</span>
+<span class="n">results</span> <span class="o">=</span> <span class="n">client</span><span class="o">.</span><span class="n">create_run_from_pipeline_func</span><span class="p">(</span><span class="n">pipeline_func</span><span class="p">,</span> 
+                                               <span class="n">experiment_name</span><span class="o">=</span><span class="n">experiment_name</span><span class="p">,</span> 
+                                               <span class="n">run_name</span><span class="o">=</span><span class="n">run_name</span><span class="p">,</span> 
+                                               <span class="n">arguments</span><span class="o">=</span><span class="n">arguments</span><span class="p">)</span>
+</pre></div>
+<div id="cell-15" class="clipboard-copy-txt">experiment_name = 'QuakeFlow Training'
+pipeline_func = quakeflow_training
+run_name = pipeline_func.__name__ + '_run'
+
+arguments = {"root_path": "/tmp/",
+             "model_path": "models",
+             "bucket_name": "models",
+#              "s3_url": "localhost:9000",
+#              "secure": False
+             "s3_url": "minio-service:9000",
+             "secure": False
+             }
+
+# Compile pipeline to generate compressed YAML definition of the pipeline.
+kfp.compiler.Compiler().compile(pipeline_func, '{}.zip'.format(experiment_name))
+
+# Submit pipeline directly from pipeline function
+results = client.create_run_from_pipeline_func(pipeline_func, 
+                                               experiment_name=experiment_name, 
+                                               run_name=run_name, 
+                                               arguments=arguments)</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output " data-mime-type="text/html">
+<a href="http://5bbb5e6223b3b3e9-dot-us-west1.pipelines.googleusercontent.com/#/experiments/details/2019a8e3-c526-4415-b9a5-a1fd4be108f3" target="_blank" >Experiment details</a>.
+</div>
+
+</div>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output " data-mime-type="text/html">
+<a href="http://5bbb5e6223b3b3e9-dot-us-west1.pipelines.googleusercontent.com/#/runs/details/cd4350c4-7133-495c-8185-013fac332420" target="_blank" >Run details</a>.
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<ul>
+<li>To check the uploaded models</li>
+</ul>
+<p>kubectl port-forward -n kubeflow svc/minio-service 9000:9000</p>
+<p>Access Key: minio
+Secret Key: minio123</p>
+<p>open: <a href="http://localhost:9000/minio/models/phasenet/">http://localhost:9000/minio/models/phasenet/</a></p>
+
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
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+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<ul>
+<li>GPU training:</li>
+</ul>
+<p><a href="https://github.com/kubeflow/pipelines/blob/master/samples/tutorials/gpu/gpu.ipynb">https://github.com/kubeflow/pipelines/blob/master/samples/tutorials/gpu/gpu.ipynb</a></p>
+
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
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+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
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+            <div class="highlight-ipynb hl-python"><pre><span></span>
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+    
+        </div>
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+
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+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
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+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
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+            <div class="highlight-ipynb hl-python"><pre><span></span>
+</pre></div>
+<div id="cell-17" class="clipboard-copy-txt"></div>
+    
+        </div>
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+
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+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
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+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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+        
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+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
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+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span>
+</pre></div>
+<div id="cell-18" class="clipboard-copy-txt"></div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
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+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
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+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span>
+</pre></div>
+<div id="cell-19" class="clipboard-copy-txt"></div>
+    
+        </div>
+    </div>
+
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+</div>
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diff --git a/kubeflow/cloud_dataset/index.html b/kubeflow/cloud_dataset/index.html
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+        <meta name="description" content="QuakeFlow: a Scalable Deep-learning-based Earthquake Monitoring Workflow with Cloud Computing">
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+  <nav class="md-header__inner md-grid" aria-label="Header">
+    <a href="../.." title="QuakeFlow" class="md-header__button md-logo" aria-label="QuakeFlow" data-md-component="logo">
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+  
+  <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54Z"/></svg>
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+          <span class="md-ellipsis">
+            QuakeFlow
+          </span>
+        </div>
+        <div class="md-header__topic" data-md-component="header-topic">
+          <span class="md-ellipsis">
+            
+              Cloud dataset
+            
+          </span>
+        </div>
+      </div>
+    </div>
+    
+    
+    
+    
+      <div class="md-header__source">
+        <a href="https://github.com/ai4eps/QuakeFlow" title="Go to repository" class="md-source" data-md-component="source">
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+      <a href="https://github.com/ai4eps/QuakeFlow" title="Go to repository" class="md-source" data-md-component="source">
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+  </div>
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+    
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+  
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+      <a href="../../data_format/" class="md-nav__link">
+        Standard Formats
+      </a>
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+</nav>
+                  </div>
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+            
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+              <div class="md-sidebar md-sidebar--secondary" data-md-component="sidebar" data-md-type="toc" >
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+                  <div class="md-sidebar__inner">
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+<nav class="md-nav md-nav--secondary" aria-label="Table of contents">
+  
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+          <div class="md-content" data-md-component="content">
+            <article class="md-content__inner md-typeset">
+              
+                
+  <a href="https://github.com/ai4eps/QuakeFlow/edit/master/docs/kubeflow/cloud_dataset.ipynb" title="Edit this page" class="md-content__button md-icon">
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
+  </a>
+
+
+
+  <h1>Cloud dataset</h1>
+
+<script>
+(function (global, factory) {
+    typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() :
+    typeof define === 'function' && define.amd ? define(factory) :
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+  }(this, function () { 'use strict';
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+    function createNode(text) {
+      const node = document.createElement('pre');
+      node.style.width = '1px';
+      node.style.height = '1px';
+      node.style.position = 'fixed';
+      node.style.top = '5px';
+      node.textContent = text;
+      return node;
+    }
+
+    function copyNode(node) {
+      if ('clipboard' in navigator) {
+        // eslint-disable-next-line flowtype/no-flow-fix-me-comments
+        // $FlowFixMe Clipboard is not defined in Flow yet.
+        return navigator.clipboard.writeText(node.textContent);
+      }
+
+      const selection = getSelection();
+
+      if (selection == null) {
+        return Promise.reject(new Error());
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+      const range = document.createRange();
+      range.selectNodeContents(node);
+      selection.addRange(range);
+      document.execCommand('copy');
+      selection.removeAllRanges();
+      return Promise.resolve();
+    }
+    function copyText(text) {
+      if ('clipboard' in navigator) {
+        // eslint-disable-next-line flowtype/no-flow-fix-me-comments
+        // $FlowFixMe Clipboard is not defined in Flow yet.
+        return navigator.clipboard.writeText(text);
+      }
+
+      const body = document.body;
+
+      if (!body) {
+        return Promise.reject(new Error());
+      }
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+      const node = createNode(text);
+      body.appendChild(node);
+      copyNode(node);
+      body.removeChild(node);
+      return Promise.resolve();
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+    function copy(button) {
+      const id = button.getAttribute('for');
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+
+      function trigger() {
+        button.dispatchEvent(new CustomEvent('clipboard-copy', {
+          bubbles: true
+        }));
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+      if (text) {
+        copyText(text).then(trigger);
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+        const root = 'getRootNode' in Element.prototype ? button.getRootNode() : button.ownerDocument;
+        if (!(root instanceof Document || 'ShadowRoot' in window && root instanceof ShadowRoot)) return;
+        const node = root.getElementById(id);
+        if (node) copyTarget(node).then(trigger);
+      }
+    }
+
+    function copyTarget(content) {
+      if (content instanceof HTMLInputElement || content instanceof HTMLTextAreaElement) {
+        return copyText(content.value);
+      } else if (content instanceof HTMLAnchorElement && content.hasAttribute('href')) {
+        return copyText(content.href);
+      } else {
+        return copyNode(content);
+      }
+    }
+
+    function clicked(event) {
+      const button = event.currentTarget;
+
+      if (button instanceof HTMLElement) {
+        copy(button);
+      }
+    }
+
+    function keydown(event) {
+      if (event.key === ' ' || event.key === 'Enter') {
+        const button = event.currentTarget;
+
+        if (button instanceof HTMLElement) {
+          event.preventDefault();
+          copy(button);
+        }
+      }
+    }
+
+    function focused(event) {
+      event.currentTarget.addEventListener('keydown', keydown);
+    }
+
+    function blurred(event) {
+      event.currentTarget.removeEventListener('keydown', keydown);
+    }
+
+    class ClipboardCopyElement extends HTMLElement {
+      constructor() {
+        super();
+        this.addEventListener('click', clicked);
+        this.addEventListener('focus', focused);
+        this.addEventListener('blur', blurred);
+      }
+
+      connectedCallback() {
+        if (!this.hasAttribute('tabindex')) {
+          this.setAttribute('tabindex', '0');
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+        if (!this.hasAttribute('role')) {
+          this.setAttribute('role', 'button');
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+
+      get value() {
+        return this.getAttribute('value') || '';
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+
+      set value(text) {
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+      }
+
+    }
+
+    if (!window.customElements.get('clipboard-copy')) {
+      window.ClipboardCopyElement = ClipboardCopyElement;
+      window.customElements.define('clipboard-copy', ClipboardCopyElement);
+    }
+
+    return ClipboardCopyElement;
+
+  }));
+</script>
+<script>
+      document.addEventListener('clipboard-copy', function(event) {
+        const notice = event.target.querySelector('.notice')
+        notice.hidden = false
+        setTimeout(function() {
+          notice.hidden = true
+        }, 1000)
+      })
+</script>
+
+
+
+
+<style type="text/css">
+    
+    pre { line-height: 125%; }
+td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+.highlight-ipynb .hll { background-color: var(--jp-cell-editor-active-background) }
+.highlight-ipynb { background: var(--jp-cell-editor-background); color: var(--jp-mirror-editor-variable-color) }
+.highlight-ipynb .c { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment */
+.highlight-ipynb .err { color: var(--jp-mirror-editor-error-color) } /* Error */
+.highlight-ipynb .k { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword */
+.highlight-ipynb .o { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator */
+.highlight-ipynb .p { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation */
+.highlight-ipynb .ch { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Hashbang */
+.highlight-ipynb .cm { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Multiline */
+.highlight-ipynb .cp { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Preproc */
+.highlight-ipynb .cpf { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.PreprocFile */
+.highlight-ipynb .c1 { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Single */
+.highlight-ipynb .cs { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Special */
+.highlight-ipynb .kc { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Constant */
+.highlight-ipynb .kd { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Declaration */
+.highlight-ipynb .kn { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Namespace */
+.highlight-ipynb .kp { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Pseudo */
+.highlight-ipynb .kr { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Reserved */
+.highlight-ipynb .kt { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Type */
+.highlight-ipynb .m { color: var(--jp-mirror-editor-number-color) } /* Literal.Number */
+.highlight-ipynb .s { color: var(--jp-mirror-editor-string-color) } /* Literal.String */
+.highlight-ipynb .ow { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator.Word */
+.highlight-ipynb .pm { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation.Marker */
+.highlight-ipynb .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */
+.highlight-ipynb .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */
+.highlight-ipynb .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */
+.highlight-ipynb .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */
+.highlight-ipynb .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */
+.highlight-ipynb .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */
+.highlight-ipynb .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */
+.highlight-ipynb .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */
+.highlight-ipynb .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */
+.highlight-ipynb .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */
+.highlight-ipynb .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */
+.highlight-ipynb .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */
+.highlight-ipynb .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */
+.highlight-ipynb .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */
+.highlight-ipynb .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */
+.highlight-ipynb .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */
+.highlight-ipynb .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */
+.highlight-ipynb .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */
+.highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */
+.highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */
+  </style>
+
+
+
+
+<style type="text/css">
+/*
+This file is taken from the built JupyterLab theme.css
+Found on share/nbconvert/templates/lab/static
+Some changes have been made and marked with CHANGE
+*/
+
+.jupyter-wrapper {
+    /* Elevation
+   *
+   * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here:
+   *
+   * https://github.com/material-components/material-components-web
+   * https://material-components-web.appspot.com/elevation.html
+   */
+
+    --jp-shadow-base-lightness: 0;
+    --jp-shadow-umbra-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.2
+    );
+    --jp-shadow-penumbra-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.14
+    );
+    --jp-shadow-ambient-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.12
+    );
+    --jp-elevation-z0: none;
+    --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color),
+        0px 1px 1px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 3px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color),
+        0px 2px 2px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 5px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color),
+        0px 4px 5px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 10px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color),
+        0px 6px 10px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 18px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color),
+        0px 8px 10px 1px var(--jp-shadow-penumbra-color),
+        0px 3px 14px 2px var(--jp-shadow-ambient-color);
+    --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color),
+        0px 12px 17px 2px var(--jp-shadow-penumbra-color),
+        0px 5px 22px 4px var(--jp-shadow-ambient-color);
+    --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color),
+        0px 16px 24px 2px var(--jp-shadow-penumbra-color),
+        0px 6px 30px 5px var(--jp-shadow-ambient-color);
+    --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color),
+        0px 20px 31px 3px var(--jp-shadow-penumbra-color),
+        0px 8px 38px 7px var(--jp-shadow-ambient-color);
+    --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color),
+        0px 24px 38px 3px var(--jp-shadow-penumbra-color),
+        0px 9px 46px 8px var(--jp-shadow-ambient-color);
+
+    /* Borders
+   *
+   * The following variables, specify the visual styling of borders in JupyterLab.
+   */
+
+    --jp-border-width: 1px;
+    --jp-border-color0: var(--md-grey-400);
+    --jp-border-color1: var(--md-grey-400);
+    --jp-border-color2: var(--md-grey-300);
+    --jp-border-color3: var(--md-grey-200);
+    --jp-border-radius: 2px;
+
+    /* UI Fonts
+   *
+   * The UI font CSS variables are used for the typography all of the JupyterLab
+   * user interface elements that are not directly user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-ui-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+    --jp-ui-font-scale-factor: 1.2;
+    --jp-ui-font-size0: 0.83333em;
+    --jp-ui-font-size1: 13px; /* Base font size */
+    --jp-ui-font-size2: 1.2em;
+    --jp-ui-font-size3: 1.44em;
+
+    --jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+
+    /*
+   * Use these font colors against the corresponding main layout colors.
+   * In a light theme, these go from dark to light.
+   */
+
+    /* Defaults use Material Design specification */
+    --jp-ui-font-color0: rgba(0, 0, 0, 1);
+    --jp-ui-font-color1: rgba(0, 0, 0, 0.87);
+    --jp-ui-font-color2: rgba(0, 0, 0, 0.54);
+    --jp-ui-font-color3: rgba(0, 0, 0, 0.38);
+
+    /*
+   * Use these against the brand/accent/warn/error colors.
+   * These will typically go from light to darker, in both a dark and light theme.
+   */
+
+    --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1);
+    --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1);
+    --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7);
+    --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5);
+
+    /* Content Fonts
+   *
+   * Content font variables are used for typography of user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-content-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
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+    --jp-content-font-scale-factor: 1.2;
+    --jp-content-font-size0: 0.83333em;
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+    --jp-content-font-size2: 1.2em;
+    --jp-content-font-size3: 1.44em;
+    --jp-content-font-size4: 1.728em;
+    --jp-content-font-size5: 2.0736em;
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-content-presentation-font-size1: 17px;
+
+    --jp-content-heading-line-height: 1;
+    --jp-content-heading-margin-top: 1.2em;
+    --jp-content-heading-margin-bottom: 0.8em;
+    --jp-content-heading-font-weight: 500;
+
+    /* Defaults use Material Design specification */
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+    --jp-content-font-color1: rgba(0, 0, 0, 0.87);
+    --jp-content-font-color2: rgba(0, 0, 0, 0.54);
+    --jp-content-font-color3: rgba(0, 0, 0, 0.38);
+
+    --jp-content-link-color: var(--md-blue-700);
+
+    --jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
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+
+    /*
+   * Code Fonts
+   *
+   * Code font variables are used for typography of code and other monospaces content.
+   */
+
+    --jp-code-font-size: 13px;
+    --jp-code-line-height: 1.3077; /* 17px for 13px base */
+    --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */
+    --jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono",
+        monospace;
+    --jp-code-font-family: var(--jp-code-font-family-default);
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-code-presentation-font-size: 16px;
+
+    /* may need to tweak cursor width if you change font size */
+    --jp-code-cursor-width0: 1.4px;
+    --jp-code-cursor-width1: 2px;
+    --jp-code-cursor-width2: 4px;
+
+    /* Layout
+   *
+   * The following are the main layout colors use in JupyterLab. In a light
+   * theme these would go from light to dark.
+   */
+
+    --jp-layout-color0: white;
+    --jp-layout-color1: white;
+    --jp-layout-color2: var(--md-grey-200);
+    --jp-layout-color3: var(--md-grey-400);
+    --jp-layout-color4: var(--md-grey-600);
+
+    /* Inverse Layout
+   *
+   * The following are the inverse layout colors use in JupyterLab. In a light
+   * theme these would go from dark to light.
+   */
+
+    --jp-inverse-layout-color0: #111111;
+    --jp-inverse-layout-color1: var(--md-grey-900);
+    --jp-inverse-layout-color2: var(--md-grey-800);
+    --jp-inverse-layout-color3: var(--md-grey-700);
+    --jp-inverse-layout-color4: var(--md-grey-600);
+
+    /* Brand/accent */
+
+    --jp-brand-color0: var(--md-blue-900);
+    --jp-brand-color1: var(--md-blue-700);
+    --jp-brand-color2: var(--md-blue-300);
+    --jp-brand-color3: var(--md-blue-100);
+    --jp-brand-color4: var(--md-blue-50);
+
+    --jp-accent-color0: var(--md-green-900);
+    --jp-accent-color1: var(--md-green-700);
+    --jp-accent-color2: var(--md-green-300);
+    --jp-accent-color3: var(--md-green-100);
+
+    /* State colors (warn, error, success, info) */
+
+    --jp-warn-color0: var(--md-orange-900);
+    --jp-warn-color1: var(--md-orange-700);
+    --jp-warn-color2: var(--md-orange-300);
+    --jp-warn-color3: var(--md-orange-100);
+
+    --jp-error-color0: var(--md-red-900);
+    --jp-error-color1: var(--md-red-700);
+    --jp-error-color2: var(--md-red-300);
+    --jp-error-color3: var(--md-red-100);
+
+    --jp-success-color0: var(--md-green-900);
+    --jp-success-color1: var(--md-green-700);
+    --jp-success-color2: var(--md-green-300);
+    --jp-success-color3: var(--md-green-100);
+
+    --jp-info-color0: var(--md-cyan-900);
+    --jp-info-color1: var(--md-cyan-700);
+    --jp-info-color2: var(--md-cyan-300);
+    --jp-info-color3: var(--md-cyan-100);
+
+    /* Cell specific styles */
+
+    --jp-cell-padding: 5px;
+
+    --jp-cell-collapser-width: 8px;
+    --jp-cell-collapser-min-height: 20px;
+    --jp-cell-collapser-not-active-hover-opacity: 0.6;
+
+    --jp-cell-editor-background: var(--md-grey-100);
+    --jp-cell-editor-border-color: var(--md-grey-300);
+    --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-cell-editor-active-background: var(--jp-layout-color0);
+    --jp-cell-editor-active-border-color: var(--jp-brand-color1);
+
+    --jp-cell-prompt-width: 64px;
+    --jp-cell-prompt-font-family: var(--jp-code-font-family-default);
+    --jp-cell-prompt-letter-spacing: 0px;
+    --jp-cell-prompt-opacity: 1;
+    --jp-cell-prompt-not-active-opacity: 0.5;
+    --jp-cell-prompt-not-active-font-color: var(--md-grey-700);
+    /* A custom blend of MD grey and blue 600
+   * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */
+    --jp-cell-inprompt-font-color: #307fc1;
+    /* A custom blend of MD grey and orange 600
+   * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */
+    --jp-cell-outprompt-font-color: #bf5b3d;
+
+    /* Notebook specific styles */
+
+    --jp-notebook-padding: 10px;
+    --jp-notebook-select-background: var(--jp-layout-color1);
+    --jp-notebook-multiselected-color: var(--md-blue-50);
+
+    /* The scroll padding is calculated to fill enough space at the bottom of the
+  notebook to show one single-line cell (with appropriate padding) at the top
+  when the notebook is scrolled all the way to the bottom. We also subtract one
+  pixel so that no scrollbar appears if we have just one single-line cell in the
+  notebook. This padding is to enable a 'scroll past end' feature in a notebook.
+  */
+    --jp-notebook-scroll-padding: calc(
+        100% - var(--jp-code-font-size) * var(--jp-code-line-height) -
+            var(--jp-code-padding) - var(--jp-cell-padding) - 1px
+    );
+
+    /* Rendermime styles */
+
+    --jp-rendermime-error-background: #fdd;
+    --jp-rendermime-table-row-background: var(--md-grey-100);
+    --jp-rendermime-table-row-hover-background: var(--md-light-blue-50);
+
+    /* Dialog specific styles */
+
+    --jp-dialog-background: rgba(0, 0, 0, 0.25);
+
+    /* Console specific styles */
+
+    --jp-console-padding: 10px;
+
+    /* Toolbar specific styles */
+
+    --jp-toolbar-border-color: var(--jp-border-color1);
+    --jp-toolbar-micro-height: 8px;
+    --jp-toolbar-background: var(--jp-layout-color1);
+    --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24);
+    --jp-toolbar-header-margin: 4px 4px 0px 4px;
+    --jp-toolbar-active-background: var(--md-grey-300);
+
+    /* Statusbar specific styles */
+
+    --jp-statusbar-height: 24px;
+
+    /* Input field styles */
+
+    --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-input-active-background: var(--jp-layout-color1);
+    --jp-input-hover-background: var(--jp-layout-color1);
+    --jp-input-background: var(--md-grey-100);
+    --jp-input-border-color: var(--jp-border-color1);
+    --jp-input-active-border-color: var(--jp-brand-color1);
+    --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3);
+
+    /* General editor styles */
+
+    --jp-editor-selected-background: #d9d9d9;
+    --jp-editor-selected-focused-background: #d7d4f0;
+    --jp-editor-cursor-color: var(--jp-ui-font-color0);
+
+    /* Code mirror specific styles */
+
+    --jp-mirror-editor-keyword-color: #008000;
+    --jp-mirror-editor-atom-color: #88f;
+    --jp-mirror-editor-number-color: #080;
+    --jp-mirror-editor-def-color: #00f;
+    --jp-mirror-editor-variable-color: var(--md-grey-900);
+    --jp-mirror-editor-variable-2-color: #05a;
+    --jp-mirror-editor-variable-3-color: #085;
+    --jp-mirror-editor-punctuation-color: #05a;
+    --jp-mirror-editor-property-color: #05a;
+    --jp-mirror-editor-operator-color: #aa22ff;
+    --jp-mirror-editor-comment-color: #408080;
+    --jp-mirror-editor-string-color: #ba2121;
+    --jp-mirror-editor-string-2-color: #708;
+    --jp-mirror-editor-meta-color: #aa22ff;
+    --jp-mirror-editor-qualifier-color: #555;
+    --jp-mirror-editor-builtin-color: #008000;
+    --jp-mirror-editor-bracket-color: #997;
+    --jp-mirror-editor-tag-color: #170;
+    --jp-mirror-editor-attribute-color: #00c;
+    --jp-mirror-editor-header-color: blue;
+    --jp-mirror-editor-quote-color: #090;
+    --jp-mirror-editor-link-color: #00c;
+    --jp-mirror-editor-error-color: #f00;
+    --jp-mirror-editor-hr-color: #999;
+
+    /* Vega extension styles */
+
+    --jp-vega-background: white;
+
+    /* Sidebar-related styles */
+
+    --jp-sidebar-min-width: 250px;
+
+    /* Search-related styles */
+
+    --jp-search-toggle-off-opacity: 0.5;
+    --jp-search-toggle-hover-opacity: 0.8;
+    --jp-search-toggle-on-opacity: 1;
+    --jp-search-selected-match-background-color: rgb(245, 200, 0);
+    --jp-search-selected-match-color: black;
+    --jp-search-unselected-match-background-color: var(
+        --jp-inverse-layout-color0
+    );
+    --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);
+
+    /* Icon colors that work well with light or dark backgrounds */
+    --jp-icon-contrast-color0: var(--md-purple-600);
+    --jp-icon-contrast-color1: var(--md-green-600);
+    --jp-icon-contrast-color2: var(--md-pink-600);
+    --jp-icon-contrast-color3: var(--md-blue-600);
+}
+
+[data-md-color-scheme="slate"] .jupyter-wrapper {
+    /* Elevation
+   *
+   * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here:
+   *
+   * https://github.com/material-components/material-components-web
+   * https://material-components-web.appspot.com/elevation.html
+   */
+
+    /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout
+   * colors used in the theme as well. */
+    --jp-shadow-base-lightness: 32;
+    --jp-shadow-umbra-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.2
+    );
+    --jp-shadow-penumbra-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.14
+    );
+    --jp-shadow-ambient-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.12
+    );
+    --jp-elevation-z0: none;
+    --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color),
+        0px 1px 1px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 3px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color),
+        0px 2px 2px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 5px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color),
+        0px 4px 5px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 10px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color),
+        0px 6px 10px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 18px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color),
+        0px 8px 10px 1px var(--jp-shadow-penumbra-color),
+        0px 3px 14px 2px var(--jp-shadow-ambient-color);
+    --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color),
+        0px 12px 17px 2px var(--jp-shadow-penumbra-color),
+        0px 5px 22px 4px var(--jp-shadow-ambient-color);
+    --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color),
+        0px 16px 24px 2px var(--jp-shadow-penumbra-color),
+        0px 6px 30px 5px var(--jp-shadow-ambient-color);
+    --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color),
+        0px 20px 31px 3px var(--jp-shadow-penumbra-color),
+        0px 8px 38px 7px var(--jp-shadow-ambient-color);
+    --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color),
+        0px 24px 38px 3px var(--jp-shadow-penumbra-color),
+        0px 9px 46px 8px var(--jp-shadow-ambient-color);
+
+    /* Borders
+   *
+   * The following variables, specify the visual styling of borders in JupyterLab.
+   */
+
+    --jp-border-width: 1px;
+    --jp-border-color0: var(--md-grey-700);
+    --jp-border-color1: var(--md-grey-700);
+    --jp-border-color2: var(--md-grey-800);
+    --jp-border-color3: var(--md-grey-900);
+    --jp-border-radius: 2px;
+
+    /* UI Fonts
+   *
+   * The UI font CSS variables are used for the typography all of the JupyterLab
+   * user interface elements that are not directly user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-ui-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+    --jp-ui-font-scale-factor: 1.2;
+    --jp-ui-font-size0: 0.83333em;
+    --jp-ui-font-size1: 13px; /* Base font size */
+    --jp-ui-font-size2: 1.2em;
+    --jp-ui-font-size3: 1.44em;
+
+    --jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+
+    /*
+   * Use these font colors against the corresponding main layout colors.
+   * In a light theme, these go from dark to light.
+   */
+
+    /* Defaults use Material Design specification */
+    --jp-ui-font-color0: rgba(255, 255, 255, 1);
+    --jp-ui-font-color1: rgba(255, 255, 255, 0.87);
+    --jp-ui-font-color2: rgba(255, 255, 255, 0.54);
+    --jp-ui-font-color3: rgba(255, 255, 255, 0.38);
+
+    /*
+   * Use these against the brand/accent/warn/error colors.
+   * These will typically go from light to darker, in both a dark and light theme.
+   */
+
+    --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1);
+    --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8);
+    --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5);
+    --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3);
+
+    /* Content Fonts
+   *
+   * Content font variables are used for typography of user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-content-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+    --jp-content-line-height: 1.6;
+    --jp-content-font-scale-factor: 1.2;
+    --jp-content-font-size0: 0.83333em;
+    --jp-content-font-size1: 14px; /* Base font size */
+    --jp-content-font-size2: 1.2em;
+    --jp-content-font-size3: 1.44em;
+    --jp-content-font-size4: 1.728em;
+    --jp-content-font-size5: 2.0736em;
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-content-presentation-font-size1: 17px;
+
+    --jp-content-heading-line-height: 1;
+    --jp-content-heading-margin-top: 1.2em;
+    --jp-content-heading-margin-bottom: 0.8em;
+    --jp-content-heading-font-weight: 500;
+
+    /* Defaults use Material Design specification */
+    --jp-content-font-color0: rgba(255, 255, 255, 1);
+    --jp-content-font-color1: rgba(255, 255, 255, 1);
+    --jp-content-font-color2: rgba(255, 255, 255, 0.7);
+    --jp-content-font-color3: rgba(255, 255, 255, 0.5);
+
+    --jp-content-link-color: var(--md-blue-300);
+
+    --jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+
+    /*
+   * Code Fonts
+   *
+   * Code font variables are used for typography of code and other monospaces content.
+   */
+
+    --jp-code-font-size: 13px;
+    --jp-code-line-height: 1.3077; /* 17px for 13px base */
+    --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */
+    --jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono",
+        monospace;
+    --jp-code-font-family: var(--jp-code-font-family-default);
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-code-presentation-font-size: 16px;
+
+    /* may need to tweak cursor width if you change font size */
+    --jp-code-cursor-width0: 1.4px;
+    --jp-code-cursor-width1: 2px;
+    --jp-code-cursor-width2: 4px;
+
+    /* Layout
+   *
+   * The following are the main layout colors use in JupyterLab. In a light
+   * theme these would go from light to dark.
+   */
+
+    --jp-layout-color0: #111111;
+    --jp-layout-color1: var(--md-grey-900);
+    --jp-layout-color2: var(--md-grey-800);
+    --jp-layout-color3: var(--md-grey-700);
+    --jp-layout-color4: var(--md-grey-600);
+
+    /* Inverse Layout
+   *
+   * The following are the inverse layout colors use in JupyterLab. In a light
+   * theme these would go from dark to light.
+   */
+
+    --jp-inverse-layout-color0: white;
+    --jp-inverse-layout-color1: white;
+    --jp-inverse-layout-color2: var(--md-grey-200);
+    --jp-inverse-layout-color3: var(--md-grey-400);
+    --jp-inverse-layout-color4: var(--md-grey-600);
+
+    /* Brand/accent */
+
+    --jp-brand-color0: var(--md-blue-700);
+    --jp-brand-color1: var(--md-blue-500);
+    --jp-brand-color2: var(--md-blue-300);
+    --jp-brand-color3: var(--md-blue-100);
+    --jp-brand-color4: var(--md-blue-50);
+
+    --jp-accent-color0: var(--md-green-700);
+    --jp-accent-color1: var(--md-green-500);
+    --jp-accent-color2: var(--md-green-300);
+    --jp-accent-color3: var(--md-green-100);
+
+    /* State colors (warn, error, success, info) */
+
+    --jp-warn-color0: var(--md-orange-700);
+    --jp-warn-color1: var(--md-orange-500);
+    --jp-warn-color2: var(--md-orange-300);
+    --jp-warn-color3: var(--md-orange-100);
+
+    --jp-error-color0: var(--md-red-700);
+    --jp-error-color1: var(--md-red-500);
+    --jp-error-color2: var(--md-red-300);
+    --jp-error-color3: var(--md-red-100);
+
+    --jp-success-color0: var(--md-green-700);
+    --jp-success-color1: var(--md-green-500);
+    --jp-success-color2: var(--md-green-300);
+    --jp-success-color3: var(--md-green-100);
+
+    --jp-info-color0: var(--md-cyan-700);
+    --jp-info-color1: var(--md-cyan-500);
+    --jp-info-color2: var(--md-cyan-300);
+    --jp-info-color3: var(--md-cyan-100);
+
+    /* Cell specific styles */
+
+    --jp-cell-padding: 5px;
+
+    --jp-cell-collapser-width: 8px;
+    --jp-cell-collapser-min-height: 20px;
+    --jp-cell-collapser-not-active-hover-opacity: 0.6;
+
+    --jp-cell-editor-background: var(--jp-layout-color1);
+    --jp-cell-editor-border-color: var(--md-grey-700);
+    --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-cell-editor-active-background: var(--jp-layout-color0);
+    --jp-cell-editor-active-border-color: var(--jp-brand-color1);
+
+    --jp-cell-prompt-width: 64px;
+    --jp-cell-prompt-font-family: var(--jp-code-font-family-default);
+    --jp-cell-prompt-letter-spacing: 0px;
+    --jp-cell-prompt-opacity: 1;
+    --jp-cell-prompt-not-active-opacity: 1;
+    --jp-cell-prompt-not-active-font-color: var(--md-grey-300);
+
+    /* A custom blend of MD grey and blue 600
+   * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */
+    --jp-cell-inprompt-font-color: #307fc1;
+    /* A custom blend of MD grey and orange 600
+   * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */
+    --jp-cell-outprompt-font-color: #bf5b3d;
+
+    /* Notebook specific styles */
+
+    --jp-notebook-padding: 10px;
+    --jp-notebook-select-background: var(--jp-layout-color1);
+    --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24);
+
+    /* The scroll padding is calculated to fill enough space at the bottom of the
+  notebook to show one single-line cell (with appropriate padding) at the top
+  when the notebook is scrolled all the way to the bottom. We also subtract one
+  pixel so that no scrollbar appears if we have just one single-line cell in the
+  notebook. This padding is to enable a 'scroll past end' feature in a notebook.
+  */
+    --jp-notebook-scroll-padding: calc(
+        100% - var(--jp-code-font-size) * var(--jp-code-line-height) -
+            var(--jp-code-padding) - var(--jp-cell-padding) - 1px
+    );
+
+    /* Rendermime styles */
+
+    --jp-rendermime-error-background: rgba(244, 67, 54, 0.28);
+    --jp-rendermime-table-row-background: var(--md-grey-900);
+    --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2);
+
+    /* Dialog specific styles */
+
+    --jp-dialog-background: rgba(0, 0, 0, 0.6);
+
+    /* Console specific styles */
+
+    --jp-console-padding: 10px;
+
+    /* Toolbar specific styles */
+
+    --jp-toolbar-border-color: var(--jp-border-color2);
+    --jp-toolbar-micro-height: 8px;
+    --jp-toolbar-background: var(--jp-layout-color1);
+    --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8);
+    --jp-toolbar-header-margin: 4px 4px 0px 4px;
+    --jp-toolbar-active-background: var(--jp-layout-color0);
+
+    /* Statusbar specific styles */
+
+    --jp-statusbar-height: 24px;
+
+    /* Input field styles */
+
+    --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-input-active-background: var(--jp-layout-color0);
+    --jp-input-hover-background: var(--jp-layout-color2);
+    --jp-input-background: var(--md-grey-800);
+    --jp-input-border-color: var(--jp-border-color1);
+    --jp-input-active-border-color: var(--jp-brand-color1);
+    --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3);
+
+    /* General editor styles */
+
+    --jp-editor-selected-background: var(--jp-layout-color2);
+    --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24);
+    --jp-editor-cursor-color: var(--jp-ui-font-color0);
+
+    /* Code mirror specific styles */
+
+    --jp-mirror-editor-keyword-color: var(--md-green-500);
+    --jp-mirror-editor-atom-color: var(--md-blue-300);
+    --jp-mirror-editor-number-color: var(--md-green-400);
+    --jp-mirror-editor-def-color: var(--md-blue-600);
+    --jp-mirror-editor-variable-color: var(--md-grey-300);
+    --jp-mirror-editor-variable-2-color: var(--md-blue-400);
+    --jp-mirror-editor-variable-3-color: var(--md-green-600);
+    --jp-mirror-editor-punctuation-color: var(--md-blue-400);
+    --jp-mirror-editor-property-color: var(--md-blue-400);
+    --jp-mirror-editor-operator-color: #aa22ff;
+    --jp-mirror-editor-comment-color: #408080;
+    --jp-mirror-editor-string-color: #ff7070;
+    --jp-mirror-editor-string-2-color: var(--md-purple-300);
+    --jp-mirror-editor-meta-color: #aa22ff;
+    --jp-mirror-editor-qualifier-color: #555;
+    --jp-mirror-editor-builtin-color: var(--md-green-600);
+    --jp-mirror-editor-bracket-color: #997;
+    --jp-mirror-editor-tag-color: var(--md-green-700);
+    --jp-mirror-editor-attribute-color: var(--md-blue-700);
+    --jp-mirror-editor-header-color: var(--md-blue-500);
+    --jp-mirror-editor-quote-color: var(--md-green-300);
+    --jp-mirror-editor-link-color: var(--md-blue-700);
+    --jp-mirror-editor-error-color: #f00;
+    --jp-mirror-editor-hr-color: #999;
+
+    /* Vega extension styles */
+
+    --jp-vega-background: var(--md-grey-400);
+
+    /* Sidebar-related styles */
+
+    --jp-sidebar-min-width: 250px;
+
+    /* Search-related styles */
+
+    --jp-search-toggle-off-opacity: 0.6;
+    --jp-search-toggle-hover-opacity: 0.8;
+    --jp-search-toggle-on-opacity: 1;
+    --jp-search-selected-match-background-color: rgb(255, 225, 0);
+    --jp-search-selected-match-color: black;
+    --jp-search-unselected-match-background-color: var(
+        --jp-inverse-layout-color0
+    );
+    --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);
+
+    /* scrollbar related styles. Supports every browser except Edge. */
+
+    /* colors based on JetBrain's Darcula theme */
+
+    --jp-scrollbar-background-color: #3f4244;
+    --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */
+
+    --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */
+
+    /* hacks for setting the thumb shape. These do nothing in Firefox */
+
+    --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */
+    --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */
+
+    /* Icon colors that work well with light or dark backgrounds */
+    --jp-icon-contrast-color0: var(--md-purple-600);
+    --jp-icon-contrast-color1: var(--md-green-600);
+    --jp-icon-contrast-color2: var(--md-pink-600);
+    --jp-icon-contrast-color3: var(--md-blue-600);
+}
+
+:root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: #7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: #ff5722;--md-deep-orange-600: #f4511e;--md-deep-orange-700: #e64a19;--md-deep-orange-800: #d84315;--md-deep-orange-900: #bf360c;--md-deep-orange-A100: #ff9e80;--md-deep-orange-A200: #ff6e40;--md-deep-orange-A400: #ff3d00;--md-deep-orange-A700: #dd2c00;--md-brown-50: #efebe9;--md-brown-100: #d7ccc8;--md-brown-200: #bcaaa4;--md-brown-300: #a1887f;--md-brown-400: #8d6e63;--md-brown-500: #795548;--md-brown-600: #6d4c41;--md-brown-700: #5d4037;--md-brown-800: #4e342e;--md-brown-900: 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+
+Copyright 2015-present Palantir Technologies, Inc. All rights reserved.
+Licensed under the Apache License, Version 2.0.
+
+*//*!
+
+Copyright 2017-present Palantir Technologies, Inc. All rights reserved.
+Licensed under the Apache License, Version 2.0.
+
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+    <!-- MathJax configuration -->
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+    init_mathjax = function() {
+        if (window.MathJax) {
+        // MathJax loaded
+            MathJax.Hub.Config({
+                TeX: {
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+
+            MathJax.Hub.Queue(["Typeset", MathJax.Hub]);
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+    }
+    init_mathjax();
+    </script>
+    <!-- End of mathjax configuration --><div class="jupyter-wrapper"><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[1]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
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+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">fsspec</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">struct</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">obspy</span>
+</pre></div>
+<div id="cell-1" class="clipboard-copy-txt">import fsspec
+import numpy as np
+import struct
+import matplotlib.pyplot as plt
+import obspy</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[2]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-2">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">def</span> <span class="nf">read_PASSCAL_segy</span><span class="p">(</span><span class="n">fid</span><span class="p">,</span> <span class="n">nTraces</span><span class="o">=</span><span class="mi">1250</span><span class="p">,</span> <span class="n">nSample</span><span class="o">=</span><span class="mi">900000</span><span class="p">,</span> <span class="n">TraceOff</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Function to read PASSCAL segy raw data</span>
+<span class="sd">    For Ridgecrest data, there are 1250 channels in total,</span>
+<span class="sd">    Sampling rate is 250 Hz so for one hour data: 250 * 3600 samples</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">fs</span> <span class="o">=</span> <span class="n">nSample</span><span class="o">/</span> <span class="mi">3600</span> <span class="c1"># sampling rate</span>
+    <span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">nTraces</span><span class="p">,</span> <span class="n">nSample</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+    <span class="n">gzFile</span> <span class="o">=</span> <span class="kc">False</span>
+    <span class="c1"># if infile.split(&quot;.&quot;)[-1] == &quot;segy&quot;:</span>
+    <span class="c1">#     fid = open(infile, &#39;rb&#39;)</span>
+    
+    <span class="n">fid</span><span class="o">.</span><span class="n">seek</span><span class="p">(</span><span class="mi">3600</span><span class="p">)</span>
+    <span class="c1"># Skipping traces if necessary</span>
+    <span class="n">fid</span><span class="o">.</span><span class="n">seek</span><span class="p">(</span><span class="n">TraceOff</span><span class="o">*</span><span class="p">(</span><span class="mi">240</span><span class="o">+</span><span class="n">nSample</span><span class="o">*</span><span class="mi">4</span><span class="p">),</span><span class="mi">1</span><span class="p">)</span>
+    <span class="c1"># Looping over traces</span>
+    <span class="k">for</span> <span class="n">ii</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">nTraces</span><span class="p">):</span>
+        <span class="n">fid</span><span class="o">.</span><span class="n">seek</span><span class="p">(</span><span class="mi">240</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">gzFile</span><span class="p">:</span>
+            <span class="c1"># np.fromfile does not work on gzip file</span>
+            <span class="n">BinDataBuffer</span> <span class="o">=</span> <span class="n">fid</span><span class="o">.</span><span class="n">read</span><span class="p">(</span><span class="n">nSample</span><span class="o">*</span><span class="mi">4</span><span class="p">)</span> <span class="c1"># read binary bytes from file</span>
+            <span class="n">data</span><span class="p">[</span><span class="n">ii</span><span class="p">,</span> <span class="p">:]</span> <span class="o">=</span> <span class="n">struct</span><span class="o">.</span><span class="n">unpack_from</span><span class="p">(</span><span class="s2">&quot;&gt;&quot;</span><span class="o">+</span><span class="p">(</span><span class="s1">&#39;f&#39;</span><span class="p">)</span><span class="o">*</span><span class="n">nSample</span><span class="p">,</span> <span class="n">BinDataBuffer</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="c1"># data[ii, :] = np.fromfile(fid, dtype=np.float32, count=nSample)</span>
+            
+            <span class="nb">bytes</span> <span class="o">=</span> <span class="n">fid</span><span class="o">.</span><span class="n">read</span><span class="p">(</span><span class="n">nSample</span><span class="o">*</span><span class="mi">4</span><span class="p">)</span>
+            <span class="n">data</span><span class="p">[</span><span class="n">ii</span><span class="p">,</span> <span class="p">:]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">frombuffer</span><span class="p">(</span><span class="nb">bytes</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+
+    <span class="n">fid</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
+
+    <span class="c1"># Convert the phase-shift to strain (in nanostrain)</span>
+    <span class="n">Ridgecrest_conversion_factor</span> <span class="o">=</span> <span class="mf">1550.12</span> <span class="o">/</span> <span class="p">(</span><span class="mf">0.78</span> <span class="o">*</span> <span class="mi">4</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="mf">1.46</span> <span class="o">*</span> <span class="mi">8</span><span class="p">)</span>
+    <span class="n">data</span> <span class="o">=</span> <span class="n">data</span> <span class="o">*</span> <span class="n">Ridgecrest_conversion_factor</span>
+
+    <span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">gradient</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="n">fs</span>
+
+    <span class="n">das_time</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="o">*</span> <span class="mi">1</span> <span class="o">/</span> <span class="n">fs</span>
+
+    <span class="k">return</span> <span class="n">data</span><span class="p">,</span> <span class="n">das_time</span>
+</pre></div>
+<div id="cell-2" class="clipboard-copy-txt">def read_PASSCAL_segy(fid, nTraces=1250, nSample=900000, TraceOff=0):
+    """Function to read PASSCAL segy raw data
+    For Ridgecrest data, there are 1250 channels in total,
+    Sampling rate is 250 Hz so for one hour data: 250 * 3600 samples
+    """
+    fs = nSample/ 3600 # sampling rate
+    data = np.zeros((nTraces, nSample), dtype=np.float32)
+    gzFile = False
+    # if infile.split(".")[-1] == "segy":
+    #     fid = open(infile, 'rb')
+    
+    fid.seek(3600)
+    # Skipping traces if necessary
+    fid.seek(TraceOff*(240+nSample*4),1)
+    # Looping over traces
+    for ii in range(nTraces):
+        fid.seek(240, 1)
+        if gzFile:
+            # np.fromfile does not work on gzip file
+            BinDataBuffer = fid.read(nSample*4) # read binary bytes from file
+            data[ii, :] = struct.unpack_from(">"+('f')*nSample, BinDataBuffer)
+        else:
+            # data[ii, :] = np.fromfile(fid, dtype=np.float32, count=nSample)
+            
+            bytes = fid.read(nSample*4)
+            data[ii, :] = np.frombuffer(bytes, dtype=np.float32)
+
+    fid.close()
+
+    # Convert the phase-shift to strain (in nanostrain)
+    Ridgecrest_conversion_factor = 1550.12 / (0.78 * 4 * np.pi * 1.46 * 8)
+    data = data * Ridgecrest_conversion_factor
+
+    data = np.gradient(data, axis=1) * fs
+
+    das_time = np.arange(0, data.shape[1]) * 1 / fs
+
+    return data, das_time</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
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+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
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+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[3]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-3">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">def</span> <span class="nf">plot_cuts</span><span class="p">(</span><span class="n">data</span><span class="p">):</span>
+    <span class="n">cut</span> <span class="o">=</span> <span class="n">data</span><span class="p">[:,</span> <span class="mi">6000</span><span class="p">:</span><span class="mi">12000</span><span class="p">]</span>
+    <span class="n">cut</span> <span class="o">-=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">cut</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">keepdims</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="n">cut</span> <span class="o">/=</span> <span class="n">np</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">cut</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">keepdims</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="n">cut</span> <span class="o">-=</span> <span class="n">np</span><span class="o">.</span><span class="n">median</span><span class="p">(</span><span class="n">cut</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">keepdims</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+    <span class="n">vmax</span> <span class="o">=</span> <span class="mi">1</span>
+    <span class="n">vmin</span> <span class="o">=</span> <span class="o">-</span><span class="n">vmax</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">cut</span><span class="p">,</span> <span class="n">vmin</span><span class="o">=</span><span class="n">vmin</span><span class="p">,</span> <span class="n">vmax</span><span class="o">=</span><span class="n">vmax</span><span class="p">,</span> <span class="n">aspect</span><span class="o">=</span><span class="s1">&#39;auto&#39;</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;seismic&#39;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+<div id="cell-3" class="clipboard-copy-txt">def plot_cuts(data):
+    cut = data[:, 6000:12000]
+    cut -= np.mean(cut, axis=1, keepdims=True)
+    cut /= np.std(cut, axis=1, keepdims=True)
+    cut -= np.median(cut, axis=0, keepdims=True)
+
+    vmax = 1
+    vmin = -vmax
+    plt.figure()
+    plt.imshow(cut, vmin=vmin, vmax=vmax, aspect='auto', cmap='seismic')
+    plt.show()</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-4">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="err">!</span><span class="n">aws</span> <span class="n">s3</span> <span class="n">cp</span> <span class="o">--</span><span class="n">no</span><span class="o">-</span><span class="n">sign</span><span class="o">-</span><span class="n">request</span> <span class="n">s3</span><span class="p">:</span><span class="o">//</span><span class="n">scedc</span><span class="o">-</span><span class="n">pds</span><span class="o">/</span><span class="n">Ridgecrest_DAS</span><span class="o">/</span><span class="n">SEG</span><span class="o">-</span><span class="n">Y</span><span class="o">/</span><span class="n">hourly</span><span class="o">/</span><span class="mf">2020062000.</span><span class="n">segy</span> <span class="o">./</span>
+</pre></div>
+<div id="cell-4" class="clipboard-copy-txt">!aws s3 cp --no-sign-request s3://scedc-pds/Ridgecrest_DAS/SEG-Y/hourly/2020062000.segy ./</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[4]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-5">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">fp</span> <span class="o">=</span> <span class="nb">open</span><span class="p">(</span><span class="s2">&quot;2020062000.segy&quot;</span><span class="p">,</span> <span class="s2">&quot;rb&quot;</span><span class="p">)</span>
+
+<span class="n">data</span><span class="p">,</span> <span class="n">das_time</span> <span class="o">=</span> <span class="n">read_PASSCAL_segy</span><span class="p">(</span><span class="n">fp</span><span class="p">)</span>
+
+<span class="n">plot_cuts</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+</pre></div>
+<div id="cell-5" class="clipboard-copy-txt">fp = open("2020062000.segy", "rb")
+
+data, das_time = read_PASSCAL_segy(fp)
+
+plot_cuts(data)</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+
+
+<div class="jp-RenderedImage jp-OutputArea-output ">
+<img 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+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">fs</span> <span class="o">=</span> <span class="n">fsspec</span><span class="o">.</span><span class="n">filesystem</span><span class="p">(</span><span class="s1">&#39;s3&#39;</span><span class="p">,</span> <span class="n">anon</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+<span class="k">for</span> <span class="n">file</span> <span class="ow">in</span> <span class="n">fs</span><span class="o">.</span><span class="n">ls</span><span class="p">(</span><span class="s2">&quot;s3://scedc-pds/Ridgecrest_DAS/SEG-Y/hourly&quot;</span><span class="p">):</span>
+    
+    <span class="nb">print</span><span class="p">(</span><span class="n">file</span><span class="p">)</span>
+    
+    <span class="n">fp</span> <span class="o">=</span> <span class="n">fs</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="n">file</span><span class="p">,</span> <span class="s2">&quot;rb&quot;</span><span class="p">)</span>
+
+    <span class="n">data</span><span class="p">,</span> <span class="n">time</span> <span class="o">=</span> <span class="n">read_PASSCAL_segy</span><span class="p">(</span><span class="n">fp</span><span class="p">)</span>
+
+    <span class="n">plot_cuts</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+    <span class="k">break</span>
+</pre></div>
+<div id="cell-6" class="clipboard-copy-txt">fs = fsspec.filesystem('s3', anon=True)
+
+for file in fs.ls("s3://scedc-pds/Ridgecrest_DAS/SEG-Y/hourly"):
+    
+    print(file)
+    
+    fp = fs.open(file, "rb")
+
+    data, time = read_PASSCAL_segy(fp)
+
+    plot_cuts(data)
+
+    break</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
+<pre>scedc-pds/Ridgecrest_DAS/SEG-Y/hourly/2020062000.segy
+</pre>
+</div>
+</div>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+
+
+<div class="jp-RenderedImage jp-OutputArea-output ">
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+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">fs</span> <span class="o">=</span> <span class="n">fsspec</span><span class="o">.</span><span class="n">filesystem</span><span class="p">(</span><span class="s1">&#39;gcs&#39;</span><span class="p">)</span>
+</pre></div>
+<div id="cell-7" class="clipboard-copy-txt">fs = fsspec.filesystem('gcs')</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[9]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-8">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">fs</span><span class="o">.</span><span class="n">ls</span><span class="p">(</span><span class="s2">&quot;quakeflow_hawaii/event_data_1986_2009&quot;</span><span class="p">)</span>
+</pre></div>
+<div id="cell-8" class="clipboard-copy-txt">fs.ls("quakeflow_hawaii/event_data_1986_2009")</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[9]:</div>
+
+
+
+
+<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
+<pre>[&#39;quakeflow_hawaii/event_data_1986_2009/&#39;,
+ &#39;quakeflow_hawaii/event_data_1986_2009/1986SAC.tar&#39;,
+ &#39;quakeflow_hawaii/event_data_1986_2009/1987SAC.tar&#39;,
+ &#39;quakeflow_hawaii/event_data_1986_2009/1988SAC.tar&#39;,
+ &#39;quakeflow_hawaii/event_data_1986_2009/1989SAC.tar&#39;,
+ &#39;quakeflow_hawaii/event_data_1986_2009/1990SAC.tar&#39;,
+ &#39;quakeflow_hawaii/event_data_1986_2009/1991SAC.tar&#39;,
+ &#39;quakeflow_hawaii/event_data_1986_2009/1992SAC.tar&#39;,
+ &#39;quakeflow_hawaii/event_data_1986_2009/1993SAC.tar&#39;,
+ &#39;quakeflow_hawaii/event_data_1986_2009/1994SAC.tar&#39;,
+ &#39;quakeflow_hawaii/event_data_1986_2009/1995SAC.tar&#39;,
+ &#39;quakeflow_hawaii/event_data_1986_2009/1996SAC.tar&#39;,
+ &#39;quakeflow_hawaii/event_data_1986_2009/1997SAC.tar&#39;,
+ &#39;quakeflow_hawaii/event_data_1986_2009/1998SAC.tar&#39;,
+ &#39;quakeflow_hawaii/event_data_1986_2009/1999SAC.tar&#39;,
+ &#39;quakeflow_hawaii/event_data_1986_2009/2000SAC.tar&#39;,
+ &#39;quakeflow_hawaii/event_data_1986_2009/2001SAC.tar&#39;,
+ &#39;quakeflow_hawaii/event_data_1986_2009/2002SAC.tar&#39;,
+ &#39;quakeflow_hawaii/event_data_1986_2009/2003SAC.tar&#39;,
+ &#39;quakeflow_hawaii/event_data_1986_2009/2004SAC.tar&#39;,
+ &#39;quakeflow_hawaii/event_data_1986_2009/2005SAC.tar&#39;,
+ &#39;quakeflow_hawaii/event_data_1986_2009/2006SAC.tar&#39;,
+ &#39;quakeflow_hawaii/event_data_1986_2009/2007SAC.tar&#39;,
+ &#39;quakeflow_hawaii/event_data_1986_2009/2008SAC.tar&#39;,
+ &#39;quakeflow_hawaii/event_data_1986_2009/2009SAC.tar&#39;]</pre>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
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+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span>
+</pre></div>
+<div id="cell-9" class="clipboard-copy-txt"></div>
+    
+        </div>
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+
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diff --git a/kubeflow/debug_magnitude/index.html b/kubeflow/debug_magnitude/index.html
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+    }
+
+    function copyNode(node) {
+      if ('clipboard' in navigator) {
+        // eslint-disable-next-line flowtype/no-flow-fix-me-comments
+        // $FlowFixMe Clipboard is not defined in Flow yet.
+        return navigator.clipboard.writeText(node.textContent);
+      }
+
+      const selection = getSelection();
+
+      if (selection == null) {
+        return Promise.reject(new Error());
+      }
+
+      selection.removeAllRanges();
+      const range = document.createRange();
+      range.selectNodeContents(node);
+      selection.addRange(range);
+      document.execCommand('copy');
+      selection.removeAllRanges();
+      return Promise.resolve();
+    }
+    function copyText(text) {
+      if ('clipboard' in navigator) {
+        // eslint-disable-next-line flowtype/no-flow-fix-me-comments
+        // $FlowFixMe Clipboard is not defined in Flow yet.
+        return navigator.clipboard.writeText(text);
+      }
+
+      const body = document.body;
+
+      if (!body) {
+        return Promise.reject(new Error());
+      }
+
+      const node = createNode(text);
+      body.appendChild(node);
+      copyNode(node);
+      body.removeChild(node);
+      return Promise.resolve();
+    }
+
+    function copy(button) {
+      const id = button.getAttribute('for');
+      const text = button.getAttribute('value');
+
+      function trigger() {
+        button.dispatchEvent(new CustomEvent('clipboard-copy', {
+          bubbles: true
+        }));
+      }
+
+      if (text) {
+        copyText(text).then(trigger);
+      } else if (id) {
+        const root = 'getRootNode' in Element.prototype ? button.getRootNode() : button.ownerDocument;
+        if (!(root instanceof Document || 'ShadowRoot' in window && root instanceof ShadowRoot)) return;
+        const node = root.getElementById(id);
+        if (node) copyTarget(node).then(trigger);
+      }
+    }
+
+    function copyTarget(content) {
+      if (content instanceof HTMLInputElement || content instanceof HTMLTextAreaElement) {
+        return copyText(content.value);
+      } else if (content instanceof HTMLAnchorElement && content.hasAttribute('href')) {
+        return copyText(content.href);
+      } else {
+        return copyNode(content);
+      }
+    }
+
+    function clicked(event) {
+      const button = event.currentTarget;
+
+      if (button instanceof HTMLElement) {
+        copy(button);
+      }
+    }
+
+    function keydown(event) {
+      if (event.key === ' ' || event.key === 'Enter') {
+        const button = event.currentTarget;
+
+        if (button instanceof HTMLElement) {
+          event.preventDefault();
+          copy(button);
+        }
+      }
+    }
+
+    function focused(event) {
+      event.currentTarget.addEventListener('keydown', keydown);
+    }
+
+    function blurred(event) {
+      event.currentTarget.removeEventListener('keydown', keydown);
+    }
+
+    class ClipboardCopyElement extends HTMLElement {
+      constructor() {
+        super();
+        this.addEventListener('click', clicked);
+        this.addEventListener('focus', focused);
+        this.addEventListener('blur', blurred);
+      }
+
+      connectedCallback() {
+        if (!this.hasAttribute('tabindex')) {
+          this.setAttribute('tabindex', '0');
+        }
+
+        if (!this.hasAttribute('role')) {
+          this.setAttribute('role', 'button');
+        }
+      }
+
+      get value() {
+        return this.getAttribute('value') || '';
+      }
+
+      set value(text) {
+        this.setAttribute('value', text);
+      }
+
+    }
+
+    if (!window.customElements.get('clipboard-copy')) {
+      window.ClipboardCopyElement = ClipboardCopyElement;
+      window.customElements.define('clipboard-copy', ClipboardCopyElement);
+    }
+
+    return ClipboardCopyElement;
+
+  }));
+</script>
+<script>
+      document.addEventListener('clipboard-copy', function(event) {
+        const notice = event.target.querySelector('.notice')
+        notice.hidden = false
+        setTimeout(function() {
+          notice.hidden = true
+        }, 1000)
+      })
+</script>
+
+
+
+
+<style type="text/css">
+    
+    pre { line-height: 125%; }
+td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+.highlight-ipynb .hll { background-color: var(--jp-cell-editor-active-background) }
+.highlight-ipynb { background: var(--jp-cell-editor-background); color: var(--jp-mirror-editor-variable-color) }
+.highlight-ipynb .c { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment */
+.highlight-ipynb .err { color: var(--jp-mirror-editor-error-color) } /* Error */
+.highlight-ipynb .k { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword */
+.highlight-ipynb .o { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator */
+.highlight-ipynb .p { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation */
+.highlight-ipynb .ch { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Hashbang */
+.highlight-ipynb .cm { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Multiline */
+.highlight-ipynb .cp { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Preproc */
+.highlight-ipynb .cpf { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.PreprocFile */
+.highlight-ipynb .c1 { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Single */
+.highlight-ipynb .cs { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Special */
+.highlight-ipynb .kc { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Constant */
+.highlight-ipynb .kd { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Declaration */
+.highlight-ipynb .kn { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Namespace */
+.highlight-ipynb .kp { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Pseudo */
+.highlight-ipynb .kr { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Reserved */
+.highlight-ipynb .kt { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Type */
+.highlight-ipynb .m { color: var(--jp-mirror-editor-number-color) } /* Literal.Number */
+.highlight-ipynb .s { color: var(--jp-mirror-editor-string-color) } /* Literal.String */
+.highlight-ipynb .ow { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator.Word */
+.highlight-ipynb .pm { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation.Marker */
+.highlight-ipynb .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */
+.highlight-ipynb .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */
+.highlight-ipynb .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */
+.highlight-ipynb .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */
+.highlight-ipynb .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */
+.highlight-ipynb .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */
+.highlight-ipynb .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */
+.highlight-ipynb .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */
+.highlight-ipynb .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */
+.highlight-ipynb .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */
+.highlight-ipynb .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */
+.highlight-ipynb .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */
+.highlight-ipynb .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */
+.highlight-ipynb .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */
+.highlight-ipynb .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */
+.highlight-ipynb .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */
+.highlight-ipynb .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */
+.highlight-ipynb .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */
+.highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */
+.highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */
+  </style>
+
+
+
+
+<style type="text/css">
+/*
+This file is taken from the built JupyterLab theme.css
+Found on share/nbconvert/templates/lab/static
+Some changes have been made and marked with CHANGE
+*/
+
+.jupyter-wrapper {
+    /* Elevation
+   *
+   * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here:
+   *
+   * https://github.com/material-components/material-components-web
+   * https://material-components-web.appspot.com/elevation.html
+   */
+
+    --jp-shadow-base-lightness: 0;
+    --jp-shadow-umbra-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.2
+    );
+    --jp-shadow-penumbra-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.14
+    );
+    --jp-shadow-ambient-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.12
+    );
+    --jp-elevation-z0: none;
+    --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color),
+        0px 1px 1px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 3px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color),
+        0px 2px 2px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 5px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color),
+        0px 4px 5px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 10px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color),
+        0px 6px 10px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 18px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color),
+        0px 8px 10px 1px var(--jp-shadow-penumbra-color),
+        0px 3px 14px 2px var(--jp-shadow-ambient-color);
+    --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color),
+        0px 12px 17px 2px var(--jp-shadow-penumbra-color),
+        0px 5px 22px 4px var(--jp-shadow-ambient-color);
+    --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color),
+        0px 16px 24px 2px var(--jp-shadow-penumbra-color),
+        0px 6px 30px 5px var(--jp-shadow-ambient-color);
+    --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color),
+        0px 20px 31px 3px var(--jp-shadow-penumbra-color),
+        0px 8px 38px 7px var(--jp-shadow-ambient-color);
+    --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color),
+        0px 24px 38px 3px var(--jp-shadow-penumbra-color),
+        0px 9px 46px 8px var(--jp-shadow-ambient-color);
+
+    /* Borders
+   *
+   * The following variables, specify the visual styling of borders in JupyterLab.
+   */
+
+    --jp-border-width: 1px;
+    --jp-border-color0: var(--md-grey-400);
+    --jp-border-color1: var(--md-grey-400);
+    --jp-border-color2: var(--md-grey-300);
+    --jp-border-color3: var(--md-grey-200);
+    --jp-border-radius: 2px;
+
+    /* UI Fonts
+   *
+   * The UI font CSS variables are used for the typography all of the JupyterLab
+   * user interface elements that are not directly user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-ui-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+    --jp-ui-font-scale-factor: 1.2;
+    --jp-ui-font-size0: 0.83333em;
+    --jp-ui-font-size1: 13px; /* Base font size */
+    --jp-ui-font-size2: 1.2em;
+    --jp-ui-font-size3: 1.44em;
+
+    --jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+
+    /*
+   * Use these font colors against the corresponding main layout colors.
+   * In a light theme, these go from dark to light.
+   */
+
+    /* Defaults use Material Design specification */
+    --jp-ui-font-color0: rgba(0, 0, 0, 1);
+    --jp-ui-font-color1: rgba(0, 0, 0, 0.87);
+    --jp-ui-font-color2: rgba(0, 0, 0, 0.54);
+    --jp-ui-font-color3: rgba(0, 0, 0, 0.38);
+
+    /*
+   * Use these against the brand/accent/warn/error colors.
+   * These will typically go from light to darker, in both a dark and light theme.
+   */
+
+    --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1);
+    --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1);
+    --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7);
+    --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5);
+
+    /* Content Fonts
+   *
+   * Content font variables are used for typography of user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-content-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+    --jp-content-line-height: 1.6;
+    --jp-content-font-scale-factor: 1.2;
+    --jp-content-font-size0: 0.83333em;
+    --jp-content-font-size1: 14px; /* Base font size */
+    --jp-content-font-size2: 1.2em;
+    --jp-content-font-size3: 1.44em;
+    --jp-content-font-size4: 1.728em;
+    --jp-content-font-size5: 2.0736em;
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-content-presentation-font-size1: 17px;
+
+    --jp-content-heading-line-height: 1;
+    --jp-content-heading-margin-top: 1.2em;
+    --jp-content-heading-margin-bottom: 0.8em;
+    --jp-content-heading-font-weight: 500;
+
+    /* Defaults use Material Design specification */
+    --jp-content-font-color0: rgba(0, 0, 0, 1);
+    --jp-content-font-color1: rgba(0, 0, 0, 0.87);
+    --jp-content-font-color2: rgba(0, 0, 0, 0.54);
+    --jp-content-font-color3: rgba(0, 0, 0, 0.38);
+
+    --jp-content-link-color: var(--md-blue-700);
+
+    --jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+
+    /*
+   * Code Fonts
+   *
+   * Code font variables are used for typography of code and other monospaces content.
+   */
+
+    --jp-code-font-size: 13px;
+    --jp-code-line-height: 1.3077; /* 17px for 13px base */
+    --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */
+    --jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono",
+        monospace;
+    --jp-code-font-family: var(--jp-code-font-family-default);
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-code-presentation-font-size: 16px;
+
+    /* may need to tweak cursor width if you change font size */
+    --jp-code-cursor-width0: 1.4px;
+    --jp-code-cursor-width1: 2px;
+    --jp-code-cursor-width2: 4px;
+
+    /* Layout
+   *
+   * The following are the main layout colors use in JupyterLab. In a light
+   * theme these would go from light to dark.
+   */
+
+    --jp-layout-color0: white;
+    --jp-layout-color1: white;
+    --jp-layout-color2: var(--md-grey-200);
+    --jp-layout-color3: var(--md-grey-400);
+    --jp-layout-color4: var(--md-grey-600);
+
+    /* Inverse Layout
+   *
+   * The following are the inverse layout colors use in JupyterLab. In a light
+   * theme these would go from dark to light.
+   */
+
+    --jp-inverse-layout-color0: #111111;
+    --jp-inverse-layout-color1: var(--md-grey-900);
+    --jp-inverse-layout-color2: var(--md-grey-800);
+    --jp-inverse-layout-color3: var(--md-grey-700);
+    --jp-inverse-layout-color4: var(--md-grey-600);
+
+    /* Brand/accent */
+
+    --jp-brand-color0: var(--md-blue-900);
+    --jp-brand-color1: var(--md-blue-700);
+    --jp-brand-color2: var(--md-blue-300);
+    --jp-brand-color3: var(--md-blue-100);
+    --jp-brand-color4: var(--md-blue-50);
+
+    --jp-accent-color0: var(--md-green-900);
+    --jp-accent-color1: var(--md-green-700);
+    --jp-accent-color2: var(--md-green-300);
+    --jp-accent-color3: var(--md-green-100);
+
+    /* State colors (warn, error, success, info) */
+
+    --jp-warn-color0: var(--md-orange-900);
+    --jp-warn-color1: var(--md-orange-700);
+    --jp-warn-color2: var(--md-orange-300);
+    --jp-warn-color3: var(--md-orange-100);
+
+    --jp-error-color0: var(--md-red-900);
+    --jp-error-color1: var(--md-red-700);
+    --jp-error-color2: var(--md-red-300);
+    --jp-error-color3: var(--md-red-100);
+
+    --jp-success-color0: var(--md-green-900);
+    --jp-success-color1: var(--md-green-700);
+    --jp-success-color2: var(--md-green-300);
+    --jp-success-color3: var(--md-green-100);
+
+    --jp-info-color0: var(--md-cyan-900);
+    --jp-info-color1: var(--md-cyan-700);
+    --jp-info-color2: var(--md-cyan-300);
+    --jp-info-color3: var(--md-cyan-100);
+
+    /* Cell specific styles */
+
+    --jp-cell-padding: 5px;
+
+    --jp-cell-collapser-width: 8px;
+    --jp-cell-collapser-min-height: 20px;
+    --jp-cell-collapser-not-active-hover-opacity: 0.6;
+
+    --jp-cell-editor-background: var(--md-grey-100);
+    --jp-cell-editor-border-color: var(--md-grey-300);
+    --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-cell-editor-active-background: var(--jp-layout-color0);
+    --jp-cell-editor-active-border-color: var(--jp-brand-color1);
+
+    --jp-cell-prompt-width: 64px;
+    --jp-cell-prompt-font-family: var(--jp-code-font-family-default);
+    --jp-cell-prompt-letter-spacing: 0px;
+    --jp-cell-prompt-opacity: 1;
+    --jp-cell-prompt-not-active-opacity: 0.5;
+    --jp-cell-prompt-not-active-font-color: var(--md-grey-700);
+    /* A custom blend of MD grey and blue 600
+   * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */
+    --jp-cell-inprompt-font-color: #307fc1;
+    /* A custom blend of MD grey and orange 600
+   * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */
+    --jp-cell-outprompt-font-color: #bf5b3d;
+
+    /* Notebook specific styles */
+
+    --jp-notebook-padding: 10px;
+    --jp-notebook-select-background: var(--jp-layout-color1);
+    --jp-notebook-multiselected-color: var(--md-blue-50);
+
+    /* The scroll padding is calculated to fill enough space at the bottom of the
+  notebook to show one single-line cell (with appropriate padding) at the top
+  when the notebook is scrolled all the way to the bottom. We also subtract one
+  pixel so that no scrollbar appears if we have just one single-line cell in the
+  notebook. This padding is to enable a 'scroll past end' feature in a notebook.
+  */
+    --jp-notebook-scroll-padding: calc(
+        100% - var(--jp-code-font-size) * var(--jp-code-line-height) -
+            var(--jp-code-padding) - var(--jp-cell-padding) - 1px
+    );
+
+    /* Rendermime styles */
+
+    --jp-rendermime-error-background: #fdd;
+    --jp-rendermime-table-row-background: var(--md-grey-100);
+    --jp-rendermime-table-row-hover-background: var(--md-light-blue-50);
+
+    /* Dialog specific styles */
+
+    --jp-dialog-background: rgba(0, 0, 0, 0.25);
+
+    /* Console specific styles */
+
+    --jp-console-padding: 10px;
+
+    /* Toolbar specific styles */
+
+    --jp-toolbar-border-color: var(--jp-border-color1);
+    --jp-toolbar-micro-height: 8px;
+    --jp-toolbar-background: var(--jp-layout-color1);
+    --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24);
+    --jp-toolbar-header-margin: 4px 4px 0px 4px;
+    --jp-toolbar-active-background: var(--md-grey-300);
+
+    /* Statusbar specific styles */
+
+    --jp-statusbar-height: 24px;
+
+    /* Input field styles */
+
+    --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-input-active-background: var(--jp-layout-color1);
+    --jp-input-hover-background: var(--jp-layout-color1);
+    --jp-input-background: var(--md-grey-100);
+    --jp-input-border-color: var(--jp-border-color1);
+    --jp-input-active-border-color: var(--jp-brand-color1);
+    --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3);
+
+    /* General editor styles */
+
+    --jp-editor-selected-background: #d9d9d9;
+    --jp-editor-selected-focused-background: #d7d4f0;
+    --jp-editor-cursor-color: var(--jp-ui-font-color0);
+
+    /* Code mirror specific styles */
+
+    --jp-mirror-editor-keyword-color: #008000;
+    --jp-mirror-editor-atom-color: #88f;
+    --jp-mirror-editor-number-color: #080;
+    --jp-mirror-editor-def-color: #00f;
+    --jp-mirror-editor-variable-color: var(--md-grey-900);
+    --jp-mirror-editor-variable-2-color: #05a;
+    --jp-mirror-editor-variable-3-color: #085;
+    --jp-mirror-editor-punctuation-color: #05a;
+    --jp-mirror-editor-property-color: #05a;
+    --jp-mirror-editor-operator-color: #aa22ff;
+    --jp-mirror-editor-comment-color: #408080;
+    --jp-mirror-editor-string-color: #ba2121;
+    --jp-mirror-editor-string-2-color: #708;
+    --jp-mirror-editor-meta-color: #aa22ff;
+    --jp-mirror-editor-qualifier-color: #555;
+    --jp-mirror-editor-builtin-color: #008000;
+    --jp-mirror-editor-bracket-color: #997;
+    --jp-mirror-editor-tag-color: #170;
+    --jp-mirror-editor-attribute-color: #00c;
+    --jp-mirror-editor-header-color: blue;
+    --jp-mirror-editor-quote-color: #090;
+    --jp-mirror-editor-link-color: #00c;
+    --jp-mirror-editor-error-color: #f00;
+    --jp-mirror-editor-hr-color: #999;
+
+    /* Vega extension styles */
+
+    --jp-vega-background: white;
+
+    /* Sidebar-related styles */
+
+    --jp-sidebar-min-width: 250px;
+
+    /* Search-related styles */
+
+    --jp-search-toggle-off-opacity: 0.5;
+    --jp-search-toggle-hover-opacity: 0.8;
+    --jp-search-toggle-on-opacity: 1;
+    --jp-search-selected-match-background-color: rgb(245, 200, 0);
+    --jp-search-selected-match-color: black;
+    --jp-search-unselected-match-background-color: var(
+        --jp-inverse-layout-color0
+    );
+    --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);
+
+    /* Icon colors that work well with light or dark backgrounds */
+    --jp-icon-contrast-color0: var(--md-purple-600);
+    --jp-icon-contrast-color1: var(--md-green-600);
+    --jp-icon-contrast-color2: var(--md-pink-600);
+    --jp-icon-contrast-color3: var(--md-blue-600);
+}
+
+[data-md-color-scheme="slate"] .jupyter-wrapper {
+    /* Elevation
+   *
+   * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here:
+   *
+   * https://github.com/material-components/material-components-web
+   * https://material-components-web.appspot.com/elevation.html
+   */
+
+    /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout
+   * colors used in the theme as well. */
+    --jp-shadow-base-lightness: 32;
+    --jp-shadow-umbra-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.2
+    );
+    --jp-shadow-penumbra-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.14
+    );
+    --jp-shadow-ambient-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.12
+    );
+    --jp-elevation-z0: none;
+    --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color),
+        0px 1px 1px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 3px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color),
+        0px 2px 2px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 5px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color),
+        0px 4px 5px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 10px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color),
+        0px 6px 10px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 18px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color),
+        0px 8px 10px 1px var(--jp-shadow-penumbra-color),
+        0px 3px 14px 2px var(--jp-shadow-ambient-color);
+    --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color),
+        0px 12px 17px 2px var(--jp-shadow-penumbra-color),
+        0px 5px 22px 4px var(--jp-shadow-ambient-color);
+    --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color),
+        0px 16px 24px 2px var(--jp-shadow-penumbra-color),
+        0px 6px 30px 5px var(--jp-shadow-ambient-color);
+    --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color),
+        0px 20px 31px 3px var(--jp-shadow-penumbra-color),
+        0px 8px 38px 7px var(--jp-shadow-ambient-color);
+    --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color),
+        0px 24px 38px 3px var(--jp-shadow-penumbra-color),
+        0px 9px 46px 8px var(--jp-shadow-ambient-color);
+
+    /* Borders
+   *
+   * The following variables, specify the visual styling of borders in JupyterLab.
+   */
+
+    --jp-border-width: 1px;
+    --jp-border-color0: var(--md-grey-700);
+    --jp-border-color1: var(--md-grey-700);
+    --jp-border-color2: var(--md-grey-800);
+    --jp-border-color3: var(--md-grey-900);
+    --jp-border-radius: 2px;
+
+    /* UI Fonts
+   *
+   * The UI font CSS variables are used for the typography all of the JupyterLab
+   * user interface elements that are not directly user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-ui-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+    --jp-ui-font-scale-factor: 1.2;
+    --jp-ui-font-size0: 0.83333em;
+    --jp-ui-font-size1: 13px; /* Base font size */
+    --jp-ui-font-size2: 1.2em;
+    --jp-ui-font-size3: 1.44em;
+
+    --jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+
+    /*
+   * Use these font colors against the corresponding main layout colors.
+   * In a light theme, these go from dark to light.
+   */
+
+    /* Defaults use Material Design specification */
+    --jp-ui-font-color0: rgba(255, 255, 255, 1);
+    --jp-ui-font-color1: rgba(255, 255, 255, 0.87);
+    --jp-ui-font-color2: rgba(255, 255, 255, 0.54);
+    --jp-ui-font-color3: rgba(255, 255, 255, 0.38);
+
+    /*
+   * Use these against the brand/accent/warn/error colors.
+   * These will typically go from light to darker, in both a dark and light theme.
+   */
+
+    --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1);
+    --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8);
+    --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5);
+    --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3);
+
+    /* Content Fonts
+   *
+   * Content font variables are used for typography of user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-content-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+    --jp-content-line-height: 1.6;
+    --jp-content-font-scale-factor: 1.2;
+    --jp-content-font-size0: 0.83333em;
+    --jp-content-font-size1: 14px; /* Base font size */
+    --jp-content-font-size2: 1.2em;
+    --jp-content-font-size3: 1.44em;
+    --jp-content-font-size4: 1.728em;
+    --jp-content-font-size5: 2.0736em;
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-content-presentation-font-size1: 17px;
+
+    --jp-content-heading-line-height: 1;
+    --jp-content-heading-margin-top: 1.2em;
+    --jp-content-heading-margin-bottom: 0.8em;
+    --jp-content-heading-font-weight: 500;
+
+    /* Defaults use Material Design specification */
+    --jp-content-font-color0: rgba(255, 255, 255, 1);
+    --jp-content-font-color1: rgba(255, 255, 255, 1);
+    --jp-content-font-color2: rgba(255, 255, 255, 0.7);
+    --jp-content-font-color3: rgba(255, 255, 255, 0.5);
+
+    --jp-content-link-color: var(--md-blue-300);
+
+    --jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+
+    /*
+   * Code Fonts
+   *
+   * Code font variables are used for typography of code and other monospaces content.
+   */
+
+    --jp-code-font-size: 13px;
+    --jp-code-line-height: 1.3077; /* 17px for 13px base */
+    --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */
+    --jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono",
+        monospace;
+    --jp-code-font-family: var(--jp-code-font-family-default);
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-code-presentation-font-size: 16px;
+
+    /* may need to tweak cursor width if you change font size */
+    --jp-code-cursor-width0: 1.4px;
+    --jp-code-cursor-width1: 2px;
+    --jp-code-cursor-width2: 4px;
+
+    /* Layout
+   *
+   * The following are the main layout colors use in JupyterLab. In a light
+   * theme these would go from light to dark.
+   */
+
+    --jp-layout-color0: #111111;
+    --jp-layout-color1: var(--md-grey-900);
+    --jp-layout-color2: var(--md-grey-800);
+    --jp-layout-color3: var(--md-grey-700);
+    --jp-layout-color4: var(--md-grey-600);
+
+    /* Inverse Layout
+   *
+   * The following are the inverse layout colors use in JupyterLab. In a light
+   * theme these would go from dark to light.
+   */
+
+    --jp-inverse-layout-color0: white;
+    --jp-inverse-layout-color1: white;
+    --jp-inverse-layout-color2: var(--md-grey-200);
+    --jp-inverse-layout-color3: var(--md-grey-400);
+    --jp-inverse-layout-color4: var(--md-grey-600);
+
+    /* Brand/accent */
+
+    --jp-brand-color0: var(--md-blue-700);
+    --jp-brand-color1: var(--md-blue-500);
+    --jp-brand-color2: var(--md-blue-300);
+    --jp-brand-color3: var(--md-blue-100);
+    --jp-brand-color4: var(--md-blue-50);
+
+    --jp-accent-color0: var(--md-green-700);
+    --jp-accent-color1: var(--md-green-500);
+    --jp-accent-color2: var(--md-green-300);
+    --jp-accent-color3: var(--md-green-100);
+
+    /* State colors (warn, error, success, info) */
+
+    --jp-warn-color0: var(--md-orange-700);
+    --jp-warn-color1: var(--md-orange-500);
+    --jp-warn-color2: var(--md-orange-300);
+    --jp-warn-color3: var(--md-orange-100);
+
+    --jp-error-color0: var(--md-red-700);
+    --jp-error-color1: var(--md-red-500);
+    --jp-error-color2: var(--md-red-300);
+    --jp-error-color3: var(--md-red-100);
+
+    --jp-success-color0: var(--md-green-700);
+    --jp-success-color1: var(--md-green-500);
+    --jp-success-color2: var(--md-green-300);
+    --jp-success-color3: var(--md-green-100);
+
+    --jp-info-color0: var(--md-cyan-700);
+    --jp-info-color1: var(--md-cyan-500);
+    --jp-info-color2: var(--md-cyan-300);
+    --jp-info-color3: var(--md-cyan-100);
+
+    /* Cell specific styles */
+
+    --jp-cell-padding: 5px;
+
+    --jp-cell-collapser-width: 8px;
+    --jp-cell-collapser-min-height: 20px;
+    --jp-cell-collapser-not-active-hover-opacity: 0.6;
+
+    --jp-cell-editor-background: var(--jp-layout-color1);
+    --jp-cell-editor-border-color: var(--md-grey-700);
+    --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-cell-editor-active-background: var(--jp-layout-color0);
+    --jp-cell-editor-active-border-color: var(--jp-brand-color1);
+
+    --jp-cell-prompt-width: 64px;
+    --jp-cell-prompt-font-family: var(--jp-code-font-family-default);
+    --jp-cell-prompt-letter-spacing: 0px;
+    --jp-cell-prompt-opacity: 1;
+    --jp-cell-prompt-not-active-opacity: 1;
+    --jp-cell-prompt-not-active-font-color: var(--md-grey-300);
+
+    /* A custom blend of MD grey and blue 600
+   * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */
+    --jp-cell-inprompt-font-color: #307fc1;
+    /* A custom blend of MD grey and orange 600
+   * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */
+    --jp-cell-outprompt-font-color: #bf5b3d;
+
+    /* Notebook specific styles */
+
+    --jp-notebook-padding: 10px;
+    --jp-notebook-select-background: var(--jp-layout-color1);
+    --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24);
+
+    /* The scroll padding is calculated to fill enough space at the bottom of the
+  notebook to show one single-line cell (with appropriate padding) at the top
+  when the notebook is scrolled all the way to the bottom. We also subtract one
+  pixel so that no scrollbar appears if we have just one single-line cell in the
+  notebook. This padding is to enable a 'scroll past end' feature in a notebook.
+  */
+    --jp-notebook-scroll-padding: calc(
+        100% - var(--jp-code-font-size) * var(--jp-code-line-height) -
+            var(--jp-code-padding) - var(--jp-cell-padding) - 1px
+    );
+
+    /* Rendermime styles */
+
+    --jp-rendermime-error-background: rgba(244, 67, 54, 0.28);
+    --jp-rendermime-table-row-background: var(--md-grey-900);
+    --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2);
+
+    /* Dialog specific styles */
+
+    --jp-dialog-background: rgba(0, 0, 0, 0.6);
+
+    /* Console specific styles */
+
+    --jp-console-padding: 10px;
+
+    /* Toolbar specific styles */
+
+    --jp-toolbar-border-color: var(--jp-border-color2);
+    --jp-toolbar-micro-height: 8px;
+    --jp-toolbar-background: var(--jp-layout-color1);
+    --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8);
+    --jp-toolbar-header-margin: 4px 4px 0px 4px;
+    --jp-toolbar-active-background: var(--jp-layout-color0);
+
+    /* Statusbar specific styles */
+
+    --jp-statusbar-height: 24px;
+
+    /* Input field styles */
+
+    --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-input-active-background: var(--jp-layout-color0);
+    --jp-input-hover-background: var(--jp-layout-color2);
+    --jp-input-background: var(--md-grey-800);
+    --jp-input-border-color: var(--jp-border-color1);
+    --jp-input-active-border-color: var(--jp-brand-color1);
+    --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3);
+
+    /* General editor styles */
+
+    --jp-editor-selected-background: var(--jp-layout-color2);
+    --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24);
+    --jp-editor-cursor-color: var(--jp-ui-font-color0);
+
+    /* Code mirror specific styles */
+
+    --jp-mirror-editor-keyword-color: var(--md-green-500);
+    --jp-mirror-editor-atom-color: var(--md-blue-300);
+    --jp-mirror-editor-number-color: var(--md-green-400);
+    --jp-mirror-editor-def-color: var(--md-blue-600);
+    --jp-mirror-editor-variable-color: var(--md-grey-300);
+    --jp-mirror-editor-variable-2-color: var(--md-blue-400);
+    --jp-mirror-editor-variable-3-color: var(--md-green-600);
+    --jp-mirror-editor-punctuation-color: var(--md-blue-400);
+    --jp-mirror-editor-property-color: var(--md-blue-400);
+    --jp-mirror-editor-operator-color: #aa22ff;
+    --jp-mirror-editor-comment-color: #408080;
+    --jp-mirror-editor-string-color: #ff7070;
+    --jp-mirror-editor-string-2-color: var(--md-purple-300);
+    --jp-mirror-editor-meta-color: #aa22ff;
+    --jp-mirror-editor-qualifier-color: #555;
+    --jp-mirror-editor-builtin-color: var(--md-green-600);
+    --jp-mirror-editor-bracket-color: #997;
+    --jp-mirror-editor-tag-color: var(--md-green-700);
+    --jp-mirror-editor-attribute-color: var(--md-blue-700);
+    --jp-mirror-editor-header-color: var(--md-blue-500);
+    --jp-mirror-editor-quote-color: var(--md-green-300);
+    --jp-mirror-editor-link-color: var(--md-blue-700);
+    --jp-mirror-editor-error-color: #f00;
+    --jp-mirror-editor-hr-color: #999;
+
+    /* Vega extension styles */
+
+    --jp-vega-background: var(--md-grey-400);
+
+    /* Sidebar-related styles */
+
+    --jp-sidebar-min-width: 250px;
+
+    /* Search-related styles */
+
+    --jp-search-toggle-off-opacity: 0.6;
+    --jp-search-toggle-hover-opacity: 0.8;
+    --jp-search-toggle-on-opacity: 1;
+    --jp-search-selected-match-background-color: rgb(255, 225, 0);
+    --jp-search-selected-match-color: black;
+    --jp-search-unselected-match-background-color: var(
+        --jp-inverse-layout-color0
+    );
+    --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);
+
+    /* scrollbar related styles. Supports every browser except Edge. */
+
+    /* colors based on JetBrain's Darcula theme */
+
+    --jp-scrollbar-background-color: #3f4244;
+    --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */
+
+    --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */
+
+    /* hacks for setting the thumb shape. These do nothing in Firefox */
+
+    --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */
+    --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */
+
+    /* Icon colors that work well with light or dark backgrounds */
+    --jp-icon-contrast-color0: var(--md-purple-600);
+    --jp-icon-contrast-color1: var(--md-green-600);
+    --jp-icon-contrast-color2: var(--md-pink-600);
+    --jp-icon-contrast-color3: var(--md-blue-600);
+}
+
+:root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: 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+
+Copyright 2015-present Palantir Technologies, Inc. All rights reserved.
+Licensed under the Apache License, Version 2.0.
+
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+
+Copyright 2017-present Palantir Technologies, Inc. All rights reserved.
+Licensed under the Apache License, Version 2.0.
+
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+
+/*# sourceMappingURL=mkdocs-jupyter.css.map*/</style>
+
+
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+    <!-- MathJax configuration -->
+    <script type="text/x-mathjax-config">
+    init_mathjax = function() {
+        if (window.MathJax) {
+        // MathJax loaded
+            MathJax.Hub.Config({
+                TeX: {
+                    equationNumbers: {
+                    autoNumber: "AMS",
+                    useLabelIds: true
+                    }
+                },
+                tex2jax: {
+                    inlineMath: [ ['$','$'], ["\\(","\\)"] ],
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+
+            MathJax.Hub.Queue(["Typeset", MathJax.Hub]);
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+    }
+    init_mathjax();
+    </script>
+    <!-- End of mathjax configuration --><div class="jupyter-wrapper"><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[1]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-1">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">obspy</span>
+<span class="kn">from</span> <span class="nn">obspy.clients.fdsn</span> <span class="kn">import</span> <span class="n">Client</span>
+</pre></div>
+<div id="cell-1" class="clipboard-copy-txt">import obspy
+from obspy.clients.fdsn import Client</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[52]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-2">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">region_name</span> <span class="o">=</span> <span class="s2">&quot;PuertoRico_threeyear&quot;</span>
+<span class="n">center</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mf">66.5</span><span class="p">,</span> <span class="mi">18</span><span class="p">)</span>
+<span class="n">horizontal_degree</span> <span class="o">=</span> <span class="mf">3.0</span>
+<span class="n">vertical_degree</span> <span class="o">=</span> <span class="mf">2.0</span>
+<span class="c1">#     starttime = obspy.UTCDateTime(&quot;2020-01-07T00&quot;)</span>
+<span class="c1">#     endtime = obspy.UTCDateTime(&quot;2020-01-14T00&quot;) </span>
+<span class="n">starttime</span> <span class="o">=</span> <span class="n">obspy</span><span class="o">.</span><span class="n">UTCDateTime</span><span class="p">(</span><span class="s2">&quot;2018-05-01T00&quot;</span><span class="p">)</span>
+<span class="n">endtime</span> <span class="o">=</span> <span class="n">obspy</span><span class="o">.</span><span class="n">UTCDateTime</span><span class="p">(</span><span class="s2">&quot;2021-05-01T00&quot;</span><span class="p">)</span> 
+<span class="n">client</span> <span class="o">=</span> <span class="s2">&quot;IRIS&quot;</span>
+<span class="n">network_list</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;*&quot;</span><span class="p">]</span>
+<span class="c1"># channel_list = &quot;HH*,BH*,EH*,HN*&quot;</span>
+<span class="c1"># channel_list = &quot;HH*,BH*,HN*&quot;</span>
+<span class="n">channel_list</span> <span class="o">=</span> <span class="s2">&quot;HN*&quot;</span>
+</pre></div>
+<div id="cell-2" class="clipboard-copy-txt">region_name = "PuertoRico_threeyear"
+center = (-66.5, 18)
+horizontal_degree = 3.0
+vertical_degree = 2.0
+#     starttime = obspy.UTCDateTime("2020-01-07T00")
+#     endtime = obspy.UTCDateTime("2020-01-14T00") 
+starttime = obspy.UTCDateTime("2018-05-01T00")
+endtime = obspy.UTCDateTime("2021-05-01T00") 
+client = "IRIS"
+network_list = ["*"]
+# channel_list = "HH*,BH*,EH*,HN*"
+# channel_list = "HH*,BH*,HN*"
+channel_list = "HN*"</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[53]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-3">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># starttime= obspy.UTCDateTime(&quot;2020-01-22T04:57:44.934&quot;)</span>
+<span class="n">starttime</span><span class="o">=</span> <span class="n">obspy</span><span class="o">.</span><span class="n">UTCDateTime</span><span class="p">(</span><span class="s2">&quot;2020-01-16T23:27:46.902&quot;</span><span class="p">)</span>
+<span class="n">endtime</span> <span class="o">=</span> <span class="n">starttime</span> <span class="o">+</span> <span class="mi">30</span>
+</pre></div>
+<div id="cell-3" class="clipboard-copy-txt"># starttime= obspy.UTCDateTime("2020-01-22T04:57:44.934")
+starttime= obspy.UTCDateTime("2020-01-16T23:27:46.902")
+endtime = starttime + 30</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[54]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-4">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">config</span> <span class="o">=</span> <span class="p">{}</span>
+<span class="n">config</span><span class="p">[</span><span class="s2">&quot;region&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">region_name</span>
+<span class="n">config</span><span class="p">[</span><span class="s2">&quot;center&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">center</span>
+<span class="n">config</span><span class="p">[</span><span class="s2">&quot;xlim_degree&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="n">center</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">-</span><span class="n">horizontal_degree</span><span class="o">/</span><span class="mi">2</span><span class="p">,</span> <span class="n">center</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">+</span><span class="n">horizontal_degree</span><span class="o">/</span><span class="mi">2</span><span class="p">]</span>
+<span class="n">config</span><span class="p">[</span><span class="s2">&quot;ylim_degree&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="n">center</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">-</span><span class="n">vertical_degree</span><span class="o">/</span><span class="mi">2</span><span class="p">,</span> <span class="n">center</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">+</span><span class="n">vertical_degree</span><span class="o">/</span><span class="mi">2</span><span class="p">]</span>
+<span class="n">config</span><span class="p">[</span><span class="s2">&quot;starttime&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">starttime</span><span class="o">.</span><span class="n">datetime</span><span class="o">.</span><span class="n">isoformat</span><span class="p">()</span>
+<span class="n">config</span><span class="p">[</span><span class="s2">&quot;endtime&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">endtime</span><span class="o">.</span><span class="n">datetime</span><span class="o">.</span><span class="n">isoformat</span><span class="p">()</span>
+<span class="n">config</span><span class="p">[</span><span class="s2">&quot;networks&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">network_list</span>
+<span class="n">config</span><span class="p">[</span><span class="s2">&quot;channels&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">channel_list</span>
+<span class="n">config</span><span class="p">[</span><span class="s2">&quot;client&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">client</span>
+</pre></div>
+<div id="cell-4" class="clipboard-copy-txt">config = {}
+config["region"] = region_name
+config["center"] = center
+config["xlim_degree"] = [center[0]-horizontal_degree/2, center[0]+horizontal_degree/2]
+config["ylim_degree"] = [center[1]-vertical_degree/2, center[1]+vertical_degree/2]
+config["starttime"] = starttime.datetime.isoformat()
+config["endtime"] = endtime.datetime.isoformat()
+config["networks"] = network_list
+config["channels"] = channel_list
+config["client"] = client</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[55]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-5">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">stations</span> <span class="o">=</span> <span class="n">Client</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;client&quot;</span><span class="p">])</span><span class="o">.</span><span class="n">get_stations</span><span class="p">(</span><span class="n">network</span> <span class="o">=</span> <span class="s2">&quot;,&quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;networks&quot;</span><span class="p">]),</span>
+                                       <span class="n">station</span> <span class="o">=</span> <span class="s2">&quot;*&quot;</span><span class="p">,</span>
+<span class="c1">#                                        station = &quot;CELP,GBPR&quot;,</span>
+                                       <span class="n">starttime</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;starttime&quot;</span><span class="p">],</span>
+                                       <span class="n">endtime</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;endtime&quot;</span><span class="p">],</span>
+                                       <span class="n">minlongitude</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;xlim_degree&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span>
+                                       <span class="n">maxlongitude</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;xlim_degree&quot;</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span>
+                                       <span class="n">minlatitude</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;ylim_degree&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span>
+                                       <span class="n">maxlatitude</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;ylim_degree&quot;</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span>
+                                       <span class="n">channel</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;channels&quot;</span><span class="p">],</span>
+                                       <span class="n">level</span><span class="o">=</span><span class="s2">&quot;response&quot;</span><span class="p">)</span><span class="c1">#,</span>
+</pre></div>
+<div id="cell-5" class="clipboard-copy-txt">stations = Client(config["client"]).get_stations(network = ",".join(config["networks"]),
+                                       station = "*",
+#                                        station = "CELP,GBPR",
+                                       starttime=config["starttime"],
+                                       endtime=config["endtime"],
+                                       minlongitude=config["xlim_degree"][0],
+                                       maxlongitude=config["xlim_degree"][1],
+                                       minlatitude=config["ylim_degree"][0],
+                                       maxlatitude=config["ylim_degree"][1],
+                                       channel=config["channels"],
+                                       level="response")#,</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[70]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-6">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">client</span> <span class="o">=</span> <span class="n">Client</span><span class="p">(</span><span class="s2">&quot;IRIS&quot;</span><span class="p">)</span>
+<span class="n">stream</span> <span class="o">=</span> <span class="n">obspy</span><span class="o">.</span><span class="n">Stream</span><span class="p">()</span>
+<span class="n">max_retry</span> <span class="o">=</span> <span class="mi">10</span>
+<span class="k">for</span> <span class="n">network</span> <span class="ow">in</span> <span class="n">stations</span><span class="p">:</span>
+    <span class="k">for</span> <span class="n">station</span> <span class="ow">in</span> <span class="n">network</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;********</span><span class="si">{</span><span class="n">network</span><span class="o">.</span><span class="n">code</span><span class="si">}</span><span class="s2">.</span><span class="si">{</span><span class="n">station</span><span class="o">.</span><span class="n">code</span><span class="si">}</span><span class="s2">********&quot;</span><span class="p">)</span>
+        <span class="n">retry</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="k">while</span> <span class="n">retry</span> <span class="o">&lt;</span> <span class="n">max_retry</span><span class="p">:</span>
+            <span class="k">try</span><span class="p">:</span>
+                <span class="n">tmp</span> <span class="o">=</span> <span class="n">client</span><span class="o">.</span><span class="n">get_waveforms</span><span class="p">(</span><span class="n">network</span><span class="o">.</span><span class="n">code</span><span class="p">,</span> <span class="n">station</span><span class="o">.</span><span class="n">code</span><span class="p">,</span> <span class="s2">&quot;*&quot;</span><span class="p">,</span> <span class="n">config</span><span class="p">[</span><span class="s2">&quot;channels&quot;</span><span class="p">],</span> <span class="n">starttime</span><span class="p">,</span> <span class="n">endtime</span><span class="p">)</span>
+<span class="c1">#                         for trace in tmp:</span>
+<span class="c1">#                             if trace.stats.sampling_rate != 100:</span>
+<span class="c1">#                                 print(trace)</span>
+<span class="c1">#                                 trace = trace.interpolate(100, method=&quot;linear&quot;)</span>
+<span class="c1">#                             trace = trace.detrend(&quot;spline&quot;, order=2, dspline=5*trace.stats.sampling_rate)</span>
+<span class="c1">#                             stream.append(trace)</span>
+                <span class="n">stream</span> <span class="o">+=</span> <span class="n">tmp</span>
+                <span class="k">break</span>
+            <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">err</span><span class="p">:</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Error </span><span class="si">{}</span><span class="s2">.</span><span class="si">{}</span><span class="s2">: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">network</span><span class="o">.</span><span class="n">code</span><span class="p">,</span> <span class="n">station</span><span class="o">.</span><span class="n">code</span><span class="p">,</span> <span class="n">err</span><span class="p">))</span>
+                <span class="n">message</span> <span class="o">=</span> <span class="s2">&quot;No data available for request.&quot;</span>
+                <span class="k">if</span> <span class="nb">str</span><span class="p">(</span><span class="n">err</span><span class="p">)[:</span><span class="nb">len</span><span class="p">(</span><span class="n">message</span><span class="p">)]</span> <span class="o">==</span> <span class="n">message</span><span class="p">:</span>
+                    <span class="k">break</span>
+                <span class="n">retry</span> <span class="o">+=</span> <span class="mi">1</span>
+                <span class="n">time</span><span class="o">.</span><span class="n">sleep</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
+                <span class="k">continue</span>
+        <span class="k">if</span> <span class="n">retry</span> <span class="o">==</span> <span class="n">max_retry</span><span class="p">:</span>
+            <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">fname</span><span class="si">}</span><span class="s2">: MAX </span><span class="si">{</span><span class="n">max_retry</span><span class="si">}</span><span class="s2"> retries reached : </span><span class="si">{</span><span class="n">network</span><span class="o">.</span><span class="n">code</span><span class="si">}</span><span class="s2">.</span><span class="si">{</span><span class="n">station</span><span class="o">.</span><span class="n">code</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+</pre></div>
+<div id="cell-6" class="clipboard-copy-txt">client = Client("IRIS")
+stream = obspy.Stream()
+max_retry = 10
+for network in stations:
+    for station in network:
+        print(f"********{network.code}.{station.code}********")
+        retry = 0
+        while retry < max_retry:
+            try:
+                tmp = client.get_waveforms(network.code, station.code, "*", config["channels"], starttime, endtime)
+#                         for trace in tmp:
+#                             if trace.stats.sampling_rate != 100:
+#                                 print(trace)
+#                                 trace = trace.interpolate(100, method="linear")
+#                             trace = trace.detrend("spline", order=2, dspline=5*trace.stats.sampling_rate)
+#                             stream.append(trace)
+                stream += tmp
+                break
+            except Exception as err:
+                print("Error {}.{}: {}".format(network.code, station.code, err))
+                message = "No data available for request."
+                if str(err)[:len(message)] == message:
+                    break
+                retry += 1
+                time.sleep(5)
+                continue
+        if retry == max_retry:
+            print(f"{fname}: MAX {max_retry} retries reached : {network.code}.{station.code}")</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
+<pre>********GS.PR01********
+Error GS.PR01: No data available for request.
+Detailed response of server:
+
+
+********GS.PR02********
+Error GS.PR02: No data available for request.
+Detailed response of server:
+
+
+********GS.PR03********
+Error GS.PR03: No data available for request.
+Detailed response of server:
+
+
+********GS.PR04********
+Error GS.PR04: No data available for request.
+Detailed response of server:
+
+
+********GS.PR05********
+Error GS.PR05: No data available for request.
+Detailed response of server:
+
+
+********GS.PR06********
+Error GS.PR06: No data available for request.
+Detailed response of server:
+
+
+********IU.SJG********
+Error IU.SJG: No data available for request.
+Detailed response of server:
+
+
+********NP.3090********
+Error NP.3090: No data available for request.
+Detailed response of server:
+
+
+********NP.3095********
+Error NP.3095: No data available for request.
+Detailed response of server:
+
+
+********NP.3096********
+Error NP.3096: No data available for request.
+Detailed response of server:
+
+
+********PR.AG02********
+Error PR.AG02: No data available for request.
+Detailed response of server:
+
+
+********PR.AGPR********
+Error PR.AGPR: No data available for request.
+Detailed response of server:
+
+
+********PR.AOPR********
+********PR.AR01********
+Error PR.AR01: No data available for request.
+Detailed response of server:
+
+
+********PR.AR02********
+Error PR.AR02: No data available for request.
+Detailed response of server:
+
+
+********PR.B03L********
+Error PR.B03L: No data available for request.
+Detailed response of server:
+
+
+********PR.BY01********
+Error PR.BY01: No data available for request.
+Detailed response of server:
+
+
+********PR.BY02********
+Error PR.BY02: No data available for request.
+Detailed response of server:
+
+
+********PR.CBR1********
+Error PR.CBR1: No data available for request.
+Detailed response of server:
+
+
+********PR.CBR2********
+Error PR.CBR2: No data available for request.
+Detailed response of server:
+
+
+********PR.CELP********
+********PR.CG01********
+Error PR.CG01: No data available for request.
+Detailed response of server:
+
+
+********PR.CR01********
+Error PR.CR01: No data available for request.
+Detailed response of server:
+
+
+********PR.CRPR********
+********PR.CULB********
+Error PR.CULB: No data available for request.
+Detailed response of server:
+
+
+********PR.CUPR********
+Error PR.CUPR: No data available for request.
+Detailed response of server:
+
+
+********PR.CVPR********
+Error PR.CVPR: No data available for request.
+Detailed response of server:
+
+
+********PR.ECPR********
+********PR.EMPR********
+********PR.GBPR********
+Error PR.GBPR: No data available for request.
+Detailed response of server:
+
+
+********PR.GCPR********
+Error PR.GCPR: No data available for request.
+Detailed response of server:
+
+
+********PR.GNC2********
+Error PR.GNC2: No data available for request.
+Detailed response of server:
+
+
+********PR.HUMP********
+********PR.ICMP********
+Error PR.ICMP: No data available for request.
+Detailed response of server:
+
+
+********PR.IGPR********
+Error PR.IGPR: No data available for request.
+Detailed response of server:
+
+
+********PR.IMPR********
+Error PR.IMPR: No data available for request.
+Detailed response of server:
+
+
+********PR.MLPR********
+********PR.MTP********
+Error PR.MTP: No data available for request.
+Detailed response of server:
+
+
+********PR.MY10********
+Error PR.MY10: No data available for request.
+Detailed response of server:
+
+
+********PR.PDPR********
+Error PR.PDPR: No data available for request.
+Detailed response of server:
+
+
+********PR.PN02********
+Error PR.PN02: No data available for request.
+Detailed response of server:
+
+
+********PR.PN12********
+Error PR.PN12: No data available for request.
+Detailed response of server:
+
+
+********PR.PN13********
+Error PR.PN13: No data available for request.
+Detailed response of server:
+
+
+********PR.PRSN********
+********PR.QBPR********
+Error PR.QBPR: No data available for request.
+Detailed response of server:
+
+
+********PR.ROPR********
+Error PR.ROPR: No data available for request.
+Detailed response of server:
+
+
+********PR.SJ07********
+Error PR.SJ07: No data available for request.
+Detailed response of server:
+
+
+********PR.TS01********
+Error PR.TS01: No data available for request.
+Detailed response of server:
+
+
+********PR.TS02********
+Error PR.TS02: No data available for request.
+Detailed response of server:
+
+
+********PR.TS05********
+Error PR.TS05: No data available for request.
+Detailed response of server:
+
+
+********PR.UTD2********
+Error PR.UTD2: No data available for request.
+Detailed response of server:
+
+
+********PR.UUPR********
+</pre>
+</div>
+</div>
+
+</div>
+
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[79]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-7">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">steam_copy</span> <span class="o">=</span> <span class="n">stream</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+<span class="c1"># stream = steam_copy.copy()</span>
+</pre></div>
+<div id="cell-7" class="clipboard-copy-txt">steam_copy = stream.copy()
+# stream = steam_copy.copy()</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[80]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-8">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">stream</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
+</pre></div>
+<div id="cell-8" class="clipboard-copy-txt">stream.plot()</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+
+
+<div class="jp-RenderedImage jp-OutputArea-output ">
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+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
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+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[81]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-9">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
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+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">stream</span> <span class="o">=</span> <span class="n">stream</span><span class="o">.</span><span class="n">detrend</span><span class="p">(</span><span class="s2">&quot;spline&quot;</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">dspline</span><span class="o">=</span><span class="mi">5</span><span class="o">*</span><span class="n">stream</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">stats</span><span class="o">.</span><span class="n">sampling_rate</span><span class="p">)</span>
+<span class="n">stream</span> <span class="o">=</span> <span class="n">stream</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">fill_value</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+</pre></div>
+<div id="cell-9" class="clipboard-copy-txt">stream = stream.detrend("spline", order=2, dspline=5*stream[0].stats.sampling_rate)
+stream = stream.merge(fill_value=0)</div>
+    
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+    </div>
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+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">WOODANDERSON</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;poles&#39;</span><span class="p">:</span> <span class="p">[</span><span class="o">-</span><span class="mf">6.283</span> <span class="o">+</span> <span class="mf">4.7124</span><span class="n">j</span><span class="p">,</span> <span class="o">-</span><span class="mf">6.283</span> <span class="o">-</span> <span class="mf">4.7124</span><span class="n">j</span><span class="p">],</span>
+                <span class="s1">&#39;zeros&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span> <span class="o">+</span> <span class="mi">0</span><span class="n">j</span><span class="p">],</span> <span class="s1">&#39;gain&#39;</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="s1">&#39;sensitivity&#39;</span><span class="p">:</span> <span class="mi">2080</span><span class="p">}</span>
+</pre></div>
+<div id="cell-10" class="clipboard-copy-txt">WOODANDERSON = {'poles': [-6.283 + 4.7124j, -6.283 - 4.7124j],
+                'zeros': [0 + 0j], 'gain': 1.0, 'sensitivity': 2080}</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[83]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
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+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">stations</span><span class="o">.</span><span class="n">select</span><span class="p">(</span><span class="n">network</span><span class="o">=</span><span class="s2">&quot;PR&quot;</span><span class="p">,</span> <span class="n">station</span><span class="o">=</span><span class="s2">&quot;GBPR&quot;</span><span class="p">)[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">response</span>
+</pre></div>
+<div id="cell-11" class="clipboard-copy-txt">stations.select(network="PR", station="GBPR")[0][0][0].response</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[83]:</div>
+
+
+
+
+<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
+<pre>Channel Response
+	From m/s**2 (Acceleration in meters per second squared) to counts (Digital Count in Digital counts)
+	Overall Sensitivity: 204000 defined at 1.000 Hz
+	6 stages:
+		Stage 1: PolesZerosResponseStage from m/s**2 to V, gain: 0.51
+		Stage 2: PolesZerosResponseStage from V to V, gain: 1
+		Stage 3: CoefficientsTypeResponseStage from V to counts, gain: 400000
+		Stage 4: FIRResponseStage from counts to counts, gain: 1
+		Stage 5: FIRResponseStage from counts to counts, gain: 1
+		Stage 6: FIRResponseStage from counts to counts, gain: 1</pre>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[84]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-12">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">stations</span><span class="o">.</span><span class="n">select</span><span class="p">(</span><span class="n">network</span><span class="o">=</span><span class="s2">&quot;PR&quot;</span><span class="p">,</span> <span class="n">station</span><span class="o">=</span><span class="s2">&quot;CELP&quot;</span><span class="p">)[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">response</span>
+</pre></div>
+<div id="cell-12" class="clipboard-copy-txt">stations.select(network="PR", station="CELP")[0][0][0].response</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[84]:</div>
+
+
+
+
+<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
+<pre>Channel Response
+	From m/s**2 (Acceleration in meters per second squared) to counts (Digital Count in Digital counts)
+	Overall Sensitivity: 204000 defined at 1.000 Hz
+	6 stages:
+		Stage 1: PolesZerosResponseStage from m/s**2 to V, gain: 0.51
+		Stage 2: PolesZerosResponseStage from V to V, gain: 1
+		Stage 3: CoefficientsTypeResponseStage from V to counts, gain: 400000
+		Stage 4: FIRResponseStage from counts to counts, gain: 1
+		Stage 5: FIRResponseStage from counts to counts, gain: 1
+		Stage 6: FIRResponseStage from counts to counts, gain: 1</pre>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[85]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-13">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># stream = stream.remove_response(stations, output=&quot;VEL&quot;)</span>
+<span class="n">stream</span> <span class="o">=</span> <span class="n">stream</span><span class="o">.</span><span class="n">remove_sensitivity</span><span class="p">(</span><span class="n">stations</span><span class="p">)</span>
+<span class="k">for</span> <span class="n">trace</span> <span class="ow">in</span> <span class="n">stream</span><span class="p">:</span>
+    <span class="k">if</span> <span class="n">trace</span><span class="o">.</span><span class="n">id</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;.&quot;</span><span class="p">)[</span><span class="o">-</span><span class="mi">1</span><span class="p">][:</span><span class="mi">2</span><span class="p">]</span><span class="o">==</span><span class="s2">&quot;HN&quot;</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="n">trace</span><span class="p">)</span>
+        <span class="n">trace</span><span class="o">.</span><span class="n">integrate</span><span class="p">()</span>
+        <span class="n">trace</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="s2">&quot;highpass&quot;</span><span class="p">,</span> <span class="n">freq</span><span class="o">=</span><span class="mf">5.0</span><span class="p">)</span>
+<span class="c1">#         trace.detrend(&quot;linear&quot;)</span>
+<span class="c1">#         trace = trace.detrend(&quot;spline&quot;, order=2, dspline=0.5*trace.stats.sampling_rate)</span>
+</pre></div>
+<div id="cell-13" class="clipboard-copy-txt"># stream = stream.remove_response(stations, output="VEL")
+stream = stream.remove_sensitivity(stations)
+for trace in stream:
+    if trace.id.split(".")[-1][:2]=="HN":
+        print(trace)
+        trace.integrate()
+        trace.filter("highpass", freq=5.0)
+#         trace.detrend("linear")
+#         trace = trace.detrend("spline", order=2, dspline=0.5*trace.stats.sampling_rate)
+</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
+<pre>PR.AOPR..HNE | 2020-01-16T23:27:46.910000Z - 2020-01-16T23:28:16.900000Z | 100.0 Hz, 3000 samples
+PR.AOPR..HNN | 2020-01-16T23:27:46.910000Z - 2020-01-16T23:28:16.900000Z | 100.0 Hz, 3000 samples
+PR.AOPR..HNZ | 2020-01-16T23:27:46.910000Z - 2020-01-16T23:28:16.900000Z | 100.0 Hz, 3000 samples
+PR.CELP.00.HN1 | 2020-01-16T23:27:46.910000Z - 2020-01-16T23:28:16.900000Z | 100.0 Hz, 3000 samples
+PR.CELP.00.HN2 | 2020-01-16T23:27:46.910000Z - 2020-01-16T23:28:16.900000Z | 100.0 Hz, 3000 samples
+PR.CELP.00.HNZ | 2020-01-16T23:27:46.910000Z - 2020-01-16T23:28:16.900000Z | 100.0 Hz, 3000 samples
+PR.CRPR.00.HN1 | 2020-01-16T23:27:46.910000Z - 2020-01-16T23:28:16.900000Z | 100.0 Hz, 3000 samples
+PR.CRPR.00.HN2 | 2020-01-16T23:27:46.910000Z - 2020-01-16T23:28:16.900000Z | 100.0 Hz, 3000 samples
+PR.CRPR.00.HNZ | 2020-01-16T23:27:46.910000Z - 2020-01-16T23:28:16.900000Z | 100.0 Hz, 3000 samples
+PR.ECPR..HNE | 2020-01-16T23:27:46.905000Z - 2020-01-16T23:28:16.895000Z | 100.0 Hz, 3000 samples
+PR.ECPR..HNN | 2020-01-16T23:27:46.905000Z - 2020-01-16T23:28:16.895000Z | 100.0 Hz, 3000 samples
+PR.ECPR..HNZ | 2020-01-16T23:27:46.905000Z - 2020-01-16T23:28:16.895000Z | 100.0 Hz, 3000 samples
+PR.ECPR.00.HN1 | 2020-01-16T23:27:46.910000Z - 2020-01-16T23:28:16.900000Z | 100.0 Hz, 3000 samples
+PR.ECPR.00.HN2 | 2020-01-16T23:27:46.910000Z - 2020-01-16T23:28:16.900000Z | 100.0 Hz, 3000 samples
+PR.ECPR.00.HNZ | 2020-01-16T23:27:46.910000Z - 2020-01-16T23:28:16.900000Z | 100.0 Hz, 3000 samples
+PR.EMPR.00.HN1 | 2020-01-16T23:27:46.910000Z - 2020-01-16T23:28:16.900000Z | 100.0 Hz, 3000 samples
+PR.EMPR.00.HN2 | 2020-01-16T23:27:46.910000Z - 2020-01-16T23:28:16.900000Z | 100.0 Hz, 3000 samples
+PR.EMPR.00.HNZ | 2020-01-16T23:27:46.910000Z - 2020-01-16T23:28:16.900000Z | 100.0 Hz, 3000 samples
+PR.HUMP..HNE | 2020-01-16T23:27:46.905000Z - 2020-01-16T23:28:16.895000Z | 100.0 Hz, 3000 samples
+PR.HUMP..HNN | 2020-01-16T23:27:46.905000Z - 2020-01-16T23:28:16.895000Z | 100.0 Hz, 3000 samples
+PR.HUMP..HNZ | 2020-01-16T23:27:46.905000Z - 2020-01-16T23:28:16.895000Z | 100.0 Hz, 3000 samples
+PR.HUMP.00.HN1 | 2020-01-16T23:27:46.910000Z - 2020-01-16T23:28:16.900000Z | 100.0 Hz, 3000 samples
+PR.HUMP.00.HN2 | 2020-01-16T23:27:46.910000Z - 2020-01-16T23:28:16.900000Z | 100.0 Hz, 3000 samples
+PR.HUMP.00.HNZ | 2020-01-16T23:27:46.910000Z - 2020-01-16T23:28:16.900000Z | 100.0 Hz, 3000 samples
+PR.MLPR..HNE | 2020-01-16T23:27:46.910000Z - 2020-01-16T23:28:16.900000Z | 100.0 Hz, 3000 samples
+PR.MLPR..HNN | 2020-01-16T23:27:46.910000Z - 2020-01-16T23:28:16.900000Z | 100.0 Hz, 3000 samples
+PR.MLPR..HNZ | 2020-01-16T23:27:46.910000Z - 2020-01-16T23:28:16.900000Z | 100.0 Hz, 3000 samples
+PR.PRSN..HNE | 2020-01-16T23:27:46.910000Z - 2020-01-16T23:28:16.900000Z | 100.0 Hz, 3000 samples
+PR.PRSN..HNN | 2020-01-16T23:27:46.910000Z - 2020-01-16T23:28:16.900000Z | 100.0 Hz, 3000 samples
+PR.PRSN..HNZ | 2020-01-16T23:27:46.910000Z - 2020-01-16T23:28:16.900000Z | 100.0 Hz, 3000 samples
+PR.PRSN.00.HN1 | 2020-01-16T23:27:46.910000Z - 2020-01-16T23:28:16.900000Z | 100.0 Hz, 3000 samples
+PR.PRSN.00.HN2 | 2020-01-16T23:27:46.910000Z - 2020-01-16T23:28:16.900000Z | 100.0 Hz, 3000 samples
+PR.PRSN.00.HNZ | 2020-01-16T23:27:46.910000Z - 2020-01-16T23:28:16.900000Z | 100.0 Hz, 3000 samples
+PR.UUPR.00.HN1 | 2020-01-16T23:27:46.909999Z - 2020-01-16T23:28:16.899999Z | 100.0 Hz, 3000 samples
+PR.UUPR.00.HN2 | 2020-01-16T23:27:46.910000Z - 2020-01-16T23:28:16.900000Z | 100.0 Hz, 3000 samples
+PR.UUPR.00.HNZ | 2020-01-16T23:27:46.910000Z - 2020-01-16T23:28:16.900000Z | 100.0 Hz, 3000 samples
+</pre>
+</div>
+</div>
+
+</div>
+
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[86]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-14">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">stream</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
+</pre></div>
+<div id="cell-14" class="clipboard-copy-txt">stream.plot()</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+
+
+<div class="jp-RenderedImage jp-OutputArea-output ">
+<img 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+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-15">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
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+            </article>
+            
+          </div>
+        </div>
+        
+      </main>
+      
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+      <script src="../../assets/javascripts/bundle.9c69f0bc.min.js"></script>
+      
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+  </body>
+</html>
\ No newline at end of file
diff --git a/kubeflow/debug_pvc.yaml b/kubeflow/debug_pvc.yaml
new file mode 100644
index 0000000..23bbb52
--- /dev/null
+++ b/kubeflow/debug_pvc.yaml
@@ -0,0 +1,16 @@
+apiVersion: v1
+kind: Pod
+metadata:
+  name: dataaccess
+spec:
+  containers:
+  - name: alpine
+    image: alpine:latest
+    command: ['sleep', 'infinity']
+    volumeMounts:
+    - name: mypvc
+      mountPath: /data
+  volumes:
+  - name: mypvc
+    persistentVolumeClaim:
+      claimName: mypvc
\ No newline at end of file
diff --git a/kubeflow/env.yml b/kubeflow/env.yml
new file mode 100644
index 0000000..ddc1e65
--- /dev/null
+++ b/kubeflow/env.yml
@@ -0,0 +1,22 @@
+name: quakeflow
+channels:
+  - defaults
+  - conda-forge
+dependencies:
+  - python=3.8
+  - numpy
+  - scipy
+  - matplotlib
+  - pandas
+  - scikit-learn
+  - tqdm
+  - obspy
+  - pymongo
+  - tensorflow
+  - pip
+  - pip:
+    - minio
+    - kfp
+    - gmma
+    - --extra-index-url https://pypi.anaconda.org/zhuwq0/simple 
+
diff --git a/kubeflow/index.html b/kubeflow/index.html
new file mode 100644
index 0000000..e15abd5
--- /dev/null
+++ b/kubeflow/index.html
@@ -0,0 +1,327 @@
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+  
+  
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+    <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
+      
+        <li class="md-nav__item">
+  <a href="#quakeflow-demo-install" class="md-nav__link">
+    QuakeFlow Demo Install
+  </a>
+  
+</li>
+      
+    </ul>
+  
+</nav>
+                  </div>
+                </div>
+              </div>
+            
+          
+          <div class="md-content" data-md-component="content">
+            <article class="md-content__inner md-typeset">
+              
+                
+  <a href="https://github.com/ai4eps/QuakeFlow/edit/master/docs/kubeflow/README.md" title="Edit this page" class="md-content__button md-icon">
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
+  </a>
+
+
+
+  <h1>Index</h1>
+
+<h2 id="quakeflow-demo-install">QuakeFlow Demo Install</h2>
+<pre><code>git clone -b factorize https://github.com/wayneweiqiang/PhaseNet.git
+git clone https://github.com/wayneweiqiang/GMMA.git
+conda env create quakeflow --file=env.yml --force
+conda activate quakeflow
+</code></pre>
+
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\ No newline at end of file
diff --git a/kubeflow/plot_catalog/index.html b/kubeflow/plot_catalog/index.html
new file mode 100644
index 0000000..a1d613b
--- /dev/null
+++ b/kubeflow/plot_catalog/index.html
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+  </label>
+  
+    <div class="md-nav__source">
+      <a href="https://github.com/ai4eps/QuakeFlow" title="Go to repository" class="md-source" data-md-component="source">
+  <div class="md-source__icon md-icon">
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+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.1.2 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+  </div>
+  <div class="md-source__repository">
+    AI4EPS/QuakeFlow
+  </div>
+</a>
+    </div>
+  
+  <ul class="md-nav__list" data-md-scrollfix>
+    
+      
+      
+      
+
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../.." class="md-nav__link">
+        Overview
+      </a>
+    </li>
+  
+
+    
+      
+      
+      
+
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../workflow/" class="md-nav__link">
+        Pipeline
+      </a>
+    </li>
+  
+
+    
+      
+      
+      
+
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../data_format/" class="md-nav__link">
+        Standard Formats
+      </a>
+    </li>
+  
+
+    
+  </ul>
+</nav>
+                  </div>
+                </div>
+              </div>
+            
+            
+              
+              <div class="md-sidebar md-sidebar--secondary" data-md-component="sidebar" data-md-type="toc" >
+                <div class="md-sidebar__scrollwrap">
+                  <div class="md-sidebar__inner">
+                    
+
+<nav class="md-nav md-nav--secondary" aria-label="Table of contents">
+  
+  
+  
+    
+  
+  
+</nav>
+                  </div>
+                </div>
+              </div>
+            
+          
+          <div class="md-content" data-md-component="content">
+            <article class="md-content__inner md-typeset">
+              
+                
+  <a href="https://github.com/ai4eps/QuakeFlow/edit/master/docs/kubeflow/plot_catalog.ipynb" title="Edit this page" class="md-content__button md-icon">
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
+  </a>
+
+
+
+<script>
+(function (global, factory) {
+    typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() :
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+  }(this, function () { 'use strict';
+
+    function createNode(text) {
+      const node = document.createElement('pre');
+      node.style.width = '1px';
+      node.style.height = '1px';
+      node.style.position = 'fixed';
+      node.style.top = '5px';
+      node.textContent = text;
+      return node;
+    }
+
+    function copyNode(node) {
+      if ('clipboard' in navigator) {
+        // eslint-disable-next-line flowtype/no-flow-fix-me-comments
+        // $FlowFixMe Clipboard is not defined in Flow yet.
+        return navigator.clipboard.writeText(node.textContent);
+      }
+
+      const selection = getSelection();
+
+      if (selection == null) {
+        return Promise.reject(new Error());
+      }
+
+      selection.removeAllRanges();
+      const range = document.createRange();
+      range.selectNodeContents(node);
+      selection.addRange(range);
+      document.execCommand('copy');
+      selection.removeAllRanges();
+      return Promise.resolve();
+    }
+    function copyText(text) {
+      if ('clipboard' in navigator) {
+        // eslint-disable-next-line flowtype/no-flow-fix-me-comments
+        // $FlowFixMe Clipboard is not defined in Flow yet.
+        return navigator.clipboard.writeText(text);
+      }
+
+      const body = document.body;
+
+      if (!body) {
+        return Promise.reject(new Error());
+      }
+
+      const node = createNode(text);
+      body.appendChild(node);
+      copyNode(node);
+      body.removeChild(node);
+      return Promise.resolve();
+    }
+
+    function copy(button) {
+      const id = button.getAttribute('for');
+      const text = button.getAttribute('value');
+
+      function trigger() {
+        button.dispatchEvent(new CustomEvent('clipboard-copy', {
+          bubbles: true
+        }));
+      }
+
+      if (text) {
+        copyText(text).then(trigger);
+      } else if (id) {
+        const root = 'getRootNode' in Element.prototype ? button.getRootNode() : button.ownerDocument;
+        if (!(root instanceof Document || 'ShadowRoot' in window && root instanceof ShadowRoot)) return;
+        const node = root.getElementById(id);
+        if (node) copyTarget(node).then(trigger);
+      }
+    }
+
+    function copyTarget(content) {
+      if (content instanceof HTMLInputElement || content instanceof HTMLTextAreaElement) {
+        return copyText(content.value);
+      } else if (content instanceof HTMLAnchorElement && content.hasAttribute('href')) {
+        return copyText(content.href);
+      } else {
+        return copyNode(content);
+      }
+    }
+
+    function clicked(event) {
+      const button = event.currentTarget;
+
+      if (button instanceof HTMLElement) {
+        copy(button);
+      }
+    }
+
+    function keydown(event) {
+      if (event.key === ' ' || event.key === 'Enter') {
+        const button = event.currentTarget;
+
+        if (button instanceof HTMLElement) {
+          event.preventDefault();
+          copy(button);
+        }
+      }
+    }
+
+    function focused(event) {
+      event.currentTarget.addEventListener('keydown', keydown);
+    }
+
+    function blurred(event) {
+      event.currentTarget.removeEventListener('keydown', keydown);
+    }
+
+    class ClipboardCopyElement extends HTMLElement {
+      constructor() {
+        super();
+        this.addEventListener('click', clicked);
+        this.addEventListener('focus', focused);
+        this.addEventListener('blur', blurred);
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+      connectedCallback() {
+        if (!this.hasAttribute('tabindex')) {
+          this.setAttribute('tabindex', '0');
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+        if (!this.hasAttribute('role')) {
+          this.setAttribute('role', 'button');
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+      }
+
+      get value() {
+        return this.getAttribute('value') || '';
+      }
+
+      set value(text) {
+        this.setAttribute('value', text);
+      }
+
+    }
+
+    if (!window.customElements.get('clipboard-copy')) {
+      window.ClipboardCopyElement = ClipboardCopyElement;
+      window.customElements.define('clipboard-copy', ClipboardCopyElement);
+    }
+
+    return ClipboardCopyElement;
+
+  }));
+</script>
+<script>
+      document.addEventListener('clipboard-copy', function(event) {
+        const notice = event.target.querySelector('.notice')
+        notice.hidden = false
+        setTimeout(function() {
+          notice.hidden = true
+        }, 1000)
+      })
+</script>
+
+
+
+
+<style type="text/css">
+    
+    pre { line-height: 125%; }
+td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+.highlight-ipynb .hll { background-color: var(--jp-cell-editor-active-background) }
+.highlight-ipynb { background: var(--jp-cell-editor-background); color: var(--jp-mirror-editor-variable-color) }
+.highlight-ipynb .c { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment */
+.highlight-ipynb .err { color: var(--jp-mirror-editor-error-color) } /* Error */
+.highlight-ipynb .k { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword */
+.highlight-ipynb .o { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator */
+.highlight-ipynb .p { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation */
+.highlight-ipynb .ch { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Hashbang */
+.highlight-ipynb .cm { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Multiline */
+.highlight-ipynb .cp { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Preproc */
+.highlight-ipynb .cpf { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.PreprocFile */
+.highlight-ipynb .c1 { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Single */
+.highlight-ipynb .cs { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Special */
+.highlight-ipynb .kc { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Constant */
+.highlight-ipynb .kd { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Declaration */
+.highlight-ipynb .kn { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Namespace */
+.highlight-ipynb .kp { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Pseudo */
+.highlight-ipynb .kr { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Reserved */
+.highlight-ipynb .kt { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Type */
+.highlight-ipynb .m { color: var(--jp-mirror-editor-number-color) } /* Literal.Number */
+.highlight-ipynb .s { color: var(--jp-mirror-editor-string-color) } /* Literal.String */
+.highlight-ipynb .ow { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator.Word */
+.highlight-ipynb .pm { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation.Marker */
+.highlight-ipynb .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */
+.highlight-ipynb .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */
+.highlight-ipynb .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */
+.highlight-ipynb .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */
+.highlight-ipynb .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */
+.highlight-ipynb .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */
+.highlight-ipynb .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */
+.highlight-ipynb .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */
+.highlight-ipynb .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */
+.highlight-ipynb .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */
+.highlight-ipynb .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */
+.highlight-ipynb .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */
+.highlight-ipynb .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */
+.highlight-ipynb .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */
+.highlight-ipynb .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */
+.highlight-ipynb .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */
+.highlight-ipynb .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */
+.highlight-ipynb .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */
+.highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */
+.highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */
+  </style>
+
+
+
+
+<style type="text/css">
+/*
+This file is taken from the built JupyterLab theme.css
+Found on share/nbconvert/templates/lab/static
+Some changes have been made and marked with CHANGE
+*/
+
+.jupyter-wrapper {
+    /* Elevation
+   *
+   * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here:
+   *
+   * https://github.com/material-components/material-components-web
+   * https://material-components-web.appspot.com/elevation.html
+   */
+
+    --jp-shadow-base-lightness: 0;
+    --jp-shadow-umbra-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.2
+    );
+    --jp-shadow-penumbra-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.14
+    );
+    --jp-shadow-ambient-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.12
+    );
+    --jp-elevation-z0: none;
+    --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color),
+        0px 1px 1px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 3px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color),
+        0px 2px 2px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 5px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color),
+        0px 4px 5px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 10px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color),
+        0px 6px 10px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 18px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color),
+        0px 8px 10px 1px var(--jp-shadow-penumbra-color),
+        0px 3px 14px 2px var(--jp-shadow-ambient-color);
+    --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color),
+        0px 12px 17px 2px var(--jp-shadow-penumbra-color),
+        0px 5px 22px 4px var(--jp-shadow-ambient-color);
+    --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color),
+        0px 16px 24px 2px var(--jp-shadow-penumbra-color),
+        0px 6px 30px 5px var(--jp-shadow-ambient-color);
+    --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color),
+        0px 20px 31px 3px var(--jp-shadow-penumbra-color),
+        0px 8px 38px 7px var(--jp-shadow-ambient-color);
+    --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color),
+        0px 24px 38px 3px var(--jp-shadow-penumbra-color),
+        0px 9px 46px 8px var(--jp-shadow-ambient-color);
+
+    /* Borders
+   *
+   * The following variables, specify the visual styling of borders in JupyterLab.
+   */
+
+    --jp-border-width: 1px;
+    --jp-border-color0: var(--md-grey-400);
+    --jp-border-color1: var(--md-grey-400);
+    --jp-border-color2: var(--md-grey-300);
+    --jp-border-color3: var(--md-grey-200);
+    --jp-border-radius: 2px;
+
+    /* UI Fonts
+   *
+   * The UI font CSS variables are used for the typography all of the JupyterLab
+   * user interface elements that are not directly user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-ui-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+    --jp-ui-font-scale-factor: 1.2;
+    --jp-ui-font-size0: 0.83333em;
+    --jp-ui-font-size1: 13px; /* Base font size */
+    --jp-ui-font-size2: 1.2em;
+    --jp-ui-font-size3: 1.44em;
+
+    --jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+
+    /*
+   * Use these font colors against the corresponding main layout colors.
+   * In a light theme, these go from dark to light.
+   */
+
+    /* Defaults use Material Design specification */
+    --jp-ui-font-color0: rgba(0, 0, 0, 1);
+    --jp-ui-font-color1: rgba(0, 0, 0, 0.87);
+    --jp-ui-font-color2: rgba(0, 0, 0, 0.54);
+    --jp-ui-font-color3: rgba(0, 0, 0, 0.38);
+
+    /*
+   * Use these against the brand/accent/warn/error colors.
+   * These will typically go from light to darker, in both a dark and light theme.
+   */
+
+    --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1);
+    --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1);
+    --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7);
+    --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5);
+
+    /* Content Fonts
+   *
+   * Content font variables are used for typography of user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-content-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+    --jp-content-line-height: 1.6;
+    --jp-content-font-scale-factor: 1.2;
+    --jp-content-font-size0: 0.83333em;
+    --jp-content-font-size1: 14px; /* Base font size */
+    --jp-content-font-size2: 1.2em;
+    --jp-content-font-size3: 1.44em;
+    --jp-content-font-size4: 1.728em;
+    --jp-content-font-size5: 2.0736em;
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-content-presentation-font-size1: 17px;
+
+    --jp-content-heading-line-height: 1;
+    --jp-content-heading-margin-top: 1.2em;
+    --jp-content-heading-margin-bottom: 0.8em;
+    --jp-content-heading-font-weight: 500;
+
+    /* Defaults use Material Design specification */
+    --jp-content-font-color0: rgba(0, 0, 0, 1);
+    --jp-content-font-color1: rgba(0, 0, 0, 0.87);
+    --jp-content-font-color2: rgba(0, 0, 0, 0.54);
+    --jp-content-font-color3: rgba(0, 0, 0, 0.38);
+
+    --jp-content-link-color: var(--md-blue-700);
+
+    --jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+
+    /*
+   * Code Fonts
+   *
+   * Code font variables are used for typography of code and other monospaces content.
+   */
+
+    --jp-code-font-size: 13px;
+    --jp-code-line-height: 1.3077; /* 17px for 13px base */
+    --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */
+    --jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono",
+        monospace;
+    --jp-code-font-family: var(--jp-code-font-family-default);
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-code-presentation-font-size: 16px;
+
+    /* may need to tweak cursor width if you change font size */
+    --jp-code-cursor-width0: 1.4px;
+    --jp-code-cursor-width1: 2px;
+    --jp-code-cursor-width2: 4px;
+
+    /* Layout
+   *
+   * The following are the main layout colors use in JupyterLab. In a light
+   * theme these would go from light to dark.
+   */
+
+    --jp-layout-color0: white;
+    --jp-layout-color1: white;
+    --jp-layout-color2: var(--md-grey-200);
+    --jp-layout-color3: var(--md-grey-400);
+    --jp-layout-color4: var(--md-grey-600);
+
+    /* Inverse Layout
+   *
+   * The following are the inverse layout colors use in JupyterLab. In a light
+   * theme these would go from dark to light.
+   */
+
+    --jp-inverse-layout-color0: #111111;
+    --jp-inverse-layout-color1: var(--md-grey-900);
+    --jp-inverse-layout-color2: var(--md-grey-800);
+    --jp-inverse-layout-color3: var(--md-grey-700);
+    --jp-inverse-layout-color4: var(--md-grey-600);
+
+    /* Brand/accent */
+
+    --jp-brand-color0: var(--md-blue-900);
+    --jp-brand-color1: var(--md-blue-700);
+    --jp-brand-color2: var(--md-blue-300);
+    --jp-brand-color3: var(--md-blue-100);
+    --jp-brand-color4: var(--md-blue-50);
+
+    --jp-accent-color0: var(--md-green-900);
+    --jp-accent-color1: var(--md-green-700);
+    --jp-accent-color2: var(--md-green-300);
+    --jp-accent-color3: var(--md-green-100);
+
+    /* State colors (warn, error, success, info) */
+
+    --jp-warn-color0: var(--md-orange-900);
+    --jp-warn-color1: var(--md-orange-700);
+    --jp-warn-color2: var(--md-orange-300);
+    --jp-warn-color3: var(--md-orange-100);
+
+    --jp-error-color0: var(--md-red-900);
+    --jp-error-color1: var(--md-red-700);
+    --jp-error-color2: var(--md-red-300);
+    --jp-error-color3: var(--md-red-100);
+
+    --jp-success-color0: var(--md-green-900);
+    --jp-success-color1: var(--md-green-700);
+    --jp-success-color2: var(--md-green-300);
+    --jp-success-color3: var(--md-green-100);
+
+    --jp-info-color0: var(--md-cyan-900);
+    --jp-info-color1: var(--md-cyan-700);
+    --jp-info-color2: var(--md-cyan-300);
+    --jp-info-color3: var(--md-cyan-100);
+
+    /* Cell specific styles */
+
+    --jp-cell-padding: 5px;
+
+    --jp-cell-collapser-width: 8px;
+    --jp-cell-collapser-min-height: 20px;
+    --jp-cell-collapser-not-active-hover-opacity: 0.6;
+
+    --jp-cell-editor-background: var(--md-grey-100);
+    --jp-cell-editor-border-color: var(--md-grey-300);
+    --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-cell-editor-active-background: var(--jp-layout-color0);
+    --jp-cell-editor-active-border-color: var(--jp-brand-color1);
+
+    --jp-cell-prompt-width: 64px;
+    --jp-cell-prompt-font-family: var(--jp-code-font-family-default);
+    --jp-cell-prompt-letter-spacing: 0px;
+    --jp-cell-prompt-opacity: 1;
+    --jp-cell-prompt-not-active-opacity: 0.5;
+    --jp-cell-prompt-not-active-font-color: var(--md-grey-700);
+    /* A custom blend of MD grey and blue 600
+   * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */
+    --jp-cell-inprompt-font-color: #307fc1;
+    /* A custom blend of MD grey and orange 600
+   * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */
+    --jp-cell-outprompt-font-color: #bf5b3d;
+
+    /* Notebook specific styles */
+
+    --jp-notebook-padding: 10px;
+    --jp-notebook-select-background: var(--jp-layout-color1);
+    --jp-notebook-multiselected-color: var(--md-blue-50);
+
+    /* The scroll padding is calculated to fill enough space at the bottom of the
+  notebook to show one single-line cell (with appropriate padding) at the top
+  when the notebook is scrolled all the way to the bottom. We also subtract one
+  pixel so that no scrollbar appears if we have just one single-line cell in the
+  notebook. This padding is to enable a 'scroll past end' feature in a notebook.
+  */
+    --jp-notebook-scroll-padding: calc(
+        100% - var(--jp-code-font-size) * var(--jp-code-line-height) -
+            var(--jp-code-padding) - var(--jp-cell-padding) - 1px
+    );
+
+    /* Rendermime styles */
+
+    --jp-rendermime-error-background: #fdd;
+    --jp-rendermime-table-row-background: var(--md-grey-100);
+    --jp-rendermime-table-row-hover-background: var(--md-light-blue-50);
+
+    /* Dialog specific styles */
+
+    --jp-dialog-background: rgba(0, 0, 0, 0.25);
+
+    /* Console specific styles */
+
+    --jp-console-padding: 10px;
+
+    /* Toolbar specific styles */
+
+    --jp-toolbar-border-color: var(--jp-border-color1);
+    --jp-toolbar-micro-height: 8px;
+    --jp-toolbar-background: var(--jp-layout-color1);
+    --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24);
+    --jp-toolbar-header-margin: 4px 4px 0px 4px;
+    --jp-toolbar-active-background: var(--md-grey-300);
+
+    /* Statusbar specific styles */
+
+    --jp-statusbar-height: 24px;
+
+    /* Input field styles */
+
+    --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-input-active-background: var(--jp-layout-color1);
+    --jp-input-hover-background: var(--jp-layout-color1);
+    --jp-input-background: var(--md-grey-100);
+    --jp-input-border-color: var(--jp-border-color1);
+    --jp-input-active-border-color: var(--jp-brand-color1);
+    --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3);
+
+    /* General editor styles */
+
+    --jp-editor-selected-background: #d9d9d9;
+    --jp-editor-selected-focused-background: #d7d4f0;
+    --jp-editor-cursor-color: var(--jp-ui-font-color0);
+
+    /* Code mirror specific styles */
+
+    --jp-mirror-editor-keyword-color: #008000;
+    --jp-mirror-editor-atom-color: #88f;
+    --jp-mirror-editor-number-color: #080;
+    --jp-mirror-editor-def-color: #00f;
+    --jp-mirror-editor-variable-color: var(--md-grey-900);
+    --jp-mirror-editor-variable-2-color: #05a;
+    --jp-mirror-editor-variable-3-color: #085;
+    --jp-mirror-editor-punctuation-color: #05a;
+    --jp-mirror-editor-property-color: #05a;
+    --jp-mirror-editor-operator-color: #aa22ff;
+    --jp-mirror-editor-comment-color: #408080;
+    --jp-mirror-editor-string-color: #ba2121;
+    --jp-mirror-editor-string-2-color: #708;
+    --jp-mirror-editor-meta-color: #aa22ff;
+    --jp-mirror-editor-qualifier-color: #555;
+    --jp-mirror-editor-builtin-color: #008000;
+    --jp-mirror-editor-bracket-color: #997;
+    --jp-mirror-editor-tag-color: #170;
+    --jp-mirror-editor-attribute-color: #00c;
+    --jp-mirror-editor-header-color: blue;
+    --jp-mirror-editor-quote-color: #090;
+    --jp-mirror-editor-link-color: #00c;
+    --jp-mirror-editor-error-color: #f00;
+    --jp-mirror-editor-hr-color: #999;
+
+    /* Vega extension styles */
+
+    --jp-vega-background: white;
+
+    /* Sidebar-related styles */
+
+    --jp-sidebar-min-width: 250px;
+
+    /* Search-related styles */
+
+    --jp-search-toggle-off-opacity: 0.5;
+    --jp-search-toggle-hover-opacity: 0.8;
+    --jp-search-toggle-on-opacity: 1;
+    --jp-search-selected-match-background-color: rgb(245, 200, 0);
+    --jp-search-selected-match-color: black;
+    --jp-search-unselected-match-background-color: var(
+        --jp-inverse-layout-color0
+    );
+    --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);
+
+    /* Icon colors that work well with light or dark backgrounds */
+    --jp-icon-contrast-color0: var(--md-purple-600);
+    --jp-icon-contrast-color1: var(--md-green-600);
+    --jp-icon-contrast-color2: var(--md-pink-600);
+    --jp-icon-contrast-color3: var(--md-blue-600);
+}
+
+[data-md-color-scheme="slate"] .jupyter-wrapper {
+    /* Elevation
+   *
+   * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here:
+   *
+   * https://github.com/material-components/material-components-web
+   * https://material-components-web.appspot.com/elevation.html
+   */
+
+    /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout
+   * colors used in the theme as well. */
+    --jp-shadow-base-lightness: 32;
+    --jp-shadow-umbra-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.2
+    );
+    --jp-shadow-penumbra-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.14
+    );
+    --jp-shadow-ambient-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.12
+    );
+    --jp-elevation-z0: none;
+    --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color),
+        0px 1px 1px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 3px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color),
+        0px 2px 2px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 5px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color),
+        0px 4px 5px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 10px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color),
+        0px 6px 10px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 18px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color),
+        0px 8px 10px 1px var(--jp-shadow-penumbra-color),
+        0px 3px 14px 2px var(--jp-shadow-ambient-color);
+    --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color),
+        0px 12px 17px 2px var(--jp-shadow-penumbra-color),
+        0px 5px 22px 4px var(--jp-shadow-ambient-color);
+    --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color),
+        0px 16px 24px 2px var(--jp-shadow-penumbra-color),
+        0px 6px 30px 5px var(--jp-shadow-ambient-color);
+    --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color),
+        0px 20px 31px 3px var(--jp-shadow-penumbra-color),
+        0px 8px 38px 7px var(--jp-shadow-ambient-color);
+    --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color),
+        0px 24px 38px 3px var(--jp-shadow-penumbra-color),
+        0px 9px 46px 8px var(--jp-shadow-ambient-color);
+
+    /* Borders
+   *
+   * The following variables, specify the visual styling of borders in JupyterLab.
+   */
+
+    --jp-border-width: 1px;
+    --jp-border-color0: var(--md-grey-700);
+    --jp-border-color1: var(--md-grey-700);
+    --jp-border-color2: var(--md-grey-800);
+    --jp-border-color3: var(--md-grey-900);
+    --jp-border-radius: 2px;
+
+    /* UI Fonts
+   *
+   * The UI font CSS variables are used for the typography all of the JupyterLab
+   * user interface elements that are not directly user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-ui-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+    --jp-ui-font-scale-factor: 1.2;
+    --jp-ui-font-size0: 0.83333em;
+    --jp-ui-font-size1: 13px; /* Base font size */
+    --jp-ui-font-size2: 1.2em;
+    --jp-ui-font-size3: 1.44em;
+
+    --jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+
+    /*
+   * Use these font colors against the corresponding main layout colors.
+   * In a light theme, these go from dark to light.
+   */
+
+    /* Defaults use Material Design specification */
+    --jp-ui-font-color0: rgba(255, 255, 255, 1);
+    --jp-ui-font-color1: rgba(255, 255, 255, 0.87);
+    --jp-ui-font-color2: rgba(255, 255, 255, 0.54);
+    --jp-ui-font-color3: rgba(255, 255, 255, 0.38);
+
+    /*
+   * Use these against the brand/accent/warn/error colors.
+   * These will typically go from light to darker, in both a dark and light theme.
+   */
+
+    --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1);
+    --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8);
+    --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5);
+    --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3);
+
+    /* Content Fonts
+   *
+   * Content font variables are used for typography of user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-content-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+    --jp-content-line-height: 1.6;
+    --jp-content-font-scale-factor: 1.2;
+    --jp-content-font-size0: 0.83333em;
+    --jp-content-font-size1: 14px; /* Base font size */
+    --jp-content-font-size2: 1.2em;
+    --jp-content-font-size3: 1.44em;
+    --jp-content-font-size4: 1.728em;
+    --jp-content-font-size5: 2.0736em;
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-content-presentation-font-size1: 17px;
+
+    --jp-content-heading-line-height: 1;
+    --jp-content-heading-margin-top: 1.2em;
+    --jp-content-heading-margin-bottom: 0.8em;
+    --jp-content-heading-font-weight: 500;
+
+    /* Defaults use Material Design specification */
+    --jp-content-font-color0: rgba(255, 255, 255, 1);
+    --jp-content-font-color1: rgba(255, 255, 255, 1);
+    --jp-content-font-color2: rgba(255, 255, 255, 0.7);
+    --jp-content-font-color3: rgba(255, 255, 255, 0.5);
+
+    --jp-content-link-color: var(--md-blue-300);
+
+    --jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+
+    /*
+   * Code Fonts
+   *
+   * Code font variables are used for typography of code and other monospaces content.
+   */
+
+    --jp-code-font-size: 13px;
+    --jp-code-line-height: 1.3077; /* 17px for 13px base */
+    --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */
+    --jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono",
+        monospace;
+    --jp-code-font-family: var(--jp-code-font-family-default);
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-code-presentation-font-size: 16px;
+
+    /* may need to tweak cursor width if you change font size */
+    --jp-code-cursor-width0: 1.4px;
+    --jp-code-cursor-width1: 2px;
+    --jp-code-cursor-width2: 4px;
+
+    /* Layout
+   *
+   * The following are the main layout colors use in JupyterLab. In a light
+   * theme these would go from light to dark.
+   */
+
+    --jp-layout-color0: #111111;
+    --jp-layout-color1: var(--md-grey-900);
+    --jp-layout-color2: var(--md-grey-800);
+    --jp-layout-color3: var(--md-grey-700);
+    --jp-layout-color4: var(--md-grey-600);
+
+    /* Inverse Layout
+   *
+   * The following are the inverse layout colors use in JupyterLab. In a light
+   * theme these would go from dark to light.
+   */
+
+    --jp-inverse-layout-color0: white;
+    --jp-inverse-layout-color1: white;
+    --jp-inverse-layout-color2: var(--md-grey-200);
+    --jp-inverse-layout-color3: var(--md-grey-400);
+    --jp-inverse-layout-color4: var(--md-grey-600);
+
+    /* Brand/accent */
+
+    --jp-brand-color0: var(--md-blue-700);
+    --jp-brand-color1: var(--md-blue-500);
+    --jp-brand-color2: var(--md-blue-300);
+    --jp-brand-color3: var(--md-blue-100);
+    --jp-brand-color4: var(--md-blue-50);
+
+    --jp-accent-color0: var(--md-green-700);
+    --jp-accent-color1: var(--md-green-500);
+    --jp-accent-color2: var(--md-green-300);
+    --jp-accent-color3: var(--md-green-100);
+
+    /* State colors (warn, error, success, info) */
+
+    --jp-warn-color0: var(--md-orange-700);
+    --jp-warn-color1: var(--md-orange-500);
+    --jp-warn-color2: var(--md-orange-300);
+    --jp-warn-color3: var(--md-orange-100);
+
+    --jp-error-color0: var(--md-red-700);
+    --jp-error-color1: var(--md-red-500);
+    --jp-error-color2: var(--md-red-300);
+    --jp-error-color3: var(--md-red-100);
+
+    --jp-success-color0: var(--md-green-700);
+    --jp-success-color1: var(--md-green-500);
+    --jp-success-color2: var(--md-green-300);
+    --jp-success-color3: var(--md-green-100);
+
+    --jp-info-color0: var(--md-cyan-700);
+    --jp-info-color1: var(--md-cyan-500);
+    --jp-info-color2: var(--md-cyan-300);
+    --jp-info-color3: var(--md-cyan-100);
+
+    /* Cell specific styles */
+
+    --jp-cell-padding: 5px;
+
+    --jp-cell-collapser-width: 8px;
+    --jp-cell-collapser-min-height: 20px;
+    --jp-cell-collapser-not-active-hover-opacity: 0.6;
+
+    --jp-cell-editor-background: var(--jp-layout-color1);
+    --jp-cell-editor-border-color: var(--md-grey-700);
+    --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-cell-editor-active-background: var(--jp-layout-color0);
+    --jp-cell-editor-active-border-color: var(--jp-brand-color1);
+
+    --jp-cell-prompt-width: 64px;
+    --jp-cell-prompt-font-family: var(--jp-code-font-family-default);
+    --jp-cell-prompt-letter-spacing: 0px;
+    --jp-cell-prompt-opacity: 1;
+    --jp-cell-prompt-not-active-opacity: 1;
+    --jp-cell-prompt-not-active-font-color: var(--md-grey-300);
+
+    /* A custom blend of MD grey and blue 600
+   * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */
+    --jp-cell-inprompt-font-color: #307fc1;
+    /* A custom blend of MD grey and orange 600
+   * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */
+    --jp-cell-outprompt-font-color: #bf5b3d;
+
+    /* Notebook specific styles */
+
+    --jp-notebook-padding: 10px;
+    --jp-notebook-select-background: var(--jp-layout-color1);
+    --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24);
+
+    /* The scroll padding is calculated to fill enough space at the bottom of the
+  notebook to show one single-line cell (with appropriate padding) at the top
+  when the notebook is scrolled all the way to the bottom. We also subtract one
+  pixel so that no scrollbar appears if we have just one single-line cell in the
+  notebook. This padding is to enable a 'scroll past end' feature in a notebook.
+  */
+    --jp-notebook-scroll-padding: calc(
+        100% - var(--jp-code-font-size) * var(--jp-code-line-height) -
+            var(--jp-code-padding) - var(--jp-cell-padding) - 1px
+    );
+
+    /* Rendermime styles */
+
+    --jp-rendermime-error-background: rgba(244, 67, 54, 0.28);
+    --jp-rendermime-table-row-background: var(--md-grey-900);
+    --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2);
+
+    /* Dialog specific styles */
+
+    --jp-dialog-background: rgba(0, 0, 0, 0.6);
+
+    /* Console specific styles */
+
+    --jp-console-padding: 10px;
+
+    /* Toolbar specific styles */
+
+    --jp-toolbar-border-color: var(--jp-border-color2);
+    --jp-toolbar-micro-height: 8px;
+    --jp-toolbar-background: var(--jp-layout-color1);
+    --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8);
+    --jp-toolbar-header-margin: 4px 4px 0px 4px;
+    --jp-toolbar-active-background: var(--jp-layout-color0);
+
+    /* Statusbar specific styles */
+
+    --jp-statusbar-height: 24px;
+
+    /* Input field styles */
+
+    --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-input-active-background: var(--jp-layout-color0);
+    --jp-input-hover-background: var(--jp-layout-color2);
+    --jp-input-background: var(--md-grey-800);
+    --jp-input-border-color: var(--jp-border-color1);
+    --jp-input-active-border-color: var(--jp-brand-color1);
+    --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3);
+
+    /* General editor styles */
+
+    --jp-editor-selected-background: var(--jp-layout-color2);
+    --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24);
+    --jp-editor-cursor-color: var(--jp-ui-font-color0);
+
+    /* Code mirror specific styles */
+
+    --jp-mirror-editor-keyword-color: var(--md-green-500);
+    --jp-mirror-editor-atom-color: var(--md-blue-300);
+    --jp-mirror-editor-number-color: var(--md-green-400);
+    --jp-mirror-editor-def-color: var(--md-blue-600);
+    --jp-mirror-editor-variable-color: var(--md-grey-300);
+    --jp-mirror-editor-variable-2-color: var(--md-blue-400);
+    --jp-mirror-editor-variable-3-color: var(--md-green-600);
+    --jp-mirror-editor-punctuation-color: var(--md-blue-400);
+    --jp-mirror-editor-property-color: var(--md-blue-400);
+    --jp-mirror-editor-operator-color: #aa22ff;
+    --jp-mirror-editor-comment-color: #408080;
+    --jp-mirror-editor-string-color: #ff7070;
+    --jp-mirror-editor-string-2-color: var(--md-purple-300);
+    --jp-mirror-editor-meta-color: #aa22ff;
+    --jp-mirror-editor-qualifier-color: #555;
+    --jp-mirror-editor-builtin-color: var(--md-green-600);
+    --jp-mirror-editor-bracket-color: #997;
+    --jp-mirror-editor-tag-color: var(--md-green-700);
+    --jp-mirror-editor-attribute-color: var(--md-blue-700);
+    --jp-mirror-editor-header-color: var(--md-blue-500);
+    --jp-mirror-editor-quote-color: var(--md-green-300);
+    --jp-mirror-editor-link-color: var(--md-blue-700);
+    --jp-mirror-editor-error-color: #f00;
+    --jp-mirror-editor-hr-color: #999;
+
+    /* Vega extension styles */
+
+    --jp-vega-background: var(--md-grey-400);
+
+    /* Sidebar-related styles */
+
+    --jp-sidebar-min-width: 250px;
+
+    /* Search-related styles */
+
+    --jp-search-toggle-off-opacity: 0.6;
+    --jp-search-toggle-hover-opacity: 0.8;
+    --jp-search-toggle-on-opacity: 1;
+    --jp-search-selected-match-background-color: rgb(255, 225, 0);
+    --jp-search-selected-match-color: black;
+    --jp-search-unselected-match-background-color: var(
+        --jp-inverse-layout-color0
+    );
+    --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);
+
+    /* scrollbar related styles. Supports every browser except Edge. */
+
+    /* colors based on JetBrain's Darcula theme */
+
+    --jp-scrollbar-background-color: #3f4244;
+    --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */
+
+    --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */
+
+    /* hacks for setting the thumb shape. These do nothing in Firefox */
+
+    --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */
+    --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */
+
+    /* Icon colors that work well with light or dark backgrounds */
+    --jp-icon-contrast-color0: var(--md-purple-600);
+    --jp-icon-contrast-color1: var(--md-green-600);
+    --jp-icon-contrast-color2: var(--md-pink-600);
+    --jp-icon-contrast-color3: var(--md-blue-600);
+}
+
+:root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: #7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: #ff5722;--md-deep-orange-600: #f4511e;--md-deep-orange-700: #e64a19;--md-deep-orange-800: #d84315;--md-deep-orange-900: #bf360c;--md-deep-orange-A100: #ff9e80;--md-deep-orange-A200: #ff6e40;--md-deep-orange-A400: #ff3d00;--md-deep-orange-A700: #dd2c00;--md-brown-50: #efebe9;--md-brown-100: #d7ccc8;--md-brown-200: #bcaaa4;--md-brown-300: #a1887f;--md-brown-400: #8d6e63;--md-brown-500: #795548;--md-brown-600: #6d4c41;--md-brown-700: #5d4037;--md-brown-800: #4e342e;--md-brown-900: 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+
+Copyright 2015-present Palantir Technologies, Inc. All rights reserved.
+Licensed under the Apache License, Version 2.0.
+
+*//*!
+
+Copyright 2017-present Palantir Technologies, Inc. All rights reserved.
+Licensed under the Apache License, Version 2.0.
+
+*/}.jupyter-wrapper [data-jp-theme-scrollbars=true]{scrollbar-color:rgb(var(--jp-scrollbar-thumb-color)) var(--jp-scrollbar-background-color)}.jupyter-wrapper [data-jp-theme-scrollbars=true] .CodeMirror-hscrollbar,.jupyter-wrapper [data-jp-theme-scrollbars=true] .CodeMirror-vscrollbar{scrollbar-color:rgba(var(--jp-scrollbar-thumb-color), 0.5) rgba(0,0,0,0)}.jupyter-wrapper [data-jp-theme-scrollbars=true] ::-webkit-scrollbar,.jupyter-wrapper [data-jp-theme-scrollbars=true] ::-webkit-scrollbar-corner{background:var(--jp-scrollbar-background-color)}.jupyter-wrapper [data-jp-theme-scrollbars=true] ::-webkit-scrollbar-thumb{background:rgb(var(--jp-scrollbar-thumb-color));border:var(--jp-scrollbar-thumb-margin) solid rgba(0,0,0,0);background-clip:content-box;border-radius:var(--jp-scrollbar-thumb-radius)}.jupyter-wrapper [data-jp-theme-scrollbars=true] ::-webkit-scrollbar-track:horizontal{border-left:var(--jp-scrollbar-endpad) solid 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+
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+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="simple-visulization">Simple Visulization<a class="anchor-link" href="#simple-visulization">&#182;</a></h1>
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+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[11]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
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+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
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+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">import</span> <span class="nn">os</span>
+<span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">datetime</span>
+<span class="kn">import</span> <span class="nn">matplotlib.dates</span> <span class="k">as</span> <span class="nn">mdates</span>
+<span class="kn">import</span> <span class="nn">pickle</span>
+<span class="kn">import</span> <span class="nn">json</span>
+<span class="c1"># import pygmt</span>
+<span class="o">%</span><span class="n">matplotlib</span> <span class="n">inline</span>
+
+<span class="n">dir_name</span> <span class="o">=</span> <span class="s2">&quot;Demo&quot;</span>
+<span class="c1"># dir_name = &quot;Ridgecrest_demo&quot;</span>
+<span class="c1"># dir_name = &quot;Ridgecrest_oneweek&quot;</span>
+<span class="c1"># dir_name = &quot;SaltonSea&quot;</span>
+<span class="c1"># dir_name = &quot;SanSimeon&quot;</span>
+<span class="c1"># dir_name = &quot;Ridgecrest&quot;</span>
+<span class="c1"># dir_name = &quot;Italy&quot;</span>
+<span class="c1"># dir_name = &quot;Hawaii&quot;</span>
+<span class="c1"># dir_name = &quot;PuertoRico&quot;</span>
+<span class="c1"># dir_name = &quot;Tahoe&quot;</span>
+<span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">dir_name</span><span class="p">):</span>
+    <span class="n">os</span><span class="o">.</span><span class="n">mkdir</span><span class="p">(</span><span class="n">dir_name</span><span class="p">)</span>
+<span class="n">root_dir</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">dir_name</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span>
+</pre></div>
+<div id="cell-1" class="clipboard-copy-txt">import pandas as pd
+import os
+from datetime import datetime
+import matplotlib.dates as mdates
+import pickle
+import json
+# import pygmt
+%matplotlib inline
+
+dir_name = "Demo"
+# dir_name = "Ridgecrest_demo"
+# dir_name = "Ridgecrest_oneweek"
+# dir_name = "SaltonSea"
+# dir_name = "SanSimeon"
+# dir_name = "Ridgecrest"
+# dir_name = "Italy"
+# dir_name = "Hawaii"
+# dir_name = "PuertoRico"
+# dir_name = "Tahoe"
+if not os.path.exists(dir_name):
+    os.mkdir(dir_name)
+root_dir = lambda x: os.path.join(dir_name, x)</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[2]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-2">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">result_label</span><span class="o">=</span><span class="s2">&quot;QuakeFlow&quot;</span>
+<span class="n">catalog_label</span><span class="o">=</span><span class="s2">&quot;Standard&quot;</span>
+<span class="c1"># catalog_label=&quot;SCSN&quot;</span>
+<span class="c1"># catalog_label=&quot;IRIS&quot;</span>
+<span class="n">catalog_file</span> <span class="o">=</span> <span class="s2">&quot;gamma_catalog.csv&quot;</span>
+<span class="n">picks_file</span> <span class="o">=</span> <span class="s2">&quot;gamm_picks.csv&quot;</span>
+</pre></div>
+<div id="cell-2" class="clipboard-copy-txt">result_label="QuakeFlow"
+catalog_label="Standard"
+# catalog_label="SCSN"
+# catalog_label="IRIS"
+catalog_file = "gamma_catalog.csv"
+picks_file = "gamm_picks.csv"</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[10]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-3">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;config.json&quot;</span><span class="p">),</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+    <span class="n">config</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">fp</span><span class="p">)</span>
+<span class="c1"># stations = pd.read_csv(root_dir(&quot;stations.csv&quot;))#, delimiter=&quot;\t&quot;)</span>
+<span class="n">stations</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_json</span><span class="p">(</span><span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;stations.json&quot;</span><span class="p">),</span> <span class="n">orient</span><span class="o">=</span><span class="s2">&quot;index&quot;</span><span class="p">)</span>
+<span class="n">events</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;standard_catalog.csv&quot;</span><span class="p">))</span><span class="c1">#, delimiter=&quot;\t&quot;)</span>
+<span class="n">events</span><span class="p">[</span><span class="s2">&quot;time&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">events</span><span class="p">[</span><span class="s2">&quot;time&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">datetime</span><span class="o">.</span><span class="n">strptime</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="s2">&quot;%Y-%m-</span><span class="si">%d</span><span class="s2">T%H:%M:%S.</span><span class="si">%f</span><span class="s2">&quot;</span><span class="p">))</span>
+
+<span class="n">catalog</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">root_dir</span><span class="p">(</span><span class="n">catalog_file</span><span class="p">))</span><span class="c1">#, delimiter=&quot;\t&quot;)</span>
+<span class="n">catalog</span><span class="p">[</span><span class="s2">&quot;time&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">catalog</span><span class="p">[</span><span class="s2">&quot;time&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">datetime</span><span class="o">.</span><span class="n">strptime</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="s2">&quot;%Y-%m-</span><span class="si">%d</span><span class="s2">T%H:%M:%S.</span><span class="si">%f</span><span class="s2">&quot;</span><span class="p">))</span>
+<span class="c1"># catalog[&quot;covariance&quot;] = catalog[&quot;covariance&quot;].apply(lambda x: [float(i) for i  in x.split(&quot;,&quot;)])</span>
+
+<span class="n">marker_size</span> <span class="o">=</span> <span class="mf">0.1</span> <span class="o">*</span> <span class="mi">10</span>
+<span class="n">bins</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">catalog</span><span class="p">[</span><span class="s2">&quot;time&quot;</span><span class="p">])</span><span class="o">//</span><span class="mi">50</span> <span class="o">+</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">50</span><span class="p">)</span>
+</pre></div>
+<div id="cell-3" class="clipboard-copy-txt">with open(root_dir("config.json"), "r") as fp:
+    config = json.load(fp)
+# stations = pd.read_csv(root_dir("stations.csv"))#, delimiter="\t")
+stations = pd.read_json(root_dir("stations.json"), orient="index")
+events = pd.read_csv(root_dir("standard_catalog.csv"))#, delimiter="\t")
+events["time"] = events["time"].apply(lambda x: datetime.strptime(x, "%Y-%m-%dT%H:%M:%S.%f"))
+
+catalog = pd.read_csv(root_dir(catalog_file))#, delimiter="\t")
+catalog["time"] = catalog["time"].apply(lambda x: datetime.strptime(x, "%Y-%m-%dT%H:%M:%S.%f"))
+# catalog["covariance"] = catalog["covariance"].apply(lambda x: [float(i) for i  in x.split(",")])
+
+marker_size = 0.1 * 10
+bins = min(len(catalog["time"])//50 + 10, 50)</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[4]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-4">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># region = config[&quot;xlim_degree&quot;] + config[&quot;ylim_degree&quot;]</span>
+<span class="c1"># fig = pygmt.Figure()</span>
+<span class="c1"># fig.basemap(region=region, projection=&quot;M8i&quot;, frame=True)</span>
+<span class="c1"># fig.grdimage(&quot;@earth_relief_15s&quot;, cmap=&quot;topo&quot;, shading=True)</span>
+<span class="c1"># # fig.grdimage(&quot;@earth_relief_15s&quot;, cmap=&quot;sealand&quot;, shading=True)</span>
+<span class="c1"># fig.plot(x=stations[&quot;longitude&quot;], y=stations[&quot;latitude&quot;], style=&quot;t0.5&quot;, color=&quot;blue&quot;, pen=&quot;black&quot;, label=&quot;Station&quot;)</span>
+<span class="c1"># fig.savefig(root_dir(f&quot;stations-{dir_name.lower()}.png&quot;))</span>
+<span class="c1"># fig.savefig(root_dir(f&quot;stations-{dir_name.lower()}.pdf&quot;))</span>
+<span class="c1"># fig.show()</span>
+</pre></div>
+<div id="cell-4" class="clipboard-copy-txt"># region = config["xlim_degree"] + config["ylim_degree"]
+# fig = pygmt.Figure()
+# fig.basemap(region=region, projection="M8i", frame=True)
+# fig.grdimage("@earth_relief_15s", cmap="topo", shading=True)
+# # fig.grdimage("@earth_relief_15s", cmap="sealand", shading=True)
+# fig.plot(x=stations["longitude"], y=stations["latitude"], style="t0.5", color="blue", pen="black", label="Station")
+# fig.savefig(root_dir(f"stations-{dir_name.lower()}.png"))
+# fig.savefig(root_dir(f"stations-{dir_name.lower()}.pdf"))
+# fig.show()</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[5]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-5">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">catalog</span><span class="p">[</span><span class="s2">&quot;time&quot;</span><span class="p">],</span> <span class="nb">range</span><span class="o">=</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;starttime&quot;</span><span class="p">],</span> <span class="n">config</span><span class="p">[</span><span class="s2">&quot;endtime&quot;</span><span class="p">]),</span> <span class="n">bins</span><span class="o">=</span><span class="n">bins</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s2">&quot;k&quot;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">result_label</span><span class="si">}</span><span class="s2">: </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">catalog</span><span class="p">[</span><span class="s1">&#39;time&#39;</span><span class="p">])</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">events</span><span class="p">[</span><span class="s2">&quot;time&quot;</span><span class="p">],</span> <span class="nb">range</span><span class="o">=</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;starttime&quot;</span><span class="p">],</span> <span class="n">config</span><span class="p">[</span><span class="s2">&quot;endtime&quot;</span><span class="p">]),</span> <span class="n">bins</span><span class="o">=</span><span class="n">bins</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s2">&quot;k&quot;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">catalog_label</span><span class="si">}</span><span class="s2">: </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">events</span><span class="p">[</span><span class="s1">&#39;time&#39;</span><span class="p">])</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">&quot;Frequency&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">&quot;Date&quot;</span><span class="p">)</span>
+<span class="c1"># plt.yscale(&quot;log&quot;)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span><span class="o">.</span><span class="n">autoscale</span><span class="p">(</span><span class="n">enable</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="s1">&#39;x&#39;</span><span class="p">,</span> <span class="n">tight</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span><span class="o">.</span><span class="n">xaxis</span><span class="o">.</span><span class="n">set_major_formatter</span><span class="p">(</span><span class="n">mdates</span><span class="o">.</span><span class="n">DateFormatter</span><span class="p">(</span><span class="s1">&#39;%m-</span><span class="si">%d</span><span class="s1">:%H&#39;</span><span class="p">))</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">gcf</span><span class="p">()</span><span class="o">.</span><span class="n">autofmt_xdate</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;earthquake_number.png&quot;</span><span class="p">),</span> <span class="n">bbox_inches</span><span class="o">=</span><span class="s2">&quot;tight&quot;</span><span class="p">,</span> <span class="n">dpi</span><span class="o">=</span><span class="mi">300</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;earthquake_number.pdf&quot;</span><span class="p">),</span> <span class="n">bbox_inches</span><span class="o">=</span><span class="s2">&quot;tight&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+<div id="cell-5" class="clipboard-copy-txt">plt.figure()
+plt.hist(catalog["time"], range=(config["starttime"], config["endtime"]), bins=bins, edgecolor="k", alpha=1.0, linewidth=0.5, label=f"{result_label}: {len(catalog['time'])}")
+plt.hist(events["time"], range=(config["starttime"], config["endtime"]), bins=bins, edgecolor="k", alpha=1.0, linewidth=0.5, label=f"{catalog_label}: {len(events['time'])}")
+plt.ylabel("Frequency")
+plt.xlabel("Date")
+# plt.yscale("log")
+plt.gca().autoscale(enable=True, axis='x', tight=True)
+plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%m-%d:%H'))
+plt.gcf().autofmt_xdate()
+plt.legend()
+plt.savefig(root_dir("earthquake_number.png"), bbox_inches="tight", dpi=300)
+plt.savefig(root_dir("earthquake_number.pdf"), bbox_inches="tight")
+plt.show()</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+
+
+<div class="jp-RenderedImage jp-OutputArea-output ">
+<img 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+            <div>
+                <span class="notice" hidden>Copied!</span>
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+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s2">&quot;figure.figsize&quot;</span><span class="p">]</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">1.5</span><span class="p">,</span><span class="mi">1</span><span class="p">]))</span>
+<span class="n">box</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">boxstyle</span><span class="o">=</span><span class="s1">&#39;round&#39;</span><span class="p">,</span> <span class="n">facecolor</span><span class="o">=</span><span class="s1">&#39;white&#39;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="n">text_loc</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.05</span><span class="p">,</span> <span class="mf">0.92</span><span class="p">]</span>
+<span class="n">grd</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_gridspec</span><span class="p">(</span><span class="n">ncols</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">nrows</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">width_ratios</span><span class="o">=</span><span class="p">[</span><span class="mf">1.5</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">height_ratios</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">])</span>
+<span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="n">grd</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">])</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">stations</span><span class="p">[</span><span class="s2">&quot;longitude&quot;</span><span class="p">],</span> <span class="n">stations</span><span class="p">[</span><span class="s2">&quot;latitude&quot;</span><span class="p">],</span> <span class="s1">&#39;k^&#39;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;Stations&quot;</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">catalog</span><span class="p">[</span><span class="s2">&quot;longitude&quot;</span><span class="p">],</span> <span class="n">catalog</span><span class="p">[</span><span class="s2">&quot;latitude&quot;</span><span class="p">],</span> <span class="s1">&#39;.&#39;</span><span class="p">,</span><span class="n">markersize</span><span class="o">=</span><span class="n">marker_size</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">rasterized</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">events</span><span class="p">[</span><span class="s2">&quot;longitude&quot;</span><span class="p">],</span> <span class="n">events</span><span class="p">[</span><span class="s2">&quot;latitude&quot;</span><span class="p">],</span> <span class="s1">&#39;.&#39;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="n">marker_size</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.6</span><span class="p">,</span> <span class="n">rasterized</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s2">&quot;scaled&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;xlim_degree&quot;</span><span class="p">]))</span><span class="c1">#+np.array([0.2,-0.27]))</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;ylim_degree&quot;</span><span class="p">]))</span><span class="c1">#+np.array([0.2,-0.27]))</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">&quot;Latitude&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">&quot;Longitude&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span><span class="o">.</span><span class="n">set_prop_cycle</span><span class="p">(</span><span class="kc">None</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;xlim_degree&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">-</span><span class="mi">10</span><span class="p">,</span> <span class="n">config</span><span class="p">[</span><span class="s2">&quot;ylim_degree&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">-</span><span class="mi">10</span><span class="p">,</span> <span class="s1">&#39;.&#39;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">result_label</span><span class="si">}</span><span class="s2">: </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">catalog</span><span class="p">[</span><span class="s1">&#39;time&#39;</span><span class="p">])</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;xlim_degree&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">-</span><span class="mi">10</span><span class="p">,</span> <span class="n">config</span><span class="p">[</span><span class="s2">&quot;ylim_degree&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">-</span><span class="mi">10</span><span class="p">,</span> <span class="s1">&#39;.&#39;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">catalog_label</span><span class="si">}</span><span class="s2">: </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">events</span><span class="p">[</span><span class="s1">&#39;time&#39;</span><span class="p">])</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s2">&quot;lower right&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="n">text_loc</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">text_loc</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="s1">&#39;(i)&#39;</span><span class="p">,</span> <span class="n">horizontalalignment</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">,</span> <span class="n">verticalalignment</span><span class="o">=</span><span class="s2">&quot;top&quot;</span><span class="p">,</span> 
+         <span class="n">transform</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span><span class="o">.</span><span class="n">transAxes</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="s2">&quot;large&quot;</span><span class="p">,</span> <span class="n">fontweight</span><span class="o">=</span><span class="s2">&quot;normal&quot;</span><span class="p">,</span> <span class="n">bbox</span><span class="o">=</span><span class="n">box</span><span class="p">)</span>
+
+<span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="n">grd</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">catalog</span><span class="p">[</span><span class="s2">&quot;longitude&quot;</span><span class="p">],</span> <span class="n">catalog</span><span class="p">[</span><span class="s2">&quot;depth(m)&quot;</span><span class="p">]</span><span class="o">/</span><span class="mf">1e3</span><span class="p">,</span> <span class="s1">&#39;.&#39;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="n">marker_size</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">rasterized</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">events</span><span class="p">[</span><span class="s2">&quot;longitude&quot;</span><span class="p">],</span> <span class="n">events</span><span class="p">[</span><span class="s2">&quot;depth(m)&quot;</span><span class="p">]</span><span class="o">/</span><span class="mf">1e3</span><span class="p">,</span> <span class="s1">&#39;.&#39;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="n">marker_size</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.6</span><span class="p">,</span> <span class="n">rasterized</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="c1"># plt.axis(&quot;scaled&quot;)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;xlim_degree&quot;</span><span class="p">]))</span><span class="c1">#+np.array([0.2,-0.27]))</span>
+<span class="c1"># plt.ylim([0,21])</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">(</span><span class="n">bottom</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">top</span><span class="o">=</span><span class="mi">41</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span><span class="o">.</span><span class="n">invert_yaxis</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">&quot;Longitude&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">&quot;Depth (km)&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span><span class="o">.</span><span class="n">set_prop_cycle</span><span class="p">(</span><span class="kc">None</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;xlim_degree&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">-</span><span class="mi">10</span><span class="p">,</span> <span class="mi">31</span><span class="p">,</span> <span class="s1">&#39;.&#39;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">result_label</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="mi">31</span><span class="p">,</span> <span class="mi">31</span><span class="p">,</span> <span class="s1">&#39;.&#39;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">catalog_label</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s2">&quot;lower right&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="n">text_loc</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">text_loc</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="s1">&#39;(ii)&#39;</span><span class="p">,</span> <span class="n">horizontalalignment</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">,</span> <span class="n">verticalalignment</span><span class="o">=</span><span class="s2">&quot;top&quot;</span><span class="p">,</span> 
+         <span class="n">transform</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span><span class="o">.</span><span class="n">transAxes</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="s2">&quot;large&quot;</span><span class="p">,</span> <span class="n">fontweight</span><span class="o">=</span><span class="s2">&quot;normal&quot;</span><span class="p">,</span> <span class="n">bbox</span><span class="o">=</span><span class="n">box</span><span class="p">)</span>
+
+<span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="n">grd</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">catalog</span><span class="p">[</span><span class="s2">&quot;latitude&quot;</span><span class="p">],</span> <span class="n">catalog</span><span class="p">[</span><span class="s2">&quot;depth(m)&quot;</span><span class="p">]</span><span class="o">/</span><span class="mf">1e3</span><span class="p">,</span> <span class="s1">&#39;.&#39;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="n">marker_size</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">rasterized</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">events</span><span class="p">[</span><span class="s2">&quot;latitude&quot;</span><span class="p">],</span> <span class="n">events</span><span class="p">[</span><span class="s2">&quot;depth(m)&quot;</span><span class="p">]</span><span class="o">/</span><span class="mf">1e3</span><span class="p">,</span> <span class="s1">&#39;.&#39;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="n">marker_size</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.6</span><span class="p">,</span> <span class="n">rasterized</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="c1"># plt.axis(&quot;scaled&quot;)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;ylim_degree&quot;</span><span class="p">]))</span><span class="c1">#+np.array([0.2,-0.27]))</span>
+<span class="c1"># plt.ylim([0,21])</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">(</span><span class="n">bottom</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">top</span><span class="o">=</span><span class="mi">41</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span><span class="o">.</span><span class="n">invert_yaxis</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">&quot;Latitude&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">&quot;Depth (km)&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span><span class="o">.</span><span class="n">set_prop_cycle</span><span class="p">(</span><span class="kc">None</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;ylim_degree&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">-</span><span class="mi">10</span><span class="p">,</span> <span class="mi">31</span><span class="p">,</span> <span class="s1">&#39;.&#39;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">result_label</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="mi">31</span><span class="p">,</span> <span class="mi">31</span><span class="p">,</span> <span class="s1">&#39;.&#39;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">catalog_label</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s2">&quot;lower left&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="n">text_loc</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">text_loc</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="s1">&#39;(iii)&#39;</span><span class="p">,</span> <span class="n">horizontalalignment</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">,</span> <span class="n">verticalalignment</span><span class="o">=</span><span class="s2">&quot;top&quot;</span><span class="p">,</span> 
+         <span class="n">transform</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span><span class="o">.</span><span class="n">transAxes</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="s2">&quot;large&quot;</span><span class="p">,</span> <span class="n">fontweight</span><span class="o">=</span><span class="s2">&quot;normal&quot;</span><span class="p">,</span> <span class="n">bbox</span><span class="o">=</span><span class="n">box</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;earthquake_location.png&quot;</span><span class="p">),</span> <span class="n">bbox_inches</span><span class="o">=</span><span class="s2">&quot;tight&quot;</span><span class="p">,</span> <span class="n">dpi</span><span class="o">=</span><span class="mi">300</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;earthquake_location.pdf&quot;</span><span class="p">),</span> <span class="n">bbox_inches</span><span class="o">=</span><span class="s2">&quot;tight&quot;</span><span class="p">,</span> <span class="n">dpi</span><span class="o">=</span><span class="mi">300</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+<div id="cell-6" class="clipboard-copy-txt">fig = plt.figure(figsize=plt.rcParams["figure.figsize"]*np.array([1.5,1]))
+box = dict(boxstyle='round', facecolor='white', alpha=1)
+text_loc = [0.05, 0.92]
+grd = fig.add_gridspec(ncols=2, nrows=2, width_ratios=[1.5, 1], height_ratios=[1,1])
+fig.add_subplot(grd[:, 0])
+plt.plot(stations["longitude"], stations["latitude"], 'k^', markersize=3, alpha=0.5, label="Stations")
+
+plt.plot(catalog["longitude"], catalog["latitude"], '.',markersize=marker_size, alpha=1.0, rasterized=True)
+plt.plot(events["longitude"], events["latitude"], '.', markersize=marker_size, alpha=0.6, rasterized=True)
+plt.axis("scaled")
+plt.xlim(np.array(config["xlim_degree"]))#+np.array([0.2,-0.27]))
+plt.ylim(np.array(config["ylim_degree"]))#+np.array([0.2,-0.27]))
+plt.ylabel("Latitude")
+plt.xlabel("Longitude")
+plt.gca().set_prop_cycle(None)
+plt.plot(config["xlim_degree"][0]-10, config["ylim_degree"][0]-10, '.', markersize=10, label=f"{result_label}: {len(catalog['time'])}")
+plt.plot(config["xlim_degree"][0]-10, config["ylim_degree"][0]-10, '.', markersize=10, label=f"{catalog_label}: {len(events['time'])}")
+plt.legend(loc="lower right")
+plt.text(text_loc[0], text_loc[1], '(i)', horizontalalignment='left', verticalalignment="top", 
+         transform=plt.gca().transAxes, fontsize="large", fontweight="normal", bbox=box)
+
+fig.add_subplot(grd[0, 1])
+plt.plot(catalog["longitude"], catalog["depth(m)"]/1e3, '.', markersize=marker_size, alpha=1.0, rasterized=True)
+plt.plot(events["longitude"], events["depth(m)"]/1e3, '.', markersize=marker_size, alpha=0.6, rasterized=True)
+# plt.axis("scaled")
+plt.xlim(np.array(config["xlim_degree"]))#+np.array([0.2,-0.27]))
+# plt.ylim([0,21])
+plt.ylim(bottom=0, top=41)
+plt.gca().invert_yaxis()
+plt.xlabel("Longitude")
+plt.ylabel("Depth (km)")
+plt.gca().set_prop_cycle(None)
+plt.plot(config["xlim_degree"][0]-10, 31, '.', markersize=10, label=f"{result_label}")
+plt.plot(31, 31, '.', markersize=10, label=f"{catalog_label}")
+plt.legend(loc="lower right")
+plt.text(text_loc[0], text_loc[1], '(ii)', horizontalalignment='left', verticalalignment="top", 
+         transform=plt.gca().transAxes, fontsize="large", fontweight="normal", bbox=box)
+
+fig.add_subplot(grd[1, 1])
+plt.plot(catalog["latitude"], catalog["depth(m)"]/1e3, '.', markersize=marker_size, alpha=1.0, rasterized=True)
+plt.plot(events["latitude"], events["depth(m)"]/1e3, '.', markersize=marker_size, alpha=0.6, rasterized=True)
+# plt.axis("scaled")
+plt.xlim(np.array(config["ylim_degree"]))#+np.array([0.2,-0.27]))
+# plt.ylim([0,21])
+plt.ylim(bottom=0, top=41)
+plt.gca().invert_yaxis()
+plt.xlabel("Latitude")
+plt.ylabel("Depth (km)")
+plt.gca().set_prop_cycle(None)
+plt.plot(config["ylim_degree"][0]-10, 31, '.', markersize=10, label=f"{result_label}")
+plt.plot(31, 31, '.', markersize=10, label=f"{catalog_label}")
+plt.legend(loc="lower left")
+plt.tight_layout()
+plt.text(text_loc[0], text_loc[1], '(iii)', horizontalalignment='left', verticalalignment="top", 
+         transform=plt.gca().transAxes, fontsize="large", fontweight="normal", bbox=box)
+plt.savefig(root_dir("earthquake_location.png"), bbox_inches="tight", dpi=300)
+plt.savefig(root_dir("earthquake_location.pdf"), bbox_inches="tight", dpi=300)
+plt.show()</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+
+
+<div class="jp-RenderedImage jp-OutputArea-output ">
+<img 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+        <div class="zeroclipboard-container">
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+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
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+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># catalog[&quot;magnitude&quot;] += np.log(np.sqrt(3))</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">xrange</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mf">1.</span><span class="p">,</span> <span class="nb">max</span><span class="p">(</span><span class="n">events</span><span class="p">[</span><span class="s2">&quot;magnitude&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">(),</span> <span class="n">catalog</span><span class="p">[</span><span class="s2">&quot;magnitude&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">()))</span>
+<span class="c1"># xrange = (-1., events[&quot;magnitude&quot;].max())</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">catalog</span><span class="p">[</span><span class="s2">&quot;magnitude&quot;</span><span class="p">],</span> <span class="nb">range</span><span class="o">=</span><span class="n">xrange</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="n">bins</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span>  <span class="n">edgecolor</span><span class="o">=</span><span class="s2">&quot;k&quot;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">result_label</span><span class="si">}</span><span class="s2">: </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">catalog</span><span class="p">[</span><span class="s1">&#39;magnitude&#39;</span><span class="p">])</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">events</span><span class="p">[</span><span class="s2">&quot;magnitude&quot;</span><span class="p">],</span> <span class="nb">range</span><span class="o">=</span><span class="n">xrange</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="n">bins</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.6</span><span class="p">,</span>  <span class="n">edgecolor</span><span class="o">=</span><span class="s2">&quot;k&quot;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">catalog_label</span><span class="si">}</span><span class="s2">: </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">events</span><span class="p">[</span><span class="s1">&#39;magnitude&#39;</span><span class="p">])</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="n">xrange</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">&quot;Magnitude&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">&quot;Frequency&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span><span class="o">.</span><span class="n">set_yscale</span><span class="p">(</span><span class="s1">&#39;log&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;earthquake_magnitude_frequency.png&quot;</span><span class="p">),</span> <span class="n">bbox_inches</span><span class="o">=</span><span class="s2">&quot;tight&quot;</span><span class="p">,</span> <span class="n">dpi</span><span class="o">=</span><span class="mi">300</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;earthquake_magnitude_frequency.pdf&quot;</span><span class="p">),</span> <span class="n">bbox_inches</span><span class="o">=</span><span class="s2">&quot;tight&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+<div id="cell-7" class="clipboard-copy-txt"># catalog["magnitude"] += np.log(np.sqrt(3))
+plt.figure()
+xrange = (-1., max(events["magnitude"].max(), catalog["magnitude"].max()))
+# xrange = (-1., events["magnitude"].max())
+plt.hist(catalog["magnitude"], range=xrange, bins=bins, alpha=1.0,  edgecolor="k", linewidth=0.5, label=f"{result_label}: {len(catalog['magnitude'])}")
+plt.hist(events["magnitude"], range=xrange, bins=bins, alpha=0.6,  edgecolor="k", linewidth=0.5, label=f"{catalog_label}: {len(events['magnitude'])}")
+plt.legend()
+plt.xlim(xrange)
+plt.xlabel("Magnitude")
+plt.ylabel("Frequency")
+plt.gca().set_yscale('log')
+plt.savefig(root_dir("earthquake_magnitude_frequency.png"), bbox_inches="tight", dpi=300)
+plt.savefig(root_dir("earthquake_magnitude_frequency.pdf"), bbox_inches="tight")
+plt.show()</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+
+
+<div class="jp-RenderedImage jp-OutputArea-output ">
+<img 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ZpkHFaWrzuYf0eFgohUkpaWRlFRkYIhybk7RUVFpKWl1ep1uvpIRCpp27YthYWFNS7FIPVfWloabdu2rdVrFAoiUknjxo1p3759otuQBEnK4SPt0SwiEh9JGQq6JFVEJD6SMhRERCQ+FAoiIhJQKIiISEChICIiAYWCiIgEFAoiIhLQh9dEaqlo82YGXj4+lFodjsnk7tsOvIWjSF1TKEi945tW0XnXF6HVa1xWElotgFJL5aOw9nv49Jlw6oiEJClDocImO4luReKgWco2zu96ZGj1nlhbFlqtsK1Z9W5oZx2gMw/55pIyFNx9LjA3Nzd3TKJ7Efkm0vd8zr5dx4RW78M3V4VWSw5NSRkKIomUsruEzh8+HEqtxqmbuazrjlBqAcxZti20WnJoUiiI1FKzJmUMD+kHeX0e2pJDky5JFRGRgEJBREQCCgUREQkoFEREJKBQEBGRgEJBREQCCgUREQkoFEREJJCUoWBmeWY2o7i4ONGtiIg0KEkZCu4+193HZmRkJLoVEZEGJSlDQURE4kOhICIiAYWCiIgEFAoiIhLQ0tkiDcju4s3MvXtyaPUaN2vHoBFXhlZP6j+FgkgDktFoL3ltt4RWb25haKUkSWj4SEREAgoFEREJaPhIQjHuxoms/SycYQvfshVoFUotEakdhYKEYu1nW1jTZlAotdqvejaUOiJSexo+EhGRgEJBREQCCgUREQkoFEREJKBQEBGRgEJBREQCSRkK2nlNRCQ+kjIUtPOaiEh8JGUoiIhIfCgUREQkoFAQEZGAQkFERAIxhYKZnRjvRkREJPFiXSV1upk1AWYCD7v71rh1JCIHbeeO7fzzxZdDqzdvfVPyxoVWTpJATKHg7meaWWfgSmCZmb0OPODuz8W1OxGplX2kUHzEsaHV27JtY2i1JDnEPKfg7u8DvwR+BvQHppnZajO7IF7NiYhI3Yp1TqGnmf0BWAWcA+S5+wnR23+IY38iIlKHYp1T+CPwZ+AX7r6z/KC7bzCzX8alMxERqXOxhsIQYKe7lwGYWQqQ5u473H1W3LoTEZE6FeucwvPA4RXup0ePiYhIAxJrKKS5+7byO9Hb6fFpSUREEiXWUNhuZjnld8ysD7CzmueLiEgSinVO4XrgUTPbEL3fChgRl45ERCRhYv3w2lIzOx7oChiw2t33xrUzERGpc7GeKQCcDGRHX3OSmeHuD8WlKxGpF3YXb2bu3ZNDqdW4WTsGjbgylFoSPzGFgpnNAjoCBUBZ9LADCgWRBiyj0V7y2m4JpdbcwlDKSJzFeqaQC3Rzd49nMyIiklixXn30NnBMPBsREZHEi/VMIQt4N7o66u7yg+5+fly6EhGRhIg1FCbHswkREakfYr0kdZGZHQd0dvfnzSwdSI1vayIiUtdivfpoDDAWaEbkKqQ2wHTg3Pi1JhWNu3Eiaz8L5yoQgA7HZHL3bVNDqyciDUOsw0fXAqcAr0Fkwx0zaxlmI2bWAZgAZLj7RWHWbgjWfraFNW0GhVfw02fCqyUiDUasVx/tdvc95XfMrBGRzylUy8zuN7PPzezt/Y4PMrM1ZvaBmd0M4O5r3f0HtWleRETCFWsoLDKzXwCHm9l/AY8Cc2N43Uyg0q+3ZpYK/AkYDHQDRppZt5g7FhGRuIl1+Ohm4AfAW8BVwDzg3ppe5O6LzSx7v8OnAB+4+1oAM/sbMAx4N5ZGzGwskfkN2rVrF2P7InIwdu7Yzj9ffDmUWvPWNyVvXCilJI5ivfpoH5HtOP8cwnu2AdZXuF8InGpmzYF8Iusq/dzdf3OAXmYAMwByc3P1CWuRONpHCsVHHBtKrS3bNoZSR+Ir1quPPqKKOQR373AQ72lVHHN3LwKuPoh6IiISktqsfVQuDbiYyOWpB6MQqPirR1tgwwGeKyIidSimiWZ3L6rw51N3vwM45yDfcynQ2czam1kT4HvAnIOsJSIiIYp1+Cinwt0UImcOR8bwur8CA4AsMysEJrn7fWZ2HfAskU9F3+/u79SmaTPLA/I6depUm5eJiEgNYh0++n2F26XAx8B/1/Qidx95gOPziFzBdFDcfS4wNzc3d8zB1hARka+L9eqjs+PdiIiIJF6sw0c/qe5xd//fcNoREZFEqs3VRyfz1YRwHrCYyp83EBGRJFebTXZy3P1LADObDDzq7j+MV2PVORQnmn3TKjrv+iK8esWbQqslIg1HrKHQDthT4f4eIDv0bmJ0KE40N0vZxvlda7zgK2Zzlm0LrZaINByxhsIs4HUze4LIJ5uHAw/FrSsREUmIWK8+yjez+cBZ0UOj3f2N+LUlIiKJEOvS2QDpQIm73wkUmln7OPUkIiIJElMomNkk4GfAz6OHGgN/iVdTIiKSGLGeKQwHzge2A7j7BmJY5iJezCzPzGYUFxcnqgURkQYp1lDY4+5OdPlsMzsifi3VzN3nuvvYjIyMRLYhItLgxBoKj5jZPcDRZjYGeJ5wNtwREZF6pMarj8zMgL8DxwMlQFdgors/F+feRESkjtUYCu7uZvaku/cBFAQiIg1YrMNHr5rZyXHtREREEi7WTzSfDVxtZh8TuQLJiJxE9IxXY9VJhrWPxt04kbWfbQmtnm/ZCrQKrV7R5s0MvHx8aPXeX/sRtAmtnDRAYX/PdTgmk7tvmxpaPYmoNhTMrJ27rwMG11E/MUmGtY/WfraFNW0GhVav/apnQ6sFUGqpfBRif7tX38VhoVWThijs7zk+fSa8WhKo6UzhSSKro35iZo+7+4V10JOIiCRITXMKVuF2h3g2IiIiiVdTKPgBbouISANU0/BRLzMrIXLGcHj0Nnw10XxUXLsTEZE6VW0ouHtqXTUiIiKJV5uls0VEpIGL9XMK9UoyfE5BRCpL2V1C5w8fDq2e9hmPj6QMhWT4nIKIVNasSRnDu+4IrZ72GY8PDR+JiEhAoSAiIgGFgoiIBBQKIiISUCiIiEhAoSAiIgGFgoiIBBQKIiISSMpQMLM8M5tRXFyc6FZERBqUpAwFd5/r7mMzMjIS3YqISIOSlKEgIiLxoVAQEZGAQkFERAIKBRERCSgUREQkoFAQEZGAQkFERAIKBRERCSgUREQkoFAQEZFAo0Q3cDDMLA/I69SpU6JbkajM3Z/S5sOHQ6nVuKwklDoiUntJGQruPheYm5ubOybRvUjEt5rsZnTXHaHUemJtWSh1RKT2NHwkIiIBhYKIiAQUCiIiElAoiIhIQKEgIiIBhYKIiAQUCiIiElAoiIhIQKEgIiIBhYKIiAQUCiIiElAoiIhIQKEgIiIBhYKIiAQUCiIiEkjK/RS0yc43l7K7hM4hbYoD2hhH6l7R5s0MvHx8aPU6HJPJ3bdNDa1eskrKUNAmO99csyZlDA9pUxzQxjhS90otlY/aDAqv4KfPhFcriWn4SEREAgoFEREJKBRERCSgUBARkYBCQUREAgoFEREJKBRERCSgUBARkYBCQUREAgoFEREJKBRERCSgUBARkYBCQUREAgoFEREJKBRERCSgUBARkYBCQUREAgoFEREJKBRERCSgUBARkYBCQUREAgoFEREJKBRERCSgUBARkYBCQUREAo0S3UA5MzsC+D9gD7DQ3WcnuCURkUNOXM8UzOx+M/vczN7e7/ggM1tjZh+Y2c3RwxcAj7n7GOD8ePYlIiJVi/fw0UxgUMUDZpYK/AkYDHQDRppZN6AtsD76tLI49yUiIlWI6/CRuy82s+z9Dp8CfODuawHM7G/AMKCQSDAUUE1YmdlYYCxAu3btwm9aRJJCyu4SOn/4cGj1vHhTaLXG3TiRtZ9tCa1e1q5P+N45OaHVq04i5hTa8NUZAUTC4FRgGvBHMxsKzD3Qi919BjADIDc31+PYp4jUY82alDG8647Q6s1Zti20Wms/28KaNoNqfmKMMgtuIa9teCFTnUSEglVxzN19OzC6rpsREZGvJOKS1ELg2Ar32wIbEtCHiIjsJxGhsBTobGbtzawJ8D1gTm0KmFmemc0oLi6OS4MiIoeqeF+S+lfgFaCrmRWa2Q/cvRS4DngWWAU84u7v1Kauu89197EZGRnhNy0icgiL99VHIw9wfB4wL57vLSIitadlLkREJKBQEBGRQFKGgiaaRUTiw9yT9/NfZvYlsCbRfdRSFvCfRDdxEJKxb/VcN9Rz3Qmr7+PcvUVVD9SbVVIP0hp3z010E7VhZsuSrWdIzr7Vc91Qz3WnLvpOyuEjERGJD4WCiIgEkj0UZiS6gYOQjD1DcvatnuuGeq47ce87qSeaRUQkXMl+piAiIiFSKIiISCDpQ8HMLjazd8xsn5nV60vMDrA3db11oD226zMzO9bMFpjZquj3xf8kuqdYmFmamb1uZm9G+56S6J5iYWapZvaGmT2d6F5iZWYfm9lbZlZgZssS3U8szOxoM3vMzFZHv7dPj9d7JX0oAG8DFwCLE91IdarZm7o+m8l+e2wngVLgp+5+AnAacG0SfJ0BdgPnuHsvoDcwyMxOS2xLMfkfIqsdJ5uz3b13En1W4U7gGXc/HuhFHL/mSR8K7r7K3ZPhU83B3tTuvgco35u63nL3xcAXie6jNtx9o7uviN7+ksj/PG0S21XNPKJ8P8jG0T/1+ioQM2sLDAXuTXQvDZmZHQX0A+4DcPc97r41Xu+X9KGQRKram7re/7BKZmaWDZwEvJbgVmISHYopAD4HnnP3+t73HcBNwL4E91FbDvzLzJab2dhENxODDsBm4IHoUN29ZnZEvN4sKULBzJ43s7er+FOvf9PeT5V7U9d5F4cIM2sKPA5c7+4lie4nFu5e5u69iWxRe4qZnZjglg7IzL4DfO7uyxPdy0Ho6+45RIZyrzWzfoluqAaNgBzgbnc/CdgOxG1OMinWPnL3bye6hxBob+o6YmaNiQTCbHf/R6L7qS1332pmC4nM59TXSf6+wPlmNgRIA44ys7+4+2UJ7qtG7r4h+t/PzewJIkO79XlOshAorHDm+BhxDIWkOFNoIL7x3tRSMzMzImOvq9z9fxPdT6zMrIWZHR29fTjwbWB1Qpuqhrv/3N3buns2ke/lF5MhEMzsCDM7svw2cB71N3gBcPfPgPVm1jV66Fzg3Xi9X9KHgpkNN7NC4HTgn2b2bKJ7qkoYe1PXtar22E50TzHoC1wOnBO95LAg+ttsfdcKWGBmK4n8AvGcuyfNZZ5J5FvAS2b2JvA68E93fybBPcViPDA7+v3RG/h/8XojLXMhIiKBpD9TEBGR8CgUREQkoFAQEZGAQkFERAIKBRERCSgU5JBjZm5msyrcb2Rmm+Ox0qeZXW1mV0RvjzKz1gdR42Mzywq7N5GqJMUnmkVCth040cwOd/edwH8Bn8bjjdx9eoW7o4h8UEqfZJd6S2cKcqiaT2SFT4CRwF/LHzCzU8zs39HFx/5d/klSM0s3s0fMbKWZ/d3MXivfw8PMtplZfnQ/hFfN7FvR45PN7AYzuwjIJfIBpAIzO7ziGYCZ5UaXtsDMmpvZv6Lvfw8V1s0ys8ui+y4UmNk90SXZRUKjUJBD1d+A75lZGtCTyiuprgb6RRcfm8hXnx69Btji7j2BW4A+FV5zBPBqdD+ExcCYim/m7o8By4BLo+v476ymt0nAS9H3nwO0AzCzE4ARRBZ06w2UAZfW9i8uUh0NH8khyd1XRpfWHgnM2+/hDOBBM+tMZCXbxtHjZxLZ7AR3fzu65EC5PUD5nMRyIkNSB6sfkY2jcPd/mtmW6PFziQTR0sgSTxxOZJltkdAoFORQNge4HRgANK9w/BZggbsPjwbHwujxqpY/L7fXv1ozpozY/t8q5auz9bT9Hqtq/RkDHnT3n8dQW+SgaPhIDmX3A1Pd/a39jmfw1cTzqArHXwL+GyC6xWePWr7fl8CRFe5/zFdDUBdWOL6Y6LCQmQ0GMqPHXwAuMrOW0ceamdlxtexBpFoKBTlkuXuhu99ZxUO/A35jZi8DFSdy/w9oER02+hmwEiiuxVvOBKaXTzQDU4A7zWwJkbOLclOAfma2gsjSzuui/b4L/JLIrmErgeeIrK4qEhqtkioSo+iVPo3dfZeZdSTym3uX6J7bIg2C5hREYpdOZM+DxkTG98cpEKSh0ZmCiIgENKcgIiIBhYKIiAQUCiIiElAoiIhIQKEgIiKB/w/JywSlS29AGwAAAABJRU5ErkJggg=="
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+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">catalog</span><span class="p">[</span><span class="s2">&quot;time&quot;</span><span class="p">],</span> <span class="n">catalog</span><span class="p">[</span><span class="s2">&quot;magnitude&quot;</span><span class="p">],</span> <span class="s1">&#39;.&#39;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="n">marker_size</span><span class="o">+</span><span class="mf">1.5</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">rasterized</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">events</span><span class="p">[</span><span class="s2">&quot;time&quot;</span><span class="p">],</span> <span class="n">events</span><span class="p">[</span><span class="s2">&quot;magnitude&quot;</span><span class="p">],</span> <span class="s1">&#39;.&#39;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="n">marker_size</span><span class="o">+</span><span class="mf">1.5</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.8</span><span class="p">,</span> <span class="n">rasterized</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="n">datetime</span><span class="o">.</span><span class="n">fromisoformat</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;starttime&quot;</span><span class="p">]),</span> <span class="n">datetime</span><span class="o">.</span><span class="n">fromisoformat</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;endtime&quot;</span><span class="p">]))</span>
+<span class="c1"># plt.ylim(top=events[&quot;magnitude&quot;].max())</span>
+<span class="n">ylim</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">(</span><span class="n">bottom</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+<span class="n">xlim</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">&quot;Magnitude&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">&quot;Date&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span><span class="o">.</span><span class="n">xaxis</span><span class="o">.</span><span class="n">set_major_formatter</span><span class="p">(</span><span class="n">mdates</span><span class="o">.</span><span class="n">DateFormatter</span><span class="p">(</span><span class="s1">&#39;%m-</span><span class="si">%d</span><span class="s1">:%H&#39;</span><span class="p">))</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">gcf</span><span class="p">()</span><span class="o">.</span><span class="n">autofmt_xdate</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span><span class="o">.</span><span class="n">set_prop_cycle</span><span class="p">(</span><span class="kc">None</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">datetime</span><span class="o">.</span><span class="n">fromisoformat</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;starttime&quot;</span><span class="p">]),</span> <span class="o">-</span><span class="mi">10</span><span class="p">,</span> <span class="s1">&#39;.&#39;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mi">15</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">result_label</span><span class="si">}</span><span class="s2">: </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">catalog</span><span class="p">[</span><span class="s1">&#39;magnitude&#39;</span><span class="p">])</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">datetime</span><span class="o">.</span><span class="n">fromisoformat</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;starttime&quot;</span><span class="p">]),</span> <span class="o">-</span><span class="mi">10</span><span class="p">,</span> <span class="s1">&#39;.&#39;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mi">15</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">catalog_label</span><span class="si">}</span><span class="s2">: </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">events</span><span class="p">[</span><span class="s1">&#39;magnitude&#39;</span><span class="p">])</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="n">xlim</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">(</span><span class="n">ylim</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;earthquake_magnitude_time.png&quot;</span><span class="p">),</span> <span class="n">bbox_inches</span><span class="o">=</span><span class="s2">&quot;tight&quot;</span><span class="p">,</span> <span class="n">dpi</span><span class="o">=</span><span class="mi">300</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;earthquake_magnitude_time.pdf&quot;</span><span class="p">),</span> <span class="n">bbox_inches</span><span class="o">=</span><span class="s2">&quot;tight&quot;</span><span class="p">,</span> <span class="n">dpi</span><span class="o">=</span><span class="mi">300</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">();</span>
+</pre></div>
+<div id="cell-8" class="clipboard-copy-txt">plt.figure()
+plt.plot(catalog["time"], catalog["magnitude"], '.', markersize=marker_size+1.5, alpha=1.0, rasterized=True)
+plt.plot(events["time"], events["magnitude"], '.', markersize=marker_size+1.5, alpha=0.8, rasterized=True)
+plt.xlim(datetime.fromisoformat(config["starttime"]), datetime.fromisoformat(config["endtime"]))
+# plt.ylim(top=events["magnitude"].max())
+ylim = plt.ylim(bottom=-1)
+xlim = plt.xlim()
+plt.ylabel("Magnitude")
+plt.xlabel("Date")
+plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%m-%d:%H'))
+plt.gcf().autofmt_xdate()
+plt.gca().set_prop_cycle(None)
+plt.plot(datetime.fromisoformat(config["starttime"]), -10, '.', markersize=15, alpha=1.0, label=f"{result_label}: {len(catalog['magnitude'])}")
+plt.plot(datetime.fromisoformat(config["starttime"]), -10, '.', markersize=15, alpha=1.0, label=f"{catalog_label}: {len(events['magnitude'])}")
+plt.legend()
+plt.xlim(xlim)
+plt.ylim(ylim)
+plt.grid()
+plt.savefig(root_dir("earthquake_magnitude_time.png"), bbox_inches="tight", dpi=300)
+plt.savefig(root_dir("earthquake_magnitude_time.pdf"), bbox_inches="tight", dpi=300)
+plt.show();</div>
+    
+        </div>
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+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s2">&quot;figure.figsize&quot;</span><span class="p">]</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">0.8</span><span class="p">,</span><span class="mf">1.1</span><span class="p">]))</span>
+<span class="n">box</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">boxstyle</span><span class="o">=</span><span class="s1">&#39;round&#39;</span><span class="p">,</span> <span class="n">facecolor</span><span class="o">=</span><span class="s1">&#39;white&#39;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="n">text_loc</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.05</span><span class="p">,</span> <span class="mf">0.90</span><span class="p">]</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">311</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">catalog</span><span class="p">[</span><span class="s2">&quot;time&quot;</span><span class="p">],</span> <span class="n">catalog</span><span class="p">[</span><span class="s2">&quot;sigma_time&quot;</span><span class="p">],</span> <span class="s1">&#39;.&#39;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="n">marker_size</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;Travel-time&quot;</span><span class="p">,</span> <span class="n">rasterized</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="sa">r</span><span class="s2">&quot;$\Sigma_</span><span class="si">{11}</span><span class="s2">$ (s)$^2$&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s2">&quot;upper right&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="n">text_loc</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">text_loc</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="s1">&#39;(i)&#39;</span><span class="p">,</span> <span class="n">horizontalalignment</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">,</span> <span class="n">verticalalignment</span><span class="o">=</span><span class="s2">&quot;top&quot;</span><span class="p">,</span> 
+         <span class="n">transform</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span><span class="o">.</span><span class="n">transAxes</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="s2">&quot;large&quot;</span><span class="p">,</span> <span class="n">fontweight</span><span class="o">=</span><span class="s2">&quot;normal&quot;</span><span class="p">,</span> <span class="n">bbox</span><span class="o">=</span><span class="n">box</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">312</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">catalog</span><span class="p">[</span><span class="s2">&quot;time&quot;</span><span class="p">],</span> <span class="n">catalog</span><span class="p">[</span><span class="s2">&quot;sigma_amp&quot;</span><span class="p">],</span> <span class="s1">&#39;.&#39;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="n">marker_size</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;Amplitude&quot;</span><span class="p">,</span> <span class="n">rasterized</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="sa">r</span><span class="s2">&quot;$\Sigma_</span><span class="si">{22}</span><span class="s2">$ ($\log10$ m/s)$^2$&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s2">&quot;upper right&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="n">text_loc</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">text_loc</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="s1">&#39;(ii)&#39;</span><span class="p">,</span> <span class="n">horizontalalignment</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">,</span> <span class="n">verticalalignment</span><span class="o">=</span><span class="s2">&quot;top&quot;</span><span class="p">,</span> 
+         <span class="n">transform</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span><span class="o">.</span><span class="n">transAxes</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="s2">&quot;large&quot;</span><span class="p">,</span> <span class="n">fontweight</span><span class="o">=</span><span class="s2">&quot;normal&quot;</span><span class="p">,</span> <span class="n">bbox</span><span class="o">=</span><span class="n">box</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">313</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">catalog</span><span class="p">[</span><span class="s2">&quot;time&quot;</span><span class="p">],</span> <span class="n">catalog</span><span class="p">[</span><span class="s2">&quot;cov_time_amp&quot;</span><span class="p">],</span> <span class="s1">&#39;.&#39;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="n">marker_size</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;Travel-time vs. Amplitude&quot;</span><span class="p">,</span> <span class="n">rasterized</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="sa">r</span><span class="s2">&quot;$\Sigma_</span><span class="si">{12}</span><span class="s2">$&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">([</span><span class="o">-</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.7</span><span class="p">])</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s2">&quot;upper right&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="n">text_loc</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">text_loc</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="s1">&#39;(iii)&#39;</span><span class="p">,</span> <span class="n">horizontalalignment</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">,</span> <span class="n">verticalalignment</span><span class="o">=</span><span class="s2">&quot;top&quot;</span><span class="p">,</span> 
+         <span class="n">transform</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span><span class="o">.</span><span class="n">transAxes</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="s2">&quot;large&quot;</span><span class="p">,</span> <span class="n">fontweight</span><span class="o">=</span><span class="s2">&quot;normal&quot;</span><span class="p">,</span> <span class="n">bbox</span><span class="o">=</span><span class="n">box</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span><span class="o">.</span><span class="n">xaxis</span><span class="o">.</span><span class="n">set_major_formatter</span><span class="p">(</span><span class="n">mdates</span><span class="o">.</span><span class="n">DateFormatter</span><span class="p">(</span><span class="s1">&#39;%m-</span><span class="si">%d</span><span class="s1">:%H&#39;</span><span class="p">))</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">gcf</span><span class="p">()</span><span class="o">.</span><span class="n">autofmt_xdate</span><span class="p">()</span>
+<span class="c1"># plt.suptitle(r&quot;Covariance Matrix ($\Sigma$) Coefficients&quot;)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">gcf</span><span class="p">()</span><span class="o">.</span><span class="n">align_labels</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;covariance.png&quot;</span><span class="p">),</span> <span class="n">bbox_inches</span><span class="o">=</span><span class="s2">&quot;tight&quot;</span><span class="p">,</span> <span class="n">dpi</span><span class="o">=</span><span class="mi">300</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;covariance.pdf&quot;</span><span class="p">),</span> <span class="n">bbox_inches</span><span class="o">=</span><span class="s2">&quot;tight&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">();</span>
+</pre></div>
+<div id="cell-9" class="clipboard-copy-txt">
+fig = plt.figure(figsize=plt.rcParams["figure.figsize"]*np.array([0.8,1.1]))
+box = dict(boxstyle='round', facecolor='white', alpha=1)
+text_loc = [0.05, 0.90]
+plt.subplot(311)
+plt.plot(catalog["time"], catalog["sigma_time"], '.', markersize=marker_size, label="Travel-time", rasterized=True)
+plt.ylim([0, 3])
+plt.ylabel(r"$\Sigma_{11}$ (s)$^2$")
+plt.legend(loc="upper right")
+plt.text(text_loc[0], text_loc[1], '(i)', horizontalalignment='left', verticalalignment="top", 
+         transform=plt.gca().transAxes, fontsize="large", fontweight="normal", bbox=box)
+plt.subplot(312)
+plt.plot(catalog["time"], catalog["sigma_amp"], '.', markersize=marker_size, label="Amplitude", rasterized=True)
+plt.ylim([0, 1])
+plt.ylabel(r"$\Sigma_{22}$ ($\log10$ m/s)$^2$")
+plt.legend(loc="upper right")
+plt.text(text_loc[0], text_loc[1], '(ii)', horizontalalignment='left', verticalalignment="top", 
+         transform=plt.gca().transAxes, fontsize="large", fontweight="normal", bbox=box)
+plt.subplot(313)
+plt.plot(catalog["time"], catalog["cov_time_amp"], '.', markersize=marker_size, label="Travel-time vs. Amplitude", rasterized=True)
+plt.ylabel(r"$\Sigma_{12}$")
+plt.ylim([-0.5, 0.7])
+plt.legend(loc="upper right")
+plt.text(text_loc[0], text_loc[1], '(iii)', horizontalalignment='left', verticalalignment="top", 
+         transform=plt.gca().transAxes, fontsize="large", fontweight="normal", bbox=box)
+plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%m-%d:%H'))
+plt.gcf().autofmt_xdate()
+# plt.suptitle(r"Covariance Matrix ($\Sigma$) Coefficients")
+plt.tight_layout()
+plt.gcf().align_labels()
+plt.savefig(root_dir("covariance.png"), bbox_inches="tight", dpi=300)
+plt.savefig(root_dir("covariance.pdf"), bbox_inches="tight")
+plt.show();</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
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+
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zzTHScOFk9jmJp1RN9ZnqlNioh3+sq3q4z9yXaVoev6u8rYdZ06/2OJvrMis5DBDYZweKGBMAbVIoNa3Kedt9HwfiKXVjO+JM1n7+35+flF5sPzRnMZhEBeord+ngYnIdmmTZ6arx1eWlIoRpqXl+eO0bmMWWrWEbeR6TFpaUDvyYWr8+Tt5ZkFPMeiro/39fU0Lna7XbfsO6zPzU3R6ycVNDiBDLCqFniIeMeBPWW6yikl69eQH/h2u13nJmVp/IuLNO75Rdr5H4v1nRWZeu7cuZA8ymDLKCGpYAdVqu2IpmT9qs9+naKNn5yv7yzPLPSAC+bhG0w+rbrHAhlU85G+ELBi3GVGzjFGzkjkgRmb3MNeMrILDhdRDc+QqmD0d8n21slXXnYfOsnoTx1Dc+x2O1NW7WT0J4kFht+8tmg7Y/o2RxBax9Skdf2avHt3e1DHcBpV5d2hjvWWV17CmH7NaVUvGlVl98ETAAjCoHYx/OvOa5lxX2f3GzPfpGTzl0828dKCDB79fAvDPtzAiI83ct/HG1i14xeeHtiS2Rv2MbZfCwRx6+qZT7vdTtr+XNJsuTS+4mL+dce13HBNbVSVXQePY7fbi3wzJyPnGKM/SeSblGzS9x9lwvytjO3bnNYxNcnIOcY9H25kxrp93Niqjlv39Oyj7ryN+HgjD8zYVKist/58nNeX7ECihNb1azKmX3P+8U06767YQUKSjeEfbiB9/1Fax9Rk3MAWoIDC1pzj7mtUVF0SEa6pEw1O5+qZW1oz+oamLEw/wKuLtjE/NcdtLFz1wlV/PfUVEXd+PWV55rVN/UsZFOe4jn/p2YR3726PIERJFIvSfubiqpWYsW6v+76Yn5LDY59vcevwzZb97rHAwQ7t8tT7LzM38e7KHaTZcrHb7WG5x8TqBCOVTp066aZNm8pEtqrjdbmWV17C/NQcGl9xMXt+PU2T2jX46+wkR8VyVkgRIT37KKM/SWRs/xbcGle/VF+xS88+yoOzNrt1alUvmq0/H3fr5soLCqNnOXQc1C6Gqat38dqiTIZ3b8Tzg9q48yE4zrl/+kZGdL+aXs3rAjByZiLjB7Zk8pJMxvRrzmuLtnNX16uYsyGLd4e2Z/WOg8z4fi/PDGrFoLj6bP35eIHyW7vrMLN/3MfQ6xrx6fq9DO3aiBuuqYOIsHrHQV7/bgev396OqKgomtSqwYOfbnbruvXn46hdeWDGJoZ3u5qPvt8DQJVKUXx4bydax9Tkm5RsXl20jaFdGzFnQxZTh3WkTf1L3Teo4DByIoLdbmfq6l3M+TGLsf2aM3Hhdj4c0Yk2DRzHp9qOsHbnYUb1bMz2X06CwoOfbmbKsA7Y7XZGfLSR529tza3XOvLZql60u4xFhFYxjvXdv5zg2YR0jp/Jo3qVKE6dszOmX3N6NavDPR/+yLl85eKLKvH0La24pk40rWKimZ+aw2uLtzPl7g6FrqeqMj81h1va1mNBag7/+CaDgXEx/KlDQ/46O4m7ul7F7B/PP5BeW7Kdd4e2Z/ehk8ReXp3vd//qzlPrmJqOlx9mbGLa8I5IlEOWS76r/DxJ25/LyBmJvH+PI01RaFo3GhHhwU83887QeHYfOknT2pcgItw/fSPn8u28MLg1g+Lqk5FzDOF8+biuiasei/Ot+Ac/ddTn1TsOMnlxJpdVr8LIGxoz+8d9PNG/Zcj3mIgkqmonX/vMoHw/uAxHqO8M+zrPZSynrNrJ60t28HjfZny2wca7d7dnyrAO7kruonVMTcb2b8Fri7fTtM4lhSpiOPH0Yl1G0dPAto6p6TYsU+/p6PZKZq/fxz3dGrEo9Wc6XHU5Akz81mFYEDiTZ2fykh189P1enru1ldPrsTPl7g60uLIGm/b8yvTv9/D0wFas2XGIyYt3ULN6FZrWcZTN6E8SubFVHT5Zt4+Lq1Ti1Nl8xvRvzugbmtKred0ChqJ1/Zo0uLw62GHigm1MG96xgK6jP0lkSJeGnM3P571Vu6gUJYzs2Zhezeu6DY2q8kTfFry6ZDtP9G/p9updLQyAD+/tRJv6l7L15+MOY+o02E3qXOIwvPuP0rp+TeKuuoK4q64oUJauMs7IPkbVypVoUrvGeaPcvwUTF2xzy8jIOcaIjzZSOQpevK01gtC41vmHCih5qpw8m88fOzZg8pJMptzdgfmpOby6aBtP9G+JIDw4a7P74TW2v8OjfeyLLQDcem0DEvceYca6fdSNvoip93SkVb1oGl5+MRMWbAWF8be05KdDp5i8JJMbW9Zhxg/7+PnYGZak/8K04R3ZdegEirL70AkmL9lRQJZnq8jzoQyw5/BpJn2bCcC0ER1RHK0Wl/fsMq7jb2mJKLy6eDuI0LT2JTz4qSNPrvKaNqIjuw+dZMKCrQjCtBEd3WXdOqYmDS6rjii8sngbN7W+ktcWWXyP+YsFVLQl1BhqceOl3ud5dsr0+OdSfWdFZlCdHqUVV/Uk0JAm79inr3hWatYR7fDSEu3w0mJt/+Ji7fDSEk21OTqR5m7ep+1fXKwJm7PcsTFXnDEh2aaNn5yvcc8v0oSkLO3xz6X69vLt7g4oV1yy+z+/07eXb9ctew9rQlLB+J6vnucuL3/n1sEzPwnJjh7xt5dv184vLynQmWO32/WdFY6OmblJWYXKw1fnmHdc0V9vf6DyTUhyxIhdHTOeIxQSkrK0y8vfaUKSrdC5rnJ5a9k2TdicpXl5eT57/T1j366y9+4g3LLvsMY9v0iT9x7yGfP3jOOeO3dO31mRqVv2HtauE5ZqQlKWe5+vkR+e6aRmFY75umLJrhcwXMPwvGPvrni6Z1zeFUP3jDUHipl71oFQR0WoBo6hlrmhK60lVINa3B5975vGdaMH6qUtSbDdSgI9RPzly18ni3dHS7A9+N43YbDl4q/H3ddN5X1tPWWm7T//4PPV4+yvjDy3F9XZ5ItAQ7MCvfXkq4c9mHMCdTj6G9rmS8dg7xPPh4y/B4Sn3nl5eUF3SHmXfTD3j7/yDgZjUIthUF0U11N1EaqBLKm8khCKrqVh+IMpC6v1CNVwB9puxbUMJn+BHlbFkefvQeQvP8HqWMDr9jHMyjOdYMsukL6BKMm1MQa1BAbVyspaHHnlhXDoHUya/kIsvprVpV2mocotqzL0JJRWSjDnFFevYPUOxfBadR9XWIOK4zPS24GdwFOBjrXqTamy9CAjmbIql2BCEeXlmoVDz1DTLI6hKcuWjdWGNxgqpEEFKgG7cMzVWhXHhNet/R1vlUEtrx5kIKzIkysNV+zLu+PNSgLpW5SHGsnXLxI81HATKQ9eF8WJpQYyqOV5YH8XYKeq7lbVs8BnwG3hFlrcOUAjGSvmv3SVy4K0n3ns8y28t2Z32D67EUhfX9fHc5tVc32qWv/yRTjqVqTV17KaINpfOcxPPf/ygCVyrKwQpYmI/Anor6ojnev3AF1V9SGPY0YBo5yrLXCEB3xRGzgURnWLoszlS5VqJ/XcmdNWJBZVvebl9tPHjkiVatWDTDPk/IeQdjDnFkt+5cvqNc3L/XmXBeVW5tf/QpYfVS36avuZ43tDOOVqVfX5GeXyPLDf1yO3wNNBVd8H3i8yIZFN6ufNh9IgEuTbz56+oPNv5Bv5VqRVnpv8NuAqj/WGQHYZ6WIwGAzl2qBuBJqJSGMRqQoMAeaVsU4Gg+ECptw2+VU1T0QeAhbj6PH/SFXTi5lckWGBMGPkG/lGfgWQX247pQwGgyHSKM9NfoPBYIgojEE1GAwGizAG1WAwGCzCGFSDwWCwCGNQDQaDwSKMQTUYDAaLMAbVYDAYLMIYVIPBYLAIY1ANBoPBIoxBNRgMBoswBtVgMBgsokiDKiI3i8gHIhLvXB9VxCklQkQ+EpFfRCTNz34RkTdFZKeIpIhIh3DqYzAYDMESjIf6V2AsMExEbgTiw6oRTMfx8T1/DACaOZdRwJQw62MwGAxBEYxBPaiquao6BugLdA6nQqq6Gvg1wCG3ATOd38taD1wmIjHh1MlgMBiCIRiDusD1Q1WfAmaGT52gaABkeazbnNsMBoOhTClygmlVTfBaf0tEagBnVDU/bJr5p8hvSbkP9PhIX40aNTq2bNkynHoZDIYLgMTExEMl+kifiETh+MTI3UAn4CxwkYgcBBYC76vqDov0LYqgvyXl+ZG+Tp066aZNm8KvncFgqNCIiN8vpAb7CZQVwFJgHJCmqnZnwlcAfYBJIvK1qs4qqbJBMA94SEQ+A7oCR1XVmo9qe5Cfn8/OnTs5efJkidKpWrUqTZs2pXr16hZpZjAYIpVgDepNqnrOe6Oq/gp8BXwlIlWsUEhE5gC9gdoiYgOeB6o45U3F4REPBHYCp4D7rJDryX/+8x8mTZpE9erVufTSSxHxFWUIjjNnzpCdnc2QIUN48803qVq1qoWaGgyGSCIog+oypiJyO7BIVY+LyLNAe+BlVd3sy+AWB1W9q4j9Cvw/K2T5YubMmUyZMoXly5fTqlUrS9L85Zdf+POf/8zDDz/Me++9Z0maBoMh8gjpI30ikqKqcSJyPfBPYDIwXlW7hktBqwg2htqjRw+eeeYZBgwYYKn8Q4cO0bRpU7Kzs6lRo4alaRsubM6dO4fNZuPMmTNlrUqFolq1ajRs2JAqVQo2vkUkUVU7+Ton1M9Iu3r1bwGmqGqCiLwQsqYRTFJSEtdff73l6dauXZurr76azMxM2rdvb3n6hgsXm81GdHQ0sbGxJQpPGc6jqhw+fBibzUbjxo2DPi/Ud/n3i8h7wB3AQhG5qBhpRDRnz54NWwdS9erVOXv2bFjSNly4nDlzhlq1ahljaiEiQq1atUL2+oMyhiLSTRxX6w5gMdBfVXOBK3C8llrhGTduHG+88QYAa9asoUWLFu59bdq0YeXKlQC8+eabPPXUU2WgoeFCxhhT6ylOmQbrXY4AEoGPgJrAcQBVzVHVJSFLLWccPHiQmTNn8pe//AWAnj17sn37dvf+9PR0evfuDcCoUaOYNWsWv/zyS1moajCUGV9//TUiwrZt2yxJb+XKlQwaNAiAefPmMWnSJADmzp1LRkZGyOn17t2bcI9FD8qgqupoVe0AvABcDkwXkXUiMlFEbhCRSuFUsqyZPn06AwcODCoUUK1aNQYMGMDMmWX9hq7BULrMmTOH66+/ns8++8zytAcPHuxu+RXXoJYGIcU/VXWbqv5bVfsDNwJrgduBH8OhXKTw7bff0qtXL/f6ypUradiwoXs9NjaWpUuXutd79+7NggULMBguFE6cOMH333/Phx9+6DaoK1eupFevXtxxxx00b96cp556ik8//ZQuXbrQrl07du3aBcC9997L6NGj6dmzJ82bN2f+/PmF0p8+fToPPfQQP/zwA/PmzWPs2LHEx8eza9euAp7noUOHiI2NBeD06dMMGTKEuLg47rzzTk6fPu1Ob8mSJXTr1o0OHTpw++23c+LECUvKodgdSqp6WlUXqurD/oYQVBRSU1MLxEyLolWrVmzZsiWMGhkMJUNVSc8+SijDJgMxd+5c+vfvT/PmzbniiivYvHkzAFu2bOE///kPqampfPLJJ2RmZrJhwwZGjhzJW2+95T5/z549rFq1igULFjB69Gi/nUHdu3dn8ODBvPbaayQnJ9O0aVO/Ok2ZMoWLL76YlJQUnn76aRITEwGH0X355ZdZunQpmzdvplOnTvzrX/+ypBxCMqgi0klEvhaRzc7JnVNEJMUSTSKY3NxcoqOjgz4+Ojqao0ePhlEjg6FkZOQc48FZm8nIOWZJenPmzGHIkCEADBkyhDlz5gDQuXNnYmJiuOiii2jatCl9+/YFoF27duzZs8d9/h133EFUVBTNmjWjSZMmlsRhV69ezbBhwwCIi4sjLi4OgPXr15ORkUGPHj2Ij49nxowZ7N3r9/X8kAh1HOqnOHr1UwG7JRqUAy6//HKOHz8e9PHHjx/n0ksvDaNGBkPJaB1TkynDOtA6pmaJ0zp8+DDLly8nLS0NESE/Px8RYeDAgVx00UXu46KiotzrUVFR5OXlufd596iH0sNeuXJl7HaHOfL2bH2lo6rcfPPNbqNvJaE2+Q+q6jxV/UlV97oWy7WKMOLi4sjMzAz6+K1bt3LttdeGUSODoWSICG3ql2yeChf//e9/GT58OHv37mXPnj1kZWXRuHFj1q5dG3QaX375JXa7nV27drF79+6AIbbo6OgCDk5sbKy7Of/f//7Xvf2GG27g008/BSAtLY2UFEdj+rrrruP7779n586dAJw6dSqk+zsQoRrU50VkmojcJSJ/cC2WaBLBDBw4kFWrVgV9/KpVqyx/ddVgiFTmzJnD73//+wLb/vjHPzJ79uyg02jRogW9evViwIABTJ06lWrVqvk9dsiQIbz22mu0b9+eXbt2MWbMGKZMmUL37t05dOiQ+7gHH3yQEydOEBcXx6uvvkqXLl0AqFOnDtOnT+euu+4iLi6O6667zrKhXqhq0AswC9gEzAA+di4fhZJGWS0dO3bUYKhUqZKeO3euwLaDBw9qgwYN9NSpU6qqumLFCm3QoIF7/9VXX63fffedqqqePn1aGzRooD///HOhtLt06aLr168PSg+DIVgyMjLKWoUSMWLECP3yyy/LWg2f+CpbYJP6sTOhxlCvVdV21pjy8kPt2rUZPnw47733Hn//+9/p3bs3NpvNvd8zuP7BBx8wdOhQrrzyyjLQ1GAwlCWhGtT1ItJaVSNzVK1FqI+hJBMnTgzq3IcffjikdA2GC53p06eXtQqWEWoM9XogWUS2O4dMpVa0YVMXX3wxx45ZM5TEm2PHjnHxxReHJW2DwVD2hOqh9g+LFhFEz549+fbbb93j16xi9+7dHD582LJJqw0GT1TVTJBiMcVpUYZkUPUCGCL12GOPMXToUKKiorjllluoWbNmiSrqb7/9xtq1a3n00Ud5/PHHqVw51GeYwRCYatWqcfjwYTOFn4Wocz7UQKMNfBHSjP3lmVC+erp06VImT57M6tWrC7z/WxwqV65MfHw8I0eOZNSoUabCGyzHzNgfHoozY78xqEVgRfkYI2owVBys/ATKBYcxhgaDIViCNqgi0hK4DWgAKJANzFPVrWHSzWAwGMoVwX4C5UngM0CADcBG5+85ImK+92EwGAwE76E+ALRR1XOeG0XkX0A6MMlqxQwGg6G8EezAfjtQ38f2GC6gafwMBoMhEMF6qH8HlonIDiDLua0RcA3g/11Lg8EQNKpKRs4xWseUbOxzJFPR8xjsR/oWAc2BF3F8RnoJjg/2tVDVb8OmncFQAVE/nx+xehb9SKSi5zHod/lV1a6q61X1K1X9r/N3vojcZ7VSItLfOV/ATl+dXiLSW0SOikiyc3nOah0MhnDhz6hYOYt+pFLR81jigf0isk9VG1mkD85PUmcCNwM2HCMK7vKc4UpEegNjVHVQsOkWd2C/wWA1Fb3ZW9Ep8cD+ADNKCWD1xJ9dgJ2qutsp+zMc418r9JSBhgsH1+dHLhQupAdIsE3+K4HhwK0+lsMW69SA8x1f4PBSG/g4rpuIbBGRb0Wkja+ERGSUiGwSkU0HDx60WE2DwRAM4Y6b+otJh3qMFQRrUOcDl6jHh/mcyx5gpcU6+XqEeZfCZuBqVb0WeAuY6yshVX1fVTupaqc6depYq6XBYAiKcMdNgzHYpdUZFmwv/wOq6vMThqo61FqVsAFXeaw3xPGaq6fMY6p6wvl7IVBFRGpbrIfBYLAAK7+wCoW9zWAMdml1hoU6Y39psBFoJiKNRaQqMASY53mAiNQT59URkS448mF16MFgMEQg3t5mMAbbaqPuj4ibbUpV80TkIRzjXSvh+KpquoiMdu6fCvwJeFBE8oDTwBC9UOYhNBjCRHnpPIrkoVchDZsSkcd8bD4KJKpqslVKhQMzbKp08HVTlpcbtSJQkrJOzz7Kg7M2M2VYhwtqFEKoBBo2FWqTvxMwGkevewNgFNAb+EBEniiJkoaKga/gf7AdAqXVE1saBJsXq/Ncks6XVvWiGdOvOa3qRVuiy4VIqAa1FtBBVR9X1cdxGNg6wA3AvRbrZiiH+GqOBdtEs7ontiwNdLB5sTrPJWkOb/35OJMXZ7L15+OW6FIcirpmEf/QVdWgF2ArUNVj/SJgq/N3UihplfbSsWNHNUQWdrtd0/bnqt1u97leUtL252rPV5Zr2v5cS9ILhWDzYnWeS0Iwulilr790irpmoV7TQPoWNy/AJvVjZ0L1UGcD60XkeRF5AfgexyTTNTBvMhkIzYMoTm9tKJRl50WweSmt3udgCEaXYDzqYOpAceczCPWaBtI3LGNT/VlafwvQEXgEx5R+nUI9v6yWSPZQPZ+UkeSxFAd/HoSvPObn51eYfIdCaeQ1XDLsdrum2o5oms1/2sF4keEuA191zCodsNBDBcjDMal0HnCuiGMNQeD5pCzv05v58yB85XHrz8fdHpGvfGukx8uCwFce/F3jYPMbzHGB6lFJylVEEBEe/NR/HQ3Giwy3Zx7MfRQWHfxZWl8LDs80Dce8qC8BqcDDoaRRVktZe6iBvNCK4KHm5+fr3KQsTc06Uigf3l6Nrzz62lbcGGiwsVmrYriBzvOVh+LGD13npdlK5gGWNLYcjnIK5dxQYr2uskq1HSngsQbyXIuCAB5qqAY1BajhsV4DSAkljbJarDKowTR5fJG2P1evn7RME5JtQd0QkUKgppNnxU5ItmmTp+Zrh5eWOCqyx03rmfdgKnIwzbVApNlyteuEpZpmy/W57j7Oy7B4rgfTmeHSLzXriHZ5+TuduzmrUL0o6uYPxVC49HMZB39ygpXpbVyKMjbBPgj94V3exTGM3nWrKLzPT0i2FfhfnHvQSoOaClTzWK8GpIaSRlktxTWo3hc9bb/j5uw6YanPixHIG0pItun1rywrdEOUtKKGE1fl9a6Adrtd5yZlaZcJ32mq7Yjm5eXp28u3a8q+X316qK68B1ORS+qZnjdw+zTNdn49ISlL8/Pz3Q9Eb8ORn59fwOj708G7TOYmZWmHl5Zoh5cW+60XoeTV30M7kEft/QALpvy885GQbNPrJy3Td1Zk6vWvLCt0vq90g/WqvWPnqbYjmrA5y/2gK8pr97xngnnY+XsYeD40iuMYqVprUB8DtuD4/MkLQDLw91DSKKslVIPq68noquipWf4vRKAmnr8nf3EqqpWE4o15emXtX1ys7V9crAlJDq/b5YUGCmv48oI8DZnrWH+VPVBZujzhuUlZmrDZYeS6TljquHk9DLrnA9GfUQrk8fnKS0rWrzp3c5am7PvVb90I9OD0zI/rod3l5e90bpJ/rzfNlqs9/rlU31mRqXl5eSE1if3lIyHZpj0mLXVfR89jfV0Tz2vnq5xc5e5dt7tOWOpRf7IK3Wdp+3M1Ly/PbxjJH8HcN6GETnxhmUF1pEVH4G/OeGr7UM8vqyVUg+rryRjKxfK+0QPF0XxVxKIMipWea1H6+Tr26837tP2Li/XtZZlurzshKcvpCRZtYD1lzk3K0iZPzde5SVkF5HiGCVwGa27iPu0yweFt+tL56837tP1Li/XrxH1ur9TTaOfl5WlK1q+akFTYE/WbZ2fIIDXriN/r4s+T9yxLXzdwoAd3QpJNu0z4zpEPD8/ZdWxK1q/6XEKKNn5qvr6zItNn/Qk1ZujLwPsyjME0xT0fcJ5OiKdjkpBUuNXmOu+5hBRtMm6BJiTbClyHQB5tMM6Bq2566xUslhrU8roU10MNthkerJfnquiBno7BNDet9Fz9eUmBhj/5ihum2o44vcLv3F6hy9vxZ2Dz8/N17uYsbf/iYp2buM9htLIK3mwJyTbt8NISjX1yvrZ+ZqHjWKdcV5mm7HMYybmJWdrkqQU6d3OW+6ZN2fervrM8U6+ftKzQjZ+fn69fb97nlu3reqdmHdGuE5a6HxgdXlpSKB7r6VV5etue18xX7NO1LyXr10LneRrWHpOWupviqbYj7u3d//mdPpeQoj3+udTtzboMT5eXv9O3l28v8ADyF3/1NMCptiM6d/P5cI73w82zTFKzzqfnq6XhMmCuVoFL96I6KBOSHXl7Z0WmO71U2xF3Gp46BWsMPa9DKCEobwIZ1KAmRxGR4xSe5Bkck0GrqkbetC9ehHtylGAmlkjPPsoD0zehqoy/pSXX1ImmdX3HJBaq5ye1APxOcOF5XKjDPYo6Nz37KKM/SWRs/xYMahfD1p+P+z1WVfkmJZvXFm9nyt0dEBFa1YsmI+cYgtC6fk1UlamrdzHnxyymDOvA7kMnCxzfOqYmGTnH+MvMTfyudV0WpR4gz+6oZpWjhHEDWxAlUdzSrh7p2Uf5b+J+FqX/zL3dY5mzMYupwzoC8MD0TZw+m8fxM3k81vcaGl1xCbe0q8eCtJ+ZsGArZ87mc/xMHmP6N+fBXtcUKN+MnGMM/3ADR06e5fIaVZlxf2cAxDnP+ehZiYzp15xr6kTTKiaab1KymbhwG9OGdyQqKqpQ+fiqB3a7nfmpOQxqF0NUVMGRiq5rgsKDn/quP66yfnXRNp7o35Jb4+qTkXPMfa1uaVuPqat38eGaPVStHMW0EY55O3YfPMnLCzLIt8OM+zojUVJAjiv/KNw/fSPn8u2M7NmYGev2ci7Pjogw477Ozrscdz5Hz0pkSJeGzFy3jw9HdKJtg8sASNufy8gZiUwb0dG9zaV/2v5cdh88Caq8uGArVStF8eG9nWlT/1J3GbSqF+2uc6paoMxUHUO9XHq0rHcJ763Zzez1+xjbv4Xfe8nz2nheB4D5qTnc0rYe2w6cCOl+CjQ5Spl7jqW1hHvYVDBNEE+Pw1czJ9wx06Jk+It5+cNfU9a7aeXyWnx55y6ZPSYtdTfBXN6pZ3PXMy1PL9But2tK1q/6deI+fWdZQS/U1axzea+e3p9rmFfKvl81ZZ8jnOBq/rm80NSs856M97Abfy0Mby8tULkHij96lqMr5OGZhwIdaLbcAvFs76a/d3Pbu86l2o7o3KQsjX9xkca/sEjfXuboXPRuJXh6d77irP5GU7jKoGDc9Py5qTZHa+ed5ec7w3yFD/x1oHnWk0D3kuf2ktxvlLTJj3Oav5IeU5ZLWY1DDRSf9BWItzI26k0wMoqjh3fMN1D82bvJ50+mr4ePy6ikZh3xebMV1RPsuS0h2aaNn5yvcc8v0lTbEfcx+fn5+tay7drhpcXuEIBnE9dlLPzFwH01Rf3FxBOSbdrUI0bojSstl9HoOmGpOzSQsu9Xtz4ug+Qa1eAvvusrfOMZFnl72XZt/+Ji7TrhuwIPJe8Hor8e8kB1x/MB5n1ewQdClt9+Bc8HmndvvaueBLqXAvVbhIIVBnUl8DDQyGt7VeBGYAZwbzBpldVSVga1uHHX8obnTRvImIXSs+ptiFwGaG5Sls+bq6hy9I6dvr0802E8PDyqtP0eHqqHofWM3xWVd38929759uXNeuff03tPs+VqQpKjDN5evt2tj3es1VcPvb/ycXmOHV5aUmhUgfd5/jw8VyvBuxXgr4x85XOuc1RGalbh8i3qPvH3wAqG4tyDVhjUasBfcUyGko1jIpTdwF7gAyA+mHTKcinrN6UqOsFWzFAqsPdN6NlTH4xx9ufleIZffHlZoXpfgeQUJ9+B0vYuA09j5/JmfRnVQOkHCgv408XbK+/w0hJt8pR/b7uo8publKWdX17i9lA9Kap5XpLme3HOLbFBLXACVAFigMtCPbcsF2NQyx/+bkJfTXxf+Gp+e6fhz2OyouUQjoeMv/M9m+OhxMF9EaqRCcZDLUpeoBcKgvFQi1t+ZeKhVoTFGNTIwQoDEkwszF/zuygdSuLx+EonGMNfEnne4Qgry7c08I6PRnoILJBBjcSvnhoqOCWdUctzlqBAaYkIt8bVZ+qwjoVmPgo005BV86i60hEkYH5LKs81xMv9v4SzKJX2HK2RNCdsSQnpI33lGfORvshB1bqP9lmZVrgIt47loQyCobx8JDDQOFRjUA0Gi6koBi5YrMpveSk3K7966pnozSLygYjEO9dHFTctQ8VDNTInhy4Nvcr7JOGhYlV+K0LTvyQx1L8CY4FhInIjEG+JRoYKQaQaldLQqyy/ZVUWXGj5DUSxm/wi8r6qjnL+ngT8TlU7W6mclZgmf+kSqc23SNXLUH4IS5MfWOD6oapPATNLkJahghGpzbdI1ctQMSiJQT3lGUMFfrNAHwBEpL+IbBeRnSLylI/9IiJvOveniEgHq2QbDAZDcalcgnP/CtwHPCMiV2BRDFVEKgHvADcDNmCjiMxT1QyPwwYAzZxLV2CK87/BYDCUGSXxUA+qaq6qjgH6AlbFT7sAO1V1t6qeBT4DbvM65jZgpvPFhfXAZSISY5F8g8FgKBYl8VALxFBF5GEL9AFoAGR5rNso7H36OqYBkON5kHMol2s41wkR2e5HZm3gUHEVtgAj38g38suP/Kv97QjKoIrIM6r6suc2VU1w7ntDVf+uqm+FoFBAcT62eQ9FCOYYVPV94P0iBYps8tdrVxoY+Ua+kV8x5Afb5P+Dh/AeInKJx74brFDEAxtwlcd6QxxTBoZ6jMFgMJQqIcVQReQZ4CMgSUR+59pssU4bgWYi0lhEqgJDgHlex8wDhjt7+68DjqpqjndCBoPBUJoEG0OtISJfAaeAa4HWwMcisg7H5NOWoap5IvIQsBioBHykqukiMtq5fyqwEBgI7HTqdF8JxRYZFggzRr6Rb+RXAPnBfvX0HPCEqv7bY1tV4B/A46paks4tg8FgqBBcMLNNGQwGQ7gxE0wbDAaDRRiDajAYDBZhDKrBYDBYhDGoBoPBYBHGoBoMBoNFGINqMBgMFmEMqsFgMFiEMagGg8FgEcagGgwGg0UYg2owGAwWYQyqwWAwWIQxqAaDwWARxqAaDAaDRRiDajAYDBZxwcxjWrt2bY2NjS1rNQwGQzknMTHxkKrW8bXvgjGosbGxbNq0qazVMBgM5RwR2etvn2nyGwwGg0VcMB5qKPz444+88cYbrF69mpMnT1qWblRUFPXr1+f//u//eOKJJ6hZs6ZlaRsMhrLHGFQv1q1bx+DBg3nhhRf45z//yaWXXoqINR92zc/PZ9euXbzxxhsMGDCAlStXUqVKFUvSNhgMZY8xqF68+uqrTJw4kT//+c9hSb9WrVrMmjWLbt26sXTpUgYMGBAWOeWZc+fOYbPZOHPmTFmrYriAqVatGg0bNgzJ6TEG1YsVK1bw3nvvhVVGVFQUgwcPZvny5cag+sBmsxEdHU1sbKxlrQODIRRUlcOHD2Oz2WjcuHHQ55lOKS9OnDjB5ZdfHnY5l19+OSdOnAi7nPLImTNnqFWrljGmhjJDRKhVq1bIraSINKgi0l9EtovIThF5ysf+3iJyVESSnctzFssvtG3cuHG88cYbAKxZs4YWLVq497Vp04aVK1cCMHHiREaOHAnAgQMHaNWqFb/99ltQMgznMeVjKGuKUwcjrskvIpWAd4CbARuwUUTmqWqG16FrVHVQaeh08OBBZs6cyc6dOwHo2bMn27dvd+9PT093/x4/frz795VXXkmfPn14//33efjhh0tDVYPBUIZEoofaBdipqrtV9SzwGXBbWSo0ffp0Bg4cSPXq1UM+9+677w57TNZgHYcPHyY+Pp74+Hjq1atHgwYN3Otnz561VNbKlSsZNMi3T/DGG29w6tQp9/rAgQPJzc21VL6V5OXlUbt2bcaNG2dpurGxsRw6dAiA7t27A7Bnzx5mz54dclrTp0/noYceslQ/byLRoDYAsjzWbc5t3nQTkS0i8q2ItAmnQt9++y29evVyr69cuZKGDRu612NjY1m6dCkAL7zwAsOGDXPv69q1K7t372bvXr8vVxgiiFq1apGcnExycjKjR4/m0Ucfda9XrVqVvLy8UtHD26AuXLiQyy67rFRkF4clS5bQokULvvjiC1Q1LDJ++OEHoPgGtTSIRIPqK3DhfYU2A1er6rXAW8BcnwmJjBKRTSKy6eDBg8VWKDU1tUDMNBQqV67MNddcw5YtW4ot31C23HvvvTz22GP06dOHJ598kg0bNtC9e3fat29P9+7d3eGfrl27Fgj/9O7dm8TERE6ePMn9999P586dad++PQkJCQHlvfnmm2RnZ9OnTx/69OkDnPfU9uzZQ8uWLRk5ciRt27bl7rvvZunSpfTo0YNmzZqxYcMGgKBk3nnnnSxcuLBAPr/66ivS09Pp0qUL8fHxxMXFsWPHjiLLaM6cOTzyyCM0atSI9evXu7fHxsYyfvx4unXrRqdOndi8eTP9+vWjadOmTJ06FXA4KDfccAO///3vad26NaNHj8ZutxeScckllwDw1FNPsWbNGuLj4/n3v/9dyPMcNGiQu0/j448/pnnz5vTq1Yvvv//efczBgwf54x//SOfOnencuXOBfSUhEg2qDbjKY70hkO15gKoeU9UTzt8LgSoiUts7IVV9X1U7qWqnOnV8zmUQFLm5uURHRxf7/Ojo6IhurlUEVJX07KNh844yMzNZunQpr7/+Oi1btmT16tUkJSXx0ksvuePmQ4YM4YsvvgAgJyeH7OxsOnbsyIQJE7jxxhvZuHEjK1asYOzYsQHfwPvb3/5G/fr1WbFiBStWrCi0f+fOnTzyyCOkpKSwbds2Zs+ezdq1a5k8eTITJ04ECErmkCFD+PzzzwE4e/Ysy5YtY+DAgUydOpVHHnmE5ORkNm3aVKA15ovTp0+zbNkyBg0axF133cWcOXMK7L/qqqtYt24dPXv25N577+W///0v69ev57nnzvclb9iwgddff53U1FR27drF//73P7/yJk2aRM+ePUlOTubRRx/1e1xOTg7PP/8833//Pd999x0ZGee7YR555BEeffRRNm7cyFdffeXuSC4pkWhQNwLNRKSxiFQFhgDzPA8QkXri7IITkS448nE4XApdfvnlHD9+vNjnHz9+PKKbaxWBjJxjPDhrMxk5x8KS/u23306lSpUAOHr0KLfffjtt27bl0UcfdXuld9xxB19++SUAX3zxBbfffjvgaA5PmjSJ+Ph4evfuzZkzZ9i3b1+xdWncuDHt2rUjKiqKNm3a8Lvf/Q4RoV27duzZsydomQMGDGD58uX89ttvfPvtt9xwww1Ur16dbt26MXHiRF555RX27t1bZN/B/Pnz6dOnDxdffDF//OMf+frrr8nPz3fvHzx4MADt2rWja9euREdHU6dOHapVq+Z2NLp06UKTJk2oVKkSd911F2vXri12+bj48ccf6d27N3Xq1KFq1arceeed7n1Lly7loYceIj4+nsGDB3Ps2LES3eMuIq6XX1XzROQhYDFQCfhIVdNFZLRz/1TgT8CDIpIHnAaGaLhcEyAuLo7MzEw6d+4c8rl5eXns3LmTa6+9NgyaGVy0jqnJlGEdaB0TnvkRatSo4f797LPP0qdPH77++mv27NlD7969AWjQoAG1atUiJSWFzz//3N0Zqap89dVXhcJGBw4ccP/u168fBw4coFOnTkybNi2gLhdddJH7d1RUlHs9KirKHeP1J9OTatWq0bt3bxYvXsznn3/OXXfdBcDQoUPp2rUrCxYsoF+/fkybNo0bb7zRbzpz5szh+++/xzU95uHDh1mxYgU33XRTAX09dfXW13uIUihDlipXrlwgROA5dtRfOna7nXXr1hWrozkQkeihoqoLVbW5qjZV1QnObVOdxhRVfVtV26jqtap6nar+EE59Bg4cyKpVq4p17oYNG4iNjeXqq6+2WCuDJyJCm/rWzbsQiKNHj9KggaOfdPr06QX2DRkyhFdffZWjR4/Srl07wGEs33rrLXc4IikpqVCaixcvJjk52W1Mo6OjS+QxBSPTpe/HH3/MmjVr6NevHwC7d++mSZMm/O1vf2Pw4MGkpKT4lXPs2DHWrl3Lvn372LNnD3v27OGdd94p1Owvig0bNvDTTz9ht9v5/PPPuf766/0e6102sbGxJCcnY7fbycrKcseRu3btysqVKzl8+DDnzp1ztx4A+vbty9tvv+1eT05ODklff0SkQY00hg8fzsKFCzl9+nTI53766aeMHj06DFoZyoonnniCcePG0aNHjwJNW4A//elPfPbZZ9xxxx3ubc8++yznzp0jLi6Otm3b8uyzzxYpY9SoUQwYMMDdKRUqwcrs27cvq1ev5qabbqJq1aoAfP7557Rt25b4+Hi2bdvG8OHDAYdjkZ1doDuD//3vf9x4440FPM/bbruNefPm+XyhxR/dunXjqaeeom3btjRu3Jjf//73fo+Ni4ujcuXKXHvttfz73/+mR48e7jDImDFj6NChAwAxMTG88MILdOvWjZtuusm9HRwdf5s2bSIuLo7WrVu7O8hKioSxpRxRdOrUSYOZYLpy5cqcOXOGypULRkPGjx9P3bp1+fvf/x60zF9++YVevXqRlJREtWrVCuybMmUKKSkpTJkyJej0LhS2bt1Kq1atyloNQymxcuVKJk+ezPz588talUL4qosikqiqnXwdH3Ex1EjF1XsaCnXr1mXr1q1h0MZgMEQixqB6UaVKFX777bdCHqrV/Pbbb2YuVIMBx3hdV8deecfEUL1o165dgYHJ4WLdunXuTgtDYS6UUJQhcilOHTQG1Yv77ruPMWPGFAq+W4VrOMuKFSsCBt4vZKpVq8bhw4eNUTWUGa75UL37PorCNPm9GD16NAcOHKBNmzY0bdrU8k+g7N69m+rVq/PNN99Qu3ahl7sMQMOGDbHZbJTkdWGDoaS4ZuwPBdPL74fTp0+zZcsWSyeBdn2kr0WLFma+T4OhnGJ6+YtB9erVue6668paDYPBUI4wMVRDsQj3ZCQGQ3nEGFRDsQj3ZCQGQ3nEGFRDsQj3ZCQGQ3nExFANxcI1GYnBYDhPsT1UEblZRD4QkXjn+ijLtDIYDIZySEma/H8FxgLDRORGIN4SjQwGQ7ExnYVlS0kM6kFVzVXVMUBfIPTZlw0Gg6WYzsKypSQGdYHrh6o+BcwsuToXBsaLKB3KezkXR3/TWVi2FNugqqr3ZxQvLqEuEY9VN2hF8SJK22CFKi9SyznYfBRH/+J8uaAsHzyhyo70h2RJOqW+8Fi+BKz5bGAEY9UNWlG8iIzsYzwwfRMZ2aVjsIItf9dN16petLucI+lGDDYfpVVPSuvB4+sahCo7Uh+SblS1WAswzWt9SnHTKo2lY8eOWlLsdrum7c9Vu90e0r5Q0yovpNqOaNcJSzXVdiQs6XuXUTBlZrfbNSHZpte/skzT9ue6t6ftz9WerywvsK0oeeHCU04k1IPSqrtp+3P1+knLNCHZFtI1tUK2lQCb1I+dKUkMdYLX+tMlSKtcEKg5Far3lJF9/njXNo0A76koXLq6vjL54YhOPsej+suT93bPdc+0vcsIgmvOZmQfY8L8rYy5uTno+Tktg/H2/HncweYlWDzzURyPy7Oc0vbnkr7/fJkVpw4FU66eeS2ul9g6piZj+7fgtcXbSc8+Snr2UYBCsn3VCVe+RITWMTXd94338YH0DmZ7SQnaoIrIM14K/eSx7w1V/dUqpUSkv4hsF5GdIvKUj/0iIm8696eISAdf6ZQE75vbs+B9XYxgm2euyqgo797dHtRxI4/+JJFvUrLdlch1o5RWRfDGn+FzGbn5qTk8OGszuw6dKHCOS29vY+ja/01KNn/5ZBPztux3Hzf6k0TmbdnPN1uyGT3LUQ73f7yRnb8cY8rdHWhVLzqg4ShwA+LYvj/3NH+ZtcldpoGMRqG8UrBs/RkQ1/b07KN+9Qv0wFBVd71pVS864HX1ZdDmp+YwckYiD8xw5POB6ZvcRqo419a1brfbSUi2kWbLdeuavv+oI31nnZxyd/FCEU1q12DK0A7sPniS0bMSC5WpZx35JiXbXY/S9ucyNymLeUkOvTzz6nl9vMvYpXeaLZeEZBupWUcK1DtX/bCKUN6U+gPwMoCI9AC2qKrrbrrBKoVEpBLwDnAzYAM2isg8Vc3wOGwA0My5dAWmOP9bhusijenXnMmLM5kyrANt6l/qvuCvLd7O1GEd3d6Z64Z1XdDWMTUREfe6ILSKiUZVeXdoewTHjf3gp5t5Z2g8d3W9itcWbadJ7RrsPnSSCQu2Iggf3lvQA3Tp5dInFFw3o0s3X/tdurp0e/fu9o5j9fy6ywAAvLpoG6pK4ysu5quk/SxKc3xrftzAFjx20zXsOnCcFnVrsCDtZ0Th1SXbGdL5Kp6bm071qpWYNqITY/o249mv06hcSXj+1taIwrl8Oy/My+DPNzRBVXlgxibO5dsZ2bMxczZmMXVYx/Oeit2xf/zAFjSpfQkjul/Np+v38rvWdXn5mwzUbueaujVpFRPtuIH1fH53HTzB/iOnmbl+H9NGdGTaiI4Igt1uZ+vPx2kdU5NW9aIZ0685repFF7qeU4Z1QO3KiI83UjlKGN69ETPX7WPa8I5ERUUVKLfdh07y2uLtjO3Xwl2nXEYpI/sYD8zYxNO3tOTWax2fqE7PPoraC+roqg/vDI1n98GTfHBPB0SEXb+c4GxePrt/OeGuFxk5x2hVL9qdD1d9dNXfd4c6dHJdl7H9WnBNnWh2HTzBo59tIbp6Ff5xWxsmL8nk8b7NUFVWZf7CJ+uz+HBEp0JeZdr+XH46dIpb2tVj24ETtKrnKG9BaHFlDf6xcCvLtx50e6lj+hVsRaRnH2X3wZO8ungbd3VpxMvfZDCi+9VuPR/7PAUF7unWCFVl98GTtKl/aQFnJiPHYSjv6noVczZkMaZvcwDW7DzE5MWZXFy1EpdUq8wHwzu677mmdS6x7K2/kF89dXqq9wBRIjJaVZcBVk7u2QXYqaq7nfI+A24DPA3qbcBMZzxjvYhcJiIxqppjlRKenkPTOpecr/g5x3ht0XbG9m/h8wntbfAyco4xckYiqsrw7lfzybp9jB/YkslLMt3GCYU5P2Yxtn8LBOG1RdsZP7Al19SJLiSjJB0V/oyxy9CiMHJGIgDTRnR06/bgrPOG1PPGbFK7Bk/0a8mEhVs5eSaPE2fzuaVdXdrEXMbEBds4dTafU2fz+W7bARak/Mxl1avw/ODW2H49xbEzefz5hsaI8+/4b/lEV6vM/twzzN6wjwd6xvLeqp94fUkmDS6vzvhbWvLiNxlM//4nhne/mp0/H2PXgeO8umQ7d3VpRJ5deWn+NgCnYbuaaat3c84OL36TAQgjezZm+g97+O2cnUpRcDZPOXk2HwVqVqvMT4dO0aR2De79+Eeua1qLpH257mvy6mJH2k1q1XAbz6cHteLWuPqkZx+lkkD/tlcy4/u95Cv8dOiU22i8c1c8qzMPMvvHfTwxoCWD2sW461R69lFGzkhk3IAW5NmViQu306TOJe6H6pmz+Rw7k4cC0RdVYnXmQVrVi2bNjkO8vmQH/7ojDlXl6a/TqFQpionfbqNxnRqs2XmI2ev3cVfXRnziNPAS5XgwuurvT4dO8djnW6hZrTK3xsfwQkI6IvDcoFY83rcZM37Yi6qdd4e2B+DpgS154ZsMRAS72t0dfxk5x9h98CQvfJPO0VN52HJP8dkGG2P6NWfiAscDt0uTy/lmy88M73YVja+42GHo7Mr9Mzby/rD2fJmYxddJP1Pjoko8M6g1TWrV4P1Vu/nXdzu4qlYNBsXFYFc72Ucc9WN490a8vCADUG5pF8POX46jdqV1fUdY4dVF23iiv6Osr6kbTcsrL0GBmT/sYdyAlvx08CTT1+5heLdG7DpwnFb1oh0PwBIS9ATTIrIdSANOAX8GWgMfA+uAXqpqyXd/ReRPQH9VHelcvwfoqqoPeRwzH5ikqmud68uAJ1V1k1dao4BRAI0aNeq4d+/eEusXjJfnud/l0ew+eJKXF2SQb4fp93UiKiqqwDGuc4CA6YdD9/Tso26jCSAIresX1i3QOfl5+Xyw9ie+33kYEXjg+sbM+GEvA9rVY2nGAe7s3IDDJ/L4Y8cG/PXTJIZe14gbrqnDX2cnObwtD0/JdSOk7c9lzY5D3NCsDm0aXEp69lHW7DjEtDU/kXvqHJddXIUHesYy44e9jB/QApEom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+    <script id="__config" type="application/json">{"base": "../..", "features": [], "search": "../../assets/javascripts/workers/search.ecf98df9.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}}</script>
+    
+    
+      <script src="../../assets/javascripts/bundle.9c69f0bc.min.js"></script>
+      
+    
+  </body>
+</html>
\ No newline at end of file
diff --git a/kubeflow/prepare_test_data/index.html b/kubeflow/prepare_test_data/index.html
new file mode 100644
index 0000000..a9687a7
--- /dev/null
+++ b/kubeflow/prepare_test_data/index.html
@@ -0,0 +1,2022 @@
+
+<!doctype html>
+<html lang="en" class="no-js">
+  <head>
+    
+      <meta charset="utf-8">
+      <meta name="viewport" content="width=device-width,initial-scale=1">
+      
+        <meta name="description" content="QuakeFlow: a Scalable Deep-learning-based Earthquake Monitoring Workflow with Cloud Computing">
+      
+      
+        <meta name="author" content="Weiqiang Zhu">
+      
+      
+      <link rel="icon" href="../../assets/images/favicon.png">
+      <meta name="generator" content="mkdocs-1.3.0, mkdocs-material-8.4.1">
+    
+    
+      
+        <title>Prepare test data - QuakeFlow</title>
+      
+    
+    
+      <link rel="stylesheet" href="../../assets/stylesheets/main.69437709.min.css">
+      
+        
+        <link rel="stylesheet" href="../../assets/stylesheets/palette.cbb835fc.min.css">
+        
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+      
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+        
+        
+        <link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
+        <link rel="stylesheet" href="https://fonts.googleapis.com/css?family=Roboto:300,300i,400,400i,700,700i%7CRoboto+Mono:400,400i,700,700i&display=fallback">
+        <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
+      
+    
+    
+    <script>__md_scope=new URL("../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
+    
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+
+  
+    <script>"undefined"!=typeof __md_analytics&&__md_analytics()</script>
+  
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+    
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+  </head>
+  
+  
+    
+    
+    
+    
+    
+    <body dir="ltr" data-md-color-scheme="" data-md-color-primary="none" data-md-color-accent="none">
+  
+    
+    
+    <input class="md-toggle" data-md-toggle="drawer" type="checkbox" id="__drawer" autocomplete="off">
+    <input class="md-toggle" data-md-toggle="search" type="checkbox" id="__search" autocomplete="off">
+    <label class="md-overlay" for="__drawer"></label>
+    <div data-md-component="skip">
+      
+        
+        <a href="#1-data" class="md-skip">
+          Skip to content
+        </a>
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+    <div data-md-component="announce">
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+<header class="md-header" data-md-component="header">
+  <nav class="md-header__inner md-grid" aria-label="Header">
+    <a href="../.." title="QuakeFlow" class="md-header__button md-logo" aria-label="QuakeFlow" data-md-component="logo">
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+
+    </a>
+    <label class="md-header__button md-icon" for="__drawer">
+      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M3 6h18v2H3V6m0 5h18v2H3v-2m0 5h18v2H3v-2Z"/></svg>
+    </label>
+    <div class="md-header__title" data-md-component="header-title">
+      <div class="md-header__ellipsis">
+        <div class="md-header__topic">
+          <span class="md-ellipsis">
+            QuakeFlow
+          </span>
+        </div>
+        <div class="md-header__topic" data-md-component="header-topic">
+          <span class="md-ellipsis">
+            
+              Prepare test data
+            
+          </span>
+        </div>
+      </div>
+    </div>
+    
+    
+    
+    
+      <div class="md-header__source">
+        <a href="https://github.com/ai4eps/QuakeFlow" title="Go to repository" class="md-source" data-md-component="source">
+  <div class="md-source__icon md-icon">
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+  </div>
+  <div class="md-source__repository">
+    AI4EPS/QuakeFlow
+  </div>
+</a>
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+  <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54Z"/></svg>
+
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+    QuakeFlow
+  </label>
+  
+    <div class="md-nav__source">
+      <a href="https://github.com/ai4eps/QuakeFlow" title="Go to repository" class="md-source" data-md-component="source">
+  <div class="md-source__icon md-icon">
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+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.1.2 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+  </div>
+  <div class="md-source__repository">
+    AI4EPS/QuakeFlow
+  </div>
+</a>
+    </div>
+  
+  <ul class="md-nav__list" data-md-scrollfix>
+    
+      
+      
+      
+
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../.." class="md-nav__link">
+        Overview
+      </a>
+    </li>
+  
+
+    
+      
+      
+      
+
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../workflow/" class="md-nav__link">
+        Pipeline
+      </a>
+    </li>
+  
+
+    
+      
+      
+      
+
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../data_format/" class="md-nav__link">
+        Standard Formats
+      </a>
+    </li>
+  
+
+    
+  </ul>
+</nav>
+                  </div>
+                </div>
+              </div>
+            
+            
+              
+              <div class="md-sidebar md-sidebar--secondary" data-md-component="sidebar" data-md-type="toc" >
+                <div class="md-sidebar__scrollwrap">
+                  <div class="md-sidebar__inner">
+                    
+
+<nav class="md-nav md-nav--secondary" aria-label="Table of contents">
+  
+  
+  
+    
+  
+  
+    <label class="md-nav__title" for="__toc">
+      <span class="md-nav__icon md-icon"></span>
+      Table of contents
+    </label>
+    <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
+      
+        <li class="md-nav__item">
+  <a href="#11-pseudo-data-generator" class="md-nav__link">
+    1.1 Pseudo Data Generator
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#12-data-aggrator" class="md-nav__link">
+    1.2 Data Aggrator
+  </a>
+  
+</li>
+      
+    </ul>
+  
+</nav>
+                  </div>
+                </div>
+              </div>
+            
+          
+          <div class="md-content" data-md-component="content">
+            <article class="md-content__inner md-typeset">
+              
+                
+  <a href="https://github.com/ai4eps/QuakeFlow/edit/master/docs/kubeflow/prepare_test_data.ipynb" title="Edit this page" class="md-content__button md-icon">
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
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+<script>
+(function (global, factory) {
+    typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() :
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+  }(this, function () { 'use strict';
+
+    function createNode(text) {
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+      node.textContent = text;
+      return node;
+    }
+
+    function copyNode(node) {
+      if ('clipboard' in navigator) {
+        // eslint-disable-next-line flowtype/no-flow-fix-me-comments
+        // $FlowFixMe Clipboard is not defined in Flow yet.
+        return navigator.clipboard.writeText(node.textContent);
+      }
+
+      const selection = getSelection();
+
+      if (selection == null) {
+        return Promise.reject(new Error());
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+      document.execCommand('copy');
+      selection.removeAllRanges();
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+    }
+    function copyText(text) {
+      if ('clipboard' in navigator) {
+        // eslint-disable-next-line flowtype/no-flow-fix-me-comments
+        // $FlowFixMe Clipboard is not defined in Flow yet.
+        return navigator.clipboard.writeText(text);
+      }
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+      body.appendChild(node);
+      copyNode(node);
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+        if (!(root instanceof Document || 'ShadowRoot' in window && root instanceof ShadowRoot)) return;
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+        if (node) copyTarget(node).then(trigger);
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+    }
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+    function copyTarget(content) {
+      if (content instanceof HTMLInputElement || content instanceof HTMLTextAreaElement) {
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+          event.preventDefault();
+          copy(button);
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+    function focused(event) {
+      event.currentTarget.addEventListener('keydown', keydown);
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+      event.currentTarget.removeEventListener('keydown', keydown);
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+    class ClipboardCopyElement extends HTMLElement {
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+</script>
+<script>
+      document.addEventListener('clipboard-copy', function(event) {
+        const notice = event.target.querySelector('.notice')
+        notice.hidden = false
+        setTimeout(function() {
+          notice.hidden = true
+        }, 1000)
+      })
+</script>
+
+
+
+
+<style type="text/css">
+    
+    pre { line-height: 125%; }
+td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+.highlight-ipynb .hll { background-color: var(--jp-cell-editor-active-background) }
+.highlight-ipynb { background: var(--jp-cell-editor-background); color: var(--jp-mirror-editor-variable-color) }
+.highlight-ipynb .c { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment */
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+.highlight-ipynb .c1 { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Single */
+.highlight-ipynb .cs { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Special */
+.highlight-ipynb .kc { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Constant */
+.highlight-ipynb .kd { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Declaration */
+.highlight-ipynb .kn { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Namespace */
+.highlight-ipynb .kp { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Pseudo */
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+.highlight-ipynb .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */
+.highlight-ipynb .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */
+.highlight-ipynb .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */
+.highlight-ipynb .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */
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+.highlight-ipynb .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */
+.highlight-ipynb .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */
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+.highlight-ipynb .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */
+.highlight-ipynb .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */
+.highlight-ipynb .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */
+.highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */
+.highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */
+  </style>
+
+
+
+
+<style type="text/css">
+/*
+This file is taken from the built JupyterLab theme.css
+Found on share/nbconvert/templates/lab/static
+Some changes have been made and marked with CHANGE
+*/
+
+.jupyter-wrapper {
+    /* Elevation
+   *
+   * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here:
+   *
+   * https://github.com/material-components/material-components-web
+   * https://material-components-web.appspot.com/elevation.html
+   */
+
+    --jp-shadow-base-lightness: 0;
+    --jp-shadow-umbra-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.2
+    );
+    --jp-shadow-penumbra-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.14
+    );
+    --jp-shadow-ambient-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.12
+    );
+    --jp-elevation-z0: none;
+    --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color),
+        0px 1px 1px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 3px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color),
+        0px 2px 2px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 5px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color),
+        0px 4px 5px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 10px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color),
+        0px 6px 10px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 18px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color),
+        0px 8px 10px 1px var(--jp-shadow-penumbra-color),
+        0px 3px 14px 2px var(--jp-shadow-ambient-color);
+    --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color),
+        0px 12px 17px 2px var(--jp-shadow-penumbra-color),
+        0px 5px 22px 4px var(--jp-shadow-ambient-color);
+    --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color),
+        0px 16px 24px 2px var(--jp-shadow-penumbra-color),
+        0px 6px 30px 5px var(--jp-shadow-ambient-color);
+    --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color),
+        0px 20px 31px 3px var(--jp-shadow-penumbra-color),
+        0px 8px 38px 7px var(--jp-shadow-ambient-color);
+    --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color),
+        0px 24px 38px 3px var(--jp-shadow-penumbra-color),
+        0px 9px 46px 8px var(--jp-shadow-ambient-color);
+
+    /* Borders
+   *
+   * The following variables, specify the visual styling of borders in JupyterLab.
+   */
+
+    --jp-border-width: 1px;
+    --jp-border-color0: var(--md-grey-400);
+    --jp-border-color1: var(--md-grey-400);
+    --jp-border-color2: var(--md-grey-300);
+    --jp-border-color3: var(--md-grey-200);
+    --jp-border-radius: 2px;
+
+    /* UI Fonts
+   *
+   * The UI font CSS variables are used for the typography all of the JupyterLab
+   * user interface elements that are not directly user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-ui-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+    --jp-ui-font-scale-factor: 1.2;
+    --jp-ui-font-size0: 0.83333em;
+    --jp-ui-font-size1: 13px; /* Base font size */
+    --jp-ui-font-size2: 1.2em;
+    --jp-ui-font-size3: 1.44em;
+
+    --jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+
+    /*
+   * Use these font colors against the corresponding main layout colors.
+   * In a light theme, these go from dark to light.
+   */
+
+    /* Defaults use Material Design specification */
+    --jp-ui-font-color0: rgba(0, 0, 0, 1);
+    --jp-ui-font-color1: rgba(0, 0, 0, 0.87);
+    --jp-ui-font-color2: rgba(0, 0, 0, 0.54);
+    --jp-ui-font-color3: rgba(0, 0, 0, 0.38);
+
+    /*
+   * Use these against the brand/accent/warn/error colors.
+   * These will typically go from light to darker, in both a dark and light theme.
+   */
+
+    --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1);
+    --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1);
+    --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7);
+    --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5);
+
+    /* Content Fonts
+   *
+   * Content font variables are used for typography of user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-content-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+    --jp-content-line-height: 1.6;
+    --jp-content-font-scale-factor: 1.2;
+    --jp-content-font-size0: 0.83333em;
+    --jp-content-font-size1: 14px; /* Base font size */
+    --jp-content-font-size2: 1.2em;
+    --jp-content-font-size3: 1.44em;
+    --jp-content-font-size4: 1.728em;
+    --jp-content-font-size5: 2.0736em;
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-content-presentation-font-size1: 17px;
+
+    --jp-content-heading-line-height: 1;
+    --jp-content-heading-margin-top: 1.2em;
+    --jp-content-heading-margin-bottom: 0.8em;
+    --jp-content-heading-font-weight: 500;
+
+    /* Defaults use Material Design specification */
+    --jp-content-font-color0: rgba(0, 0, 0, 1);
+    --jp-content-font-color1: rgba(0, 0, 0, 0.87);
+    --jp-content-font-color2: rgba(0, 0, 0, 0.54);
+    --jp-content-font-color3: rgba(0, 0, 0, 0.38);
+
+    --jp-content-link-color: var(--md-blue-700);
+
+    --jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+
+    /*
+   * Code Fonts
+   *
+   * Code font variables are used for typography of code and other monospaces content.
+   */
+
+    --jp-code-font-size: 13px;
+    --jp-code-line-height: 1.3077; /* 17px for 13px base */
+    --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */
+    --jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono",
+        monospace;
+    --jp-code-font-family: var(--jp-code-font-family-default);
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-code-presentation-font-size: 16px;
+
+    /* may need to tweak cursor width if you change font size */
+    --jp-code-cursor-width0: 1.4px;
+    --jp-code-cursor-width1: 2px;
+    --jp-code-cursor-width2: 4px;
+
+    /* Layout
+   *
+   * The following are the main layout colors use in JupyterLab. In a light
+   * theme these would go from light to dark.
+   */
+
+    --jp-layout-color0: white;
+    --jp-layout-color1: white;
+    --jp-layout-color2: var(--md-grey-200);
+    --jp-layout-color3: var(--md-grey-400);
+    --jp-layout-color4: var(--md-grey-600);
+
+    /* Inverse Layout
+   *
+   * The following are the inverse layout colors use in JupyterLab. In a light
+   * theme these would go from dark to light.
+   */
+
+    --jp-inverse-layout-color0: #111111;
+    --jp-inverse-layout-color1: var(--md-grey-900);
+    --jp-inverse-layout-color2: var(--md-grey-800);
+    --jp-inverse-layout-color3: var(--md-grey-700);
+    --jp-inverse-layout-color4: var(--md-grey-600);
+
+    /* Brand/accent */
+
+    --jp-brand-color0: var(--md-blue-900);
+    --jp-brand-color1: var(--md-blue-700);
+    --jp-brand-color2: var(--md-blue-300);
+    --jp-brand-color3: var(--md-blue-100);
+    --jp-brand-color4: var(--md-blue-50);
+
+    --jp-accent-color0: var(--md-green-900);
+    --jp-accent-color1: var(--md-green-700);
+    --jp-accent-color2: var(--md-green-300);
+    --jp-accent-color3: var(--md-green-100);
+
+    /* State colors (warn, error, success, info) */
+
+    --jp-warn-color0: var(--md-orange-900);
+    --jp-warn-color1: var(--md-orange-700);
+    --jp-warn-color2: var(--md-orange-300);
+    --jp-warn-color3: var(--md-orange-100);
+
+    --jp-error-color0: var(--md-red-900);
+    --jp-error-color1: var(--md-red-700);
+    --jp-error-color2: var(--md-red-300);
+    --jp-error-color3: var(--md-red-100);
+
+    --jp-success-color0: var(--md-green-900);
+    --jp-success-color1: var(--md-green-700);
+    --jp-success-color2: var(--md-green-300);
+    --jp-success-color3: var(--md-green-100);
+
+    --jp-info-color0: var(--md-cyan-900);
+    --jp-info-color1: var(--md-cyan-700);
+    --jp-info-color2: var(--md-cyan-300);
+    --jp-info-color3: var(--md-cyan-100);
+
+    /* Cell specific styles */
+
+    --jp-cell-padding: 5px;
+
+    --jp-cell-collapser-width: 8px;
+    --jp-cell-collapser-min-height: 20px;
+    --jp-cell-collapser-not-active-hover-opacity: 0.6;
+
+    --jp-cell-editor-background: var(--md-grey-100);
+    --jp-cell-editor-border-color: var(--md-grey-300);
+    --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-cell-editor-active-background: var(--jp-layout-color0);
+    --jp-cell-editor-active-border-color: var(--jp-brand-color1);
+
+    --jp-cell-prompt-width: 64px;
+    --jp-cell-prompt-font-family: var(--jp-code-font-family-default);
+    --jp-cell-prompt-letter-spacing: 0px;
+    --jp-cell-prompt-opacity: 1;
+    --jp-cell-prompt-not-active-opacity: 0.5;
+    --jp-cell-prompt-not-active-font-color: var(--md-grey-700);
+    /* A custom blend of MD grey and blue 600
+   * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */
+    --jp-cell-inprompt-font-color: #307fc1;
+    /* A custom blend of MD grey and orange 600
+   * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */
+    --jp-cell-outprompt-font-color: #bf5b3d;
+
+    /* Notebook specific styles */
+
+    --jp-notebook-padding: 10px;
+    --jp-notebook-select-background: var(--jp-layout-color1);
+    --jp-notebook-multiselected-color: var(--md-blue-50);
+
+    /* The scroll padding is calculated to fill enough space at the bottom of the
+  notebook to show one single-line cell (with appropriate padding) at the top
+  when the notebook is scrolled all the way to the bottom. We also subtract one
+  pixel so that no scrollbar appears if we have just one single-line cell in the
+  notebook. This padding is to enable a 'scroll past end' feature in a notebook.
+  */
+    --jp-notebook-scroll-padding: calc(
+        100% - var(--jp-code-font-size) * var(--jp-code-line-height) -
+            var(--jp-code-padding) - var(--jp-cell-padding) - 1px
+    );
+
+    /* Rendermime styles */
+
+    --jp-rendermime-error-background: #fdd;
+    --jp-rendermime-table-row-background: var(--md-grey-100);
+    --jp-rendermime-table-row-hover-background: var(--md-light-blue-50);
+
+    /* Dialog specific styles */
+
+    --jp-dialog-background: rgba(0, 0, 0, 0.25);
+
+    /* Console specific styles */
+
+    --jp-console-padding: 10px;
+
+    /* Toolbar specific styles */
+
+    --jp-toolbar-border-color: var(--jp-border-color1);
+    --jp-toolbar-micro-height: 8px;
+    --jp-toolbar-background: var(--jp-layout-color1);
+    --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24);
+    --jp-toolbar-header-margin: 4px 4px 0px 4px;
+    --jp-toolbar-active-background: var(--md-grey-300);
+
+    /* Statusbar specific styles */
+
+    --jp-statusbar-height: 24px;
+
+    /* Input field styles */
+
+    --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-input-active-background: var(--jp-layout-color1);
+    --jp-input-hover-background: var(--jp-layout-color1);
+    --jp-input-background: var(--md-grey-100);
+    --jp-input-border-color: var(--jp-border-color1);
+    --jp-input-active-border-color: var(--jp-brand-color1);
+    --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3);
+
+    /* General editor styles */
+
+    --jp-editor-selected-background: #d9d9d9;
+    --jp-editor-selected-focused-background: #d7d4f0;
+    --jp-editor-cursor-color: var(--jp-ui-font-color0);
+
+    /* Code mirror specific styles */
+
+    --jp-mirror-editor-keyword-color: #008000;
+    --jp-mirror-editor-atom-color: #88f;
+    --jp-mirror-editor-number-color: #080;
+    --jp-mirror-editor-def-color: #00f;
+    --jp-mirror-editor-variable-color: var(--md-grey-900);
+    --jp-mirror-editor-variable-2-color: #05a;
+    --jp-mirror-editor-variable-3-color: #085;
+    --jp-mirror-editor-punctuation-color: #05a;
+    --jp-mirror-editor-property-color: #05a;
+    --jp-mirror-editor-operator-color: #aa22ff;
+    --jp-mirror-editor-comment-color: #408080;
+    --jp-mirror-editor-string-color: #ba2121;
+    --jp-mirror-editor-string-2-color: #708;
+    --jp-mirror-editor-meta-color: #aa22ff;
+    --jp-mirror-editor-qualifier-color: #555;
+    --jp-mirror-editor-builtin-color: #008000;
+    --jp-mirror-editor-bracket-color: #997;
+    --jp-mirror-editor-tag-color: #170;
+    --jp-mirror-editor-attribute-color: #00c;
+    --jp-mirror-editor-header-color: blue;
+    --jp-mirror-editor-quote-color: #090;
+    --jp-mirror-editor-link-color: #00c;
+    --jp-mirror-editor-error-color: #f00;
+    --jp-mirror-editor-hr-color: #999;
+
+    /* Vega extension styles */
+
+    --jp-vega-background: white;
+
+    /* Sidebar-related styles */
+
+    --jp-sidebar-min-width: 250px;
+
+    /* Search-related styles */
+
+    --jp-search-toggle-off-opacity: 0.5;
+    --jp-search-toggle-hover-opacity: 0.8;
+    --jp-search-toggle-on-opacity: 1;
+    --jp-search-selected-match-background-color: rgb(245, 200, 0);
+    --jp-search-selected-match-color: black;
+    --jp-search-unselected-match-background-color: var(
+        --jp-inverse-layout-color0
+    );
+    --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);
+
+    /* Icon colors that work well with light or dark backgrounds */
+    --jp-icon-contrast-color0: var(--md-purple-600);
+    --jp-icon-contrast-color1: var(--md-green-600);
+    --jp-icon-contrast-color2: var(--md-pink-600);
+    --jp-icon-contrast-color3: var(--md-blue-600);
+}
+
+[data-md-color-scheme="slate"] .jupyter-wrapper {
+    /* Elevation
+   *
+   * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here:
+   *
+   * https://github.com/material-components/material-components-web
+   * https://material-components-web.appspot.com/elevation.html
+   */
+
+    /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout
+   * colors used in the theme as well. */
+    --jp-shadow-base-lightness: 32;
+    --jp-shadow-umbra-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.2
+    );
+    --jp-shadow-penumbra-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.14
+    );
+    --jp-shadow-ambient-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.12
+    );
+    --jp-elevation-z0: none;
+    --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color),
+        0px 1px 1px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 3px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color),
+        0px 2px 2px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 5px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color),
+        0px 4px 5px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 10px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color),
+        0px 6px 10px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 18px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color),
+        0px 8px 10px 1px var(--jp-shadow-penumbra-color),
+        0px 3px 14px 2px var(--jp-shadow-ambient-color);
+    --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color),
+        0px 12px 17px 2px var(--jp-shadow-penumbra-color),
+        0px 5px 22px 4px var(--jp-shadow-ambient-color);
+    --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color),
+        0px 16px 24px 2px var(--jp-shadow-penumbra-color),
+        0px 6px 30px 5px var(--jp-shadow-ambient-color);
+    --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color),
+        0px 20px 31px 3px var(--jp-shadow-penumbra-color),
+        0px 8px 38px 7px var(--jp-shadow-ambient-color);
+    --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color),
+        0px 24px 38px 3px var(--jp-shadow-penumbra-color),
+        0px 9px 46px 8px var(--jp-shadow-ambient-color);
+
+    /* Borders
+   *
+   * The following variables, specify the visual styling of borders in JupyterLab.
+   */
+
+    --jp-border-width: 1px;
+    --jp-border-color0: var(--md-grey-700);
+    --jp-border-color1: var(--md-grey-700);
+    --jp-border-color2: var(--md-grey-800);
+    --jp-border-color3: var(--md-grey-900);
+    --jp-border-radius: 2px;
+
+    /* UI Fonts
+   *
+   * The UI font CSS variables are used for the typography all of the JupyterLab
+   * user interface elements that are not directly user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-ui-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+    --jp-ui-font-scale-factor: 1.2;
+    --jp-ui-font-size0: 0.83333em;
+    --jp-ui-font-size1: 13px; /* Base font size */
+    --jp-ui-font-size2: 1.2em;
+    --jp-ui-font-size3: 1.44em;
+
+    --jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+
+    /*
+   * Use these font colors against the corresponding main layout colors.
+   * In a light theme, these go from dark to light.
+   */
+
+    /* Defaults use Material Design specification */
+    --jp-ui-font-color0: rgba(255, 255, 255, 1);
+    --jp-ui-font-color1: rgba(255, 255, 255, 0.87);
+    --jp-ui-font-color2: rgba(255, 255, 255, 0.54);
+    --jp-ui-font-color3: rgba(255, 255, 255, 0.38);
+
+    /*
+   * Use these against the brand/accent/warn/error colors.
+   * These will typically go from light to darker, in both a dark and light theme.
+   */
+
+    --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1);
+    --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8);
+    --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5);
+    --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3);
+
+    /* Content Fonts
+   *
+   * Content font variables are used for typography of user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-content-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+    --jp-content-line-height: 1.6;
+    --jp-content-font-scale-factor: 1.2;
+    --jp-content-font-size0: 0.83333em;
+    --jp-content-font-size1: 14px; /* Base font size */
+    --jp-content-font-size2: 1.2em;
+    --jp-content-font-size3: 1.44em;
+    --jp-content-font-size4: 1.728em;
+    --jp-content-font-size5: 2.0736em;
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-content-presentation-font-size1: 17px;
+
+    --jp-content-heading-line-height: 1;
+    --jp-content-heading-margin-top: 1.2em;
+    --jp-content-heading-margin-bottom: 0.8em;
+    --jp-content-heading-font-weight: 500;
+
+    /* Defaults use Material Design specification */
+    --jp-content-font-color0: rgba(255, 255, 255, 1);
+    --jp-content-font-color1: rgba(255, 255, 255, 1);
+    --jp-content-font-color2: rgba(255, 255, 255, 0.7);
+    --jp-content-font-color3: rgba(255, 255, 255, 0.5);
+
+    --jp-content-link-color: var(--md-blue-300);
+
+    --jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+
+    /*
+   * Code Fonts
+   *
+   * Code font variables are used for typography of code and other monospaces content.
+   */
+
+    --jp-code-font-size: 13px;
+    --jp-code-line-height: 1.3077; /* 17px for 13px base */
+    --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */
+    --jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono",
+        monospace;
+    --jp-code-font-family: var(--jp-code-font-family-default);
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-code-presentation-font-size: 16px;
+
+    /* may need to tweak cursor width if you change font size */
+    --jp-code-cursor-width0: 1.4px;
+    --jp-code-cursor-width1: 2px;
+    --jp-code-cursor-width2: 4px;
+
+    /* Layout
+   *
+   * The following are the main layout colors use in JupyterLab. In a light
+   * theme these would go from light to dark.
+   */
+
+    --jp-layout-color0: #111111;
+    --jp-layout-color1: var(--md-grey-900);
+    --jp-layout-color2: var(--md-grey-800);
+    --jp-layout-color3: var(--md-grey-700);
+    --jp-layout-color4: var(--md-grey-600);
+
+    /* Inverse Layout
+   *
+   * The following are the inverse layout colors use in JupyterLab. In a light
+   * theme these would go from dark to light.
+   */
+
+    --jp-inverse-layout-color0: white;
+    --jp-inverse-layout-color1: white;
+    --jp-inverse-layout-color2: var(--md-grey-200);
+    --jp-inverse-layout-color3: var(--md-grey-400);
+    --jp-inverse-layout-color4: var(--md-grey-600);
+
+    /* Brand/accent */
+
+    --jp-brand-color0: var(--md-blue-700);
+    --jp-brand-color1: var(--md-blue-500);
+    --jp-brand-color2: var(--md-blue-300);
+    --jp-brand-color3: var(--md-blue-100);
+    --jp-brand-color4: var(--md-blue-50);
+
+    --jp-accent-color0: var(--md-green-700);
+    --jp-accent-color1: var(--md-green-500);
+    --jp-accent-color2: var(--md-green-300);
+    --jp-accent-color3: var(--md-green-100);
+
+    /* State colors (warn, error, success, info) */
+
+    --jp-warn-color0: var(--md-orange-700);
+    --jp-warn-color1: var(--md-orange-500);
+    --jp-warn-color2: var(--md-orange-300);
+    --jp-warn-color3: var(--md-orange-100);
+
+    --jp-error-color0: var(--md-red-700);
+    --jp-error-color1: var(--md-red-500);
+    --jp-error-color2: var(--md-red-300);
+    --jp-error-color3: var(--md-red-100);
+
+    --jp-success-color0: var(--md-green-700);
+    --jp-success-color1: var(--md-green-500);
+    --jp-success-color2: var(--md-green-300);
+    --jp-success-color3: var(--md-green-100);
+
+    --jp-info-color0: var(--md-cyan-700);
+    --jp-info-color1: var(--md-cyan-500);
+    --jp-info-color2: var(--md-cyan-300);
+    --jp-info-color3: var(--md-cyan-100);
+
+    /* Cell specific styles */
+
+    --jp-cell-padding: 5px;
+
+    --jp-cell-collapser-width: 8px;
+    --jp-cell-collapser-min-height: 20px;
+    --jp-cell-collapser-not-active-hover-opacity: 0.6;
+
+    --jp-cell-editor-background: var(--jp-layout-color1);
+    --jp-cell-editor-border-color: var(--md-grey-700);
+    --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-cell-editor-active-background: var(--jp-layout-color0);
+    --jp-cell-editor-active-border-color: var(--jp-brand-color1);
+
+    --jp-cell-prompt-width: 64px;
+    --jp-cell-prompt-font-family: var(--jp-code-font-family-default);
+    --jp-cell-prompt-letter-spacing: 0px;
+    --jp-cell-prompt-opacity: 1;
+    --jp-cell-prompt-not-active-opacity: 1;
+    --jp-cell-prompt-not-active-font-color: var(--md-grey-300);
+
+    /* A custom blend of MD grey and blue 600
+   * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */
+    --jp-cell-inprompt-font-color: #307fc1;
+    /* A custom blend of MD grey and orange 600
+   * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */
+    --jp-cell-outprompt-font-color: #bf5b3d;
+
+    /* Notebook specific styles */
+
+    --jp-notebook-padding: 10px;
+    --jp-notebook-select-background: var(--jp-layout-color1);
+    --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24);
+
+    /* The scroll padding is calculated to fill enough space at the bottom of the
+  notebook to show one single-line cell (with appropriate padding) at the top
+  when the notebook is scrolled all the way to the bottom. We also subtract one
+  pixel so that no scrollbar appears if we have just one single-line cell in the
+  notebook. This padding is to enable a 'scroll past end' feature in a notebook.
+  */
+    --jp-notebook-scroll-padding: calc(
+        100% - var(--jp-code-font-size) * var(--jp-code-line-height) -
+            var(--jp-code-padding) - var(--jp-cell-padding) - 1px
+    );
+
+    /* Rendermime styles */
+
+    --jp-rendermime-error-background: rgba(244, 67, 54, 0.28);
+    --jp-rendermime-table-row-background: var(--md-grey-900);
+    --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2);
+
+    /* Dialog specific styles */
+
+    --jp-dialog-background: rgba(0, 0, 0, 0.6);
+
+    /* Console specific styles */
+
+    --jp-console-padding: 10px;
+
+    /* Toolbar specific styles */
+
+    --jp-toolbar-border-color: var(--jp-border-color2);
+    --jp-toolbar-micro-height: 8px;
+    --jp-toolbar-background: var(--jp-layout-color1);
+    --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8);
+    --jp-toolbar-header-margin: 4px 4px 0px 4px;
+    --jp-toolbar-active-background: var(--jp-layout-color0);
+
+    /* Statusbar specific styles */
+
+    --jp-statusbar-height: 24px;
+
+    /* Input field styles */
+
+    --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-input-active-background: var(--jp-layout-color0);
+    --jp-input-hover-background: var(--jp-layout-color2);
+    --jp-input-background: var(--md-grey-800);
+    --jp-input-border-color: var(--jp-border-color1);
+    --jp-input-active-border-color: var(--jp-brand-color1);
+    --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3);
+
+    /* General editor styles */
+
+    --jp-editor-selected-background: var(--jp-layout-color2);
+    --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24);
+    --jp-editor-cursor-color: var(--jp-ui-font-color0);
+
+    /* Code mirror specific styles */
+
+    --jp-mirror-editor-keyword-color: var(--md-green-500);
+    --jp-mirror-editor-atom-color: var(--md-blue-300);
+    --jp-mirror-editor-number-color: var(--md-green-400);
+    --jp-mirror-editor-def-color: var(--md-blue-600);
+    --jp-mirror-editor-variable-color: var(--md-grey-300);
+    --jp-mirror-editor-variable-2-color: var(--md-blue-400);
+    --jp-mirror-editor-variable-3-color: var(--md-green-600);
+    --jp-mirror-editor-punctuation-color: var(--md-blue-400);
+    --jp-mirror-editor-property-color: var(--md-blue-400);
+    --jp-mirror-editor-operator-color: #aa22ff;
+    --jp-mirror-editor-comment-color: #408080;
+    --jp-mirror-editor-string-color: #ff7070;
+    --jp-mirror-editor-string-2-color: var(--md-purple-300);
+    --jp-mirror-editor-meta-color: #aa22ff;
+    --jp-mirror-editor-qualifier-color: #555;
+    --jp-mirror-editor-builtin-color: var(--md-green-600);
+    --jp-mirror-editor-bracket-color: #997;
+    --jp-mirror-editor-tag-color: var(--md-green-700);
+    --jp-mirror-editor-attribute-color: var(--md-blue-700);
+    --jp-mirror-editor-header-color: var(--md-blue-500);
+    --jp-mirror-editor-quote-color: var(--md-green-300);
+    --jp-mirror-editor-link-color: var(--md-blue-700);
+    --jp-mirror-editor-error-color: #f00;
+    --jp-mirror-editor-hr-color: #999;
+
+    /* Vega extension styles */
+
+    --jp-vega-background: var(--md-grey-400);
+
+    /* Sidebar-related styles */
+
+    --jp-sidebar-min-width: 250px;
+
+    /* Search-related styles */
+
+    --jp-search-toggle-off-opacity: 0.6;
+    --jp-search-toggle-hover-opacity: 0.8;
+    --jp-search-toggle-on-opacity: 1;
+    --jp-search-selected-match-background-color: rgb(255, 225, 0);
+    --jp-search-selected-match-color: black;
+    --jp-search-unselected-match-background-color: var(
+        --jp-inverse-layout-color0
+    );
+    --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);
+
+    /* scrollbar related styles. Supports every browser except Edge. */
+
+    /* colors based on JetBrain's Darcula theme */
+
+    --jp-scrollbar-background-color: #3f4244;
+    --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */
+
+    --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */
+
+    /* hacks for setting the thumb shape. These do nothing in Firefox */
+
+    --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */
+    --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */
+
+    /* Icon colors that work well with light or dark backgrounds */
+    --jp-icon-contrast-color0: var(--md-purple-600);
+    --jp-icon-contrast-color1: var(--md-green-600);
+    --jp-icon-contrast-color2: var(--md-pink-600);
+    --jp-icon-contrast-color3: var(--md-blue-600);
+}
+
+:root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: #7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: #ff5722;--md-deep-orange-600: #f4511e;--md-deep-orange-700: #e64a19;--md-deep-orange-800: #d84315;--md-deep-orange-900: #bf360c;--md-deep-orange-A100: #ff9e80;--md-deep-orange-A200: #ff6e40;--md-deep-orange-A400: #ff3d00;--md-deep-orange-A700: #dd2c00;--md-brown-50: #efebe9;--md-brown-100: #d7ccc8;--md-brown-200: #bcaaa4;--md-brown-300: #a1887f;--md-brown-400: #8d6e63;--md-brown-500: #795548;--md-brown-600: #6d4c41;--md-brown-700: #5d4037;--md-brown-800: #4e342e;--md-brown-900: #3e2723;--md-grey-50: #fafafa;--md-grey-100: #f5f5f5;--md-grey-200: #eeeeee;--md-grey-300: #e0e0e0;--md-grey-400: #bdbdbd;--md-grey-500: #9e9e9e;--md-grey-600: #757575;--md-grey-700: #616161;--md-grey-800: #424242;--md-grey-900: #212121;--md-blue-grey-50: #eceff1;--md-blue-grey-100: #cfd8dc;--md-blue-grey-200: #b0bec5;--md-blue-grey-300: #90a4ae;--md-blue-grey-400: #78909c;--md-blue-grey-500: #607d8b;--md-blue-grey-600: #546e7a;--md-blue-grey-700: #455a64;--md-blue-grey-800: #37474f;--md-blue-grey-900: #263238}.jupyter-wrapper{/*!
+
+Copyright 2015-present Palantir Technologies, Inc. All rights reserved.
+Licensed under the Apache License, Version 2.0.
+
+*//*!
+
+Copyright 2017-present Palantir Technologies, Inc. All rights reserved.
+Licensed under the Apache License, Version 2.0.
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+    init_mathjax();
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+    <!-- End of mathjax configuration --><div class="jupyter-wrapper">
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="1-data">1. Data<a class="anchor-link" href="#1-data">&#182;</a></h1><h3 id="11-pseudo-data-generator">1.1 Pseudo Data Generator<a class="anchor-link" href="#11-pseudo-data-generator">&#182;</a></h3><ul>
+<li>Output: (id, timestamp, data vec of 1s (100 data points))</li>
+</ul>
+<h3 id="12-data-aggrator">1.2 Data Aggrator<a class="anchor-link" href="#12-data-aggrator">&#182;</a></h3><ul>
+<li><p>Input: (id, timestamp, data vec of 1s (100 data points))</p>
+</li>
+<li><p>Process: Data buffer (Kafka)</p>
+</li>
+<li><p>Output: (id, timestamp, data vec of 30s (3000 data points)) for every second</p>
+</li>
+</ul>
+
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
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+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="2-phase-picking">2. Phase Picking<a class="anchor-link" href="#2-phase-picking">&#182;</a></h1><ul>
+<li><p>Preload: NN model of PhaseNet</p>
+</li>
+<li><p>Input: (id, timestamp, data vec of 30s (3000 data points))</p>
+</li>
+<li><p>Process: PhaseNet</p>
+</li>
+<li><p>Output: (id, timestamp, idx_p, prob_p, amp_p, idx_s, prob_s, amp_s)</p>
+</li>
+</ul>
+
+</div>
+</div>
+</div>
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+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="3-data-buffer">3. Data buffer<a class="anchor-link" href="#3-data-buffer">&#182;</a></h2><ul>
+<li><p>Input: (id, timestamp, idx_p, prob_p, amp_p, idx_s, prob_s, amp_s) of overlapping 30s</p>
+</li>
+<li><p>Output: (id, timestamp, idx_p, prob_p, amp_p, idx_s, prob_s, amp_s) with no overlapping and reprot the recent 10 s.</p>
+</li>
+</ul>
+
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
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+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="4-association">4. Association<a class="anchor-link" href="#4-association">&#182;</a></h1><ul>
+<li><p>Preload: csv file of seismic station information (id, latitude, longitude, elevation)</p>
+</li>
+<li><p>Input: (id, timestamp, idx_p, prob_p, amp_p, idx_s, prob_s, amp_s)</p>
+</li>
+<li><p>Process: GMMA</p>
+</li>
+<li><p>Output: (latitude, longtidue, depth, magnitude) of earthquakes</p>
+</li>
+</ul>
+
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
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+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="5-visulization-and-notification">5. Visulization and Notification<a class="anchor-link" href="#5-visulization-and-notification">&#182;</a></h1><ul>
+<li><p>Input: (latitude, longtidue, depth, magnitude) of earthquakes</p>
+</li>
+<li><p>WebUI + Twitter Bot</p>
+</li>
+</ul>
+
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
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+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="code">Code:<a class="anchor-link" href="#code">&#182;</a></h2>
+</div>
+</div>
+</div>
+</div>
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+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="1-data">1. Data<a class="anchor-link" href="#1-data">&#182;</a></h2>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
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+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[8]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
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+                <span class="notice" hidden>Copied!</span>
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+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">obspy</span>
+<span class="kn">import</span> <span class="nn">os</span>
+<span class="kn">import</span> <span class="nn">pickle</span>
+<span class="kn">import</span> <span class="nn">datetime</span>
+
+<span class="c1"># Location</span>
+<span class="n">pi</span> <span class="o">=</span> <span class="mf">3.1415926</span>
+<span class="n">degree2km</span> <span class="o">=</span> <span class="n">pi</span><span class="o">*</span><span class="mi">6371</span><span class="o">/</span><span class="mi">180</span>
+<span class="n">center</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mf">115.53</span><span class="p">,</span> <span class="mf">32.98</span><span class="p">)</span> <span class="c1">#salton sea</span>
+<span class="n">horizontal_degree</span> <span class="o">=</span> <span class="mf">0.5</span>
+<span class="n">vertical_degree</span> <span class="o">=</span> <span class="mf">0.5</span>
+<span class="n">zero_anchor</span> <span class="o">=</span> <span class="p">(</span><span class="n">center</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">-</span><span class="n">horizontal_degree</span><span class="p">,</span> <span class="n">center</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">-</span><span class="n">vertical_degree</span><span class="p">)</span>
+
+<span class="c1"># Time</span>
+<span class="n">starttime</span> <span class="o">=</span> <span class="n">obspy</span><span class="o">.</span><span class="n">UTCDateTime</span><span class="p">(</span><span class="s2">&quot;2020-10-01T00&quot;</span><span class="p">)</span>
+<span class="n">endtime</span> <span class="o">=</span> <span class="n">obspy</span><span class="o">.</span><span class="n">UTCDateTime</span><span class="p">(</span><span class="s2">&quot;2020-10-01T02&quot;</span><span class="p">)</span> <span class="c1">## not included</span>
+
+<span class="c1"># seismic stations</span>
+<span class="n">network_list</span> <span class="o">=</span> <span class="s2">&quot;CI&quot;</span>
+<span class="c1"># channel_list = &quot;HNE,HNN,HNZ,HHE,HHN,HHZ,BHE,BHN,BHZ,EHE,EHN,EHZ&quot;</span>
+<span class="n">channel_list</span> <span class="o">=</span> <span class="s2">&quot;HHE,HHN,HHZ&quot;</span>
+
+<span class="n">config_file</span> <span class="o">=</span> <span class="s2">&quot;config.pkl&quot;</span>
+<span class="n">datetime_file</span> <span class="o">=</span> <span class="s2">&quot;datetimes.pkl&quot;</span>
+<span class="n">station_file</span> <span class="o">=</span> <span class="s2">&quot;stations.pkl&quot;</span>
+<span class="n">data_path</span> <span class="o">=</span> <span class="s2">&quot;data&quot;</span>
+<span class="n">station_list</span> <span class="o">=</span> <span class="s2">&quot;stations.csv&quot;</span>
+<span class="n">fname_list</span> <span class="o">=</span> <span class="s2">&quot;fname.csv&quot;</span>
+<span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">data_path</span><span class="p">):</span>
+    <span class="n">os</span><span class="o">.</span><span class="n">mkdir</span><span class="p">(</span><span class="n">data_path</span><span class="p">)</span>
+<span class="c1">####### save config ########</span>
+<span class="n">config</span> <span class="o">=</span> <span class="p">{}</span>
+<span class="n">config</span><span class="p">[</span><span class="s2">&quot;center&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">center</span>
+<span class="n">config</span><span class="p">[</span><span class="s2">&quot;horizontal_degree&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">horizontal_degree</span>
+<span class="n">config</span><span class="p">[</span><span class="s2">&quot;vertical_degree&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">vertical_degree</span>
+<span class="n">config</span><span class="p">[</span><span class="s2">&quot;zero_anchor&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">zero_anchor</span>
+<span class="n">config</span><span class="p">[</span><span class="s2">&quot;xlim&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="n">horizontal_degree</span><span class="o">*</span><span class="mi">2</span><span class="o">*</span><span class="n">degree2km</span><span class="p">]</span>
+<span class="n">config</span><span class="p">[</span><span class="s2">&quot;ylim&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="n">vertical_degree</span><span class="o">*</span><span class="mi">2</span><span class="o">*</span><span class="n">degree2km</span><span class="p">]</span>
+<span class="n">config</span><span class="p">[</span><span class="s2">&quot;anchor&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">zero_anchor</span>
+<span class="n">config</span><span class="p">[</span><span class="s2">&quot;degree2km&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">degree2km</span>
+<span class="n">config</span><span class="p">[</span><span class="s2">&quot;starttime&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">starttime</span>
+<span class="n">config</span><span class="p">[</span><span class="s2">&quot;endtime&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">endtime</span>
+<span class="n">config</span><span class="p">[</span><span class="s2">&quot;networks&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">network_list</span>
+<span class="n">config</span><span class="p">[</span><span class="s2">&quot;channels&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">channel_list</span>
+<span class="n">config</span><span class="p">[</span><span class="s2">&quot;network_list&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">network_list</span>
+<span class="n">config</span><span class="p">[</span><span class="s2">&quot;channel_list&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">channel_list</span>
+<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">config_file</span><span class="p">,</span> <span class="s2">&quot;wb&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+    <span class="n">pickle</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="n">config</span><span class="p">,</span> <span class="n">fp</span><span class="p">)</span>
+
+<span class="n">one_day</span> <span class="o">=</span> <span class="n">datetime</span><span class="o">.</span><span class="n">timedelta</span><span class="p">(</span><span class="n">days</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="n">one_hour</span> <span class="o">=</span> <span class="n">datetime</span><span class="o">.</span><span class="n">timedelta</span><span class="p">(</span><span class="n">hours</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="n">starttimes</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="n">tmp_start</span> <span class="o">=</span> <span class="n">starttime</span>
+<span class="k">while</span> <span class="n">tmp_start</span> <span class="o">&lt;</span> <span class="n">endtime</span><span class="p">:</span>
+    <span class="n">starttimes</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp_start</span><span class="p">)</span>
+    <span class="n">tmp_start</span> <span class="o">+=</span> <span class="n">one_hour</span>
+
+<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">datetime_file</span><span class="p">,</span> <span class="s2">&quot;wb&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+    <span class="n">pickle</span><span class="o">.</span><span class="n">dump</span><span class="p">({</span><span class="s2">&quot;starttimes&quot;</span><span class="p">:</span> <span class="n">starttimes</span><span class="p">,</span> <span class="s2">&quot;interval&quot;</span><span class="p">:</span> <span class="n">one_hour</span><span class="p">},</span> <span class="n">fp</span><span class="p">)</span>
+</pre></div>
+<div id="cell-1" class="clipboard-copy-txt">import obspy
+import os
+import pickle
+import datetime
+
+# Location
+pi = 3.1415926
+degree2km = pi*6371/180
+center = (-115.53, 32.98) #salton sea
+horizontal_degree = 0.5
+vertical_degree = 0.5
+zero_anchor = (center[0]-horizontal_degree, center[1]-vertical_degree)
+
+# Time
+starttime = obspy.UTCDateTime("2020-10-01T00")
+endtime = obspy.UTCDateTime("2020-10-01T02") ## not included
+
+# seismic stations
+network_list = "CI"
+# channel_list = "HNE,HNN,HNZ,HHE,HHN,HHZ,BHE,BHN,BHZ,EHE,EHN,EHZ"
+channel_list = "HHE,HHN,HHZ"
+
+config_file = "config.pkl"
+datetime_file = "datetimes.pkl"
+station_file = "stations.pkl"
+data_path = "data"
+station_list = "stations.csv"
+fname_list = "fname.csv"
+if not os.path.exists(data_path):
+    os.mkdir(data_path)
+####### save config ########
+config = {}
+config["center"] = center
+config["horizontal_degree"] = horizontal_degree
+config["vertical_degree"] = vertical_degree
+config["zero_anchor"] = zero_anchor
+config["xlim"] = [0, horizontal_degree*2*degree2km]
+config["ylim"] = [0, vertical_degree*2*degree2km]
+config["anchor"] = zero_anchor
+config["degree2km"] = degree2km
+config["starttime"] = starttime
+config["endtime"] = endtime
+config["networks"] = network_list
+config["channels"] = channel_list
+config["network_list"] = network_list
+config["channel_list"] = channel_list
+with open(config_file, "wb") as fp:
+    pickle.dump(config, fp)
+
+one_day = datetime.timedelta(days=1)
+one_hour = datetime.timedelta(hours=1)
+starttimes = []
+tmp_start = starttime
+while tmp_start < endtime:
+    starttimes.append(tmp_start)
+    tmp_start += one_hour
+
+with open(datetime_file, "wb") as fp:
+    pickle.dump({"starttimes": starttimes, "interval": one_hour}, fp)
+</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[6]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-2">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">from</span> <span class="nn">obspy.clients.fdsn</span> <span class="kn">import</span> <span class="n">Client</span>
+<span class="kn">import</span> <span class="nn">matplotlib</span>
+<span class="c1"># matplotlib.use(&quot;agg&quot;)</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">defaultdict</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+
+<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">config_file</span><span class="p">,</span> <span class="s2">&quot;rb&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+    <span class="n">config</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">fp</span><span class="p">)</span>
+
+<span class="c1">####### Download stations ########</span>
+<span class="n">stations</span> <span class="o">=</span> <span class="n">Client</span><span class="p">(</span><span class="s2">&quot;IRIS&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">get_stations</span><span class="p">(</span><span class="n">network</span> <span class="o">=</span> <span class="n">config</span><span class="p">[</span><span class="s2">&quot;network_list&quot;</span><span class="p">],</span>
+                                       <span class="n">station</span> <span class="o">=</span> <span class="s2">&quot;*&quot;</span><span class="p">,</span>
+                                       <span class="n">starttime</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;starttime&quot;</span><span class="p">],</span>
+                                       <span class="n">endtime</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;endtime&quot;</span><span class="p">],</span>
+                                       <span class="n">minlatitude</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;center&quot;</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span><span class="o">-</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;vertical_degree&quot;</span><span class="p">],</span>
+                                       <span class="n">maxlatitude</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;center&quot;</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span><span class="o">+</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;vertical_degree&quot;</span><span class="p">],</span>
+                                       <span class="n">minlongitude</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;center&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">-</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;horizontal_degree&quot;</span><span class="p">],</span>
+                                       <span class="n">maxlongitude</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;center&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">+</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;horizontal_degree&quot;</span><span class="p">],</span>
+                                       <span class="n">channel</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;channel_list&quot;</span><span class="p">],</span>
+                                       <span class="n">level</span><span class="o">=</span><span class="s2">&quot;response&quot;</span><span class="p">,</span>
+                                       <span class="n">filename</span><span class="o">=</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">data_path</span><span class="p">,</span> <span class="s1">&#39;stations.xml&#39;</span><span class="p">))</span>
+
+<span class="n">stations</span> <span class="o">=</span> <span class="n">obspy</span><span class="o">.</span><span class="n">read_inventory</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">data_path</span><span class="p">,</span> <span class="s1">&#39;stations.xml&#39;</span><span class="p">))</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Number of stations: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">sum</span><span class="p">([</span><span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">stations</span><span class="p">])))</span>
+<span class="c1"># stations.plot(&#39;local&#39;, outfile=&quot;stations.png&quot;)</span>
+
+<span class="c1">####### Save stations ########</span>
+<span class="n">station_locs</span> <span class="o">=</span> <span class="n">defaultdict</span><span class="p">(</span><span class="nb">dict</span><span class="p">)</span>
+<span class="k">for</span> <span class="n">network</span> <span class="ow">in</span> <span class="n">stations</span><span class="p">:</span>
+    <span class="k">for</span> <span class="n">station</span> <span class="ow">in</span> <span class="n">network</span><span class="p">:</span>
+        <span class="k">for</span> <span class="n">chn</span> <span class="ow">in</span> <span class="n">station</span><span class="p">:</span>
+            <span class="n">x</span> <span class="o">=</span> <span class="p">(</span><span class="n">chn</span><span class="o">.</span><span class="n">longitude</span> <span class="o">-</span> <span class="n">config</span><span class="p">[</span><span class="s2">&quot;zero_anchor&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span><span class="o">*</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;degree2km&quot;</span><span class="p">]</span>
+            <span class="n">y</span> <span class="o">=</span> <span class="p">(</span><span class="n">chn</span><span class="o">.</span><span class="n">latitude</span> <span class="o">-</span> <span class="n">config</span><span class="p">[</span><span class="s2">&quot;zero_anchor&quot;</span><span class="p">][</span><span class="mi">1</span><span class="p">])</span><span class="o">*</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;degree2km&quot;</span><span class="p">]</span>
+            <span class="n">z</span> <span class="o">=</span> <span class="o">-</span><span class="n">chn</span><span class="o">.</span><span class="n">elevation</span> <span class="o">/</span> <span class="mf">1e3</span> <span class="c1">#km</span>
+            <span class="n">sid</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">network</span><span class="o">.</span><span class="n">code</span><span class="si">}</span><span class="s2">.</span><span class="si">{</span><span class="n">station</span><span class="o">.</span><span class="n">code</span><span class="si">}</span><span class="s2">.</span><span class="si">{</span><span class="n">chn</span><span class="o">.</span><span class="n">location_code</span><span class="si">}</span><span class="s2">.</span><span class="si">{</span><span class="n">chn</span><span class="o">.</span><span class="n">code</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="si">}</span><span class="s2">&quot;</span>
+            <span class="k">if</span> <span class="n">sid</span> <span class="ow">in</span> <span class="n">station_locs</span><span class="p">:</span>
+                <span class="n">station_locs</span><span class="p">[</span><span class="n">sid</span><span class="p">][</span><span class="s2">&quot;component&quot;</span><span class="p">]</span> <span class="o">+=</span> <span class="sa">f</span><span class="s2">&quot;,</span><span class="si">{</span><span class="n">chn</span><span class="o">.</span><span class="n">code</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="si">}</span><span class="s2">&quot;</span>
+                <span class="n">station_locs</span><span class="p">[</span><span class="n">sid</span><span class="p">][</span><span class="s2">&quot;response&quot;</span><span class="p">]</span> <span class="o">+=</span> <span class="sa">f</span><span class="s2">&quot;,</span><span class="si">{</span><span class="n">chn</span><span class="o">.</span><span class="n">response</span><span class="o">.</span><span class="n">instrument_sensitivity</span><span class="o">.</span><span class="n">value</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">&quot;</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">component</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">chn</span><span class="o">.</span><span class="n">code</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="si">}</span><span class="s2">&quot;</span>
+                <span class="n">response</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">chn</span><span class="o">.</span><span class="n">response</span><span class="o">.</span><span class="n">instrument_sensitivity</span><span class="o">.</span><span class="n">value</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">&quot;</span>
+                <span class="n">dtype</span> <span class="o">=</span> <span class="n">chn</span><span class="o">.</span><span class="n">response</span><span class="o">.</span><span class="n">instrument_sensitivity</span><span class="o">.</span><span class="n">input_units</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span>
+                <span class="n">tmp_dict</span> <span class="o">=</span> <span class="p">{}</span>
+                <span class="n">tmp_dict</span><span class="p">[</span><span class="s2">&quot;x(km)&quot;</span><span class="p">],</span> <span class="n">tmp_dict</span><span class="p">[</span><span class="s2">&quot;y(km)&quot;</span><span class="p">],</span> <span class="n">tmp_dict</span><span class="p">[</span><span class="s2">&quot;z(km)&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">z</span>
+                <span class="n">tmp_dict</span><span class="p">[</span><span class="s2">&quot;lng&quot;</span><span class="p">],</span> <span class="n">tmp_dict</span><span class="p">[</span><span class="s2">&quot;lat&quot;</span><span class="p">],</span> <span class="n">tmp_dict</span><span class="p">[</span><span class="s2">&quot;elv(m)&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">chn</span><span class="o">.</span><span class="n">longitude</span><span class="p">,</span> <span class="n">chn</span><span class="o">.</span><span class="n">latitude</span><span class="p">,</span> <span class="n">chn</span><span class="o">.</span><span class="n">elevation</span>
+                <span class="n">tmp_dict</span><span class="p">[</span><span class="s2">&quot;component&quot;</span><span class="p">],</span> <span class="n">tmp_dict</span><span class="p">[</span><span class="s2">&quot;response&quot;</span><span class="p">],</span> <span class="n">tmp_dict</span><span class="p">[</span><span class="s2">&quot;type&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">component</span><span class="p">,</span> <span class="n">response</span><span class="p">,</span> <span class="n">dtype</span>
+                <span class="n">station_locs</span><span class="p">[</span><span class="n">sid</span><span class="p">]</span> <span class="o">=</span> <span class="n">tmp_dict</span>
+<span class="n">station_locs</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">from_dict</span><span class="p">(</span><span class="n">station_locs</span><span class="p">,</span> <span class="n">orient</span><span class="o">=</span><span class="s1">&#39;index&#39;</span><span class="p">)</span>
+<span class="n">station_locs</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="n">station_list</span><span class="p">,</span>
+                    <span class="n">sep</span><span class="o">=</span><span class="s2">&quot;</span><span class="se">\t</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">float_format</span><span class="o">=</span><span class="s2">&quot;</span><span class="si">%.3f</span><span class="s2">&quot;</span><span class="p">,</span>
+                    <span class="n">index_label</span><span class="o">=</span><span class="s2">&quot;station&quot;</span><span class="p">,</span>
+                    <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;x(km)&quot;</span><span class="p">,</span> <span class="s2">&quot;y(km)&quot;</span><span class="p">,</span> <span class="s2">&quot;z(km)&quot;</span><span class="p">,</span> <span class="s2">&quot;lat&quot;</span><span class="p">,</span> <span class="s2">&quot;lng&quot;</span><span class="p">,</span> <span class="s2">&quot;elv(m)&quot;</span><span class="p">,</span> <span class="s2">&quot;type&quot;</span><span class="p">,</span> <span class="s2">&quot;component&quot;</span><span class="p">,</span> <span class="s2">&quot;response&quot;</span><span class="p">])</span>
+
+<span class="c1">#     ####### Plot stations ########</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">station_locs</span><span class="p">[</span><span class="s2">&quot;x(km)&quot;</span><span class="p">],</span> <span class="n">station_locs</span><span class="p">[</span><span class="s2">&quot;y(km)&quot;</span><span class="p">],</span> <span class="s2">&quot;^&quot;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;Stations&quot;</span><span class="p">)</span>
+<span class="c1">#     plt.plot(catalog[&quot;x(km)&quot;], catalog[&quot;y(km)&quot;], &quot;k.&quot;, label=&quot;Earthquakes&quot;)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">&quot;X (km)&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">&quot;Y (km)&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s2">&quot;scaled&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Number of stations: </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">station_locs</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="c1">#     plt.savefig(os.path.join(data_path, &quot;stations_events.png&quot;))</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+
+<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">station_file</span><span class="p">,</span> <span class="s2">&quot;wb&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+    <span class="n">pickle</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="n">stations</span><span class="p">,</span> <span class="n">fp</span><span class="p">)</span>
+</pre></div>
+<div id="cell-2" class="clipboard-copy-txt">from obspy.clients.fdsn import Client
+import matplotlib
+# matplotlib.use("agg")
+import matplotlib.pyplot as plt
+from collections import defaultdict
+import pandas as pd
+
+with open(config_file, "rb") as fp:
+    config = pickle.load(fp)
+
+####### Download stations ########
+stations = Client("IRIS").get_stations(network = config["network_list"],
+                                       station = "*",
+                                       starttime=config["starttime"],
+                                       endtime=config["endtime"],
+                                       minlatitude=config["center"][1]-config["vertical_degree"],
+                                       maxlatitude=config["center"][1]+config["vertical_degree"],
+                                       minlongitude=config["center"][0]-config["horizontal_degree"],
+                                       maxlongitude=config["center"][0]+config["horizontal_degree"],
+                                       channel=config["channel_list"],
+                                       level="response",
+                                       filename=os.path.join(data_path, 'stations.xml'))
+
+stations = obspy.read_inventory(os.path.join(data_path, 'stations.xml'))
+print("Number of stations: {}".format(sum([len(x) for x in stations])))
+# stations.plot('local', outfile="stations.png")
+
+####### Save stations ########
+station_locs = defaultdict(dict)
+for network in stations:
+    for station in network:
+        for chn in station:
+            x = (chn.longitude - config["zero_anchor"][0])*config["degree2km"]
+            y = (chn.latitude - config["zero_anchor"][1])*config["degree2km"]
+            z = -chn.elevation / 1e3 #km
+            sid = f"{network.code}.{station.code}.{chn.location_code}.{chn.code[:-1]}"
+            if sid in station_locs:
+                station_locs[sid]["component"] += f",{chn.code[-1]}"
+                station_locs[sid]["response"] += f",{chn.response.instrument_sensitivity.value:.2f}"
+            else:
+                component = f"{chn.code[-1]}"
+                response = f"{chn.response.instrument_sensitivity.value:.2f}"
+                dtype = chn.response.instrument_sensitivity.input_units.lower()
+                tmp_dict = {}
+                tmp_dict["x(km)"], tmp_dict["y(km)"], tmp_dict["z(km)"] = x, y, z
+                tmp_dict["lng"], tmp_dict["lat"], tmp_dict["elv(m)"] = chn.longitude, chn.latitude, chn.elevation
+                tmp_dict["component"], tmp_dict["response"], tmp_dict["type"] = component, response, dtype
+                station_locs[sid] = tmp_dict
+station_locs = pd.DataFrame.from_dict(station_locs, orient='index')
+station_locs.to_csv(station_list,
+                    sep="\t", float_format="%.3f",
+                    index_label="station",
+                    columns=["x(km)", "y(km)", "z(km)", "lat", "lng", "elv(m)", "type", "component", "response"])
+
+#     ####### Plot stations ########
+plt.figure()
+plt.plot(station_locs["x(km)"], station_locs["y(km)"], "^", label="Stations")
+#     plt.plot(catalog["x(km)"], catalog["y(km)"], "k.", label="Earthquakes")
+plt.xlabel("X (km)")
+plt.ylabel("Y (km)")
+plt.axis("scaled")
+plt.legend()
+plt.title(f"Number of stations: {len(station_locs)}")
+#     plt.savefig(os.path.join(data_path, "stations_events.png"))
+plt.show()
+
+with open(station_file, "wb") as fp:
+    pickle.dump(stations, fp)</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
+<pre>Number of stations: 16
+</pre>
+</div>
+</div>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+
+
+<div class="jp-RenderedImage jp-OutputArea-output ">
+<img 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+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[12]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-3">
+            <div>
+                <span class="notice" hidden>Copied!</span>
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+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
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+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">config_file</span><span class="p">,</span> <span class="s2">&quot;rb&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+    <span class="n">config</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">fp</span><span class="p">)</span>
+<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">datetime_file</span><span class="p">,</span> <span class="s2">&quot;rb&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+    <span class="n">tmp</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">fp</span><span class="p">)</span>
+    <span class="n">starttimes</span> <span class="o">=</span> <span class="n">tmp</span><span class="p">[</span><span class="s2">&quot;starttimes&quot;</span><span class="p">]</span>
+    <span class="n">interval</span> <span class="o">=</span> <span class="n">tmp</span><span class="p">[</span><span class="s2">&quot;interval&quot;</span><span class="p">]</span>
+<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">station_file</span><span class="p">,</span> <span class="s2">&quot;rb&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+    <span class="n">stations</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">fp</span><span class="p">)</span>
+
+<span class="n">waveform_dir</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">data_path</span><span class="p">,</span> <span class="s2">&quot;waveforms&quot;</span><span class="p">)</span>
+<span class="c1">####### Download data ########</span>
+<span class="n">client</span> <span class="o">=</span> <span class="n">Client</span><span class="p">(</span><span class="s2">&quot;SCEDC&quot;</span><span class="p">)</span>
+<span class="n">fp</span> <span class="o">=</span> <span class="nb">open</span><span class="p">(</span><span class="n">fname_list</span><span class="p">,</span> <span class="s2">&quot;w&quot;</span><span class="p">)</span>
+<span class="n">fp</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s2">&quot;fname</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="c1"># for i in idx: </span>
+<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">starttimes</span><span class="p">)):</span>
+    <span class="n">starttime</span> <span class="o">=</span> <span class="n">starttimes</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> 
+    <span class="n">endtime</span> <span class="o">=</span> <span class="n">starttime</span> <span class="o">+</span> <span class="n">interval</span>
+    <span class="n">fname</span> <span class="o">=</span> <span class="s2">&quot;</span><span class="si">{}</span><span class="s2">.mseed&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">starttime</span><span class="o">.</span><span class="n">datetime</span><span class="o">.</span><span class="n">strftime</span><span class="p">(</span><span class="s2">&quot;%Y-%m-</span><span class="si">%d</span><span class="s2">T%H&quot;</span><span class="p">))</span>
+
+    <span class="n">max_retry</span> <span class="o">=</span> <span class="mi">3</span>
+    <span class="n">stream</span> <span class="o">=</span> <span class="n">obspy</span><span class="o">.</span><span class="n">Stream</span><span class="p">()</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">fname</span><span class="si">}</span><span class="s2"> download starts&quot;</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">network</span> <span class="ow">in</span> <span class="n">stations</span><span class="p">:</span>
+        <span class="k">for</span> <span class="n">station</span> <span class="ow">in</span> <span class="n">network</span><span class="p">:</span>
+            <span class="n">retry</span> <span class="o">=</span> <span class="mi">0</span>
+            <span class="k">while</span> <span class="n">retry</span> <span class="o">&lt;</span> <span class="n">max_retry</span><span class="p">:</span>
+                <span class="k">try</span><span class="p">:</span>
+                    <span class="n">tmp</span> <span class="o">=</span> <span class="n">client</span><span class="o">.</span><span class="n">get_waveforms</span><span class="p">(</span><span class="n">network</span><span class="o">.</span><span class="n">code</span><span class="p">,</span> <span class="n">station</span><span class="o">.</span><span class="n">code</span><span class="p">,</span> <span class="s2">&quot;*&quot;</span><span class="p">,</span> <span class="n">config</span><span class="p">[</span><span class="s2">&quot;channel_list&quot;</span><span class="p">],</span> <span class="n">starttime</span><span class="p">,</span> <span class="n">endtime</span><span class="p">)</span>
+                    <span class="n">stream</span> <span class="o">+=</span> <span class="n">tmp</span>
+                    <span class="k">break</span>
+                <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
+                    <span class="n">err</span> <span class="o">=</span> <span class="n">e</span>
+                    <span class="n">retry</span> <span class="o">+=</span> <span class="mi">1</span>
+                    <span class="n">time</span><span class="o">.</span><span class="n">sleep</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+                    <span class="k">continue</span>
+            <span class="k">if</span> <span class="n">retry</span> <span class="o">==</span> <span class="n">max_retry</span><span class="p">:</span>
+                <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">fname</span><span class="si">}</span><span class="s2">: MAX </span><span class="si">{</span><span class="n">max_retry</span><span class="si">}</span><span class="s2"> retries reached : </span><span class="si">{</span><span class="n">network</span><span class="o">.</span><span class="n">code</span><span class="si">}</span><span class="s2">.</span><span class="si">{</span><span class="n">station</span><span class="o">.</span><span class="n">code</span><span class="si">}</span><span class="s2"> with error: </span><span class="si">{</span><span class="n">err</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">waveform_dir</span><span class="p">):</span>
+        <span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">waveform_dir</span><span class="p">)</span>
+    <span class="n">stream</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">waveform_dir</span><span class="p">,</span> <span class="n">fname</span><span class="p">))</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">fname</span><span class="si">}</span><span class="s2"> download succeeds&quot;</span><span class="p">)</span>
+    <span class="n">fp</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">fname</span><span class="si">}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
+
+<span class="n">fp</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">listdir</span><span class="p">(</span><span class="n">waveform_dir</span><span class="p">))</span>
+</pre></div>
+<div id="cell-3" class="clipboard-copy-txt">with open(config_file, "rb") as fp:
+    config = pickle.load(fp)
+with open(datetime_file, "rb") as fp:
+    tmp = pickle.load(fp)
+    starttimes = tmp["starttimes"]
+    interval = tmp["interval"]
+with open(station_file, "rb") as fp:
+    stations = pickle.load(fp)
+
+waveform_dir = os.path.join(data_path, "waveforms")
+####### Download data ########
+client = Client("SCEDC")
+fp = open(fname_list, "w")
+fp.write("fname\n")
+# for i in idx: 
+for i in range(len(starttimes)):
+    starttime = starttimes[i] 
+    endtime = starttime + interval
+    fname = "{}.mseed".format(starttime.datetime.strftime("%Y-%m-%dT%H"))
+
+    max_retry = 3
+    stream = obspy.Stream()
+    print(f"{fname} download starts")
+    for network in stations:
+        for station in network:
+            retry = 0
+            while retry < max_retry:
+                try:
+                    tmp = client.get_waveforms(network.code, station.code, "*", config["channel_list"], starttime, endtime)
+                    stream += tmp
+                    break
+                except Exception as e:
+                    err = e
+                    retry += 1
+                    time.sleep(1)
+                    continue
+            if retry == max_retry:
+                print(f"{fname}: MAX {max_retry} retries reached : {network.code}.{station.code} with error: {err}")
+
+    if not os.path.exists(waveform_dir):
+        os.makedirs(waveform_dir)
+    stream.write(os.path.join(waveform_dir, fname))
+    print(f"{fname} download succeeds")
+    fp.write(f"{fname}\n")
+
+fp.close()
+print(os.listdir(waveform_dir))</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
+<pre>2020-10-01T00.mseed download starts
+2020-10-01T00.mseed download succeeds
+2020-10-01T01.mseed download starts
+2020-10-01T01.mseed download succeeds
+[&#39;2020-10-01T01.mseed&#39;, &#39;2020-10-01T00.mseed&#39;]
+</pre>
+</div>
+</div>
+
+</div>
+
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-4">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span>
+</pre></div>
+<div id="cell-4" class="clipboard-copy-txt"></div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+</div>
+
+
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\ No newline at end of file
diff --git a/kubeflow/rsync.yaml b/kubeflow/rsync.yaml
new file mode 100644
index 0000000..10d8a0b
--- /dev/null
+++ b/kubeflow/rsync.yaml
@@ -0,0 +1,16 @@
+apiVersion: v1
+kind: Pod
+metadata:
+  name: dataaccess52
+spec:
+  containers:
+  - name: alpine
+    image: zhuwq0/waveform-env:1.1
+    command: ['sleep', 'infinity']
+    volumeMounts:
+    - name: mypvc
+      mountPath: /data
+  volumes:
+  - name: mypvc
+    persistentVolumeClaim:
+      claimName: quakeflow-w8gfg-data-volume-52
diff --git a/kubeflow/tweepy_test/index.html b/kubeflow/tweepy_test/index.html
new file mode 100644
index 0000000..0f5f0bb
--- /dev/null
+++ b/kubeflow/tweepy_test/index.html
@@ -0,0 +1,1983 @@
+
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+    
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+      <meta name="viewport" content="width=device-width,initial-scale=1">
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+        <meta name="description" content="QuakeFlow: a Scalable Deep-learning-based Earthquake Monitoring Workflow with Cloud Computing">
+      
+      
+        <meta name="author" content="Weiqiang Zhu">
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+            QuakeFlow
+          </span>
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+        <div class="md-header__topic" data-md-component="header-topic">
+          <span class="md-ellipsis">
+            
+              Tweepy test
+            
+          </span>
+        </div>
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+    
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+
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+  </label>
+  
+    <div class="md-nav__source">
+      <a href="https://github.com/ai4eps/QuakeFlow" title="Go to repository" class="md-source" data-md-component="source">
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+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.1.2 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+  </div>
+  <div class="md-source__repository">
+    AI4EPS/QuakeFlow
+  </div>
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+    
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+      <a href="../.." class="md-nav__link">
+        Overview
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+  
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+      <a href="../../data_format/" class="md-nav__link">
+        Standard Formats
+      </a>
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+                  </div>
+                </div>
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+            
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+                <div class="md-sidebar__scrollwrap">
+                  <div class="md-sidebar__inner">
+                    
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+<nav class="md-nav md-nav--secondary" aria-label="Table of contents">
+  
+  
+  
+  
+    <label class="md-nav__title" for="__toc">
+      <span class="md-nav__icon md-icon"></span>
+      Table of contents
+    </label>
+    <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
+      
+        <li class="md-nav__item">
+  <a href="#load-access-credentials-to-bot" class="md-nav__link">
+    Load access credentials to bot
+  </a>
+  
+</li>
+      
+    </ul>
+  
+</nav>
+                  </div>
+                </div>
+              </div>
+            
+          
+          <div class="md-content" data-md-component="content">
+            <article class="md-content__inner md-typeset">
+              
+                
+  <a href="https://github.com/ai4eps/QuakeFlow/edit/master/docs/kubeflow/tweepy_test.ipynb" title="Edit this page" class="md-content__button md-icon">
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
+  </a>
+
+
+
+  <h1>Tweepy test</h1>
+
+<script>
+(function (global, factory) {
+    typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() :
+    typeof define === 'function' && define.amd ? define(factory) :
+    (global = global || self, global.ClipboardCopyElement = factory());
+  }(this, function () { 'use strict';
+
+    function createNode(text) {
+      const node = document.createElement('pre');
+      node.style.width = '1px';
+      node.style.height = '1px';
+      node.style.position = 'fixed';
+      node.style.top = '5px';
+      node.textContent = text;
+      return node;
+    }
+
+    function copyNode(node) {
+      if ('clipboard' in navigator) {
+        // eslint-disable-next-line flowtype/no-flow-fix-me-comments
+        // $FlowFixMe Clipboard is not defined in Flow yet.
+        return navigator.clipboard.writeText(node.textContent);
+      }
+
+      const selection = getSelection();
+
+      if (selection == null) {
+        return Promise.reject(new Error());
+      }
+
+      selection.removeAllRanges();
+      const range = document.createRange();
+      range.selectNodeContents(node);
+      selection.addRange(range);
+      document.execCommand('copy');
+      selection.removeAllRanges();
+      return Promise.resolve();
+    }
+    function copyText(text) {
+      if ('clipboard' in navigator) {
+        // eslint-disable-next-line flowtype/no-flow-fix-me-comments
+        // $FlowFixMe Clipboard is not defined in Flow yet.
+        return navigator.clipboard.writeText(text);
+      }
+
+      const body = document.body;
+
+      if (!body) {
+        return Promise.reject(new Error());
+      }
+
+      const node = createNode(text);
+      body.appendChild(node);
+      copyNode(node);
+      body.removeChild(node);
+      return Promise.resolve();
+    }
+
+    function copy(button) {
+      const id = button.getAttribute('for');
+      const text = button.getAttribute('value');
+
+      function trigger() {
+        button.dispatchEvent(new CustomEvent('clipboard-copy', {
+          bubbles: true
+        }));
+      }
+
+      if (text) {
+        copyText(text).then(trigger);
+      } else if (id) {
+        const root = 'getRootNode' in Element.prototype ? button.getRootNode() : button.ownerDocument;
+        if (!(root instanceof Document || 'ShadowRoot' in window && root instanceof ShadowRoot)) return;
+        const node = root.getElementById(id);
+        if (node) copyTarget(node).then(trigger);
+      }
+    }
+
+    function copyTarget(content) {
+      if (content instanceof HTMLInputElement || content instanceof HTMLTextAreaElement) {
+        return copyText(content.value);
+      } else if (content instanceof HTMLAnchorElement && content.hasAttribute('href')) {
+        return copyText(content.href);
+      } else {
+        return copyNode(content);
+      }
+    }
+
+    function clicked(event) {
+      const button = event.currentTarget;
+
+      if (button instanceof HTMLElement) {
+        copy(button);
+      }
+    }
+
+    function keydown(event) {
+      if (event.key === ' ' || event.key === 'Enter') {
+        const button = event.currentTarget;
+
+        if (button instanceof HTMLElement) {
+          event.preventDefault();
+          copy(button);
+        }
+      }
+    }
+
+    function focused(event) {
+      event.currentTarget.addEventListener('keydown', keydown);
+    }
+
+    function blurred(event) {
+      event.currentTarget.removeEventListener('keydown', keydown);
+    }
+
+    class ClipboardCopyElement extends HTMLElement {
+      constructor() {
+        super();
+        this.addEventListener('click', clicked);
+        this.addEventListener('focus', focused);
+        this.addEventListener('blur', blurred);
+      }
+
+      connectedCallback() {
+        if (!this.hasAttribute('tabindex')) {
+          this.setAttribute('tabindex', '0');
+        }
+
+        if (!this.hasAttribute('role')) {
+          this.setAttribute('role', 'button');
+        }
+      }
+
+      get value() {
+        return this.getAttribute('value') || '';
+      }
+
+      set value(text) {
+        this.setAttribute('value', text);
+      }
+
+    }
+
+    if (!window.customElements.get('clipboard-copy')) {
+      window.ClipboardCopyElement = ClipboardCopyElement;
+      window.customElements.define('clipboard-copy', ClipboardCopyElement);
+    }
+
+    return ClipboardCopyElement;
+
+  }));
+</script>
+<script>
+      document.addEventListener('clipboard-copy', function(event) {
+        const notice = event.target.querySelector('.notice')
+        notice.hidden = false
+        setTimeout(function() {
+          notice.hidden = true
+        }, 1000)
+      })
+</script>
+
+
+
+
+<style type="text/css">
+    
+    pre { line-height: 125%; }
+td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+.highlight-ipynb .hll { background-color: var(--jp-cell-editor-active-background) }
+.highlight-ipynb { background: var(--jp-cell-editor-background); color: var(--jp-mirror-editor-variable-color) }
+.highlight-ipynb .c { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment */
+.highlight-ipynb .err { color: var(--jp-mirror-editor-error-color) } /* Error */
+.highlight-ipynb .k { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword */
+.highlight-ipynb .o { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator */
+.highlight-ipynb .p { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation */
+.highlight-ipynb .ch { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Hashbang */
+.highlight-ipynb .cm { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Multiline */
+.highlight-ipynb .cp { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Preproc */
+.highlight-ipynb .cpf { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.PreprocFile */
+.highlight-ipynb .c1 { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Single */
+.highlight-ipynb .cs { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Special */
+.highlight-ipynb .kc { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Constant */
+.highlight-ipynb .kd { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Declaration */
+.highlight-ipynb .kn { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Namespace */
+.highlight-ipynb .kp { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Pseudo */
+.highlight-ipynb .kr { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Reserved */
+.highlight-ipynb .kt { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Type */
+.highlight-ipynb .m { color: var(--jp-mirror-editor-number-color) } /* Literal.Number */
+.highlight-ipynb .s { color: var(--jp-mirror-editor-string-color) } /* Literal.String */
+.highlight-ipynb .ow { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator.Word */
+.highlight-ipynb .pm { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation.Marker */
+.highlight-ipynb .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */
+.highlight-ipynb .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */
+.highlight-ipynb .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */
+.highlight-ipynb .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */
+.highlight-ipynb .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */
+.highlight-ipynb .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */
+.highlight-ipynb .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */
+.highlight-ipynb .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */
+.highlight-ipynb .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */
+.highlight-ipynb .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */
+.highlight-ipynb .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */
+.highlight-ipynb .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */
+.highlight-ipynb .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */
+.highlight-ipynb .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */
+.highlight-ipynb .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */
+.highlight-ipynb .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */
+.highlight-ipynb .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */
+.highlight-ipynb .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */
+.highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */
+.highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */
+  </style>
+
+
+
+
+<style type="text/css">
+/*
+This file is taken from the built JupyterLab theme.css
+Found on share/nbconvert/templates/lab/static
+Some changes have been made and marked with CHANGE
+*/
+
+.jupyter-wrapper {
+    /* Elevation
+   *
+   * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here:
+   *
+   * https://github.com/material-components/material-components-web
+   * https://material-components-web.appspot.com/elevation.html
+   */
+
+    --jp-shadow-base-lightness: 0;
+    --jp-shadow-umbra-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.2
+    );
+    --jp-shadow-penumbra-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.14
+    );
+    --jp-shadow-ambient-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.12
+    );
+    --jp-elevation-z0: none;
+    --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color),
+        0px 1px 1px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 3px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color),
+        0px 2px 2px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 5px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color),
+        0px 4px 5px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 10px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color),
+        0px 6px 10px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 18px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color),
+        0px 8px 10px 1px var(--jp-shadow-penumbra-color),
+        0px 3px 14px 2px var(--jp-shadow-ambient-color);
+    --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color),
+        0px 12px 17px 2px var(--jp-shadow-penumbra-color),
+        0px 5px 22px 4px var(--jp-shadow-ambient-color);
+    --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color),
+        0px 16px 24px 2px var(--jp-shadow-penumbra-color),
+        0px 6px 30px 5px var(--jp-shadow-ambient-color);
+    --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color),
+        0px 20px 31px 3px var(--jp-shadow-penumbra-color),
+        0px 8px 38px 7px var(--jp-shadow-ambient-color);
+    --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color),
+        0px 24px 38px 3px var(--jp-shadow-penumbra-color),
+        0px 9px 46px 8px var(--jp-shadow-ambient-color);
+
+    /* Borders
+   *
+   * The following variables, specify the visual styling of borders in JupyterLab.
+   */
+
+    --jp-border-width: 1px;
+    --jp-border-color0: var(--md-grey-400);
+    --jp-border-color1: var(--md-grey-400);
+    --jp-border-color2: var(--md-grey-300);
+    --jp-border-color3: var(--md-grey-200);
+    --jp-border-radius: 2px;
+
+    /* UI Fonts
+   *
+   * The UI font CSS variables are used for the typography all of the JupyterLab
+   * user interface elements that are not directly user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-ui-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+    --jp-ui-font-scale-factor: 1.2;
+    --jp-ui-font-size0: 0.83333em;
+    --jp-ui-font-size1: 13px; /* Base font size */
+    --jp-ui-font-size2: 1.2em;
+    --jp-ui-font-size3: 1.44em;
+
+    --jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+
+    /*
+   * Use these font colors against the corresponding main layout colors.
+   * In a light theme, these go from dark to light.
+   */
+
+    /* Defaults use Material Design specification */
+    --jp-ui-font-color0: rgba(0, 0, 0, 1);
+    --jp-ui-font-color1: rgba(0, 0, 0, 0.87);
+    --jp-ui-font-color2: rgba(0, 0, 0, 0.54);
+    --jp-ui-font-color3: rgba(0, 0, 0, 0.38);
+
+    /*
+   * Use these against the brand/accent/warn/error colors.
+   * These will typically go from light to darker, in both a dark and light theme.
+   */
+
+    --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1);
+    --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1);
+    --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7);
+    --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5);
+
+    /* Content Fonts
+   *
+   * Content font variables are used for typography of user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-content-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+    --jp-content-line-height: 1.6;
+    --jp-content-font-scale-factor: 1.2;
+    --jp-content-font-size0: 0.83333em;
+    --jp-content-font-size1: 14px; /* Base font size */
+    --jp-content-font-size2: 1.2em;
+    --jp-content-font-size3: 1.44em;
+    --jp-content-font-size4: 1.728em;
+    --jp-content-font-size5: 2.0736em;
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-content-presentation-font-size1: 17px;
+
+    --jp-content-heading-line-height: 1;
+    --jp-content-heading-margin-top: 1.2em;
+    --jp-content-heading-margin-bottom: 0.8em;
+    --jp-content-heading-font-weight: 500;
+
+    /* Defaults use Material Design specification */
+    --jp-content-font-color0: rgba(0, 0, 0, 1);
+    --jp-content-font-color1: rgba(0, 0, 0, 0.87);
+    --jp-content-font-color2: rgba(0, 0, 0, 0.54);
+    --jp-content-font-color3: rgba(0, 0, 0, 0.38);
+
+    --jp-content-link-color: var(--md-blue-700);
+
+    --jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+
+    /*
+   * Code Fonts
+   *
+   * Code font variables are used for typography of code and other monospaces content.
+   */
+
+    --jp-code-font-size: 13px;
+    --jp-code-line-height: 1.3077; /* 17px for 13px base */
+    --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */
+    --jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono",
+        monospace;
+    --jp-code-font-family: var(--jp-code-font-family-default);
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-code-presentation-font-size: 16px;
+
+    /* may need to tweak cursor width if you change font size */
+    --jp-code-cursor-width0: 1.4px;
+    --jp-code-cursor-width1: 2px;
+    --jp-code-cursor-width2: 4px;
+
+    /* Layout
+   *
+   * The following are the main layout colors use in JupyterLab. In a light
+   * theme these would go from light to dark.
+   */
+
+    --jp-layout-color0: white;
+    --jp-layout-color1: white;
+    --jp-layout-color2: var(--md-grey-200);
+    --jp-layout-color3: var(--md-grey-400);
+    --jp-layout-color4: var(--md-grey-600);
+
+    /* Inverse Layout
+   *
+   * The following are the inverse layout colors use in JupyterLab. In a light
+   * theme these would go from dark to light.
+   */
+
+    --jp-inverse-layout-color0: #111111;
+    --jp-inverse-layout-color1: var(--md-grey-900);
+    --jp-inverse-layout-color2: var(--md-grey-800);
+    --jp-inverse-layout-color3: var(--md-grey-700);
+    --jp-inverse-layout-color4: var(--md-grey-600);
+
+    /* Brand/accent */
+
+    --jp-brand-color0: var(--md-blue-900);
+    --jp-brand-color1: var(--md-blue-700);
+    --jp-brand-color2: var(--md-blue-300);
+    --jp-brand-color3: var(--md-blue-100);
+    --jp-brand-color4: var(--md-blue-50);
+
+    --jp-accent-color0: var(--md-green-900);
+    --jp-accent-color1: var(--md-green-700);
+    --jp-accent-color2: var(--md-green-300);
+    --jp-accent-color3: var(--md-green-100);
+
+    /* State colors (warn, error, success, info) */
+
+    --jp-warn-color0: var(--md-orange-900);
+    --jp-warn-color1: var(--md-orange-700);
+    --jp-warn-color2: var(--md-orange-300);
+    --jp-warn-color3: var(--md-orange-100);
+
+    --jp-error-color0: var(--md-red-900);
+    --jp-error-color1: var(--md-red-700);
+    --jp-error-color2: var(--md-red-300);
+    --jp-error-color3: var(--md-red-100);
+
+    --jp-success-color0: var(--md-green-900);
+    --jp-success-color1: var(--md-green-700);
+    --jp-success-color2: var(--md-green-300);
+    --jp-success-color3: var(--md-green-100);
+
+    --jp-info-color0: var(--md-cyan-900);
+    --jp-info-color1: var(--md-cyan-700);
+    --jp-info-color2: var(--md-cyan-300);
+    --jp-info-color3: var(--md-cyan-100);
+
+    /* Cell specific styles */
+
+    --jp-cell-padding: 5px;
+
+    --jp-cell-collapser-width: 8px;
+    --jp-cell-collapser-min-height: 20px;
+    --jp-cell-collapser-not-active-hover-opacity: 0.6;
+
+    --jp-cell-editor-background: var(--md-grey-100);
+    --jp-cell-editor-border-color: var(--md-grey-300);
+    --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-cell-editor-active-background: var(--jp-layout-color0);
+    --jp-cell-editor-active-border-color: var(--jp-brand-color1);
+
+    --jp-cell-prompt-width: 64px;
+    --jp-cell-prompt-font-family: var(--jp-code-font-family-default);
+    --jp-cell-prompt-letter-spacing: 0px;
+    --jp-cell-prompt-opacity: 1;
+    --jp-cell-prompt-not-active-opacity: 0.5;
+    --jp-cell-prompt-not-active-font-color: var(--md-grey-700);
+    /* A custom blend of MD grey and blue 600
+   * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */
+    --jp-cell-inprompt-font-color: #307fc1;
+    /* A custom blend of MD grey and orange 600
+   * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */
+    --jp-cell-outprompt-font-color: #bf5b3d;
+
+    /* Notebook specific styles */
+
+    --jp-notebook-padding: 10px;
+    --jp-notebook-select-background: var(--jp-layout-color1);
+    --jp-notebook-multiselected-color: var(--md-blue-50);
+
+    /* The scroll padding is calculated to fill enough space at the bottom of the
+  notebook to show one single-line cell (with appropriate padding) at the top
+  when the notebook is scrolled all the way to the bottom. We also subtract one
+  pixel so that no scrollbar appears if we have just one single-line cell in the
+  notebook. This padding is to enable a 'scroll past end' feature in a notebook.
+  */
+    --jp-notebook-scroll-padding: calc(
+        100% - var(--jp-code-font-size) * var(--jp-code-line-height) -
+            var(--jp-code-padding) - var(--jp-cell-padding) - 1px
+    );
+
+    /* Rendermime styles */
+
+    --jp-rendermime-error-background: #fdd;
+    --jp-rendermime-table-row-background: var(--md-grey-100);
+    --jp-rendermime-table-row-hover-background: var(--md-light-blue-50);
+
+    /* Dialog specific styles */
+
+    --jp-dialog-background: rgba(0, 0, 0, 0.25);
+
+    /* Console specific styles */
+
+    --jp-console-padding: 10px;
+
+    /* Toolbar specific styles */
+
+    --jp-toolbar-border-color: var(--jp-border-color1);
+    --jp-toolbar-micro-height: 8px;
+    --jp-toolbar-background: var(--jp-layout-color1);
+    --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24);
+    --jp-toolbar-header-margin: 4px 4px 0px 4px;
+    --jp-toolbar-active-background: var(--md-grey-300);
+
+    /* Statusbar specific styles */
+
+    --jp-statusbar-height: 24px;
+
+    /* Input field styles */
+
+    --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-input-active-background: var(--jp-layout-color1);
+    --jp-input-hover-background: var(--jp-layout-color1);
+    --jp-input-background: var(--md-grey-100);
+    --jp-input-border-color: var(--jp-border-color1);
+    --jp-input-active-border-color: var(--jp-brand-color1);
+    --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3);
+
+    /* General editor styles */
+
+    --jp-editor-selected-background: #d9d9d9;
+    --jp-editor-selected-focused-background: #d7d4f0;
+    --jp-editor-cursor-color: var(--jp-ui-font-color0);
+
+    /* Code mirror specific styles */
+
+    --jp-mirror-editor-keyword-color: #008000;
+    --jp-mirror-editor-atom-color: #88f;
+    --jp-mirror-editor-number-color: #080;
+    --jp-mirror-editor-def-color: #00f;
+    --jp-mirror-editor-variable-color: var(--md-grey-900);
+    --jp-mirror-editor-variable-2-color: #05a;
+    --jp-mirror-editor-variable-3-color: #085;
+    --jp-mirror-editor-punctuation-color: #05a;
+    --jp-mirror-editor-property-color: #05a;
+    --jp-mirror-editor-operator-color: #aa22ff;
+    --jp-mirror-editor-comment-color: #408080;
+    --jp-mirror-editor-string-color: #ba2121;
+    --jp-mirror-editor-string-2-color: #708;
+    --jp-mirror-editor-meta-color: #aa22ff;
+    --jp-mirror-editor-qualifier-color: #555;
+    --jp-mirror-editor-builtin-color: #008000;
+    --jp-mirror-editor-bracket-color: #997;
+    --jp-mirror-editor-tag-color: #170;
+    --jp-mirror-editor-attribute-color: #00c;
+    --jp-mirror-editor-header-color: blue;
+    --jp-mirror-editor-quote-color: #090;
+    --jp-mirror-editor-link-color: #00c;
+    --jp-mirror-editor-error-color: #f00;
+    --jp-mirror-editor-hr-color: #999;
+
+    /* Vega extension styles */
+
+    --jp-vega-background: white;
+
+    /* Sidebar-related styles */
+
+    --jp-sidebar-min-width: 250px;
+
+    /* Search-related styles */
+
+    --jp-search-toggle-off-opacity: 0.5;
+    --jp-search-toggle-hover-opacity: 0.8;
+    --jp-search-toggle-on-opacity: 1;
+    --jp-search-selected-match-background-color: rgb(245, 200, 0);
+    --jp-search-selected-match-color: black;
+    --jp-search-unselected-match-background-color: var(
+        --jp-inverse-layout-color0
+    );
+    --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);
+
+    /* Icon colors that work well with light or dark backgrounds */
+    --jp-icon-contrast-color0: var(--md-purple-600);
+    --jp-icon-contrast-color1: var(--md-green-600);
+    --jp-icon-contrast-color2: var(--md-pink-600);
+    --jp-icon-contrast-color3: var(--md-blue-600);
+}
+
+[data-md-color-scheme="slate"] .jupyter-wrapper {
+    /* Elevation
+   *
+   * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here:
+   *
+   * https://github.com/material-components/material-components-web
+   * https://material-components-web.appspot.com/elevation.html
+   */
+
+    /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout
+   * colors used in the theme as well. */
+    --jp-shadow-base-lightness: 32;
+    --jp-shadow-umbra-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.2
+    );
+    --jp-shadow-penumbra-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.14
+    );
+    --jp-shadow-ambient-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.12
+    );
+    --jp-elevation-z0: none;
+    --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color),
+        0px 1px 1px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 3px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color),
+        0px 2px 2px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 5px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color),
+        0px 4px 5px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 10px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color),
+        0px 6px 10px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 18px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color),
+        0px 8px 10px 1px var(--jp-shadow-penumbra-color),
+        0px 3px 14px 2px var(--jp-shadow-ambient-color);
+    --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color),
+        0px 12px 17px 2px var(--jp-shadow-penumbra-color),
+        0px 5px 22px 4px var(--jp-shadow-ambient-color);
+    --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color),
+        0px 16px 24px 2px var(--jp-shadow-penumbra-color),
+        0px 6px 30px 5px var(--jp-shadow-ambient-color);
+    --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color),
+        0px 20px 31px 3px var(--jp-shadow-penumbra-color),
+        0px 8px 38px 7px var(--jp-shadow-ambient-color);
+    --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color),
+        0px 24px 38px 3px var(--jp-shadow-penumbra-color),
+        0px 9px 46px 8px var(--jp-shadow-ambient-color);
+
+    /* Borders
+   *
+   * The following variables, specify the visual styling of borders in JupyterLab.
+   */
+
+    --jp-border-width: 1px;
+    --jp-border-color0: var(--md-grey-700);
+    --jp-border-color1: var(--md-grey-700);
+    --jp-border-color2: var(--md-grey-800);
+    --jp-border-color3: var(--md-grey-900);
+    --jp-border-radius: 2px;
+
+    /* UI Fonts
+   *
+   * The UI font CSS variables are used for the typography all of the JupyterLab
+   * user interface elements that are not directly user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-ui-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+    --jp-ui-font-scale-factor: 1.2;
+    --jp-ui-font-size0: 0.83333em;
+    --jp-ui-font-size1: 13px; /* Base font size */
+    --jp-ui-font-size2: 1.2em;
+    --jp-ui-font-size3: 1.44em;
+
+    --jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+
+    /*
+   * Use these font colors against the corresponding main layout colors.
+   * In a light theme, these go from dark to light.
+   */
+
+    /* Defaults use Material Design specification */
+    --jp-ui-font-color0: rgba(255, 255, 255, 1);
+    --jp-ui-font-color1: rgba(255, 255, 255, 0.87);
+    --jp-ui-font-color2: rgba(255, 255, 255, 0.54);
+    --jp-ui-font-color3: rgba(255, 255, 255, 0.38);
+
+    /*
+   * Use these against the brand/accent/warn/error colors.
+   * These will typically go from light to darker, in both a dark and light theme.
+   */
+
+    --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1);
+    --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8);
+    --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5);
+    --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3);
+
+    /* Content Fonts
+   *
+   * Content font variables are used for typography of user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-content-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+    --jp-content-line-height: 1.6;
+    --jp-content-font-scale-factor: 1.2;
+    --jp-content-font-size0: 0.83333em;
+    --jp-content-font-size1: 14px; /* Base font size */
+    --jp-content-font-size2: 1.2em;
+    --jp-content-font-size3: 1.44em;
+    --jp-content-font-size4: 1.728em;
+    --jp-content-font-size5: 2.0736em;
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-content-presentation-font-size1: 17px;
+
+    --jp-content-heading-line-height: 1;
+    --jp-content-heading-margin-top: 1.2em;
+    --jp-content-heading-margin-bottom: 0.8em;
+    --jp-content-heading-font-weight: 500;
+
+    /* Defaults use Material Design specification */
+    --jp-content-font-color0: rgba(255, 255, 255, 1);
+    --jp-content-font-color1: rgba(255, 255, 255, 1);
+    --jp-content-font-color2: rgba(255, 255, 255, 0.7);
+    --jp-content-font-color3: rgba(255, 255, 255, 0.5);
+
+    --jp-content-link-color: var(--md-blue-300);
+
+    --jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+
+    /*
+   * Code Fonts
+   *
+   * Code font variables are used for typography of code and other monospaces content.
+   */
+
+    --jp-code-font-size: 13px;
+    --jp-code-line-height: 1.3077; /* 17px for 13px base */
+    --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */
+    --jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono",
+        monospace;
+    --jp-code-font-family: var(--jp-code-font-family-default);
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-code-presentation-font-size: 16px;
+
+    /* may need to tweak cursor width if you change font size */
+    --jp-code-cursor-width0: 1.4px;
+    --jp-code-cursor-width1: 2px;
+    --jp-code-cursor-width2: 4px;
+
+    /* Layout
+   *
+   * The following are the main layout colors use in JupyterLab. In a light
+   * theme these would go from light to dark.
+   */
+
+    --jp-layout-color0: #111111;
+    --jp-layout-color1: var(--md-grey-900);
+    --jp-layout-color2: var(--md-grey-800);
+    --jp-layout-color3: var(--md-grey-700);
+    --jp-layout-color4: var(--md-grey-600);
+
+    /* Inverse Layout
+   *
+   * The following are the inverse layout colors use in JupyterLab. In a light
+   * theme these would go from dark to light.
+   */
+
+    --jp-inverse-layout-color0: white;
+    --jp-inverse-layout-color1: white;
+    --jp-inverse-layout-color2: var(--md-grey-200);
+    --jp-inverse-layout-color3: var(--md-grey-400);
+    --jp-inverse-layout-color4: var(--md-grey-600);
+
+    /* Brand/accent */
+
+    --jp-brand-color0: var(--md-blue-700);
+    --jp-brand-color1: var(--md-blue-500);
+    --jp-brand-color2: var(--md-blue-300);
+    --jp-brand-color3: var(--md-blue-100);
+    --jp-brand-color4: var(--md-blue-50);
+
+    --jp-accent-color0: var(--md-green-700);
+    --jp-accent-color1: var(--md-green-500);
+    --jp-accent-color2: var(--md-green-300);
+    --jp-accent-color3: var(--md-green-100);
+
+    /* State colors (warn, error, success, info) */
+
+    --jp-warn-color0: var(--md-orange-700);
+    --jp-warn-color1: var(--md-orange-500);
+    --jp-warn-color2: var(--md-orange-300);
+    --jp-warn-color3: var(--md-orange-100);
+
+    --jp-error-color0: var(--md-red-700);
+    --jp-error-color1: var(--md-red-500);
+    --jp-error-color2: var(--md-red-300);
+    --jp-error-color3: var(--md-red-100);
+
+    --jp-success-color0: var(--md-green-700);
+    --jp-success-color1: var(--md-green-500);
+    --jp-success-color2: var(--md-green-300);
+    --jp-success-color3: var(--md-green-100);
+
+    --jp-info-color0: var(--md-cyan-700);
+    --jp-info-color1: var(--md-cyan-500);
+    --jp-info-color2: var(--md-cyan-300);
+    --jp-info-color3: var(--md-cyan-100);
+
+    /* Cell specific styles */
+
+    --jp-cell-padding: 5px;
+
+    --jp-cell-collapser-width: 8px;
+    --jp-cell-collapser-min-height: 20px;
+    --jp-cell-collapser-not-active-hover-opacity: 0.6;
+
+    --jp-cell-editor-background: var(--jp-layout-color1);
+    --jp-cell-editor-border-color: var(--md-grey-700);
+    --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-cell-editor-active-background: var(--jp-layout-color0);
+    --jp-cell-editor-active-border-color: var(--jp-brand-color1);
+
+    --jp-cell-prompt-width: 64px;
+    --jp-cell-prompt-font-family: var(--jp-code-font-family-default);
+    --jp-cell-prompt-letter-spacing: 0px;
+    --jp-cell-prompt-opacity: 1;
+    --jp-cell-prompt-not-active-opacity: 1;
+    --jp-cell-prompt-not-active-font-color: var(--md-grey-300);
+
+    /* A custom blend of MD grey and blue 600
+   * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */
+    --jp-cell-inprompt-font-color: #307fc1;
+    /* A custom blend of MD grey and orange 600
+   * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */
+    --jp-cell-outprompt-font-color: #bf5b3d;
+
+    /* Notebook specific styles */
+
+    --jp-notebook-padding: 10px;
+    --jp-notebook-select-background: var(--jp-layout-color1);
+    --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24);
+
+    /* The scroll padding is calculated to fill enough space at the bottom of the
+  notebook to show one single-line cell (with appropriate padding) at the top
+  when the notebook is scrolled all the way to the bottom. We also subtract one
+  pixel so that no scrollbar appears if we have just one single-line cell in the
+  notebook. This padding is to enable a 'scroll past end' feature in a notebook.
+  */
+    --jp-notebook-scroll-padding: calc(
+        100% - var(--jp-code-font-size) * var(--jp-code-line-height) -
+            var(--jp-code-padding) - var(--jp-cell-padding) - 1px
+    );
+
+    /* Rendermime styles */
+
+    --jp-rendermime-error-background: rgba(244, 67, 54, 0.28);
+    --jp-rendermime-table-row-background: var(--md-grey-900);
+    --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2);
+
+    /* Dialog specific styles */
+
+    --jp-dialog-background: rgba(0, 0, 0, 0.6);
+
+    /* Console specific styles */
+
+    --jp-console-padding: 10px;
+
+    /* Toolbar specific styles */
+
+    --jp-toolbar-border-color: var(--jp-border-color2);
+    --jp-toolbar-micro-height: 8px;
+    --jp-toolbar-background: var(--jp-layout-color1);
+    --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8);
+    --jp-toolbar-header-margin: 4px 4px 0px 4px;
+    --jp-toolbar-active-background: var(--jp-layout-color0);
+
+    /* Statusbar specific styles */
+
+    --jp-statusbar-height: 24px;
+
+    /* Input field styles */
+
+    --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-input-active-background: var(--jp-layout-color0);
+    --jp-input-hover-background: var(--jp-layout-color2);
+    --jp-input-background: var(--md-grey-800);
+    --jp-input-border-color: var(--jp-border-color1);
+    --jp-input-active-border-color: var(--jp-brand-color1);
+    --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3);
+
+    /* General editor styles */
+
+    --jp-editor-selected-background: var(--jp-layout-color2);
+    --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24);
+    --jp-editor-cursor-color: var(--jp-ui-font-color0);
+
+    /* Code mirror specific styles */
+
+    --jp-mirror-editor-keyword-color: var(--md-green-500);
+    --jp-mirror-editor-atom-color: var(--md-blue-300);
+    --jp-mirror-editor-number-color: var(--md-green-400);
+    --jp-mirror-editor-def-color: var(--md-blue-600);
+    --jp-mirror-editor-variable-color: var(--md-grey-300);
+    --jp-mirror-editor-variable-2-color: var(--md-blue-400);
+    --jp-mirror-editor-variable-3-color: var(--md-green-600);
+    --jp-mirror-editor-punctuation-color: var(--md-blue-400);
+    --jp-mirror-editor-property-color: var(--md-blue-400);
+    --jp-mirror-editor-operator-color: #aa22ff;
+    --jp-mirror-editor-comment-color: #408080;
+    --jp-mirror-editor-string-color: #ff7070;
+    --jp-mirror-editor-string-2-color: var(--md-purple-300);
+    --jp-mirror-editor-meta-color: #aa22ff;
+    --jp-mirror-editor-qualifier-color: #555;
+    --jp-mirror-editor-builtin-color: var(--md-green-600);
+    --jp-mirror-editor-bracket-color: #997;
+    --jp-mirror-editor-tag-color: var(--md-green-700);
+    --jp-mirror-editor-attribute-color: var(--md-blue-700);
+    --jp-mirror-editor-header-color: var(--md-blue-500);
+    --jp-mirror-editor-quote-color: var(--md-green-300);
+    --jp-mirror-editor-link-color: var(--md-blue-700);
+    --jp-mirror-editor-error-color: #f00;
+    --jp-mirror-editor-hr-color: #999;
+
+    /* Vega extension styles */
+
+    --jp-vega-background: var(--md-grey-400);
+
+    /* Sidebar-related styles */
+
+    --jp-sidebar-min-width: 250px;
+
+    /* Search-related styles */
+
+    --jp-search-toggle-off-opacity: 0.6;
+    --jp-search-toggle-hover-opacity: 0.8;
+    --jp-search-toggle-on-opacity: 1;
+    --jp-search-selected-match-background-color: rgb(255, 225, 0);
+    --jp-search-selected-match-color: black;
+    --jp-search-unselected-match-background-color: var(
+        --jp-inverse-layout-color0
+    );
+    --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);
+
+    /* scrollbar related styles. Supports every browser except Edge. */
+
+    /* colors based on JetBrain's Darcula theme */
+
+    --jp-scrollbar-background-color: #3f4244;
+    --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */
+
+    --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */
+
+    /* hacks for setting the thumb shape. These do nothing in Firefox */
+
+    --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */
+    --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */
+
+    /* Icon colors that work well with light or dark backgrounds */
+    --jp-icon-contrast-color0: var(--md-purple-600);
+    --jp-icon-contrast-color1: var(--md-green-600);
+    --jp-icon-contrast-color2: var(--md-pink-600);
+    --jp-icon-contrast-color3: var(--md-blue-600);
+}
+
+:root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: #7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: #ff5722;--md-deep-orange-600: #f4511e;--md-deep-orange-700: #e64a19;--md-deep-orange-800: #d84315;--md-deep-orange-900: #bf360c;--md-deep-orange-A100: #ff9e80;--md-deep-orange-A200: #ff6e40;--md-deep-orange-A400: #ff3d00;--md-deep-orange-A700: #dd2c00;--md-brown-50: #efebe9;--md-brown-100: #d7ccc8;--md-brown-200: #bcaaa4;--md-brown-300: #a1887f;--md-brown-400: #8d6e63;--md-brown-500: #795548;--md-brown-600: #6d4c41;--md-brown-700: #5d4037;--md-brown-800: #4e342e;--md-brown-900: 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+
+Copyright 2015-present Palantir Technologies, Inc. All rights reserved.
+Licensed under the Apache License, Version 2.0.
+
+*//*!
+
+Copyright 2017-present Palantir Technologies, Inc. All rights reserved.
+Licensed under the Apache License, Version 2.0.
+
+*/}.jupyter-wrapper [data-jp-theme-scrollbars=true]{scrollbar-color:rgb(var(--jp-scrollbar-thumb-color)) var(--jp-scrollbar-background-color)}.jupyter-wrapper [data-jp-theme-scrollbars=true] .CodeMirror-hscrollbar,.jupyter-wrapper [data-jp-theme-scrollbars=true] .CodeMirror-vscrollbar{scrollbar-color:rgba(var(--jp-scrollbar-thumb-color), 0.5) rgba(0,0,0,0)}.jupyter-wrapper [data-jp-theme-scrollbars=true] ::-webkit-scrollbar,.jupyter-wrapper [data-jp-theme-scrollbars=true] ::-webkit-scrollbar-corner{background:var(--jp-scrollbar-background-color)}.jupyter-wrapper [data-jp-theme-scrollbars=true] ::-webkit-scrollbar-thumb{background:rgb(var(--jp-scrollbar-thumb-color));border:var(--jp-scrollbar-thumb-margin) solid rgba(0,0,0,0);background-clip:content-box;border-radius:var(--jp-scrollbar-thumb-radius)}.jupyter-wrapper [data-jp-theme-scrollbars=true] ::-webkit-scrollbar-track:horizontal{border-left:var(--jp-scrollbar-endpad) solid 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+
+/*# sourceMappingURL=mkdocs-jupyter.css.map*/</style>
+
+
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+    <!-- MathJax configuration -->
+    <script type="text/x-mathjax-config">
+    init_mathjax = function() {
+        if (window.MathJax) {
+        // MathJax loaded
+            MathJax.Hub.Config({
+                TeX: {
+                    equationNumbers: {
+                    autoNumber: "AMS",
+                    useLabelIds: true
+                    }
+                },
+                tex2jax: {
+                    inlineMath: [ ['$','$'], ["\\(","\\)"] ],
+                    displayMath: [ ['$$','$$'], ["\\[","\\]"] ],
+                    processEscapes: true,
+                    processEnvironments: true
+                },
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+                    linebreaks: {
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+                    }
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+            });
+
+            MathJax.Hub.Queue(["Typeset", MathJax.Hub]);
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+    }
+    init_mathjax();
+    </script>
+    <!-- End of mathjax configuration --><div class="jupyter-wrapper"><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[6]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-1">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
+<span class="kn">import</span> <span class="nn">tweepy</span>
+<span class="kn">import</span> <span class="nn">logging</span>
+</pre></div>
+<div id="cell-1" class="clipboard-copy-txt">import os
+import tweepy
+import logging</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[3]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-2">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">sys</span><span class="p">;</span> <span class="n">sys</span><span class="o">.</span><span class="n">executable</span>
+</pre></div>
+<div id="cell-2" class="clipboard-copy-txt">import sys; sys.executable</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[3]:</div>
+
+
+
+
+<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
+<pre>&#39;/home/avoy/anaconda3/envs/new_cs329s/bin/python&#39;</pre>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h3 id="load-access-credentials-to-bot">Load access credentials to bot<a class="anchor-link" href="#load-access-credentials-to-bot">&#182;</a></h3>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[4]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-3">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">consumer_key</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">getenv</span><span class="p">(</span><span class="s1">&#39;CONSUMER_KEY&#39;</span><span class="p">)</span>
+<span class="n">consumer_secret</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">getenv</span><span class="p">(</span><span class="s1">&#39;CONSUMER_SECRET&#39;</span><span class="p">)</span>
+<span class="n">access_token</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">getenv</span><span class="p">(</span><span class="s1">&#39;ACCESS_TOKEN&#39;</span><span class="p">)</span>
+<span class="n">access_token_secret</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">getenv</span><span class="p">(</span><span class="s1">&#39;ACCESS_TOKEN_SECRET&#39;</span><span class="p">)</span>
+</pre></div>
+<div id="cell-3" class="clipboard-copy-txt">consumer_key = os.getenv('CONSUMER_KEY')
+consumer_secret = os.getenv('CONSUMER_SECRET')
+access_token = os.getenv('ACCESS_TOKEN')
+access_token_secret = os.getenv('ACCESS_TOKEN_SECRET')
+</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
+<pre></pre>
+</div>
+</div>
+
+</div>
+
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[7]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-4">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">logger</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">()</span>
+
+<span class="k">def</span> <span class="nf">create_api</span><span class="p">():</span>
+    <span class="n">consumer_key</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">getenv</span><span class="p">(</span><span class="s2">&quot;CONSUMER_KEY&quot;</span><span class="p">)</span>
+    <span class="n">consumer_secret</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">getenv</span><span class="p">(</span><span class="s2">&quot;CONSUMER_SECRET&quot;</span><span class="p">)</span>
+    <span class="n">access_token</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">getenv</span><span class="p">(</span><span class="s2">&quot;ACCESS_TOKEN&quot;</span><span class="p">)</span>
+    <span class="n">access_token_secret</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">getenv</span><span class="p">(</span><span class="s2">&quot;ACCESS_TOKEN_SECRET&quot;</span><span class="p">)</span>
+
+    <span class="n">auth</span> <span class="o">=</span> <span class="n">tweepy</span><span class="o">.</span><span class="n">OAuthHandler</span><span class="p">(</span><span class="n">consumer_key</span><span class="p">,</span> <span class="n">consumer_secret</span><span class="p">)</span>
+    <span class="n">auth</span><span class="o">.</span><span class="n">set_access_token</span><span class="p">(</span><span class="n">access_token</span><span class="p">,</span> <span class="n">access_token_secret</span><span class="p">)</span>
+    <span class="n">api</span> <span class="o">=</span> <span class="n">tweepy</span><span class="o">.</span><span class="n">API</span><span class="p">(</span><span class="n">auth</span><span class="p">,</span> <span class="n">wait_on_rate_limit</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> 
+        <span class="n">wait_on_rate_limit_notify</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="k">try</span><span class="p">:</span>
+        <span class="n">api</span><span class="o">.</span><span class="n">verify_credentials</span><span class="p">()</span>
+    <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
+        <span class="n">logger</span><span class="o">.</span><span class="n">error</span><span class="p">(</span><span class="s2">&quot;Error creating API&quot;</span><span class="p">,</span> <span class="n">exc_info</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+        <span class="k">raise</span> <span class="n">e</span>
+    <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;API created&quot;</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">api</span>
+</pre></div>
+<div id="cell-4" class="clipboard-copy-txt">logger = logging.getLogger()
+
+def create_api():
+    consumer_key = os.getenv("CONSUMER_KEY")
+    consumer_secret = os.getenv("CONSUMER_SECRET")
+    access_token = os.getenv("ACCESS_TOKEN")
+    access_token_secret = os.getenv("ACCESS_TOKEN_SECRET")
+
+    auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
+    auth.set_access_token(access_token, access_token_secret)
+    api = tweepy.API(auth, wait_on_rate_limit=True, 
+        wait_on_rate_limit_notify=True)
+    try:
+        api.verify_credentials()
+    except Exception as e:
+        logger.error("Error creating API", exc_info=True)
+        raise e
+    logger.info("API created")
+    return api
+</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[9]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-5">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">api</span> <span class="o">=</span> <span class="n">create_api</span><span class="p">()</span>
+</pre></div>
+<div id="cell-5" class="clipboard-copy-txt">api = create_api()
+</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[10]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-6">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">lat</span><span class="p">,</span> <span class="n">lon</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span>
+</pre></div>
+<div id="cell-6" class="clipboard-copy-txt">lat, lon = 0, 0
+</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[10]:</div>
+
+
+
+
+<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
+<pre>Status(_api=&lt;tweepy.api.API object at 0x7fb41c4d6550&gt;, _json={&#39;created_at&#39;: &#39;Fri Mar 12 22:38:34 +0000 2021&#39;, &#39;id&#39;: 1370504518895497218, &#39;id_str&#39;: &#39;1370504518895497218&#39;, &#39;text&#39;: &#39;Earthquake at (0, 0)&#39;, &#39;truncated&#39;: False, &#39;entities&#39;: {&#39;hashtags&#39;: [], &#39;symbols&#39;: [], &#39;user_mentions&#39;: [], &#39;urls&#39;: []}, &#39;source&#39;: &#39;&#39;, &#39;in_reply_to_status_id&#39;: None, &#39;in_reply_to_status_id_str&#39;: None, &#39;in_reply_to_user_id&#39;: None, &#39;in_reply_to_user_id_str&#39;: None, &#39;in_reply_to_screen_name&#39;: None, &#39;user&#39;: {&#39;id&#39;: 1370235376275578885, &#39;id_str&#39;: &#39;1370235376275578885&#39;, &#39;name&#39;: &#39;QuakeFlow&#39;, &#39;screen_name&#39;: &#39;QuakeFlow_bot&#39;, &#39;location&#39;: &#39;&#39;, &#39;description&#39;: &#39;QuakeFlow Project&#39;, &#39;url&#39;: None, &#39;entities&#39;: {&#39;description&#39;: {&#39;urls&#39;: []}}, &#39;protected&#39;: False, &#39;followers_count&#39;: 0, &#39;friends_count&#39;: 0, &#39;listed_count&#39;: 0, &#39;created_at&#39;: &#39;Fri Mar 12 04:49:20 +0000 2021&#39;, &#39;favourites_count&#39;: 0, &#39;utc_offset&#39;: None, &#39;time_zone&#39;: None, &#39;geo_enabled&#39;: False, &#39;verified&#39;: False, &#39;statuses_count&#39;: 2, &#39;lang&#39;: None, &#39;contributors_enabled&#39;: False, &#39;is_translator&#39;: False, &#39;is_translation_enabled&#39;: False, &#39;profile_background_color&#39;: &#39;F5F8FA&#39;, &#39;profile_background_image_url&#39;: None, &#39;profile_background_image_url_https&#39;: None, &#39;profile_background_tile&#39;: False, &#39;profile_image_url&#39;: &#39;http://abs.twimg.com/sticky/default_profile_images/default_profile_normal.png&#39;, &#39;profile_image_url_https&#39;: &#39;https://abs.twimg.com/sticky/default_profile_images/default_profile_normal.png&#39;, &#39;profile_link_color&#39;: &#39;1DA1F2&#39;, &#39;profile_sidebar_border_color&#39;: &#39;C0DEED&#39;, &#39;profile_sidebar_fill_color&#39;: &#39;DDEEF6&#39;, &#39;profile_text_color&#39;: &#39;333333&#39;, &#39;profile_use_background_image&#39;: True, &#39;has_extended_profile&#39;: True, &#39;default_profile&#39;: True, &#39;default_profile_image&#39;: True, &#39;following&#39;: False, &#39;follow_request_sent&#39;: False, &#39;notifications&#39;: False, &#39;translator_type&#39;: &#39;none&#39;}, &#39;geo&#39;: None, &#39;coordinates&#39;: None, &#39;place&#39;: None, &#39;contributors&#39;: None, &#39;is_quote_status&#39;: False, &#39;retweet_count&#39;: 0, &#39;favorite_count&#39;: 0, &#39;favorited&#39;: False, &#39;retweeted&#39;: False, &#39;lang&#39;: &#39;en&#39;}, created_at=datetime.datetime(2021, 3, 12, 22, 38, 34), id=1370504518895497218, id_str=&#39;1370504518895497218&#39;, text=&#39;Earthquake at (0, 0)&#39;, truncated=False, entities={&#39;hashtags&#39;: [], &#39;symbols&#39;: [], &#39;user_mentions&#39;: [], &#39;urls&#39;: []}, source=&#39;&#39;, source_url=None, in_reply_to_status_id=None, in_reply_to_status_id_str=None, in_reply_to_user_id=None, in_reply_to_user_id_str=None, in_reply_to_screen_name=None, author=User(_api=&lt;tweepy.api.API object at 0x7fb41c4d6550&gt;, _json={&#39;id&#39;: 1370235376275578885, &#39;id_str&#39;: &#39;1370235376275578885&#39;, &#39;name&#39;: &#39;QuakeFlow&#39;, &#39;screen_name&#39;: &#39;QuakeFlow_bot&#39;, &#39;location&#39;: &#39;&#39;, &#39;description&#39;: &#39;QuakeFlow Project&#39;, &#39;url&#39;: None, &#39;entities&#39;: {&#39;description&#39;: {&#39;urls&#39;: []}}, &#39;protected&#39;: False, &#39;followers_count&#39;: 0, &#39;friends_count&#39;: 0, &#39;listed_count&#39;: 0, &#39;created_at&#39;: &#39;Fri Mar 12 04:49:20 +0000 2021&#39;, &#39;favourites_count&#39;: 0, &#39;utc_offset&#39;: None, &#39;time_zone&#39;: None, &#39;geo_enabled&#39;: False, &#39;verified&#39;: False, &#39;statuses_count&#39;: 2, &#39;lang&#39;: None, 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&#39;listed_count&#39;: 0, &#39;created_at&#39;: &#39;Fri Mar 12 04:49:20 +0000 2021&#39;, &#39;favourites_count&#39;: 0, &#39;utc_offset&#39;: None, &#39;time_zone&#39;: None, &#39;geo_enabled&#39;: False, &#39;verified&#39;: False, &#39;statuses_count&#39;: 2, &#39;lang&#39;: None, &#39;contributors_enabled&#39;: False, &#39;is_translator&#39;: False, &#39;is_translation_enabled&#39;: False, &#39;profile_background_color&#39;: &#39;F5F8FA&#39;, &#39;profile_background_image_url&#39;: None, &#39;profile_background_image_url_https&#39;: None, &#39;profile_background_tile&#39;: False, &#39;profile_image_url&#39;: &#39;http://abs.twimg.com/sticky/default_profile_images/default_profile_normal.png&#39;, &#39;profile_image_url_https&#39;: &#39;https://abs.twimg.com/sticky/default_profile_images/default_profile_normal.png&#39;, &#39;profile_link_color&#39;: &#39;1DA1F2&#39;, &#39;profile_sidebar_border_color&#39;: &#39;C0DEED&#39;, &#39;profile_sidebar_fill_color&#39;: &#39;DDEEF6&#39;, 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profile_background_image_url_https=None, profile_background_tile=False, profile_image_url=&#39;http://abs.twimg.com/sticky/default_profile_images/default_profile_normal.png&#39;, profile_image_url_https=&#39;https://abs.twimg.com/sticky/default_profile_images/default_profile_normal.png&#39;, profile_link_color=&#39;1DA1F2&#39;, profile_sidebar_border_color=&#39;C0DEED&#39;, profile_sidebar_fill_color=&#39;DDEEF6&#39;, profile_text_color=&#39;333333&#39;, profile_use_background_image=True, has_extended_profile=True, default_profile=True, default_profile_image=True, following=False, follow_request_sent=False, notifications=False, translator_type=&#39;none&#39;), geo=None, coordinates=None, place=None, contributors=None, is_quote_status=False, retweet_count=0, favorite_count=0, favorited=False, retweeted=False, lang=&#39;en&#39;)</pre>
+</div>
+
+</div>
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+</div>
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+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[11]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-7">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">api</span><span class="o">.</span><span class="n">update_with_media</span><span class="p">(</span><span class="s1">&#39;quakeflow logo design 2.jpg&#39;</span><span class="p">)</span>
+</pre></div>
+<div id="cell-7" class="clipboard-copy-txt">api.update_with_media('quakeflow logo design 2.jpg')</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
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+<div class="jp-OutputArea jp-Cell-outputArea">
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+<div class="jp-OutputArea-child">
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+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[11]:</div>
+
+
+
+
+<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
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&#39;is_translator&#39;: False, &#39;is_translation_enabled&#39;: False, &#39;profile_background_color&#39;: &#39;F5F8FA&#39;, &#39;profile_background_image_url&#39;: None, &#39;profile_background_image_url_https&#39;: None, &#39;profile_background_tile&#39;: False, &#39;profile_image_url&#39;: &#39;http://abs.twimg.com/sticky/default_profile_images/default_profile_normal.png&#39;, &#39;profile_image_url_https&#39;: &#39;https://abs.twimg.com/sticky/default_profile_images/default_profile_normal.png&#39;, &#39;profile_link_color&#39;: &#39;1DA1F2&#39;, &#39;profile_sidebar_border_color&#39;: &#39;C0DEED&#39;, &#39;profile_sidebar_fill_color&#39;: &#39;DDEEF6&#39;, &#39;profile_text_color&#39;: &#39;333333&#39;, &#39;profile_use_background_image&#39;: True, &#39;has_extended_profile&#39;: True, &#39;default_profile&#39;: True, &#39;default_profile_image&#39;: True, &#39;following&#39;: False, &#39;follow_request_sent&#39;: False, &#39;notifications&#39;: False, &#39;translator_type&#39;: &#39;none&#39;}, id=1370235376275578885, id_str=&#39;1370235376275578885&#39;, name=&#39;QuakeFlow&#39;, screen_name=&#39;QuakeFlow_bot&#39;, location=&#39;&#39;, description=&#39;QuakeFlow Project&#39;, url=None, entities={&#39;description&#39;: {&#39;urls&#39;: []}}, protected=False, followers_count=0, friends_count=0, listed_count=0, created_at=datetime.datetime(2021, 3, 12, 4, 49, 20), favourites_count=0, utc_offset=None, time_zone=None, geo_enabled=False, verified=False, statuses_count=3, lang=None, contributors_enabled=False, is_translator=False, is_translation_enabled=False, profile_background_color=&#39;F5F8FA&#39;, profile_background_image_url=None, profile_background_image_url_https=None, profile_background_tile=False, profile_image_url=&#39;http://abs.twimg.com/sticky/default_profile_images/default_profile_normal.png&#39;, profile_image_url_https=&#39;https://abs.twimg.com/sticky/default_profile_images/default_profile_normal.png&#39;, profile_link_color=&#39;1DA1F2&#39;, profile_sidebar_border_color=&#39;C0DEED&#39;, profile_sidebar_fill_color=&#39;DDEEF6&#39;, profile_text_color=&#39;333333&#39;, profile_use_background_image=True, has_extended_profile=True, default_profile=True, default_profile_image=True, following=False, follow_request_sent=False, notifications=False, translator_type=&#39;none&#39;), geo=None, coordinates=None, place=None, contributors=None, is_quote_status=False, retweet_count=0, favorite_count=0, favorited=False, retweeted=False, possibly_sensitive=False, lang=&#39;und&#39;)</pre>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
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+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
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+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+<span class="c1"># Lambdas and Constants</span>
+<span class="n">normalize</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="p">(</span><span class="n">x</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">finfo</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span><span class="o">.</span><span class="n">eps</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">x</span><span class="p">)</span><span class="o">+</span><span class="n">np</span><span class="o">.</span><span class="n">finfo</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span><span class="o">.</span><span class="n">eps</span><span class="p">)</span>
+<span class="n">timestamp_seconds</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">datetime</span><span class="o">.</span><span class="n">fromisoformat</span><span class="p">(</span><span class="n">x</span><span class="p">)</span><span class="o">.</span><span class="n">timestamp</span><span class="p">()</span>
+<span class="n">wave_dict</span> <span class="o">=</span> <span class="n">defaultdict</span><span class="p">(</span><span class="nb">list</span><span class="p">)</span>
+<span class="n">pick_dict</span> <span class="o">=</span> <span class="n">defaultdict</span><span class="p">(</span><span class="nb">list</span><span class="p">)</span>
+<span class="n">event_dict</span> <span class="o">=</span> <span class="n">defaultdict</span><span class="p">(</span><span class="nb">dict</span><span class="p">)</span>
+<span class="n">event_min_gap</span> <span class="o">=</span> <span class="mi">5</span>
+</pre></div>
+<div id="cell-8" class="clipboard-copy-txt">import numpy as np
+
+# Lambdas and Constants
+normalize = lambda x: (x - np.mean(x) + np.finfo(x.dtype).eps)/(np.std(x)+np.finfo(x.dtype).eps)
+timestamp_seconds = lambda x: datetime.fromisoformat(x).timestamp()
+wave_dict = defaultdict(list)
+pick_dict = defaultdict(list)
+event_dict = defaultdict(dict)
+event_min_gap = 5</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
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+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">from</span> <span class="nn">kafka</span> <span class="kn">import</span> <span class="n">KafkaConsumer</span>
+
+<span class="c1"># Connection to Kafka</span>
+<span class="n">consumer</span> <span class="o">=</span> <span class="n">KafkaConsumer</span><span class="p">(</span>
+    <span class="n">bootstrap_servers</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;localhost:9092&#39;</span><span class="p">],</span>
+    <span class="n">auto_offset_reset</span><span class="o">=</span><span class="s1">&#39;earliest&#39;</span><span class="p">,</span>
+    <span class="n">enable_auto_commit</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">key_deserializer</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">loads</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">decode</span><span class="p">(</span><span class="s1">&#39;utf-8&#39;</span><span class="p">)),</span>
+    <span class="n">value_deserializer</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">loads</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">decode</span><span class="p">(</span><span class="s1">&#39;utf-8&#39;</span><span class="p">))</span>
+<span class="p">)</span>
+
+<span class="n">consumer</span><span class="o">.</span><span class="n">subscribe</span><span class="p">([</span><span class="s1">&#39;waveform_raw&#39;</span><span class="p">,</span> <span class="s1">&#39;phasenet_picks&#39;</span><span class="p">,</span> <span class="s1">&#39;gmma_events&#39;</span><span class="p">])</span>
+</pre></div>
+<div id="cell-9" class="clipboard-copy-txt">from kafka import KafkaConsumer
+
+# Connection to Kafka
+consumer = KafkaConsumer(
+    bootstrap_servers=['localhost:9092'],
+    auto_offset_reset='earliest',
+    enable_auto_commit=True,
+    key_deserializer=lambda x: loads(x.decode('utf-8')),
+    value_deserializer=lambda x: loads(x.decode('utf-8'))
+)
+
+consumer.subscribe(['waveform_raw', 'phasenet_picks', 'gmma_events'])</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
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+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Handle messages from Kafka</span>
+<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">message</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">consumer</span><span class="p">):</span>
+
+    <span class="k">if</span> <span class="n">message</span><span class="o">.</span><span class="n">topic</span> <span class="o">==</span> <span class="s2">&quot;waveform_raw&quot;</span><span class="p">:</span>
+        <span class="n">key</span> <span class="o">=</span> <span class="n">message</span><span class="o">.</span><span class="n">key</span>
+        <span class="n">timestamp</span> <span class="o">=</span> <span class="n">message</span><span class="o">.</span><span class="n">value</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="n">vec</span> <span class="o">=</span> <span class="n">message</span><span class="o">.</span><span class="n">value</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+        <span class="n">wave_dict</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">message</span><span class="o">.</span><span class="n">value</span><span class="p">)</span>
+        <span class="n">wave_dict</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">wave_dict</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="o">-</span><span class="n">window_number</span><span class="p">:]</span>
+    
+    <span class="k">elif</span> <span class="n">message</span><span class="o">.</span><span class="n">topic</span> <span class="o">==</span> <span class="s2">&quot;phasenet_picks&quot;</span><span class="p">:</span>
+        <span class="c1"># print(&quot;phasenet!&quot;)</span>
+        <span class="n">key</span> <span class="o">=</span> <span class="n">message</span><span class="o">.</span><span class="n">key</span>
+        <span class="n">pick</span> <span class="o">=</span> <span class="n">message</span><span class="o">.</span><span class="n">value</span>
+        <span class="n">pick_dict</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">pick</span><span class="p">)</span>
+
+    <span class="k">elif</span> <span class="n">message</span><span class="o">.</span><span class="n">topic</span> <span class="o">==</span> <span class="s2">&quot;gmma_events&quot;</span><span class="p">:</span>
+        <span class="c1"># print(&quot;gmma!&quot;)</span>
+        <span class="n">key</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">timestamp_seconds</span><span class="p">(</span><span class="n">message</span><span class="o">.</span><span class="n">key</span><span class="p">)</span><span class="o">/</span><span class="n">event_min_gap</span><span class="p">)</span> <span class="o">*</span> <span class="n">event_min_gap</span>
+        <span class="n">event</span> <span class="o">=</span> <span class="n">message</span><span class="o">.</span><span class="n">value</span>
+        <span class="c1"># event_list.extend(event)</span>
+        <span class="c1"># event_dict[key].append(event)</span>
+        <span class="n">event_dict</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">event</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">raise</span><span class="p">(</span><span class="s2">&quot;Topic Error!&quot;</span><span class="p">)</span>
+</pre></div>
+<div id="cell-10" class="clipboard-copy-txt"># Handle messages from Kafka
+for i, message in enumerate(consumer):
+
+    if message.topic == "waveform_raw":
+        key = message.key
+        timestamp = message.value[0]
+        vec = message.value[1]
+        wave_dict[key].append(message.value)
+        wave_dict[key] = wave_dict[key][-window_number:]
+    
+    elif message.topic == "phasenet_picks":
+        # print("phasenet!")
+        key = message.key
+        pick = message.value
+        pick_dict[key].append(pick)
+
+    elif message.topic == "gmma_events":
+        # print("gmma!")
+        key = np.round(timestamp_seconds(message.key)/event_min_gap) * event_min_gap
+        event = message.value
+        # event_list.extend(event)
+        # event_dict[key].append(event)
+        event_dict[key] = event
+    else:
+        raise("Topic Error!")</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
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+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
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+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">def</span> <span class="nf">get_plot_events</span><span class="p">(</span><span class="n">message</span><span class="p">,</span> <span class="n">t0</span><span class="p">,</span> <span class="n">tn</span><span class="p">):</span>
+    <span class="n">t0_idx</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="n">t_events</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">mag_events</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">loc_events</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">message</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+        <span class="k">if</span> <span class="n">timestamp_seconds</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="s2">&quot;time&quot;</span><span class="p">])</span> <span class="o">&gt;=</span> <span class="n">t0</span><span class="p">:</span>
+            <span class="c1"># if t0_idx == 0:</span>
+            <span class="c1">#     t0_idx = i</span>
+            <span class="k">if</span> <span class="n">timestamp_seconds</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="s2">&quot;time&quot;</span><span class="p">])</span> <span class="o">&lt;=</span> <span class="n">tn</span> <span class="o">-</span> <span class="mi">8</span> <span class="p">:</span>
+                <span class="n">t_events</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">timestamp_seconds</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="s2">&quot;time&quot;</span><span class="p">])</span> <span class="o">-</span> <span class="n">t0</span><span class="p">)</span>
+                <span class="n">mag_events</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="s2">&quot;magnitude&quot;</span><span class="p">])</span>
+                <span class="n">loc_events</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="s2">&quot;location&quot;</span><span class="p">])</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">return</span> <span class="n">t_events</span><span class="p">,</span> <span class="n">mag_events</span><span class="p">,</span> <span class="n">loc_events</span><span class="p">,</span> <span class="n">t0_idx</span> 
+    <span class="k">return</span> <span class="n">t_events</span><span class="p">,</span> <span class="n">mag_events</span><span class="p">,</span> <span class="n">loc_events</span><span class="p">,</span> <span class="n">t0_idx</span>
+</pre></div>
+<div id="cell-11" class="clipboard-copy-txt">def get_plot_events(message, t0, tn):
+    t0_idx = 0
+    t_events = []
+    mag_events = []
+    loc_events = []
+    for k, x in message.items():
+        if timestamp_seconds(x["time"]) >= t0:
+            # if t0_idx == 0:
+            #     t0_idx = i
+            if timestamp_seconds(x["time"]) <= tn - 8 :
+                t_events.append(timestamp_seconds(x["time"]) - t0)
+                mag_events.append(x["magnitude"])
+                loc_events.append(x["location"])
+            else:
+                return t_events, mag_events, loc_events, t0_idx 
+    return t_events, mag_events, loc_events, t0_idx 
+
+</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+</div>
+
+
+
+
+  
+
+
+
+              
+            </article>
+            
+          </div>
+        </div>
+        
+      </main>
+      
+        <footer class="md-footer">
+  
+  <div class="md-footer-meta md-typeset">
+    <div class="md-footer-meta__inner md-grid">
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+  
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+    
+    
+      <script src="../../assets/javascripts/bundle.9c69f0bc.min.js"></script>
+      
+    
+  </body>
+</html>
\ No newline at end of file
diff --git a/kubeflow/waveforms/Dockerfile b/kubeflow/waveforms/Dockerfile
new file mode 100644
index 0000000..68d450e
--- /dev/null
+++ b/kubeflow/waveforms/Dockerfile
@@ -0,0 +1,12 @@
+FROM python:3.8
+
+RUN apt-get update && apt-get install -y vim ssh rsync && rm -rf /var/lib/apt/lists/*
+
+RUN python -m pip install --upgrade obspy && rm -rf /var/cache/apk/*
+
+WORKDIR /opt
+
+# RUN ssh-keygen -q -t rsa -N "" <<< $"\ny" >/dev/null 2>&1
+# RUN ssh-copy-id zhuwq@wintermute.gps.caltech.edu
+
+# ARG CACHEBUST=1
\ No newline at end of file
diff --git a/kubeflow/waveforms/download_waveform/index.html b/kubeflow/waveforms/download_waveform/index.html
new file mode 100644
index 0000000..6d148ca
--- /dev/null
+++ b/kubeflow/waveforms/download_waveform/index.html
@@ -0,0 +1,3021 @@
+
+<!doctype html>
+<html lang="en" class="no-js">
+  <head>
+    
+      <meta charset="utf-8">
+      <meta name="viewport" content="width=device-width,initial-scale=1">
+      
+        <meta name="description" content="QuakeFlow: a Scalable Deep-learning-based Earthquake Monitoring Workflow with Cloud Computing">
+      
+      
+        <meta name="author" content="Weiqiang Zhu">
+      
+      
+      <link rel="icon" href="../../../assets/images/favicon.png">
+      <meta name="generator" content="mkdocs-1.3.0, mkdocs-material-8.4.1">
+    
+    
+      
+        <title>Download waveform - QuakeFlow</title>
+      
+    
+    
+      <link rel="stylesheet" href="../../../assets/stylesheets/main.69437709.min.css">
+      
+        
+        <link rel="stylesheet" href="../../../assets/stylesheets/palette.cbb835fc.min.css">
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+        <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
+      
+    
+    
+    <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
+    
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+
+  
+    <script>"undefined"!=typeof __md_analytics&&__md_analytics()</script>
+  
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+    
+    
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+  
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+    
+    
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+    
+    <body dir="ltr" data-md-color-scheme="" data-md-color-primary="none" data-md-color-accent="none">
+  
+    
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+    <input class="md-toggle" data-md-toggle="drawer" type="checkbox" id="__drawer" autocomplete="off">
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+    <label class="md-overlay" for="__drawer"></label>
+    <div data-md-component="skip">
+      
+        
+        <a href="#2-set-configurations" class="md-skip">
+          Skip to content
+        </a>
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+  <nav class="md-header__inner md-grid" aria-label="Header">
+    <a href="../../.." title="QuakeFlow" class="md-header__button md-logo" aria-label="QuakeFlow" data-md-component="logo">
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+  
+  <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54Z"/></svg>
+
+    </a>
+    <label class="md-header__button md-icon" for="__drawer">
+      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M3 6h18v2H3V6m0 5h18v2H3v-2m0 5h18v2H3v-2Z"/></svg>
+    </label>
+    <div class="md-header__title" data-md-component="header-title">
+      <div class="md-header__ellipsis">
+        <div class="md-header__topic">
+          <span class="md-ellipsis">
+            QuakeFlow
+          </span>
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+        <div class="md-header__topic" data-md-component="header-topic">
+          <span class="md-ellipsis">
+            
+              Download waveform
+            
+          </span>
+        </div>
+      </div>
+    </div>
+    
+    
+    
+    
+      <div class="md-header__source">
+        <a href="https://github.com/ai4eps/QuakeFlow" title="Go to repository" class="md-source" data-md-component="source">
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+  <ul class="md-nav__list" data-md-scrollfix>
+    
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+      <a href="../../.." class="md-nav__link">
+        Overview
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+    <li class="md-nav__item">
+      <a href="../../workflow/" class="md-nav__link">
+        Pipeline
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+
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+      
+      
+
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../../data_format/" class="md-nav__link">
+        Standard Formats
+      </a>
+    </li>
+  
+
+    
+  </ul>
+</nav>
+                  </div>
+                </div>
+              </div>
+            
+            
+              
+              <div class="md-sidebar md-sidebar--secondary" data-md-component="sidebar" data-md-type="toc" >
+                <div class="md-sidebar__scrollwrap">
+                  <div class="md-sidebar__inner">
+                    
+
+<nav class="md-nav md-nav--secondary" aria-label="Table of contents">
+  
+  
+  
+  
+    <label class="md-nav__title" for="__toc">
+      <span class="md-nav__icon md-icon"></span>
+      Table of contents
+    </label>
+    <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
+      
+        <li class="md-nav__item">
+  <a href="#2-set-configurations" class="md-nav__link">
+    2. Set configurations
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#3-download-events-in-the-routine-catalog" class="md-nav__link">
+    3. Download events in the routine catalog
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#4-download-stations" class="md-nav__link">
+    4. Download stations
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#5-download-waveform-data" class="md-nav__link">
+    5. Download waveform data
+  </a>
+  
+</li>
+      
+    </ul>
+  
+</nav>
+                  </div>
+                </div>
+              </div>
+            
+          
+          <div class="md-content" data-md-component="content">
+            <article class="md-content__inner md-typeset">
+              
+                
+  <a href="https://github.com/ai4eps/QuakeFlow/edit/master/docs/kubeflow/waveforms/download_waveform.ipynb" title="Edit this page" class="md-content__button md-icon">
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
+  </a>
+
+
+
+  <h1>Download waveform</h1>
+
+<script>
+(function (global, factory) {
+    typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() :
+    typeof define === 'function' && define.amd ? define(factory) :
+    (global = global || self, global.ClipboardCopyElement = factory());
+  }(this, function () { 'use strict';
+
+    function createNode(text) {
+      const node = document.createElement('pre');
+      node.style.width = '1px';
+      node.style.height = '1px';
+      node.style.position = 'fixed';
+      node.style.top = '5px';
+      node.textContent = text;
+      return node;
+    }
+
+    function copyNode(node) {
+      if ('clipboard' in navigator) {
+        // eslint-disable-next-line flowtype/no-flow-fix-me-comments
+        // $FlowFixMe Clipboard is not defined in Flow yet.
+        return navigator.clipboard.writeText(node.textContent);
+      }
+
+      const selection = getSelection();
+
+      if (selection == null) {
+        return Promise.reject(new Error());
+      }
+
+      selection.removeAllRanges();
+      const range = document.createRange();
+      range.selectNodeContents(node);
+      selection.addRange(range);
+      document.execCommand('copy');
+      selection.removeAllRanges();
+      return Promise.resolve();
+    }
+    function copyText(text) {
+      if ('clipboard' in navigator) {
+        // eslint-disable-next-line flowtype/no-flow-fix-me-comments
+        // $FlowFixMe Clipboard is not defined in Flow yet.
+        return navigator.clipboard.writeText(text);
+      }
+
+      const body = document.body;
+
+      if (!body) {
+        return Promise.reject(new Error());
+      }
+
+      const node = createNode(text);
+      body.appendChild(node);
+      copyNode(node);
+      body.removeChild(node);
+      return Promise.resolve();
+    }
+
+    function copy(button) {
+      const id = button.getAttribute('for');
+      const text = button.getAttribute('value');
+
+      function trigger() {
+        button.dispatchEvent(new CustomEvent('clipboard-copy', {
+          bubbles: true
+        }));
+      }
+
+      if (text) {
+        copyText(text).then(trigger);
+      } else if (id) {
+        const root = 'getRootNode' in Element.prototype ? button.getRootNode() : button.ownerDocument;
+        if (!(root instanceof Document || 'ShadowRoot' in window && root instanceof ShadowRoot)) return;
+        const node = root.getElementById(id);
+        if (node) copyTarget(node).then(trigger);
+      }
+    }
+
+    function copyTarget(content) {
+      if (content instanceof HTMLInputElement || content instanceof HTMLTextAreaElement) {
+        return copyText(content.value);
+      } else if (content instanceof HTMLAnchorElement && content.hasAttribute('href')) {
+        return copyText(content.href);
+      } else {
+        return copyNode(content);
+      }
+    }
+
+    function clicked(event) {
+      const button = event.currentTarget;
+
+      if (button instanceof HTMLElement) {
+        copy(button);
+      }
+    }
+
+    function keydown(event) {
+      if (event.key === ' ' || event.key === 'Enter') {
+        const button = event.currentTarget;
+
+        if (button instanceof HTMLElement) {
+          event.preventDefault();
+          copy(button);
+        }
+      }
+    }
+
+    function focused(event) {
+      event.currentTarget.addEventListener('keydown', keydown);
+    }
+
+    function blurred(event) {
+      event.currentTarget.removeEventListener('keydown', keydown);
+    }
+
+    class ClipboardCopyElement extends HTMLElement {
+      constructor() {
+        super();
+        this.addEventListener('click', clicked);
+        this.addEventListener('focus', focused);
+        this.addEventListener('blur', blurred);
+      }
+
+      connectedCallback() {
+        if (!this.hasAttribute('tabindex')) {
+          this.setAttribute('tabindex', '0');
+        }
+
+        if (!this.hasAttribute('role')) {
+          this.setAttribute('role', 'button');
+        }
+      }
+
+      get value() {
+        return this.getAttribute('value') || '';
+      }
+
+      set value(text) {
+        this.setAttribute('value', text);
+      }
+
+    }
+
+    if (!window.customElements.get('clipboard-copy')) {
+      window.ClipboardCopyElement = ClipboardCopyElement;
+      window.customElements.define('clipboard-copy', ClipboardCopyElement);
+    }
+
+    return ClipboardCopyElement;
+
+  }));
+</script>
+<script>
+      document.addEventListener('clipboard-copy', function(event) {
+        const notice = event.target.querySelector('.notice')
+        notice.hidden = false
+        setTimeout(function() {
+          notice.hidden = true
+        }, 1000)
+      })
+</script>
+
+
+
+
+<style type="text/css">
+    
+    pre { line-height: 125%; }
+td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+.highlight-ipynb .hll { background-color: var(--jp-cell-editor-active-background) }
+.highlight-ipynb { background: var(--jp-cell-editor-background); color: var(--jp-mirror-editor-variable-color) }
+.highlight-ipynb .c { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment */
+.highlight-ipynb .err { color: var(--jp-mirror-editor-error-color) } /* Error */
+.highlight-ipynb .k { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword */
+.highlight-ipynb .o { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator */
+.highlight-ipynb .p { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation */
+.highlight-ipynb .ch { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Hashbang */
+.highlight-ipynb .cm { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Multiline */
+.highlight-ipynb .cp { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Preproc */
+.highlight-ipynb .cpf { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.PreprocFile */
+.highlight-ipynb .c1 { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Single */
+.highlight-ipynb .cs { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Special */
+.highlight-ipynb .kc { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Constant */
+.highlight-ipynb .kd { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Declaration */
+.highlight-ipynb .kn { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Namespace */
+.highlight-ipynb .kp { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Pseudo */
+.highlight-ipynb .kr { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Reserved */
+.highlight-ipynb .kt { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Type */
+.highlight-ipynb .m { color: var(--jp-mirror-editor-number-color) } /* Literal.Number */
+.highlight-ipynb .s { color: var(--jp-mirror-editor-string-color) } /* Literal.String */
+.highlight-ipynb .ow { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator.Word */
+.highlight-ipynb .pm { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation.Marker */
+.highlight-ipynb .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */
+.highlight-ipynb .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */
+.highlight-ipynb .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */
+.highlight-ipynb .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */
+.highlight-ipynb .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */
+.highlight-ipynb .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */
+.highlight-ipynb .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */
+.highlight-ipynb .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */
+.highlight-ipynb .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */
+.highlight-ipynb .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */
+.highlight-ipynb .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */
+.highlight-ipynb .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */
+.highlight-ipynb .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */
+.highlight-ipynb .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */
+.highlight-ipynb .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */
+.highlight-ipynb .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */
+.highlight-ipynb .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */
+.highlight-ipynb .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */
+.highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */
+.highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */
+  </style>
+
+
+
+
+<style type="text/css">
+/*
+This file is taken from the built JupyterLab theme.css
+Found on share/nbconvert/templates/lab/static
+Some changes have been made and marked with CHANGE
+*/
+
+.jupyter-wrapper {
+    /* Elevation
+   *
+   * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here:
+   *
+   * https://github.com/material-components/material-components-web
+   * https://material-components-web.appspot.com/elevation.html
+   */
+
+    --jp-shadow-base-lightness: 0;
+    --jp-shadow-umbra-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.2
+    );
+    --jp-shadow-penumbra-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.14
+    );
+    --jp-shadow-ambient-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.12
+    );
+    --jp-elevation-z0: none;
+    --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color),
+        0px 1px 1px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 3px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color),
+        0px 2px 2px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 5px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color),
+        0px 4px 5px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 10px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color),
+        0px 6px 10px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 18px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color),
+        0px 8px 10px 1px var(--jp-shadow-penumbra-color),
+        0px 3px 14px 2px var(--jp-shadow-ambient-color);
+    --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color),
+        0px 12px 17px 2px var(--jp-shadow-penumbra-color),
+        0px 5px 22px 4px var(--jp-shadow-ambient-color);
+    --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color),
+        0px 16px 24px 2px var(--jp-shadow-penumbra-color),
+        0px 6px 30px 5px var(--jp-shadow-ambient-color);
+    --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color),
+        0px 20px 31px 3px var(--jp-shadow-penumbra-color),
+        0px 8px 38px 7px var(--jp-shadow-ambient-color);
+    --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color),
+        0px 24px 38px 3px var(--jp-shadow-penumbra-color),
+        0px 9px 46px 8px var(--jp-shadow-ambient-color);
+
+    /* Borders
+   *
+   * The following variables, specify the visual styling of borders in JupyterLab.
+   */
+
+    --jp-border-width: 1px;
+    --jp-border-color0: var(--md-grey-400);
+    --jp-border-color1: var(--md-grey-400);
+    --jp-border-color2: var(--md-grey-300);
+    --jp-border-color3: var(--md-grey-200);
+    --jp-border-radius: 2px;
+
+    /* UI Fonts
+   *
+   * The UI font CSS variables are used for the typography all of the JupyterLab
+   * user interface elements that are not directly user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-ui-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+    --jp-ui-font-scale-factor: 1.2;
+    --jp-ui-font-size0: 0.83333em;
+    --jp-ui-font-size1: 13px; /* Base font size */
+    --jp-ui-font-size2: 1.2em;
+    --jp-ui-font-size3: 1.44em;
+
+    --jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+
+    /*
+   * Use these font colors against the corresponding main layout colors.
+   * In a light theme, these go from dark to light.
+   */
+
+    /* Defaults use Material Design specification */
+    --jp-ui-font-color0: rgba(0, 0, 0, 1);
+    --jp-ui-font-color1: rgba(0, 0, 0, 0.87);
+    --jp-ui-font-color2: rgba(0, 0, 0, 0.54);
+    --jp-ui-font-color3: rgba(0, 0, 0, 0.38);
+
+    /*
+   * Use these against the brand/accent/warn/error colors.
+   * These will typically go from light to darker, in both a dark and light theme.
+   */
+
+    --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1);
+    --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1);
+    --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7);
+    --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5);
+
+    /* Content Fonts
+   *
+   * Content font variables are used for typography of user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-content-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+    --jp-content-line-height: 1.6;
+    --jp-content-font-scale-factor: 1.2;
+    --jp-content-font-size0: 0.83333em;
+    --jp-content-font-size1: 14px; /* Base font size */
+    --jp-content-font-size2: 1.2em;
+    --jp-content-font-size3: 1.44em;
+    --jp-content-font-size4: 1.728em;
+    --jp-content-font-size5: 2.0736em;
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-content-presentation-font-size1: 17px;
+
+    --jp-content-heading-line-height: 1;
+    --jp-content-heading-margin-top: 1.2em;
+    --jp-content-heading-margin-bottom: 0.8em;
+    --jp-content-heading-font-weight: 500;
+
+    /* Defaults use Material Design specification */
+    --jp-content-font-color0: rgba(0, 0, 0, 1);
+    --jp-content-font-color1: rgba(0, 0, 0, 0.87);
+    --jp-content-font-color2: rgba(0, 0, 0, 0.54);
+    --jp-content-font-color3: rgba(0, 0, 0, 0.38);
+
+    --jp-content-link-color: var(--md-blue-700);
+
+    --jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+
+    /*
+   * Code Fonts
+   *
+   * Code font variables are used for typography of code and other monospaces content.
+   */
+
+    --jp-code-font-size: 13px;
+    --jp-code-line-height: 1.3077; /* 17px for 13px base */
+    --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */
+    --jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono",
+        monospace;
+    --jp-code-font-family: var(--jp-code-font-family-default);
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-code-presentation-font-size: 16px;
+
+    /* may need to tweak cursor width if you change font size */
+    --jp-code-cursor-width0: 1.4px;
+    --jp-code-cursor-width1: 2px;
+    --jp-code-cursor-width2: 4px;
+
+    /* Layout
+   *
+   * The following are the main layout colors use in JupyterLab. In a light
+   * theme these would go from light to dark.
+   */
+
+    --jp-layout-color0: white;
+    --jp-layout-color1: white;
+    --jp-layout-color2: var(--md-grey-200);
+    --jp-layout-color3: var(--md-grey-400);
+    --jp-layout-color4: var(--md-grey-600);
+
+    /* Inverse Layout
+   *
+   * The following are the inverse layout colors use in JupyterLab. In a light
+   * theme these would go from dark to light.
+   */
+
+    --jp-inverse-layout-color0: #111111;
+    --jp-inverse-layout-color1: var(--md-grey-900);
+    --jp-inverse-layout-color2: var(--md-grey-800);
+    --jp-inverse-layout-color3: var(--md-grey-700);
+    --jp-inverse-layout-color4: var(--md-grey-600);
+
+    /* Brand/accent */
+
+    --jp-brand-color0: var(--md-blue-900);
+    --jp-brand-color1: var(--md-blue-700);
+    --jp-brand-color2: var(--md-blue-300);
+    --jp-brand-color3: var(--md-blue-100);
+    --jp-brand-color4: var(--md-blue-50);
+
+    --jp-accent-color0: var(--md-green-900);
+    --jp-accent-color1: var(--md-green-700);
+    --jp-accent-color2: var(--md-green-300);
+    --jp-accent-color3: var(--md-green-100);
+
+    /* State colors (warn, error, success, info) */
+
+    --jp-warn-color0: var(--md-orange-900);
+    --jp-warn-color1: var(--md-orange-700);
+    --jp-warn-color2: var(--md-orange-300);
+    --jp-warn-color3: var(--md-orange-100);
+
+    --jp-error-color0: var(--md-red-900);
+    --jp-error-color1: var(--md-red-700);
+    --jp-error-color2: var(--md-red-300);
+    --jp-error-color3: var(--md-red-100);
+
+    --jp-success-color0: var(--md-green-900);
+    --jp-success-color1: var(--md-green-700);
+    --jp-success-color2: var(--md-green-300);
+    --jp-success-color3: var(--md-green-100);
+
+    --jp-info-color0: var(--md-cyan-900);
+    --jp-info-color1: var(--md-cyan-700);
+    --jp-info-color2: var(--md-cyan-300);
+    --jp-info-color3: var(--md-cyan-100);
+
+    /* Cell specific styles */
+
+    --jp-cell-padding: 5px;
+
+    --jp-cell-collapser-width: 8px;
+    --jp-cell-collapser-min-height: 20px;
+    --jp-cell-collapser-not-active-hover-opacity: 0.6;
+
+    --jp-cell-editor-background: var(--md-grey-100);
+    --jp-cell-editor-border-color: var(--md-grey-300);
+    --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-cell-editor-active-background: var(--jp-layout-color0);
+    --jp-cell-editor-active-border-color: var(--jp-brand-color1);
+
+    --jp-cell-prompt-width: 64px;
+    --jp-cell-prompt-font-family: var(--jp-code-font-family-default);
+    --jp-cell-prompt-letter-spacing: 0px;
+    --jp-cell-prompt-opacity: 1;
+    --jp-cell-prompt-not-active-opacity: 0.5;
+    --jp-cell-prompt-not-active-font-color: var(--md-grey-700);
+    /* A custom blend of MD grey and blue 600
+   * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */
+    --jp-cell-inprompt-font-color: #307fc1;
+    /* A custom blend of MD grey and orange 600
+   * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */
+    --jp-cell-outprompt-font-color: #bf5b3d;
+
+    /* Notebook specific styles */
+
+    --jp-notebook-padding: 10px;
+    --jp-notebook-select-background: var(--jp-layout-color1);
+    --jp-notebook-multiselected-color: var(--md-blue-50);
+
+    /* The scroll padding is calculated to fill enough space at the bottom of the
+  notebook to show one single-line cell (with appropriate padding) at the top
+  when the notebook is scrolled all the way to the bottom. We also subtract one
+  pixel so that no scrollbar appears if we have just one single-line cell in the
+  notebook. This padding is to enable a 'scroll past end' feature in a notebook.
+  */
+    --jp-notebook-scroll-padding: calc(
+        100% - var(--jp-code-font-size) * var(--jp-code-line-height) -
+            var(--jp-code-padding) - var(--jp-cell-padding) - 1px
+    );
+
+    /* Rendermime styles */
+
+    --jp-rendermime-error-background: #fdd;
+    --jp-rendermime-table-row-background: var(--md-grey-100);
+    --jp-rendermime-table-row-hover-background: var(--md-light-blue-50);
+
+    /* Dialog specific styles */
+
+    --jp-dialog-background: rgba(0, 0, 0, 0.25);
+
+    /* Console specific styles */
+
+    --jp-console-padding: 10px;
+
+    /* Toolbar specific styles */
+
+    --jp-toolbar-border-color: var(--jp-border-color1);
+    --jp-toolbar-micro-height: 8px;
+    --jp-toolbar-background: var(--jp-layout-color1);
+    --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24);
+    --jp-toolbar-header-margin: 4px 4px 0px 4px;
+    --jp-toolbar-active-background: var(--md-grey-300);
+
+    /* Statusbar specific styles */
+
+    --jp-statusbar-height: 24px;
+
+    /* Input field styles */
+
+    --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-input-active-background: var(--jp-layout-color1);
+    --jp-input-hover-background: var(--jp-layout-color1);
+    --jp-input-background: var(--md-grey-100);
+    --jp-input-border-color: var(--jp-border-color1);
+    --jp-input-active-border-color: var(--jp-brand-color1);
+    --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3);
+
+    /* General editor styles */
+
+    --jp-editor-selected-background: #d9d9d9;
+    --jp-editor-selected-focused-background: #d7d4f0;
+    --jp-editor-cursor-color: var(--jp-ui-font-color0);
+
+    /* Code mirror specific styles */
+
+    --jp-mirror-editor-keyword-color: #008000;
+    --jp-mirror-editor-atom-color: #88f;
+    --jp-mirror-editor-number-color: #080;
+    --jp-mirror-editor-def-color: #00f;
+    --jp-mirror-editor-variable-color: var(--md-grey-900);
+    --jp-mirror-editor-variable-2-color: #05a;
+    --jp-mirror-editor-variable-3-color: #085;
+    --jp-mirror-editor-punctuation-color: #05a;
+    --jp-mirror-editor-property-color: #05a;
+    --jp-mirror-editor-operator-color: #aa22ff;
+    --jp-mirror-editor-comment-color: #408080;
+    --jp-mirror-editor-string-color: #ba2121;
+    --jp-mirror-editor-string-2-color: #708;
+    --jp-mirror-editor-meta-color: #aa22ff;
+    --jp-mirror-editor-qualifier-color: #555;
+    --jp-mirror-editor-builtin-color: #008000;
+    --jp-mirror-editor-bracket-color: #997;
+    --jp-mirror-editor-tag-color: #170;
+    --jp-mirror-editor-attribute-color: #00c;
+    --jp-mirror-editor-header-color: blue;
+    --jp-mirror-editor-quote-color: #090;
+    --jp-mirror-editor-link-color: #00c;
+    --jp-mirror-editor-error-color: #f00;
+    --jp-mirror-editor-hr-color: #999;
+
+    /* Vega extension styles */
+
+    --jp-vega-background: white;
+
+    /* Sidebar-related styles */
+
+    --jp-sidebar-min-width: 250px;
+
+    /* Search-related styles */
+
+    --jp-search-toggle-off-opacity: 0.5;
+    --jp-search-toggle-hover-opacity: 0.8;
+    --jp-search-toggle-on-opacity: 1;
+    --jp-search-selected-match-background-color: rgb(245, 200, 0);
+    --jp-search-selected-match-color: black;
+    --jp-search-unselected-match-background-color: var(
+        --jp-inverse-layout-color0
+    );
+    --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);
+
+    /* Icon colors that work well with light or dark backgrounds */
+    --jp-icon-contrast-color0: var(--md-purple-600);
+    --jp-icon-contrast-color1: var(--md-green-600);
+    --jp-icon-contrast-color2: var(--md-pink-600);
+    --jp-icon-contrast-color3: var(--md-blue-600);
+}
+
+[data-md-color-scheme="slate"] .jupyter-wrapper {
+    /* Elevation
+   *
+   * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here:
+   *
+   * https://github.com/material-components/material-components-web
+   * https://material-components-web.appspot.com/elevation.html
+   */
+
+    /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout
+   * colors used in the theme as well. */
+    --jp-shadow-base-lightness: 32;
+    --jp-shadow-umbra-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.2
+    );
+    --jp-shadow-penumbra-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.14
+    );
+    --jp-shadow-ambient-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.12
+    );
+    --jp-elevation-z0: none;
+    --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color),
+        0px 1px 1px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 3px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color),
+        0px 2px 2px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 5px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color),
+        0px 4px 5px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 10px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color),
+        0px 6px 10px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 18px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color),
+        0px 8px 10px 1px var(--jp-shadow-penumbra-color),
+        0px 3px 14px 2px var(--jp-shadow-ambient-color);
+    --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color),
+        0px 12px 17px 2px var(--jp-shadow-penumbra-color),
+        0px 5px 22px 4px var(--jp-shadow-ambient-color);
+    --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color),
+        0px 16px 24px 2px var(--jp-shadow-penumbra-color),
+        0px 6px 30px 5px var(--jp-shadow-ambient-color);
+    --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color),
+        0px 20px 31px 3px var(--jp-shadow-penumbra-color),
+        0px 8px 38px 7px var(--jp-shadow-ambient-color);
+    --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color),
+        0px 24px 38px 3px var(--jp-shadow-penumbra-color),
+        0px 9px 46px 8px var(--jp-shadow-ambient-color);
+
+    /* Borders
+   *
+   * The following variables, specify the visual styling of borders in JupyterLab.
+   */
+
+    --jp-border-width: 1px;
+    --jp-border-color0: var(--md-grey-700);
+    --jp-border-color1: var(--md-grey-700);
+    --jp-border-color2: var(--md-grey-800);
+    --jp-border-color3: var(--md-grey-900);
+    --jp-border-radius: 2px;
+
+    /* UI Fonts
+   *
+   * The UI font CSS variables are used for the typography all of the JupyterLab
+   * user interface elements that are not directly user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-ui-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+    --jp-ui-font-scale-factor: 1.2;
+    --jp-ui-font-size0: 0.83333em;
+    --jp-ui-font-size1: 13px; /* Base font size */
+    --jp-ui-font-size2: 1.2em;
+    --jp-ui-font-size3: 1.44em;
+
+    --jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+
+    /*
+   * Use these font colors against the corresponding main layout colors.
+   * In a light theme, these go from dark to light.
+   */
+
+    /* Defaults use Material Design specification */
+    --jp-ui-font-color0: rgba(255, 255, 255, 1);
+    --jp-ui-font-color1: rgba(255, 255, 255, 0.87);
+    --jp-ui-font-color2: rgba(255, 255, 255, 0.54);
+    --jp-ui-font-color3: rgba(255, 255, 255, 0.38);
+
+    /*
+   * Use these against the brand/accent/warn/error colors.
+   * These will typically go from light to darker, in both a dark and light theme.
+   */
+
+    --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1);
+    --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8);
+    --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5);
+    --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3);
+
+    /* Content Fonts
+   *
+   * Content font variables are used for typography of user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-content-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+    --jp-content-line-height: 1.6;
+    --jp-content-font-scale-factor: 1.2;
+    --jp-content-font-size0: 0.83333em;
+    --jp-content-font-size1: 14px; /* Base font size */
+    --jp-content-font-size2: 1.2em;
+    --jp-content-font-size3: 1.44em;
+    --jp-content-font-size4: 1.728em;
+    --jp-content-font-size5: 2.0736em;
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-content-presentation-font-size1: 17px;
+
+    --jp-content-heading-line-height: 1;
+    --jp-content-heading-margin-top: 1.2em;
+    --jp-content-heading-margin-bottom: 0.8em;
+    --jp-content-heading-font-weight: 500;
+
+    /* Defaults use Material Design specification */
+    --jp-content-font-color0: rgba(255, 255, 255, 1);
+    --jp-content-font-color1: rgba(255, 255, 255, 1);
+    --jp-content-font-color2: rgba(255, 255, 255, 0.7);
+    --jp-content-font-color3: rgba(255, 255, 255, 0.5);
+
+    --jp-content-link-color: var(--md-blue-300);
+
+    --jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+
+    /*
+   * Code Fonts
+   *
+   * Code font variables are used for typography of code and other monospaces content.
+   */
+
+    --jp-code-font-size: 13px;
+    --jp-code-line-height: 1.3077; /* 17px for 13px base */
+    --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */
+    --jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono",
+        monospace;
+    --jp-code-font-family: var(--jp-code-font-family-default);
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-code-presentation-font-size: 16px;
+
+    /* may need to tweak cursor width if you change font size */
+    --jp-code-cursor-width0: 1.4px;
+    --jp-code-cursor-width1: 2px;
+    --jp-code-cursor-width2: 4px;
+
+    /* Layout
+   *
+   * The following are the main layout colors use in JupyterLab. In a light
+   * theme these would go from light to dark.
+   */
+
+    --jp-layout-color0: #111111;
+    --jp-layout-color1: var(--md-grey-900);
+    --jp-layout-color2: var(--md-grey-800);
+    --jp-layout-color3: var(--md-grey-700);
+    --jp-layout-color4: var(--md-grey-600);
+
+    /* Inverse Layout
+   *
+   * The following are the inverse layout colors use in JupyterLab. In a light
+   * theme these would go from dark to light.
+   */
+
+    --jp-inverse-layout-color0: white;
+    --jp-inverse-layout-color1: white;
+    --jp-inverse-layout-color2: var(--md-grey-200);
+    --jp-inverse-layout-color3: var(--md-grey-400);
+    --jp-inverse-layout-color4: var(--md-grey-600);
+
+    /* Brand/accent */
+
+    --jp-brand-color0: var(--md-blue-700);
+    --jp-brand-color1: var(--md-blue-500);
+    --jp-brand-color2: var(--md-blue-300);
+    --jp-brand-color3: var(--md-blue-100);
+    --jp-brand-color4: var(--md-blue-50);
+
+    --jp-accent-color0: var(--md-green-700);
+    --jp-accent-color1: var(--md-green-500);
+    --jp-accent-color2: var(--md-green-300);
+    --jp-accent-color3: var(--md-green-100);
+
+    /* State colors (warn, error, success, info) */
+
+    --jp-warn-color0: var(--md-orange-700);
+    --jp-warn-color1: var(--md-orange-500);
+    --jp-warn-color2: var(--md-orange-300);
+    --jp-warn-color3: var(--md-orange-100);
+
+    --jp-error-color0: var(--md-red-700);
+    --jp-error-color1: var(--md-red-500);
+    --jp-error-color2: var(--md-red-300);
+    --jp-error-color3: var(--md-red-100);
+
+    --jp-success-color0: var(--md-green-700);
+    --jp-success-color1: var(--md-green-500);
+    --jp-success-color2: var(--md-green-300);
+    --jp-success-color3: var(--md-green-100);
+
+    --jp-info-color0: var(--md-cyan-700);
+    --jp-info-color1: var(--md-cyan-500);
+    --jp-info-color2: var(--md-cyan-300);
+    --jp-info-color3: var(--md-cyan-100);
+
+    /* Cell specific styles */
+
+    --jp-cell-padding: 5px;
+
+    --jp-cell-collapser-width: 8px;
+    --jp-cell-collapser-min-height: 20px;
+    --jp-cell-collapser-not-active-hover-opacity: 0.6;
+
+    --jp-cell-editor-background: var(--jp-layout-color1);
+    --jp-cell-editor-border-color: var(--md-grey-700);
+    --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-cell-editor-active-background: var(--jp-layout-color0);
+    --jp-cell-editor-active-border-color: var(--jp-brand-color1);
+
+    --jp-cell-prompt-width: 64px;
+    --jp-cell-prompt-font-family: var(--jp-code-font-family-default);
+    --jp-cell-prompt-letter-spacing: 0px;
+    --jp-cell-prompt-opacity: 1;
+    --jp-cell-prompt-not-active-opacity: 1;
+    --jp-cell-prompt-not-active-font-color: var(--md-grey-300);
+
+    /* A custom blend of MD grey and blue 600
+   * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */
+    --jp-cell-inprompt-font-color: #307fc1;
+    /* A custom blend of MD grey and orange 600
+   * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */
+    --jp-cell-outprompt-font-color: #bf5b3d;
+
+    /* Notebook specific styles */
+
+    --jp-notebook-padding: 10px;
+    --jp-notebook-select-background: var(--jp-layout-color1);
+    --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24);
+
+    /* The scroll padding is calculated to fill enough space at the bottom of the
+  notebook to show one single-line cell (with appropriate padding) at the top
+  when the notebook is scrolled all the way to the bottom. We also subtract one
+  pixel so that no scrollbar appears if we have just one single-line cell in the
+  notebook. This padding is to enable a 'scroll past end' feature in a notebook.
+  */
+    --jp-notebook-scroll-padding: calc(
+        100% - var(--jp-code-font-size) * var(--jp-code-line-height) -
+            var(--jp-code-padding) - var(--jp-cell-padding) - 1px
+    );
+
+    /* Rendermime styles */
+
+    --jp-rendermime-error-background: rgba(244, 67, 54, 0.28);
+    --jp-rendermime-table-row-background: var(--md-grey-900);
+    --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2);
+
+    /* Dialog specific styles */
+
+    --jp-dialog-background: rgba(0, 0, 0, 0.6);
+
+    /* Console specific styles */
+
+    --jp-console-padding: 10px;
+
+    /* Toolbar specific styles */
+
+    --jp-toolbar-border-color: var(--jp-border-color2);
+    --jp-toolbar-micro-height: 8px;
+    --jp-toolbar-background: var(--jp-layout-color1);
+    --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8);
+    --jp-toolbar-header-margin: 4px 4px 0px 4px;
+    --jp-toolbar-active-background: var(--jp-layout-color0);
+
+    /* Statusbar specific styles */
+
+    --jp-statusbar-height: 24px;
+
+    /* Input field styles */
+
+    --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-input-active-background: var(--jp-layout-color0);
+    --jp-input-hover-background: var(--jp-layout-color2);
+    --jp-input-background: var(--md-grey-800);
+    --jp-input-border-color: var(--jp-border-color1);
+    --jp-input-active-border-color: var(--jp-brand-color1);
+    --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3);
+
+    /* General editor styles */
+
+    --jp-editor-selected-background: var(--jp-layout-color2);
+    --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24);
+    --jp-editor-cursor-color: var(--jp-ui-font-color0);
+
+    /* Code mirror specific styles */
+
+    --jp-mirror-editor-keyword-color: var(--md-green-500);
+    --jp-mirror-editor-atom-color: var(--md-blue-300);
+    --jp-mirror-editor-number-color: var(--md-green-400);
+    --jp-mirror-editor-def-color: var(--md-blue-600);
+    --jp-mirror-editor-variable-color: var(--md-grey-300);
+    --jp-mirror-editor-variable-2-color: var(--md-blue-400);
+    --jp-mirror-editor-variable-3-color: var(--md-green-600);
+    --jp-mirror-editor-punctuation-color: var(--md-blue-400);
+    --jp-mirror-editor-property-color: var(--md-blue-400);
+    --jp-mirror-editor-operator-color: #aa22ff;
+    --jp-mirror-editor-comment-color: #408080;
+    --jp-mirror-editor-string-color: #ff7070;
+    --jp-mirror-editor-string-2-color: var(--md-purple-300);
+    --jp-mirror-editor-meta-color: #aa22ff;
+    --jp-mirror-editor-qualifier-color: #555;
+    --jp-mirror-editor-builtin-color: var(--md-green-600);
+    --jp-mirror-editor-bracket-color: #997;
+    --jp-mirror-editor-tag-color: var(--md-green-700);
+    --jp-mirror-editor-attribute-color: var(--md-blue-700);
+    --jp-mirror-editor-header-color: var(--md-blue-500);
+    --jp-mirror-editor-quote-color: var(--md-green-300);
+    --jp-mirror-editor-link-color: var(--md-blue-700);
+    --jp-mirror-editor-error-color: #f00;
+    --jp-mirror-editor-hr-color: #999;
+
+    /* Vega extension styles */
+
+    --jp-vega-background: var(--md-grey-400);
+
+    /* Sidebar-related styles */
+
+    --jp-sidebar-min-width: 250px;
+
+    /* Search-related styles */
+
+    --jp-search-toggle-off-opacity: 0.6;
+    --jp-search-toggle-hover-opacity: 0.8;
+    --jp-search-toggle-on-opacity: 1;
+    --jp-search-selected-match-background-color: rgb(255, 225, 0);
+    --jp-search-selected-match-color: black;
+    --jp-search-unselected-match-background-color: var(
+        --jp-inverse-layout-color0
+    );
+    --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);
+
+    /* scrollbar related styles. Supports every browser except Edge. */
+
+    /* colors based on JetBrain's Darcula theme */
+
+    --jp-scrollbar-background-color: #3f4244;
+    --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */
+
+    --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */
+
+    /* hacks for setting the thumb shape. These do nothing in Firefox */
+
+    --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */
+    --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */
+
+    /* Icon colors that work well with light or dark backgrounds */
+    --jp-icon-contrast-color0: var(--md-purple-600);
+    --jp-icon-contrast-color1: var(--md-green-600);
+    --jp-icon-contrast-color2: var(--md-pink-600);
+    --jp-icon-contrast-color3: var(--md-blue-600);
+}
+
+:root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: #7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: 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+Copyright 2015-present Palantir Technologies, Inc. All rights reserved.
+Licensed under the Apache License, Version 2.0.
+
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+
+Copyright 2017-present Palantir Technologies, Inc. All rights reserved.
+Licensed under the Apache License, Version 2.0.
+
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+
+/*# sourceMappingURL=mkdocs-jupyter.css.map*/</style>
+
+
+<!-- Load mathjax -->
+    <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.7/latest.js?config=TeX-AMS_CHTML-full,Safe"> </script>
+    <!-- MathJax configuration -->
+    <script type="text/x-mathjax-config">
+    init_mathjax = function() {
+        if (window.MathJax) {
+        // MathJax loaded
+            MathJax.Hub.Config({
+                TeX: {
+                    equationNumbers: {
+                    autoNumber: "AMS",
+                    useLabelIds: true
+                    }
+                },
+                tex2jax: {
+                    inlineMath: [ ['$','$'], ["\\(","\\)"] ],
+                    displayMath: [ ['$$','$$'], ["\\[","\\]"] ],
+                    processEscapes: true,
+                    processEnvironments: true
+                },
+                displayAlign: 'center',
+                CommonHTML: {
+                    linebreaks: {
+                    automatic: true
+                    }
+                }
+            });
+
+            MathJax.Hub.Queue(["Typeset", MathJax.Hub]);
+        }
+    }
+    init_mathjax();
+    </script>
+    <!-- End of mathjax configuration --><div class="jupyter-wrapper"><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[49]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-1">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">warnings</span>
+
+<span class="kn">import</span> <span class="nn">kfp</span>
+<span class="kn">import</span> <span class="nn">kfp.dsl</span> <span class="k">as</span> <span class="nn">dsl</span>
+<span class="kn">import</span> <span class="nn">kfp.components</span> <span class="k">as</span> <span class="nn">comp</span>
+<span class="kn">from</span> <span class="nn">kfp.components</span> <span class="kn">import</span> <span class="n">InputPath</span><span class="p">,</span> <span class="n">OutputPath</span>
+
+<span class="n">warnings</span><span class="o">.</span><span class="n">filterwarnings</span><span class="p">(</span><span class="s2">&quot;ignore&quot;</span><span class="p">)</span>
+</pre></div>
+<div id="cell-1" class="clipboard-copy-txt">import warnings
+
+import kfp
+import kfp.dsl as dsl
+import kfp.components as comp
+from kfp.components import InputPath, OutputPath
+
+warnings.filterwarnings("ignore")
+</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="2-set-configurations">2. Set configurations<a class="anchor-link" href="#2-set-configurations">&#182;</a></h2>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[50]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-2">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
+<span class="kn">import</span> <span class="nn">matplotlib</span>
+
+<span class="c1"># matplotlib.use(&quot;agg&quot;)</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+
+<span class="n">region_name</span> <span class="o">=</span> <span class="s2">&quot;Hawaii_201801_202206&quot;</span>
+<span class="n">dir_name</span> <span class="o">=</span> <span class="n">region_name</span>
+<span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">dir_name</span><span class="p">):</span>
+    <span class="n">os</span><span class="o">.</span><span class="n">mkdir</span><span class="p">(</span><span class="n">dir_name</span><span class="p">)</span>
+<span class="n">root_dir</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">dir_name</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span>
+
+<span class="n">run_local</span> <span class="o">=</span> <span class="kc">False</span>
+</pre></div>
+<div id="cell-2" class="clipboard-copy-txt">import os
+import matplotlib
+
+# matplotlib.use("agg")
+import matplotlib.pyplot as plt
+
+region_name = "Hawaii_201801_202206"
+dir_name = region_name
+if not os.path.exists(dir_name):
+    os.mkdir(dir_name)
+root_dir = lambda x: os.path.join(dir_name, x)
+
+run_local = False
+</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[51]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-3">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">def</span> <span class="nf">set_config</span><span class="p">(</span>
+    <span class="n">index_json</span><span class="p">:</span> <span class="n">OutputPath</span><span class="p">(</span><span class="s2">&quot;json&quot;</span><span class="p">),</span>
+    <span class="n">config_json</span><span class="p">:</span> <span class="n">OutputPath</span><span class="p">(</span><span class="s2">&quot;json&quot;</span><span class="p">),</span>
+    <span class="n">datetime_json</span><span class="p">:</span> <span class="n">OutputPath</span><span class="p">(</span><span class="s2">&quot;json&quot;</span><span class="p">),</span>
+    <span class="n">num_parallel</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">:</span>
+
+    <span class="kn">import</span> <span class="nn">obspy</span>
+    <span class="kn">import</span> <span class="nn">os</span>
+    <span class="kn">import</span> <span class="nn">pickle</span>
+    <span class="kn">import</span> <span class="nn">datetime</span>
+    <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+    <span class="kn">import</span> <span class="nn">json</span>
+
+    <span class="n">pi</span> <span class="o">=</span> <span class="mf">3.1415926</span>
+    <span class="n">degree2km</span> <span class="o">=</span> <span class="n">pi</span> <span class="o">*</span> <span class="mi">6371</span> <span class="o">/</span> <span class="mi">180</span>
+
+    <span class="n">region_name</span> <span class="o">=</span> <span class="s2">&quot;Hawaii&quot;</span>
+    <span class="n">center</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mf">155.32</span><span class="p">,</span> <span class="mf">19.39</span><span class="p">)</span>
+    <span class="n">horizontal_degree</span> <span class="o">=</span> <span class="mf">2.0</span>
+    <span class="n">vertical_degree</span> <span class="o">=</span> <span class="mf">2.0</span>
+    <span class="c1"># starttime = obspy.UTCDateTime(&quot;2018-01-01T00&quot;)</span>
+    <span class="n">starttime</span> <span class="o">=</span> <span class="n">obspy</span><span class="o">.</span><span class="n">UTCDateTime</span><span class="p">(</span><span class="s2">&quot;2021-01-01T00&quot;</span><span class="p">)</span>
+    <span class="n">endtime</span> <span class="o">=</span> <span class="n">obspy</span><span class="o">.</span><span class="n">UTCDateTime</span><span class="p">(</span><span class="s2">&quot;2022-05-18T00&quot;</span><span class="p">)</span>
+    <span class="c1"># endtime = obspy.UTCDateTime(&quot;2018-01-03T00&quot;)</span>
+    <span class="n">client</span> <span class="o">=</span> <span class="s2">&quot;IRIS&quot;</span>
+    <span class="n">network_list</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;HV&quot;</span><span class="p">,</span> <span class="s2">&quot;PT&quot;</span><span class="p">]</span>
+    <span class="n">channel_list</span> <span class="o">=</span> <span class="s2">&quot;HH*,BH*,EH*,HN*&quot;</span>
+
+    <span class="c1">####### save config ########</span>
+    <span class="n">config</span> <span class="o">=</span> <span class="p">{}</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;region&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">region_name</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;center&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">center</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;xlim_degree&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span>
+        <span class="n">center</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">horizontal_degree</span> <span class="o">/</span> <span class="mi">2</span><span class="p">,</span>
+        <span class="n">center</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">horizontal_degree</span> <span class="o">/</span> <span class="mi">2</span><span class="p">,</span>
+    <span class="p">]</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;ylim_degree&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span>
+        <span class="n">center</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">vertical_degree</span> <span class="o">/</span> <span class="mi">2</span><span class="p">,</span>
+        <span class="n">center</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">vertical_degree</span> <span class="o">/</span> <span class="mi">2</span><span class="p">,</span>
+    <span class="p">]</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;degree2km&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">degree2km</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;starttime&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">starttime</span><span class="o">.</span><span class="n">datetime</span><span class="o">.</span><span class="n">isoformat</span><span class="p">()</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;endtime&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">endtime</span><span class="o">.</span><span class="n">datetime</span><span class="o">.</span><span class="n">isoformat</span><span class="p">()</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;networks&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">network_list</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;channels&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">channel_list</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;client&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">client</span>
+
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">config_json</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">json</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="n">config</span><span class="p">,</span> <span class="n">fp</span><span class="p">)</span>
+
+    <span class="n">one_day</span> <span class="o">=</span> <span class="n">datetime</span><span class="o">.</span><span class="n">timedelta</span><span class="p">(</span><span class="n">days</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+    <span class="c1"># one_hour = datetime.timedelta(hours=1)</span>
+    <span class="n">starttimes</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">tmp_start</span> <span class="o">=</span> <span class="n">starttime</span>
+    <span class="k">while</span> <span class="n">tmp_start</span> <span class="o">&lt;</span> <span class="n">endtime</span><span class="p">:</span>
+        <span class="n">starttimes</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp_start</span><span class="o">.</span><span class="n">datetime</span><span class="o">.</span><span class="n">isoformat</span><span class="p">())</span>
+        <span class="c1"># tmp_start += one_hour</span>
+        <span class="n">tmp_start</span> <span class="o">+=</span> <span class="n">one_day</span>
+
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">datetime_json</span><span class="p">,</span> <span class="s2">&quot;w&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="c1"># json.dump({&quot;starttimes&quot;: starttimes, &quot;interval&quot;: one_hour.total_seconds()}, fp)</span>
+        <span class="n">json</span><span class="o">.</span><span class="n">dump</span><span class="p">({</span><span class="s2">&quot;starttimes&quot;</span><span class="p">:</span> <span class="n">starttimes</span><span class="p">,</span> <span class="s2">&quot;interval&quot;</span><span class="p">:</span> <span class="n">one_day</span><span class="o">.</span><span class="n">total_seconds</span><span class="p">()},</span> <span class="n">fp</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">num_parallel</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="c1"># num_parallel = min(60, len(starttimes)//6)</span>
+        <span class="n">num_parallel</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="mi">30</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">starttimes</span><span class="p">))</span>
+
+    <span class="n">idx</span> <span class="o">=</span> <span class="p">[[]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_parallel</span><span class="p">)]</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">starttimes</span><span class="p">)):</span>
+        <span class="n">idx</span><span class="p">[</span><span class="n">i</span> <span class="o">-</span> <span class="n">i</span> <span class="o">//</span> <span class="n">num_parallel</span> <span class="o">*</span> <span class="n">num_parallel</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
+
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">index_json</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">json</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="n">idx</span><span class="p">,</span> <span class="n">fp</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">num_parallel</span><span class="p">))</span>
+</pre></div>
+<div id="cell-3" class="clipboard-copy-txt">def set_config(
+    index_json: OutputPath("json"),
+    config_json: OutputPath("json"),
+    datetime_json: OutputPath("json"),
+    num_parallel: int = 1,
+) -> list:
+
+    import obspy
+    import os
+    import pickle
+    import datetime
+    import numpy as np
+    import json
+
+    pi = 3.1415926
+    degree2km = pi * 6371 / 180
+
+    region_name = "Hawaii"
+    center = (-155.32, 19.39)
+    horizontal_degree = 2.0
+    vertical_degree = 2.0
+    # starttime = obspy.UTCDateTime("2018-01-01T00")
+    starttime = obspy.UTCDateTime("2021-01-01T00")
+    endtime = obspy.UTCDateTime("2022-05-18T00")
+    # endtime = obspy.UTCDateTime("2018-01-03T00")
+    client = "IRIS"
+    network_list = ["HV", "PT"]
+    channel_list = "HH*,BH*,EH*,HN*"
+
+    ####### save config ########
+    config = {}
+    config["region"] = region_name
+    config["center"] = center
+    config["xlim_degree"] = [
+        center[0] - horizontal_degree / 2,
+        center[0] + horizontal_degree / 2,
+    ]
+    config["ylim_degree"] = [
+        center[1] - vertical_degree / 2,
+        center[1] + vertical_degree / 2,
+    ]
+    config["degree2km"] = degree2km
+    config["starttime"] = starttime.datetime.isoformat()
+    config["endtime"] = endtime.datetime.isoformat()
+    config["networks"] = network_list
+    config["channels"] = channel_list
+    config["client"] = client
+
+    with open(config_json, 'w') as fp:
+        json.dump(config, fp)
+
+    one_day = datetime.timedelta(days=1)
+    # one_hour = datetime.timedelta(hours=1)
+    starttimes = []
+    tmp_start = starttime
+    while tmp_start < endtime:
+        starttimes.append(tmp_start.datetime.isoformat())
+        # tmp_start += one_hour
+        tmp_start += one_day
+
+    with open(datetime_json, "w") as fp:
+        # json.dump({"starttimes": starttimes, "interval": one_hour.total_seconds()}, fp)
+        json.dump({"starttimes": starttimes, "interval": one_day.total_seconds()}, fp)
+
+    if num_parallel == 0:
+        # num_parallel = min(60, len(starttimes)//6)
+        num_parallel = min(30, len(starttimes))
+
+    idx = [[] for i in range(num_parallel)]
+    for i in range(len(starttimes)):
+        idx[i - i // num_parallel * num_parallel].append(i)
+
+    with open(index_json, 'w') as fp:
+        json.dump(idx, fp)
+
+    return list(range(num_parallel))
+</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[52]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-4">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">if</span> <span class="n">run_local</span><span class="p">:</span>
+    <span class="n">idx</span> <span class="o">=</span> <span class="n">set_config</span><span class="p">(</span><span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;index.json&quot;</span><span class="p">),</span> <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;config.json&quot;</span><span class="p">),</span> <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;datetimes.json&quot;</span><span class="p">),</span> <span class="n">num_parallel</span><span class="o">=</span><span class="mi">1</span><span class="p">,)</span>
+</pre></div>
+<div id="cell-4" class="clipboard-copy-txt">if run_local:
+    idx = set_config(root_dir("index.json"), root_dir("config.json"), root_dir("datetimes.json"), num_parallel=1,)</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[53]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-5">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">config_op</span> <span class="o">=</span> <span class="n">comp</span><span class="o">.</span><span class="n">func_to_container_op</span><span class="p">(</span>
+    <span class="n">set_config</span><span class="p">,</span>
+    <span class="c1"># base_image=&#39;zhuwq0/quakeflow-env:latest&#39;,</span>
+    <span class="n">base_image</span><span class="o">=</span><span class="s1">&#39;python:3.8&#39;</span><span class="p">,</span>
+    <span class="n">packages_to_install</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;numpy&quot;</span><span class="p">,</span> <span class="s2">&quot;obspy&quot;</span><span class="p">,],</span>
+<span class="p">)</span>
+</pre></div>
+<div id="cell-5" class="clipboard-copy-txt">config_op = comp.func_to_container_op(
+    set_config,
+    # base_image='zhuwq0/quakeflow-env:latest',
+    base_image='python:3.8',
+    packages_to_install=["numpy", "obspy",],
+)
+</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="3-download-events-in-the-routine-catalog">3. Download events in the routine catalog<a class="anchor-link" href="#3-download-events-in-the-routine-catalog">&#182;</a></h2><p>This catalog is not used by QuakeFolow. It is only used for comparing detection results.</p>
+
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[54]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-6">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">def</span> <span class="nf">download_events</span><span class="p">(</span><span class="n">config_json</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="s2">&quot;json&quot;</span><span class="p">),</span> <span class="n">event_csv</span><span class="p">:</span> <span class="n">OutputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">)):</span>
+
+    <span class="kn">import</span> <span class="nn">pickle</span><span class="o">,</span> <span class="nn">os</span>
+    <span class="kn">import</span> <span class="nn">obspy</span>
+    <span class="kn">from</span> <span class="nn">obspy.clients.fdsn</span> <span class="kn">import</span> <span class="n">Client</span>
+    <span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">defaultdict</span>
+    <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+    <span class="kn">import</span> <span class="nn">json</span>
+    <span class="kn">import</span> <span class="nn">matplotlib</span>
+
+    <span class="c1">#     matplotlib.use(&quot;agg&quot;)</span>
+    <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">config_json</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">config</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">fp</span><span class="p">)</span>
+
+    <span class="c1">####### IRIS catalog ########</span>
+    <span class="k">try</span><span class="p">:</span>
+        <span class="n">events</span> <span class="o">=</span> <span class="n">Client</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;client&quot;</span><span class="p">])</span><span class="o">.</span><span class="n">get_events</span><span class="p">(</span>
+            <span class="n">starttime</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;starttime&quot;</span><span class="p">],</span>
+            <span class="n">endtime</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;endtime&quot;</span><span class="p">],</span>
+            <span class="n">minlongitude</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;xlim_degree&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span>
+            <span class="n">maxlongitude</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;xlim_degree&quot;</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span>
+            <span class="n">minlatitude</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;ylim_degree&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span>
+            <span class="n">maxlatitude</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;ylim_degree&quot;</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span>
+            <span class="c1"># filename=&#39;events.xml&#39;,</span>
+        <span class="p">)</span>
+    <span class="k">except</span><span class="p">:</span>
+        <span class="n">events</span> <span class="o">=</span> <span class="n">Client</span><span class="p">(</span><span class="s2">&quot;iris&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">get_events</span><span class="p">(</span>
+            <span class="n">starttime</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;starttime&quot;</span><span class="p">],</span>
+            <span class="n">endtime</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;endtime&quot;</span><span class="p">],</span>
+            <span class="n">minlongitude</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;xlim_degree&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span>
+            <span class="n">maxlongitude</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;xlim_degree&quot;</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span>
+            <span class="n">minlatitude</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;ylim_degree&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span>
+            <span class="n">maxlatitude</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;ylim_degree&quot;</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span>
+            <span class="c1"># filename=&#39;events.xml&#39;,</span>
+        <span class="p">)</span>
+
+    <span class="c1">#     events = obspy.read_events(&#39;events.xml&#39;)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Number of events: </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">events</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+    <span class="c1">#     events.plot(&#39;local&#39;, outfile=&quot;events.png&quot;)</span>
+    <span class="c1">#     events.plot(&#39;local&#39;)</span>
+
+    <span class="c1">####### Save catalog ########</span>
+    <span class="n">catalog</span> <span class="o">=</span> <span class="n">defaultdict</span><span class="p">(</span><span class="nb">list</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">event</span> <span class="ow">in</span> <span class="n">events</span><span class="p">:</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">event</span><span class="o">.</span><span class="n">magnitudes</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="n">catalog</span><span class="p">[</span><span class="s2">&quot;time&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">event</span><span class="o">.</span><span class="n">origins</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">time</span><span class="o">.</span><span class="n">datetime</span><span class="p">)</span>
+            <span class="n">catalog</span><span class="p">[</span><span class="s2">&quot;magnitude&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">event</span><span class="o">.</span><span class="n">magnitudes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">mag</span><span class="p">)</span>
+            <span class="n">catalog</span><span class="p">[</span><span class="s2">&quot;longitude&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">event</span><span class="o">.</span><span class="n">origins</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">longitude</span><span class="p">)</span>
+            <span class="n">catalog</span><span class="p">[</span><span class="s2">&quot;latitude&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">event</span><span class="o">.</span><span class="n">origins</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">latitude</span><span class="p">)</span>
+            <span class="n">catalog</span><span class="p">[</span><span class="s2">&quot;depth(m)&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">event</span><span class="o">.</span><span class="n">origins</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">depth</span><span class="p">)</span>
+    <span class="n">catalog</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">from_dict</span><span class="p">(</span><span class="n">catalog</span><span class="p">)</span><span class="o">.</span><span class="n">sort_values</span><span class="p">([</span><span class="s2">&quot;time&quot;</span><span class="p">])</span>
+    <span class="n">catalog</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span>
+        <span class="n">event_csv</span><span class="p">,</span>
+        <span class="n">sep</span><span class="o">=</span><span class="s2">&quot;</span><span class="se">\t</span><span class="s2">&quot;</span><span class="p">,</span>
+        <span class="n">index</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+        <span class="n">float_format</span><span class="o">=</span><span class="s2">&quot;</span><span class="si">%.3f</span><span class="s2">&quot;</span><span class="p">,</span>
+        <span class="n">date_format</span><span class="o">=</span><span class="s1">&#39;%Y-%m-</span><span class="si">%d</span><span class="s1">T%H:%M:%S.</span><span class="si">%f</span><span class="s1">&#39;</span><span class="p">,</span>
+        <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;time&quot;</span><span class="p">,</span> <span class="s2">&quot;magnitude&quot;</span><span class="p">,</span> <span class="s2">&quot;longitude&quot;</span><span class="p">,</span> <span class="s2">&quot;latitude&quot;</span><span class="p">,</span> <span class="s2">&quot;depth(m)&quot;</span><span class="p">],</span>
+    <span class="p">)</span>
+
+    <span class="c1">####### Plot catalog ########</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">catalog</span><span class="p">[</span><span class="s2">&quot;longitude&quot;</span><span class="p">],</span> <span class="n">catalog</span><span class="p">[</span><span class="s2">&quot;latitude&quot;</span><span class="p">],</span> <span class="s1">&#39;.&#39;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">&quot;Longitude&quot;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">&quot;Latitude&quot;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s2">&quot;scaled&quot;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;xlim_degree&quot;</span><span class="p">])</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;ylim_degree&quot;</span><span class="p">])</span>
+    <span class="c1">#     plt.savefig(os.path.join(data_path, &quot;events_loc.png&quot;))</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+
+    <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">plot_date</span><span class="p">(</span><span class="n">catalog</span><span class="p">[</span><span class="s2">&quot;time&quot;</span><span class="p">],</span> <span class="n">catalog</span><span class="p">[</span><span class="s2">&quot;magnitude&quot;</span><span class="p">],</span> <span class="s1">&#39;.&#39;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">gcf</span><span class="p">()</span><span class="o">.</span><span class="n">autofmt_xdate</span><span class="p">()</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">&quot;Magnitude&quot;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Number of events: </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">events</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="s2">&quot;events_mag_time.png&quot;</span><span class="p">))</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+<div id="cell-6" class="clipboard-copy-txt">def download_events(config_json: InputPath("json"), event_csv: OutputPath(str)):
+
+    import pickle, os
+    import obspy
+    from obspy.clients.fdsn import Client
+    from collections import defaultdict
+    import pandas as pd
+    import json
+    import matplotlib
+
+    #     matplotlib.use("agg")
+    import matplotlib.pyplot as plt
+
+    with open(config_json, "r") as fp:
+        config = json.load(fp)
+
+    ####### IRIS catalog ########
+    try:
+        events = Client(config["client"]).get_events(
+            starttime=config["starttime"],
+            endtime=config["endtime"],
+            minlongitude=config["xlim_degree"][0],
+            maxlongitude=config["xlim_degree"][1],
+            minlatitude=config["ylim_degree"][0],
+            maxlatitude=config["ylim_degree"][1],
+            # filename='events.xml',
+        )
+    except:
+        events = Client("iris").get_events(
+            starttime=config["starttime"],
+            endtime=config["endtime"],
+            minlongitude=config["xlim_degree"][0],
+            maxlongitude=config["xlim_degree"][1],
+            minlatitude=config["ylim_degree"][0],
+            maxlatitude=config["ylim_degree"][1],
+            # filename='events.xml',
+        )
+
+    #     events = obspy.read_events('events.xml')
+    print(f"Number of events: {len(events)}")
+    #     events.plot('local', outfile="events.png")
+    #     events.plot('local')
+
+    ####### Save catalog ########
+    catalog = defaultdict(list)
+    for event in events:
+        if len(event.magnitudes) > 0:
+            catalog["time"].append(event.origins[0].time.datetime)
+            catalog["magnitude"].append(event.magnitudes[0].mag)
+            catalog["longitude"].append(event.origins[0].longitude)
+            catalog["latitude"].append(event.origins[0].latitude)
+            catalog["depth(m)"].append(event.origins[0].depth)
+    catalog = pd.DataFrame.from_dict(catalog).sort_values(["time"])
+    catalog.to_csv(
+        event_csv,
+        sep="\t",
+        index=False,
+        float_format="%.3f",
+        date_format='%Y-%m-%dT%H:%M:%S.%f',
+        columns=["time", "magnitude", "longitude", "latitude", "depth(m)"],
+    )
+
+    ####### Plot catalog ########
+    plt.figure()
+    plt.plot(catalog["longitude"], catalog["latitude"], '.', markersize=1)
+    plt.xlabel("Longitude")
+    plt.ylabel("Latitude")
+    plt.axis("scaled")
+    plt.xlim(config["xlim_degree"])
+    plt.ylim(config["ylim_degree"])
+    #     plt.savefig(os.path.join(data_path, "events_loc.png"))
+    plt.show()
+
+    plt.figure()
+    plt.plot_date(catalog["time"], catalog["magnitude"], '.', markersize=1)
+    plt.gcf().autofmt_xdate()
+    plt.ylabel("Magnitude")
+    plt.title(f"Number of events: {len(events)}")
+    plt.savefig(os.path.join("events_mag_time.png"))
+    plt.show()
+</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[55]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
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+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">if</span> <span class="n">run_local</span><span class="p">:</span>
+    <span class="n">download_events</span><span class="p">(</span><span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;config.json&quot;</span><span class="p">),</span> <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;events.csv&quot;</span><span class="p">))</span>
+</pre></div>
+<div id="cell-7" class="clipboard-copy-txt">if run_local:
+    download_events(root_dir("config.json"), root_dir("events.csv"))
+</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[56]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-8">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">download_events_op</span> <span class="o">=</span> <span class="n">comp</span><span class="o">.</span><span class="n">func_to_container_op</span><span class="p">(</span>
+    <span class="n">download_events</span><span class="p">,</span>
+    <span class="c1"># base_image=&#39;zhuwq0/quakeflow-env:latest&#39;,</span>
+    <span class="n">base_image</span><span class="o">=</span><span class="s1">&#39;python:3.8&#39;</span><span class="p">,</span>
+    <span class="n">packages_to_install</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;obspy&quot;</span><span class="p">,</span> <span class="s2">&quot;pandas&quot;</span><span class="p">,</span> <span class="s2">&quot;matplotlib&quot;</span><span class="p">,],</span>
+<span class="p">)</span>
+</pre></div>
+<div id="cell-8" class="clipboard-copy-txt">download_events_op = comp.func_to_container_op(
+    download_events,
+    # base_image='zhuwq0/quakeflow-env:latest',
+    base_image='python:3.8',
+    packages_to_install=["obspy", "pandas", "matplotlib",],
+)
+</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="4-download-stations">4. Download stations<a class="anchor-link" href="#4-download-stations">&#182;</a></h2>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[57]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-9">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">def</span> <span class="nf">download_stations</span><span class="p">(</span>
+    <span class="n">config_json</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="s2">&quot;json&quot;</span><span class="p">),</span> <span class="n">station_csv</span><span class="p">:</span> <span class="n">OutputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span> <span class="n">station_pkl</span><span class="p">:</span> <span class="n">OutputPath</span><span class="p">(</span><span class="s2">&quot;pickle&quot;</span><span class="p">),</span>
+<span class="p">):</span>
+
+    <span class="kn">import</span> <span class="nn">pickle</span><span class="o">,</span> <span class="nn">os</span>
+    <span class="kn">import</span> <span class="nn">obspy</span>
+    <span class="kn">from</span> <span class="nn">obspy.clients.fdsn</span> <span class="kn">import</span> <span class="n">Client</span>
+    <span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">defaultdict</span>
+    <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+    <span class="kn">import</span> <span class="nn">json</span>
+    <span class="kn">import</span> <span class="nn">matplotlib</span>
+
+    <span class="c1">#     matplotlib.use(&quot;agg&quot;)</span>
+    <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">config_json</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">config</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">fp</span><span class="p">)</span>
+
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Network:&quot;</span><span class="p">,</span> <span class="s2">&quot;,&quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;networks&quot;</span><span class="p">]))</span>
+    <span class="c1">####### Download stations ########</span>
+    <span class="n">stations</span> <span class="o">=</span> <span class="n">Client</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;client&quot;</span><span class="p">])</span><span class="o">.</span><span class="n">get_stations</span><span class="p">(</span>
+        <span class="n">network</span><span class="o">=</span><span class="s2">&quot;,&quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;networks&quot;</span><span class="p">]),</span>
+        <span class="n">station</span><span class="o">=</span><span class="s2">&quot;*&quot;</span><span class="p">,</span>
+        <span class="n">starttime</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;starttime&quot;</span><span class="p">],</span>
+        <span class="n">endtime</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;endtime&quot;</span><span class="p">],</span>
+        <span class="n">minlongitude</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;xlim_degree&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span>
+        <span class="n">maxlongitude</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;xlim_degree&quot;</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span>
+        <span class="n">minlatitude</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;ylim_degree&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span>
+        <span class="n">maxlatitude</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;ylim_degree&quot;</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span>
+        <span class="n">channel</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;channels&quot;</span><span class="p">],</span>
+        <span class="n">level</span><span class="o">=</span><span class="s2">&quot;response&quot;</span><span class="p">,</span>
+    <span class="p">)</span>  <span class="c1"># ,</span>
+    <span class="c1">#                                            filename=&quot;stations.xml&quot;)</span>
+
+    <span class="c1">#     stations = obspy.read_inventory(&quot;stations.xml&quot;)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Number of stations: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">sum</span><span class="p">([</span><span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">stations</span><span class="p">])))</span>
+    <span class="c1"># stations.plot(&#39;local&#39;, outfile=&quot;stations.png&quot;)</span>
+    <span class="c1">#     stations.plot(&#39;local&#39;)</span>
+
+    <span class="c1">####### Save stations ########</span>
+    <span class="n">station_locs</span> <span class="o">=</span> <span class="n">defaultdict</span><span class="p">(</span><span class="nb">dict</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">network</span> <span class="ow">in</span> <span class="n">stations</span><span class="p">:</span>
+        <span class="k">for</span> <span class="n">station</span> <span class="ow">in</span> <span class="n">network</span><span class="p">:</span>
+            <span class="k">for</span> <span class="n">chn</span> <span class="ow">in</span> <span class="n">station</span><span class="p">:</span>
+                <span class="n">sid</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">network</span><span class="o">.</span><span class="n">code</span><span class="si">}</span><span class="s2">.</span><span class="si">{</span><span class="n">station</span><span class="o">.</span><span class="n">code</span><span class="si">}</span><span class="s2">.</span><span class="si">{</span><span class="n">chn</span><span class="o">.</span><span class="n">location_code</span><span class="si">}</span><span class="s2">.</span><span class="si">{</span><span class="n">chn</span><span class="o">.</span><span class="n">code</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="si">}</span><span class="s2">&quot;</span>
+                <span class="k">if</span> <span class="n">sid</span> <span class="ow">in</span> <span class="n">station_locs</span><span class="p">:</span>
+                    <span class="n">station_locs</span><span class="p">[</span><span class="n">sid</span><span class="p">][</span><span class="s2">&quot;component&quot;</span><span class="p">]</span> <span class="o">+=</span> <span class="sa">f</span><span class="s2">&quot;,</span><span class="si">{</span><span class="n">chn</span><span class="o">.</span><span class="n">code</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="si">}</span><span class="s2">&quot;</span>
+                    <span class="n">station_locs</span><span class="p">[</span><span class="n">sid</span><span class="p">][</span><span class="s2">&quot;response&quot;</span><span class="p">]</span> <span class="o">+=</span> <span class="sa">f</span><span class="s2">&quot;,</span><span class="si">{</span><span class="n">chn</span><span class="o">.</span><span class="n">response</span><span class="o">.</span><span class="n">instrument_sensitivity</span><span class="o">.</span><span class="n">value</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">&quot;</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">component</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">chn</span><span class="o">.</span><span class="n">code</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="si">}</span><span class="s2">&quot;</span>
+                    <span class="n">response</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">chn</span><span class="o">.</span><span class="n">response</span><span class="o">.</span><span class="n">instrument_sensitivity</span><span class="o">.</span><span class="n">value</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">&quot;</span>
+                    <span class="n">dtype</span> <span class="o">=</span> <span class="n">chn</span><span class="o">.</span><span class="n">response</span><span class="o">.</span><span class="n">instrument_sensitivity</span><span class="o">.</span><span class="n">input_units</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span>
+                    <span class="n">tmp_dict</span> <span class="o">=</span> <span class="p">{}</span>
+                    <span class="p">(</span><span class="n">tmp_dict</span><span class="p">[</span><span class="s2">&quot;longitude&quot;</span><span class="p">],</span> <span class="n">tmp_dict</span><span class="p">[</span><span class="s2">&quot;latitude&quot;</span><span class="p">],</span> <span class="n">tmp_dict</span><span class="p">[</span><span class="s2">&quot;elevation(m)&quot;</span><span class="p">],)</span> <span class="o">=</span> <span class="p">(</span>
+                        <span class="n">chn</span><span class="o">.</span><span class="n">longitude</span><span class="p">,</span>
+                        <span class="n">chn</span><span class="o">.</span><span class="n">latitude</span><span class="p">,</span>
+                        <span class="n">chn</span><span class="o">.</span><span class="n">elevation</span><span class="p">,</span>
+                    <span class="p">)</span>
+                    <span class="n">tmp_dict</span><span class="p">[</span><span class="s2">&quot;component&quot;</span><span class="p">],</span> <span class="n">tmp_dict</span><span class="p">[</span><span class="s2">&quot;response&quot;</span><span class="p">],</span> <span class="n">tmp_dict</span><span class="p">[</span><span class="s2">&quot;unit&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span>
+                        <span class="n">component</span><span class="p">,</span>
+                        <span class="n">response</span><span class="p">,</span>
+                        <span class="n">dtype</span><span class="p">,</span>
+                    <span class="p">)</span>
+                    <span class="n">station_locs</span><span class="p">[</span><span class="n">sid</span><span class="p">]</span> <span class="o">=</span> <span class="n">tmp_dict</span>
+
+    <span class="n">station_locs</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">from_dict</span><span class="p">(</span><span class="n">station_locs</span><span class="p">,</span> <span class="n">orient</span><span class="o">=</span><span class="s1">&#39;index&#39;</span><span class="p">)</span>
+    <span class="n">station_locs</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span>
+        <span class="n">station_csv</span><span class="p">,</span>
+        <span class="n">sep</span><span class="o">=</span><span class="s2">&quot;</span><span class="se">\t</span><span class="s2">&quot;</span><span class="p">,</span>
+        <span class="n">float_format</span><span class="o">=</span><span class="s2">&quot;</span><span class="si">%.3f</span><span class="s2">&quot;</span><span class="p">,</span>
+        <span class="n">index_label</span><span class="o">=</span><span class="s2">&quot;station&quot;</span><span class="p">,</span>
+        <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;longitude&quot;</span><span class="p">,</span> <span class="s2">&quot;latitude&quot;</span><span class="p">,</span> <span class="s2">&quot;elevation(m)&quot;</span><span class="p">,</span> <span class="s2">&quot;unit&quot;</span><span class="p">,</span> <span class="s2">&quot;component&quot;</span><span class="p">,</span> <span class="s2">&quot;response&quot;</span><span class="p">,],</span>
+    <span class="p">)</span>
+
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">station_pkl</span><span class="p">,</span> <span class="s2">&quot;wb&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">pickle</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="n">stations</span><span class="p">,</span> <span class="n">fp</span><span class="p">)</span>
+
+    <span class="c1">#     ####### Plot stations ########</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">station_locs</span><span class="p">[</span><span class="s2">&quot;longitude&quot;</span><span class="p">],</span> <span class="n">station_locs</span><span class="p">[</span><span class="s2">&quot;latitude&quot;</span><span class="p">],</span> <span class="s2">&quot;^&quot;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;Stations&quot;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">&quot;X (km)&quot;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">&quot;Y (km)&quot;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s2">&quot;scaled&quot;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;xlim_degree&quot;</span><span class="p">])</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;ylim_degree&quot;</span><span class="p">])</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Number of stations: </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">station_locs</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+    <span class="c1">#     plt.savefig(os.path.join(data_path, &quot;stations_loc.png&quot;))</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+<div id="cell-9" class="clipboard-copy-txt">def download_stations(
+    config_json: InputPath("json"), station_csv: OutputPath(str), station_pkl: OutputPath("pickle"),
+):
+
+    import pickle, os
+    import obspy
+    from obspy.clients.fdsn import Client
+    from collections import defaultdict
+    import pandas as pd
+    import json
+    import matplotlib
+
+    #     matplotlib.use("agg")
+    import matplotlib.pyplot as plt
+
+    with open(config_json, "r") as fp:
+        config = json.load(fp)
+
+    print("Network:", ",".join(config["networks"]))
+    ####### Download stations ########
+    stations = Client(config["client"]).get_stations(
+        network=",".join(config["networks"]),
+        station="*",
+        starttime=config["starttime"],
+        endtime=config["endtime"],
+        minlongitude=config["xlim_degree"][0],
+        maxlongitude=config["xlim_degree"][1],
+        minlatitude=config["ylim_degree"][0],
+        maxlatitude=config["ylim_degree"][1],
+        channel=config["channels"],
+        level="response",
+    )  # ,
+    #                                            filename="stations.xml")
+
+    #     stations = obspy.read_inventory("stations.xml")
+    print("Number of stations: {}".format(sum([len(x) for x in stations])))
+    # stations.plot('local', outfile="stations.png")
+    #     stations.plot('local')
+
+    ####### Save stations ########
+    station_locs = defaultdict(dict)
+    for network in stations:
+        for station in network:
+            for chn in station:
+                sid = f"{network.code}.{station.code}.{chn.location_code}.{chn.code[:-1]}"
+                if sid in station_locs:
+                    station_locs[sid]["component"] += f",{chn.code[-1]}"
+                    station_locs[sid]["response"] += f",{chn.response.instrument_sensitivity.value:.2f}"
+                else:
+                    component = f"{chn.code[-1]}"
+                    response = f"{chn.response.instrument_sensitivity.value:.2f}"
+                    dtype = chn.response.instrument_sensitivity.input_units.lower()
+                    tmp_dict = {}
+                    (tmp_dict["longitude"], tmp_dict["latitude"], tmp_dict["elevation(m)"],) = (
+                        chn.longitude,
+                        chn.latitude,
+                        chn.elevation,
+                    )
+                    tmp_dict["component"], tmp_dict["response"], tmp_dict["unit"] = (
+                        component,
+                        response,
+                        dtype,
+                    )
+                    station_locs[sid] = tmp_dict
+
+    station_locs = pd.DataFrame.from_dict(station_locs, orient='index')
+    station_locs.to_csv(
+        station_csv,
+        sep="\t",
+        float_format="%.3f",
+        index_label="station",
+        columns=["longitude", "latitude", "elevation(m)", "unit", "component", "response",],
+    )
+
+    with open(station_pkl, "wb") as fp:
+        pickle.dump(stations, fp)
+
+    #     ####### Plot stations ########
+    plt.figure()
+    plt.plot(station_locs["longitude"], station_locs["latitude"], "^", label="Stations")
+    plt.xlabel("X (km)")
+    plt.ylabel("Y (km)")
+    plt.axis("scaled")
+    plt.xlim(config["xlim_degree"])
+    plt.ylim(config["ylim_degree"])
+    plt.legend()
+    plt.title(f"Number of stations: {len(station_locs)}")
+    #     plt.savefig(os.path.join(data_path, "stations_loc.png"))
+    plt.show()
+</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[58]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-10">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">if</span> <span class="n">run_local</span><span class="p">:</span>
+    <span class="n">download_stations</span><span class="p">(</span><span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;config.json&quot;</span><span class="p">),</span> <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;stations.csv&quot;</span><span class="p">),</span> <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;stations.pkl&quot;</span><span class="p">))</span>
+</pre></div>
+<div id="cell-10" class="clipboard-copy-txt">if run_local:
+    download_stations(root_dir("config.json"), root_dir("stations.csv"), root_dir("stations.pkl"))
+</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[59]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-11">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">download_stations_op</span> <span class="o">=</span> <span class="n">comp</span><span class="o">.</span><span class="n">func_to_container_op</span><span class="p">(</span>
+    <span class="n">download_stations</span><span class="p">,</span>
+    <span class="c1"># base_image=&#39;zhuwq0/quakeflow-env:latest&#39;,</span>
+    <span class="n">base_image</span><span class="o">=</span><span class="s1">&#39;python:3.8&#39;</span><span class="p">,</span>
+    <span class="n">packages_to_install</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;obspy&quot;</span><span class="p">,</span> <span class="s2">&quot;pandas&quot;</span><span class="p">,</span> <span class="s2">&quot;matplotlib&quot;</span><span class="p">,],</span>
+<span class="p">)</span>
+</pre></div>
+<div id="cell-11" class="clipboard-copy-txt">download_stations_op = comp.func_to_container_op(
+    download_stations,
+    # base_image='zhuwq0/quakeflow-env:latest',
+    base_image='python:3.8',
+    packages_to_install=["obspy", "pandas", "matplotlib",],
+)
+</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="5-download-waveform-data">5. Download waveform data<a class="anchor-link" href="#5-download-waveform-data">&#182;</a></h2>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[60]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-12">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">def</span> <span class="nf">download_waveform</span><span class="p">(</span>
+    <span class="n">i</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
+    <span class="n">index_json</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="s2">&quot;json&quot;</span><span class="p">),</span>
+    <span class="n">config_json</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="s2">&quot;json&quot;</span><span class="p">),</span>
+    <span class="n">datetime_json</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="s2">&quot;json&quot;</span><span class="p">),</span>
+    <span class="n">station_pkl</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="s2">&quot;pickle&quot;</span><span class="p">),</span>
+    <span class="n">fname_csv</span><span class="p">:</span> <span class="n">OutputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span>
+    <span class="n">data_path</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
+    <span class="n">bucket_name</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;waveforms&quot;</span><span class="p">,</span>
+    <span class="n">s3_url</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;minio-service:9000&quot;</span><span class="p">,</span>
+    <span class="n">secure</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
+
+    <span class="kn">import</span> <span class="nn">pickle</span><span class="o">,</span> <span class="nn">os</span>
+    <span class="kn">import</span> <span class="nn">obspy</span>
+    <span class="kn">from</span> <span class="nn">obspy.clients.fdsn</span> <span class="kn">import</span> <span class="n">Client</span>
+    <span class="kn">import</span> <span class="nn">time</span>
+    <span class="kn">import</span> <span class="nn">json</span>
+    <span class="kn">import</span> <span class="nn">random</span>
+    <span class="kn">import</span> <span class="nn">threading</span>
+
+    <span class="n">lock</span> <span class="o">=</span> <span class="n">threading</span><span class="o">.</span><span class="n">Lock</span><span class="p">()</span>
+
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">index_json</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">index</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">fp</span><span class="p">)</span>
+    <span class="n">idx</span> <span class="o">=</span> <span class="n">index</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">config_json</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">config</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">fp</span><span class="p">)</span>
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">datetime_json</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">tmp</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">fp</span><span class="p">)</span>
+        <span class="n">starttimes</span> <span class="o">=</span> <span class="n">tmp</span><span class="p">[</span><span class="s2">&quot;starttimes&quot;</span><span class="p">]</span>
+        <span class="n">interval</span> <span class="o">=</span> <span class="n">tmp</span><span class="p">[</span><span class="s2">&quot;interval&quot;</span><span class="p">]</span>
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">station_pkl</span><span class="p">,</span> <span class="s2">&quot;rb&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">stations</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">fp</span><span class="p">)</span>
+
+    <span class="n">waveform_dir</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">data_path</span><span class="p">,</span> <span class="n">config</span><span class="p">[</span><span class="s2">&quot;region&quot;</span><span class="p">],</span> <span class="s2">&quot;waveforms&quot;</span><span class="p">)</span>
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">waveform_dir</span><span class="p">):</span>
+        <span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">waveform_dir</span><span class="p">)</span>
+
+    <span class="c1">####### Download data ########</span>
+    <span class="n">client</span> <span class="o">=</span> <span class="n">Client</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;client&quot;</span><span class="p">])</span>
+    <span class="n">fname_list</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;fname&quot;</span><span class="p">]</span>
+
+    <span class="k">def</span> <span class="nf">download</span><span class="p">(</span><span class="n">i</span><span class="p">):</span>
+        <span class="c1">#     for i in idx:</span>
+        <span class="n">starttime</span> <span class="o">=</span> <span class="n">obspy</span><span class="o">.</span><span class="n">UTCDateTime</span><span class="p">(</span><span class="n">starttimes</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+        <span class="n">endtime</span> <span class="o">=</span> <span class="n">starttime</span> <span class="o">+</span> <span class="n">interval</span>
+        <span class="c1"># fname = &quot;{}.mseed&quot;.format(starttime.datetime.strftime(&quot;%Y-%m-%dT%H:%M:%S&quot;))</span>
+        <span class="n">folder</span> <span class="o">=</span> <span class="n">starttime</span><span class="o">.</span><span class="n">datetime</span><span class="o">.</span><span class="n">strftime</span><span class="p">(</span><span class="s2">&quot;%Y/%j&quot;</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">waveform_dir</span><span class="p">,</span> <span class="n">folder</span><span class="p">)):</span>
+            <span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">waveform_dir</span><span class="p">,</span> <span class="n">folder</span><span class="p">),</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+        <span class="c1"># status = os.system(f&quot;ssh zhuwq@wintermute.gps.caltech.edu mkdir -p /scratch/zhuwq/Hawaii/wf/{folder}/&quot;)</span>
+        <span class="c1"># if status != 0:</span>
+        <span class="c1">#     print(f&quot;Failed: ssh zhuwq@wintermute.gps.caltech.edu mkdir -p /scratch/zhuwq/Hawaii/wf/{folder}/&quot;)</span>
+
+        <span class="n">max_retry</span> <span class="o">=</span> <span class="mi">10</span>
+
+        <span class="n">status</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
+        <span class="n">retry_rsync</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="k">while</span> <span class="p">(</span><span class="n">status</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="n">retry_rsync</span> <span class="o">&lt;</span> <span class="n">max_retry</span><span class="p">):</span>
+            <span class="n">status</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;ssh zhuwq@wintermute mkdir -p /scratch/zhuwq/Hawaii/wf/</span><span class="si">{</span><span class="n">folder</span><span class="si">}</span><span class="s2">/&quot;</span><span class="p">)</span>
+            <span class="n">retry_rsync</span> <span class="o">+=</span> <span class="mi">1</span>
+            <span class="n">time</span><span class="o">.</span><span class="n">sleep</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">status</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Failed: ssh zhuwq@wintermute mkdir -p /scratch/zhuwq/Hawaii/wf/</span><span class="si">{</span><span class="n">folder</span><span class="si">}</span><span class="s2">/&quot;</span><span class="p">)</span>
+
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">folder</span><span class="si">}</span><span class="s2"> download starts&quot;</span><span class="p">)</span>
+        <span class="n">num_sta</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="k">for</span> <span class="n">network</span> <span class="ow">in</span> <span class="n">stations</span><span class="p">:</span>
+            <span class="k">for</span> <span class="n">station</span> <span class="ow">in</span> <span class="n">network</span><span class="p">:</span>
+                <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;********</span><span class="si">{</span><span class="n">network</span><span class="o">.</span><span class="n">code</span><span class="si">}</span><span class="s2">.</span><span class="si">{</span><span class="n">station</span><span class="o">.</span><span class="n">code</span><span class="si">}</span><span class="s2">********&quot;</span><span class="p">)</span>
+                <span class="n">retry</span> <span class="o">=</span> <span class="mi">0</span>
+                <span class="k">while</span> <span class="n">retry</span> <span class="o">&lt;</span> <span class="n">max_retry</span><span class="p">:</span>
+                    <span class="k">try</span><span class="p">:</span>
+                        <span class="n">stream</span> <span class="o">=</span> <span class="n">client</span><span class="o">.</span><span class="n">get_waveforms</span><span class="p">(</span>
+                            <span class="n">network</span><span class="o">.</span><span class="n">code</span><span class="p">,</span> <span class="n">station</span><span class="o">.</span><span class="n">code</span><span class="p">,</span> <span class="s2">&quot;*&quot;</span><span class="p">,</span> <span class="n">config</span><span class="p">[</span><span class="s2">&quot;channels&quot;</span><span class="p">],</span> <span class="n">starttime</span><span class="p">,</span> <span class="n">endtime</span><span class="p">,</span>
+                        <span class="p">)</span>
+                        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">stream</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+                            <span class="n">stream</span> <span class="o">=</span> <span class="n">stream</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">fill_value</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+                            <span class="n">stream</span> <span class="o">=</span> <span class="n">stream</span><span class="o">.</span><span class="n">trim</span><span class="p">(</span><span class="n">starttime</span><span class="p">,</span> <span class="n">endtime</span><span class="p">,</span> <span class="n">pad</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">fill_value</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+
+                            <span class="k">for</span> <span class="n">trace</span> <span class="ow">in</span> <span class="n">stream</span><span class="p">:</span>
+                                <span class="k">if</span> <span class="n">trace</span><span class="o">.</span><span class="n">stats</span><span class="o">.</span><span class="n">sampling_rate</span> <span class="o">!=</span> <span class="mi">100</span><span class="p">:</span>
+                                    <span class="n">trace</span> <span class="o">=</span> <span class="n">trace</span><span class="o">.</span><span class="n">interpolate</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s2">&quot;linear&quot;</span><span class="p">)</span>
+                                <span class="n">trace_name</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">trace</span><span class="o">.</span><span class="n">stats</span><span class="o">.</span><span class="n">network</span><span class="si">}</span><span class="s2">.</span><span class="si">{</span><span class="n">trace</span><span class="o">.</span><span class="n">stats</span><span class="o">.</span><span class="n">station</span><span class="si">}</span><span class="s2">.</span><span class="si">{</span><span class="n">trace</span><span class="o">.</span><span class="n">stats</span><span class="o">.</span><span class="n">channel</span><span class="si">}</span><span class="s2">.mseed&quot;</span>
+                                <span class="n">trace</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">waveform_dir</span><span class="p">,</span> <span class="n">folder</span><span class="p">,</span> <span class="n">trace_name</span><span class="p">),</span> <span class="nb">format</span><span class="o">=</span><span class="s2">&quot;mseed&quot;</span><span class="p">)</span>
+                                <span class="c1"># os.system(f&quot;scp {trace_name} zhuwq@wintermute.gps.caltech.edu:/scratch/zhuwq/Hawaii/wf/{folder}/&quot;)</span>
+                                <span class="c1"># scp.put(f&quot;{trace_name}&quot;, f&quot;/scratch/zhuwq/Hawaii/wf/{folder}/&quot;)</span>
+                                <span class="c1"># status = os.system(f&quot;rsync -av {os.path.join(waveform_dir, folder, trace_name)} zhuwq@wintermute.gps.caltech.edu:/scratch/zhuwq/Hawaii/wf/{folder}/{trace_name}&quot;)</span>
+                                <span class="c1"># if status != 0:</span>
+                                <span class="c1">#     print(f&quot;Failed: rsync -av {os.path.join(waveform_dir, folder, trace_name)} zhuwq@wintermute.gps.caltech.edu:/scratch/zhuwq/Hawaii/wf/{folder}/{trace_name}&quot;)</span>
+
+                                <span class="n">status</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
+                                <span class="n">retry_rsync</span> <span class="o">=</span> <span class="mi">0</span>
+                                <span class="k">while</span> <span class="p">(</span><span class="n">status</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="n">retry_rsync</span> <span class="o">&lt;</span> <span class="n">max_retry</span><span class="p">):</span>
+                                    <span class="n">status</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;rsync -av </span><span class="si">{</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">waveform_dir</span><span class="p">,</span><span class="w"> </span><span class="n">folder</span><span class="p">,</span><span class="w"> </span><span class="n">trace_name</span><span class="p">)</span><span class="si">}</span><span class="s2"> zhuwq@wintermute:/scratch/zhuwq/Hawaii/wf/</span><span class="si">{</span><span class="n">folder</span><span class="si">}</span><span class="s2">/</span><span class="si">{</span><span class="n">trace_name</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+                                    <span class="n">retry_rsync</span> <span class="o">+=</span> <span class="mi">1</span>
+                                    <span class="n">time</span><span class="o">.</span><span class="n">sleep</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
+
+                                <span class="k">if</span> <span class="n">status</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+                                    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Failed: rsync -av </span><span class="si">{</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">waveform_dir</span><span class="p">,</span><span class="w"> </span><span class="n">folder</span><span class="p">,</span><span class="w"> </span><span class="n">trace_name</span><span class="p">)</span><span class="si">}</span><span class="s2"> zhuwq@wintermute:/scratch/zhuwq/Hawaii/wf/</span><span class="si">{</span><span class="n">folder</span><span class="si">}</span><span class="s2">/</span><span class="si">{</span><span class="n">trace_name</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+                                <span class="k">else</span><span class="p">:</span>
+                                    <span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;rm -f </span><span class="si">{</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">waveform_dir</span><span class="p">,</span><span class="w"> </span><span class="n">folder</span><span class="p">,</span><span class="w"> </span><span class="n">trace_name</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+
+                        <span class="k">break</span>
+                    <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">err</span><span class="p">:</span>
+                        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Error </span><span class="si">{}</span><span class="s2">.</span><span class="si">{}</span><span class="s2">: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">network</span><span class="o">.</span><span class="n">code</span><span class="p">,</span> <span class="n">station</span><span class="o">.</span><span class="n">code</span><span class="p">,</span> <span class="n">err</span><span class="p">))</span>
+                        <span class="n">message</span> <span class="o">=</span> <span class="s2">&quot;No data available for request.&quot;</span>
+                        <span class="k">if</span> <span class="nb">str</span><span class="p">(</span><span class="n">err</span><span class="p">)[:</span> <span class="nb">len</span><span class="p">(</span><span class="n">message</span><span class="p">)]</span> <span class="o">==</span> <span class="n">message</span><span class="p">:</span>
+                            <span class="k">break</span>
+                        <span class="n">retry</span> <span class="o">+=</span> <span class="mi">1</span>
+                        <span class="n">time</span><span class="o">.</span><span class="n">sleep</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
+                        <span class="k">continue</span>
+
+                <span class="k">if</span> <span class="n">retry</span> <span class="o">==</span> <span class="n">max_retry</span><span class="p">:</span>
+                    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">folder</span><span class="si">}</span><span class="s2">: MAX </span><span class="si">{</span><span class="n">max_retry</span><span class="si">}</span><span class="s2"> retries reached : </span><span class="si">{</span><span class="n">network</span><span class="o">.</span><span class="n">code</span><span class="si">}</span><span class="s2">.</span><span class="si">{</span><span class="n">station</span><span class="o">.</span><span class="n">code</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">folder</span><span class="si">}</span><span class="s2"> download succeeds&quot;</span><span class="p">)</span>
+
+        <span class="n">lock</span><span class="o">.</span><span class="n">acquire</span><span class="p">()</span>
+        <span class="n">fname_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">folder</span><span class="p">)</span>
+        <span class="n">lock</span><span class="o">.</span><span class="n">release</span><span class="p">()</span>
+
+    <span class="n">threads</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">MAX_THREADS</span> <span class="o">=</span> <span class="mi">4</span>
+    <span class="c1"># MAX_THREADS = 1</span>
+    <span class="k">for</span> <span class="n">ii</span><span class="p">,</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">idx</span><span class="p">):</span>
+        <span class="n">t</span> <span class="o">=</span> <span class="n">threading</span><span class="o">.</span><span class="n">Thread</span><span class="p">(</span><span class="n">target</span><span class="o">=</span><span class="n">download</span><span class="p">,</span> <span class="n">args</span><span class="o">=</span><span class="p">(</span><span class="n">i</span><span class="p">,))</span>
+        <span class="n">t</span><span class="o">.</span><span class="n">start</span><span class="p">()</span>
+        <span class="n">time</span><span class="o">.</span><span class="n">sleep</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+        <span class="n">threads</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">t</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">ii</span> <span class="o">%</span> <span class="n">MAX_THREADS</span> <span class="o">==</span> <span class="n">MAX_THREADS</span> <span class="o">-</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">threads</span><span class="p">:</span>
+                <span class="n">t</span><span class="o">.</span><span class="n">join</span><span class="p">()</span>
+            <span class="n">threads</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">threads</span><span class="p">:</span>
+        <span class="n">t</span><span class="o">.</span><span class="n">join</span><span class="p">()</span>
+
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">fname_csv</span><span class="p">,</span> <span class="s2">&quot;w&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">fp</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">fname_list</span><span class="p">))</span>
+
+    <span class="k">return</span> <span class="n">waveform_dir</span>
+</pre></div>
+<div id="cell-12" class="clipboard-copy-txt">def download_waveform(
+    i: int,
+    index_json: InputPath("json"),
+    config_json: InputPath("json"),
+    datetime_json: InputPath("json"),
+    station_pkl: InputPath("pickle"),
+    fname_csv: OutputPath(str),
+    data_path: str,
+    bucket_name: str = "waveforms",
+    s3_url: str = "minio-service:9000",
+    secure: bool = True,
+) -> str:
+
+    import pickle, os
+    import obspy
+    from obspy.clients.fdsn import Client
+    import time
+    import json
+    import random
+    import threading
+
+    lock = threading.Lock()
+
+    with open(index_json, "r") as fp:
+        index = json.load(fp)
+    idx = index[i]
+    with open(config_json, "r") as fp:
+        config = json.load(fp)
+    with open(datetime_json, "r") as fp:
+        tmp = json.load(fp)
+        starttimes = tmp["starttimes"]
+        interval = tmp["interval"]
+    with open(station_pkl, "rb") as fp:
+        stations = pickle.load(fp)
+
+    waveform_dir = os.path.join(data_path, config["region"], "waveforms")
+    if not os.path.exists(waveform_dir):
+        os.makedirs(waveform_dir)
+
+    ####### Download data ########
+    client = Client(config["client"])
+    fname_list = ["fname"]
+
+    def download(i):
+        #     for i in idx:
+        starttime = obspy.UTCDateTime(starttimes[i])
+        endtime = starttime + interval
+        # fname = "{}.mseed".format(starttime.datetime.strftime("%Y-%m-%dT%H:%M:%S"))
+        folder = starttime.datetime.strftime("%Y/%j")
+
+        if not os.path.exists(os.path.join(waveform_dir, folder)):
+            os.makedirs(os.path.join(waveform_dir, folder), exist_ok=True)
+        # status = os.system(f"ssh zhuwq@wintermute.gps.caltech.edu mkdir -p /scratch/zhuwq/Hawaii/wf/{folder}/")
+        # if status != 0:
+        #     print(f"Failed: ssh zhuwq@wintermute.gps.caltech.edu mkdir -p /scratch/zhuwq/Hawaii/wf/{folder}/")
+
+        max_retry = 10
+
+        status = -1
+        retry_rsync = 0
+        while (status != 0) and (retry_rsync < max_retry):
+            status = os.system(f"ssh zhuwq@wintermute mkdir -p /scratch/zhuwq/Hawaii/wf/{folder}/")
+            retry_rsync += 1
+            time.sleep(5)
+
+        if status != 0:
+            print(f"Failed: ssh zhuwq@wintermute mkdir -p /scratch/zhuwq/Hawaii/wf/{folder}/")
+
+        print(f"{folder} download starts")
+        num_sta = 0
+        for network in stations:
+            for station in network:
+                print(f"********{network.code}.{station.code}********")
+                retry = 0
+                while retry < max_retry:
+                    try:
+                        stream = client.get_waveforms(
+                            network.code, station.code, "*", config["channels"], starttime, endtime,
+                        )
+                        if len(stream) > 0:
+                            stream = stream.merge(fill_value=0)
+                            stream = stream.trim(starttime, endtime, pad=True, fill_value=0)
+
+                            for trace in stream:
+                                if trace.stats.sampling_rate != 100:
+                                    trace = trace.interpolate(100, method="linear")
+                                trace_name = f"{trace.stats.network}.{trace.stats.station}.{trace.stats.channel}.mseed"
+                                trace.write(os.path.join(waveform_dir, folder, trace_name), format="mseed")
+                                # os.system(f"scp {trace_name} zhuwq@wintermute.gps.caltech.edu:/scratch/zhuwq/Hawaii/wf/{folder}/")
+                                # scp.put(f"{trace_name}", f"/scratch/zhuwq/Hawaii/wf/{folder}/")
+                                # status = os.system(f"rsync -av {os.path.join(waveform_dir, folder, trace_name)} zhuwq@wintermute.gps.caltech.edu:/scratch/zhuwq/Hawaii/wf/{folder}/{trace_name}")
+                                # if status != 0:
+                                #     print(f"Failed: rsync -av {os.path.join(waveform_dir, folder, trace_name)} zhuwq@wintermute.gps.caltech.edu:/scratch/zhuwq/Hawaii/wf/{folder}/{trace_name}")
+
+                                status = -1
+                                retry_rsync = 0
+                                while (status != 0) and (retry_rsync < max_retry):
+                                    status = os.system(f"rsync -av {os.path.join(waveform_dir, folder, trace_name)} zhuwq@wintermute:/scratch/zhuwq/Hawaii/wf/{folder}/{trace_name}")
+                                    retry_rsync += 1
+                                    time.sleep(5)
+
+                                if status != 0:
+                                    print(f"Failed: rsync -av {os.path.join(waveform_dir, folder, trace_name)} zhuwq@wintermute:/scratch/zhuwq/Hawaii/wf/{folder}/{trace_name}")
+                                else:
+                                    os.system(f"rm -f {os.path.join(waveform_dir, folder, trace_name)}")
+
+                        break
+                    except Exception as err:
+                        print("Error {}.{}: {}".format(network.code, station.code, err))
+                        message = "No data available for request."
+                        if str(err)[: len(message)] == message:
+                            break
+                        retry += 1
+                        time.sleep(5)
+                        continue
+
+                if retry == max_retry:
+                    print(f"{folder}: MAX {max_retry} retries reached : {network.code}.{station.code}")
+
+        print(f"{folder} download succeeds")
+
+        lock.acquire()
+        fname_list.append(folder)
+        lock.release()
+
+    threads = []
+    MAX_THREADS = 4
+    # MAX_THREADS = 1
+    for ii, i in enumerate(idx):
+        t = threading.Thread(target=download, args=(i,))
+        t.start()
+        time.sleep(1)
+        threads.append(t)
+        if ii % MAX_THREADS == MAX_THREADS - 1:
+            for t in threads:
+                t.join()
+            threads = []
+    for t in threads:
+        t.join()
+
+    with open(fname_csv, "w") as fp:
+        fp.write("\n".join(fname_list))
+
+    return waveform_dir
+</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[61]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-13">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">if</span> <span class="n">run_local</span><span class="p">:</span>
+    <span class="n">waveform_path</span> <span class="o">=</span> <span class="n">download_waveform</span><span class="p">(</span>
+        <span class="mi">0</span><span class="p">,</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;index.json&quot;</span><span class="p">),</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;config.json&quot;</span><span class="p">),</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;datetimes.json&quot;</span><span class="p">),</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;stations.pkl&quot;</span><span class="p">),</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;fname.csv&quot;</span><span class="p">),</span>
+        <span class="n">data_path</span><span class="o">=</span><span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;&quot;</span><span class="p">),</span>
+    <span class="p">)</span>
+</pre></div>
+<div id="cell-13" class="clipboard-copy-txt">if run_local:
+    waveform_path = download_waveform(
+        0,
+        root_dir("index.json"),
+        root_dir("config.json"),
+        root_dir("datetimes.json"),
+        root_dir("stations.pkl"),
+        root_dir("fname.csv"),
+        data_path=root_dir(""),
+    )
+</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[62]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-14">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">download_waveform_op</span> <span class="o">=</span> <span class="n">comp</span><span class="o">.</span><span class="n">func_to_container_op</span><span class="p">(</span>
+    <span class="n">download_waveform</span><span class="p">,</span>
+    <span class="n">base_image</span><span class="o">=</span><span class="s2">&quot;zhuwq0/waveform-env:1.1&quot;</span><span class="p">,</span>
+    <span class="c1"># base_image=&#39;python:3.8&#39;,</span>
+    <span class="c1"># packages_to_install=[&quot;obspy&quot;, &quot;minio&quot;],</span>
+<span class="p">)</span>
+</pre></div>
+<div id="cell-14" class="clipboard-copy-txt">download_waveform_op = comp.func_to_container_op(
+    download_waveform,
+    base_image="zhuwq0/waveform-env:1.1",
+    # base_image='python:3.8',
+    # packages_to_install=["obspy", "minio"],
+)
+</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[63]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-15">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="nd">@dsl</span><span class="o">.</span><span class="n">pipeline</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;QuakeFlow&#39;</span><span class="p">,</span> <span class="n">description</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">)</span>
+<span class="k">def</span> <span class="nf">quakeflow_pipeline</span><span class="p">(</span>
+    <span class="n">data_path</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;/tmp/&quot;</span><span class="p">,</span>
+    <span class="n">num_parallel</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+    <span class="n">bucket_catalog</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;catalogs&quot;</span><span class="p">,</span>
+    <span class="n">s3_url</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;minio-service:9000&quot;</span><span class="p">,</span>
+    <span class="n">secure</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+<span class="p">):</span>
+
+    <span class="n">config</span> <span class="o">=</span> <span class="n">config_op</span><span class="p">(</span><span class="n">num_parallel</span><span class="p">)</span>
+
+    <span class="n">events</span> <span class="o">=</span> <span class="n">download_events_op</span><span class="p">(</span><span class="n">config</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;config_json&quot;</span><span class="p">])</span><span class="o">.</span><span class="n">set_display_name</span><span class="p">(</span><span class="s1">&#39;Download Events&#39;</span><span class="p">)</span>
+
+    <span class="n">stations</span> <span class="o">=</span> <span class="n">download_stations_op</span><span class="p">(</span><span class="n">config</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;config_json&quot;</span><span class="p">])</span><span class="o">.</span><span class="n">set_display_name</span><span class="p">(</span><span class="s1">&#39;Download Stations&#39;</span><span class="p">)</span>
+
+    <span class="k">with</span> <span class="n">kfp</span><span class="o">.</span><span class="n">dsl</span><span class="o">.</span><span class="n">ParallelFor</span><span class="p">(</span><span class="n">config</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;output&quot;</span><span class="p">])</span> <span class="k">as</span> <span class="n">i</span><span class="p">:</span>
+
+        <span class="c1"># vop_ = dsl.VolumeOp(</span>
+        <span class="c1">#     name=f&quot;Create volume 2&quot;,</span>
+        <span class="c1">#     resource_name=f&quot;data-volume-{str(i)}&quot;,</span>
+        <span class="c1">#     size=&quot;50Gi&quot;,</span>
+        <span class="c1">#     modes=dsl.VOLUME_MODE_RWO,</span>
+        <span class="c1"># ).set_retry(3)</span>
+
+        <span class="n">download_op_</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="n">download_waveform_op</span><span class="p">(</span>
+                <span class="n">i</span><span class="p">,</span>
+                <span class="n">config</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;index_json&quot;</span><span class="p">],</span>
+                <span class="n">config</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;config_json&quot;</span><span class="p">],</span>
+                <span class="n">config</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;datetime_json&quot;</span><span class="p">],</span>
+                <span class="n">stations</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;station_pkl&quot;</span><span class="p">],</span>
+                <span class="n">data_path</span><span class="o">=</span><span class="n">data_path</span><span class="p">,</span>
+                <span class="n">bucket_name</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;waveforms&quot;</span><span class="p">,</span>
+                <span class="n">s3_url</span><span class="o">=</span><span class="n">s3_url</span><span class="p">,</span>
+                <span class="n">secure</span><span class="o">=</span><span class="n">secure</span><span class="p">,</span>
+            <span class="p">)</span>
+            <span class="c1"># .add_pvolumes({data_path: vop_.volume})</span>
+            <span class="o">.</span><span class="n">set_cpu_request</span><span class="p">(</span><span class="s2">&quot;800m&quot;</span><span class="p">)</span>
+            <span class="o">.</span><span class="n">set_retry</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
+            <span class="o">.</span><span class="n">set_display_name</span><span class="p">(</span><span class="s1">&#39;Download Waveforms&#39;</span><span class="p">)</span>
+        <span class="p">)</span>
+        <span class="n">download_op_</span><span class="o">.</span><span class="n">execution_options</span><span class="o">.</span><span class="n">caching_strategy</span><span class="o">.</span><span class="n">max_cache_staleness</span> <span class="o">=</span> <span class="s2">&quot;P30D&quot;</span>
+        <span class="n">download_op_</span><span class="o">.</span><span class="n">set_image_pull_policy</span><span class="p">(</span><span class="s2">&quot;Always&quot;</span><span class="p">)</span>
+</pre></div>
+<div id="cell-15" class="clipboard-copy-txt">@dsl.pipeline(name='QuakeFlow', description='')
+def quakeflow_pipeline(
+    data_path: str = "/tmp/",
+    num_parallel=0,
+    bucket_catalog: str = "catalogs",
+    s3_url: str = "minio-service:9000",
+    secure: bool = False,
+):
+
+    config = config_op(num_parallel)
+
+    events = download_events_op(config.outputs["config_json"]).set_display_name('Download Events')
+
+    stations = download_stations_op(config.outputs["config_json"]).set_display_name('Download Stations')
+
+    with kfp.dsl.ParallelFor(config.outputs["output"]) as i:
+
+        # vop_ = dsl.VolumeOp(
+        #     name=f"Create volume 2",
+        #     resource_name=f"data-volume-{str(i)}",
+        #     size="50Gi",
+        #     modes=dsl.VOLUME_MODE_RWO,
+        # ).set_retry(3)
+
+        download_op_ = (
+            download_waveform_op(
+                i,
+                config.outputs["index_json"],
+                config.outputs["config_json"],
+                config.outputs["datetime_json"],
+                stations.outputs["station_pkl"],
+                data_path=data_path,
+                bucket_name=f"waveforms",
+                s3_url=s3_url,
+                secure=secure,
+            )
+            # .add_pvolumes({data_path: vop_.volume})
+            .set_cpu_request("800m")
+            .set_retry(3)
+            .set_display_name('Download Waveforms')
+        )
+        download_op_.execution_options.caching_strategy.max_cache_staleness = "P30D"
+        download_op_.set_image_pull_policy("Always")
+</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[64]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-16">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
+
+<span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s2">&quot;GOOGLE_APPLICATION_CREDENTIALS&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;/home/weiqiang/.dotbot/cloud/quakeflow_zhuwq.json&quot;</span>
+<span class="n">experiment_name</span> <span class="o">=</span> <span class="s1">&#39;QuakeFlow&#39;</span>
+<span class="n">pipeline_func</span> <span class="o">=</span> <span class="n">quakeflow_pipeline</span>
+<span class="n">run_name</span> <span class="o">=</span> <span class="n">pipeline_func</span><span class="o">.</span><span class="vm">__name__</span> <span class="o">+</span> <span class="s1">&#39;_run&#39;</span>
+
+<span class="n">arguments</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;data_path&quot;</span><span class="p">:</span> <span class="s2">&quot;/tmp&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;num_parallel&quot;</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span>
+    <span class="s2">&quot;bucket_catalog&quot;</span><span class="p">:</span> <span class="s2">&quot;catalogs&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;s3_url&quot;</span><span class="p">:</span> <span class="s2">&quot;minio-service:9000&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;secure&quot;</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span>
+<span class="p">}</span>
+
+<span class="k">if</span> <span class="ow">not</span> <span class="n">run_local</span><span class="p">:</span>
+    <span class="n">pipeline_conf</span> <span class="o">=</span> <span class="n">kfp</span><span class="o">.</span><span class="n">dsl</span><span class="o">.</span><span class="n">PipelineConf</span><span class="p">()</span>
+    <span class="n">pipeline_conf</span><span class="o">.</span><span class="n">set_image_pull_policy</span><span class="p">(</span><span class="s2">&quot;Always&quot;</span><span class="p">)</span>
+    <span class="n">pipeline_conf</span><span class="o">.</span><span class="n">ttl_seconds_after_finished</span> <span class="o">=</span> <span class="mi">60</span> <span class="o">*</span> <span class="mi">10</span>
+    <span class="n">client</span> <span class="o">=</span> <span class="n">kfp</span><span class="o">.</span><span class="n">Client</span><span class="p">(</span><span class="n">host</span><span class="o">=</span><span class="s2">&quot;7f176775ae43f263-dot-us-west1.pipelines.googleusercontent.com&quot;</span><span class="p">)</span>
+    <span class="c1"># client = kfp.Client(host=&quot;http://localhost:8080&quot;)</span>
+    <span class="n">kfp</span><span class="o">.</span><span class="n">compiler</span><span class="o">.</span><span class="n">Compiler</span><span class="p">()</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">pipeline_func</span><span class="p">,</span> <span class="s1">&#39;</span><span class="si">{}</span><span class="s1">.zip&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">experiment_name</span><span class="p">),</span> <span class="n">pipeline_conf</span><span class="o">=</span><span class="n">pipeline_conf</span><span class="p">)</span>
+    <span class="n">results</span> <span class="o">=</span> <span class="n">client</span><span class="o">.</span><span class="n">create_run_from_pipeline_func</span><span class="p">(</span>
+        <span class="n">pipeline_func</span><span class="p">,</span> <span class="n">experiment_name</span><span class="o">=</span><span class="n">experiment_name</span><span class="p">,</span> <span class="n">run_name</span><span class="o">=</span><span class="n">run_name</span><span class="p">,</span> <span class="n">arguments</span><span class="o">=</span><span class="n">arguments</span><span class="p">,</span>
+    <span class="p">)</span>
+</pre></div>
+<div id="cell-16" class="clipboard-copy-txt">import os
+
+os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "/home/weiqiang/.dotbot/cloud/quakeflow_zhuwq.json"
+experiment_name = 'QuakeFlow'
+pipeline_func = quakeflow_pipeline
+run_name = pipeline_func.__name__ + '_run'
+
+arguments = {
+    "data_path": "/tmp",
+    "num_parallel": 0,
+    "bucket_catalog": "catalogs",
+    "s3_url": "minio-service:9000",
+    "secure": False,
+}
+
+if not run_local:
+    pipeline_conf = kfp.dsl.PipelineConf()
+    pipeline_conf.set_image_pull_policy("Always")
+    pipeline_conf.ttl_seconds_after_finished = 60 * 10
+    client = kfp.Client(host="7f176775ae43f263-dot-us-west1.pipelines.googleusercontent.com")
+    # client = kfp.Client(host="http://localhost:8080")
+    kfp.compiler.Compiler().compile(pipeline_func, '{}.zip'.format(experiment_name), pipeline_conf=pipeline_conf)
+    results = client.create_run_from_pipeline_func(
+        pipeline_func, experiment_name=experiment_name, run_name=run_name, arguments=arguments,
+    )
+</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output " data-mime-type="text/html">
+<a href="http://7f176775ae43f263-dot-us-west1.pipelines.googleusercontent.com/#/experiments/details/a6bdb01d-5e26-4462-84c6-dd2b40455c1f" target="_blank" >Experiment details</a>.
+</div>
+
+</div>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output " data-mime-type="text/html">
+<a href="http://7f176775ae43f263-dot-us-west1.pipelines.googleusercontent.com/#/runs/details/2df93322-0b65-45b1-8691-a84c34508ae1" target="_blank" >Run details</a>.
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-17">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span>
+</pre></div>
+<div id="cell-17" class="clipboard-copy-txt"></div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+</div>
+
+
+
+
+  
+
+
+
+              
+            </article>
+            
+          </div>
+        </div>
+        
+      </main>
+      
+        <footer class="md-footer">
+  
+  <div class="md-footer-meta md-typeset">
+    <div class="md-footer-meta__inner md-grid">
+      <div class="md-copyright">
+  
+  
+    Made with
+    <a href="https://squidfunk.github.io/mkdocs-material/" target="_blank" rel="noopener">
+      Material for MkDocs
+    </a>
+  
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+      
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+    <div class="md-dialog" data-md-component="dialog">
+      <div class="md-dialog__inner md-typeset"></div>
+    </div>
+    
+    <script id="__config" type="application/json">{"base": "../../..", "features": [], "search": "../../../assets/javascripts/workers/search.ecf98df9.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}}</script>
+    
+    
+      <script src="../../../assets/javascripts/bundle.9c69f0bc.min.js"></script>
+      
+    
+  </body>
+</html>
\ No newline at end of file
diff --git a/kubeflow/workflow-api/index.html b/kubeflow/workflow-api/index.html
new file mode 100644
index 0000000..353340a
--- /dev/null
+++ b/kubeflow/workflow-api/index.html
@@ -0,0 +1,4319 @@
+
+<!doctype html>
+<html lang="en" class="no-js">
+  <head>
+    
+      <meta charset="utf-8">
+      <meta name="viewport" content="width=device-width,initial-scale=1">
+      
+        <meta name="description" content="QuakeFlow: a Scalable Deep-learning-based Earthquake Monitoring Workflow with Cloud Computing">
+      
+      
+        <meta name="author" content="Weiqiang Zhu">
+      
+      
+      <link rel="icon" href="../../assets/images/favicon.png">
+      <meta name="generator" content="mkdocs-1.3.0, mkdocs-material-8.4.1">
+    
+    
+      
+        <title>Workflow api - QuakeFlow</title>
+      
+    
+    
+      <link rel="stylesheet" href="../../assets/stylesheets/main.69437709.min.css">
+      
+        
+        <link rel="stylesheet" href="../../assets/stylesheets/palette.cbb835fc.min.css">
+        
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+        <link rel="stylesheet" href="https://fonts.googleapis.com/css?family=Roboto:300,300i,400,400i,700,700i%7CRoboto+Mono:400,400i,700,700i&display=fallback">
+        <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
+      
+    
+    
+    <script>__md_scope=new URL("../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
+    
+      
+  
+
+
+  
+  
+
+
+  <script id="__analytics">function __md_analytics(){function n(){dataLayer.push(arguments)}window.dataLayer=window.dataLayer||[],n("js",new Date),n("config","G-69DX3B35RK"),document.addEventListener("DOMContentLoaded",function(){if(document.forms.search&&document.forms.search.query.addEventListener("blur",function(){this.value&&n("event","search",{search_term:this.value})}),document.forms.feedback){var e,a=document.forms.feedback;for(e of a.querySelectorAll("[type=submit]"))e.addEventListener("click",function(e){e.preventDefault();var t=document.location.pathname,e=this.getAttribute("data-md-value");n("event","feedback",{page:t,data:e}),a.firstElementChild.disabled=!0;e=a.querySelector(".md-feedback__note [data-md-value='"+e+"']");e&&(e.hidden=!1)}),a.hidden=!1}"undefined"!=typeof location$&&location$.subscribe(function(e){n("config","G-69DX3B35RK",{page_path:e.pathname})})});var e=document.createElement("script");e.async=!0,e.src="https://www.googletagmanager.com/gtag/js?id=G-69DX3B35RK",document.getElementById("__analytics").insertAdjacentElement("afterEnd",e)}</script>
+
+  
+    <script>"undefined"!=typeof __md_analytics&&__md_analytics()</script>
+  
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+    
+    
+  </head>
+  
+  
+    
+    
+    
+    
+    
+    <body dir="ltr" data-md-color-scheme="" data-md-color-primary="none" data-md-color-accent="none">
+  
+    
+    
+    <input class="md-toggle" data-md-toggle="drawer" type="checkbox" id="__drawer" autocomplete="off">
+    <input class="md-toggle" data-md-toggle="search" type="checkbox" id="__search" autocomplete="off">
+    <label class="md-overlay" for="__drawer"></label>
+    <div data-md-component="skip">
+      
+        
+        <a href="#earthquake-detection-workflow" class="md-skip">
+          Skip to content
+        </a>
+      
+    </div>
+    <div data-md-component="announce">
+      
+    </div>
+    
+    
+      
+
+<header class="md-header" data-md-component="header">
+  <nav class="md-header__inner md-grid" aria-label="Header">
+    <a href="../.." title="QuakeFlow" class="md-header__button md-logo" aria-label="QuakeFlow" data-md-component="logo">
+      
+  
+  <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54Z"/></svg>
+
+    </a>
+    <label class="md-header__button md-icon" for="__drawer">
+      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M3 6h18v2H3V6m0 5h18v2H3v-2m0 5h18v2H3v-2Z"/></svg>
+    </label>
+    <div class="md-header__title" data-md-component="header-title">
+      <div class="md-header__ellipsis">
+        <div class="md-header__topic">
+          <span class="md-ellipsis">
+            QuakeFlow
+          </span>
+        </div>
+        <div class="md-header__topic" data-md-component="header-topic">
+          <span class="md-ellipsis">
+            
+              Workflow api
+            
+          </span>
+        </div>
+      </div>
+    </div>
+    
+    
+    
+    
+      <div class="md-header__source">
+        <a href="https://github.com/ai4eps/QuakeFlow" title="Go to repository" class="md-source" data-md-component="source">
+  <div class="md-source__icon md-icon">
+    
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.1.2 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+  </div>
+  <div class="md-source__repository">
+    AI4EPS/QuakeFlow
+  </div>
+</a>
+      </div>
+    
+  </nav>
+  
+</header>
+    
+    <div class="md-container" data-md-component="container">
+      
+      
+        
+          
+        
+      
+      <main class="md-main" data-md-component="main">
+        <div class="md-main__inner md-grid">
+          
+            
+              
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+                  <div class="md-sidebar__inner">
+                    
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+    <a href="../.." title="QuakeFlow" class="md-nav__button md-logo" aria-label="QuakeFlow" data-md-component="logo">
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+
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+    QuakeFlow
+  </label>
+  
+    <div class="md-nav__source">
+      <a href="https://github.com/ai4eps/QuakeFlow" title="Go to repository" class="md-source" data-md-component="source">
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+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.1.2 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+  </div>
+  <div class="md-source__repository">
+    AI4EPS/QuakeFlow
+  </div>
+</a>
+    </div>
+  
+  <ul class="md-nav__list" data-md-scrollfix>
+    
+      
+      
+      
+
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../.." class="md-nav__link">
+        Overview
+      </a>
+    </li>
+  
+
+    
+      
+      
+      
+
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../workflow/" class="md-nav__link">
+        Pipeline
+      </a>
+    </li>
+  
+
+    
+      
+      
+      
+
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../data_format/" class="md-nav__link">
+        Standard Formats
+      </a>
+    </li>
+  
+
+    
+  </ul>
+</nav>
+                  </div>
+                </div>
+              </div>
+            
+            
+              
+              <div class="md-sidebar md-sidebar--secondary" data-md-component="sidebar" data-md-type="toc" >
+                <div class="md-sidebar__scrollwrap">
+                  <div class="md-sidebar__inner">
+                    
+
+<nav class="md-nav md-nav--secondary" aria-label="Table of contents">
+  
+  
+  
+    
+  
+  
+    <label class="md-nav__title" for="__toc">
+      <span class="md-nav__icon md-icon"></span>
+      Table of contents
+    </label>
+    <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
+      
+        <li class="md-nav__item">
+  <a href="#outline" class="md-nav__link">
+    Outline
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#1-install-miniconda-and-download-packages" class="md-nav__link">
+    1. Install miniconda and download packages
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#2-set-configurations" class="md-nav__link">
+    2. Set configurations
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#3-download-events-in-the-routine-catalog" class="md-nav__link">
+    3. Download events in the routine catalog
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#4-download-stations" class="md-nav__link">
+    4. Download stations
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#5-download-waveform-data" class="md-nav__link">
+    5. Download waveform data
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#8-plot-catalogs" class="md-nav__link">
+    8. Plot catalogs
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#9-parallel-processing-on-cloud" class="md-nav__link">
+    9. Parallel processing on cloud
+  </a>
+  
+</li>
+      
+    </ul>
+  
+</nav>
+                  </div>
+                </div>
+              </div>
+            
+          
+          <div class="md-content" data-md-component="content">
+            <article class="md-content__inner md-typeset">
+              
+                
+  <a href="https://github.com/ai4eps/QuakeFlow/edit/master/docs/kubeflow/workflow-api.ipynb" title="Edit this page" class="md-content__button md-icon">
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
+  </a>
+
+
+
+<script>
+(function (global, factory) {
+    typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() :
+    typeof define === 'function' && define.amd ? define(factory) :
+    (global = global || self, global.ClipboardCopyElement = factory());
+  }(this, function () { 'use strict';
+
+    function createNode(text) {
+      const node = document.createElement('pre');
+      node.style.width = '1px';
+      node.style.height = '1px';
+      node.style.position = 'fixed';
+      node.style.top = '5px';
+      node.textContent = text;
+      return node;
+    }
+
+    function copyNode(node) {
+      if ('clipboard' in navigator) {
+        // eslint-disable-next-line flowtype/no-flow-fix-me-comments
+        // $FlowFixMe Clipboard is not defined in Flow yet.
+        return navigator.clipboard.writeText(node.textContent);
+      }
+
+      const selection = getSelection();
+
+      if (selection == null) {
+        return Promise.reject(new Error());
+      }
+
+      selection.removeAllRanges();
+      const range = document.createRange();
+      range.selectNodeContents(node);
+      selection.addRange(range);
+      document.execCommand('copy');
+      selection.removeAllRanges();
+      return Promise.resolve();
+    }
+    function copyText(text) {
+      if ('clipboard' in navigator) {
+        // eslint-disable-next-line flowtype/no-flow-fix-me-comments
+        // $FlowFixMe Clipboard is not defined in Flow yet.
+        return navigator.clipboard.writeText(text);
+      }
+
+      const body = document.body;
+
+      if (!body) {
+        return Promise.reject(new Error());
+      }
+
+      const node = createNode(text);
+      body.appendChild(node);
+      copyNode(node);
+      body.removeChild(node);
+      return Promise.resolve();
+    }
+
+    function copy(button) {
+      const id = button.getAttribute('for');
+      const text = button.getAttribute('value');
+
+      function trigger() {
+        button.dispatchEvent(new CustomEvent('clipboard-copy', {
+          bubbles: true
+        }));
+      }
+
+      if (text) {
+        copyText(text).then(trigger);
+      } else if (id) {
+        const root = 'getRootNode' in Element.prototype ? button.getRootNode() : button.ownerDocument;
+        if (!(root instanceof Document || 'ShadowRoot' in window && root instanceof ShadowRoot)) return;
+        const node = root.getElementById(id);
+        if (node) copyTarget(node).then(trigger);
+      }
+    }
+
+    function copyTarget(content) {
+      if (content instanceof HTMLInputElement || content instanceof HTMLTextAreaElement) {
+        return copyText(content.value);
+      } else if (content instanceof HTMLAnchorElement && content.hasAttribute('href')) {
+        return copyText(content.href);
+      } else {
+        return copyNode(content);
+      }
+    }
+
+    function clicked(event) {
+      const button = event.currentTarget;
+
+      if (button instanceof HTMLElement) {
+        copy(button);
+      }
+    }
+
+    function keydown(event) {
+      if (event.key === ' ' || event.key === 'Enter') {
+        const button = event.currentTarget;
+
+        if (button instanceof HTMLElement) {
+          event.preventDefault();
+          copy(button);
+        }
+      }
+    }
+
+    function focused(event) {
+      event.currentTarget.addEventListener('keydown', keydown);
+    }
+
+    function blurred(event) {
+      event.currentTarget.removeEventListener('keydown', keydown);
+    }
+
+    class ClipboardCopyElement extends HTMLElement {
+      constructor() {
+        super();
+        this.addEventListener('click', clicked);
+        this.addEventListener('focus', focused);
+        this.addEventListener('blur', blurred);
+      }
+
+      connectedCallback() {
+        if (!this.hasAttribute('tabindex')) {
+          this.setAttribute('tabindex', '0');
+        }
+
+        if (!this.hasAttribute('role')) {
+          this.setAttribute('role', 'button');
+        }
+      }
+
+      get value() {
+        return this.getAttribute('value') || '';
+      }
+
+      set value(text) {
+        this.setAttribute('value', text);
+      }
+
+    }
+
+    if (!window.customElements.get('clipboard-copy')) {
+      window.ClipboardCopyElement = ClipboardCopyElement;
+      window.customElements.define('clipboard-copy', ClipboardCopyElement);
+    }
+
+    return ClipboardCopyElement;
+
+  }));
+</script>
+<script>
+      document.addEventListener('clipboard-copy', function(event) {
+        const notice = event.target.querySelector('.notice')
+        notice.hidden = false
+        setTimeout(function() {
+          notice.hidden = true
+        }, 1000)
+      })
+</script>
+
+
+
+
+<style type="text/css">
+    
+    pre { line-height: 125%; }
+td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+.highlight-ipynb .hll { background-color: var(--jp-cell-editor-active-background) }
+.highlight-ipynb { background: var(--jp-cell-editor-background); color: var(--jp-mirror-editor-variable-color) }
+.highlight-ipynb .c { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment */
+.highlight-ipynb .err { color: var(--jp-mirror-editor-error-color) } /* Error */
+.highlight-ipynb .k { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword */
+.highlight-ipynb .o { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator */
+.highlight-ipynb .p { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation */
+.highlight-ipynb .ch { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Hashbang */
+.highlight-ipynb .cm { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Multiline */
+.highlight-ipynb .cp { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Preproc */
+.highlight-ipynb .cpf { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.PreprocFile */
+.highlight-ipynb .c1 { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Single */
+.highlight-ipynb .cs { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Special */
+.highlight-ipynb .kc { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Constant */
+.highlight-ipynb .kd { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Declaration */
+.highlight-ipynb .kn { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Namespace */
+.highlight-ipynb .kp { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Pseudo */
+.highlight-ipynb .kr { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Reserved */
+.highlight-ipynb .kt { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Type */
+.highlight-ipynb .m { color: var(--jp-mirror-editor-number-color) } /* Literal.Number */
+.highlight-ipynb .s { color: var(--jp-mirror-editor-string-color) } /* Literal.String */
+.highlight-ipynb .ow { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator.Word */
+.highlight-ipynb .pm { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation.Marker */
+.highlight-ipynb .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */
+.highlight-ipynb .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */
+.highlight-ipynb .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */
+.highlight-ipynb .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */
+.highlight-ipynb .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */
+.highlight-ipynb .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */
+.highlight-ipynb .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */
+.highlight-ipynb .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */
+.highlight-ipynb .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */
+.highlight-ipynb .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */
+.highlight-ipynb .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */
+.highlight-ipynb .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */
+.highlight-ipynb .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */
+.highlight-ipynb .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */
+.highlight-ipynb .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */
+.highlight-ipynb .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */
+.highlight-ipynb .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */
+.highlight-ipynb .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */
+.highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */
+.highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */
+  </style>
+
+
+
+
+<style type="text/css">
+/*
+This file is taken from the built JupyterLab theme.css
+Found on share/nbconvert/templates/lab/static
+Some changes have been made and marked with CHANGE
+*/
+
+.jupyter-wrapper {
+    /* Elevation
+   *
+   * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here:
+   *
+   * https://github.com/material-components/material-components-web
+   * https://material-components-web.appspot.com/elevation.html
+   */
+
+    --jp-shadow-base-lightness: 0;
+    --jp-shadow-umbra-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.2
+    );
+    --jp-shadow-penumbra-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.14
+    );
+    --jp-shadow-ambient-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.12
+    );
+    --jp-elevation-z0: none;
+    --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color),
+        0px 1px 1px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 3px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color),
+        0px 2px 2px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 5px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color),
+        0px 4px 5px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 10px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color),
+        0px 6px 10px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 18px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color),
+        0px 8px 10px 1px var(--jp-shadow-penumbra-color),
+        0px 3px 14px 2px var(--jp-shadow-ambient-color);
+    --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color),
+        0px 12px 17px 2px var(--jp-shadow-penumbra-color),
+        0px 5px 22px 4px var(--jp-shadow-ambient-color);
+    --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color),
+        0px 16px 24px 2px var(--jp-shadow-penumbra-color),
+        0px 6px 30px 5px var(--jp-shadow-ambient-color);
+    --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color),
+        0px 20px 31px 3px var(--jp-shadow-penumbra-color),
+        0px 8px 38px 7px var(--jp-shadow-ambient-color);
+    --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color),
+        0px 24px 38px 3px var(--jp-shadow-penumbra-color),
+        0px 9px 46px 8px var(--jp-shadow-ambient-color);
+
+    /* Borders
+   *
+   * The following variables, specify the visual styling of borders in JupyterLab.
+   */
+
+    --jp-border-width: 1px;
+    --jp-border-color0: var(--md-grey-400);
+    --jp-border-color1: var(--md-grey-400);
+    --jp-border-color2: var(--md-grey-300);
+    --jp-border-color3: var(--md-grey-200);
+    --jp-border-radius: 2px;
+
+    /* UI Fonts
+   *
+   * The UI font CSS variables are used for the typography all of the JupyterLab
+   * user interface elements that are not directly user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-ui-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+    --jp-ui-font-scale-factor: 1.2;
+    --jp-ui-font-size0: 0.83333em;
+    --jp-ui-font-size1: 13px; /* Base font size */
+    --jp-ui-font-size2: 1.2em;
+    --jp-ui-font-size3: 1.44em;
+
+    --jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+
+    /*
+   * Use these font colors against the corresponding main layout colors.
+   * In a light theme, these go from dark to light.
+   */
+
+    /* Defaults use Material Design specification */
+    --jp-ui-font-color0: rgba(0, 0, 0, 1);
+    --jp-ui-font-color1: rgba(0, 0, 0, 0.87);
+    --jp-ui-font-color2: rgba(0, 0, 0, 0.54);
+    --jp-ui-font-color3: rgba(0, 0, 0, 0.38);
+
+    /*
+   * Use these against the brand/accent/warn/error colors.
+   * These will typically go from light to darker, in both a dark and light theme.
+   */
+
+    --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1);
+    --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1);
+    --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7);
+    --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5);
+
+    /* Content Fonts
+   *
+   * Content font variables are used for typography of user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-content-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+    --jp-content-line-height: 1.6;
+    --jp-content-font-scale-factor: 1.2;
+    --jp-content-font-size0: 0.83333em;
+    --jp-content-font-size1: 14px; /* Base font size */
+    --jp-content-font-size2: 1.2em;
+    --jp-content-font-size3: 1.44em;
+    --jp-content-font-size4: 1.728em;
+    --jp-content-font-size5: 2.0736em;
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-content-presentation-font-size1: 17px;
+
+    --jp-content-heading-line-height: 1;
+    --jp-content-heading-margin-top: 1.2em;
+    --jp-content-heading-margin-bottom: 0.8em;
+    --jp-content-heading-font-weight: 500;
+
+    /* Defaults use Material Design specification */
+    --jp-content-font-color0: rgba(0, 0, 0, 1);
+    --jp-content-font-color1: rgba(0, 0, 0, 0.87);
+    --jp-content-font-color2: rgba(0, 0, 0, 0.54);
+    --jp-content-font-color3: rgba(0, 0, 0, 0.38);
+
+    --jp-content-link-color: var(--md-blue-700);
+
+    --jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+
+    /*
+   * Code Fonts
+   *
+   * Code font variables are used for typography of code and other monospaces content.
+   */
+
+    --jp-code-font-size: 13px;
+    --jp-code-line-height: 1.3077; /* 17px for 13px base */
+    --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */
+    --jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono",
+        monospace;
+    --jp-code-font-family: var(--jp-code-font-family-default);
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-code-presentation-font-size: 16px;
+
+    /* may need to tweak cursor width if you change font size */
+    --jp-code-cursor-width0: 1.4px;
+    --jp-code-cursor-width1: 2px;
+    --jp-code-cursor-width2: 4px;
+
+    /* Layout
+   *
+   * The following are the main layout colors use in JupyterLab. In a light
+   * theme these would go from light to dark.
+   */
+
+    --jp-layout-color0: white;
+    --jp-layout-color1: white;
+    --jp-layout-color2: var(--md-grey-200);
+    --jp-layout-color3: var(--md-grey-400);
+    --jp-layout-color4: var(--md-grey-600);
+
+    /* Inverse Layout
+   *
+   * The following are the inverse layout colors use in JupyterLab. In a light
+   * theme these would go from dark to light.
+   */
+
+    --jp-inverse-layout-color0: #111111;
+    --jp-inverse-layout-color1: var(--md-grey-900);
+    --jp-inverse-layout-color2: var(--md-grey-800);
+    --jp-inverse-layout-color3: var(--md-grey-700);
+    --jp-inverse-layout-color4: var(--md-grey-600);
+
+    /* Brand/accent */
+
+    --jp-brand-color0: var(--md-blue-900);
+    --jp-brand-color1: var(--md-blue-700);
+    --jp-brand-color2: var(--md-blue-300);
+    --jp-brand-color3: var(--md-blue-100);
+    --jp-brand-color4: var(--md-blue-50);
+
+    --jp-accent-color0: var(--md-green-900);
+    --jp-accent-color1: var(--md-green-700);
+    --jp-accent-color2: var(--md-green-300);
+    --jp-accent-color3: var(--md-green-100);
+
+    /* State colors (warn, error, success, info) */
+
+    --jp-warn-color0: var(--md-orange-900);
+    --jp-warn-color1: var(--md-orange-700);
+    --jp-warn-color2: var(--md-orange-300);
+    --jp-warn-color3: var(--md-orange-100);
+
+    --jp-error-color0: var(--md-red-900);
+    --jp-error-color1: var(--md-red-700);
+    --jp-error-color2: var(--md-red-300);
+    --jp-error-color3: var(--md-red-100);
+
+    --jp-success-color0: var(--md-green-900);
+    --jp-success-color1: var(--md-green-700);
+    --jp-success-color2: var(--md-green-300);
+    --jp-success-color3: var(--md-green-100);
+
+    --jp-info-color0: var(--md-cyan-900);
+    --jp-info-color1: var(--md-cyan-700);
+    --jp-info-color2: var(--md-cyan-300);
+    --jp-info-color3: var(--md-cyan-100);
+
+    /* Cell specific styles */
+
+    --jp-cell-padding: 5px;
+
+    --jp-cell-collapser-width: 8px;
+    --jp-cell-collapser-min-height: 20px;
+    --jp-cell-collapser-not-active-hover-opacity: 0.6;
+
+    --jp-cell-editor-background: var(--md-grey-100);
+    --jp-cell-editor-border-color: var(--md-grey-300);
+    --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-cell-editor-active-background: var(--jp-layout-color0);
+    --jp-cell-editor-active-border-color: var(--jp-brand-color1);
+
+    --jp-cell-prompt-width: 64px;
+    --jp-cell-prompt-font-family: var(--jp-code-font-family-default);
+    --jp-cell-prompt-letter-spacing: 0px;
+    --jp-cell-prompt-opacity: 1;
+    --jp-cell-prompt-not-active-opacity: 0.5;
+    --jp-cell-prompt-not-active-font-color: var(--md-grey-700);
+    /* A custom blend of MD grey and blue 600
+   * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */
+    --jp-cell-inprompt-font-color: #307fc1;
+    /* A custom blend of MD grey and orange 600
+   * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */
+    --jp-cell-outprompt-font-color: #bf5b3d;
+
+    /* Notebook specific styles */
+
+    --jp-notebook-padding: 10px;
+    --jp-notebook-select-background: var(--jp-layout-color1);
+    --jp-notebook-multiselected-color: var(--md-blue-50);
+
+    /* The scroll padding is calculated to fill enough space at the bottom of the
+  notebook to show one single-line cell (with appropriate padding) at the top
+  when the notebook is scrolled all the way to the bottom. We also subtract one
+  pixel so that no scrollbar appears if we have just one single-line cell in the
+  notebook. This padding is to enable a 'scroll past end' feature in a notebook.
+  */
+    --jp-notebook-scroll-padding: calc(
+        100% - var(--jp-code-font-size) * var(--jp-code-line-height) -
+            var(--jp-code-padding) - var(--jp-cell-padding) - 1px
+    );
+
+    /* Rendermime styles */
+
+    --jp-rendermime-error-background: #fdd;
+    --jp-rendermime-table-row-background: var(--md-grey-100);
+    --jp-rendermime-table-row-hover-background: var(--md-light-blue-50);
+
+    /* Dialog specific styles */
+
+    --jp-dialog-background: rgba(0, 0, 0, 0.25);
+
+    /* Console specific styles */
+
+    --jp-console-padding: 10px;
+
+    /* Toolbar specific styles */
+
+    --jp-toolbar-border-color: var(--jp-border-color1);
+    --jp-toolbar-micro-height: 8px;
+    --jp-toolbar-background: var(--jp-layout-color1);
+    --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24);
+    --jp-toolbar-header-margin: 4px 4px 0px 4px;
+    --jp-toolbar-active-background: var(--md-grey-300);
+
+    /* Statusbar specific styles */
+
+    --jp-statusbar-height: 24px;
+
+    /* Input field styles */
+
+    --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-input-active-background: var(--jp-layout-color1);
+    --jp-input-hover-background: var(--jp-layout-color1);
+    --jp-input-background: var(--md-grey-100);
+    --jp-input-border-color: var(--jp-border-color1);
+    --jp-input-active-border-color: var(--jp-brand-color1);
+    --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3);
+
+    /* General editor styles */
+
+    --jp-editor-selected-background: #d9d9d9;
+    --jp-editor-selected-focused-background: #d7d4f0;
+    --jp-editor-cursor-color: var(--jp-ui-font-color0);
+
+    /* Code mirror specific styles */
+
+    --jp-mirror-editor-keyword-color: #008000;
+    --jp-mirror-editor-atom-color: #88f;
+    --jp-mirror-editor-number-color: #080;
+    --jp-mirror-editor-def-color: #00f;
+    --jp-mirror-editor-variable-color: var(--md-grey-900);
+    --jp-mirror-editor-variable-2-color: #05a;
+    --jp-mirror-editor-variable-3-color: #085;
+    --jp-mirror-editor-punctuation-color: #05a;
+    --jp-mirror-editor-property-color: #05a;
+    --jp-mirror-editor-operator-color: #aa22ff;
+    --jp-mirror-editor-comment-color: #408080;
+    --jp-mirror-editor-string-color: #ba2121;
+    --jp-mirror-editor-string-2-color: #708;
+    --jp-mirror-editor-meta-color: #aa22ff;
+    --jp-mirror-editor-qualifier-color: #555;
+    --jp-mirror-editor-builtin-color: #008000;
+    --jp-mirror-editor-bracket-color: #997;
+    --jp-mirror-editor-tag-color: #170;
+    --jp-mirror-editor-attribute-color: #00c;
+    --jp-mirror-editor-header-color: blue;
+    --jp-mirror-editor-quote-color: #090;
+    --jp-mirror-editor-link-color: #00c;
+    --jp-mirror-editor-error-color: #f00;
+    --jp-mirror-editor-hr-color: #999;
+
+    /* Vega extension styles */
+
+    --jp-vega-background: white;
+
+    /* Sidebar-related styles */
+
+    --jp-sidebar-min-width: 250px;
+
+    /* Search-related styles */
+
+    --jp-search-toggle-off-opacity: 0.5;
+    --jp-search-toggle-hover-opacity: 0.8;
+    --jp-search-toggle-on-opacity: 1;
+    --jp-search-selected-match-background-color: rgb(245, 200, 0);
+    --jp-search-selected-match-color: black;
+    --jp-search-unselected-match-background-color: var(
+        --jp-inverse-layout-color0
+    );
+    --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);
+
+    /* Icon colors that work well with light or dark backgrounds */
+    --jp-icon-contrast-color0: var(--md-purple-600);
+    --jp-icon-contrast-color1: var(--md-green-600);
+    --jp-icon-contrast-color2: var(--md-pink-600);
+    --jp-icon-contrast-color3: var(--md-blue-600);
+}
+
+[data-md-color-scheme="slate"] .jupyter-wrapper {
+    /* Elevation
+   *
+   * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here:
+   *
+   * https://github.com/material-components/material-components-web
+   * https://material-components-web.appspot.com/elevation.html
+   */
+
+    /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout
+   * colors used in the theme as well. */
+    --jp-shadow-base-lightness: 32;
+    --jp-shadow-umbra-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.2
+    );
+    --jp-shadow-penumbra-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.14
+    );
+    --jp-shadow-ambient-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.12
+    );
+    --jp-elevation-z0: none;
+    --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color),
+        0px 1px 1px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 3px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color),
+        0px 2px 2px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 5px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color),
+        0px 4px 5px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 10px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color),
+        0px 6px 10px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 18px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color),
+        0px 8px 10px 1px var(--jp-shadow-penumbra-color),
+        0px 3px 14px 2px var(--jp-shadow-ambient-color);
+    --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color),
+        0px 12px 17px 2px var(--jp-shadow-penumbra-color),
+        0px 5px 22px 4px var(--jp-shadow-ambient-color);
+    --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color),
+        0px 16px 24px 2px var(--jp-shadow-penumbra-color),
+        0px 6px 30px 5px var(--jp-shadow-ambient-color);
+    --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color),
+        0px 20px 31px 3px var(--jp-shadow-penumbra-color),
+        0px 8px 38px 7px var(--jp-shadow-ambient-color);
+    --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color),
+        0px 24px 38px 3px var(--jp-shadow-penumbra-color),
+        0px 9px 46px 8px var(--jp-shadow-ambient-color);
+
+    /* Borders
+   *
+   * The following variables, specify the visual styling of borders in JupyterLab.
+   */
+
+    --jp-border-width: 1px;
+    --jp-border-color0: var(--md-grey-700);
+    --jp-border-color1: var(--md-grey-700);
+    --jp-border-color2: var(--md-grey-800);
+    --jp-border-color3: var(--md-grey-900);
+    --jp-border-radius: 2px;
+
+    /* UI Fonts
+   *
+   * The UI font CSS variables are used for the typography all of the JupyterLab
+   * user interface elements that are not directly user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-ui-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+    --jp-ui-font-scale-factor: 1.2;
+    --jp-ui-font-size0: 0.83333em;
+    --jp-ui-font-size1: 13px; /* Base font size */
+    --jp-ui-font-size2: 1.2em;
+    --jp-ui-font-size3: 1.44em;
+
+    --jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+
+    /*
+   * Use these font colors against the corresponding main layout colors.
+   * In a light theme, these go from dark to light.
+   */
+
+    /* Defaults use Material Design specification */
+    --jp-ui-font-color0: rgba(255, 255, 255, 1);
+    --jp-ui-font-color1: rgba(255, 255, 255, 0.87);
+    --jp-ui-font-color2: rgba(255, 255, 255, 0.54);
+    --jp-ui-font-color3: rgba(255, 255, 255, 0.38);
+
+    /*
+   * Use these against the brand/accent/warn/error colors.
+   * These will typically go from light to darker, in both a dark and light theme.
+   */
+
+    --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1);
+    --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8);
+    --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5);
+    --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3);
+
+    /* Content Fonts
+   *
+   * Content font variables are used for typography of user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-content-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+    --jp-content-line-height: 1.6;
+    --jp-content-font-scale-factor: 1.2;
+    --jp-content-font-size0: 0.83333em;
+    --jp-content-font-size1: 14px; /* Base font size */
+    --jp-content-font-size2: 1.2em;
+    --jp-content-font-size3: 1.44em;
+    --jp-content-font-size4: 1.728em;
+    --jp-content-font-size5: 2.0736em;
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-content-presentation-font-size1: 17px;
+
+    --jp-content-heading-line-height: 1;
+    --jp-content-heading-margin-top: 1.2em;
+    --jp-content-heading-margin-bottom: 0.8em;
+    --jp-content-heading-font-weight: 500;
+
+    /* Defaults use Material Design specification */
+    --jp-content-font-color0: rgba(255, 255, 255, 1);
+    --jp-content-font-color1: rgba(255, 255, 255, 1);
+    --jp-content-font-color2: rgba(255, 255, 255, 0.7);
+    --jp-content-font-color3: rgba(255, 255, 255, 0.5);
+
+    --jp-content-link-color: var(--md-blue-300);
+
+    --jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+
+    /*
+   * Code Fonts
+   *
+   * Code font variables are used for typography of code and other monospaces content.
+   */
+
+    --jp-code-font-size: 13px;
+    --jp-code-line-height: 1.3077; /* 17px for 13px base */
+    --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */
+    --jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono",
+        monospace;
+    --jp-code-font-family: var(--jp-code-font-family-default);
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-code-presentation-font-size: 16px;
+
+    /* may need to tweak cursor width if you change font size */
+    --jp-code-cursor-width0: 1.4px;
+    --jp-code-cursor-width1: 2px;
+    --jp-code-cursor-width2: 4px;
+
+    /* Layout
+   *
+   * The following are the main layout colors use in JupyterLab. In a light
+   * theme these would go from light to dark.
+   */
+
+    --jp-layout-color0: #111111;
+    --jp-layout-color1: var(--md-grey-900);
+    --jp-layout-color2: var(--md-grey-800);
+    --jp-layout-color3: var(--md-grey-700);
+    --jp-layout-color4: var(--md-grey-600);
+
+    /* Inverse Layout
+   *
+   * The following are the inverse layout colors use in JupyterLab. In a light
+   * theme these would go from dark to light.
+   */
+
+    --jp-inverse-layout-color0: white;
+    --jp-inverse-layout-color1: white;
+    --jp-inverse-layout-color2: var(--md-grey-200);
+    --jp-inverse-layout-color3: var(--md-grey-400);
+    --jp-inverse-layout-color4: var(--md-grey-600);
+
+    /* Brand/accent */
+
+    --jp-brand-color0: var(--md-blue-700);
+    --jp-brand-color1: var(--md-blue-500);
+    --jp-brand-color2: var(--md-blue-300);
+    --jp-brand-color3: var(--md-blue-100);
+    --jp-brand-color4: var(--md-blue-50);
+
+    --jp-accent-color0: var(--md-green-700);
+    --jp-accent-color1: var(--md-green-500);
+    --jp-accent-color2: var(--md-green-300);
+    --jp-accent-color3: var(--md-green-100);
+
+    /* State colors (warn, error, success, info) */
+
+    --jp-warn-color0: var(--md-orange-700);
+    --jp-warn-color1: var(--md-orange-500);
+    --jp-warn-color2: var(--md-orange-300);
+    --jp-warn-color3: var(--md-orange-100);
+
+    --jp-error-color0: var(--md-red-700);
+    --jp-error-color1: var(--md-red-500);
+    --jp-error-color2: var(--md-red-300);
+    --jp-error-color3: var(--md-red-100);
+
+    --jp-success-color0: var(--md-green-700);
+    --jp-success-color1: var(--md-green-500);
+    --jp-success-color2: var(--md-green-300);
+    --jp-success-color3: var(--md-green-100);
+
+    --jp-info-color0: var(--md-cyan-700);
+    --jp-info-color1: var(--md-cyan-500);
+    --jp-info-color2: var(--md-cyan-300);
+    --jp-info-color3: var(--md-cyan-100);
+
+    /* Cell specific styles */
+
+    --jp-cell-padding: 5px;
+
+    --jp-cell-collapser-width: 8px;
+    --jp-cell-collapser-min-height: 20px;
+    --jp-cell-collapser-not-active-hover-opacity: 0.6;
+
+    --jp-cell-editor-background: var(--jp-layout-color1);
+    --jp-cell-editor-border-color: var(--md-grey-700);
+    --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-cell-editor-active-background: var(--jp-layout-color0);
+    --jp-cell-editor-active-border-color: var(--jp-brand-color1);
+
+    --jp-cell-prompt-width: 64px;
+    --jp-cell-prompt-font-family: var(--jp-code-font-family-default);
+    --jp-cell-prompt-letter-spacing: 0px;
+    --jp-cell-prompt-opacity: 1;
+    --jp-cell-prompt-not-active-opacity: 1;
+    --jp-cell-prompt-not-active-font-color: var(--md-grey-300);
+
+    /* A custom blend of MD grey and blue 600
+   * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */
+    --jp-cell-inprompt-font-color: #307fc1;
+    /* A custom blend of MD grey and orange 600
+   * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */
+    --jp-cell-outprompt-font-color: #bf5b3d;
+
+    /* Notebook specific styles */
+
+    --jp-notebook-padding: 10px;
+    --jp-notebook-select-background: var(--jp-layout-color1);
+    --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24);
+
+    /* The scroll padding is calculated to fill enough space at the bottom of the
+  notebook to show one single-line cell (with appropriate padding) at the top
+  when the notebook is scrolled all the way to the bottom. We also subtract one
+  pixel so that no scrollbar appears if we have just one single-line cell in the
+  notebook. This padding is to enable a 'scroll past end' feature in a notebook.
+  */
+    --jp-notebook-scroll-padding: calc(
+        100% - var(--jp-code-font-size) * var(--jp-code-line-height) -
+            var(--jp-code-padding) - var(--jp-cell-padding) - 1px
+    );
+
+    /* Rendermime styles */
+
+    --jp-rendermime-error-background: rgba(244, 67, 54, 0.28);
+    --jp-rendermime-table-row-background: var(--md-grey-900);
+    --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2);
+
+    /* Dialog specific styles */
+
+    --jp-dialog-background: rgba(0, 0, 0, 0.6);
+
+    /* Console specific styles */
+
+    --jp-console-padding: 10px;
+
+    /* Toolbar specific styles */
+
+    --jp-toolbar-border-color: var(--jp-border-color2);
+    --jp-toolbar-micro-height: 8px;
+    --jp-toolbar-background: var(--jp-layout-color1);
+    --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8);
+    --jp-toolbar-header-margin: 4px 4px 0px 4px;
+    --jp-toolbar-active-background: var(--jp-layout-color0);
+
+    /* Statusbar specific styles */
+
+    --jp-statusbar-height: 24px;
+
+    /* Input field styles */
+
+    --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-input-active-background: var(--jp-layout-color0);
+    --jp-input-hover-background: var(--jp-layout-color2);
+    --jp-input-background: var(--md-grey-800);
+    --jp-input-border-color: var(--jp-border-color1);
+    --jp-input-active-border-color: var(--jp-brand-color1);
+    --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3);
+
+    /* General editor styles */
+
+    --jp-editor-selected-background: var(--jp-layout-color2);
+    --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24);
+    --jp-editor-cursor-color: var(--jp-ui-font-color0);
+
+    /* Code mirror specific styles */
+
+    --jp-mirror-editor-keyword-color: var(--md-green-500);
+    --jp-mirror-editor-atom-color: var(--md-blue-300);
+    --jp-mirror-editor-number-color: var(--md-green-400);
+    --jp-mirror-editor-def-color: var(--md-blue-600);
+    --jp-mirror-editor-variable-color: var(--md-grey-300);
+    --jp-mirror-editor-variable-2-color: var(--md-blue-400);
+    --jp-mirror-editor-variable-3-color: var(--md-green-600);
+    --jp-mirror-editor-punctuation-color: var(--md-blue-400);
+    --jp-mirror-editor-property-color: var(--md-blue-400);
+    --jp-mirror-editor-operator-color: #aa22ff;
+    --jp-mirror-editor-comment-color: #408080;
+    --jp-mirror-editor-string-color: #ff7070;
+    --jp-mirror-editor-string-2-color: var(--md-purple-300);
+    --jp-mirror-editor-meta-color: #aa22ff;
+    --jp-mirror-editor-qualifier-color: #555;
+    --jp-mirror-editor-builtin-color: var(--md-green-600);
+    --jp-mirror-editor-bracket-color: #997;
+    --jp-mirror-editor-tag-color: var(--md-green-700);
+    --jp-mirror-editor-attribute-color: var(--md-blue-700);
+    --jp-mirror-editor-header-color: var(--md-blue-500);
+    --jp-mirror-editor-quote-color: var(--md-green-300);
+    --jp-mirror-editor-link-color: var(--md-blue-700);
+    --jp-mirror-editor-error-color: #f00;
+    --jp-mirror-editor-hr-color: #999;
+
+    /* Vega extension styles */
+
+    --jp-vega-background: var(--md-grey-400);
+
+    /* Sidebar-related styles */
+
+    --jp-sidebar-min-width: 250px;
+
+    /* Search-related styles */
+
+    --jp-search-toggle-off-opacity: 0.6;
+    --jp-search-toggle-hover-opacity: 0.8;
+    --jp-search-toggle-on-opacity: 1;
+    --jp-search-selected-match-background-color: rgb(255, 225, 0);
+    --jp-search-selected-match-color: black;
+    --jp-search-unselected-match-background-color: var(
+        --jp-inverse-layout-color0
+    );
+    --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);
+
+    /* scrollbar related styles. Supports every browser except Edge. */
+
+    /* colors based on JetBrain's Darcula theme */
+
+    --jp-scrollbar-background-color: #3f4244;
+    --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */
+
+    --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */
+
+    /* hacks for setting the thumb shape. These do nothing in Firefox */
+
+    --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */
+    --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */
+
+    /* Icon colors that work well with light or dark backgrounds */
+    --jp-icon-contrast-color0: var(--md-purple-600);
+    --jp-icon-contrast-color1: var(--md-green-600);
+    --jp-icon-contrast-color2: var(--md-pink-600);
+    --jp-icon-contrast-color3: var(--md-blue-600);
+}
+
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+Copyright 2015-present Palantir Technologies, Inc. All rights reserved.
+Licensed under the Apache License, Version 2.0.
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+
+Copyright 2017-present Palantir Technologies, Inc. All rights reserved.
+Licensed under the Apache License, Version 2.0.
+
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+
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+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
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+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="earthquake-detection-workflow">Earthquake Detection Workflow<a class="anchor-link" href="#earthquake-detection-workflow">&#182;</a></h1><h2 id="outline">Outline<a class="anchor-link" href="#outline">&#182;</a></h2><p>Here we show an example of the current modules in QuakeFlow</p>
+<ol>
+<li><p>Download data using Obpsy:</p>
+<p><a href="https://docs.obspy.org/packages/obspy.clients.fdsn.html#module-obspy.clients.fdsn">FDSN web service client for ObsPy</a></p>
+<p><a href="https://docs.obspy.org/packages/autogen/obspy.clients.fdsn.mass_downloader.html#module-obspy.clients.fdsn.mass_downloader">Mass Downloader for FDSN Compliant Web Services</a></p>
+</li>
+<li><p>PhaseNet for picking P/S phases</p>
+<p>Find more details in <a href="https://wayneweiqiang.github.io/PhaseNet/">PhaseNet github page</a></p>
+</li>
+<li><p>GaMMA for associating picking and estimate approximate location and magnitude</p>
+<p>Find more details in <a href="https://wayneweiqiang.github.io/GMMA/">GaMMA github page</a></p>
+</li>
+<li><p>Earthquake location, magnitude estimation, etc. (to be continued)</p>
+</li>
+</ol>
+
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="1-install-miniconda-and-download-packages">1. Install <a href="https://docs.conda.io/en/latest/miniconda.html">miniconda</a> and download packages<a class="anchor-link" href="#1-install-miniconda-and-download-packages">&#182;</a></h2>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<p><!-- # %%capture --></p>
+<div class="highlight"><pre><span></span>git<span class="w"> </span>clone<span class="w"> </span>https://github.com/wayneweiqiang/PhaseNet.git
+git<span class="w"> </span>clone<span class="w"> </span>https://github.com/wayneweiqiang/GMMA.git
+conda<span class="w"> </span>env<span class="w"> </span>update<span class="w"> </span>-f<span class="o">=</span>env.yml<span class="w"> </span>-n<span class="w"> </span>base
+</pre></div>
+<p><strong>Second option: install to quakeflow environment, but need to select jupyter notebook kernel to quakflow</strong></p>
+<div class="highlight"><pre><span></span>conda<span class="w"> </span>env<span class="w"> </span>create<span class="w"> </span>-f<span class="o">=</span>env.yml<span class="w"> </span>-n<span class="w"> </span>quakeflow
+python<span class="w"> </span>-m<span class="w"> </span>ipykernel<span class="w"> </span>install<span class="w"> </span>--user<span class="w"> </span>--name<span class="o">=</span>quakeflow
+</pre></div>
+
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
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+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[24]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
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+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">kfp</span>
+<span class="kn">import</span> <span class="nn">kfp.dsl</span> <span class="k">as</span> <span class="nn">dsl</span>
+<span class="kn">import</span> <span class="nn">kfp.components</span> <span class="k">as</span> <span class="nn">comp</span>
+<span class="kn">from</span> <span class="nn">kfp.components</span> <span class="kn">import</span> <span class="n">InputPath</span><span class="p">,</span> <span class="n">OutputPath</span>
+<span class="kn">import</span> <span class="nn">warnings</span>
+<span class="n">warnings</span><span class="o">.</span><span class="n">filterwarnings</span><span class="p">(</span><span class="s2">&quot;ignore&quot;</span><span class="p">)</span>
+</pre></div>
+<div id="cell-1" class="clipboard-copy-txt">import kfp
+import kfp.dsl as dsl
+import kfp.components as comp
+from kfp.components import InputPath, OutputPath
+import warnings
+warnings.filterwarnings("ignore")</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="2-set-configurations">2. Set configurations<a class="anchor-link" href="#2-set-configurations">&#182;</a></h2>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[25]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-2">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
+<span class="kn">import</span> <span class="nn">matplotlib</span>
+<span class="c1"># matplotlib.use(&quot;agg&quot;)</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+
+<span class="c1"># region_name = &quot;Ridgecrest_demo&quot;</span>
+<span class="c1"># region_name = &quot;Ridgecrest_oneweek&quot;</span>
+<span class="c1"># region_name = &quot;SaltonSea&quot;</span>
+<span class="c1"># region_name = &quot;Ridgecrest&quot;</span>
+<span class="c1"># region_name = &quot;SanSimeon&quot;</span>
+<span class="c1"># region_name = &quot;Italy&quot;</span>
+<span class="c1"># region_name = &quot;PNSN&quot;</span>
+<span class="n">region_name</span> <span class="o">=</span> <span class="s2">&quot;Hawaii&quot;</span>
+<span class="c1"># region_name = &quot;PuertoRico&quot;</span>
+<span class="c1"># region_name = &quot;SmithValley&quot;</span>
+<span class="c1"># region_name = &quot;Antilles&quot;</span>
+<span class="n">dir_name</span> <span class="o">=</span> <span class="n">region_name</span>
+<span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">dir_name</span><span class="p">):</span>
+    <span class="n">os</span><span class="o">.</span><span class="n">mkdir</span><span class="p">(</span><span class="n">dir_name</span><span class="p">)</span>
+<span class="n">root_dir</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">dir_name</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span>
+
+<span class="n">run_local</span> <span class="o">=</span> <span class="kc">False</span>
+</pre></div>
+<div id="cell-2" class="clipboard-copy-txt">import os
+import matplotlib
+# matplotlib.use("agg")
+import matplotlib.pyplot as plt
+
+# region_name = "Ridgecrest_demo"
+# region_name = "Ridgecrest_oneweek"
+# region_name = "SaltonSea"
+# region_name = "Ridgecrest"
+# region_name = "SanSimeon"
+# region_name = "Italy"
+# region_name = "PNSN"
+region_name = "Hawaii"
+# region_name = "PuertoRico"
+# region_name = "SmithValley"
+# region_name = "Antilles"
+dir_name = region_name
+if not os.path.exists(dir_name):
+    os.mkdir(dir_name)
+root_dir = lambda x: os.path.join(dir_name, x)
+
+run_local = False</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[26]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-3">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">def</span> <span class="nf">set_config</span><span class="p">(</span>
+    <span class="n">index_json</span><span class="p">:</span> <span class="n">OutputPath</span><span class="p">(</span><span class="s2">&quot;json&quot;</span><span class="p">),</span>
+    <span class="n">config_json</span><span class="p">:</span> <span class="n">OutputPath</span><span class="p">(</span><span class="s2">&quot;json&quot;</span><span class="p">),</span>
+    <span class="n">datetime_json</span><span class="p">:</span> <span class="n">OutputPath</span><span class="p">(</span><span class="s2">&quot;json&quot;</span><span class="p">),</span>
+    <span class="n">num_parallel</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">:</span>
+
+    <span class="kn">import</span> <span class="nn">obspy</span>
+    <span class="kn">import</span> <span class="nn">datetime</span>
+    <span class="kn">import</span> <span class="nn">json</span>
+
+    <span class="n">pi</span> <span class="o">=</span> <span class="mf">3.1415926</span>
+    <span class="n">degree2km</span> <span class="o">=</span> <span class="n">pi</span> <span class="o">*</span> <span class="mi">6371</span> <span class="o">/</span> <span class="mi">180</span>
+
+    <span class="c1"># region_name = &quot;Ridgecrest_demo&quot;</span>
+    <span class="c1"># center = (-117.504, 35.705)</span>
+    <span class="c1"># horizontal_degree = 1.0</span>
+    <span class="c1"># vertical_degree = 1.0</span>
+    <span class="c1"># starttime = obspy.UTCDateTime(&quot;2019-07-04T17&quot;)</span>
+    <span class="c1"># endtime = obspy.UTCDateTime(&quot;2019-07-04T19&quot;)</span>
+    <span class="c1"># client = &quot;SCEDC&quot;</span>
+    <span class="c1"># network_list = [&quot;CI&quot;]</span>
+    <span class="c1"># channel_list = &quot;HH*,BH*,EH*,HN*&quot;</span>
+
+    <span class="c1"># region_name = &quot;Ridgecrest_oneweek&quot;</span>
+    <span class="c1"># center = (-117.504, 35.705)</span>
+    <span class="c1"># horizontal_degree = 1.0</span>
+    <span class="c1"># vertical_degree = 1.0</span>
+    <span class="c1"># starttime = obspy.UTCDateTime(&quot;2019-07-04T00&quot;)</span>
+    <span class="c1"># endtime = obspy.UTCDateTime(&quot;2019-07-10T00&quot;)</span>
+    <span class="c1"># client = &quot;SCEDC&quot;</span>
+    <span class="c1"># network_list = [&quot;CI&quot;]</span>
+    <span class="c1"># channel_list = &quot;HH*,BH*,EH*,HN*&quot;</span>
+
+    <span class="n">region_name</span> <span class="o">=</span> <span class="s2">&quot;Hawaii&quot;</span>
+    <span class="n">center</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mf">155.32</span><span class="p">,</span> <span class="mf">19.39</span><span class="p">)</span>
+    <span class="n">horizontal_degree</span> <span class="o">=</span> <span class="mf">2.0</span>
+    <span class="n">vertical_degree</span> <span class="o">=</span> <span class="mf">2.0</span>
+    <span class="n">starttime</span> <span class="o">=</span> <span class="n">obspy</span><span class="o">.</span><span class="n">UTCDateTime</span><span class="p">(</span><span class="s2">&quot;2021-07-01T00&quot;</span><span class="p">)</span>
+    <span class="n">endtime</span> <span class="o">=</span> <span class="n">obspy</span><span class="o">.</span><span class="n">UTCDateTime</span><span class="p">(</span><span class="s2">&quot;2021-11-01T00&quot;</span><span class="p">)</span>
+    <span class="n">client</span> <span class="o">=</span> <span class="s2">&quot;IRIS&quot;</span>
+    <span class="n">network_list</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;HV&quot;</span><span class="p">,</span> <span class="s2">&quot;PT&quot;</span><span class="p">]</span>
+    <span class="n">channel_list</span> <span class="o">=</span> <span class="s2">&quot;HH*,BH*,EH*,HN*&quot;</span>
+
+    <span class="c1"># region_name = &quot;PuertoRico&quot;</span>
+    <span class="c1"># center = (-66.5, 18)</span>
+    <span class="c1"># horizontal_degree = 3.0</span>
+    <span class="c1"># vertical_degree = 2.0</span>
+    <span class="c1"># # starttime = obspy.UTCDateTime(&quot;2020-01-01T00&quot;)</span>
+    <span class="c1"># # endtime = obspy.UTCDateTime(&quot;2020-01-05T05&quot;)</span>
+    <span class="c1"># starttime = obspy.UTCDateTime(&quot;2018-01-01T00&quot;)</span>
+    <span class="c1"># endtime = obspy.UTCDateTime(&quot;2018-01-01T06&quot;)</span>
+    <span class="c1"># # endtime = obspy.UTCDateTime(&quot;2018-01-06T00&quot;)</span>
+    <span class="c1"># # endtime = obspy.UTCDateTime(&quot;2021-08-01T00&quot;)</span>
+    <span class="c1"># client = &quot;IRIS&quot;</span>
+    <span class="c1"># network_list = [&quot;*&quot;]</span>
+    <span class="c1"># channel_list = &quot;HH*,BH*,HN*&quot;</span>
+
+    <span class="c1"># region_name = &quot;SaltonSea&quot;</span>
+    <span class="c1"># center = (-115.53, 32.98)</span>
+    <span class="c1"># horizontal_degree = 1.0</span>
+    <span class="c1"># vertical_degree = 1.0</span>
+    <span class="c1"># starttime = obspy.UTCDateTime(&quot;2020-10-01T00&quot;)</span>
+    <span class="c1"># endtime = obspy.UTCDateTime(&quot;2020-10-01T02&quot;)</span>
+    <span class="c1"># client = &quot;SCEDC&quot;</span>
+    <span class="c1"># network_list = [&quot;CI&quot;]</span>
+    <span class="c1"># channel_list = &quot;HH*,BH*,EH*,HN*&quot;</span>
+
+    <span class="c1"># region_name = &quot;2003SanSimeon&quot;</span>
+    <span class="c1"># center = (-121.101, 35.701)</span>
+    <span class="c1"># horizontal_degree = 1.0</span>
+    <span class="c1"># vertical_degree = 1.0</span>
+    <span class="c1"># starttime = obspy.UTCDateTime(&quot;2003-12-22T00&quot;)</span>
+    <span class="c1"># endtime = obspy.UTCDateTime(&quot;2003-12-24T00&quot;)</span>
+    <span class="c1"># client = &quot;NCEDC&quot;</span>
+    <span class="c1"># network_list = [&quot;*&quot;]</span>
+    <span class="c1"># channel_list = &quot;HH*,BH*,EH*,HN*&quot;</span>
+
+    <span class="c1"># region_name = &quot;Italy&quot;</span>
+    <span class="c1"># center = (13.188, 42.723)</span>
+    <span class="c1"># horizontal_degree = 1.0</span>
+    <span class="c1"># vertical_degree = 1.0</span>
+    <span class="c1"># starttime = obspy.UTCDateTime(&quot;2016-08-24T00&quot;)</span>
+    <span class="c1"># endtime = obspy.UTCDateTime(&quot;2016-08-26T00&quot;)</span>
+    <span class="c1"># client = &quot;INGV&quot;</span>
+    <span class="c1"># network_list = [&quot;*&quot;]</span>
+    <span class="c1"># channel_list = &quot;HH*,BH*,EH*,HN*&quot;</span>
+
+    <span class="c1"># region_name = &quot;SmithValley&quot;</span>
+    <span class="c1"># center = (-119.5, 38.51)</span>
+    <span class="c1"># horizontal_degree = 1.0</span>
+    <span class="c1"># vertical_degree = 1.0</span>
+    <span class="c1"># starttime = obspy.UTCDateTime(&quot;2021-07-08T00:00&quot;)</span>
+    <span class="c1"># endtime = obspy.UTCDateTime(&quot;2021-07-16T00:00&quot;)</span>
+    <span class="c1"># client = &quot;NCEDC&quot;</span>
+    <span class="c1"># network_list = [&quot;*&quot;]</span>
+    <span class="c1"># channel_list = &quot;HH*,BH*,EH*,HN*&quot;</span>
+
+    <span class="c1"># region_name = &quot;Antilles&quot;</span>
+    <span class="c1"># center = (-61.14867, 14.79683)</span>
+    <span class="c1"># horizontal_degree = 0.2</span>
+    <span class="c1"># vertical_degree = 0.2</span>
+    <span class="c1"># starttime = obspy.UTCDateTime(&quot;2021-04-10T00&quot;)</span>
+    <span class="c1"># endtime = obspy.UTCDateTime(&quot;2021-04-15T00&quot;)</span>
+    <span class="c1"># client = &quot;RESIF&quot;</span>
+    <span class="c1"># network_list = [&quot;*&quot;]</span>
+    <span class="c1"># channel_list = &quot;HH*,BH*,EH*,HN*&quot;</span>
+
+    <span class="c1">####### save config ########</span>
+    <span class="n">config</span> <span class="o">=</span> <span class="p">{}</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;region&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">region_name</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;center&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">center</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;xlim_degree&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="n">center</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">horizontal_degree</span> <span class="o">/</span> <span class="mi">2</span><span class="p">,</span> <span class="n">center</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">horizontal_degree</span> <span class="o">/</span> <span class="mi">2</span><span class="p">]</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;ylim_degree&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="n">center</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">vertical_degree</span> <span class="o">/</span> <span class="mi">2</span><span class="p">,</span> <span class="n">center</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">vertical_degree</span> <span class="o">/</span> <span class="mi">2</span><span class="p">]</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;degree2km&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">degree2km</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;starttime&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">starttime</span><span class="o">.</span><span class="n">datetime</span><span class="o">.</span><span class="n">isoformat</span><span class="p">()</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;endtime&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">endtime</span><span class="o">.</span><span class="n">datetime</span><span class="o">.</span><span class="n">isoformat</span><span class="p">()</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;networks&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">network_list</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;channels&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">channel_list</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;client&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">client</span>
+
+    <span class="n">one_day</span> <span class="o">=</span> <span class="n">datetime</span><span class="o">.</span><span class="n">timedelta</span><span class="p">(</span><span class="n">days</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">one_hour</span> <span class="o">=</span> <span class="n">datetime</span><span class="o">.</span><span class="n">timedelta</span><span class="p">(</span><span class="n">hours</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">starttimes</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">tmp_start</span> <span class="o">=</span> <span class="n">starttime</span>
+    <span class="k">while</span> <span class="n">tmp_start</span> <span class="o">&lt;</span> <span class="n">endtime</span><span class="p">:</span>
+        <span class="n">starttimes</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp_start</span><span class="o">.</span><span class="n">datetime</span><span class="o">.</span><span class="n">isoformat</span><span class="p">())</span>
+        <span class="n">tmp_start</span> <span class="o">+=</span> <span class="n">one_hour</span>
+
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">datetime_json</span><span class="p">,</span> <span class="s2">&quot;w&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">json</span><span class="o">.</span><span class="n">dump</span><span class="p">({</span><span class="s2">&quot;starttimes&quot;</span><span class="p">:</span> <span class="n">starttimes</span><span class="p">,</span> <span class="s2">&quot;interval&quot;</span><span class="p">:</span> <span class="n">one_hour</span><span class="o">.</span><span class="n">total_seconds</span><span class="p">()},</span> <span class="n">fp</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">num_parallel</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="n">num_parallel</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="mi">24</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">starttimes</span><span class="p">))</span>
+        <span class="c1"># num_parallel = min(60, len(starttimes)//12)</span>
+        <span class="c1"># num_parallel = (len(starttimes)-1)//(24) + 1</span>
+        <span class="c1"># num_parallel = (len(starttimes)-1)//(24*7) + 1</span>
+        <span class="c1"># num_parallel = (len(starttimes)-1)//(24*10) + 1</span>
+        <span class="c1"># num_parallel = (len(starttimes)-1)//(24*14) + 1</span>
+        <span class="c1"># num_parallel = min(60, (len(starttimes)-1)//(24) + 1)</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;num_parallel = </span><span class="si">{</span><span class="n">num_parallel</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;num_parallel&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">num_parallel</span>
+
+    <span class="n">idx</span> <span class="o">=</span> <span class="p">[[]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_parallel</span><span class="p">)]</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">starttimes</span><span class="p">)):</span>
+        <span class="n">idx</span><span class="p">[</span><span class="n">i</span> <span class="o">-</span> <span class="n">i</span> <span class="o">//</span> <span class="n">num_parallel</span> <span class="o">*</span> <span class="n">num_parallel</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
+
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">config_json</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">json</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="n">config</span><span class="p">,</span> <span class="n">fp</span><span class="p">)</span>
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">index_json</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">json</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="n">idx</span><span class="p">,</span> <span class="n">fp</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">num_parallel</span><span class="p">))</span>
+</pre></div>
+<div id="cell-3" class="clipboard-copy-txt">def set_config(
+    index_json: OutputPath("json"),
+    config_json: OutputPath("json"),
+    datetime_json: OutputPath("json"),
+    num_parallel: int = 1,
+) -> list:
+
+    import obspy
+    import datetime
+    import json
+
+    pi = 3.1415926
+    degree2km = pi * 6371 / 180
+
+    # region_name = "Ridgecrest_demo"
+    # center = (-117.504, 35.705)
+    # horizontal_degree = 1.0
+    # vertical_degree = 1.0
+    # starttime = obspy.UTCDateTime("2019-07-04T17")
+    # endtime = obspy.UTCDateTime("2019-07-04T19")
+    # client = "SCEDC"
+    # network_list = ["CI"]
+    # channel_list = "HH*,BH*,EH*,HN*"
+
+    # region_name = "Ridgecrest_oneweek"
+    # center = (-117.504, 35.705)
+    # horizontal_degree = 1.0
+    # vertical_degree = 1.0
+    # starttime = obspy.UTCDateTime("2019-07-04T00")
+    # endtime = obspy.UTCDateTime("2019-07-10T00")
+    # client = "SCEDC"
+    # network_list = ["CI"]
+    # channel_list = "HH*,BH*,EH*,HN*"
+
+    region_name = "Hawaii"
+    center = (-155.32, 19.39)
+    horizontal_degree = 2.0
+    vertical_degree = 2.0
+    starttime = obspy.UTCDateTime("2021-07-01T00")
+    endtime = obspy.UTCDateTime("2021-11-01T00")
+    client = "IRIS"
+    network_list = ["HV", "PT"]
+    channel_list = "HH*,BH*,EH*,HN*"
+
+    # region_name = "PuertoRico"
+    # center = (-66.5, 18)
+    # horizontal_degree = 3.0
+    # vertical_degree = 2.0
+    # # starttime = obspy.UTCDateTime("2020-01-01T00")
+    # # endtime = obspy.UTCDateTime("2020-01-05T05")
+    # starttime = obspy.UTCDateTime("2018-01-01T00")
+    # endtime = obspy.UTCDateTime("2018-01-01T06")
+    # # endtime = obspy.UTCDateTime("2018-01-06T00")
+    # # endtime = obspy.UTCDateTime("2021-08-01T00")
+    # client = "IRIS"
+    # network_list = ["*"]
+    # channel_list = "HH*,BH*,HN*"
+
+    # region_name = "SaltonSea"
+    # center = (-115.53, 32.98)
+    # horizontal_degree = 1.0
+    # vertical_degree = 1.0
+    # starttime = obspy.UTCDateTime("2020-10-01T00")
+    # endtime = obspy.UTCDateTime("2020-10-01T02")
+    # client = "SCEDC"
+    # network_list = ["CI"]
+    # channel_list = "HH*,BH*,EH*,HN*"
+
+    # region_name = "2003SanSimeon"
+    # center = (-121.101, 35.701)
+    # horizontal_degree = 1.0
+    # vertical_degree = 1.0
+    # starttime = obspy.UTCDateTime("2003-12-22T00")
+    # endtime = obspy.UTCDateTime("2003-12-24T00")
+    # client = "NCEDC"
+    # network_list = ["*"]
+    # channel_list = "HH*,BH*,EH*,HN*"
+
+    # region_name = "Italy"
+    # center = (13.188, 42.723)
+    # horizontal_degree = 1.0
+    # vertical_degree = 1.0
+    # starttime = obspy.UTCDateTime("2016-08-24T00")
+    # endtime = obspy.UTCDateTime("2016-08-26T00")
+    # client = "INGV"
+    # network_list = ["*"]
+    # channel_list = "HH*,BH*,EH*,HN*"
+
+    # region_name = "SmithValley"
+    # center = (-119.5, 38.51)
+    # horizontal_degree = 1.0
+    # vertical_degree = 1.0
+    # starttime = obspy.UTCDateTime("2021-07-08T00:00")
+    # endtime = obspy.UTCDateTime("2021-07-16T00:00")
+    # client = "NCEDC"
+    # network_list = ["*"]
+    # channel_list = "HH*,BH*,EH*,HN*"
+
+    # region_name = "Antilles"
+    # center = (-61.14867, 14.79683)
+    # horizontal_degree = 0.2
+    # vertical_degree = 0.2
+    # starttime = obspy.UTCDateTime("2021-04-10T00")
+    # endtime = obspy.UTCDateTime("2021-04-15T00")
+    # client = "RESIF"
+    # network_list = ["*"]
+    # channel_list = "HH*,BH*,EH*,HN*"
+
+    ####### save config ########
+    config = {}
+    config["region"] = region_name
+    config["center"] = center
+    config["xlim_degree"] = [center[0] - horizontal_degree / 2, center[0] + horizontal_degree / 2]
+    config["ylim_degree"] = [center[1] - vertical_degree / 2, center[1] + vertical_degree / 2]
+    config["degree2km"] = degree2km
+    config["starttime"] = starttime.datetime.isoformat()
+    config["endtime"] = endtime.datetime.isoformat()
+    config["networks"] = network_list
+    config["channels"] = channel_list
+    config["client"] = client
+
+    one_day = datetime.timedelta(days=1)
+    one_hour = datetime.timedelta(hours=1)
+    starttimes = []
+    tmp_start = starttime
+    while tmp_start < endtime:
+        starttimes.append(tmp_start.datetime.isoformat())
+        tmp_start += one_hour
+
+    with open(datetime_json, "w") as fp:
+        json.dump({"starttimes": starttimes, "interval": one_hour.total_seconds()}, fp)
+
+    if num_parallel == 0:
+        num_parallel = min(24, len(starttimes))
+        # num_parallel = min(60, len(starttimes)//12)
+        # num_parallel = (len(starttimes)-1)//(24) + 1
+        # num_parallel = (len(starttimes)-1)//(24*7) + 1
+        # num_parallel = (len(starttimes)-1)//(24*10) + 1
+        # num_parallel = (len(starttimes)-1)//(24*14) + 1
+        # num_parallel = min(60, (len(starttimes)-1)//(24) + 1)
+        print(f"num_parallel = {num_parallel}")
+    config["num_parallel"] = num_parallel
+
+    idx = [[] for i in range(num_parallel)]
+    for i in range(len(starttimes)):
+        idx[i - i // num_parallel * num_parallel].append(i)
+
+    with open(config_json, 'w') as fp:
+        json.dump(config, fp)
+    with open(index_json, 'w') as fp:
+        json.dump(idx, fp)
+
+    return list(range(num_parallel))
+</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[27]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-4">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">if</span> <span class="n">run_local</span><span class="p">:</span>
+    <span class="n">idx</span> <span class="o">=</span> <span class="n">set_config</span><span class="p">(</span><span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;index.json&quot;</span><span class="p">),</span> <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;config.json&quot;</span><span class="p">),</span> <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;datetimes.json&quot;</span><span class="p">),</span> <span class="n">num_parallel</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+</pre></div>
+<div id="cell-4" class="clipboard-copy-txt">if run_local:
+    idx = set_config(root_dir("index.json"), root_dir("config.json"), root_dir("datetimes.json"), num_parallel=1)
+</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[28]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-5">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">config_op</span> <span class="o">=</span> <span class="n">comp</span><span class="o">.</span><span class="n">func_to_container_op</span><span class="p">(</span>
+    <span class="n">set_config</span><span class="p">,</span>
+    <span class="c1"># base_image=&#39;zhuwq0/quakeflow-env:latest&#39;,</span>
+    <span class="n">base_image</span><span class="o">=</span><span class="s1">&#39;python:3.8&#39;</span><span class="p">,</span>
+    <span class="n">packages_to_install</span><span class="o">=</span><span class="p">[</span>
+        <span class="s2">&quot;obspy&quot;</span><span class="p">,</span>
+    <span class="p">],</span>
+<span class="p">)</span>
+</pre></div>
+<div id="cell-5" class="clipboard-copy-txt">config_op = comp.func_to_container_op(
+    set_config,
+    # base_image='zhuwq0/quakeflow-env:latest',
+    base_image='python:3.8',
+    packages_to_install=[
+        "obspy",
+    ],
+)
+</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="3-download-events-in-the-routine-catalog">3. Download events in the routine catalog<a class="anchor-link" href="#3-download-events-in-the-routine-catalog">&#182;</a></h2><p>This catalog is not used by QuakeFolow. It is only used for comparing detection results.</p>
+
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[29]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-6">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">def</span> <span class="nf">download_events</span><span class="p">(</span><span class="n">config_json</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="s2">&quot;json&quot;</span><span class="p">),</span> <span class="n">event_csv</span><span class="p">:</span> <span class="n">OutputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">)):</span>
+
+    <span class="kn">from</span> <span class="nn">obspy.clients.fdsn</span> <span class="kn">import</span> <span class="n">Client</span>
+    <span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">defaultdict</span>
+    <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+    <span class="kn">import</span> <span class="nn">json</span>
+    <span class="kn">import</span> <span class="nn">matplotlib</span>
+    <span class="c1">#     matplotlib.use(&quot;agg&quot;)</span>
+    <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">config_json</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">config</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">fp</span><span class="p">)</span>
+
+    <span class="c1">####### IRIS catalog ########</span>
+    <span class="k">try</span><span class="p">:</span>
+        <span class="n">events</span> <span class="o">=</span> <span class="n">Client</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;client&quot;</span><span class="p">])</span><span class="o">.</span><span class="n">get_events</span><span class="p">(</span>
+            <span class="n">starttime</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;starttime&quot;</span><span class="p">],</span>
+            <span class="n">endtime</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;endtime&quot;</span><span class="p">],</span>
+            <span class="n">minlongitude</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;xlim_degree&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span>
+            <span class="n">maxlongitude</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;xlim_degree&quot;</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span>
+            <span class="n">minlatitude</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;ylim_degree&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span>
+            <span class="n">maxlatitude</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;ylim_degree&quot;</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span>
+        <span class="p">)</span>
+    <span class="k">except</span><span class="p">:</span>
+        <span class="n">events</span> <span class="o">=</span> <span class="n">Client</span><span class="p">(</span><span class="s2">&quot;iris&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">get_events</span><span class="p">(</span>
+            <span class="n">starttime</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;starttime&quot;</span><span class="p">],</span>
+            <span class="n">endtime</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;endtime&quot;</span><span class="p">],</span>
+            <span class="n">minlongitude</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;xlim_degree&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span>
+            <span class="n">maxlongitude</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;xlim_degree&quot;</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span>
+            <span class="n">minlatitude</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;ylim_degree&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span>
+            <span class="n">maxlatitude</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;ylim_degree&quot;</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span>
+        <span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Number of events: </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">events</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+
+    <span class="c1">####### Save catalog ########</span>
+    <span class="n">catalog</span> <span class="o">=</span> <span class="n">defaultdict</span><span class="p">(</span><span class="nb">list</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">event</span> <span class="ow">in</span> <span class="n">events</span><span class="p">:</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">event</span><span class="o">.</span><span class="n">magnitudes</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="n">catalog</span><span class="p">[</span><span class="s2">&quot;time&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">event</span><span class="o">.</span><span class="n">origins</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">time</span><span class="o">.</span><span class="n">datetime</span><span class="p">)</span>
+            <span class="n">catalog</span><span class="p">[</span><span class="s2">&quot;magnitude&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">event</span><span class="o">.</span><span class="n">magnitudes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">mag</span><span class="p">)</span>
+            <span class="n">catalog</span><span class="p">[</span><span class="s2">&quot;longitude&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">event</span><span class="o">.</span><span class="n">origins</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">longitude</span><span class="p">)</span>
+            <span class="n">catalog</span><span class="p">[</span><span class="s2">&quot;latitude&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">event</span><span class="o">.</span><span class="n">origins</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">latitude</span><span class="p">)</span>
+            <span class="n">catalog</span><span class="p">[</span><span class="s2">&quot;depth(m)&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">event</span><span class="o">.</span><span class="n">origins</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">depth</span><span class="p">)</span>
+    <span class="n">catalog</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">from_dict</span><span class="p">(</span><span class="n">catalog</span><span class="p">)</span><span class="o">.</span><span class="n">sort_values</span><span class="p">([</span><span class="s2">&quot;time&quot;</span><span class="p">])</span>
+    <span class="n">catalog</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span>
+        <span class="n">event_csv</span><span class="p">,</span>
+        <span class="n">sep</span><span class="o">=</span><span class="s2">&quot;</span><span class="se">\t</span><span class="s2">&quot;</span><span class="p">,</span>
+        <span class="n">index</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+        <span class="n">float_format</span><span class="o">=</span><span class="s2">&quot;</span><span class="si">%.3f</span><span class="s2">&quot;</span><span class="p">,</span>
+        <span class="n">date_format</span><span class="o">=</span><span class="s1">&#39;%Y-%m-</span><span class="si">%d</span><span class="s1">T%H:%M:%S.</span><span class="si">%f</span><span class="s1">&#39;</span><span class="p">,</span>
+        <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;time&quot;</span><span class="p">,</span> <span class="s2">&quot;magnitude&quot;</span><span class="p">,</span> <span class="s2">&quot;longitude&quot;</span><span class="p">,</span> <span class="s2">&quot;latitude&quot;</span><span class="p">,</span> <span class="s2">&quot;depth(m)&quot;</span><span class="p">],</span>
+    <span class="p">)</span>
+
+    <span class="c1">####### Plot catalog ########</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">catalog</span><span class="p">[</span><span class="s2">&quot;longitude&quot;</span><span class="p">],</span> <span class="n">catalog</span><span class="p">[</span><span class="s2">&quot;latitude&quot;</span><span class="p">],</span> <span class="s1">&#39;.&#39;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">&quot;Longitude&quot;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">&quot;Latitude&quot;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s2">&quot;scaled&quot;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;xlim_degree&quot;</span><span class="p">])</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;ylim_degree&quot;</span><span class="p">])</span>
+    <span class="c1">#  plt.savefig(os.path.join(data_path, &quot;events_loc.png&quot;))</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+
+    <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">plot_date</span><span class="p">(</span><span class="n">catalog</span><span class="p">[</span><span class="s2">&quot;time&quot;</span><span class="p">],</span> <span class="n">catalog</span><span class="p">[</span><span class="s2">&quot;magnitude&quot;</span><span class="p">],</span> <span class="s1">&#39;.&#39;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">gcf</span><span class="p">()</span><span class="o">.</span><span class="n">autofmt_xdate</span><span class="p">()</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">&quot;Magnitude&quot;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Number of events: </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">events</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+    <span class="c1">#  plt.savefig(os.path.join(data_path, &quot;events_mag_time.png&quot;))</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+<div id="cell-6" class="clipboard-copy-txt">def download_events(config_json: InputPath("json"), event_csv: OutputPath(str)):
+
+    from obspy.clients.fdsn import Client
+    from collections import defaultdict
+    import pandas as pd
+    import json
+    import matplotlib
+    #     matplotlib.use("agg")
+    import matplotlib.pyplot as plt
+
+    with open(config_json, "r") as fp:
+        config = json.load(fp)
+
+    ####### IRIS catalog ########
+    try:
+        events = Client(config["client"]).get_events(
+            starttime=config["starttime"],
+            endtime=config["endtime"],
+            minlongitude=config["xlim_degree"][0],
+            maxlongitude=config["xlim_degree"][1],
+            minlatitude=config["ylim_degree"][0],
+            maxlatitude=config["ylim_degree"][1],
+        )
+    except:
+        events = Client("iris").get_events(
+            starttime=config["starttime"],
+            endtime=config["endtime"],
+            minlongitude=config["xlim_degree"][0],
+            maxlongitude=config["xlim_degree"][1],
+            minlatitude=config["ylim_degree"][0],
+            maxlatitude=config["ylim_degree"][1],
+        )
+    print(f"Number of events: {len(events)}")
+
+    ####### Save catalog ########
+    catalog = defaultdict(list)
+    for event in events:
+        if len(event.magnitudes) > 0:
+            catalog["time"].append(event.origins[0].time.datetime)
+            catalog["magnitude"].append(event.magnitudes[0].mag)
+            catalog["longitude"].append(event.origins[0].longitude)
+            catalog["latitude"].append(event.origins[0].latitude)
+            catalog["depth(m)"].append(event.origins[0].depth)
+    catalog = pd.DataFrame.from_dict(catalog).sort_values(["time"])
+    catalog.to_csv(
+        event_csv,
+        sep="\t",
+        index=False,
+        float_format="%.3f",
+        date_format='%Y-%m-%dT%H:%M:%S.%f',
+        columns=["time", "magnitude", "longitude", "latitude", "depth(m)"],
+    )
+
+    ####### Plot catalog ########
+    plt.figure()
+    plt.plot(catalog["longitude"], catalog["latitude"], '.', markersize=1)
+    plt.xlabel("Longitude")
+    plt.ylabel("Latitude")
+    plt.axis("scaled")
+    plt.xlim(config["xlim_degree"])
+    plt.ylim(config["ylim_degree"])
+    #  plt.savefig(os.path.join(data_path, "events_loc.png"))
+    plt.show()
+
+    plt.figure()
+    plt.plot_date(catalog["time"], catalog["magnitude"], '.', markersize=1)
+    plt.gcf().autofmt_xdate()
+    plt.ylabel("Magnitude")
+    plt.title(f"Number of events: {len(events)}")
+    #  plt.savefig(os.path.join(data_path, "events_mag_time.png"))
+    plt.show()
+</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[30]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-7">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">if</span> <span class="n">run_local</span><span class="p">:</span>
+    <span class="n">download_events</span><span class="p">(</span><span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;config.json&quot;</span><span class="p">),</span> <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;events.csv&quot;</span><span class="p">))</span>
+</pre></div>
+<div id="cell-7" class="clipboard-copy-txt">if run_local:
+    download_events(root_dir("config.json"), root_dir("events.csv"))
+</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[31]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-8">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">download_events_op</span> <span class="o">=</span> <span class="n">comp</span><span class="o">.</span><span class="n">func_to_container_op</span><span class="p">(</span>
+    <span class="n">download_events</span><span class="p">,</span>
+    <span class="c1">#base_image=&#39;zhuwq0/quakeflow-env:latest&#39;,</span>
+    <span class="n">base_image</span><span class="o">=</span><span class="s1">&#39;python:3.8&#39;</span><span class="p">,</span>
+    <span class="n">packages_to_install</span><span class="o">=</span><span class="p">[</span>
+        <span class="s2">&quot;obspy&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;pandas&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;matplotlib&quot;</span><span class="p">,</span>
+    <span class="p">],</span>
+<span class="p">)</span>
+</pre></div>
+<div id="cell-8" class="clipboard-copy-txt">download_events_op = comp.func_to_container_op(
+    download_events,
+    #base_image='zhuwq0/quakeflow-env:latest',
+    base_image='python:3.8',
+    packages_to_install=[
+        "obspy",
+        "pandas",
+        "matplotlib",
+    ],
+)
+</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="4-download-stations">4. Download stations<a class="anchor-link" href="#4-download-stations">&#182;</a></h2>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[32]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-9">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">def</span> <span class="nf">download_stations</span><span class="p">(</span><span class="n">config_json</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="s2">&quot;json&quot;</span><span class="p">),</span> <span class="n">station_csv</span><span class="p">:</span> <span class="n">OutputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span> <span class="n">station_pkl</span><span class="p">:</span> <span class="n">OutputPath</span><span class="p">(</span><span class="s2">&quot;pickle&quot;</span><span class="p">)):</span>
+
+    <span class="kn">import</span> <span class="nn">pickle</span>
+    <span class="kn">from</span> <span class="nn">obspy.clients.fdsn</span> <span class="kn">import</span> <span class="n">Client</span>
+    <span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">defaultdict</span>
+    <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+    <span class="kn">import</span> <span class="nn">json</span>
+    <span class="kn">import</span> <span class="nn">matplotlib</span>
+    <span class="c1">#     matplotlib.use(&quot;agg&quot;)</span>
+    <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">config_json</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">config</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">fp</span><span class="p">)</span>
+
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Network:&quot;</span><span class="p">,</span> <span class="s2">&quot;,&quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;networks&quot;</span><span class="p">]))</span>
+    <span class="c1">####### Download stations ########</span>
+    <span class="n">stations</span> <span class="o">=</span> <span class="n">Client</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;client&quot;</span><span class="p">])</span><span class="o">.</span><span class="n">get_stations</span><span class="p">(</span>
+        <span class="n">network</span><span class="o">=</span><span class="s2">&quot;,&quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;networks&quot;</span><span class="p">]),</span>
+        <span class="n">station</span><span class="o">=</span><span class="s2">&quot;*&quot;</span><span class="p">,</span>
+        <span class="n">starttime</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;starttime&quot;</span><span class="p">],</span>
+        <span class="n">endtime</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;endtime&quot;</span><span class="p">],</span>
+        <span class="n">minlongitude</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;xlim_degree&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span>
+        <span class="n">maxlongitude</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;xlim_degree&quot;</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span>
+        <span class="n">minlatitude</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;ylim_degree&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span>
+        <span class="n">maxlatitude</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;ylim_degree&quot;</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span>
+        <span class="n">channel</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;channels&quot;</span><span class="p">],</span>
+        <span class="n">level</span><span class="o">=</span><span class="s2">&quot;response&quot;</span><span class="p">,</span>
+    <span class="p">)</span> 
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Number of stations: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">sum</span><span class="p">([</span><span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">stations</span><span class="p">])))</span>
+
+    <span class="c1">####### Save stations ########</span>
+    <span class="n">station_locs</span> <span class="o">=</span> <span class="n">defaultdict</span><span class="p">(</span><span class="nb">dict</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">network</span> <span class="ow">in</span> <span class="n">stations</span><span class="p">:</span>
+        <span class="k">for</span> <span class="n">station</span> <span class="ow">in</span> <span class="n">network</span><span class="p">:</span>
+            <span class="k">for</span> <span class="n">chn</span> <span class="ow">in</span> <span class="n">station</span><span class="p">:</span>
+                <span class="n">sid</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">network</span><span class="o">.</span><span class="n">code</span><span class="si">}</span><span class="s2">.</span><span class="si">{</span><span class="n">station</span><span class="o">.</span><span class="n">code</span><span class="si">}</span><span class="s2">.</span><span class="si">{</span><span class="n">chn</span><span class="o">.</span><span class="n">location_code</span><span class="si">}</span><span class="s2">.</span><span class="si">{</span><span class="n">chn</span><span class="o">.</span><span class="n">code</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="si">}</span><span class="s2">&quot;</span>
+                <span class="k">if</span> <span class="n">sid</span> <span class="ow">in</span> <span class="n">station_locs</span><span class="p">:</span>
+                    <span class="n">station_locs</span><span class="p">[</span><span class="n">sid</span><span class="p">][</span><span class="s2">&quot;component&quot;</span><span class="p">]</span> <span class="o">+=</span> <span class="sa">f</span><span class="s2">&quot;,</span><span class="si">{</span><span class="n">chn</span><span class="o">.</span><span class="n">code</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="si">}</span><span class="s2">&quot;</span>
+                    <span class="n">station_locs</span><span class="p">[</span><span class="n">sid</span><span class="p">][</span><span class="s2">&quot;response&quot;</span><span class="p">]</span> <span class="o">+=</span> <span class="sa">f</span><span class="s2">&quot;,</span><span class="si">{</span><span class="n">chn</span><span class="o">.</span><span class="n">response</span><span class="o">.</span><span class="n">instrument_sensitivity</span><span class="o">.</span><span class="n">value</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">&quot;</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">component</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">chn</span><span class="o">.</span><span class="n">code</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="si">}</span><span class="s2">&quot;</span>
+                    <span class="n">response</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">chn</span><span class="o">.</span><span class="n">response</span><span class="o">.</span><span class="n">instrument_sensitivity</span><span class="o">.</span><span class="n">value</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">&quot;</span>
+                    <span class="n">dtype</span> <span class="o">=</span> <span class="n">chn</span><span class="o">.</span><span class="n">response</span><span class="o">.</span><span class="n">instrument_sensitivity</span><span class="o">.</span><span class="n">input_units</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span>
+                    <span class="n">tmp_dict</span> <span class="o">=</span> <span class="p">{}</span>
+                    <span class="n">tmp_dict</span><span class="p">[</span><span class="s2">&quot;longitude&quot;</span><span class="p">],</span> <span class="n">tmp_dict</span><span class="p">[</span><span class="s2">&quot;latitude&quot;</span><span class="p">],</span> <span class="n">tmp_dict</span><span class="p">[</span><span class="s2">&quot;elevation(m)&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span>
+                        <span class="n">chn</span><span class="o">.</span><span class="n">longitude</span><span class="p">,</span>
+                        <span class="n">chn</span><span class="o">.</span><span class="n">latitude</span><span class="p">,</span>
+                        <span class="n">chn</span><span class="o">.</span><span class="n">elevation</span><span class="p">,</span>
+                    <span class="p">)</span>
+                    <span class="n">tmp_dict</span><span class="p">[</span><span class="s2">&quot;component&quot;</span><span class="p">],</span> <span class="n">tmp_dict</span><span class="p">[</span><span class="s2">&quot;response&quot;</span><span class="p">],</span> <span class="n">tmp_dict</span><span class="p">[</span><span class="s2">&quot;unit&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">component</span><span class="p">,</span> <span class="n">response</span><span class="p">,</span> <span class="n">dtype</span>
+                    <span class="n">station_locs</span><span class="p">[</span><span class="n">sid</span><span class="p">]</span> <span class="o">=</span> <span class="n">tmp_dict</span>
+
+    <span class="n">station_locs</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">from_dict</span><span class="p">(</span><span class="n">station_locs</span><span class="p">,</span> <span class="n">orient</span><span class="o">=</span><span class="s1">&#39;index&#39;</span><span class="p">)</span>
+    <span class="n">station_locs</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span>
+        <span class="n">station_csv</span><span class="p">,</span>
+        <span class="n">sep</span><span class="o">=</span><span class="s2">&quot;</span><span class="se">\t</span><span class="s2">&quot;</span><span class="p">,</span>
+        <span class="n">float_format</span><span class="o">=</span><span class="s2">&quot;</span><span class="si">%.3f</span><span class="s2">&quot;</span><span class="p">,</span>
+        <span class="n">index_label</span><span class="o">=</span><span class="s2">&quot;station&quot;</span><span class="p">,</span>
+        <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;longitude&quot;</span><span class="p">,</span> <span class="s2">&quot;latitude&quot;</span><span class="p">,</span> <span class="s2">&quot;elevation(m)&quot;</span><span class="p">,</span> <span class="s2">&quot;unit&quot;</span><span class="p">,</span> <span class="s2">&quot;component&quot;</span><span class="p">,</span> <span class="s2">&quot;response&quot;</span><span class="p">],</span>
+    <span class="p">)</span>
+
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">station_pkl</span><span class="p">,</span> <span class="s2">&quot;wb&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">pickle</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="n">stations</span><span class="p">,</span> <span class="n">fp</span><span class="p">)</span>
+
+    <span class="c1">######## Plot stations ########</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">station_locs</span><span class="p">[</span><span class="s2">&quot;longitude&quot;</span><span class="p">],</span> <span class="n">station_locs</span><span class="p">[</span><span class="s2">&quot;latitude&quot;</span><span class="p">],</span> <span class="s2">&quot;^&quot;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;Stations&quot;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">&quot;X (km)&quot;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">&quot;Y (km)&quot;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s2">&quot;scaled&quot;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;xlim_degree&quot;</span><span class="p">])</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;ylim_degree&quot;</span><span class="p">])</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Number of stations: </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">station_locs</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+    <span class="c1">#     plt.savefig(os.path.join(data_path, &quot;stations_loc.png&quot;))</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+<div id="cell-9" class="clipboard-copy-txt">def download_stations(config_json: InputPath("json"), station_csv: OutputPath(str), station_pkl: OutputPath("pickle")):
+
+    import pickle
+    from obspy.clients.fdsn import Client
+    from collections import defaultdict
+    import pandas as pd
+    import json
+    import matplotlib
+    #     matplotlib.use("agg")
+    import matplotlib.pyplot as plt
+
+    with open(config_json, "r") as fp:
+        config = json.load(fp)
+
+    print("Network:", ",".join(config["networks"]))
+    ####### Download stations ########
+    stations = Client(config["client"]).get_stations(
+        network=",".join(config["networks"]),
+        station="*",
+        starttime=config["starttime"],
+        endtime=config["endtime"],
+        minlongitude=config["xlim_degree"][0],
+        maxlongitude=config["xlim_degree"][1],
+        minlatitude=config["ylim_degree"][0],
+        maxlatitude=config["ylim_degree"][1],
+        channel=config["channels"],
+        level="response",
+    ) 
+    print("Number of stations: {}".format(sum([len(x) for x in stations])))
+
+    ####### Save stations ########
+    station_locs = defaultdict(dict)
+    for network in stations:
+        for station in network:
+            for chn in station:
+                sid = f"{network.code}.{station.code}.{chn.location_code}.{chn.code[:-1]}"
+                if sid in station_locs:
+                    station_locs[sid]["component"] += f",{chn.code[-1]}"
+                    station_locs[sid]["response"] += f",{chn.response.instrument_sensitivity.value:.2f}"
+                else:
+                    component = f"{chn.code[-1]}"
+                    response = f"{chn.response.instrument_sensitivity.value:.2f}"
+                    dtype = chn.response.instrument_sensitivity.input_units.lower()
+                    tmp_dict = {}
+                    tmp_dict["longitude"], tmp_dict["latitude"], tmp_dict["elevation(m)"] = (
+                        chn.longitude,
+                        chn.latitude,
+                        chn.elevation,
+                    )
+                    tmp_dict["component"], tmp_dict["response"], tmp_dict["unit"] = component, response, dtype
+                    station_locs[sid] = tmp_dict
+
+    station_locs = pd.DataFrame.from_dict(station_locs, orient='index')
+    station_locs.to_csv(
+        station_csv,
+        sep="\t",
+        float_format="%.3f",
+        index_label="station",
+        columns=["longitude", "latitude", "elevation(m)", "unit", "component", "response"],
+    )
+
+    with open(station_pkl, "wb") as fp:
+        pickle.dump(stations, fp)
+
+    ######## Plot stations ########
+    plt.figure()
+    plt.plot(station_locs["longitude"], station_locs["latitude"], "^", label="Stations")
+    plt.xlabel("X (km)")
+    plt.ylabel("Y (km)")
+    plt.axis("scaled")
+    plt.xlim(config["xlim_degree"])
+    plt.ylim(config["ylim_degree"])
+    plt.legend()
+    plt.title(f"Number of stations: {len(station_locs)}")
+    #     plt.savefig(os.path.join(data_path, "stations_loc.png"))
+    plt.show()
+</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[33]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-10">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">if</span> <span class="n">run_local</span><span class="p">:</span>
+    <span class="n">download_stations</span><span class="p">(</span><span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;config.json&quot;</span><span class="p">),</span> <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;stations.csv&quot;</span><span class="p">),</span> <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;stations.pkl&quot;</span><span class="p">))</span>
+</pre></div>
+<div id="cell-10" class="clipboard-copy-txt">if run_local:
+    download_stations(root_dir("config.json"), root_dir("stations.csv"), root_dir("stations.pkl"))
+</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[34]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-11">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">download_stations_op</span> <span class="o">=</span> <span class="n">comp</span><span class="o">.</span><span class="n">func_to_container_op</span><span class="p">(</span>
+    <span class="n">download_stations</span><span class="p">,</span>
+    <span class="c1"># base_image=&#39;zhuwq0/quakeflow-env:latest&#39;,</span>
+    <span class="n">base_image</span><span class="o">=</span><span class="s1">&#39;python:3.8&#39;</span><span class="p">,</span>
+    <span class="n">packages_to_install</span><span class="o">=</span><span class="p">[</span>
+        <span class="s2">&quot;obspy&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;pandas&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;matplotlib&quot;</span><span class="p">,</span>
+    <span class="p">],</span>
+<span class="p">)</span>
+</pre></div>
+<div id="cell-11" class="clipboard-copy-txt">download_stations_op = comp.func_to_container_op(
+    download_stations,
+    # base_image='zhuwq0/quakeflow-env:latest',
+    base_image='python:3.8',
+    packages_to_install=[
+        "obspy",
+        "pandas",
+        "matplotlib",
+    ],
+)
+</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="5-download-waveform-data">5. Download waveform data<a class="anchor-link" href="#5-download-waveform-data">&#182;</a></h2>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[35]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-12">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">def</span> <span class="nf">download_waveform</span><span class="p">(</span>
+    <span class="n">i</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
+    <span class="n">index_json</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="s2">&quot;json&quot;</span><span class="p">),</span>
+    <span class="n">config_json</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="s2">&quot;json&quot;</span><span class="p">),</span>
+    <span class="n">datetime_json</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="s2">&quot;json&quot;</span><span class="p">),</span>
+    <span class="n">station_pkl</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="s2">&quot;pickle&quot;</span><span class="p">),</span>
+    <span class="n">fname_csv</span><span class="p">:</span> <span class="n">OutputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span>
+    <span class="n">data_path</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
+    <span class="n">bucket_name</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;waveforms&quot;</span><span class="p">,</span>
+    <span class="n">s3_url</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;minio-service:9000&quot;</span><span class="p">,</span>
+    <span class="n">secure</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
+
+    <span class="kn">import</span> <span class="nn">pickle</span><span class="o">,</span> <span class="nn">os</span>
+    <span class="kn">import</span> <span class="nn">obspy</span>
+    <span class="kn">from</span> <span class="nn">obspy.clients.fdsn</span> <span class="kn">import</span> <span class="n">Client</span>
+    <span class="kn">import</span> <span class="nn">time</span>
+    <span class="kn">import</span> <span class="nn">json</span>
+    <span class="kn">import</span> <span class="nn">random</span>
+    <span class="kn">import</span> <span class="nn">threading</span>
+
+    <span class="n">lock</span> <span class="o">=</span> <span class="n">threading</span><span class="o">.</span><span class="n">Lock</span><span class="p">()</span>
+
+    <span class="n">upload_minio</span> <span class="o">=</span> <span class="kc">False</span>
+    <span class="k">try</span><span class="p">:</span>
+        <span class="kn">from</span> <span class="nn">minio</span> <span class="kn">import</span> <span class="n">Minio</span>
+
+        <span class="n">minioClient</span> <span class="o">=</span> <span class="n">Minio</span><span class="p">(</span><span class="n">s3_url</span><span class="p">,</span> <span class="n">access_key</span><span class="o">=</span><span class="s1">&#39;minio&#39;</span><span class="p">,</span> <span class="n">secret_key</span><span class="o">=</span><span class="s1">&#39;minio123&#39;</span><span class="p">,</span> <span class="n">secure</span><span class="o">=</span><span class="n">secure</span><span class="p">)</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">minioClient</span><span class="o">.</span><span class="n">bucket_exists</span><span class="p">(</span><span class="n">bucket_name</span><span class="p">):</span>
+            <span class="n">minioClient</span><span class="o">.</span><span class="n">make_bucket</span><span class="p">(</span><span class="n">bucket_name</span><span class="p">)</span>
+        <span class="n">upload_minio</span> <span class="o">=</span> <span class="kc">True</span>
+    <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">err</span><span class="p">:</span>
+        <span class="c1"># print(f&quot;ERROR: can not access minio service! \n{err}&quot;)</span>
+        <span class="k">pass</span>
+
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">index_json</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">index</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">fp</span><span class="p">)</span>
+    <span class="n">idx</span> <span class="o">=</span> <span class="n">index</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">config_json</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">config</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">fp</span><span class="p">)</span>
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">datetime_json</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">tmp</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">fp</span><span class="p">)</span>
+        <span class="n">starttimes</span> <span class="o">=</span> <span class="n">tmp</span><span class="p">[</span><span class="s2">&quot;starttimes&quot;</span><span class="p">]</span>
+        <span class="n">interval</span> <span class="o">=</span> <span class="n">tmp</span><span class="p">[</span><span class="s2">&quot;interval&quot;</span><span class="p">]</span>
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">station_pkl</span><span class="p">,</span> <span class="s2">&quot;rb&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">stations</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">fp</span><span class="p">)</span>
+
+    <span class="n">waveform_dir</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">data_path</span><span class="p">,</span> <span class="n">config</span><span class="p">[</span><span class="s2">&quot;region&quot;</span><span class="p">],</span> <span class="s2">&quot;waveforms&quot;</span><span class="p">)</span>
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">waveform_dir</span><span class="p">):</span>
+        <span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">waveform_dir</span><span class="p">)</span>
+
+    <span class="c1">####### Download data ########</span>
+    <span class="n">client</span> <span class="o">=</span> <span class="n">Client</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;client&quot;</span><span class="p">])</span>
+    <span class="n">fname_list</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;fname&quot;</span><span class="p">]</span>
+
+    <span class="k">def</span> <span class="nf">download</span><span class="p">(</span><span class="n">i</span><span class="p">):</span>
+        <span class="c1">#     for i in idx:</span>
+        <span class="n">starttime</span> <span class="o">=</span> <span class="n">obspy</span><span class="o">.</span><span class="n">UTCDateTime</span><span class="p">(</span><span class="n">starttimes</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+        <span class="n">endtime</span> <span class="o">=</span> <span class="n">starttime</span> <span class="o">+</span> <span class="n">interval</span>
+        <span class="n">fname</span> <span class="o">=</span> <span class="s2">&quot;</span><span class="si">{}</span><span class="s2">.mseed&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">starttime</span><span class="o">.</span><span class="n">datetime</span><span class="o">.</span><span class="n">strftime</span><span class="p">(</span><span class="s2">&quot;%Y-%m-</span><span class="si">%d</span><span class="s2">T%H:%M:%S&quot;</span><span class="p">))</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">upload_minio</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">waveform_dir</span><span class="p">,</span> <span class="n">fname</span><span class="p">)):</span>
+                <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">fname</span><span class="si">}</span><span class="s2"> exists&quot;</span><span class="p">)</span>
+                <span class="n">fname_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">fname</span><span class="p">)</span>
+                <span class="k">return</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">try</span><span class="p">:</span>
+                <span class="n">minioClient</span><span class="o">.</span><span class="n">fget_object</span><span class="p">(</span><span class="n">bucket_name</span><span class="p">,</span>  <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;region&#39;</span><span class="p">],</span> <span class="n">fname</span><span class="p">),</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">waveform_dir</span><span class="p">,</span> <span class="n">fname</span><span class="p">))</span>
+                <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">bucket_name</span><span class="si">}</span><span class="s2">/</span><span class="si">{</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;region&#39;</span><span class="p">],</span><span class="w"> </span><span class="n">fname</span><span class="p">)</span><span class="si">}</span><span class="s2"> download to </span><span class="si">{</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">waveform_dir</span><span class="p">,</span><span class="w"> </span><span class="n">fname</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+                <span class="n">fname_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">fname</span><span class="p">)</span>
+                <span class="k">return</span>
+            <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">err</span><span class="p">:</span>
+                <span class="nb">print</span><span class="p">(</span><span class="n">err</span><span class="p">)</span>
+
+        <span class="n">max_retry</span> <span class="o">=</span> <span class="mi">10</span>
+        <span class="n">stream</span> <span class="o">=</span> <span class="n">obspy</span><span class="o">.</span><span class="n">Stream</span><span class="p">()</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">fname</span><span class="si">}</span><span class="s2"> download starts&quot;</span><span class="p">)</span>
+        <span class="n">num_sta</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="k">for</span> <span class="n">network</span> <span class="ow">in</span> <span class="n">stations</span><span class="p">:</span>
+            <span class="k">for</span> <span class="n">station</span> <span class="ow">in</span> <span class="n">network</span><span class="p">:</span>
+                <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;********</span><span class="si">{</span><span class="n">network</span><span class="o">.</span><span class="n">code</span><span class="si">}</span><span class="s2">.</span><span class="si">{</span><span class="n">station</span><span class="o">.</span><span class="n">code</span><span class="si">}</span><span class="s2">********&quot;</span><span class="p">)</span>
+                <span class="n">retry</span> <span class="o">=</span> <span class="mi">0</span>
+                <span class="k">while</span> <span class="n">retry</span> <span class="o">&lt;</span> <span class="n">max_retry</span><span class="p">:</span>
+                    <span class="k">try</span><span class="p">:</span>
+                        <span class="n">tmp</span> <span class="o">=</span> <span class="n">client</span><span class="o">.</span><span class="n">get_waveforms</span><span class="p">(</span>
+                            <span class="n">network</span><span class="o">.</span><span class="n">code</span><span class="p">,</span> <span class="n">station</span><span class="o">.</span><span class="n">code</span><span class="p">,</span> <span class="s2">&quot;*&quot;</span><span class="p">,</span> <span class="n">config</span><span class="p">[</span><span class="s2">&quot;channels&quot;</span><span class="p">],</span> <span class="n">starttime</span><span class="p">,</span> <span class="n">endtime</span>
+                        <span class="p">)</span>
+                        <span class="c1">#  for trace in tmp:</span>
+                        <span class="c1">#      if trace.stats.sampling_rate != 100:</span>
+                        <span class="c1">#          print(trace)</span>
+                        <span class="c1">#          trace = trace.interpolate(100, method=&quot;linear&quot;)</span>
+                        <span class="c1">#      trace = trace.detrend(&quot;spline&quot;, order=2, dspline=5*trace.stats.sampling_rate)</span>
+                        <span class="c1">#      stream.append(trace)</span>
+                        <span class="n">stream</span> <span class="o">+=</span> <span class="n">tmp</span>
+                        <span class="n">num_sta</span> <span class="o">+=</span> <span class="nb">len</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
+                        <span class="k">break</span>
+                    <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">err</span><span class="p">:</span>
+                        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Error </span><span class="si">{}</span><span class="s2">.</span><span class="si">{}</span><span class="s2">: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">network</span><span class="o">.</span><span class="n">code</span><span class="p">,</span> <span class="n">station</span><span class="o">.</span><span class="n">code</span><span class="p">,</span> <span class="n">err</span><span class="p">))</span>
+                        <span class="n">message</span> <span class="o">=</span> <span class="s2">&quot;No data available for request.&quot;</span>
+                        <span class="k">if</span> <span class="nb">str</span><span class="p">(</span><span class="n">err</span><span class="p">)[:</span> <span class="nb">len</span><span class="p">(</span><span class="n">message</span><span class="p">)]</span> <span class="o">==</span> <span class="n">message</span><span class="p">:</span>
+                            <span class="k">break</span>
+                        <span class="n">retry</span> <span class="o">+=</span> <span class="mi">1</span>
+                        <span class="n">time</span><span class="o">.</span><span class="n">sleep</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
+                        <span class="k">continue</span>
+                <span class="k">if</span> <span class="n">retry</span> <span class="o">==</span> <span class="n">max_retry</span><span class="p">:</span>
+                    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">fname</span><span class="si">}</span><span class="s2">: MAX </span><span class="si">{</span><span class="n">max_retry</span><span class="si">}</span><span class="s2"> retries reached : </span><span class="si">{</span><span class="n">network</span><span class="o">.</span><span class="n">code</span><span class="si">}</span><span class="s2">.</span><span class="si">{</span><span class="n">station</span><span class="o">.</span><span class="n">code</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+
+        <span class="c1"># stream = stream.merge(fill_value=0)</span>
+        <span class="c1"># stream = stream.trim(starttime, endtime, pad=True, fill_value=0)</span>
+        <span class="n">stream</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">waveform_dir</span><span class="p">,</span> <span class="n">fname</span><span class="p">))</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">fname</span><span class="si">}</span><span class="s2"> download succeeds&quot;</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">upload_minio</span><span class="p">:</span>
+            <span class="n">minioClient</span><span class="o">.</span><span class="n">fput_object</span><span class="p">(</span><span class="n">bucket_name</span><span class="p">,</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;region&#39;</span><span class="p">],</span> <span class="n">fname</span><span class="p">),</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">waveform_dir</span><span class="p">,</span> <span class="n">fname</span><span class="p">))</span>
+            <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">fname</span><span class="si">}</span><span class="s2"> upload to minio </span><span class="si">{</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;region&#39;</span><span class="p">],</span><span class="w"> </span><span class="n">fname</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+        <span class="n">lock</span><span class="o">.</span><span class="n">acquire</span><span class="p">()</span>
+        <span class="n">fname_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">fname</span><span class="p">)</span>
+        <span class="n">lock</span><span class="o">.</span><span class="n">release</span><span class="p">()</span>
+
+    <span class="n">threads</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">MAX_THREADS</span> <span class="o">=</span> <span class="mi">4</span>
+    <span class="k">for</span> <span class="n">ii</span><span class="p">,</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">idx</span><span class="p">):</span>
+        <span class="n">t</span> <span class="o">=</span> <span class="n">threading</span><span class="o">.</span><span class="n">Thread</span><span class="p">(</span><span class="n">target</span><span class="o">=</span><span class="n">download</span><span class="p">,</span> <span class="n">args</span><span class="o">=</span><span class="p">(</span><span class="n">i</span><span class="p">,))</span>
+        <span class="n">t</span><span class="o">.</span><span class="n">start</span><span class="p">()</span>
+        <span class="n">time</span><span class="o">.</span><span class="n">sleep</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+        <span class="n">threads</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">t</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">ii</span> <span class="o">%</span> <span class="n">MAX_THREADS</span> <span class="o">==</span> <span class="n">MAX_THREADS</span> <span class="o">-</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">threads</span><span class="p">:</span>
+                <span class="n">t</span><span class="o">.</span><span class="n">join</span><span class="p">()</span>
+            <span class="n">threads</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">threads</span><span class="p">:</span>
+        <span class="n">t</span><span class="o">.</span><span class="n">join</span><span class="p">()</span>
+
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">fname_csv</span><span class="p">,</span> <span class="s2">&quot;w&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">fp</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">fname_list</span><span class="p">))</span>
+
+    <span class="k">return</span> <span class="n">waveform_dir</span>
+</pre></div>
+<div id="cell-12" class="clipboard-copy-txt">def download_waveform(
+    i: int,
+    index_json: InputPath("json"),
+    config_json: InputPath("json"),
+    datetime_json: InputPath("json"),
+    station_pkl: InputPath("pickle"),
+    fname_csv: OutputPath(str),
+    data_path: str,
+    bucket_name: str = "waveforms",
+    s3_url: str = "minio-service:9000",
+    secure: bool = True,
+) -> str:
+
+    import pickle, os
+    import obspy
+    from obspy.clients.fdsn import Client
+    import time
+    import json
+    import random
+    import threading
+
+    lock = threading.Lock()
+
+    upload_minio = False
+    try:
+        from minio import Minio
+
+        minioClient = Minio(s3_url, access_key='minio', secret_key='minio123', secure=secure)
+        if not minioClient.bucket_exists(bucket_name):
+            minioClient.make_bucket(bucket_name)
+        upload_minio = True
+    except Exception as err:
+        # print(f"ERROR: can not access minio service! \n{err}")
+        pass
+
+    with open(index_json, "r") as fp:
+        index = json.load(fp)
+    idx = index[i]
+    with open(config_json, "r") as fp:
+        config = json.load(fp)
+    with open(datetime_json, "r") as fp:
+        tmp = json.load(fp)
+        starttimes = tmp["starttimes"]
+        interval = tmp["interval"]
+    with open(station_pkl, "rb") as fp:
+        stations = pickle.load(fp)
+
+    waveform_dir = os.path.join(data_path, config["region"], "waveforms")
+    if not os.path.exists(waveform_dir):
+        os.makedirs(waveform_dir)
+
+    ####### Download data ########
+    client = Client(config["client"])
+    fname_list = ["fname"]
+
+    def download(i):
+        #     for i in idx:
+        starttime = obspy.UTCDateTime(starttimes[i])
+        endtime = starttime + interval
+        fname = "{}.mseed".format(starttime.datetime.strftime("%Y-%m-%dT%H:%M:%S"))
+        if not upload_minio:
+            if os.path.exists(os.path.join(waveform_dir, fname)):
+                print(f"{fname} exists")
+                fname_list.append(fname)
+                return
+        else:
+            try:
+                minioClient.fget_object(bucket_name,  os.path.join(config['region'], fname), os.path.join(waveform_dir, fname))
+                print(f"{bucket_name}/{os.path.join(config['region'], fname)} download to {os.path.join(waveform_dir, fname)}")
+                fname_list.append(fname)
+                return
+            except Exception as err:
+                print(err)
+
+        max_retry = 10
+        stream = obspy.Stream()
+        print(f"{fname} download starts")
+        num_sta = 0
+        for network in stations:
+            for station in network:
+                print(f"********{network.code}.{station.code}********")
+                retry = 0
+                while retry < max_retry:
+                    try:
+                        tmp = client.get_waveforms(
+                            network.code, station.code, "*", config["channels"], starttime, endtime
+                        )
+                        #  for trace in tmp:
+                        #      if trace.stats.sampling_rate != 100:
+                        #          print(trace)
+                        #          trace = trace.interpolate(100, method="linear")
+                        #      trace = trace.detrend("spline", order=2, dspline=5*trace.stats.sampling_rate)
+                        #      stream.append(trace)
+                        stream += tmp
+                        num_sta += len(tmp)
+                        break
+                    except Exception as err:
+                        print("Error {}.{}: {}".format(network.code, station.code, err))
+                        message = "No data available for request."
+                        if str(err)[: len(message)] == message:
+                            break
+                        retry += 1
+                        time.sleep(5)
+                        continue
+                if retry == max_retry:
+                    print(f"{fname}: MAX {max_retry} retries reached : {network.code}.{station.code}")
+
+        # stream = stream.merge(fill_value=0)
+        # stream = stream.trim(starttime, endtime, pad=True, fill_value=0)
+        stream.write(os.path.join(waveform_dir, fname))
+        print(f"{fname} download succeeds")
+        if upload_minio:
+            minioClient.fput_object(bucket_name, os.path.join(config['region'], fname), os.path.join(waveform_dir, fname))
+            print(f"{fname} upload to minio {os.path.join(config['region'], fname)}")
+        lock.acquire()
+        fname_list.append(fname)
+        lock.release()
+
+    threads = []
+    MAX_THREADS = 4
+    for ii, i in enumerate(idx):
+        t = threading.Thread(target=download, args=(i,))
+        t.start()
+        time.sleep(1)
+        threads.append(t)
+        if ii % MAX_THREADS == MAX_THREADS - 1:
+            for t in threads:
+                t.join()
+            threads = []
+    for t in threads:
+        t.join()
+
+    with open(fname_csv, "w") as fp:
+        fp.write("\n".join(fname_list))
+
+    return waveform_dir
+</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[36]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-13">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">if</span> <span class="n">run_local</span><span class="p">:</span>
+    <span class="n">waveform_path</span> <span class="o">=</span> <span class="n">download_waveform</span><span class="p">(</span>
+        <span class="mi">0</span><span class="p">,</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;index.json&quot;</span><span class="p">),</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;config.json&quot;</span><span class="p">),</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;datetimes.json&quot;</span><span class="p">),</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;stations.pkl&quot;</span><span class="p">),</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;fname.csv&quot;</span><span class="p">),</span>
+        <span class="n">data_path</span><span class="o">=</span><span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;&quot;</span><span class="p">),</span>
+    <span class="p">)</span>
+</pre></div>
+<div id="cell-13" class="clipboard-copy-txt">if run_local:
+    waveform_path = download_waveform(
+        0,
+        root_dir("index.json"),
+        root_dir("config.json"),
+        root_dir("datetimes.json"),
+        root_dir("stations.pkl"),
+        root_dir("fname.csv"),
+        data_path=root_dir(""),
+    )
+</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[37]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-14">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">download_waveform_op</span> <span class="o">=</span> <span class="n">comp</span><span class="o">.</span><span class="n">func_to_container_op</span><span class="p">(</span>
+    <span class="n">download_waveform</span><span class="p">,</span>
+    <span class="c1"># base_image=&#39;zhuwq0/quakeflow-env:latest&#39;,</span>
+    <span class="n">base_image</span><span class="o">=</span><span class="s1">&#39;python:3.8&#39;</span><span class="p">,</span>
+    <span class="n">packages_to_install</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;obspy&quot;</span><span class="p">,</span> <span class="s2">&quot;minio&quot;</span><span class="p">],</span>
+<span class="p">)</span>
+</pre></div>
+<div id="cell-14" class="clipboard-copy-txt">download_waveform_op = comp.func_to_container_op(
+    download_waveform,
+    # base_image='zhuwq0/quakeflow-env:latest',
+    base_image='python:3.8',
+    packages_to_install=["obspy", "minio"],
+)
+</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[38]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-15">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">def</span> <span class="nf">phasenet2gamma</span><span class="p">(</span>
+    <span class="n">i</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
+    <span class="n">index_json</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="s2">&quot;json&quot;</span><span class="p">),</span>
+    <span class="n">config_json</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="s2">&quot;json&quot;</span><span class="p">),</span>
+    <span class="n">station_csv</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span>
+    <span class="n">data_path</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
+    <span class="n">data_list</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span>
+    <span class="n">catalog_csv</span><span class="p">:</span> <span class="n">OutputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span>
+    <span class="n">picks_csv</span><span class="p">:</span> <span class="n">OutputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span>
+    <span class="n">bucket_name</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;catalogs&quot;</span><span class="p">,</span>
+    <span class="n">s3_url</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;localhost:9000&quot;</span><span class="p">,</span>
+    <span class="n">secure</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
+
+    <span class="kn">import</span> <span class="nn">json</span>
+    <span class="kn">import</span> <span class="nn">os</span>
+    <span class="kn">import</span> <span class="nn">obspy</span>
+    <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+    <span class="kn">import</span> <span class="nn">requests</span>
+    <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+    <span class="kn">import</span> <span class="nn">multiprocessing</span>
+    <span class="kn">from</span> <span class="nn">multiprocessing</span> <span class="kn">import</span> <span class="n">dummy</span>
+    <span class="kn">import</span> <span class="nn">time</span>
+
+    <span class="kn">import</span> <span class="nn">threading</span>
+
+    <span class="n">lock</span> <span class="o">=</span> <span class="n">threading</span><span class="o">.</span><span class="n">Lock</span><span class="p">()</span>
+
+    <span class="c1"># from requests.adapters import HTTPAdapter</span>
+    <span class="c1"># from requests.packages.urllib3.util.retry import Retry</span>
+    <span class="c1"># retry_strategy = Retry(</span>
+    <span class="c1">#     total=10,</span>
+    <span class="c1">#     status_forcelist=[104, 502],</span>
+    <span class="c1"># )</span>
+    <span class="c1"># adapter = HTTPAdapter(max_retries=retry_strategy)</span>
+    <span class="c1"># http = requests.Session()</span>
+    <span class="c1"># http.mount(&quot;http://&quot;, adapter)</span>
+
+    <span class="k">def</span> <span class="nf">convert_mseed</span><span class="p">(</span>
+        <span class="n">mseed</span><span class="p">,</span> <span class="n">stations</span><span class="p">,</span> <span class="n">sampling_rate</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">n_channel</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s2">&quot;float32&quot;</span><span class="p">,</span> <span class="n">amplitude</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">remove_resp</span><span class="o">=</span><span class="kc">True</span>
+    <span class="p">):</span>
+        <span class="k">try</span><span class="p">:</span>
+            <span class="n">mseed</span> <span class="o">=</span> <span class="n">mseed</span><span class="o">.</span><span class="n">detrend</span><span class="p">(</span><span class="s2">&quot;spline&quot;</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">dspline</span><span class="o">=</span><span class="mi">5</span> <span class="o">*</span> <span class="n">mseed</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">stats</span><span class="o">.</span><span class="n">sampling_rate</span><span class="p">)</span>
+        <span class="k">except</span><span class="p">:</span>
+            <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Error: spline detrend failed at file </span><span class="si">{</span><span class="n">mseed</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+            <span class="n">mseed</span> <span class="o">=</span> <span class="n">mseed</span><span class="o">.</span><span class="n">detrend</span><span class="p">(</span><span class="s2">&quot;demean&quot;</span><span class="p">)</span>
+        <span class="n">mseed</span> <span class="o">=</span> <span class="n">mseed</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">fill_value</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+        <span class="n">starttime</span> <span class="o">=</span> <span class="nb">min</span><span class="p">([</span><span class="n">st</span><span class="o">.</span><span class="n">stats</span><span class="o">.</span><span class="n">starttime</span> <span class="k">for</span> <span class="n">st</span> <span class="ow">in</span> <span class="n">mseed</span><span class="p">])</span>
+        <span class="n">endtime</span> <span class="o">=</span> <span class="nb">max</span><span class="p">([</span><span class="n">st</span><span class="o">.</span><span class="n">stats</span><span class="o">.</span><span class="n">endtime</span> <span class="k">for</span> <span class="n">st</span> <span class="ow">in</span> <span class="n">mseed</span><span class="p">])</span>
+        <span class="c1"># endtime = starttime + 60</span>
+        <span class="n">mseed</span> <span class="o">=</span> <span class="n">mseed</span><span class="o">.</span><span class="n">trim</span><span class="p">(</span><span class="n">starttime</span><span class="p">,</span> <span class="n">endtime</span><span class="p">,</span> <span class="n">pad</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">fill_value</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">mseed</span><span class="p">)):</span>
+            <span class="k">if</span> <span class="n">mseed</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">stats</span><span class="o">.</span><span class="n">sampling_rate</span> <span class="o">!=</span> <span class="n">sampling_rate</span><span class="p">:</span>
+                <span class="c1"># print(f&quot;Resampling {mseed[i].id} from {mseed[i].stats.sampling_rate} to {sampling_rate} Hz&quot;)</span>
+                <span class="n">mseed</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">mseed</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">interpolate</span><span class="p">(</span><span class="n">sampling_rate</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s2">&quot;linear&quot;</span><span class="p">)</span>
+
+        <span class="n">order</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;3&#39;</span><span class="p">,</span> <span class="s1">&#39;2&#39;</span><span class="p">,</span> <span class="s1">&#39;1&#39;</span><span class="p">,</span> <span class="s1">&#39;E&#39;</span><span class="p">,</span> <span class="s1">&#39;N&#39;</span><span class="p">,</span> <span class="s1">&#39;Z&#39;</span><span class="p">]</span>
+        <span class="n">order</span> <span class="o">=</span> <span class="p">{</span><span class="n">key</span><span class="p">:</span> <span class="n">i</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">key</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">order</span><span class="p">)}</span>
+        <span class="n">comp2idx</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;3&quot;</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span> <span class="s2">&quot;2&quot;</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span> <span class="s2">&quot;1&quot;</span><span class="p">:</span> <span class="mi">2</span><span class="p">,</span> <span class="s2">&quot;E&quot;</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span> <span class="s2">&quot;N&quot;</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span> <span class="s2">&quot;Z&quot;</span><span class="p">:</span> <span class="mi">2</span><span class="p">}</span>
+
+        <span class="n">nsta</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">stations</span><span class="p">)</span>
+        <span class="n">nt</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">mseed</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">data</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">mseed</span><span class="p">)))</span>
+        <span class="n">data</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">station_id</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">t0</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">nsta</span><span class="p">):</span>
+            <span class="n">trace_data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">([</span><span class="n">nt</span><span class="p">,</span> <span class="n">n_channel</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span>
+            <span class="n">empty_station</span> <span class="o">=</span> <span class="kc">True</span>
+            <span class="c1"># sta = stations.iloc[i][&quot;station&quot;]</span>
+            <span class="c1"># sta = stations.index[i]</span>
+            <span class="n">sta</span> <span class="o">=</span> <span class="n">stations</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="s2">&quot;id&quot;</span><span class="p">]</span>
+            <span class="n">comp</span> <span class="o">=</span> <span class="n">stations</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="s2">&quot;component&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;,&quot;</span><span class="p">)</span>
+            <span class="k">if</span> <span class="n">remove_resp</span><span class="p">:</span>
+                <span class="n">resp</span> <span class="o">=</span> <span class="n">stations</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="s2">&quot;response&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;,&quot;</span><span class="p">)</span>
+                <span class="c1"># resp = station_locs.iloc[i][&quot;response&quot;]</span>
+
+            <span class="k">for</span> <span class="n">j</span><span class="p">,</span> <span class="n">c</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">sorted</span><span class="p">(</span><span class="n">comp</span><span class="p">,</span> <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">order</span><span class="p">[</span><span class="n">x</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]])):</span>
+                <span class="n">resp_j</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">resp</span><span class="p">[</span><span class="n">j</span><span class="p">])</span>
+                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">comp</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">3</span><span class="p">:</span>  <span class="c1">## less than 3 component</span>
+                    <span class="n">j</span> <span class="o">=</span> <span class="n">comp2idx</span><span class="p">[</span><span class="n">c</span><span class="p">]</span>
+                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">mseed</span><span class="o">.</span><span class="n">select</span><span class="p">(</span><span class="nb">id</span><span class="o">=</span><span class="n">sta</span> <span class="o">+</span> <span class="n">c</span><span class="p">))</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+                    <span class="c1"># print(f&quot;Empty trace: {sta+c} {starttime}&quot;)</span>
+                    <span class="k">continue</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">empty_station</span> <span class="o">=</span> <span class="kc">False</span>
+
+                <span class="n">tmp</span> <span class="o">=</span> <span class="n">mseed</span><span class="o">.</span><span class="n">select</span><span class="p">(</span><span class="nb">id</span><span class="o">=</span><span class="n">sta</span> <span class="o">+</span> <span class="n">c</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">dtype</span><span class="p">)</span>
+                <span class="n">trace_data</span><span class="p">[:</span> <span class="nb">len</span><span class="p">(</span><span class="n">tmp</span><span class="p">),</span> <span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">tmp</span><span class="p">[:</span><span class="n">nt</span><span class="p">]</span>
+
+                <span class="k">if</span> <span class="n">stations</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="s2">&quot;unit&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;m/s**2&quot;</span><span class="p">:</span>
+                    <span class="n">tmp</span> <span class="o">=</span> <span class="n">mseed</span><span class="o">.</span><span class="n">select</span><span class="p">(</span><span class="nb">id</span><span class="o">=</span><span class="n">sta</span> <span class="o">+</span> <span class="n">c</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
+                    <span class="n">tmp</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">integrate</span><span class="p">()</span>
+                    <span class="n">tmp</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="s2">&quot;highpass&quot;</span><span class="p">,</span> <span class="n">freq</span><span class="o">=</span><span class="mf">1.0</span><span class="p">)</span>
+                    <span class="n">tmp</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">dtype</span><span class="p">)</span>
+                    <span class="n">trace_data</span><span class="p">[:</span> <span class="nb">len</span><span class="p">(</span><span class="n">tmp</span><span class="p">),</span> <span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">tmp</span><span class="p">[:</span><span class="n">nt</span><span class="p">]</span>
+                <span class="k">elif</span> <span class="n">stations</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="s2">&quot;unit&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;m/s&quot;</span><span class="p">:</span>
+                    <span class="n">tmp</span> <span class="o">=</span> <span class="n">mseed</span><span class="o">.</span><span class="n">select</span><span class="p">(</span><span class="nb">id</span><span class="o">=</span><span class="n">sta</span> <span class="o">+</span> <span class="n">c</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">dtype</span><span class="p">)</span>
+                    <span class="n">trace_data</span><span class="p">[:</span> <span class="nb">len</span><span class="p">(</span><span class="n">tmp</span><span class="p">),</span> <span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">tmp</span><span class="p">[:</span><span class="n">nt</span><span class="p">]</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="nb">print</span><span class="p">(</span>
+                        <span class="sa">f</span><span class="s2">&quot;Error in </span><span class="si">{</span><span class="n">stations</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="s1">&#39;station&#39;</span><span class="p">]</span><span class="si">}</span><span class="se">\n</span><span class="si">{</span><span class="n">stations</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="s1">&#39;unit&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2"> should be m/s**2 or m/s!&quot;</span>
+                    <span class="p">)</span>
+
+                <span class="k">if</span> <span class="n">remove_resp</span><span class="p">:</span>
+                    <span class="n">trace_data</span><span class="p">[:,</span> <span class="n">j</span><span class="p">]</span> <span class="o">/=</span> <span class="n">resp_j</span>
+
+            <span class="k">if</span> <span class="ow">not</span> <span class="n">empty_station</span><span class="p">:</span>
+                <span class="n">data</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">trace_data</span><span class="p">)</span>
+                <span class="n">station_id</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">sta</span><span class="p">)</span>
+                <span class="n">t0</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">starttime</span><span class="o">.</span><span class="n">strftime</span><span class="p">(</span><span class="s2">&quot;%Y-%m-</span><span class="si">%d</span><span class="s2">T%H:%M:%S.</span><span class="si">%f</span><span class="s2">&quot;</span><span class="p">)[:</span><span class="o">-</span><span class="mi">3</span><span class="p">])</span>
+
+        <span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+        <span class="n">meta</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;data&quot;</span><span class="p">:</span> <span class="n">data</span><span class="p">,</span> <span class="s2">&quot;t0&quot;</span><span class="p">:</span> <span class="n">t0</span><span class="p">,</span> <span class="s2">&quot;station_id&quot;</span><span class="p">:</span> <span class="n">station_id</span><span class="p">}</span>
+
+        <span class="k">return</span> <span class="n">meta</span>
+
+    <span class="c1"># PHASENET_API_URL = &quot;http://phasenet.quakeflow.com&quot;</span>
+    <span class="c1"># GAMMA_API_URL = &quot;http://gamma.quakeflow.com&quot;</span>
+    <span class="n">PHASENET_API_URL</span> <span class="o">=</span> <span class="s2">&quot;http://phasenet-api.default.svc.cluster.local:8000&quot;</span>
+    <span class="n">GAMMA_API_URL</span> <span class="o">=</span> <span class="s2">&quot;http://gamma-api.default.svc.cluster.local:8001&quot;</span>
+
+    <span class="c1">## read config</span>
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">index_json</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">index</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">fp</span><span class="p">)</span>
+    <span class="n">idx</span> <span class="o">=</span> <span class="n">index</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">config_json</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">config</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">fp</span><span class="p">)</span>
+
+    <span class="c1">## read stations</span>
+    <span class="n">stations</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">station_csv</span><span class="p">,</span> <span class="n">delimiter</span><span class="o">=</span><span class="s2">&quot;</span><span class="se">\t</span><span class="s2">&quot;</span><span class="p">)</span>
+    <span class="n">stations</span> <span class="o">=</span> <span class="n">stations</span><span class="o">.</span><span class="n">rename</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;station&quot;</span><span class="p">:</span> <span class="s2">&quot;id&quot;</span><span class="p">})</span>
+
+    <span class="c1">## read meed list</span>
+    <span class="n">data_list</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">data_list</span><span class="p">)</span>
+
+    <span class="n">manager</span> <span class="o">=</span> <span class="n">multiprocessing</span><span class="o">.</span><span class="n">Manager</span><span class="p">()</span>
+    <span class="c1"># catalog_list = manager.list()</span>
+    <span class="c1"># picks_list = manager.list()</span>
+    <span class="n">catalog_list</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">picks_list</span> <span class="o">=</span> <span class="p">[]</span>
+
+    <span class="k">for</span> <span class="n">fname</span> <span class="ow">in</span> <span class="n">data_list</span><span class="p">[</span><span class="s1">&#39;fname&#39;</span><span class="p">]:</span>
+        <span class="c1"># def process(fname):</span>
+        <span class="c1"># print(f&quot;Process {fname}\n&quot;)</span>
+        <span class="n">mseed</span> <span class="o">=</span> <span class="n">obspy</span><span class="o">.</span><span class="n">read</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">data_path</span><span class="p">,</span> <span class="n">fname</span><span class="p">))</span>
+        <span class="n">meta</span> <span class="o">=</span> <span class="n">convert_mseed</span><span class="p">(</span><span class="n">mseed</span><span class="p">,</span> <span class="n">stations</span><span class="p">)</span>
+
+        <span class="n">batch</span> <span class="o">=</span> <span class="mi">2</span>
+        <span class="c1"># phasenet_picks = manager.list()</span>
+        <span class="n">phasenet_picks</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="c1"># for j in range(0, len(meta[&quot;station_id&quot;]), batch):</span>
+        <span class="k">def</span> <span class="nf">run_phasenet</span><span class="p">(</span><span class="n">j</span><span class="p">):</span>
+            <span class="n">req</span> <span class="o">=</span> <span class="p">{</span>
+                <span class="s2">&quot;id&quot;</span><span class="p">:</span> <span class="n">meta</span><span class="p">[</span><span class="s1">&#39;station_id&#39;</span><span class="p">][</span><span class="n">j</span> <span class="p">:</span> <span class="n">j</span> <span class="o">+</span> <span class="n">batch</span><span class="p">],</span>
+                <span class="s2">&quot;timestamp&quot;</span><span class="p">:</span> <span class="n">meta</span><span class="p">[</span><span class="s2">&quot;t0&quot;</span><span class="p">][</span><span class="n">j</span> <span class="p">:</span> <span class="n">j</span> <span class="o">+</span> <span class="n">batch</span><span class="p">],</span>
+                <span class="s2">&quot;vec&quot;</span><span class="p">:</span> <span class="n">meta</span><span class="p">[</span><span class="s2">&quot;data&quot;</span><span class="p">][</span><span class="n">j</span> <span class="p">:</span> <span class="n">j</span> <span class="o">+</span> <span class="n">batch</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">(),</span>
+            <span class="p">}</span>
+            <span class="c1"># resp = requests.post(f&#39;{PHASENET_API_URL}/predict&#39;, json=req)</span>
+            <span class="c1"># phasenet_picks.extend(resp.json())</span>
+            <span class="n">counts</span> <span class="o">=</span> <span class="mi">0</span>
+            <span class="k">while</span> <span class="kc">True</span><span class="p">:</span>
+                <span class="k">try</span><span class="p">:</span>
+                    <span class="n">resp</span> <span class="o">=</span> <span class="n">requests</span><span class="o">.</span><span class="n">post</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">PHASENET_API_URL</span><span class="si">}</span><span class="s1">/predict&#39;</span><span class="p">,</span> <span class="n">json</span><span class="o">=</span><span class="n">req</span><span class="p">,</span> <span class="n">timeout</span><span class="o">=</span><span class="mi">30</span><span class="p">)</span>
+                    <span class="n">lock</span><span class="o">.</span><span class="n">acquire</span><span class="p">()</span>
+                    <span class="n">phasenet_picks</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">resp</span><span class="o">.</span><span class="n">json</span><span class="p">())</span>
+                    <span class="n">lock</span><span class="o">.</span><span class="n">release</span><span class="p">()</span>
+                    <span class="k">break</span>
+                <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
+                    <span class="k">if</span> <span class="n">counts</span> <span class="o">&gt;</span> <span class="mi">30</span><span class="p">:</span>
+                        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Error PhaseNet on </span><span class="si">{</span><span class="n">fname</span><span class="si">}</span><span class="s2"> at </span><span class="si">{</span><span class="n">j</span><span class="si">}</span><span class="s2"> trace: </span><span class="si">{</span><span class="n">e</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+                        <span class="k">break</span>
+                    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Retry PhaseNet on </span><span class="si">{</span><span class="n">fname</span><span class="si">}</span><span class="s2"> at </span><span class="si">{</span><span class="n">j</span><span class="si">}</span><span class="s2"> trace&quot;</span><span class="p">)</span>
+                    <span class="c1"># time.sleep(3)</span>
+                    <span class="n">counts</span> <span class="o">+=</span> <span class="mi">1</span>
+
+        <span class="n">time_prev</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;PhaseNet start on </span><span class="si">{</span><span class="n">fname</span><span class="si">}</span><span class="s2">...&quot;</span><span class="p">)</span>
+        <span class="n">threads</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">MAX_THREADS</span> <span class="o">=</span> <span class="mi">4</span>
+        <span class="c1"># for ii, i in enumerate(idx):</span>
+        <span class="k">for</span> <span class="n">ii</span><span class="p">,</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">meta</span><span class="p">[</span><span class="s2">&quot;station_id&quot;</span><span class="p">]),</span> <span class="n">batch</span><span class="p">)):</span>
+            <span class="n">t</span> <span class="o">=</span> <span class="n">threading</span><span class="o">.</span><span class="n">Thread</span><span class="p">(</span><span class="n">target</span><span class="o">=</span><span class="n">run_phasenet</span><span class="p">,</span> <span class="n">args</span><span class="o">=</span><span class="p">(</span><span class="n">j</span><span class="p">,))</span>
+            <span class="n">t</span><span class="o">.</span><span class="n">start</span><span class="p">()</span>
+            <span class="n">time</span><span class="o">.</span><span class="n">sleep</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
+            <span class="n">threads</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">t</span><span class="p">)</span>
+            <span class="k">if</span> <span class="n">ii</span> <span class="o">%</span> <span class="n">MAX_THREADS</span> <span class="o">==</span> <span class="n">MAX_THREADS</span> <span class="o">-</span> <span class="mi">1</span><span class="p">:</span>
+                <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">threads</span><span class="p">:</span>
+                    <span class="n">t</span><span class="o">.</span><span class="n">join</span><span class="p">()</span>
+                <span class="n">threads</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">threads</span><span class="p">:</span>
+            <span class="n">t</span><span class="o">.</span><span class="n">join</span><span class="p">()</span>
+
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;PhaseNet finish on </span><span class="si">{</span><span class="n">fname</span><span class="si">}</span><span class="s2"> using </span><span class="si">{</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span><span class="o">-</span><span class="n">time_prev</span><span class="si">}</span><span class="s2">s&quot;</span><span class="p">)</span>
+
+        <span class="c1"># print(f&quot;PhaseNet on {fname}...&quot;)</span>
+        <span class="c1"># # with multiprocessing.dummy.Pool(processes=min(8, (len(meta[&quot;station_id&quot;])-1)//batch+1)) as pool:</span>
+        <span class="c1"># with multiprocessing.dummy.Pool(processes=min(8, (len(meta[&quot;station_id&quot;])-1)//batch+1)) as pool:</span>
+        <span class="c1">#     pool.map(run_phasenet, range(0, len(meta[&quot;station_id&quot;]), batch))</span>
+        <span class="c1"># phasenet_picks = list(phasenet_picks)</span>
+
+        <span class="n">time_prev</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;GaMMA start on </span><span class="si">{</span><span class="n">fname</span><span class="si">}</span><span class="s2">...&quot;</span><span class="p">)</span>
+        <span class="n">counts</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="k">while</span> <span class="kc">True</span><span class="p">:</span>
+            <span class="k">try</span><span class="p">:</span>
+                <span class="n">resp</span> <span class="o">=</span> <span class="n">requests</span><span class="o">.</span><span class="n">post</span><span class="p">(</span>
+                    <span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">GAMMA_API_URL</span><span class="si">}</span><span class="s1">/predict&#39;</span><span class="p">,</span>
+                    <span class="n">json</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;picks&quot;</span><span class="p">:</span> <span class="n">phasenet_picks</span><span class="p">,</span> <span class="s2">&quot;stations&quot;</span><span class="p">:</span> <span class="n">stations</span><span class="o">.</span><span class="n">to_dict</span><span class="p">(</span><span class="n">orient</span><span class="o">=</span><span class="s2">&quot;records&quot;</span><span class="p">),</span> <span class="s2">&quot;config&quot;</span><span class="p">:</span> <span class="n">config</span><span class="p">},</span>
+                <span class="p">)</span>
+                <span class="n">result</span> <span class="o">=</span> <span class="n">resp</span><span class="o">.</span><span class="n">json</span><span class="p">()</span>
+                <span class="n">catalog_gamma</span> <span class="o">=</span> <span class="n">result</span><span class="p">[</span><span class="s2">&quot;catalog&quot;</span><span class="p">]</span>
+                <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">catalog_gamma</span><span class="p">:</span>
+                    <span class="n">c</span><span class="p">[</span><span class="s2">&quot;file_idx&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">fname</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;/&quot;</span><span class="p">)[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+                <span class="n">picks_gamma</span> <span class="o">=</span> <span class="n">result</span><span class="p">[</span><span class="s2">&quot;picks&quot;</span><span class="p">]</span>
+                <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">picks_gamma</span><span class="p">:</span>
+                    <span class="n">c</span><span class="p">[</span><span class="s2">&quot;file_idx&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">fname</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;/&quot;</span><span class="p">)[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+                <span class="k">break</span>
+            <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
+                <span class="k">if</span> <span class="n">counts</span> <span class="o">&gt;</span> <span class="mi">30</span><span class="p">:</span>
+                    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Error in GaMMA on </span><span class="si">{</span><span class="n">fname</span><span class="si">}</span><span class="s2">: </span><span class="si">{</span><span class="n">e</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+                    <span class="k">break</span>
+                <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Retry GaMMA on </span><span class="si">{</span><span class="n">fname</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+                <span class="n">time</span><span class="o">.</span><span class="n">sleep</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
+                <span class="n">counts</span> <span class="o">+=</span> <span class="mi">1</span>
+
+        <span class="n">catalog_list</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">catalog_gamma</span><span class="p">)</span>
+        <span class="n">picks_list</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">picks_gamma</span><span class="p">)</span>
+
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;GaMMA finish on </span><span class="si">{</span><span class="n">fname</span><span class="si">}</span><span class="s2"> using </span><span class="si">{</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span><span class="o">-</span><span class="n">time_prev</span><span class="si">}</span><span class="s2">s&quot;</span><span class="p">)</span>
+
+    <span class="c1"># with multiprocessing.dummy.Pool(processes=min(12, len(data_list[&quot;fname&quot;]))) as pool:</span>
+    <span class="c1">#     pool.map(process, data_list[&quot;fname&quot;])</span>
+
+    <span class="n">catalog_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">catalog_list</span><span class="p">))</span>
+    <span class="n">picks_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">picks_list</span><span class="p">))</span>
+    <span class="n">manager</span><span class="o">.</span><span class="n">shutdown</span><span class="p">()</span>
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">catalog_df</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;GaMMA catalog:&quot;</span><span class="p">)</span>
+        <span class="nb">print</span><span class="p">(</span>
+            <span class="n">catalog_df</span><span class="p">[</span>
+                <span class="p">[</span><span class="s2">&quot;time&quot;</span><span class="p">,</span> <span class="s2">&quot;latitude&quot;</span><span class="p">,</span> <span class="s2">&quot;longitude&quot;</span><span class="p">,</span> <span class="s2">&quot;depth(m)&quot;</span><span class="p">,</span> <span class="s2">&quot;magnitude&quot;</span><span class="p">,</span> <span class="s2">&quot;covariance&quot;</span><span class="p">,</span> <span class="s2">&quot;event_idx&quot;</span><span class="p">,</span> <span class="s2">&quot;file_idx&quot;</span><span class="p">]</span>
+            <span class="p">]</span>
+        <span class="p">)</span>
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">picks_df</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;GaMMA association:&quot;</span><span class="p">)</span>
+        <span class="nb">print</span><span class="p">(</span><span class="n">picks_df</span><span class="p">)</span>
+
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">catalog_csv</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">catalog_df</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span>
+            <span class="n">fp</span><span class="p">,</span>
+            <span class="n">sep</span><span class="o">=</span><span class="s2">&quot;</span><span class="se">\t</span><span class="s2">&quot;</span><span class="p">,</span>
+            <span class="n">index</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+            <span class="n">float_format</span><span class="o">=</span><span class="s2">&quot;</span><span class="si">%.3f</span><span class="s2">&quot;</span><span class="p">,</span>
+            <span class="n">date_format</span><span class="o">=</span><span class="s1">&#39;%Y-%m-</span><span class="si">%d</span><span class="s1">T%H:%M:%S.</span><span class="si">%f</span><span class="s1">&#39;</span><span class="p">,</span>
+            <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;time&quot;</span><span class="p">,</span> <span class="s2">&quot;magnitude&quot;</span><span class="p">,</span> <span class="s2">&quot;longitude&quot;</span><span class="p">,</span> <span class="s2">&quot;latitude&quot;</span><span class="p">,</span> <span class="s2">&quot;depth(m)&quot;</span><span class="p">,</span> <span class="s2">&quot;covariance&quot;</span><span class="p">,</span> <span class="s2">&quot;event_idx&quot;</span><span class="p">,</span> <span class="s2">&quot;file_idx&quot;</span><span class="p">],</span>
+        <span class="p">)</span>
+
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">picks_csv</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">picks_df</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span>
+            <span class="n">fp</span><span class="p">,</span>
+            <span class="n">sep</span><span class="o">=</span><span class="s2">&quot;</span><span class="se">\t</span><span class="s2">&quot;</span><span class="p">,</span>
+            <span class="n">index</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+            <span class="n">date_format</span><span class="o">=</span><span class="s1">&#39;%Y-%m-</span><span class="si">%d</span><span class="s1">T%H:%M:%S.</span><span class="si">%f</span><span class="s1">&#39;</span><span class="p">,</span>
+            <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;id&quot;</span><span class="p">,</span> <span class="s2">&quot;timestamp&quot;</span><span class="p">,</span> <span class="s2">&quot;type&quot;</span><span class="p">,</span> <span class="s2">&quot;prob&quot;</span><span class="p">,</span> <span class="s2">&quot;amp&quot;</span><span class="p">,</span> <span class="s2">&quot;prob_gmma&quot;</span><span class="p">,</span> <span class="s2">&quot;event_idx&quot;</span><span class="p">,</span> <span class="s2">&quot;file_idx&quot;</span><span class="p">],</span>
+        <span class="p">)</span>
+
+    <span class="c1">## upload to s3 bucket</span>
+    <span class="k">try</span><span class="p">:</span>
+        <span class="kn">from</span> <span class="nn">minio</span> <span class="kn">import</span> <span class="n">Minio</span>
+
+        <span class="n">catalog_dir</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="s2">&quot;/tmp/&quot;</span><span class="p">,</span> <span class="n">bucket_name</span><span class="p">)</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">catalog_dir</span><span class="p">):</span>
+            <span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">catalog_dir</span><span class="p">)</span>
+        <span class="n">minioClient</span> <span class="o">=</span> <span class="n">Minio</span><span class="p">(</span><span class="n">s3_url</span><span class="p">,</span> <span class="n">access_key</span><span class="o">=</span><span class="s1">&#39;minio&#39;</span><span class="p">,</span> <span class="n">secret_key</span><span class="o">=</span><span class="s1">&#39;minio123&#39;</span><span class="p">,</span> <span class="n">secure</span><span class="o">=</span><span class="n">secure</span><span class="p">)</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">minioClient</span><span class="o">.</span><span class="n">bucket_exists</span><span class="p">(</span><span class="n">bucket_name</span><span class="p">):</span>
+            <span class="n">minioClient</span><span class="o">.</span><span class="n">make_bucket</span><span class="p">(</span><span class="n">bucket_name</span><span class="p">)</span>
+
+        <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">catalog_dir</span><span class="p">,</span> <span class="sa">f</span><span class="s2">&quot;catalog_</span><span class="si">{</span><span class="n">idx</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">:</span><span class="s2">04d</span><span class="si">}</span><span class="s2">.csv&quot;</span><span class="p">),</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+            <span class="n">catalog_df</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span>
+                <span class="n">fp</span><span class="p">,</span>
+                <span class="n">sep</span><span class="o">=</span><span class="s2">&quot;</span><span class="se">\t</span><span class="s2">&quot;</span><span class="p">,</span>
+                <span class="n">index</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                <span class="n">float_format</span><span class="o">=</span><span class="s2">&quot;</span><span class="si">%.3f</span><span class="s2">&quot;</span><span class="p">,</span>
+                <span class="n">date_format</span><span class="o">=</span><span class="s1">&#39;%Y-%m-</span><span class="si">%d</span><span class="s1">T%H:%M:%S.</span><span class="si">%f</span><span class="s1">&#39;</span><span class="p">,</span>
+                <span class="n">columns</span><span class="o">=</span><span class="p">[</span>
+                    <span class="s2">&quot;time&quot;</span><span class="p">,</span>
+                    <span class="s2">&quot;magnitude&quot;</span><span class="p">,</span>
+                    <span class="s2">&quot;longitude&quot;</span><span class="p">,</span>
+                    <span class="s2">&quot;latitude&quot;</span><span class="p">,</span>
+                    <span class="s2">&quot;depth(m)&quot;</span><span class="p">,</span>
+                    <span class="s2">&quot;covariance&quot;</span><span class="p">,</span>
+                    <span class="s2">&quot;event_idx&quot;</span><span class="p">,</span>
+                    <span class="s2">&quot;file_idx&quot;</span><span class="p">,</span>
+                <span class="p">],</span>
+            <span class="p">)</span>
+        <span class="n">minioClient</span><span class="o">.</span><span class="n">fput_object</span><span class="p">(</span>
+            <span class="n">bucket_name</span><span class="p">,</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;region&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">/catalog_</span><span class="si">{</span><span class="n">idx</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">:</span><span class="s2">04d</span><span class="si">}</span><span class="s2">.csv&quot;</span><span class="p">,</span>
+            <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">catalog_dir</span><span class="p">,</span> <span class="sa">f</span><span class="s2">&quot;catalog_</span><span class="si">{</span><span class="n">idx</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">:</span><span class="s2">04d</span><span class="si">}</span><span class="s2">.csv&quot;</span><span class="p">),</span>
+        <span class="p">)</span>
+
+        <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">catalog_dir</span><span class="p">,</span> <span class="sa">f</span><span class="s2">&quot;picks_</span><span class="si">{</span><span class="n">idx</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">:</span><span class="s2">04d</span><span class="si">}</span><span class="s2">.csv&quot;</span><span class="p">),</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+            <span class="n">picks_df</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span>
+                <span class="n">fp</span><span class="p">,</span>
+                <span class="n">sep</span><span class="o">=</span><span class="s2">&quot;</span><span class="se">\t</span><span class="s2">&quot;</span><span class="p">,</span>
+                <span class="n">index</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                <span class="n">date_format</span><span class="o">=</span><span class="s1">&#39;%Y-%m-</span><span class="si">%d</span><span class="s1">T%H:%M:%S.</span><span class="si">%f</span><span class="s1">&#39;</span><span class="p">,</span>
+                <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;id&quot;</span><span class="p">,</span> <span class="s2">&quot;timestamp&quot;</span><span class="p">,</span> <span class="s2">&quot;type&quot;</span><span class="p">,</span> <span class="s2">&quot;prob&quot;</span><span class="p">,</span> <span class="s2">&quot;amp&quot;</span><span class="p">,</span> <span class="s2">&quot;prob_gmma&quot;</span><span class="p">,</span> <span class="s2">&quot;event_idx&quot;</span><span class="p">,</span> <span class="s2">&quot;file_idx&quot;</span><span class="p">],</span>
+            <span class="p">)</span>
+        <span class="n">minioClient</span><span class="o">.</span><span class="n">fput_object</span><span class="p">(</span>
+            <span class="n">bucket_name</span><span class="p">,</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;region&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">/picks_</span><span class="si">{</span><span class="n">idx</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">:</span><span class="s2">04d</span><span class="si">}</span><span class="s2">.csv&quot;</span><span class="p">,</span>
+            <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">catalog_dir</span><span class="p">,</span> <span class="sa">f</span><span class="s2">&quot;picks_</span><span class="si">{</span><span class="n">idx</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">:</span><span class="s2">04d</span><span class="si">}</span><span class="s2">.csv&quot;</span><span class="p">),</span>
+        <span class="p">)</span>
+
+    <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">err</span><span class="p">:</span>
+        <span class="c1"># print(f&quot;ERROR: can not access minio service! \n{err}&quot;)</span>
+        <span class="k">pass</span>
+
+    <span class="k">return</span> <span class="sa">f</span><span class="s2">&quot;catalog_</span><span class="si">{</span><span class="n">idx</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">:</span><span class="s2">04d</span><span class="si">}</span><span class="s2">.csv&quot;</span>
+</pre></div>
+<div id="cell-15" class="clipboard-copy-txt">def phasenet2gamma(
+    i: int,
+    index_json: InputPath("json"),
+    config_json: InputPath("json"),
+    station_csv: InputPath(str),
+    data_path: str,
+    data_list: InputPath(str),
+    catalog_csv: OutputPath(str),
+    picks_csv: OutputPath(str),
+    bucket_name: str = "catalogs",
+    s3_url: str = "localhost:9000",
+    secure: bool = True,
+) -> str:
+
+    import json
+    import os
+    import obspy
+    import pandas as pd
+    import requests
+    import numpy as np
+    import multiprocessing
+    from multiprocessing import dummy
+    import time
+
+    import threading
+
+    lock = threading.Lock()
+
+    # from requests.adapters import HTTPAdapter
+    # from requests.packages.urllib3.util.retry import Retry
+    # retry_strategy = Retry(
+    #     total=10,
+    #     status_forcelist=[104, 502],
+    # )
+    # adapter = HTTPAdapter(max_retries=retry_strategy)
+    # http = requests.Session()
+    # http.mount("http://", adapter)
+
+    def convert_mseed(
+        mseed, stations, sampling_rate=100, n_channel=3, dtype="float32", amplitude=True, remove_resp=True
+    ):
+        try:
+            mseed = mseed.detrend("spline", order=2, dspline=5 * mseed[0].stats.sampling_rate)
+        except:
+            print(f"Error: spline detrend failed at file {mseed}")
+            mseed = mseed.detrend("demean")
+        mseed = mseed.merge(fill_value=0)
+        starttime = min([st.stats.starttime for st in mseed])
+        endtime = max([st.stats.endtime for st in mseed])
+        # endtime = starttime + 60
+        mseed = mseed.trim(starttime, endtime, pad=True, fill_value=0)
+
+        for i in range(len(mseed)):
+            if mseed[i].stats.sampling_rate != sampling_rate:
+                # print(f"Resampling {mseed[i].id} from {mseed[i].stats.sampling_rate} to {sampling_rate} Hz")
+                mseed[i] = mseed[i].interpolate(sampling_rate, method="linear")
+
+        order = ['3', '2', '1', 'E', 'N', 'Z']
+        order = {key: i for i, key in enumerate(order)}
+        comp2idx = {"3": 0, "2": 1, "1": 2, "E": 0, "N": 1, "Z": 2}
+
+        nsta = len(stations)
+        nt = max(len(mseed[i].data) for i in range(len(mseed)))
+        data = []
+        station_id = []
+        t0 = []
+        for i in range(nsta):
+            trace_data = np.zeros([nt, n_channel], dtype=dtype)
+            empty_station = True
+            # sta = stations.iloc[i]["station"]
+            # sta = stations.index[i]
+            sta = stations.iloc[i]["id"]
+            comp = stations.iloc[i]["component"].split(",")
+            if remove_resp:
+                resp = stations.iloc[i]["response"].split(",")
+                # resp = station_locs.iloc[i]["response"]
+
+            for j, c in enumerate(sorted(comp, key=lambda x: order[x[-1]])):
+                resp_j = float(resp[j])
+                if len(comp) != 3:  ## less than 3 component
+                    j = comp2idx[c]
+                if len(mseed.select(id=sta + c)) == 0:
+                    # print(f"Empty trace: {sta+c} {starttime}")
+                    continue
+                else:
+                    empty_station = False
+
+                tmp = mseed.select(id=sta + c)[0].data.astype(dtype)
+                trace_data[: len(tmp), j] = tmp[:nt]
+
+                if stations.iloc[i]["unit"] == "m/s**2":
+                    tmp = mseed.select(id=sta + c)[0]
+                    tmp = tmp.integrate()
+                    tmp = tmp.filter("highpass", freq=1.0)
+                    tmp = tmp.data.astype(dtype)
+                    trace_data[: len(tmp), j] = tmp[:nt]
+                elif stations.iloc[i]["unit"] == "m/s":
+                    tmp = mseed.select(id=sta + c)[0].data.astype(dtype)
+                    trace_data[: len(tmp), j] = tmp[:nt]
+                else:
+                    print(
+                        f"Error in {stations.iloc[i]['station']}\n{stations.iloc[i]['unit']} should be m/s**2 or m/s!"
+                    )
+
+                if remove_resp:
+                    trace_data[:, j] /= resp_j
+
+            if not empty_station:
+                data.append(trace_data)
+                station_id.append(sta)
+                t0.append(starttime.strftime("%Y-%m-%dT%H:%M:%S.%f")[:-3])
+
+        data = np.stack(data)
+
+        meta = {"data": data, "t0": t0, "station_id": station_id}
+
+        return meta
+
+    # PHASENET_API_URL = "http://phasenet.quakeflow.com"
+    # GAMMA_API_URL = "http://gamma.quakeflow.com"
+    PHASENET_API_URL = "http://phasenet-api.default.svc.cluster.local:8000"
+    GAMMA_API_URL = "http://gamma-api.default.svc.cluster.local:8001"
+
+    ## read config
+    with open(index_json, "r") as fp:
+        index = json.load(fp)
+    idx = index[i]
+
+    with open(config_json, "r") as fp:
+        config = json.load(fp)
+
+    ## read stations
+    stations = pd.read_csv(station_csv, delimiter="\t")
+    stations = stations.rename(columns={"station": "id"})
+
+    ## read meed list
+    data_list = pd.read_csv(data_list)
+
+    manager = multiprocessing.Manager()
+    # catalog_list = manager.list()
+    # picks_list = manager.list()
+    catalog_list = []
+    picks_list = []
+
+    for fname in data_list['fname']:
+        # def process(fname):
+        # print(f"Process {fname}\n")
+        mseed = obspy.read(os.path.join(data_path, fname))
+        meta = convert_mseed(mseed, stations)
+
+        batch = 2
+        # phasenet_picks = manager.list()
+        phasenet_picks = []
+
+        # for j in range(0, len(meta["station_id"]), batch):
+        def run_phasenet(j):
+            req = {
+                "id": meta['station_id'][j : j + batch],
+                "timestamp": meta["t0"][j : j + batch],
+                "vec": meta["data"][j : j + batch].tolist(),
+            }
+            # resp = requests.post(f'{PHASENET_API_URL}/predict', json=req)
+            # phasenet_picks.extend(resp.json())
+            counts = 0
+            while True:
+                try:
+                    resp = requests.post(f'{PHASENET_API_URL}/predict', json=req, timeout=30)
+                    lock.acquire()
+                    phasenet_picks.extend(resp.json())
+                    lock.release()
+                    break
+                except Exception as e:
+                    if counts > 30:
+                        print(f"Error PhaseNet on {fname} at {j} trace: {e}")
+                        break
+                    print(f"Retry PhaseNet on {fname} at {j} trace")
+                    # time.sleep(3)
+                    counts += 1
+
+        time_prev = time.time()
+        print(f"PhaseNet start on {fname}...")
+        threads = []
+        MAX_THREADS = 4
+        # for ii, i in enumerate(idx):
+        for ii, j in enumerate(range(0, len(meta["station_id"]), batch)):
+            t = threading.Thread(target=run_phasenet, args=(j,))
+            t.start()
+            time.sleep(3)
+            threads.append(t)
+            if ii % MAX_THREADS == MAX_THREADS - 1:
+                for t in threads:
+                    t.join()
+                threads = []
+        for t in threads:
+            t.join()
+
+        print(f"PhaseNet finish on {fname} using {time.time()-time_prev}s")
+
+        # print(f"PhaseNet on {fname}...")
+        # # with multiprocessing.dummy.Pool(processes=min(8, (len(meta["station_id"])-1)//batch+1)) as pool:
+        # with multiprocessing.dummy.Pool(processes=min(8, (len(meta["station_id"])-1)//batch+1)) as pool:
+        #     pool.map(run_phasenet, range(0, len(meta["station_id"]), batch))
+        # phasenet_picks = list(phasenet_picks)
+
+        time_prev = time.time()
+        print(f"GaMMA start on {fname}...")
+        counts = 0
+        while True:
+            try:
+                resp = requests.post(
+                    f'{GAMMA_API_URL}/predict',
+                    json={"picks": phasenet_picks, "stations": stations.to_dict(orient="records"), "config": config},
+                )
+                result = resp.json()
+                catalog_gamma = result["catalog"]
+                for c in catalog_gamma:
+                    c["file_idx"] = fname.split("/")[-1]
+                picks_gamma = result["picks"]
+                for c in picks_gamma:
+                    c["file_idx"] = fname.split("/")[-1]
+                break
+            except Exception as e:
+                if counts > 30:
+                    print(f"Error in GaMMA on {fname}: {e}")
+                    break
+                print(f"Retry GaMMA on {fname}")
+                time.sleep(3)
+                counts += 1
+
+        catalog_list.extend(catalog_gamma)
+        picks_list.extend(picks_gamma)
+
+        print(f"GaMMA finish on {fname} using {time.time()-time_prev}s")
+
+    # with multiprocessing.dummy.Pool(processes=min(12, len(data_list["fname"]))) as pool:
+    #     pool.map(process, data_list["fname"])
+
+    catalog_df = pd.DataFrame(list(catalog_list))
+    picks_df = pd.DataFrame(list(picks_list))
+    manager.shutdown()
+    if len(catalog_df) > 0:
+        print("GaMMA catalog:")
+        print(
+            catalog_df[
+                ["time", "latitude", "longitude", "depth(m)", "magnitude", "covariance", "event_idx", "file_idx"]
+            ]
+        )
+    if len(picks_df) > 0:
+        print("GaMMA association:")
+        print(picks_df)
+
+    with open(catalog_csv, 'w') as fp:
+        catalog_df.to_csv(
+            fp,
+            sep="\t",
+            index=False,
+            float_format="%.3f",
+            date_format='%Y-%m-%dT%H:%M:%S.%f',
+            columns=["time", "magnitude", "longitude", "latitude", "depth(m)", "covariance", "event_idx", "file_idx"],
+        )
+
+    with open(picks_csv, 'w') as fp:
+        picks_df.to_csv(
+            fp,
+            sep="\t",
+            index=False,
+            date_format='%Y-%m-%dT%H:%M:%S.%f',
+            columns=["id", "timestamp", "type", "prob", "amp", "prob_gmma", "event_idx", "file_idx"],
+        )
+
+    ## upload to s3 bucket
+    try:
+        from minio import Minio
+
+        catalog_dir = os.path.join("/tmp/", bucket_name)
+        if not os.path.exists(catalog_dir):
+            os.makedirs(catalog_dir)
+        minioClient = Minio(s3_url, access_key='minio', secret_key='minio123', secure=secure)
+        if not minioClient.bucket_exists(bucket_name):
+            minioClient.make_bucket(bucket_name)
+
+        with open(os.path.join(catalog_dir, f"catalog_{idx[0]:04d}.csv"), 'w') as fp:
+            catalog_df.to_csv(
+                fp,
+                sep="\t",
+                index=False,
+                float_format="%.3f",
+                date_format='%Y-%m-%dT%H:%M:%S.%f',
+                columns=[
+                    "time",
+                    "magnitude",
+                    "longitude",
+                    "latitude",
+                    "depth(m)",
+                    "covariance",
+                    "event_idx",
+                    "file_idx",
+                ],
+            )
+        minioClient.fput_object(
+            bucket_name,
+            f"{config['region']}/catalog_{idx[0]:04d}.csv",
+            os.path.join(catalog_dir, f"catalog_{idx[0]:04d}.csv"),
+        )
+
+        with open(os.path.join(catalog_dir, f"picks_{idx[0]:04d}.csv"), 'w') as fp:
+            picks_df.to_csv(
+                fp,
+                sep="\t",
+                index=False,
+                date_format='%Y-%m-%dT%H:%M:%S.%f',
+                columns=["id", "timestamp", "type", "prob", "amp", "prob_gmma", "event_idx", "file_idx"],
+            )
+        minioClient.fput_object(
+            bucket_name,
+            f"{config['region']}/picks_{idx[0]:04d}.csv",
+            os.path.join(catalog_dir, f"picks_{idx[0]:04d}.csv"),
+        )
+
+    except Exception as err:
+        # print(f"ERROR: can not access minio service! \n{err}")
+        pass
+
+    return f"catalog_{idx[0]:04d}.csv"
+</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[39]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-16">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">if</span> <span class="n">run_local</span><span class="p">:</span>
+    <span class="n">phasenet2gamma</span><span class="p">(</span>
+        <span class="mi">0</span><span class="p">,</span> 
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;index.json&quot;</span><span class="p">),</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;config.json&quot;</span><span class="p">),</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;stations.csv&quot;</span><span class="p">),</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="n">root_dir</span><span class="p">(</span><span class="s1">&#39;waveforms&#39;</span><span class="p">)),</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s1">&#39;fname.csv&#39;</span><span class="p">),</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;catalog.csv&quot;</span><span class="p">),</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;picks.csv&quot;</span><span class="p">),</span>
+    <span class="p">)</span>
+</pre></div>
+<div id="cell-16" class="clipboard-copy-txt">if run_local:
+    phasenet2gamma(
+        0, 
+        root_dir("index.json"),
+        root_dir("config.json"),
+        root_dir("stations.csv"),
+        root_dir(root_dir('waveforms')),
+        root_dir('fname.csv'),
+        root_dir("catalog.csv"),
+        root_dir("picks.csv"),
+    )</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[40]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-17">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">phasenet2gamma_op</span> <span class="o">=</span> <span class="n">comp</span><span class="o">.</span><span class="n">func_to_container_op</span><span class="p">(</span>
+    <span class="n">phasenet2gamma</span><span class="p">,</span>
+    <span class="c1"># base_image=&#39;zhuwq0/quakeflow-env:latest&#39;,</span>
+    <span class="n">base_image</span><span class="o">=</span><span class="s1">&#39;python:3.8&#39;</span><span class="p">,</span>
+    <span class="n">packages_to_install</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;pandas&quot;</span><span class="p">,</span> <span class="s2">&quot;numpy&quot;</span><span class="p">,</span> <span class="s2">&quot;tqdm&quot;</span><span class="p">,</span> <span class="s2">&quot;minio&quot;</span><span class="p">,</span> <span class="s2">&quot;obspy&quot;</span><span class="p">],</span>
+<span class="p">)</span>
+</pre></div>
+<div id="cell-17" class="clipboard-copy-txt">phasenet2gamma_op = comp.func_to_container_op(
+    phasenet2gamma,
+    # base_image='zhuwq0/quakeflow-env:latest',
+    base_image='python:3.8',
+    packages_to_install=["pandas", "numpy", "tqdm", "minio", "obspy"],
+)</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="8-plot-catalogs">8. Plot catalogs<a class="anchor-link" href="#8-plot-catalogs">&#182;</a></h2>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[41]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-18">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">if</span> <span class="n">run_local</span><span class="p">:</span>
+    <span class="o">%</span><span class="n">run</span> <span class="n">plot_catalog</span><span class="o">.</span><span class="n">ipynb</span>
+</pre></div>
+<div id="cell-18" class="clipboard-copy-txt">if run_local:
+    %run plot_catalog.ipynb</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="9-parallel-processing-on-cloud">9. Parallel processing on cloud<a class="anchor-link" href="#9-parallel-processing-on-cloud">&#182;</a></h2><p>Only run this section for parallel jobs on cloud. Setting cloud environment is needed.</p>
+
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[42]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-19">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">def</span> <span class="nf">merge_catalog</span><span class="p">(</span>
+    <span class="n">config_json</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="s2">&quot;json&quot;</span><span class="p">),</span>
+    <span class="n">catalog_csv</span><span class="p">:</span> <span class="n">OutputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span>
+    <span class="n">picks_csv</span><span class="p">:</span> <span class="n">OutputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span>
+    <span class="n">bucket_name</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;catalogs&quot;</span><span class="p">,</span>
+    <span class="n">s3_url</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;minio-service:9000&quot;</span><span class="p">,</span>
+    <span class="n">secure</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+<span class="p">):</span>
+
+    <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+    <span class="kn">from</span> <span class="nn">glob</span> <span class="kn">import</span> <span class="n">glob</span>
+    <span class="kn">import</span> <span class="nn">os</span>
+    <span class="kn">import</span> <span class="nn">json</span>
+
+    <span class="kn">from</span> <span class="nn">minio</span> <span class="kn">import</span> <span class="n">Minio</span>
+
+    <span class="n">minioClient</span> <span class="o">=</span> <span class="n">Minio</span><span class="p">(</span><span class="n">s3_url</span><span class="p">,</span> <span class="n">access_key</span><span class="o">=</span><span class="s1">&#39;minio&#39;</span><span class="p">,</span> <span class="n">secret_key</span><span class="o">=</span><span class="s1">&#39;minio123&#39;</span><span class="p">,</span> <span class="n">secure</span><span class="o">=</span><span class="n">secure</span><span class="p">)</span>
+
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">config_json</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">config</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">fp</span><span class="p">)</span>
+
+    <span class="n">objects</span> <span class="o">=</span> <span class="n">minioClient</span><span class="o">.</span><span class="n">list_objects</span><span class="p">(</span><span class="n">bucket_name</span><span class="p">,</span> <span class="n">prefix</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;region&quot;</span><span class="p">],</span> <span class="n">recursive</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+    <span class="n">tmp_path</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="s2">&quot;/tmp/&quot;</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">obj</span> <span class="ow">in</span> <span class="n">objects</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="n">obj</span><span class="o">.</span><span class="n">_object_name</span><span class="p">)</span>
+        <span class="n">minioClient</span><span class="o">.</span><span class="n">fget_object</span><span class="p">(</span><span class="n">bucket_name</span><span class="p">,</span> <span class="n">obj</span><span class="o">.</span><span class="n">_object_name</span><span class="p">,</span> <span class="n">tmp_path</span><span class="p">(</span><span class="n">obj</span><span class="o">.</span><span class="n">_object_name</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;/&quot;</span><span class="p">)[</span><span class="o">-</span><span class="mi">1</span><span class="p">]))</span>
+
+    <span class="n">files_catalog</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">glob</span><span class="p">(</span><span class="n">tmp_path</span><span class="p">(</span><span class="s2">&quot;catalog_*.csv&quot;</span><span class="p">)))</span>
+    <span class="n">files_picks</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">glob</span><span class="p">(</span><span class="n">tmp_path</span><span class="p">(</span><span class="s2">&quot;picks_*.csv&quot;</span><span class="p">)))</span>
+
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">files_catalog</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="n">catalog_list</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="n">files_catalog</span><span class="p">:</span>
+            <span class="n">tmp</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="n">sep</span><span class="o">=</span><span class="s2">&quot;</span><span class="se">\t</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">str</span><span class="p">)</span>
+            <span class="n">catalog_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
+        <span class="n">merged_catalog</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">(</span><span class="n">catalog_list</span><span class="p">)</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">by</span><span class="o">=</span><span class="s2">&quot;time&quot;</span><span class="p">)</span>
+        <span class="n">merged_catalog</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="n">tmp_path</span><span class="p">(</span><span class="s2">&quot;merged_catalog.csv&quot;</span><span class="p">),</span> <span class="n">sep</span><span class="o">=</span><span class="s2">&quot;</span><span class="se">\t</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+        <span class="n">minioClient</span><span class="o">.</span><span class="n">fput_object</span><span class="p">(</span><span class="n">bucket_name</span><span class="p">,</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;region&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">/merged_catalog.csv&quot;</span><span class="p">,</span> <span class="n">tmp_path</span><span class="p">(</span><span class="s2">&quot;merged_catalog.csv&quot;</span><span class="p">))</span>
+
+        <span class="n">pick_list</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="n">files_picks</span><span class="p">:</span>
+            <span class="n">tmp</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="n">sep</span><span class="o">=</span><span class="s2">&quot;</span><span class="se">\t</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">str</span><span class="p">)</span>
+            <span class="n">pick_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
+        <span class="n">merged_picks</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">(</span><span class="n">pick_list</span><span class="p">)</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">by</span><span class="o">=</span><span class="s2">&quot;timestamp&quot;</span><span class="p">)</span>
+        <span class="n">merged_picks</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="n">tmp_path</span><span class="p">(</span><span class="s2">&quot;merged_picks.csv&quot;</span><span class="p">),</span> <span class="n">sep</span><span class="o">=</span><span class="s2">&quot;</span><span class="se">\t</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+        <span class="n">minioClient</span><span class="o">.</span><span class="n">fput_object</span><span class="p">(</span><span class="n">bucket_name</span><span class="p">,</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;region&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">/merged_picks.csv&quot;</span><span class="p">,</span> <span class="n">tmp_path</span><span class="p">(</span><span class="s2">&quot;merged_picks.csv&quot;</span><span class="p">))</span>
+
+        <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">catalog_csv</span><span class="p">,</span> <span class="s2">&quot;w&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fout</span><span class="p">:</span>
+            <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">tmp_path</span><span class="p">(</span><span class="s2">&quot;merged_catalog.csv&quot;</span><span class="p">),</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fin</span><span class="p">:</span>
+                <span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">fin</span><span class="p">:</span>
+                    <span class="n">fout</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">line</span><span class="p">)</span>
+        <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">picks_csv</span><span class="p">,</span> <span class="s2">&quot;w&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fout</span><span class="p">:</span>
+            <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">tmp_path</span><span class="p">(</span><span class="s2">&quot;merged_picks.csv&quot;</span><span class="p">),</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fin</span><span class="p">:</span>
+                <span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">fin</span><span class="p">:</span>
+                    <span class="n">fout</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">line</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">catalog_csv</span><span class="p">,</span> <span class="s2">&quot;w&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fout</span><span class="p">:</span>
+            <span class="k">pass</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;No catalog.csv found!&quot;</span><span class="p">)</span>
+        <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">picks_csv</span><span class="p">,</span> <span class="s2">&quot;w&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fout</span><span class="p">:</span>
+            <span class="k">pass</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;No picks.csv found!&quot;</span><span class="p">)</span>
+</pre></div>
+<div id="cell-19" class="clipboard-copy-txt">def merge_catalog(
+    config_json: InputPath("json"),
+    catalog_csv: OutputPath(str),
+    picks_csv: OutputPath(str),
+    bucket_name: str = "catalogs",
+    s3_url: str = "minio-service:9000",
+    secure: bool = True,
+):
+
+    import pandas as pd
+    from glob import glob
+    import os
+    import json
+
+    from minio import Minio
+
+    minioClient = Minio(s3_url, access_key='minio', secret_key='minio123', secure=secure)
+
+    with open(config_json, "r") as fp:
+        config = json.load(fp)
+
+    objects = minioClient.list_objects(bucket_name, prefix=config["region"], recursive=True)
+
+    tmp_path = lambda x: os.path.join("/tmp/", x)
+    for obj in objects:
+        print(obj._object_name)
+        minioClient.fget_object(bucket_name, obj._object_name, tmp_path(obj._object_name.split("/")[-1]))
+
+    files_catalog = sorted(glob(tmp_path("catalog_*.csv")))
+    files_picks = sorted(glob(tmp_path("picks_*.csv")))
+
+    if len(files_catalog) > 0:
+        catalog_list = []
+        for f in files_catalog:
+            tmp = pd.read_csv(f, sep="\t", dtype=str)
+            catalog_list.append(tmp)
+        merged_catalog = pd.concat(catalog_list).sort_values(by="time")
+        merged_catalog.to_csv(tmp_path("merged_catalog.csv"), sep="\t", index=False)
+        minioClient.fput_object(bucket_name, f"{config['region']}/merged_catalog.csv", tmp_path("merged_catalog.csv"))
+
+        pick_list = []
+        for f in files_picks:
+            tmp = pd.read_csv(f, sep="\t", dtype=str)
+            pick_list.append(tmp)
+        merged_picks = pd.concat(pick_list).sort_values(by="timestamp")
+        merged_picks.to_csv(tmp_path("merged_picks.csv"), sep="\t", index=False)
+        minioClient.fput_object(bucket_name, f"{config['region']}/merged_picks.csv", tmp_path("merged_picks.csv"))
+
+        with open(catalog_csv, "w") as fout:
+            with open(tmp_path("merged_catalog.csv"), "r") as fin:
+                for line in fin:
+                    fout.write(line)
+        with open(picks_csv, "w") as fout:
+            with open(tmp_path("merged_picks.csv"), "r") as fin:
+                for line in fin:
+                    fout.write(line)
+    else:
+        with open(catalog_csv, "w") as fout:
+            pass
+        print("No catalog.csv found!")
+        with open(picks_csv, "w") as fout:
+            pass
+        print("No picks.csv found!")
+</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[43]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-20">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># if run_local</span>
+<span class="c1"># combine_catalog(root_dir(&quot;config.json&quot;), root_dir(&quot;combined_catalog.csv&quot;), root_dir(&quot;combined_picks.csv&quot;), bucket_name=&quot;catalogs&quot;, s3_url=&quot;localhost:9000&quot;, secure=False)</span>
+</pre></div>
+<div id="cell-20" class="clipboard-copy-txt"># if run_local
+# combine_catalog(root_dir("config.json"), root_dir("combined_catalog.csv"), root_dir("combined_picks.csv"), bucket_name="catalogs", s3_url="localhost:9000", secure=False)
+</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[44]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-21">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">merge_op</span> <span class="o">=</span> <span class="n">comp</span><span class="o">.</span><span class="n">func_to_container_op</span><span class="p">(</span>
+    <span class="n">merge_catalog</span><span class="p">,</span>
+    <span class="c1"># base_image=&#39;zhuwq0/quakeflow-env:latest&#39;,</span>
+    <span class="n">base_image</span><span class="o">=</span><span class="s1">&#39;python:3.8&#39;</span><span class="p">,</span>
+    <span class="n">packages_to_install</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;pandas&quot;</span><span class="p">,</span> <span class="s2">&quot;minio&quot;</span><span class="p">],</span>
+<span class="p">)</span>
+</pre></div>
+<div id="cell-21" class="clipboard-copy-txt">merge_op = comp.func_to_container_op(
+    merge_catalog,
+    # base_image='zhuwq0/quakeflow-env:latest',
+    base_image='python:3.8',
+    packages_to_install=["pandas", "minio"],
+)
+</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[45]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-22">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="c1">## Define QuakeFlow pipeline</span>
+<span class="nd">@dsl</span><span class="o">.</span><span class="n">pipeline</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;QuakeFlow&#39;</span><span class="p">,</span> <span class="n">description</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">)</span>
+<span class="k">def</span> <span class="nf">quakeflow_pipeline</span><span class="p">(</span>
+    <span class="n">data_path</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;/tmp/&quot;</span><span class="p">,</span>
+    <span class="n">num_parallel</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+    <span class="n">bucket_catalog</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;catalogs&quot;</span><span class="p">,</span>
+    <span class="n">s3_url</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;minio-service:9000&quot;</span><span class="p">,</span>
+    <span class="n">secure</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+<span class="p">):</span>
+
+    <span class="n">config</span> <span class="o">=</span> <span class="n">config_op</span><span class="p">(</span><span class="n">num_parallel</span><span class="p">)</span>
+
+    <span class="n">events</span> <span class="o">=</span> <span class="n">download_events_op</span><span class="p">(</span><span class="n">config</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;config_json&quot;</span><span class="p">])</span>
+
+    <span class="n">stations</span> <span class="o">=</span> <span class="n">download_stations_op</span><span class="p">(</span><span class="n">config</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;config_json&quot;</span><span class="p">])</span>
+
+    <span class="k">with</span> <span class="n">kfp</span><span class="o">.</span><span class="n">dsl</span><span class="o">.</span><span class="n">ParallelFor</span><span class="p">(</span><span class="n">config</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;output&quot;</span><span class="p">])</span> <span class="k">as</span> <span class="n">i</span><span class="p">:</span>
+
+        <span class="n">vop_</span> <span class="o">=</span> <span class="n">dsl</span><span class="o">.</span><span class="n">VolumeOp</span><span class="p">(</span>
+            <span class="n">name</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;Create volume&quot;</span><span class="p">,</span> <span class="n">resource_name</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;data-volume-</span><span class="si">{</span><span class="nb">str</span><span class="p">(</span><span class="n">i</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="s2">&quot;20Gi&quot;</span><span class="p">,</span> <span class="n">modes</span><span class="o">=</span><span class="n">dsl</span><span class="o">.</span><span class="n">VOLUME_MODE_RWO</span>
+        <span class="p">)</span><span class="o">.</span><span class="n">set_retry</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
+
+        <span class="n">download_op_</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="n">download_waveform_op</span><span class="p">(</span>
+                <span class="n">i</span><span class="p">,</span>
+                <span class="n">config</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;index_json&quot;</span><span class="p">],</span>
+                <span class="n">config</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;config_json&quot;</span><span class="p">],</span>
+                <span class="n">config</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;datetime_json&quot;</span><span class="p">],</span>
+                <span class="n">stations</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;station_pkl&quot;</span><span class="p">],</span>
+                <span class="n">data_path</span><span class="o">=</span><span class="n">data_path</span><span class="p">,</span>
+                <span class="n">bucket_name</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;waveforms&quot;</span><span class="p">,</span>
+                <span class="c1"># s3_url=s3_url,</span>
+                <span class="n">s3_url</span><span class="o">=</span><span class="s2">&quot;quakeflow-minio.default.svc.cluster.local:9000&quot;</span><span class="p">,</span>
+                <span class="n">secure</span><span class="o">=</span><span class="n">secure</span><span class="p">,</span>
+            <span class="p">)</span>
+            <span class="o">.</span><span class="n">add_pvolumes</span><span class="p">({</span><span class="n">data_path</span><span class="p">:</span> <span class="n">vop_</span><span class="o">.</span><span class="n">volume</span><span class="p">})</span>
+            <span class="o">.</span><span class="n">set_cpu_request</span><span class="p">(</span><span class="s2">&quot;860m&quot;</span><span class="p">)</span> <span class="c1">#2CPU per node</span>
+            <span class="o">.</span><span class="n">set_retry</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
+            <span class="o">.</span><span class="n">set_display_name</span><span class="p">(</span><span class="s1">&#39;Download Waveforms&#39;</span><span class="p">)</span>
+        <span class="p">)</span>
+        <span class="n">download_op_</span><span class="o">.</span><span class="n">execution_options</span><span class="o">.</span><span class="n">caching_strategy</span><span class="o">.</span><span class="n">max_cache_staleness</span> <span class="o">=</span> <span class="s2">&quot;P30D&quot;</span>
+
+        <span class="n">phasenet2gamma_op_</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="n">phasenet2gamma_op</span><span class="p">(</span>
+                <span class="n">i</span><span class="p">,</span>
+                <span class="n">config</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;index_json&quot;</span><span class="p">],</span>
+                <span class="n">config</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;config_json&quot;</span><span class="p">],</span>
+                <span class="n">station_csv</span><span class="o">=</span><span class="n">stations</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;station_csv&quot;</span><span class="p">],</span>
+                <span class="n">data_path</span><span class="o">=</span><span class="n">download_op_</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;Output&quot;</span><span class="p">],</span>
+                <span class="n">data_list</span><span class="o">=</span><span class="n">download_op_</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;fname_csv&quot;</span><span class="p">],</span>
+                <span class="n">bucket_name</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;catalogs&quot;</span><span class="p">,</span>
+                <span class="n">s3_url</span><span class="o">=</span><span class="n">s3_url</span><span class="p">,</span>
+                <span class="n">secure</span><span class="o">=</span><span class="n">secure</span><span class="p">,</span>
+            <span class="p">)</span><span class="o">.</span><span class="n">add_pvolumes</span><span class="p">({</span><span class="n">data_path</span><span class="p">:</span> <span class="n">download_op_</span><span class="o">.</span><span class="n">pvolume</span><span class="p">})</span>
+            <span class="o">.</span><span class="n">set_cpu_request</span><span class="p">(</span><span class="s2">&quot;860m&quot;</span><span class="p">)</span>
+            <span class="c1"># .set_memory_request(&quot;2500M&quot;) #6GB per node</span>
+            <span class="o">.</span><span class="n">set_display_name</span><span class="p">(</span><span class="s1">&#39;PhaseNet + GaMMA&#39;</span><span class="p">)</span>
+        <span class="p">)</span>
+        <span class="n">phasenet2gamma_op_</span><span class="o">.</span><span class="n">execution_options</span><span class="o">.</span><span class="n">caching_strategy</span><span class="o">.</span><span class="n">max_cache_staleness</span> <span class="o">=</span> <span class="s2">&quot;P30D&quot;</span>
+
+    <span class="n">merge_op_</span> <span class="o">=</span> <span class="n">merge_op</span><span class="p">(</span><span class="n">config</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;config_json&quot;</span><span class="p">],</span> <span class="n">bucket_name</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;catalogs&quot;</span><span class="p">,</span> <span class="n">s3_url</span><span class="o">=</span><span class="n">s3_url</span><span class="p">,</span> <span class="n">secure</span><span class="o">=</span><span class="n">secure</span><span class="p">)</span><span class="o">.</span><span class="n">after</span><span class="p">(</span>
+        <span class="n">phasenet2gamma_op_</span>
+    <span class="p">)</span>
+    <span class="n">merge_op_</span><span class="o">.</span><span class="n">execution_options</span><span class="o">.</span><span class="n">caching_strategy</span><span class="o">.</span><span class="n">max_cache_staleness</span> <span class="o">=</span> <span class="s2">&quot;P0D&quot;</span>
+
+    <span class="n">vop_</span><span class="o">.</span><span class="n">delete</span><span class="p">()</span><span class="o">.</span><span class="n">after</span><span class="p">(</span><span class="n">merge_op_</span><span class="p">)</span>
+</pre></div>
+<div id="cell-22" class="clipboard-copy-txt">## Define QuakeFlow pipeline
+@dsl.pipeline(name='QuakeFlow', description='')
+def quakeflow_pipeline(
+    data_path: str = "/tmp/",
+    num_parallel=0,
+    bucket_catalog: str = "catalogs",
+    s3_url: str = "minio-service:9000",
+    secure: bool = False,
+):
+
+    config = config_op(num_parallel)
+
+    events = download_events_op(config.outputs["config_json"])
+
+    stations = download_stations_op(config.outputs["config_json"])
+
+    with kfp.dsl.ParallelFor(config.outputs["output"]) as i:
+
+        vop_ = dsl.VolumeOp(
+            name=f"Create volume", resource_name=f"data-volume-{str(i)}", size="20Gi", modes=dsl.VOLUME_MODE_RWO
+        ).set_retry(3)
+
+        download_op_ = (
+            download_waveform_op(
+                i,
+                config.outputs["index_json"],
+                config.outputs["config_json"],
+                config.outputs["datetime_json"],
+                stations.outputs["station_pkl"],
+                data_path=data_path,
+                bucket_name=f"waveforms",
+                # s3_url=s3_url,
+                s3_url="quakeflow-minio.default.svc.cluster.local:9000",
+                secure=secure,
+            )
+            .add_pvolumes({data_path: vop_.volume})
+            .set_cpu_request("860m") #2CPU per node
+            .set_retry(3)
+            .set_display_name('Download Waveforms')
+        )
+        download_op_.execution_options.caching_strategy.max_cache_staleness = "P30D"
+
+        phasenet2gamma_op_ = (
+            phasenet2gamma_op(
+                i,
+                config.outputs["index_json"],
+                config.outputs["config_json"],
+                station_csv=stations.outputs["station_csv"],
+                data_path=download_op_.outputs["Output"],
+                data_list=download_op_.outputs["fname_csv"],
+                bucket_name=f"catalogs",
+                s3_url=s3_url,
+                secure=secure,
+            ).add_pvolumes({data_path: download_op_.pvolume})
+            .set_cpu_request("860m")
+            # .set_memory_request("2500M") #6GB per node
+            .set_display_name('PhaseNet + GaMMA')
+        )
+        phasenet2gamma_op_.execution_options.caching_strategy.max_cache_staleness = "P30D"
+
+    merge_op_ = merge_op(config.outputs["config_json"], bucket_name=f"catalogs", s3_url=s3_url, secure=secure).after(
+        phasenet2gamma_op_
+    )
+    merge_op_.execution_options.caching_strategy.max_cache_staleness = "P0D"
+
+    vop_.delete().after(merge_op_)
+</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+
+<pre><code>helm install quakeflow-minio --set accessKey.password=minio --set secretKey.password=minio123 --set persistence.size=1T  bitnami/minio</code></pre>
+
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[46]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-23">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
+
+<span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s2">&quot;GOOGLE_APPLICATION_CREDENTIALS&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;/Users/weiqiang/.dotbot/cloud/quakeflow_zhuwq.json&quot;</span>
+<span class="n">experiment_name</span> <span class="o">=</span> <span class="s1">&#39;QuakeFlow&#39;</span>
+<span class="n">pipeline_func</span> <span class="o">=</span> <span class="n">quakeflow_pipeline</span>
+<span class="n">run_name</span> <span class="o">=</span> <span class="n">pipeline_func</span><span class="o">.</span><span class="vm">__name__</span> <span class="o">+</span> <span class="s1">&#39;_run&#39;</span>
+
+<span class="n">arguments</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;data_path&quot;</span><span class="p">:</span> <span class="s2">&quot;/tmp&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;num_parallel&quot;</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span>
+    <span class="s2">&quot;bucket_catalog&quot;</span><span class="p">:</span> <span class="s2">&quot;catalogs&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;s3_url&quot;</span><span class="p">:</span> <span class="s2">&quot;minio-service:9000&quot;</span><span class="p">,</span>
+    <span class="c1">#  &quot;s3_url&quot;: &quot;quakeflow-minio.default.svc.cluster.local:9000&quot;,</span>
+    <span class="s2">&quot;secure&quot;</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span>
+<span class="p">}</span>
+
+<span class="k">if</span> <span class="ow">not</span> <span class="n">run_local</span><span class="p">:</span>
+    <span class="n">client</span> <span class="o">=</span> <span class="n">kfp</span><span class="o">.</span><span class="n">Client</span><span class="p">(</span><span class="n">host</span><span class="o">=</span><span class="s2">&quot;66bece58bfa6ae5b-dot-us-west1.pipelines.googleusercontent.com&quot;</span><span class="p">)</span>
+    <span class="n">kfp</span><span class="o">.</span><span class="n">compiler</span><span class="o">.</span><span class="n">Compiler</span><span class="p">()</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">pipeline_func</span><span class="p">,</span> <span class="s1">&#39;</span><span class="si">{}</span><span class="s1">.zip&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">experiment_name</span><span class="p">))</span>
+    <span class="n">results</span> <span class="o">=</span> <span class="n">client</span><span class="o">.</span><span class="n">create_run_from_pipeline_func</span><span class="p">(</span>
+        <span class="n">pipeline_func</span><span class="p">,</span> <span class="n">experiment_name</span><span class="o">=</span><span class="n">experiment_name</span><span class="p">,</span> <span class="n">run_name</span><span class="o">=</span><span class="n">run_name</span><span class="p">,</span> <span class="n">arguments</span><span class="o">=</span><span class="n">arguments</span>
+    <span class="p">)</span>
+</pre></div>
+<div id="cell-23" class="clipboard-copy-txt">import os
+
+os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "/Users/weiqiang/.dotbot/cloud/quakeflow_zhuwq.json"
+experiment_name = 'QuakeFlow'
+pipeline_func = quakeflow_pipeline
+run_name = pipeline_func.__name__ + '_run'
+
+arguments = {
+    "data_path": "/tmp",
+    "num_parallel": 0,
+    "bucket_catalog": "catalogs",
+    "s3_url": "minio-service:9000",
+    #  "s3_url": "quakeflow-minio.default.svc.cluster.local:9000",
+    "secure": False,
+}
+
+if not run_local:
+    client = kfp.Client(host="66bece58bfa6ae5b-dot-us-west1.pipelines.googleusercontent.com")
+    kfp.compiler.Compiler().compile(pipeline_func, '{}.zip'.format(experiment_name))
+    results = client.create_run_from_pipeline_func(
+        pipeline_func, experiment_name=experiment_name, run_name=run_name, arguments=arguments
+    )
+</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output " data-mime-type="text/html">
+<a href="http://66bece58bfa6ae5b-dot-us-west1.pipelines.googleusercontent.com/#/experiments/details/e07314b3-f96b-49c9-987f-ed6c33379f11" target="_blank" >Experiment details</a>.
+</div>
+
+</div>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output " data-mime-type="text/html">
+<a href="http://66bece58bfa6ae5b-dot-us-west1.pipelines.googleusercontent.com/#/runs/details/1cb10618-74cd-4b42-a393-4d863056bddf" target="_blank" >Run details</a>.
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div>
+</div>
+</div>
+
+
+
+
+  
+
+
+
+              
+            </article>
+            
+          </div>
+        </div>
+        
+      </main>
+      
+        <footer class="md-footer">
+  
+  <div class="md-footer-meta md-typeset">
+    <div class="md-footer-meta__inner md-grid">
+      <div class="md-copyright">
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+    Made with
+    <a href="https://squidfunk.github.io/mkdocs-material/" target="_blank" rel="noopener">
+      Material for MkDocs
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+    
+    
+      <script src="../../assets/javascripts/bundle.9c69f0bc.min.js"></script>
+      
+    
+  </body>
+</html>
\ No newline at end of file
diff --git a/kubeflow/workflow-kfp2/index.html b/kubeflow/workflow-kfp2/index.html
new file mode 100644
index 0000000..815197d
--- /dev/null
+++ b/kubeflow/workflow-kfp2/index.html
@@ -0,0 +1,2278 @@
+
+<!doctype html>
+<html lang="en" class="no-js">
+  <head>
+    
+      <meta charset="utf-8">
+      <meta name="viewport" content="width=device-width,initial-scale=1">
+      
+        <meta name="description" content="QuakeFlow: a Scalable Deep-learning-based Earthquake Monitoring Workflow with Cloud Computing">
+      
+      
+        <meta name="author" content="Weiqiang Zhu">
+      
+      
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+
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+    <script>"undefined"!=typeof __md_analytics&&__md_analytics()</script>
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+  
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+    
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+    
+    
+    <body dir="ltr" data-md-color-scheme="" data-md-color-primary="none" data-md-color-accent="none">
+  
+    
+    
+    <input class="md-toggle" data-md-toggle="drawer" type="checkbox" id="__drawer" autocomplete="off">
+    <input class="md-toggle" data-md-toggle="search" type="checkbox" id="__search" autocomplete="off">
+    <label class="md-overlay" for="__drawer"></label>
+    <div data-md-component="skip">
+      
+    </div>
+    <div data-md-component="announce">
+      
+    </div>
+    
+    
+      
+
+<header class="md-header" data-md-component="header">
+  <nav class="md-header__inner md-grid" aria-label="Header">
+    <a href="../.." title="QuakeFlow" class="md-header__button md-logo" aria-label="QuakeFlow" data-md-component="logo">
+      
+  
+  <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54Z"/></svg>
+
+    </a>
+    <label class="md-header__button md-icon" for="__drawer">
+      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M3 6h18v2H3V6m0 5h18v2H3v-2m0 5h18v2H3v-2Z"/></svg>
+    </label>
+    <div class="md-header__title" data-md-component="header-title">
+      <div class="md-header__ellipsis">
+        <div class="md-header__topic">
+          <span class="md-ellipsis">
+            QuakeFlow
+          </span>
+        </div>
+        <div class="md-header__topic" data-md-component="header-topic">
+          <span class="md-ellipsis">
+            
+              Workflow kfp2
+            
+          </span>
+        </div>
+      </div>
+    </div>
+    
+    
+    
+    
+      <div class="md-header__source">
+        <a href="https://github.com/ai4eps/QuakeFlow" title="Go to repository" class="md-source" data-md-component="source">
+  <div class="md-source__icon md-icon">
+    
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+  </div>
+  <div class="md-source__repository">
+    AI4EPS/QuakeFlow
+  </div>
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+        <div class="md-main__inner md-grid">
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+                <div class="md-sidebar__scrollwrap">
+                  <div class="md-sidebar__inner">
+                    
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+<nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
+  <label class="md-nav__title" for="__drawer">
+    <a href="../.." title="QuakeFlow" class="md-nav__button md-logo" aria-label="QuakeFlow" data-md-component="logo">
+      
+  
+  <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54Z"/></svg>
+
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+    QuakeFlow
+  </label>
+  
+    <div class="md-nav__source">
+      <a href="https://github.com/ai4eps/QuakeFlow" title="Go to repository" class="md-source" data-md-component="source">
+  <div class="md-source__icon md-icon">
+    
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.1.2 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+  </div>
+  <div class="md-source__repository">
+    AI4EPS/QuakeFlow
+  </div>
+</a>
+    </div>
+  
+  <ul class="md-nav__list" data-md-scrollfix>
+    
+      
+      
+      
+
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../.." class="md-nav__link">
+        Overview
+      </a>
+    </li>
+  
+
+    
+      
+      
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+  
+    <li class="md-nav__item">
+      <a href="../workflow/" class="md-nav__link">
+        Pipeline
+      </a>
+    </li>
+  
+
+    
+      
+      
+      
+
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../data_format/" class="md-nav__link">
+        Standard Formats
+      </a>
+    </li>
+  
+
+    
+  </ul>
+</nav>
+                  </div>
+                </div>
+              </div>
+            
+            
+              
+              <div class="md-sidebar md-sidebar--secondary" data-md-component="sidebar" data-md-type="toc" >
+                <div class="md-sidebar__scrollwrap">
+                  <div class="md-sidebar__inner">
+                    
+
+<nav class="md-nav md-nav--secondary" aria-label="Table of contents">
+  
+  
+  
+  
+</nav>
+                  </div>
+                </div>
+              </div>
+            
+          
+          <div class="md-content" data-md-component="content">
+            <article class="md-content__inner md-typeset">
+              
+                
+  <a href="https://github.com/ai4eps/QuakeFlow/edit/master/docs/kubeflow/workflow-kfp2.ipynb" title="Edit this page" class="md-content__button md-icon">
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
+  </a>
+
+
+
+  <h1>Workflow kfp2</h1>
+
+<script>
+(function (global, factory) {
+    typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() :
+    typeof define === 'function' && define.amd ? define(factory) :
+    (global = global || self, global.ClipboardCopyElement = factory());
+  }(this, function () { 'use strict';
+
+    function createNode(text) {
+      const node = document.createElement('pre');
+      node.style.width = '1px';
+      node.style.height = '1px';
+      node.style.position = 'fixed';
+      node.style.top = '5px';
+      node.textContent = text;
+      return node;
+    }
+
+    function copyNode(node) {
+      if ('clipboard' in navigator) {
+        // eslint-disable-next-line flowtype/no-flow-fix-me-comments
+        // $FlowFixMe Clipboard is not defined in Flow yet.
+        return navigator.clipboard.writeText(node.textContent);
+      }
+
+      const selection = getSelection();
+
+      if (selection == null) {
+        return Promise.reject(new Error());
+      }
+
+      selection.removeAllRanges();
+      const range = document.createRange();
+      range.selectNodeContents(node);
+      selection.addRange(range);
+      document.execCommand('copy');
+      selection.removeAllRanges();
+      return Promise.resolve();
+    }
+    function copyText(text) {
+      if ('clipboard' in navigator) {
+        // eslint-disable-next-line flowtype/no-flow-fix-me-comments
+        // $FlowFixMe Clipboard is not defined in Flow yet.
+        return navigator.clipboard.writeText(text);
+      }
+
+      const body = document.body;
+
+      if (!body) {
+        return Promise.reject(new Error());
+      }
+
+      const node = createNode(text);
+      body.appendChild(node);
+      copyNode(node);
+      body.removeChild(node);
+      return Promise.resolve();
+    }
+
+    function copy(button) {
+      const id = button.getAttribute('for');
+      const text = button.getAttribute('value');
+
+      function trigger() {
+        button.dispatchEvent(new CustomEvent('clipboard-copy', {
+          bubbles: true
+        }));
+      }
+
+      if (text) {
+        copyText(text).then(trigger);
+      } else if (id) {
+        const root = 'getRootNode' in Element.prototype ? button.getRootNode() : button.ownerDocument;
+        if (!(root instanceof Document || 'ShadowRoot' in window && root instanceof ShadowRoot)) return;
+        const node = root.getElementById(id);
+        if (node) copyTarget(node).then(trigger);
+      }
+    }
+
+    function copyTarget(content) {
+      if (content instanceof HTMLInputElement || content instanceof HTMLTextAreaElement) {
+        return copyText(content.value);
+      } else if (content instanceof HTMLAnchorElement && content.hasAttribute('href')) {
+        return copyText(content.href);
+      } else {
+        return copyNode(content);
+      }
+    }
+
+    function clicked(event) {
+      const button = event.currentTarget;
+
+      if (button instanceof HTMLElement) {
+        copy(button);
+      }
+    }
+
+    function keydown(event) {
+      if (event.key === ' ' || event.key === 'Enter') {
+        const button = event.currentTarget;
+
+        if (button instanceof HTMLElement) {
+          event.preventDefault();
+          copy(button);
+        }
+      }
+    }
+
+    function focused(event) {
+      event.currentTarget.addEventListener('keydown', keydown);
+    }
+
+    function blurred(event) {
+      event.currentTarget.removeEventListener('keydown', keydown);
+    }
+
+    class ClipboardCopyElement extends HTMLElement {
+      constructor() {
+        super();
+        this.addEventListener('click', clicked);
+        this.addEventListener('focus', focused);
+        this.addEventListener('blur', blurred);
+      }
+
+      connectedCallback() {
+        if (!this.hasAttribute('tabindex')) {
+          this.setAttribute('tabindex', '0');
+        }
+
+        if (!this.hasAttribute('role')) {
+          this.setAttribute('role', 'button');
+        }
+      }
+
+      get value() {
+        return this.getAttribute('value') || '';
+      }
+
+      set value(text) {
+        this.setAttribute('value', text);
+      }
+
+    }
+
+    if (!window.customElements.get('clipboard-copy')) {
+      window.ClipboardCopyElement = ClipboardCopyElement;
+      window.customElements.define('clipboard-copy', ClipboardCopyElement);
+    }
+
+    return ClipboardCopyElement;
+
+  }));
+</script>
+<script>
+      document.addEventListener('clipboard-copy', function(event) {
+        const notice = event.target.querySelector('.notice')
+        notice.hidden = false
+        setTimeout(function() {
+          notice.hidden = true
+        }, 1000)
+      })
+</script>
+
+
+
+
+<style type="text/css">
+    
+    pre { line-height: 125%; }
+td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+.highlight-ipynb .hll { background-color: var(--jp-cell-editor-active-background) }
+.highlight-ipynb { background: var(--jp-cell-editor-background); color: var(--jp-mirror-editor-variable-color) }
+.highlight-ipynb .c { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment */
+.highlight-ipynb .err { color: var(--jp-mirror-editor-error-color) } /* Error */
+.highlight-ipynb .k { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword */
+.highlight-ipynb .o { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator */
+.highlight-ipynb .p { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation */
+.highlight-ipynb .ch { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Hashbang */
+.highlight-ipynb .cm { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Multiline */
+.highlight-ipynb .cp { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Preproc */
+.highlight-ipynb .cpf { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.PreprocFile */
+.highlight-ipynb .c1 { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Single */
+.highlight-ipynb .cs { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Special */
+.highlight-ipynb .kc { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Constant */
+.highlight-ipynb .kd { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Declaration */
+.highlight-ipynb .kn { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Namespace */
+.highlight-ipynb .kp { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Pseudo */
+.highlight-ipynb .kr { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Reserved */
+.highlight-ipynb .kt { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Type */
+.highlight-ipynb .m { color: var(--jp-mirror-editor-number-color) } /* Literal.Number */
+.highlight-ipynb .s { color: var(--jp-mirror-editor-string-color) } /* Literal.String */
+.highlight-ipynb .ow { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator.Word */
+.highlight-ipynb .pm { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation.Marker */
+.highlight-ipynb .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */
+.highlight-ipynb .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */
+.highlight-ipynb .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */
+.highlight-ipynb .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */
+.highlight-ipynb .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */
+.highlight-ipynb .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */
+.highlight-ipynb .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */
+.highlight-ipynb .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */
+.highlight-ipynb .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */
+.highlight-ipynb .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */
+.highlight-ipynb .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */
+.highlight-ipynb .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */
+.highlight-ipynb .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */
+.highlight-ipynb .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */
+.highlight-ipynb .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */
+.highlight-ipynb .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */
+.highlight-ipynb .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */
+.highlight-ipynb .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */
+.highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */
+.highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */
+  </style>
+
+
+
+
+<style type="text/css">
+/*
+This file is taken from the built JupyterLab theme.css
+Found on share/nbconvert/templates/lab/static
+Some changes have been made and marked with CHANGE
+*/
+
+.jupyter-wrapper {
+    /* Elevation
+   *
+   * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here:
+   *
+   * https://github.com/material-components/material-components-web
+   * https://material-components-web.appspot.com/elevation.html
+   */
+
+    --jp-shadow-base-lightness: 0;
+    --jp-shadow-umbra-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.2
+    );
+    --jp-shadow-penumbra-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.14
+    );
+    --jp-shadow-ambient-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.12
+    );
+    --jp-elevation-z0: none;
+    --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color),
+        0px 1px 1px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 3px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color),
+        0px 2px 2px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 5px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color),
+        0px 4px 5px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 10px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color),
+        0px 6px 10px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 18px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color),
+        0px 8px 10px 1px var(--jp-shadow-penumbra-color),
+        0px 3px 14px 2px var(--jp-shadow-ambient-color);
+    --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color),
+        0px 12px 17px 2px var(--jp-shadow-penumbra-color),
+        0px 5px 22px 4px var(--jp-shadow-ambient-color);
+    --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color),
+        0px 16px 24px 2px var(--jp-shadow-penumbra-color),
+        0px 6px 30px 5px var(--jp-shadow-ambient-color);
+    --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color),
+        0px 20px 31px 3px var(--jp-shadow-penumbra-color),
+        0px 8px 38px 7px var(--jp-shadow-ambient-color);
+    --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color),
+        0px 24px 38px 3px var(--jp-shadow-penumbra-color),
+        0px 9px 46px 8px var(--jp-shadow-ambient-color);
+
+    /* Borders
+   *
+   * The following variables, specify the visual styling of borders in JupyterLab.
+   */
+
+    --jp-border-width: 1px;
+    --jp-border-color0: var(--md-grey-400);
+    --jp-border-color1: var(--md-grey-400);
+    --jp-border-color2: var(--md-grey-300);
+    --jp-border-color3: var(--md-grey-200);
+    --jp-border-radius: 2px;
+
+    /* UI Fonts
+   *
+   * The UI font CSS variables are used for the typography all of the JupyterLab
+   * user interface elements that are not directly user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-ui-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+    --jp-ui-font-scale-factor: 1.2;
+    --jp-ui-font-size0: 0.83333em;
+    --jp-ui-font-size1: 13px; /* Base font size */
+    --jp-ui-font-size2: 1.2em;
+    --jp-ui-font-size3: 1.44em;
+
+    --jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+
+    /*
+   * Use these font colors against the corresponding main layout colors.
+   * In a light theme, these go from dark to light.
+   */
+
+    /* Defaults use Material Design specification */
+    --jp-ui-font-color0: rgba(0, 0, 0, 1);
+    --jp-ui-font-color1: rgba(0, 0, 0, 0.87);
+    --jp-ui-font-color2: rgba(0, 0, 0, 0.54);
+    --jp-ui-font-color3: rgba(0, 0, 0, 0.38);
+
+    /*
+   * Use these against the brand/accent/warn/error colors.
+   * These will typically go from light to darker, in both a dark and light theme.
+   */
+
+    --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1);
+    --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1);
+    --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7);
+    --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5);
+
+    /* Content Fonts
+   *
+   * Content font variables are used for typography of user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-content-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+    --jp-content-line-height: 1.6;
+    --jp-content-font-scale-factor: 1.2;
+    --jp-content-font-size0: 0.83333em;
+    --jp-content-font-size1: 14px; /* Base font size */
+    --jp-content-font-size2: 1.2em;
+    --jp-content-font-size3: 1.44em;
+    --jp-content-font-size4: 1.728em;
+    --jp-content-font-size5: 2.0736em;
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-content-presentation-font-size1: 17px;
+
+    --jp-content-heading-line-height: 1;
+    --jp-content-heading-margin-top: 1.2em;
+    --jp-content-heading-margin-bottom: 0.8em;
+    --jp-content-heading-font-weight: 500;
+
+    /* Defaults use Material Design specification */
+    --jp-content-font-color0: rgba(0, 0, 0, 1);
+    --jp-content-font-color1: rgba(0, 0, 0, 0.87);
+    --jp-content-font-color2: rgba(0, 0, 0, 0.54);
+    --jp-content-font-color3: rgba(0, 0, 0, 0.38);
+
+    --jp-content-link-color: var(--md-blue-700);
+
+    --jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+
+    /*
+   * Code Fonts
+   *
+   * Code font variables are used for typography of code and other monospaces content.
+   */
+
+    --jp-code-font-size: 13px;
+    --jp-code-line-height: 1.3077; /* 17px for 13px base */
+    --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */
+    --jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono",
+        monospace;
+    --jp-code-font-family: var(--jp-code-font-family-default);
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-code-presentation-font-size: 16px;
+
+    /* may need to tweak cursor width if you change font size */
+    --jp-code-cursor-width0: 1.4px;
+    --jp-code-cursor-width1: 2px;
+    --jp-code-cursor-width2: 4px;
+
+    /* Layout
+   *
+   * The following are the main layout colors use in JupyterLab. In a light
+   * theme these would go from light to dark.
+   */
+
+    --jp-layout-color0: white;
+    --jp-layout-color1: white;
+    --jp-layout-color2: var(--md-grey-200);
+    --jp-layout-color3: var(--md-grey-400);
+    --jp-layout-color4: var(--md-grey-600);
+
+    /* Inverse Layout
+   *
+   * The following are the inverse layout colors use in JupyterLab. In a light
+   * theme these would go from dark to light.
+   */
+
+    --jp-inverse-layout-color0: #111111;
+    --jp-inverse-layout-color1: var(--md-grey-900);
+    --jp-inverse-layout-color2: var(--md-grey-800);
+    --jp-inverse-layout-color3: var(--md-grey-700);
+    --jp-inverse-layout-color4: var(--md-grey-600);
+
+    /* Brand/accent */
+
+    --jp-brand-color0: var(--md-blue-900);
+    --jp-brand-color1: var(--md-blue-700);
+    --jp-brand-color2: var(--md-blue-300);
+    --jp-brand-color3: var(--md-blue-100);
+    --jp-brand-color4: var(--md-blue-50);
+
+    --jp-accent-color0: var(--md-green-900);
+    --jp-accent-color1: var(--md-green-700);
+    --jp-accent-color2: var(--md-green-300);
+    --jp-accent-color3: var(--md-green-100);
+
+    /* State colors (warn, error, success, info) */
+
+    --jp-warn-color0: var(--md-orange-900);
+    --jp-warn-color1: var(--md-orange-700);
+    --jp-warn-color2: var(--md-orange-300);
+    --jp-warn-color3: var(--md-orange-100);
+
+    --jp-error-color0: var(--md-red-900);
+    --jp-error-color1: var(--md-red-700);
+    --jp-error-color2: var(--md-red-300);
+    --jp-error-color3: var(--md-red-100);
+
+    --jp-success-color0: var(--md-green-900);
+    --jp-success-color1: var(--md-green-700);
+    --jp-success-color2: var(--md-green-300);
+    --jp-success-color3: var(--md-green-100);
+
+    --jp-info-color0: var(--md-cyan-900);
+    --jp-info-color1: var(--md-cyan-700);
+    --jp-info-color2: var(--md-cyan-300);
+    --jp-info-color3: var(--md-cyan-100);
+
+    /* Cell specific styles */
+
+    --jp-cell-padding: 5px;
+
+    --jp-cell-collapser-width: 8px;
+    --jp-cell-collapser-min-height: 20px;
+    --jp-cell-collapser-not-active-hover-opacity: 0.6;
+
+    --jp-cell-editor-background: var(--md-grey-100);
+    --jp-cell-editor-border-color: var(--md-grey-300);
+    --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-cell-editor-active-background: var(--jp-layout-color0);
+    --jp-cell-editor-active-border-color: var(--jp-brand-color1);
+
+    --jp-cell-prompt-width: 64px;
+    --jp-cell-prompt-font-family: var(--jp-code-font-family-default);
+    --jp-cell-prompt-letter-spacing: 0px;
+    --jp-cell-prompt-opacity: 1;
+    --jp-cell-prompt-not-active-opacity: 0.5;
+    --jp-cell-prompt-not-active-font-color: var(--md-grey-700);
+    /* A custom blend of MD grey and blue 600
+   * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */
+    --jp-cell-inprompt-font-color: #307fc1;
+    /* A custom blend of MD grey and orange 600
+   * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */
+    --jp-cell-outprompt-font-color: #bf5b3d;
+
+    /* Notebook specific styles */
+
+    --jp-notebook-padding: 10px;
+    --jp-notebook-select-background: var(--jp-layout-color1);
+    --jp-notebook-multiselected-color: var(--md-blue-50);
+
+    /* The scroll padding is calculated to fill enough space at the bottom of the
+  notebook to show one single-line cell (with appropriate padding) at the top
+  when the notebook is scrolled all the way to the bottom. We also subtract one
+  pixel so that no scrollbar appears if we have just one single-line cell in the
+  notebook. This padding is to enable a 'scroll past end' feature in a notebook.
+  */
+    --jp-notebook-scroll-padding: calc(
+        100% - var(--jp-code-font-size) * var(--jp-code-line-height) -
+            var(--jp-code-padding) - var(--jp-cell-padding) - 1px
+    );
+
+    /* Rendermime styles */
+
+    --jp-rendermime-error-background: #fdd;
+    --jp-rendermime-table-row-background: var(--md-grey-100);
+    --jp-rendermime-table-row-hover-background: var(--md-light-blue-50);
+
+    /* Dialog specific styles */
+
+    --jp-dialog-background: rgba(0, 0, 0, 0.25);
+
+    /* Console specific styles */
+
+    --jp-console-padding: 10px;
+
+    /* Toolbar specific styles */
+
+    --jp-toolbar-border-color: var(--jp-border-color1);
+    --jp-toolbar-micro-height: 8px;
+    --jp-toolbar-background: var(--jp-layout-color1);
+    --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24);
+    --jp-toolbar-header-margin: 4px 4px 0px 4px;
+    --jp-toolbar-active-background: var(--md-grey-300);
+
+    /* Statusbar specific styles */
+
+    --jp-statusbar-height: 24px;
+
+    /* Input field styles */
+
+    --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-input-active-background: var(--jp-layout-color1);
+    --jp-input-hover-background: var(--jp-layout-color1);
+    --jp-input-background: var(--md-grey-100);
+    --jp-input-border-color: var(--jp-border-color1);
+    --jp-input-active-border-color: var(--jp-brand-color1);
+    --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3);
+
+    /* General editor styles */
+
+    --jp-editor-selected-background: #d9d9d9;
+    --jp-editor-selected-focused-background: #d7d4f0;
+    --jp-editor-cursor-color: var(--jp-ui-font-color0);
+
+    /* Code mirror specific styles */
+
+    --jp-mirror-editor-keyword-color: #008000;
+    --jp-mirror-editor-atom-color: #88f;
+    --jp-mirror-editor-number-color: #080;
+    --jp-mirror-editor-def-color: #00f;
+    --jp-mirror-editor-variable-color: var(--md-grey-900);
+    --jp-mirror-editor-variable-2-color: #05a;
+    --jp-mirror-editor-variable-3-color: #085;
+    --jp-mirror-editor-punctuation-color: #05a;
+    --jp-mirror-editor-property-color: #05a;
+    --jp-mirror-editor-operator-color: #aa22ff;
+    --jp-mirror-editor-comment-color: #408080;
+    --jp-mirror-editor-string-color: #ba2121;
+    --jp-mirror-editor-string-2-color: #708;
+    --jp-mirror-editor-meta-color: #aa22ff;
+    --jp-mirror-editor-qualifier-color: #555;
+    --jp-mirror-editor-builtin-color: #008000;
+    --jp-mirror-editor-bracket-color: #997;
+    --jp-mirror-editor-tag-color: #170;
+    --jp-mirror-editor-attribute-color: #00c;
+    --jp-mirror-editor-header-color: blue;
+    --jp-mirror-editor-quote-color: #090;
+    --jp-mirror-editor-link-color: #00c;
+    --jp-mirror-editor-error-color: #f00;
+    --jp-mirror-editor-hr-color: #999;
+
+    /* Vega extension styles */
+
+    --jp-vega-background: white;
+
+    /* Sidebar-related styles */
+
+    --jp-sidebar-min-width: 250px;
+
+    /* Search-related styles */
+
+    --jp-search-toggle-off-opacity: 0.5;
+    --jp-search-toggle-hover-opacity: 0.8;
+    --jp-search-toggle-on-opacity: 1;
+    --jp-search-selected-match-background-color: rgb(245, 200, 0);
+    --jp-search-selected-match-color: black;
+    --jp-search-unselected-match-background-color: var(
+        --jp-inverse-layout-color0
+    );
+    --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);
+
+    /* Icon colors that work well with light or dark backgrounds */
+    --jp-icon-contrast-color0: var(--md-purple-600);
+    --jp-icon-contrast-color1: var(--md-green-600);
+    --jp-icon-contrast-color2: var(--md-pink-600);
+    --jp-icon-contrast-color3: var(--md-blue-600);
+}
+
+[data-md-color-scheme="slate"] .jupyter-wrapper {
+    /* Elevation
+   *
+   * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here:
+   *
+   * https://github.com/material-components/material-components-web
+   * https://material-components-web.appspot.com/elevation.html
+   */
+
+    /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout
+   * colors used in the theme as well. */
+    --jp-shadow-base-lightness: 32;
+    --jp-shadow-umbra-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.2
+    );
+    --jp-shadow-penumbra-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.14
+    );
+    --jp-shadow-ambient-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.12
+    );
+    --jp-elevation-z0: none;
+    --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color),
+        0px 1px 1px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 3px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color),
+        0px 2px 2px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 5px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color),
+        0px 4px 5px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 10px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color),
+        0px 6px 10px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 18px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color),
+        0px 8px 10px 1px var(--jp-shadow-penumbra-color),
+        0px 3px 14px 2px var(--jp-shadow-ambient-color);
+    --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color),
+        0px 12px 17px 2px var(--jp-shadow-penumbra-color),
+        0px 5px 22px 4px var(--jp-shadow-ambient-color);
+    --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color),
+        0px 16px 24px 2px var(--jp-shadow-penumbra-color),
+        0px 6px 30px 5px var(--jp-shadow-ambient-color);
+    --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color),
+        0px 20px 31px 3px var(--jp-shadow-penumbra-color),
+        0px 8px 38px 7px var(--jp-shadow-ambient-color);
+    --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color),
+        0px 24px 38px 3px var(--jp-shadow-penumbra-color),
+        0px 9px 46px 8px var(--jp-shadow-ambient-color);
+
+    /* Borders
+   *
+   * The following variables, specify the visual styling of borders in JupyterLab.
+   */
+
+    --jp-border-width: 1px;
+    --jp-border-color0: var(--md-grey-700);
+    --jp-border-color1: var(--md-grey-700);
+    --jp-border-color2: var(--md-grey-800);
+    --jp-border-color3: var(--md-grey-900);
+    --jp-border-radius: 2px;
+
+    /* UI Fonts
+   *
+   * The UI font CSS variables are used for the typography all of the JupyterLab
+   * user interface elements that are not directly user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-ui-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+    --jp-ui-font-scale-factor: 1.2;
+    --jp-ui-font-size0: 0.83333em;
+    --jp-ui-font-size1: 13px; /* Base font size */
+    --jp-ui-font-size2: 1.2em;
+    --jp-ui-font-size3: 1.44em;
+
+    --jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+
+    /*
+   * Use these font colors against the corresponding main layout colors.
+   * In a light theme, these go from dark to light.
+   */
+
+    /* Defaults use Material Design specification */
+    --jp-ui-font-color0: rgba(255, 255, 255, 1);
+    --jp-ui-font-color1: rgba(255, 255, 255, 0.87);
+    --jp-ui-font-color2: rgba(255, 255, 255, 0.54);
+    --jp-ui-font-color3: rgba(255, 255, 255, 0.38);
+
+    /*
+   * Use these against the brand/accent/warn/error colors.
+   * These will typically go from light to darker, in both a dark and light theme.
+   */
+
+    --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1);
+    --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8);
+    --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5);
+    --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3);
+
+    /* Content Fonts
+   *
+   * Content font variables are used for typography of user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-content-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+    --jp-content-line-height: 1.6;
+    --jp-content-font-scale-factor: 1.2;
+    --jp-content-font-size0: 0.83333em;
+    --jp-content-font-size1: 14px; /* Base font size */
+    --jp-content-font-size2: 1.2em;
+    --jp-content-font-size3: 1.44em;
+    --jp-content-font-size4: 1.728em;
+    --jp-content-font-size5: 2.0736em;
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-content-presentation-font-size1: 17px;
+
+    --jp-content-heading-line-height: 1;
+    --jp-content-heading-margin-top: 1.2em;
+    --jp-content-heading-margin-bottom: 0.8em;
+    --jp-content-heading-font-weight: 500;
+
+    /* Defaults use Material Design specification */
+    --jp-content-font-color0: rgba(255, 255, 255, 1);
+    --jp-content-font-color1: rgba(255, 255, 255, 1);
+    --jp-content-font-color2: rgba(255, 255, 255, 0.7);
+    --jp-content-font-color3: rgba(255, 255, 255, 0.5);
+
+    --jp-content-link-color: var(--md-blue-300);
+
+    --jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+
+    /*
+   * Code Fonts
+   *
+   * Code font variables are used for typography of code and other monospaces content.
+   */
+
+    --jp-code-font-size: 13px;
+    --jp-code-line-height: 1.3077; /* 17px for 13px base */
+    --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */
+    --jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono",
+        monospace;
+    --jp-code-font-family: var(--jp-code-font-family-default);
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-code-presentation-font-size: 16px;
+
+    /* may need to tweak cursor width if you change font size */
+    --jp-code-cursor-width0: 1.4px;
+    --jp-code-cursor-width1: 2px;
+    --jp-code-cursor-width2: 4px;
+
+    /* Layout
+   *
+   * The following are the main layout colors use in JupyterLab. In a light
+   * theme these would go from light to dark.
+   */
+
+    --jp-layout-color0: #111111;
+    --jp-layout-color1: var(--md-grey-900);
+    --jp-layout-color2: var(--md-grey-800);
+    --jp-layout-color3: var(--md-grey-700);
+    --jp-layout-color4: var(--md-grey-600);
+
+    /* Inverse Layout
+   *
+   * The following are the inverse layout colors use in JupyterLab. In a light
+   * theme these would go from dark to light.
+   */
+
+    --jp-inverse-layout-color0: white;
+    --jp-inverse-layout-color1: white;
+    --jp-inverse-layout-color2: var(--md-grey-200);
+    --jp-inverse-layout-color3: var(--md-grey-400);
+    --jp-inverse-layout-color4: var(--md-grey-600);
+
+    /* Brand/accent */
+
+    --jp-brand-color0: var(--md-blue-700);
+    --jp-brand-color1: var(--md-blue-500);
+    --jp-brand-color2: var(--md-blue-300);
+    --jp-brand-color3: var(--md-blue-100);
+    --jp-brand-color4: var(--md-blue-50);
+
+    --jp-accent-color0: var(--md-green-700);
+    --jp-accent-color1: var(--md-green-500);
+    --jp-accent-color2: var(--md-green-300);
+    --jp-accent-color3: var(--md-green-100);
+
+    /* State colors (warn, error, success, info) */
+
+    --jp-warn-color0: var(--md-orange-700);
+    --jp-warn-color1: var(--md-orange-500);
+    --jp-warn-color2: var(--md-orange-300);
+    --jp-warn-color3: var(--md-orange-100);
+
+    --jp-error-color0: var(--md-red-700);
+    --jp-error-color1: var(--md-red-500);
+    --jp-error-color2: var(--md-red-300);
+    --jp-error-color3: var(--md-red-100);
+
+    --jp-success-color0: var(--md-green-700);
+    --jp-success-color1: var(--md-green-500);
+    --jp-success-color2: var(--md-green-300);
+    --jp-success-color3: var(--md-green-100);
+
+    --jp-info-color0: var(--md-cyan-700);
+    --jp-info-color1: var(--md-cyan-500);
+    --jp-info-color2: var(--md-cyan-300);
+    --jp-info-color3: var(--md-cyan-100);
+
+    /* Cell specific styles */
+
+    --jp-cell-padding: 5px;
+
+    --jp-cell-collapser-width: 8px;
+    --jp-cell-collapser-min-height: 20px;
+    --jp-cell-collapser-not-active-hover-opacity: 0.6;
+
+    --jp-cell-editor-background: var(--jp-layout-color1);
+    --jp-cell-editor-border-color: var(--md-grey-700);
+    --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-cell-editor-active-background: var(--jp-layout-color0);
+    --jp-cell-editor-active-border-color: var(--jp-brand-color1);
+
+    --jp-cell-prompt-width: 64px;
+    --jp-cell-prompt-font-family: var(--jp-code-font-family-default);
+    --jp-cell-prompt-letter-spacing: 0px;
+    --jp-cell-prompt-opacity: 1;
+    --jp-cell-prompt-not-active-opacity: 1;
+    --jp-cell-prompt-not-active-font-color: var(--md-grey-300);
+
+    /* A custom blend of MD grey and blue 600
+   * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */
+    --jp-cell-inprompt-font-color: #307fc1;
+    /* A custom blend of MD grey and orange 600
+   * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */
+    --jp-cell-outprompt-font-color: #bf5b3d;
+
+    /* Notebook specific styles */
+
+    --jp-notebook-padding: 10px;
+    --jp-notebook-select-background: var(--jp-layout-color1);
+    --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24);
+
+    /* The scroll padding is calculated to fill enough space at the bottom of the
+  notebook to show one single-line cell (with appropriate padding) at the top
+  when the notebook is scrolled all the way to the bottom. We also subtract one
+  pixel so that no scrollbar appears if we have just one single-line cell in the
+  notebook. This padding is to enable a 'scroll past end' feature in a notebook.
+  */
+    --jp-notebook-scroll-padding: calc(
+        100% - var(--jp-code-font-size) * var(--jp-code-line-height) -
+            var(--jp-code-padding) - var(--jp-cell-padding) - 1px
+    );
+
+    /* Rendermime styles */
+
+    --jp-rendermime-error-background: rgba(244, 67, 54, 0.28);
+    --jp-rendermime-table-row-background: var(--md-grey-900);
+    --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2);
+
+    /* Dialog specific styles */
+
+    --jp-dialog-background: rgba(0, 0, 0, 0.6);
+
+    /* Console specific styles */
+
+    --jp-console-padding: 10px;
+
+    /* Toolbar specific styles */
+
+    --jp-toolbar-border-color: var(--jp-border-color2);
+    --jp-toolbar-micro-height: 8px;
+    --jp-toolbar-background: var(--jp-layout-color1);
+    --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8);
+    --jp-toolbar-header-margin: 4px 4px 0px 4px;
+    --jp-toolbar-active-background: var(--jp-layout-color0);
+
+    /* Statusbar specific styles */
+
+    --jp-statusbar-height: 24px;
+
+    /* Input field styles */
+
+    --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-input-active-background: var(--jp-layout-color0);
+    --jp-input-hover-background: var(--jp-layout-color2);
+    --jp-input-background: var(--md-grey-800);
+    --jp-input-border-color: var(--jp-border-color1);
+    --jp-input-active-border-color: var(--jp-brand-color1);
+    --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3);
+
+    /* General editor styles */
+
+    --jp-editor-selected-background: var(--jp-layout-color2);
+    --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24);
+    --jp-editor-cursor-color: var(--jp-ui-font-color0);
+
+    /* Code mirror specific styles */
+
+    --jp-mirror-editor-keyword-color: var(--md-green-500);
+    --jp-mirror-editor-atom-color: var(--md-blue-300);
+    --jp-mirror-editor-number-color: var(--md-green-400);
+    --jp-mirror-editor-def-color: var(--md-blue-600);
+    --jp-mirror-editor-variable-color: var(--md-grey-300);
+    --jp-mirror-editor-variable-2-color: var(--md-blue-400);
+    --jp-mirror-editor-variable-3-color: var(--md-green-600);
+    --jp-mirror-editor-punctuation-color: var(--md-blue-400);
+    --jp-mirror-editor-property-color: var(--md-blue-400);
+    --jp-mirror-editor-operator-color: #aa22ff;
+    --jp-mirror-editor-comment-color: #408080;
+    --jp-mirror-editor-string-color: #ff7070;
+    --jp-mirror-editor-string-2-color: var(--md-purple-300);
+    --jp-mirror-editor-meta-color: #aa22ff;
+    --jp-mirror-editor-qualifier-color: #555;
+    --jp-mirror-editor-builtin-color: var(--md-green-600);
+    --jp-mirror-editor-bracket-color: #997;
+    --jp-mirror-editor-tag-color: var(--md-green-700);
+    --jp-mirror-editor-attribute-color: var(--md-blue-700);
+    --jp-mirror-editor-header-color: var(--md-blue-500);
+    --jp-mirror-editor-quote-color: var(--md-green-300);
+    --jp-mirror-editor-link-color: var(--md-blue-700);
+    --jp-mirror-editor-error-color: #f00;
+    --jp-mirror-editor-hr-color: #999;
+
+    /* Vega extension styles */
+
+    --jp-vega-background: var(--md-grey-400);
+
+    /* Sidebar-related styles */
+
+    --jp-sidebar-min-width: 250px;
+
+    /* Search-related styles */
+
+    --jp-search-toggle-off-opacity: 0.6;
+    --jp-search-toggle-hover-opacity: 0.8;
+    --jp-search-toggle-on-opacity: 1;
+    --jp-search-selected-match-background-color: rgb(255, 225, 0);
+    --jp-search-selected-match-color: black;
+    --jp-search-unselected-match-background-color: var(
+        --jp-inverse-layout-color0
+    );
+    --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);
+
+    /* scrollbar related styles. Supports every browser except Edge. */
+
+    /* colors based on JetBrain's Darcula theme */
+
+    --jp-scrollbar-background-color: #3f4244;
+    --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */
+
+    --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */
+
+    /* hacks for setting the thumb shape. These do nothing in Firefox */
+
+    --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */
+    --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */
+
+    /* Icon colors that work well with light or dark backgrounds */
+    --jp-icon-contrast-color0: var(--md-purple-600);
+    --jp-icon-contrast-color1: var(--md-green-600);
+    --jp-icon-contrast-color2: var(--md-pink-600);
+    --jp-icon-contrast-color3: var(--md-blue-600);
+}
+
+:root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: #7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: #ff5722;--md-deep-orange-600: #f4511e;--md-deep-orange-700: #e64a19;--md-deep-orange-800: #d84315;--md-deep-orange-900: #bf360c;--md-deep-orange-A100: #ff9e80;--md-deep-orange-A200: #ff6e40;--md-deep-orange-A400: #ff3d00;--md-deep-orange-A700: #dd2c00;--md-brown-50: #efebe9;--md-brown-100: #d7ccc8;--md-brown-200: #bcaaa4;--md-brown-300: #a1887f;--md-brown-400: #8d6e63;--md-brown-500: #795548;--md-brown-600: #6d4c41;--md-brown-700: #5d4037;--md-brown-800: #4e342e;--md-brown-900: 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+
+Copyright 2015-present Palantir Technologies, Inc. All rights reserved.
+Licensed under the Apache License, Version 2.0.
+
+*//*!
+
+Copyright 2017-present Palantir Technologies, Inc. All rights reserved.
+Licensed under the Apache License, Version 2.0.
+
+*/}.jupyter-wrapper [data-jp-theme-scrollbars=true]{scrollbar-color:rgb(var(--jp-scrollbar-thumb-color)) var(--jp-scrollbar-background-color)}.jupyter-wrapper [data-jp-theme-scrollbars=true] .CodeMirror-hscrollbar,.jupyter-wrapper [data-jp-theme-scrollbars=true] .CodeMirror-vscrollbar{scrollbar-color:rgba(var(--jp-scrollbar-thumb-color), 0.5) rgba(0,0,0,0)}.jupyter-wrapper [data-jp-theme-scrollbars=true] ::-webkit-scrollbar,.jupyter-wrapper [data-jp-theme-scrollbars=true] ::-webkit-scrollbar-corner{background:var(--jp-scrollbar-background-color)}.jupyter-wrapper [data-jp-theme-scrollbars=true] ::-webkit-scrollbar-thumb{background:rgb(var(--jp-scrollbar-thumb-color));border:var(--jp-scrollbar-thumb-margin) solid rgba(0,0,0,0);background-clip:content-box;border-radius:var(--jp-scrollbar-thumb-radius)}.jupyter-wrapper [data-jp-theme-scrollbars=true] ::-webkit-scrollbar-track:horizontal{border-left:var(--jp-scrollbar-endpad) solid 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+
+/*# sourceMappingURL=mkdocs-jupyter.css.map*/</style>
+
+
+<!-- Load mathjax -->
+    <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.7/latest.js?config=TeX-AMS_CHTML-full,Safe"> </script>
+    <!-- MathJax configuration -->
+    <script type="text/x-mathjax-config">
+    init_mathjax = function() {
+        if (window.MathJax) {
+        // MathJax loaded
+            MathJax.Hub.Config({
+                TeX: {
+                    equationNumbers: {
+                    autoNumber: "AMS",
+                    useLabelIds: true
+                    }
+                },
+                tex2jax: {
+                    inlineMath: [ ['$','$'], ["\\(","\\)"] ],
+                    displayMath: [ ['$$','$$'], ["\\[","\\]"] ],
+                    processEscapes: true,
+                    processEnvironments: true
+                },
+                displayAlign: 'center',
+                CommonHTML: {
+                    linebreaks: {
+                    automatic: true
+                    }
+                }
+            });
+
+            MathJax.Hub.Queue(["Typeset", MathJax.Hub]);
+        }
+    }
+    init_mathjax();
+    </script>
+    <!-- End of mathjax configuration --><div class="jupyter-wrapper"><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[2]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-1">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
+<span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">namedtuple</span>
+<span class="kn">import</span> <span class="nn">matplotlib</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+
+<span class="n">matplotlib</span><span class="o">.</span><span class="n">use</span><span class="p">(</span><span class="s2">&quot;agg&quot;</span><span class="p">)</span>
+<span class="o">%</span><span class="n">matplotlib</span> <span class="n">inline</span>
+
+<span class="n">run_local</span> <span class="o">=</span> <span class="kc">False</span>
+
+<span class="n">region_name</span> <span class="o">=</span> <span class="s2">&quot;Demo&quot;</span>
+<span class="n">dir_name</span> <span class="o">=</span> <span class="n">region_name</span>
+<span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">dir_name</span><span class="p">):</span>
+    <span class="n">os</span><span class="o">.</span><span class="n">mkdir</span><span class="p">(</span><span class="n">dir_name</span><span class="p">)</span>
+<span class="n">root_dir</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">dir_name</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span>
+<span class="n">Artifact2</span> <span class="o">=</span> <span class="n">namedtuple</span><span class="p">(</span><span class="s2">&quot;Artifact&quot;</span><span class="p">,</span> <span class="p">[</span><span class="s2">&quot;path&quot;</span><span class="p">])</span>
+</pre></div>
+<div id="cell-1" class="clipboard-copy-txt">import os
+from collections import namedtuple
+import matplotlib
+import matplotlib.pyplot as plt
+
+matplotlib.use("agg")
+%matplotlib inline
+
+run_local = False
+
+region_name = "Demo"
+dir_name = region_name
+if not os.path.exists(dir_name):
+    os.mkdir(dir_name)
+root_dir = lambda x: os.path.join(dir_name, x)
+Artifact2 = namedtuple("Artifact", ["path"])</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[4]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-2">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">from</span> <span class="nn">kfp</span> <span class="kn">import</span> <span class="n">dsl</span>
+
+<span class="nd">@dsl</span><span class="o">.</span><span class="n">component</span>
+<span class="k">def</span> <span class="nf">square</span><span class="p">(</span><span class="n">x</span><span class="p">:</span> <span class="nb">float</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
+    <span class="k">return</span> <span class="n">x</span> <span class="o">**</span> <span class="mi">2</span>
+</pre></div>
+<div id="cell-2" class="clipboard-copy-txt">from kfp import dsl
+
+@dsl.component
+def square(x: float) -> float:
+    return x ** 2</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[9]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-3">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">square</span><span class="o">.</span><span class="n">component_spec</span>
+</pre></div>
+<div id="cell-3" class="clipboard-copy-txt">square.component_spec</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[9]:</div>
+
+
+
+
+<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
+<pre>ComponentSpec(name=&#39;square&#39;, implementation=Implementation(container=ContainerSpecImplementation(image=&#39;python:3.7&#39;, command=[&#39;sh&#39;, &#39;-c&#39;, &#39;\nif ! [ -x &#34;$(command -v pip)&#34; ]; then\n    python3 -m ensurepip || python3 -m ensurepip --user || apt-get install python3-pip\nfi\n\nPIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet     --no-warn-script-location \&#39;kfp==2.0.1\&#39; &amp;&amp; &#34;$0&#34; &#34;$@&#34;\n&#39;, &#39;sh&#39;, &#39;-ec&#39;, &#39;program_path=$(mktemp -d)\nprintf &#34;%s&#34; &#34;$0&#34; &gt; &#34;$program_path/ephemeral_component.py&#34;\npython3 -m kfp.components.executor_main                         --component_module_path                         &#34;$program_path/ephemeral_component.py&#34;                         &#34;$@&#34;\n&#39;, &#39;\nimport kfp\nfrom kfp import dsl\nfrom kfp.dsl import *\nfrom typing import *\n\ndef square(x: float) -&gt; float:\n    return x ** 2\n\n&#39;], args=[&#39;--executor_input&#39;, &lt;kfp.components.placeholders.ExecutorInputPlaceholder object at 0x11673f490&gt;, &#39;--function_to_execute&#39;, &#39;square&#39;], env=None, resources=None), importer=None, graph=None), description=None, inputs={&#39;x&#39;: InputSpec(type=&#39;Float&#39;, default=None, optional=False, is_artifact_list=False, description=None)}, outputs={&#39;Output&#39;: OutputSpec(type=&#39;Float&#39;, is_artifact_list=False, description=None)}, platform_spec=)</pre>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[2]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-4">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">from</span> <span class="nn">kfp</span> <span class="kn">import</span> <span class="n">client</span>
+<span class="kn">from</span> <span class="nn">kfp</span> <span class="kn">import</span> <span class="n">dsl</span>
+<span class="kn">from</span> <span class="nn">kfp.dsl</span> <span class="kn">import</span> <span class="n">component</span><span class="p">,</span> <span class="n">Input</span><span class="p">,</span> <span class="n">Output</span><span class="p">,</span> <span class="n">Artifact</span><span class="p">,</span> <span class="n">OutputPath</span><span class="p">,</span> <span class="n">InputPath</span>
+</pre></div>
+<div id="cell-4" class="clipboard-copy-txt">from kfp import client
+from kfp import dsl
+from kfp.dsl import component, Input, Output, Artifact, OutputPath, InputPath</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[3]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-5">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="nd">@dsl</span><span class="o">.</span><span class="n">component</span><span class="p">(</span><span class="n">packages_to_install</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;obspy&quot;</span><span class="p">,</span> <span class="s2">&quot;numpy&quot;</span><span class="p">,</span> <span class="s2">&quot;minio&quot;</span><span class="p">,</span> <span class="s2">&quot;matplotlib&quot;</span><span class="p">])</span>
+<span class="k">def</span> <span class="nf">set_config</span><span class="p">(</span>
+    <span class="n">region_name</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
+    <span class="n">index_json</span><span class="p">:</span> <span class="n">Output</span><span class="p">[</span><span class="n">Artifact</span><span class="p">],</span>
+    <span class="n">config_json</span><span class="p">:</span> <span class="n">Output</span><span class="p">[</span><span class="n">Artifact</span><span class="p">],</span>
+    <span class="n">datetime_json</span><span class="p">:</span> <span class="n">Output</span><span class="p">[</span><span class="n">Artifact</span><span class="p">],</span>
+    <span class="n">num_parallel</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span>
+    <span class="n">bucket_name</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;catalogs&quot;</span><span class="p">,</span>
+    <span class="n">s3_url</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;minio-service:9000&quot;</span><span class="p">,</span>
+    <span class="n">secure</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+<span class="p">):</span>
+
+    <span class="kn">import</span> <span class="nn">datetime</span>
+    <span class="kn">import</span> <span class="nn">json</span>
+    <span class="kn">import</span> <span class="nn">os</span>
+    <span class="kn">import</span> <span class="nn">pickle</span>
+    <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+    <span class="kn">import</span> <span class="nn">obspy</span>
+
+    <span class="n">degree2km</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="mi">6371</span> <span class="o">/</span> <span class="mi">180</span>
+
+    <span class="k">if</span> <span class="n">region_name</span> <span class="o">==</span> <span class="s2">&quot;Demo&quot;</span><span class="p">:</span>
+        <span class="n">center</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mf">117.504</span><span class="p">,</span> <span class="mf">35.705</span><span class="p">)</span>
+        <span class="n">horizontal_degree</span> <span class="o">=</span> <span class="mf">1.0</span>
+        <span class="n">vertical_degree</span> <span class="o">=</span> <span class="mf">1.0</span>
+        <span class="n">starttime</span> <span class="o">=</span> <span class="n">obspy</span><span class="o">.</span><span class="n">UTCDateTime</span><span class="p">(</span><span class="s2">&quot;2019-07-04T17&quot;</span><span class="p">)</span>
+        <span class="n">endtime</span> <span class="o">=</span> <span class="n">obspy</span><span class="o">.</span><span class="n">UTCDateTime</span><span class="p">(</span><span class="s2">&quot;2019-07-04T19&quot;</span><span class="p">)</span>
+        <span class="n">client</span> <span class="o">=</span> <span class="s2">&quot;SCEDC&quot;</span>
+        <span class="n">network_list</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;CI&quot;</span><span class="p">]</span>
+        <span class="n">channel_list</span> <span class="o">=</span> <span class="s2">&quot;HH*,BH*,EH*,HN*&quot;</span>
+
+
+    <span class="c1">####### save config ########</span>
+    <span class="n">config</span> <span class="o">=</span> <span class="p">{}</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;region&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">region_name</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;center&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">center</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;xlim_degree&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span>
+        <span class="n">center</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">horizontal_degree</span> <span class="o">/</span> <span class="mi">2</span><span class="p">,</span>
+        <span class="n">center</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">horizontal_degree</span> <span class="o">/</span> <span class="mi">2</span><span class="p">,</span>
+    <span class="p">]</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;ylim_degree&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span>
+        <span class="n">center</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">vertical_degree</span> <span class="o">/</span> <span class="mi">2</span><span class="p">,</span>
+        <span class="n">center</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">vertical_degree</span> <span class="o">/</span> <span class="mi">2</span><span class="p">,</span>
+    <span class="p">]</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;min_longitude&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">center</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">horizontal_degree</span> <span class="o">/</span> <span class="mi">2</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;max_longitude&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">center</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">horizontal_degree</span> <span class="o">/</span> <span class="mi">2</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;min_latitude&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">center</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">vertical_degree</span> <span class="o">/</span> <span class="mi">2</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;max_latitude&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">center</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">vertical_degree</span> <span class="o">/</span> <span class="mi">2</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;degree2km&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">degree2km</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;starttime&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">starttime</span><span class="o">.</span><span class="n">datetime</span><span class="o">.</span><span class="n">isoformat</span><span class="p">(</span><span class="n">timespec</span><span class="o">=</span><span class="s2">&quot;milliseconds&quot;</span><span class="p">)</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;endtime&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">endtime</span><span class="o">.</span><span class="n">datetime</span><span class="o">.</span><span class="n">isoformat</span><span class="p">(</span><span class="n">timespec</span><span class="o">=</span><span class="s2">&quot;milliseconds&quot;</span><span class="p">)</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;networks&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">network_list</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;channels&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">channel_list</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;client&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">client</span>
+
+    <span class="c1">## PhaseNet</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;phasenet&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">{}</span>
+    <span class="c1">## GaMMA</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;gamma&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">{}</span>
+    <span class="c1">## HypoDD</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;hypodd&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;MAXEVENT&quot;</span><span class="p">:</span> <span class="mf">1e4</span><span class="p">}</span>
+
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">config_json</span><span class="o">.</span><span class="n">path</span><span class="p">,</span> <span class="s2">&quot;w&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">json</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="n">config</span><span class="p">,</span> <span class="n">fp</span><span class="p">,</span> <span class="n">indent</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+
+    <span class="nb">print</span><span class="p">(</span><span class="n">json</span><span class="o">.</span><span class="n">dumps</span><span class="p">(</span><span class="n">config</span><span class="p">,</span> <span class="n">indent</span><span class="o">=</span><span class="mi">4</span><span class="p">))</span>
+
+    <span class="c1">####### set paraell for cloud ########</span>
+    <span class="c1">## split data by hours</span>
+    <span class="c1"># one_day = datetime.timedelta(days=1)</span>
+    <span class="n">one_hour</span> <span class="o">=</span> <span class="n">datetime</span><span class="o">.</span><span class="n">timedelta</span><span class="p">(</span><span class="n">hours</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">starttimes</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">tmp_start</span> <span class="o">=</span> <span class="n">starttime</span>
+    <span class="k">while</span> <span class="n">tmp_start</span> <span class="o">&lt;</span> <span class="n">endtime</span><span class="p">:</span>
+        <span class="n">starttimes</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp_start</span><span class="o">.</span><span class="n">datetime</span><span class="o">.</span><span class="n">isoformat</span><span class="p">(</span><span class="n">timespec</span><span class="o">=</span><span class="s2">&quot;milliseconds&quot;</span><span class="p">))</span>
+        <span class="n">tmp_start</span> <span class="o">+=</span> <span class="n">one_hour</span>
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">datetime_json</span><span class="o">.</span><span class="n">path</span><span class="p">,</span> <span class="s2">&quot;w&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">json</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span>
+            <span class="p">{</span><span class="s2">&quot;starttimes&quot;</span><span class="p">:</span> <span class="n">starttimes</span><span class="p">,</span> <span class="s2">&quot;interval&quot;</span><span class="p">:</span> <span class="n">one_hour</span><span class="o">.</span><span class="n">total_seconds</span><span class="p">()},</span>
+            <span class="n">fp</span><span class="p">,</span>
+            <span class="n">indent</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
+        <span class="p">)</span>
+
+    <span class="c1">## split stattimes into N parallel process</span>
+    <span class="k">if</span> <span class="n">num_parallel</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="n">num_parallel</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="mi">60</span><span class="p">,</span> <span class="nb">int</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">starttimes</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">//</span> <span class="mi">6</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
+        <span class="c1"># num_parallel = min(30, int((len(starttimes)-1)//6+1))</span>
+        <span class="c1"># num_parallel = min(24, len(starttimes))</span>
+    <span class="n">idx</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">array_split</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">starttimes</span><span class="p">)),</span> <span class="n">num_parallel</span><span class="p">)]</span>
+
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">index_json</span><span class="o">.</span><span class="n">path</span><span class="p">,</span> <span class="s2">&quot;w&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">json</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="n">idx</span><span class="p">,</span> <span class="n">fp</span><span class="p">,</span> <span class="n">indent</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+
+    <span class="c1">## upload to s3 bucket</span>
+    <span class="k">try</span><span class="p">:</span>
+        <span class="kn">from</span> <span class="nn">minio</span> <span class="kn">import</span> <span class="n">Minio</span>
+
+        <span class="n">minioClient</span> <span class="o">=</span> <span class="n">Minio</span><span class="p">(</span><span class="n">s3_url</span><span class="p">,</span> <span class="n">access_key</span><span class="o">=</span><span class="s2">&quot;minio&quot;</span><span class="p">,</span> <span class="n">secret_key</span><span class="o">=</span><span class="s2">&quot;minio123&quot;</span><span class="p">,</span> <span class="n">secure</span><span class="o">=</span><span class="n">secure</span><span class="p">)</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">minioClient</span><span class="o">.</span><span class="n">bucket_exists</span><span class="p">(</span><span class="n">bucket_name</span><span class="p">):</span>
+            <span class="n">minioClient</span><span class="o">.</span><span class="n">make_bucket</span><span class="p">(</span><span class="n">bucket_name</span><span class="p">)</span>
+
+        <span class="n">minioClient</span><span class="o">.</span><span class="n">fput_object</span><span class="p">(</span>
+            <span class="n">bucket_name</span><span class="p">,</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;region&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">/config.json&quot;</span><span class="p">,</span>
+            <span class="n">config_json</span><span class="p">,</span>
+        <span class="p">)</span>
+
+        <span class="n">minioClient</span><span class="o">.</span><span class="n">fput_object</span><span class="p">(</span>
+            <span class="n">bucket_name</span><span class="p">,</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;region&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">/datetime.json&quot;</span><span class="p">,</span>
+            <span class="n">datetime_json</span><span class="p">,</span>
+        <span class="p">)</span>
+
+        <span class="n">minioClient</span><span class="o">.</span><span class="n">fput_object</span><span class="p">(</span>
+            <span class="n">bucket_name</span><span class="p">,</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;region&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">/index.json&quot;</span><span class="p">,</span>
+            <span class="n">index_json</span><span class="p">,</span>
+        <span class="p">)</span>
+
+    <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">err</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;ERROR: can not access minio service! </span><span class="se">\n</span><span class="si">{</span><span class="n">err</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+        <span class="k">pass</span>
+</pre></div>
+<div id="cell-5" class="clipboard-copy-txt">@dsl.component(packages_to_install=["obspy", "numpy", "minio", "matplotlib"])
+def set_config(
+    region_name: str,
+    index_json: Output[Artifact],
+    config_json: Output[Artifact],
+    datetime_json: Output[Artifact],
+    num_parallel: int = 1,
+    bucket_name: str = "catalogs",
+    s3_url: str = "minio-service:9000",
+    secure: bool = True,
+):
+
+    import datetime
+    import json
+    import os
+    import pickle
+    import numpy as np
+    import obspy
+
+    degree2km = np.pi * 6371 / 180
+
+    if region_name == "Demo":
+        center = (-117.504, 35.705)
+        horizontal_degree = 1.0
+        vertical_degree = 1.0
+        starttime = obspy.UTCDateTime("2019-07-04T17")
+        endtime = obspy.UTCDateTime("2019-07-04T19")
+        client = "SCEDC"
+        network_list = ["CI"]
+        channel_list = "HH*,BH*,EH*,HN*"
+
+
+    ####### save config ########
+    config = {}
+    config["region"] = region_name
+    config["center"] = center
+    config["xlim_degree"] = [
+        center[0] - horizontal_degree / 2,
+        center[0] + horizontal_degree / 2,
+    ]
+    config["ylim_degree"] = [
+        center[1] - vertical_degree / 2,
+        center[1] + vertical_degree / 2,
+    ]
+    config["min_longitude"] = center[0] - horizontal_degree / 2
+    config["max_longitude"] = center[0] + horizontal_degree / 2
+    config["min_latitude"] = center[1] - vertical_degree / 2
+    config["max_latitude"] = center[1] + vertical_degree / 2
+    config["degree2km"] = degree2km
+    config["starttime"] = starttime.datetime.isoformat(timespec="milliseconds")
+    config["endtime"] = endtime.datetime.isoformat(timespec="milliseconds")
+    config["networks"] = network_list
+    config["channels"] = channel_list
+    config["client"] = client
+
+    ## PhaseNet
+    config["phasenet"] = {}
+    ## GaMMA
+    config["gamma"] = {}
+    ## HypoDD
+    config["hypodd"] = {"MAXEVENT": 1e4}
+
+    with open(config_json.path, "w") as fp:
+        json.dump(config, fp, indent=2)
+
+    print(json.dumps(config, indent=4))
+
+    ####### set paraell for cloud ########
+    ## split data by hours
+    # one_day = datetime.timedelta(days=1)
+    one_hour = datetime.timedelta(hours=1)
+    starttimes = []
+    tmp_start = starttime
+    while tmp_start < endtime:
+        starttimes.append(tmp_start.datetime.isoformat(timespec="milliseconds"))
+        tmp_start += one_hour
+    with open(datetime_json.path, "w") as fp:
+        json.dump(
+            {"starttimes": starttimes, "interval": one_hour.total_seconds()},
+            fp,
+            indent=2,
+        )
+
+    ## split stattimes into N parallel process
+    if num_parallel == 0:
+        num_parallel = min(60, int((len(starttimes) - 1) // 6 + 1))
+        # num_parallel = min(30, int((len(starttimes)-1)//6+1))
+        # num_parallel = min(24, len(starttimes))
+    idx = [x.tolist() for x in np.array_split(np.arange(len(starttimes)), num_parallel)]
+
+    with open(index_json.path, "w") as fp:
+        json.dump(idx, fp, indent=2)
+
+    ## upload to s3 bucket
+    try:
+        from minio import Minio
+
+        minioClient = Minio(s3_url, access_key="minio", secret_key="minio123", secure=secure)
+        if not minioClient.bucket_exists(bucket_name):
+            minioClient.make_bucket(bucket_name)
+
+        minioClient.fput_object(
+            bucket_name,
+            f"{config['region']}/config.json",
+            config_json,
+        )
+
+        minioClient.fput_object(
+            bucket_name,
+            f"{config['region']}/datetime.json",
+            datetime_json,
+        )
+
+        minioClient.fput_object(
+            bucket_name,
+            f"{config['region']}/index.json",
+            index_json,
+        )
+
+    except Exception as err:
+        print(f"ERROR: can not access minio service! \n{err}")
+        pass</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[4]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-6">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">if</span> <span class="n">run_local</span><span class="p">:</span>
+    <span class="n">set_config</span><span class="o">.</span><span class="n">python_func</span><span class="p">(</span><span class="n">region_name</span><span class="o">=</span><span class="s2">&quot;Demo&quot;</span><span class="p">,</span> <span class="n">index_json</span><span class="o">=</span><span class="n">Artifact2</span><span class="p">(</span><span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;index.json&quot;</span><span class="p">)),</span> <span class="n">config_json</span><span class="o">=</span><span class="n">Artifact2</span><span class="p">(</span><span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;config.json&quot;</span><span class="p">)),</span> <span class="n">datetime_json</span><span class="o">=</span><span class="n">Artifact2</span><span class="p">(</span><span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;datetime.json&quot;</span><span class="p">)),</span> <span class="n">num_parallel</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">bucket_name</span><span class="o">=</span><span class="s2">&quot;catalogs&quot;</span><span class="p">,</span> <span class="n">s3_url</span><span class="o">=</span><span class="s2">&quot;minio-service:9000&quot;</span><span class="p">,</span> <span class="n">secure</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</pre></div>
+<div id="cell-6" class="clipboard-copy-txt">if run_local:
+    set_config.python_func(region_name="Demo", index_json=Artifact2(root_dir("index.json")), config_json=Artifact2(root_dir("config.json")), datetime_json=Artifact2(root_dir("datetime.json")), num_parallel=1, bucket_name="catalogs", s3_url="minio-service:9000", secure=True)</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[5]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-7">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="nd">@dsl</span><span class="o">.</span><span class="n">component</span><span class="p">(</span><span class="n">packages_to_install</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;obspy&quot;</span><span class="p">,</span> <span class="s2">&quot;numpy&quot;</span><span class="p">,</span> <span class="s2">&quot;minio&quot;</span><span class="p">,</span> <span class="s2">&quot;pandas&quot;</span><span class="p">,</span> <span class="s2">&quot;matplotlib&quot;</span><span class="p">])</span>
+<span class="k">def</span> <span class="nf">download_events</span><span class="p">(</span>
+    <span class="n">config_json</span><span class="p">:</span> <span class="n">Input</span><span class="p">[</span><span class="n">Artifact</span><span class="p">],</span> 
+    <span class="n">standard_catalog_csv</span><span class="p">:</span> <span class="n">Output</span><span class="p">[</span><span class="n">Artifact</span><span class="p">],</span>
+    <span class="n">catalog_location_png</span><span class="p">:</span> <span class="n">Output</span><span class="p">[</span><span class="n">Artifact</span><span class="p">],</span>
+    <span class="n">catalog_magnitude_png</span><span class="p">:</span> <span class="n">Output</span><span class="p">[</span><span class="n">Artifact</span><span class="p">],</span>
+    <span class="n">bucket_name</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;catalogs&quot;</span><span class="p">,</span>
+    <span class="n">s3_url</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;minio-service:9000&quot;</span><span class="p">,</span>
+    <span class="n">secure</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+<span class="p">):</span>
+
+    <span class="kn">import</span> <span class="nn">json</span>
+    <span class="kn">import</span> <span class="nn">os</span>
+    <span class="kn">import</span> <span class="nn">pickle</span>
+    <span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">defaultdict</span>
+
+    <span class="kn">import</span> <span class="nn">matplotlib</span>
+    <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+    <span class="kn">import</span> <span class="nn">obspy</span>
+    <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+    <span class="kn">from</span> <span class="nn">obspy.clients.fdsn</span> <span class="kn">import</span> <span class="n">Client</span>
+
+    <span class="c1"># matplotlib.use(&quot;agg&quot;)</span>
+    <span class="c1"># %matplotlib inline</span>
+
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">config_json</span><span class="o">.</span><span class="n">path</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">config</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">fp</span><span class="p">)</span>
+
+    <span class="c1">####### IRIS catalog ########</span>
+    <span class="k">try</span><span class="p">:</span>
+        <span class="n">events</span> <span class="o">=</span> <span class="n">Client</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;client&quot;</span><span class="p">])</span><span class="o">.</span><span class="n">get_events</span><span class="p">(</span>
+            <span class="n">starttime</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;starttime&quot;</span><span class="p">],</span>
+            <span class="n">endtime</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;endtime&quot;</span><span class="p">],</span>
+            <span class="n">minlongitude</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;xlim_degree&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span>
+            <span class="n">maxlongitude</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;xlim_degree&quot;</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span>
+            <span class="n">minlatitude</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;ylim_degree&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span>
+            <span class="n">maxlatitude</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;ylim_degree&quot;</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span>
+            <span class="c1"># filename=&#39;events.xml&#39;,</span>
+        <span class="p">)</span>
+    <span class="k">except</span><span class="p">:</span>
+        <span class="n">events</span> <span class="o">=</span> <span class="n">Client</span><span class="p">(</span><span class="s2">&quot;USGS&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">get_events</span><span class="p">(</span>
+            <span class="n">starttime</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;starttime&quot;</span><span class="p">],</span>
+            <span class="n">endtime</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;endtime&quot;</span><span class="p">],</span>
+            <span class="n">minlongitude</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;xlim_degree&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span>
+            <span class="n">maxlongitude</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;xlim_degree&quot;</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span>
+            <span class="n">minlatitude</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;ylim_degree&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span>
+            <span class="n">maxlatitude</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;ylim_degree&quot;</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span>
+            <span class="c1"># filename=&#39;events.xml&#39;,</span>
+        <span class="p">)</span>
+
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Number of events: </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">events</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+
+    <span class="c1">####### Save catalog ########</span>
+    <span class="n">catalog</span> <span class="o">=</span> <span class="n">defaultdict</span><span class="p">(</span><span class="nb">list</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">event</span> <span class="ow">in</span> <span class="n">events</span><span class="p">:</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">event</span><span class="o">.</span><span class="n">magnitudes</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="n">catalog</span><span class="p">[</span><span class="s2">&quot;time&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">event</span><span class="o">.</span><span class="n">origins</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">time</span><span class="o">.</span><span class="n">datetime</span><span class="p">)</span>
+            <span class="n">catalog</span><span class="p">[</span><span class="s2">&quot;magnitude&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">event</span><span class="o">.</span><span class="n">magnitudes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">mag</span><span class="p">)</span>
+            <span class="n">catalog</span><span class="p">[</span><span class="s2">&quot;longitude&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">event</span><span class="o">.</span><span class="n">origins</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">longitude</span><span class="p">)</span>
+            <span class="n">catalog</span><span class="p">[</span><span class="s2">&quot;latitude&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">event</span><span class="o">.</span><span class="n">origins</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">latitude</span><span class="p">)</span>
+            <span class="n">catalog</span><span class="p">[</span><span class="s2">&quot;depth(m)&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">event</span><span class="o">.</span><span class="n">origins</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">depth</span><span class="p">)</span>
+    <span class="n">catalog</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">from_dict</span><span class="p">(</span><span class="n">catalog</span><span class="p">)</span><span class="o">.</span><span class="n">sort_values</span><span class="p">([</span><span class="s2">&quot;time&quot;</span><span class="p">])</span>
+    <span class="n">catalog</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span>
+        <span class="n">standard_catalog_csv</span><span class="o">.</span><span class="n">path</span><span class="p">,</span>
+        <span class="n">index</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+        <span class="n">float_format</span><span class="o">=</span><span class="s2">&quot;</span><span class="si">%.3f</span><span class="s2">&quot;</span><span class="p">,</span>
+        <span class="n">date_format</span><span class="o">=</span><span class="s2">&quot;%Y-%m-</span><span class="si">%d</span><span class="s2">T%H:%M:%S.</span><span class="si">%f</span><span class="s2">&quot;</span><span class="p">,</span>
+        <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;time&quot;</span><span class="p">,</span> <span class="s2">&quot;magnitude&quot;</span><span class="p">,</span> <span class="s2">&quot;longitude&quot;</span><span class="p">,</span> <span class="s2">&quot;latitude&quot;</span><span class="p">,</span> <span class="s2">&quot;depth(m)&quot;</span><span class="p">],</span>
+    <span class="p">)</span>
+
+    <span class="c1">## upload to s3 bucket</span>
+    <span class="k">try</span><span class="p">:</span>
+        <span class="kn">from</span> <span class="nn">minio</span> <span class="kn">import</span> <span class="n">Minio</span>
+
+        <span class="n">minioClient</span> <span class="o">=</span> <span class="n">Minio</span><span class="p">(</span><span class="n">s3_url</span><span class="p">,</span> <span class="n">access_key</span><span class="o">=</span><span class="s2">&quot;minio&quot;</span><span class="p">,</span> <span class="n">secret_key</span><span class="o">=</span><span class="s2">&quot;minio123&quot;</span><span class="p">,</span> <span class="n">secure</span><span class="o">=</span><span class="n">secure</span><span class="p">)</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">minioClient</span><span class="o">.</span><span class="n">bucket_exists</span><span class="p">(</span><span class="n">bucket_name</span><span class="p">):</span>
+            <span class="n">minioClient</span><span class="o">.</span><span class="n">make_bucket</span><span class="p">(</span><span class="n">bucket_name</span><span class="p">)</span>
+
+        <span class="n">minioClient</span><span class="o">.</span><span class="n">fput_object</span><span class="p">(</span>
+            <span class="n">bucket_name</span><span class="p">,</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;region&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">/standard_catalog.csv&quot;</span><span class="p">,</span>
+            <span class="n">standard_catalog_csv</span><span class="p">,</span>
+        <span class="p">)</span>
+
+    <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">err</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;ERROR: can not access minio service! </span><span class="se">\n</span><span class="si">{</span><span class="n">err</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+        <span class="k">pass</span>
+
+    <span class="c1">####### Plot catalog ########</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">catalog</span><span class="p">[</span><span class="s2">&quot;longitude&quot;</span><span class="p">],</span> <span class="n">catalog</span><span class="p">[</span><span class="s2">&quot;latitude&quot;</span><span class="p">],</span> <span class="s2">&quot;.&quot;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">&quot;Longitude&quot;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">&quot;Latitude&quot;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s2">&quot;scaled&quot;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;xlim_degree&quot;</span><span class="p">])</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;ylim_degree&quot;</span><span class="p">])</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="n">catalog_location_png</span><span class="o">.</span><span class="n">path</span><span class="p">,</span> <span class="nb">format</span><span class="o">=</span><span class="s2">&quot;png&quot;</span><span class="p">,</span> <span class="n">dpi</span><span class="o">=</span><span class="mi">300</span><span class="p">)</span>
+
+    <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">plot_date</span><span class="p">(</span><span class="n">catalog</span><span class="p">[</span><span class="s2">&quot;time&quot;</span><span class="p">],</span> <span class="n">catalog</span><span class="p">[</span><span class="s2">&quot;magnitude&quot;</span><span class="p">],</span> <span class="s2">&quot;.&quot;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">gcf</span><span class="p">()</span><span class="o">.</span><span class="n">autofmt_xdate</span><span class="p">()</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">&quot;Magnitude&quot;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Number of events: </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">events</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="n">catalog_magnitude_png</span><span class="o">.</span><span class="n">path</span><span class="p">,</span> <span class="nb">format</span><span class="o">=</span><span class="s2">&quot;png&quot;</span><span class="p">,</span> <span class="n">dpi</span><span class="o">=</span><span class="mi">300</span><span class="p">)</span>
+</pre></div>
+<div id="cell-7" class="clipboard-copy-txt">@dsl.component(packages_to_install=["obspy", "numpy", "minio", "pandas", "matplotlib"])
+def download_events(
+    config_json: Input[Artifact], 
+    standard_catalog_csv: Output[Artifact],
+    catalog_location_png: Output[Artifact],
+    catalog_magnitude_png: Output[Artifact],
+    bucket_name: str = "catalogs",
+    s3_url: str = "minio-service:9000",
+    secure: bool = True,
+):
+
+    import json
+    import os
+    import pickle
+    from collections import defaultdict
+
+    import matplotlib
+    import matplotlib.pyplot as plt
+    import obspy
+    import pandas as pd
+    from obspy.clients.fdsn import Client
+
+    # matplotlib.use("agg")
+    # %matplotlib inline
+
+    with open(config_json.path, "r") as fp:
+        config = json.load(fp)
+
+    ####### IRIS catalog ########
+    try:
+        events = Client(config["client"]).get_events(
+            starttime=config["starttime"],
+            endtime=config["endtime"],
+            minlongitude=config["xlim_degree"][0],
+            maxlongitude=config["xlim_degree"][1],
+            minlatitude=config["ylim_degree"][0],
+            maxlatitude=config["ylim_degree"][1],
+            # filename='events.xml',
+        )
+    except:
+        events = Client("USGS").get_events(
+            starttime=config["starttime"],
+            endtime=config["endtime"],
+            minlongitude=config["xlim_degree"][0],
+            maxlongitude=config["xlim_degree"][1],
+            minlatitude=config["ylim_degree"][0],
+            maxlatitude=config["ylim_degree"][1],
+            # filename='events.xml',
+        )
+
+    print(f"Number of events: {len(events)}")
+
+    ####### Save catalog ########
+    catalog = defaultdict(list)
+    for event in events:
+        if len(event.magnitudes) > 0:
+            catalog["time"].append(event.origins[0].time.datetime)
+            catalog["magnitude"].append(event.magnitudes[0].mag)
+            catalog["longitude"].append(event.origins[0].longitude)
+            catalog["latitude"].append(event.origins[0].latitude)
+            catalog["depth(m)"].append(event.origins[0].depth)
+    catalog = pd.DataFrame.from_dict(catalog).sort_values(["time"])
+    catalog.to_csv(
+        standard_catalog_csv.path,
+        index=False,
+        float_format="%.3f",
+        date_format="%Y-%m-%dT%H:%M:%S.%f",
+        columns=["time", "magnitude", "longitude", "latitude", "depth(m)"],
+    )
+
+    ## upload to s3 bucket
+    try:
+        from minio import Minio
+
+        minioClient = Minio(s3_url, access_key="minio", secret_key="minio123", secure=secure)
+        if not minioClient.bucket_exists(bucket_name):
+            minioClient.make_bucket(bucket_name)
+
+        minioClient.fput_object(
+            bucket_name,
+            f"{config['region']}/standard_catalog.csv",
+            standard_catalog_csv,
+        )
+
+    except Exception as err:
+        print(f"ERROR: can not access minio service! \n{err}")
+        pass
+
+    ####### Plot catalog ########
+    plt.figure()
+    plt.plot(catalog["longitude"], catalog["latitude"], ".", markersize=1)
+    plt.xlabel("Longitude")
+    plt.ylabel("Latitude")
+    plt.axis("scaled")
+    plt.xlim(config["xlim_degree"])
+    plt.ylim(config["ylim_degree"])
+    plt.savefig(catalog_location_png.path, format="png", dpi=300)
+
+    plt.figure()
+    plt.plot_date(catalog["time"], catalog["magnitude"], ".", markersize=1)
+    plt.gcf().autofmt_xdate()
+    plt.ylabel("Magnitude")
+    plt.title(f"Number of events: {len(events)}")
+    plt.savefig(catalog_magnitude_png.path, format="png", dpi=300)</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[6]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-8">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="nd">@dsl</span><span class="o">.</span><span class="n">pipeline</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;quakeflow-pipeline&#39;</span><span class="p">)</span>
+<span class="k">def</span> <span class="nf">quakeflow_pipeline</span><span class="p">(</span>
+    <span class="n">data_path</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;/tmp/&quot;</span><span class="p">,</span>
+    <span class="n">num_parallel</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span>
+    <span class="n">bucket_catalog</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;catalogs&quot;</span><span class="p">,</span>
+    <span class="n">s3_url</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;minio-service:9000&quot;</span><span class="p">,</span>
+    <span class="n">secure</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+<span class="p">):</span>
+
+    <span class="n">config</span> <span class="o">=</span> <span class="n">set_config</span><span class="p">(</span>
+        <span class="n">region_name</span><span class="o">=</span><span class="n">region_name</span><span class="p">,</span> <span class="n">num_parallel</span><span class="o">=</span><span class="n">num_parallel</span><span class="p">,</span> <span class="n">bucket_name</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;catalogs&quot;</span><span class="p">,</span> <span class="n">s3_url</span><span class="o">=</span><span class="n">s3_url</span><span class="p">,</span> <span class="n">secure</span><span class="o">=</span><span class="n">secure</span>
+    <span class="p">)</span>
+
+    <span class="n">events</span> <span class="o">=</span> <span class="n">download_events</span><span class="p">(</span><span class="n">config_json</span> <span class="o">=</span> <span class="n">config</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;config_json&quot;</span><span class="p">],</span> <span class="n">bucket_name</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;catalogs&quot;</span><span class="p">,</span> <span class="n">s3_url</span><span class="o">=</span><span class="n">s3_url</span><span class="p">,</span> <span class="n">secure</span><span class="o">=</span><span class="n">secure</span><span class="p">)</span>
+</pre></div>
+<div id="cell-8" class="clipboard-copy-txt">@dsl.pipeline(name='quakeflow-pipeline')
+def quakeflow_pipeline(
+    data_path: str = "/tmp/",
+    num_parallel: int = 0,
+    bucket_catalog: str = "catalogs",
+    s3_url: str = "minio-service:9000",
+    secure: bool = False,
+):
+
+    config = set_config(
+        region_name=region_name, num_parallel=num_parallel, bucket_name=f"catalogs", s3_url=s3_url, secure=secure
+    )
+
+    events = download_events(config_json = config.outputs["config_json"], bucket_name=f"catalogs", s3_url=s3_url, secure=secure)
+    
+
+    
+</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[9]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-9">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">endpoint</span> <span class="o">=</span> <span class="s1">&#39;http://test.quakeflow.com:8080&#39;</span>
+<span class="n">kfp_client</span> <span class="o">=</span> <span class="n">client</span><span class="o">.</span><span class="n">Client</span><span class="p">(</span><span class="n">host</span><span class="o">=</span><span class="n">endpoint</span><span class="p">)</span>
+<span class="n">run</span> <span class="o">=</span> <span class="n">kfp_client</span><span class="o">.</span><span class="n">create_run_from_pipeline_func</span><span class="p">(</span>
+    <span class="n">quakeflow_pipeline</span><span class="p">,</span>
+    <span class="c1"># arguments={},</span>
+<span class="p">)</span>
+<span class="n">url</span> <span class="o">=</span> <span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">endpoint</span><span class="si">}</span><span class="s1">/#/runs/details/</span><span class="si">{</span><span class="n">run</span><span class="o">.</span><span class="n">run_id</span><span class="si">}</span><span class="s1">&#39;</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">url</span><span class="p">)</span>
+</pre></div>
+<div id="cell-9" class="clipboard-copy-txt">endpoint = 'http://test.quakeflow.com:8080'
+kfp_client = client.Client(host=endpoint)
+run = kfp_client.create_run_from_pipeline_func(
+    quakeflow_pipeline,
+    # arguments={},
+)
+url = f'{endpoint}/#/runs/details/{run.run_id}'
+print(url)</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>/Users/weiqiang/.local/miniconda3/lib/python3.9/site-packages/kfp/client/client.py:158: FutureWarning: This client only works with Kubeflow Pipeline v2.0.0-beta.2 and later versions.
+  warnings.warn(
+</pre>
+</div>
+</div>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output " data-mime-type="text/html">
+<a href="http://test.quakeflow.com:8080/#/experiments/details/fcfd54d4-7241-4438-bbf6-21ea3013aa87" target="_blank" >Experiment details</a>.
+</div>
+
+</div>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output " data-mime-type="text/html">
+<a href="http://test.quakeflow.com:8080/#/runs/details/5b0a8d58-a561-4e31-b962-9738309b523b" target="_blank" >Run details</a>.
+</div>
+
+</div>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
+<pre>http://test.quakeflow.com:8080/#/runs/details/5b0a8d58-a561-4e31-b962-9738309b523b
+</pre>
+</div>
+</div>
+
+</div>
+
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-10">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span>
+</pre></div>
+<div id="cell-10" class="clipboard-copy-txt"></div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+</div>
+
+
+
+
+  
+
+
+
+              
+            </article>
+            
+          </div>
+        </div>
+        
+      </main>
+      
+        <footer class="md-footer">
+  
+  <div class="md-footer-meta md-typeset">
+    <div class="md-footer-meta__inner md-grid">
+      <div class="md-copyright">
+  
+  
+    Made with
+    <a href="https://squidfunk.github.io/mkdocs-material/" target="_blank" rel="noopener">
+      Material for MkDocs
+    </a>
+  
+</div>
+      
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+      
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+    <div class="md-dialog" data-md-component="dialog">
+      <div class="md-dialog__inner md-typeset"></div>
+    </div>
+    
+    <script id="__config" type="application/json">{"base": "../..", "features": [], "search": "../../assets/javascripts/workers/search.ecf98df9.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}}</script>
+    
+    
+      <script src="../../assets/javascripts/bundle.9c69f0bc.min.js"></script>
+      
+    
+  </body>
+</html>
\ No newline at end of file
diff --git a/kubeflow/workflow/index.html b/kubeflow/workflow/index.html
new file mode 100644
index 0000000..7e30709
--- /dev/null
+++ b/kubeflow/workflow/index.html
@@ -0,0 +1,9755 @@
+
+<!doctype html>
+<html lang="en" class="no-js">
+  <head>
+    
+      <meta charset="utf-8">
+      <meta name="viewport" content="width=device-width,initial-scale=1">
+      
+        <meta name="description" content="QuakeFlow: a Scalable Deep-learning-based Earthquake Monitoring Workflow with Cloud Computing">
+      
+      
+        <meta name="author" content="Weiqiang Zhu">
+      
+      
+      <link rel="icon" href="../../assets/images/favicon.png">
+      <meta name="generator" content="mkdocs-1.3.0, mkdocs-material-8.4.1">
+    
+    
+      
+        <title>Pipeline - QuakeFlow</title>
+      
+    
+    
+      <link rel="stylesheet" href="../../assets/stylesheets/main.69437709.min.css">
+      
+        
+        <link rel="stylesheet" href="../../assets/stylesheets/palette.cbb835fc.min.css">
+        
+      
+      
+    
+    
+    
+      
+        
+        
+        <link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
+        <link rel="stylesheet" href="https://fonts.googleapis.com/css?family=Roboto:300,300i,400,400i,700,700i%7CRoboto+Mono:400,400i,700,700i&display=fallback">
+        <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
+      
+    
+    
+    <script>__md_scope=new URL("../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
+    
+      
+  
+
+
+  
+  
+
+
+  <script id="__analytics">function __md_analytics(){function n(){dataLayer.push(arguments)}window.dataLayer=window.dataLayer||[],n("js",new Date),n("config","G-69DX3B35RK"),document.addEventListener("DOMContentLoaded",function(){if(document.forms.search&&document.forms.search.query.addEventListener("blur",function(){this.value&&n("event","search",{search_term:this.value})}),document.forms.feedback){var e,a=document.forms.feedback;for(e of a.querySelectorAll("[type=submit]"))e.addEventListener("click",function(e){e.preventDefault();var t=document.location.pathname,e=this.getAttribute("data-md-value");n("event","feedback",{page:t,data:e}),a.firstElementChild.disabled=!0;e=a.querySelector(".md-feedback__note [data-md-value='"+e+"']");e&&(e.hidden=!1)}),a.hidden=!1}"undefined"!=typeof location$&&location$.subscribe(function(e){n("config","G-69DX3B35RK",{page_path:e.pathname})})});var e=document.createElement("script");e.async=!0,e.src="https://www.googletagmanager.com/gtag/js?id=G-69DX3B35RK",document.getElementById("__analytics").insertAdjacentElement("afterEnd",e)}</script>
+
+  
+    <script>"undefined"!=typeof __md_analytics&&__md_analytics()</script>
+  
+
+    
+    
+  </head>
+  
+  
+    
+    
+    
+    
+    
+    <body dir="ltr" data-md-color-scheme="" data-md-color-primary="none" data-md-color-accent="none">
+  
+    
+    
+    <input class="md-toggle" data-md-toggle="drawer" type="checkbox" id="__drawer" autocomplete="off">
+    <input class="md-toggle" data-md-toggle="search" type="checkbox" id="__search" autocomplete="off">
+    <label class="md-overlay" for="__drawer"></label>
+    <div data-md-component="skip">
+      
+        
+        <a href="#earthquake-detection-workflow-using-quakeflow" class="md-skip">
+          Skip to content
+        </a>
+      
+    </div>
+    <div data-md-component="announce">
+      
+    </div>
+    
+    
+      
+
+<header class="md-header" data-md-component="header">
+  <nav class="md-header__inner md-grid" aria-label="Header">
+    <a href="../.." title="QuakeFlow" class="md-header__button md-logo" aria-label="QuakeFlow" data-md-component="logo">
+      
+  
+  <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54Z"/></svg>
+
+    </a>
+    <label class="md-header__button md-icon" for="__drawer">
+      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M3 6h18v2H3V6m0 5h18v2H3v-2m0 5h18v2H3v-2Z"/></svg>
+    </label>
+    <div class="md-header__title" data-md-component="header-title">
+      <div class="md-header__ellipsis">
+        <div class="md-header__topic">
+          <span class="md-ellipsis">
+            QuakeFlow
+          </span>
+        </div>
+        <div class="md-header__topic" data-md-component="header-topic">
+          <span class="md-ellipsis">
+            
+              Pipeline
+            
+          </span>
+        </div>
+      </div>
+    </div>
+    
+    
+    
+    
+      <div class="md-header__source">
+        <a href="https://github.com/ai4eps/QuakeFlow" title="Go to repository" class="md-source" data-md-component="source">
+  <div class="md-source__icon md-icon">
+    
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.1.2 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+  </div>
+  <div class="md-source__repository">
+    AI4EPS/QuakeFlow
+  </div>
+</a>
+      </div>
+    
+  </nav>
+  
+</header>
+    
+    <div class="md-container" data-md-component="container">
+      
+      
+        
+          
+        
+      
+      <main class="md-main" data-md-component="main">
+        <div class="md-main__inner md-grid">
+          
+            
+              
+              <div class="md-sidebar md-sidebar--primary" data-md-component="sidebar" data-md-type="navigation" >
+                <div class="md-sidebar__scrollwrap">
+                  <div class="md-sidebar__inner">
+                    
+
+
+<nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
+  <label class="md-nav__title" for="__drawer">
+    <a href="../.." title="QuakeFlow" class="md-nav__button md-logo" aria-label="QuakeFlow" data-md-component="logo">
+      
+  
+  <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54Z"/></svg>
+
+    </a>
+    QuakeFlow
+  </label>
+  
+    <div class="md-nav__source">
+      <a href="https://github.com/ai4eps/QuakeFlow" title="Go to repository" class="md-source" data-md-component="source">
+  <div class="md-source__icon md-icon">
+    
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.1.2 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+  </div>
+  <div class="md-source__repository">
+    AI4EPS/QuakeFlow
+  </div>
+</a>
+    </div>
+  
+  <ul class="md-nav__list" data-md-scrollfix>
+    
+      
+      
+      
+
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../.." class="md-nav__link">
+        Overview
+      </a>
+    </li>
+  
+
+    
+      
+      
+      
+
+  
+  
+    
+  
+  
+    <li class="md-nav__item md-nav__item--active">
+      
+      <input class="md-nav__toggle md-toggle" data-md-toggle="toc" type="checkbox" id="__toc">
+      
+      
+        
+      
+      
+        <label class="md-nav__link md-nav__link--active" for="__toc">
+          Pipeline
+          <span class="md-nav__icon md-icon"></span>
+        </label>
+      
+      <a href="./" class="md-nav__link md-nav__link--active">
+        Pipeline
+      </a>
+      
+        
+
+<nav class="md-nav md-nav--secondary" aria-label="Table of contents">
+  
+  
+  
+    
+  
+  
+    <label class="md-nav__title" for="__toc">
+      <span class="md-nav__icon md-icon"></span>
+      Table of contents
+    </label>
+    <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
+      
+        <li class="md-nav__item">
+  <a href="#outline" class="md-nav__link">
+    Outline
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#0-install-miniconda-and-download-packages" class="md-nav__link">
+    0. Install miniconda and download packages
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#1-set-configurations" class="md-nav__link">
+    1. Set configurations
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#2-download-events-of-the-standard-catalog" class="md-nav__link">
+    2. Download events of the standard catalog
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#3-download-stations" class="md-nav__link">
+    3. Download stations
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#4-download-waveform-data" class="md-nav__link">
+    4. Download waveform data
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#5-run-phasenet-to-pick-ps-picks" class="md-nav__link">
+    5. Run PhaseNet to pick P/S picks
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#6-run-gamma-to-associate-ps-picks" class="md-nav__link">
+    6. Run GaMMA to associate P/S picks
+  </a>
+  
+    <nav class="md-nav" aria-label="6. Run GaMMA to associate P/S picks">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#plot-catalogs" class="md-nav__link">
+    Plot catalogs
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#merge-parallel-processing-on-cloud" class="md-nav__link">
+    Merge parallel processing on cloud
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#7-hypodd-earthquake-relocation" class="md-nav__link">
+    7. HypoDD earthquake relocation
+  </a>
+  
+    <nav class="md-nav" aria-label="7. HypoDD earthquake relocation">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#download-and-compile-hypodd" class="md-nav__link">
+    Download and compile HypoDD
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#71-convert-station-format" class="md-nav__link">
+    7.1. Convert station format
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#split-large-catalog-due-to-memory-and-time" class="md-nav__link">
+    Split large catalog due to memory and time
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#72-convert-phase-format" class="md-nav__link">
+    7.2. Convert phase format
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#73-run-ph2dt-to-calculate-differential-time-between-phases" class="md-nav__link">
+    7.3. Run ph2dt to calculate differential time between phases
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#74-run-hypodd-re-location" class="md-nav__link">
+    7.4. Run HypoDD re-location
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#merge-hypodd-results" class="md-nav__link">
+    Merge hypodd results
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#8-visulization" class="md-nav__link">
+    8. Visulization
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#9-parallel-process-on-cloud" class="md-nav__link">
+    9. Parallel process on cloud
+  </a>
+  
+</li>
+      
+    </ul>
+  
+</nav>
+      
+    </li>
+  
+
+    
+      
+      
+      
+
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../data_format/" class="md-nav__link">
+        Standard Formats
+      </a>
+    </li>
+  
+
+    
+  </ul>
+</nav>
+                  </div>
+                </div>
+              </div>
+            
+            
+              
+              <div class="md-sidebar md-sidebar--secondary" data-md-component="sidebar" data-md-type="toc" >
+                <div class="md-sidebar__scrollwrap">
+                  <div class="md-sidebar__inner">
+                    
+
+<nav class="md-nav md-nav--secondary" aria-label="Table of contents">
+  
+  
+  
+    
+  
+  
+    <label class="md-nav__title" for="__toc">
+      <span class="md-nav__icon md-icon"></span>
+      Table of contents
+    </label>
+    <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
+      
+        <li class="md-nav__item">
+  <a href="#outline" class="md-nav__link">
+    Outline
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#0-install-miniconda-and-download-packages" class="md-nav__link">
+    0. Install miniconda and download packages
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#1-set-configurations" class="md-nav__link">
+    1. Set configurations
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#2-download-events-of-the-standard-catalog" class="md-nav__link">
+    2. Download events of the standard catalog
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#3-download-stations" class="md-nav__link">
+    3. Download stations
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#4-download-waveform-data" class="md-nav__link">
+    4. Download waveform data
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#5-run-phasenet-to-pick-ps-picks" class="md-nav__link">
+    5. Run PhaseNet to pick P/S picks
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#6-run-gamma-to-associate-ps-picks" class="md-nav__link">
+    6. Run GaMMA to associate P/S picks
+  </a>
+  
+    <nav class="md-nav" aria-label="6. Run GaMMA to associate P/S picks">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#plot-catalogs" class="md-nav__link">
+    Plot catalogs
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#merge-parallel-processing-on-cloud" class="md-nav__link">
+    Merge parallel processing on cloud
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#7-hypodd-earthquake-relocation" class="md-nav__link">
+    7. HypoDD earthquake relocation
+  </a>
+  
+    <nav class="md-nav" aria-label="7. HypoDD earthquake relocation">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#download-and-compile-hypodd" class="md-nav__link">
+    Download and compile HypoDD
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#71-convert-station-format" class="md-nav__link">
+    7.1. Convert station format
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#split-large-catalog-due-to-memory-and-time" class="md-nav__link">
+    Split large catalog due to memory and time
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#72-convert-phase-format" class="md-nav__link">
+    7.2. Convert phase format
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#73-run-ph2dt-to-calculate-differential-time-between-phases" class="md-nav__link">
+    7.3. Run ph2dt to calculate differential time between phases
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#74-run-hypodd-re-location" class="md-nav__link">
+    7.4. Run HypoDD re-location
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#merge-hypodd-results" class="md-nav__link">
+    Merge hypodd results
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#8-visulization" class="md-nav__link">
+    8. Visulization
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#9-parallel-process-on-cloud" class="md-nav__link">
+    9. Parallel process on cloud
+  </a>
+  
+</li>
+      
+    </ul>
+  
+</nav>
+                  </div>
+                </div>
+              </div>
+            
+          
+          <div class="md-content" data-md-component="content">
+            <article class="md-content__inner md-typeset">
+              
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+  <a href="https://github.com/ai4eps/QuakeFlow/edit/master/docs/kubeflow/workflow.ipynb" title="Edit this page" class="md-content__button md-icon">
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+    --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color),
+        0px 24px 38px 3px var(--jp-shadow-penumbra-color),
+        0px 9px 46px 8px var(--jp-shadow-ambient-color);
+
+    /* Borders
+   *
+   * The following variables, specify the visual styling of borders in JupyterLab.
+   */
+
+    --jp-border-width: 1px;
+    --jp-border-color0: var(--md-grey-400);
+    --jp-border-color1: var(--md-grey-400);
+    --jp-border-color2: var(--md-grey-300);
+    --jp-border-color3: var(--md-grey-200);
+    --jp-border-radius: 2px;
+
+    /* UI Fonts
+   *
+   * The UI font CSS variables are used for the typography all of the JupyterLab
+   * user interface elements that are not directly user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-ui-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+    --jp-ui-font-scale-factor: 1.2;
+    --jp-ui-font-size0: 0.83333em;
+    --jp-ui-font-size1: 13px; /* Base font size */
+    --jp-ui-font-size2: 1.2em;
+    --jp-ui-font-size3: 1.44em;
+
+    --jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+
+    /*
+   * Use these font colors against the corresponding main layout colors.
+   * In a light theme, these go from dark to light.
+   */
+
+    /* Defaults use Material Design specification */
+    --jp-ui-font-color0: rgba(0, 0, 0, 1);
+    --jp-ui-font-color1: rgba(0, 0, 0, 0.87);
+    --jp-ui-font-color2: rgba(0, 0, 0, 0.54);
+    --jp-ui-font-color3: rgba(0, 0, 0, 0.38);
+
+    /*
+   * Use these against the brand/accent/warn/error colors.
+   * These will typically go from light to darker, in both a dark and light theme.
+   */
+
+    --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1);
+    --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1);
+    --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7);
+    --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5);
+
+    /* Content Fonts
+   *
+   * Content font variables are used for typography of user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-content-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+    --jp-content-line-height: 1.6;
+    --jp-content-font-scale-factor: 1.2;
+    --jp-content-font-size0: 0.83333em;
+    --jp-content-font-size1: 14px; /* Base font size */
+    --jp-content-font-size2: 1.2em;
+    --jp-content-font-size3: 1.44em;
+    --jp-content-font-size4: 1.728em;
+    --jp-content-font-size5: 2.0736em;
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-content-presentation-font-size1: 17px;
+
+    --jp-content-heading-line-height: 1;
+    --jp-content-heading-margin-top: 1.2em;
+    --jp-content-heading-margin-bottom: 0.8em;
+    --jp-content-heading-font-weight: 500;
+
+    /* Defaults use Material Design specification */
+    --jp-content-font-color0: rgba(0, 0, 0, 1);
+    --jp-content-font-color1: rgba(0, 0, 0, 0.87);
+    --jp-content-font-color2: rgba(0, 0, 0, 0.54);
+    --jp-content-font-color3: rgba(0, 0, 0, 0.38);
+
+    --jp-content-link-color: var(--md-blue-700);
+
+    --jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+
+    /*
+   * Code Fonts
+   *
+   * Code font variables are used for typography of code and other monospaces content.
+   */
+
+    --jp-code-font-size: 13px;
+    --jp-code-line-height: 1.3077; /* 17px for 13px base */
+    --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */
+    --jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono",
+        monospace;
+    --jp-code-font-family: var(--jp-code-font-family-default);
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-code-presentation-font-size: 16px;
+
+    /* may need to tweak cursor width if you change font size */
+    --jp-code-cursor-width0: 1.4px;
+    --jp-code-cursor-width1: 2px;
+    --jp-code-cursor-width2: 4px;
+
+    /* Layout
+   *
+   * The following are the main layout colors use in JupyterLab. In a light
+   * theme these would go from light to dark.
+   */
+
+    --jp-layout-color0: white;
+    --jp-layout-color1: white;
+    --jp-layout-color2: var(--md-grey-200);
+    --jp-layout-color3: var(--md-grey-400);
+    --jp-layout-color4: var(--md-grey-600);
+
+    /* Inverse Layout
+   *
+   * The following are the inverse layout colors use in JupyterLab. In a light
+   * theme these would go from dark to light.
+   */
+
+    --jp-inverse-layout-color0: #111111;
+    --jp-inverse-layout-color1: var(--md-grey-900);
+    --jp-inverse-layout-color2: var(--md-grey-800);
+    --jp-inverse-layout-color3: var(--md-grey-700);
+    --jp-inverse-layout-color4: var(--md-grey-600);
+
+    /* Brand/accent */
+
+    --jp-brand-color0: var(--md-blue-900);
+    --jp-brand-color1: var(--md-blue-700);
+    --jp-brand-color2: var(--md-blue-300);
+    --jp-brand-color3: var(--md-blue-100);
+    --jp-brand-color4: var(--md-blue-50);
+
+    --jp-accent-color0: var(--md-green-900);
+    --jp-accent-color1: var(--md-green-700);
+    --jp-accent-color2: var(--md-green-300);
+    --jp-accent-color3: var(--md-green-100);
+
+    /* State colors (warn, error, success, info) */
+
+    --jp-warn-color0: var(--md-orange-900);
+    --jp-warn-color1: var(--md-orange-700);
+    --jp-warn-color2: var(--md-orange-300);
+    --jp-warn-color3: var(--md-orange-100);
+
+    --jp-error-color0: var(--md-red-900);
+    --jp-error-color1: var(--md-red-700);
+    --jp-error-color2: var(--md-red-300);
+    --jp-error-color3: var(--md-red-100);
+
+    --jp-success-color0: var(--md-green-900);
+    --jp-success-color1: var(--md-green-700);
+    --jp-success-color2: var(--md-green-300);
+    --jp-success-color3: var(--md-green-100);
+
+    --jp-info-color0: var(--md-cyan-900);
+    --jp-info-color1: var(--md-cyan-700);
+    --jp-info-color2: var(--md-cyan-300);
+    --jp-info-color3: var(--md-cyan-100);
+
+    /* Cell specific styles */
+
+    --jp-cell-padding: 5px;
+
+    --jp-cell-collapser-width: 8px;
+    --jp-cell-collapser-min-height: 20px;
+    --jp-cell-collapser-not-active-hover-opacity: 0.6;
+
+    --jp-cell-editor-background: var(--md-grey-100);
+    --jp-cell-editor-border-color: var(--md-grey-300);
+    --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-cell-editor-active-background: var(--jp-layout-color0);
+    --jp-cell-editor-active-border-color: var(--jp-brand-color1);
+
+    --jp-cell-prompt-width: 64px;
+    --jp-cell-prompt-font-family: var(--jp-code-font-family-default);
+    --jp-cell-prompt-letter-spacing: 0px;
+    --jp-cell-prompt-opacity: 1;
+    --jp-cell-prompt-not-active-opacity: 0.5;
+    --jp-cell-prompt-not-active-font-color: var(--md-grey-700);
+    /* A custom blend of MD grey and blue 600
+   * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */
+    --jp-cell-inprompt-font-color: #307fc1;
+    /* A custom blend of MD grey and orange 600
+   * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */
+    --jp-cell-outprompt-font-color: #bf5b3d;
+
+    /* Notebook specific styles */
+
+    --jp-notebook-padding: 10px;
+    --jp-notebook-select-background: var(--jp-layout-color1);
+    --jp-notebook-multiselected-color: var(--md-blue-50);
+
+    /* The scroll padding is calculated to fill enough space at the bottom of the
+  notebook to show one single-line cell (with appropriate padding) at the top
+  when the notebook is scrolled all the way to the bottom. We also subtract one
+  pixel so that no scrollbar appears if we have just one single-line cell in the
+  notebook. This padding is to enable a 'scroll past end' feature in a notebook.
+  */
+    --jp-notebook-scroll-padding: calc(
+        100% - var(--jp-code-font-size) * var(--jp-code-line-height) -
+            var(--jp-code-padding) - var(--jp-cell-padding) - 1px
+    );
+
+    /* Rendermime styles */
+
+    --jp-rendermime-error-background: #fdd;
+    --jp-rendermime-table-row-background: var(--md-grey-100);
+    --jp-rendermime-table-row-hover-background: var(--md-light-blue-50);
+
+    /* Dialog specific styles */
+
+    --jp-dialog-background: rgba(0, 0, 0, 0.25);
+
+    /* Console specific styles */
+
+    --jp-console-padding: 10px;
+
+    /* Toolbar specific styles */
+
+    --jp-toolbar-border-color: var(--jp-border-color1);
+    --jp-toolbar-micro-height: 8px;
+    --jp-toolbar-background: var(--jp-layout-color1);
+    --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24);
+    --jp-toolbar-header-margin: 4px 4px 0px 4px;
+    --jp-toolbar-active-background: var(--md-grey-300);
+
+    /* Statusbar specific styles */
+
+    --jp-statusbar-height: 24px;
+
+    /* Input field styles */
+
+    --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-input-active-background: var(--jp-layout-color1);
+    --jp-input-hover-background: var(--jp-layout-color1);
+    --jp-input-background: var(--md-grey-100);
+    --jp-input-border-color: var(--jp-border-color1);
+    --jp-input-active-border-color: var(--jp-brand-color1);
+    --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3);
+
+    /* General editor styles */
+
+    --jp-editor-selected-background: #d9d9d9;
+    --jp-editor-selected-focused-background: #d7d4f0;
+    --jp-editor-cursor-color: var(--jp-ui-font-color0);
+
+    /* Code mirror specific styles */
+
+    --jp-mirror-editor-keyword-color: #008000;
+    --jp-mirror-editor-atom-color: #88f;
+    --jp-mirror-editor-number-color: #080;
+    --jp-mirror-editor-def-color: #00f;
+    --jp-mirror-editor-variable-color: var(--md-grey-900);
+    --jp-mirror-editor-variable-2-color: #05a;
+    --jp-mirror-editor-variable-3-color: #085;
+    --jp-mirror-editor-punctuation-color: #05a;
+    --jp-mirror-editor-property-color: #05a;
+    --jp-mirror-editor-operator-color: #aa22ff;
+    --jp-mirror-editor-comment-color: #408080;
+    --jp-mirror-editor-string-color: #ba2121;
+    --jp-mirror-editor-string-2-color: #708;
+    --jp-mirror-editor-meta-color: #aa22ff;
+    --jp-mirror-editor-qualifier-color: #555;
+    --jp-mirror-editor-builtin-color: #008000;
+    --jp-mirror-editor-bracket-color: #997;
+    --jp-mirror-editor-tag-color: #170;
+    --jp-mirror-editor-attribute-color: #00c;
+    --jp-mirror-editor-header-color: blue;
+    --jp-mirror-editor-quote-color: #090;
+    --jp-mirror-editor-link-color: #00c;
+    --jp-mirror-editor-error-color: #f00;
+    --jp-mirror-editor-hr-color: #999;
+
+    /* Vega extension styles */
+
+    --jp-vega-background: white;
+
+    /* Sidebar-related styles */
+
+    --jp-sidebar-min-width: 250px;
+
+    /* Search-related styles */
+
+    --jp-search-toggle-off-opacity: 0.5;
+    --jp-search-toggle-hover-opacity: 0.8;
+    --jp-search-toggle-on-opacity: 1;
+    --jp-search-selected-match-background-color: rgb(245, 200, 0);
+    --jp-search-selected-match-color: black;
+    --jp-search-unselected-match-background-color: var(
+        --jp-inverse-layout-color0
+    );
+    --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);
+
+    /* Icon colors that work well with light or dark backgrounds */
+    --jp-icon-contrast-color0: var(--md-purple-600);
+    --jp-icon-contrast-color1: var(--md-green-600);
+    --jp-icon-contrast-color2: var(--md-pink-600);
+    --jp-icon-contrast-color3: var(--md-blue-600);
+}
+
+[data-md-color-scheme="slate"] .jupyter-wrapper {
+    /* Elevation
+   *
+   * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here:
+   *
+   * https://github.com/material-components/material-components-web
+   * https://material-components-web.appspot.com/elevation.html
+   */
+
+    /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout
+   * colors used in the theme as well. */
+    --jp-shadow-base-lightness: 32;
+    --jp-shadow-umbra-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.2
+    );
+    --jp-shadow-penumbra-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.14
+    );
+    --jp-shadow-ambient-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.12
+    );
+    --jp-elevation-z0: none;
+    --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color),
+        0px 1px 1px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 3px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color),
+        0px 2px 2px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 5px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color),
+        0px 4px 5px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 10px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color),
+        0px 6px 10px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 18px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color),
+        0px 8px 10px 1px var(--jp-shadow-penumbra-color),
+        0px 3px 14px 2px var(--jp-shadow-ambient-color);
+    --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color),
+        0px 12px 17px 2px var(--jp-shadow-penumbra-color),
+        0px 5px 22px 4px var(--jp-shadow-ambient-color);
+    --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color),
+        0px 16px 24px 2px var(--jp-shadow-penumbra-color),
+        0px 6px 30px 5px var(--jp-shadow-ambient-color);
+    --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color),
+        0px 20px 31px 3px var(--jp-shadow-penumbra-color),
+        0px 8px 38px 7px var(--jp-shadow-ambient-color);
+    --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color),
+        0px 24px 38px 3px var(--jp-shadow-penumbra-color),
+        0px 9px 46px 8px var(--jp-shadow-ambient-color);
+
+    /* Borders
+   *
+   * The following variables, specify the visual styling of borders in JupyterLab.
+   */
+
+    --jp-border-width: 1px;
+    --jp-border-color0: var(--md-grey-700);
+    --jp-border-color1: var(--md-grey-700);
+    --jp-border-color2: var(--md-grey-800);
+    --jp-border-color3: var(--md-grey-900);
+    --jp-border-radius: 2px;
+
+    /* UI Fonts
+   *
+   * The UI font CSS variables are used for the typography all of the JupyterLab
+   * user interface elements that are not directly user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-ui-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+    --jp-ui-font-scale-factor: 1.2;
+    --jp-ui-font-size0: 0.83333em;
+    --jp-ui-font-size1: 13px; /* Base font size */
+    --jp-ui-font-size2: 1.2em;
+    --jp-ui-font-size3: 1.44em;
+
+    --jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+
+    /*
+   * Use these font colors against the corresponding main layout colors.
+   * In a light theme, these go from dark to light.
+   */
+
+    /* Defaults use Material Design specification */
+    --jp-ui-font-color0: rgba(255, 255, 255, 1);
+    --jp-ui-font-color1: rgba(255, 255, 255, 0.87);
+    --jp-ui-font-color2: rgba(255, 255, 255, 0.54);
+    --jp-ui-font-color3: rgba(255, 255, 255, 0.38);
+
+    /*
+   * Use these against the brand/accent/warn/error colors.
+   * These will typically go from light to darker, in both a dark and light theme.
+   */
+
+    --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1);
+    --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8);
+    --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5);
+    --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3);
+
+    /* Content Fonts
+   *
+   * Content font variables are used for typography of user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-content-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+    --jp-content-line-height: 1.6;
+    --jp-content-font-scale-factor: 1.2;
+    --jp-content-font-size0: 0.83333em;
+    --jp-content-font-size1: 14px; /* Base font size */
+    --jp-content-font-size2: 1.2em;
+    --jp-content-font-size3: 1.44em;
+    --jp-content-font-size4: 1.728em;
+    --jp-content-font-size5: 2.0736em;
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-content-presentation-font-size1: 17px;
+
+    --jp-content-heading-line-height: 1;
+    --jp-content-heading-margin-top: 1.2em;
+    --jp-content-heading-margin-bottom: 0.8em;
+    --jp-content-heading-font-weight: 500;
+
+    /* Defaults use Material Design specification */
+    --jp-content-font-color0: rgba(255, 255, 255, 1);
+    --jp-content-font-color1: rgba(255, 255, 255, 1);
+    --jp-content-font-color2: rgba(255, 255, 255, 0.7);
+    --jp-content-font-color3: rgba(255, 255, 255, 0.5);
+
+    --jp-content-link-color: var(--md-blue-300);
+
+    --jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+
+    /*
+   * Code Fonts
+   *
+   * Code font variables are used for typography of code and other monospaces content.
+   */
+
+    --jp-code-font-size: 13px;
+    --jp-code-line-height: 1.3077; /* 17px for 13px base */
+    --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */
+    --jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono",
+        monospace;
+    --jp-code-font-family: var(--jp-code-font-family-default);
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-code-presentation-font-size: 16px;
+
+    /* may need to tweak cursor width if you change font size */
+    --jp-code-cursor-width0: 1.4px;
+    --jp-code-cursor-width1: 2px;
+    --jp-code-cursor-width2: 4px;
+
+    /* Layout
+   *
+   * The following are the main layout colors use in JupyterLab. In a light
+   * theme these would go from light to dark.
+   */
+
+    --jp-layout-color0: #111111;
+    --jp-layout-color1: var(--md-grey-900);
+    --jp-layout-color2: var(--md-grey-800);
+    --jp-layout-color3: var(--md-grey-700);
+    --jp-layout-color4: var(--md-grey-600);
+
+    /* Inverse Layout
+   *
+   * The following are the inverse layout colors use in JupyterLab. In a light
+   * theme these would go from dark to light.
+   */
+
+    --jp-inverse-layout-color0: white;
+    --jp-inverse-layout-color1: white;
+    --jp-inverse-layout-color2: var(--md-grey-200);
+    --jp-inverse-layout-color3: var(--md-grey-400);
+    --jp-inverse-layout-color4: var(--md-grey-600);
+
+    /* Brand/accent */
+
+    --jp-brand-color0: var(--md-blue-700);
+    --jp-brand-color1: var(--md-blue-500);
+    --jp-brand-color2: var(--md-blue-300);
+    --jp-brand-color3: var(--md-blue-100);
+    --jp-brand-color4: var(--md-blue-50);
+
+    --jp-accent-color0: var(--md-green-700);
+    --jp-accent-color1: var(--md-green-500);
+    --jp-accent-color2: var(--md-green-300);
+    --jp-accent-color3: var(--md-green-100);
+
+    /* State colors (warn, error, success, info) */
+
+    --jp-warn-color0: var(--md-orange-700);
+    --jp-warn-color1: var(--md-orange-500);
+    --jp-warn-color2: var(--md-orange-300);
+    --jp-warn-color3: var(--md-orange-100);
+
+    --jp-error-color0: var(--md-red-700);
+    --jp-error-color1: var(--md-red-500);
+    --jp-error-color2: var(--md-red-300);
+    --jp-error-color3: var(--md-red-100);
+
+    --jp-success-color0: var(--md-green-700);
+    --jp-success-color1: var(--md-green-500);
+    --jp-success-color2: var(--md-green-300);
+    --jp-success-color3: var(--md-green-100);
+
+    --jp-info-color0: var(--md-cyan-700);
+    --jp-info-color1: var(--md-cyan-500);
+    --jp-info-color2: var(--md-cyan-300);
+    --jp-info-color3: var(--md-cyan-100);
+
+    /* Cell specific styles */
+
+    --jp-cell-padding: 5px;
+
+    --jp-cell-collapser-width: 8px;
+    --jp-cell-collapser-min-height: 20px;
+    --jp-cell-collapser-not-active-hover-opacity: 0.6;
+
+    --jp-cell-editor-background: var(--jp-layout-color1);
+    --jp-cell-editor-border-color: var(--md-grey-700);
+    --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-cell-editor-active-background: var(--jp-layout-color0);
+    --jp-cell-editor-active-border-color: var(--jp-brand-color1);
+
+    --jp-cell-prompt-width: 64px;
+    --jp-cell-prompt-font-family: var(--jp-code-font-family-default);
+    --jp-cell-prompt-letter-spacing: 0px;
+    --jp-cell-prompt-opacity: 1;
+    --jp-cell-prompt-not-active-opacity: 1;
+    --jp-cell-prompt-not-active-font-color: var(--md-grey-300);
+
+    /* A custom blend of MD grey and blue 600
+   * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */
+    --jp-cell-inprompt-font-color: #307fc1;
+    /* A custom blend of MD grey and orange 600
+   * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */
+    --jp-cell-outprompt-font-color: #bf5b3d;
+
+    /* Notebook specific styles */
+
+    --jp-notebook-padding: 10px;
+    --jp-notebook-select-background: var(--jp-layout-color1);
+    --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24);
+
+    /* The scroll padding is calculated to fill enough space at the bottom of the
+  notebook to show one single-line cell (with appropriate padding) at the top
+  when the notebook is scrolled all the way to the bottom. We also subtract one
+  pixel so that no scrollbar appears if we have just one single-line cell in the
+  notebook. This padding is to enable a 'scroll past end' feature in a notebook.
+  */
+    --jp-notebook-scroll-padding: calc(
+        100% - var(--jp-code-font-size) * var(--jp-code-line-height) -
+            var(--jp-code-padding) - var(--jp-cell-padding) - 1px
+    );
+
+    /* Rendermime styles */
+
+    --jp-rendermime-error-background: rgba(244, 67, 54, 0.28);
+    --jp-rendermime-table-row-background: var(--md-grey-900);
+    --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2);
+
+    /* Dialog specific styles */
+
+    --jp-dialog-background: rgba(0, 0, 0, 0.6);
+
+    /* Console specific styles */
+
+    --jp-console-padding: 10px;
+
+    /* Toolbar specific styles */
+
+    --jp-toolbar-border-color: var(--jp-border-color2);
+    --jp-toolbar-micro-height: 8px;
+    --jp-toolbar-background: var(--jp-layout-color1);
+    --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8);
+    --jp-toolbar-header-margin: 4px 4px 0px 4px;
+    --jp-toolbar-active-background: var(--jp-layout-color0);
+
+    /* Statusbar specific styles */
+
+    --jp-statusbar-height: 24px;
+
+    /* Input field styles */
+
+    --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-input-active-background: var(--jp-layout-color0);
+    --jp-input-hover-background: var(--jp-layout-color2);
+    --jp-input-background: var(--md-grey-800);
+    --jp-input-border-color: var(--jp-border-color1);
+    --jp-input-active-border-color: var(--jp-brand-color1);
+    --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3);
+
+    /* General editor styles */
+
+    --jp-editor-selected-background: var(--jp-layout-color2);
+    --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24);
+    --jp-editor-cursor-color: var(--jp-ui-font-color0);
+
+    /* Code mirror specific styles */
+
+    --jp-mirror-editor-keyword-color: var(--md-green-500);
+    --jp-mirror-editor-atom-color: var(--md-blue-300);
+    --jp-mirror-editor-number-color: var(--md-green-400);
+    --jp-mirror-editor-def-color: var(--md-blue-600);
+    --jp-mirror-editor-variable-color: var(--md-grey-300);
+    --jp-mirror-editor-variable-2-color: var(--md-blue-400);
+    --jp-mirror-editor-variable-3-color: var(--md-green-600);
+    --jp-mirror-editor-punctuation-color: var(--md-blue-400);
+    --jp-mirror-editor-property-color: var(--md-blue-400);
+    --jp-mirror-editor-operator-color: #aa22ff;
+    --jp-mirror-editor-comment-color: #408080;
+    --jp-mirror-editor-string-color: #ff7070;
+    --jp-mirror-editor-string-2-color: var(--md-purple-300);
+    --jp-mirror-editor-meta-color: #aa22ff;
+    --jp-mirror-editor-qualifier-color: #555;
+    --jp-mirror-editor-builtin-color: var(--md-green-600);
+    --jp-mirror-editor-bracket-color: #997;
+    --jp-mirror-editor-tag-color: var(--md-green-700);
+    --jp-mirror-editor-attribute-color: var(--md-blue-700);
+    --jp-mirror-editor-header-color: var(--md-blue-500);
+    --jp-mirror-editor-quote-color: var(--md-green-300);
+    --jp-mirror-editor-link-color: var(--md-blue-700);
+    --jp-mirror-editor-error-color: #f00;
+    --jp-mirror-editor-hr-color: #999;
+
+    /* Vega extension styles */
+
+    --jp-vega-background: var(--md-grey-400);
+
+    /* Sidebar-related styles */
+
+    --jp-sidebar-min-width: 250px;
+
+    /* Search-related styles */
+
+    --jp-search-toggle-off-opacity: 0.6;
+    --jp-search-toggle-hover-opacity: 0.8;
+    --jp-search-toggle-on-opacity: 1;
+    --jp-search-selected-match-background-color: rgb(255, 225, 0);
+    --jp-search-selected-match-color: black;
+    --jp-search-unselected-match-background-color: var(
+        --jp-inverse-layout-color0
+    );
+    --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);
+
+    /* scrollbar related styles. Supports every browser except Edge. */
+
+    /* colors based on JetBrain's Darcula theme */
+
+    --jp-scrollbar-background-color: #3f4244;
+    --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */
+
+    --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */
+
+    /* hacks for setting the thumb shape. These do nothing in Firefox */
+
+    --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */
+    --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */
+
+    /* Icon colors that work well with light or dark backgrounds */
+    --jp-icon-contrast-color0: var(--md-purple-600);
+    --jp-icon-contrast-color1: var(--md-green-600);
+    --jp-icon-contrast-color2: var(--md-pink-600);
+    --jp-icon-contrast-color3: var(--md-blue-600);
+}
+
+:root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: #7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: #ff5722;--md-deep-orange-600: #f4511e;--md-deep-orange-700: #e64a19;--md-deep-orange-800: #d84315;--md-deep-orange-900: #bf360c;--md-deep-orange-A100: #ff9e80;--md-deep-orange-A200: #ff6e40;--md-deep-orange-A400: #ff3d00;--md-deep-orange-A700: #dd2c00;--md-brown-50: #efebe9;--md-brown-100: #d7ccc8;--md-brown-200: #bcaaa4;--md-brown-300: #a1887f;--md-brown-400: #8d6e63;--md-brown-500: #795548;--md-brown-600: #6d4c41;--md-brown-700: #5d4037;--md-brown-800: #4e342e;--md-brown-900: #3e2723;--md-grey-50: #fafafa;--md-grey-100: #f5f5f5;--md-grey-200: #eeeeee;--md-grey-300: #e0e0e0;--md-grey-400: #bdbdbd;--md-grey-500: #9e9e9e;--md-grey-600: #757575;--md-grey-700: #616161;--md-grey-800: #424242;--md-grey-900: #212121;--md-blue-grey-50: #eceff1;--md-blue-grey-100: #cfd8dc;--md-blue-grey-200: #b0bec5;--md-blue-grey-300: #90a4ae;--md-blue-grey-400: #78909c;--md-blue-grey-500: #607d8b;--md-blue-grey-600: #546e7a;--md-blue-grey-700: #455a64;--md-blue-grey-800: #37474f;--md-blue-grey-900: #263238}.jupyter-wrapper{/*!
+
+Copyright 2015-present Palantir Technologies, Inc. All rights reserved.
+Licensed under the Apache License, Version 2.0.
+
+*//*!
+
+Copyright 2017-present Palantir Technologies, Inc. All rights reserved.
+Licensed under the Apache License, Version 2.0.
+
+*/}.jupyter-wrapper [data-jp-theme-scrollbars=true]{scrollbar-color:rgb(var(--jp-scrollbar-thumb-color)) var(--jp-scrollbar-background-color)}.jupyter-wrapper [data-jp-theme-scrollbars=true] .CodeMirror-hscrollbar,.jupyter-wrapper [data-jp-theme-scrollbars=true] .CodeMirror-vscrollbar{scrollbar-color:rgba(var(--jp-scrollbar-thumb-color), 0.5) rgba(0,0,0,0)}.jupyter-wrapper [data-jp-theme-scrollbars=true] ::-webkit-scrollbar,.jupyter-wrapper [data-jp-theme-scrollbars=true] ::-webkit-scrollbar-corner{background:var(--jp-scrollbar-background-color)}.jupyter-wrapper [data-jp-theme-scrollbars=true] ::-webkit-scrollbar-thumb{background:rgb(var(--jp-scrollbar-thumb-color));border:var(--jp-scrollbar-thumb-margin) solid rgba(0,0,0,0);background-clip:content-box;border-radius:var(--jp-scrollbar-thumb-radius)}.jupyter-wrapper [data-jp-theme-scrollbars=true] ::-webkit-scrollbar-track:horizontal{border-left:var(--jp-scrollbar-endpad) solid 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+
+/*# sourceMappingURL=mkdocs-jupyter.css.map*/</style>
+
+
+<!-- Load mathjax -->
+    <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.7/latest.js?config=TeX-AMS_CHTML-full,Safe"> </script>
+    <!-- MathJax configuration -->
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+    }
+    init_mathjax();
+    </script>
+    <!-- End of mathjax configuration --><div class="jupyter-wrapper">
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="earthquake-detection-workflow-using-quakeflow">Earthquake Detection Workflow using QuakeFlow<a class="anchor-link" href="#earthquake-detection-workflow-using-quakeflow">&#182;</a></h1><h2 id="outline">Outline<a class="anchor-link" href="#outline">&#182;</a></h2><p>Here we show an example of the current modules in QuakeFlow</p>
+<ol>
+<li><p>Download data using Obpsy:</p>
+<p><a href="https://docs.obspy.org/packages/obspy.clients.fdsn.html#module-obspy.clients.fdsn">FDSN web service client for ObsPy</a></p>
+<p><a href="https://docs.obspy.org/packages/autogen/obspy.clients.fdsn.mass_downloader.html#module-obspy.clients.fdsn.mass_downloader">Mass Downloader for FDSN Compliant Web Services</a></p>
+</li>
+<li><p>PhaseNet for picking P/S phases</p>
+<p>Find more details in <a href="https://wayneweiqiang.github.io/PhaseNet/">PhaseNet github page</a></p>
+</li>
+<li><p>GaMMA for associating picking and estimate approximate location and magnitude</p>
+<p>Find more details in <a href="https://wayneweiqiang.github.io/GaMMA/">GaMMA github page</a></p>
+</li>
+<li><p>Earthquake location, magnitude estimation, etc.</p>
+</li>
+</ol>
+
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="0-install-miniconda-and-download-packages">0. Install <a href="https://docs.conda.io/en/latest/miniconda.html">miniconda</a> and download packages<a class="anchor-link" href="#0-install-miniconda-and-download-packages">&#182;</a></h2>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+
+<pre><code>git clone https://github.com/wayneweiqiang/PhaseNet.git
+git clone https://github.com/wayneweiqiang/GaMMA.git
+conda env update -f=env.yml -n base</code></pre>
+<p><strong>Second option: install to quakeflow environment, but need to select jupyter notebook kernel to quakeflow</strong></p>
+
+<pre><code>conda env create -f=env.yml -n quakeflow
+python -m ipykernel install --user --name=quakeflow</code></pre>
+
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[19]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-1">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">warnings</span>
+
+<span class="kn">import</span> <span class="nn">kfp</span>
+<span class="kn">import</span> <span class="nn">kfp.components</span> <span class="k">as</span> <span class="nn">comp</span>
+<span class="kn">import</span> <span class="nn">kfp.dsl</span> <span class="k">as</span> <span class="nn">dsl</span>
+<span class="kn">from</span> <span class="nn">kfp.components</span> <span class="kn">import</span> <span class="n">InputPath</span><span class="p">,</span> <span class="n">OutputPath</span>
+
+<span class="n">warnings</span><span class="o">.</span><span class="n">filterwarnings</span><span class="p">(</span><span class="s2">&quot;ignore&quot;</span><span class="p">)</span>
+</pre></div>
+<div id="cell-1" class="clipboard-copy-txt">import warnings
+
+import kfp
+import kfp.components as comp
+import kfp.dsl as dsl
+from kfp.components import InputPath, OutputPath
+
+warnings.filterwarnings("ignore")</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="1-set-configurations">1. Set configurations<a class="anchor-link" href="#1-set-configurations">&#182;</a></h2>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[20]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-2">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
+
+<span class="kn">import</span> <span class="nn">matplotlib</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+
+<span class="n">matplotlib</span><span class="o">.</span><span class="n">use</span><span class="p">(</span><span class="s2">&quot;agg&quot;</span><span class="p">)</span>
+<span class="o">%</span><span class="n">matplotlib</span> <span class="n">inline</span>
+
+<span class="n">region_name</span> <span class="o">=</span> <span class="s2">&quot;Demo&quot;</span>
+<span class="c1"># region_name = &quot;Ridgecrest&quot;</span>
+<span class="c1"># region_name = &quot;SaltonSea&quot;</span>
+<span class="c1"># region_name = &quot;Ridgecrest&quot;</span>
+<span class="c1"># region_name = &quot;SanSimeon&quot;</span>
+<span class="c1"># region_name = &quot;Italy&quot;</span>
+<span class="c1"># region_name = &quot;PNSN&quot;</span>
+<span class="c1"># region_name = &quot;Hawaii2&quot;</span>
+<span class="c1"># region_name = &quot;SierraNegra&quot;</span>
+<span class="c1"># region_name = &quot;PuertoRico&quot;</span>
+<span class="c1"># region_name = &quot;SmithValley&quot;</span>
+<span class="c1"># region_name = &quot;Antilles&quot;</span>
+<span class="c1"># region_name = &quot;LongValley&quot;</span>
+
+<span class="n">dir_name</span> <span class="o">=</span> <span class="n">region_name</span>
+<span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">dir_name</span><span class="p">):</span>
+    <span class="n">os</span><span class="o">.</span><span class="n">mkdir</span><span class="p">(</span><span class="n">dir_name</span><span class="p">)</span>
+<span class="n">root_dir</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">dir_name</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span>
+
+<span class="n">run_local</span> <span class="o">=</span> <span class="kc">True</span>
+</pre></div>
+<div id="cell-2" class="clipboard-copy-txt">import os
+
+import matplotlib
+import matplotlib.pyplot as plt
+
+matplotlib.use("agg")
+%matplotlib inline
+
+region_name = "Demo"
+# region_name = "Ridgecrest"
+# region_name = "SaltonSea"
+# region_name = "Ridgecrest"
+# region_name = "SanSimeon"
+# region_name = "Italy"
+# region_name = "PNSN"
+# region_name = "Hawaii2"
+# region_name = "SierraNegra"
+# region_name = "PuertoRico"
+# region_name = "SmithValley"
+# region_name = "Antilles"
+# region_name = "LongValley"
+
+dir_name = region_name
+if not os.path.exists(dir_name):
+    os.mkdir(dir_name)
+root_dir = lambda x: os.path.join(dir_name, x)
+
+run_local = True</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[21]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-3">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">def</span> <span class="nf">set_config</span><span class="p">(</span>
+    <span class="n">region_name</span><span class="p">,</span>
+    <span class="n">index_json</span><span class="p">:</span> <span class="n">OutputPath</span><span class="p">(</span><span class="s2">&quot;json&quot;</span><span class="p">),</span>
+    <span class="n">config_json</span><span class="p">:</span> <span class="n">OutputPath</span><span class="p">(</span><span class="s2">&quot;json&quot;</span><span class="p">),</span>
+    <span class="n">datetime_json</span><span class="p">:</span> <span class="n">OutputPath</span><span class="p">(</span><span class="s2">&quot;json&quot;</span><span class="p">),</span>
+    <span class="n">num_parallel</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span>
+    <span class="n">bucket_name</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;catalogs&quot;</span><span class="p">,</span>
+    <span class="n">s3_url</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;minio-service:9000&quot;</span><span class="p">,</span>
+    <span class="n">secure</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">:</span>
+
+    <span class="kn">import</span> <span class="nn">datetime</span>
+    <span class="kn">import</span> <span class="nn">json</span>
+    <span class="kn">import</span> <span class="nn">os</span>
+    <span class="kn">import</span> <span class="nn">pickle</span>
+
+    <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+    <span class="kn">import</span> <span class="nn">obspy</span>
+
+    <span class="n">degree2km</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="mi">6371</span> <span class="o">/</span> <span class="mi">180</span>
+
+    <span class="k">if</span> <span class="n">region_name</span> <span class="o">==</span> <span class="s2">&quot;Demo&quot;</span><span class="p">:</span>
+        <span class="n">center</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mf">117.504</span><span class="p">,</span> <span class="mf">35.705</span><span class="p">)</span>
+        <span class="n">horizontal_degree</span> <span class="o">=</span> <span class="mf">1.0</span>
+        <span class="n">vertical_degree</span> <span class="o">=</span> <span class="mf">1.0</span>
+        <span class="n">starttime</span> <span class="o">=</span> <span class="n">obspy</span><span class="o">.</span><span class="n">UTCDateTime</span><span class="p">(</span><span class="s2">&quot;2019-07-04T17&quot;</span><span class="p">)</span>
+        <span class="n">endtime</span> <span class="o">=</span> <span class="n">obspy</span><span class="o">.</span><span class="n">UTCDateTime</span><span class="p">(</span><span class="s2">&quot;2019-07-04T19&quot;</span><span class="p">)</span>
+        <span class="n">client</span> <span class="o">=</span> <span class="s2">&quot;SCEDC&quot;</span>
+        <span class="n">network_list</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;CI&quot;</span><span class="p">]</span>
+        <span class="n">channel_list</span> <span class="o">=</span> <span class="s2">&quot;HH*,BH*,EH*,HN*&quot;</span>
+
+    <span class="k">if</span> <span class="n">region_name</span> <span class="o">==</span> <span class="s2">&quot;Ridgecrest&quot;</span><span class="p">:</span>
+        <span class="n">center</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mf">117.504</span><span class="p">,</span> <span class="mf">35.705</span><span class="p">)</span>
+        <span class="n">horizontal_degree</span> <span class="o">=</span> <span class="mf">1.0</span>
+        <span class="n">vertical_degree</span> <span class="o">=</span> <span class="mf">1.0</span>
+        <span class="n">starttime</span> <span class="o">=</span> <span class="n">obspy</span><span class="o">.</span><span class="n">UTCDateTime</span><span class="p">(</span><span class="s2">&quot;2019-07-04T00&quot;</span><span class="p">)</span>
+        <span class="n">endtime</span> <span class="o">=</span> <span class="n">obspy</span><span class="o">.</span><span class="n">UTCDateTime</span><span class="p">(</span><span class="s2">&quot;2019-07-10T00&quot;</span><span class="p">)</span>
+        <span class="n">client</span> <span class="o">=</span> <span class="s2">&quot;SCEDC&quot;</span>
+        <span class="n">network_list</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;CI&quot;</span><span class="p">]</span>
+        <span class="n">channel_list</span> <span class="o">=</span> <span class="s2">&quot;HH*,BH*,EH*,HN*&quot;</span>
+
+    <span class="k">if</span> <span class="n">region_name</span> <span class="o">==</span> <span class="s2">&quot;Hawaii&quot;</span><span class="p">:</span>
+        <span class="n">center</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mf">155.32</span><span class="p">,</span> <span class="mf">19.39</span><span class="p">)</span>
+        <span class="n">horizontal_degree</span> <span class="o">=</span> <span class="mf">2.0</span>
+        <span class="n">vertical_degree</span> <span class="o">=</span> <span class="mf">2.0</span>
+        <span class="n">starttime</span> <span class="o">=</span> <span class="n">obspy</span><span class="o">.</span><span class="n">UTCDateTime</span><span class="p">(</span><span class="s2">&quot;2018-01-01T00&quot;</span><span class="p">)</span>
+        <span class="n">endtime</span> <span class="o">=</span> <span class="n">obspy</span><span class="o">.</span><span class="n">UTCDateTime</span><span class="p">(</span><span class="s2">&quot;2022-08-12T00&quot;</span><span class="p">)</span>
+        <span class="n">client</span> <span class="o">=</span> <span class="s2">&quot;IRIS&quot;</span>
+        <span class="n">network_list</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;HV&quot;</span><span class="p">,</span> <span class="s2">&quot;PT&quot;</span><span class="p">]</span>
+        <span class="n">channel_list</span> <span class="o">=</span> <span class="s2">&quot;HH*,BH*,EH*,HN*&quot;</span>
+
+    <span class="k">if</span> <span class="n">region_name</span> <span class="o">==</span> <span class="s2">&quot;Hawaii2&quot;</span><span class="p">:</span>
+        <span class="n">center</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mf">155.32</span><span class="p">,</span> <span class="mf">19.39</span><span class="p">)</span>
+        <span class="n">horizontal_degree</span> <span class="o">=</span> <span class="mf">2.0</span>
+        <span class="n">vertical_degree</span> <span class="o">=</span> <span class="mf">2.0</span>
+        <span class="n">starttime</span> <span class="o">=</span> <span class="n">obspy</span><span class="o">.</span><span class="n">UTCDateTime</span><span class="p">(</span><span class="s2">&quot;2022-08-01T00&quot;</span><span class="p">)</span>
+        <span class="n">endtime</span> <span class="o">=</span> <span class="n">obspy</span><span class="o">.</span><span class="n">UTCDateTime</span><span class="p">(</span><span class="s2">&quot;2022-08-16T00&quot;</span><span class="p">)</span>
+        <span class="n">client</span> <span class="o">=</span> <span class="s2">&quot;IRIS&quot;</span>
+        <span class="n">network_list</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;HV&quot;</span><span class="p">,</span> <span class="s2">&quot;PT&quot;</span><span class="p">]</span>
+        <span class="n">channel_list</span> <span class="o">=</span> <span class="s2">&quot;HH*,BH*,EH*,HN*&quot;</span>
+
+    <span class="k">if</span> <span class="n">region_name</span> <span class="o">==</span> <span class="s2">&quot;PuertoRico&quot;</span><span class="p">:</span>
+        <span class="n">center</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mf">66.5</span><span class="p">,</span> <span class="mi">18</span><span class="p">)</span>
+        <span class="n">horizontal_degree</span> <span class="o">=</span> <span class="mf">3.0</span>
+        <span class="n">vertical_degree</span> <span class="o">=</span> <span class="mf">2.0</span>
+        <span class="n">starttime</span> <span class="o">=</span> <span class="n">obspy</span><span class="o">.</span><span class="n">UTCDateTime</span><span class="p">(</span><span class="s2">&quot;2018-05-01T00&quot;</span><span class="p">)</span>
+        <span class="n">endtime</span> <span class="o">=</span> <span class="n">obspy</span><span class="o">.</span><span class="n">UTCDateTime</span><span class="p">(</span><span class="s2">&quot;2021-11-01T00&quot;</span><span class="p">)</span>
+        <span class="n">client</span> <span class="o">=</span> <span class="s2">&quot;IRIS&quot;</span>
+        <span class="n">network_list</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;*&quot;</span><span class="p">]</span>
+        <span class="n">channel_list</span> <span class="o">=</span> <span class="s2">&quot;HH*,BH*,HN*&quot;</span>
+
+    <span class="k">if</span> <span class="n">region_name</span> <span class="o">==</span> <span class="s2">&quot;SaltonSea&quot;</span><span class="p">:</span>
+        <span class="n">center</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mf">115.53</span><span class="p">,</span> <span class="mf">32.98</span><span class="p">)</span>
+        <span class="n">horizontal_degree</span> <span class="o">=</span> <span class="mf">1.0</span>
+        <span class="n">vertical_degree</span> <span class="o">=</span> <span class="mf">1.0</span>
+        <span class="n">starttime</span> <span class="o">=</span> <span class="n">obspy</span><span class="o">.</span><span class="n">UTCDateTime</span><span class="p">(</span><span class="s2">&quot;2020-10-01T00&quot;</span><span class="p">)</span>
+        <span class="n">endtime</span> <span class="o">=</span> <span class="n">obspy</span><span class="o">.</span><span class="n">UTCDateTime</span><span class="p">(</span><span class="s2">&quot;2020-10-01T02&quot;</span><span class="p">)</span>
+        <span class="n">client</span> <span class="o">=</span> <span class="s2">&quot;SCEDC&quot;</span>
+        <span class="n">network_list</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;CI&quot;</span><span class="p">]</span>
+        <span class="n">channel_list</span> <span class="o">=</span> <span class="s2">&quot;HH*,BH*,EH*,HN*&quot;</span>
+
+    <span class="k">if</span> <span class="n">region_name</span> <span class="o">==</span> <span class="s2">&quot;2003SanSimeon&quot;</span><span class="p">:</span>
+        <span class="n">center</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mf">121.101</span><span class="p">,</span> <span class="mf">35.701</span><span class="p">)</span>
+        <span class="n">horizontal_degree</span> <span class="o">=</span> <span class="mf">1.0</span>
+        <span class="n">vertical_degree</span> <span class="o">=</span> <span class="mf">1.0</span>
+        <span class="n">starttime</span> <span class="o">=</span> <span class="n">obspy</span><span class="o">.</span><span class="n">UTCDateTime</span><span class="p">(</span><span class="s2">&quot;2003-12-22T00&quot;</span><span class="p">)</span>
+        <span class="n">endtime</span> <span class="o">=</span> <span class="n">obspy</span><span class="o">.</span><span class="n">UTCDateTime</span><span class="p">(</span><span class="s2">&quot;2003-12-24T00&quot;</span><span class="p">)</span>
+        <span class="n">client</span> <span class="o">=</span> <span class="s2">&quot;NCEDC&quot;</span>
+        <span class="n">network_list</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;*&quot;</span><span class="p">]</span>
+        <span class="n">channel_list</span> <span class="o">=</span> <span class="s2">&quot;HH*,BH*,EH*,HN*&quot;</span>
+
+    <span class="k">if</span> <span class="n">region_name</span> <span class="o">==</span> <span class="s2">&quot;Italy&quot;</span><span class="p">:</span>
+        <span class="n">center</span> <span class="o">=</span> <span class="p">(</span><span class="mf">13.188</span><span class="p">,</span> <span class="mf">42.723</span><span class="p">)</span>
+        <span class="n">horizontal_degree</span> <span class="o">=</span> <span class="mf">1.0</span>
+        <span class="n">vertical_degree</span> <span class="o">=</span> <span class="mf">1.0</span>
+        <span class="n">starttime</span> <span class="o">=</span> <span class="n">obspy</span><span class="o">.</span><span class="n">UTCDateTime</span><span class="p">(</span><span class="s2">&quot;2016-08-24T00&quot;</span><span class="p">)</span>
+        <span class="n">endtime</span> <span class="o">=</span> <span class="n">obspy</span><span class="o">.</span><span class="n">UTCDateTime</span><span class="p">(</span><span class="s2">&quot;2016-08-26T00&quot;</span><span class="p">)</span>
+        <span class="n">client</span> <span class="o">=</span> <span class="s2">&quot;INGV&quot;</span>
+        <span class="n">network_list</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;*&quot;</span><span class="p">]</span>
+        <span class="n">channel_list</span> <span class="o">=</span> <span class="s2">&quot;HH*,BH*,EH*,HN*&quot;</span>
+
+    <span class="k">if</span> <span class="n">region_name</span> <span class="o">==</span> <span class="s2">&quot;SmithValley&quot;</span><span class="p">:</span>
+        <span class="n">center</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mf">119.5</span><span class="p">,</span> <span class="mf">38.51</span><span class="p">)</span>
+        <span class="n">horizontal_degree</span> <span class="o">=</span> <span class="mf">1.0</span>
+        <span class="n">vertical_degree</span> <span class="o">=</span> <span class="mf">1.0</span>
+        <span class="n">starttime</span> <span class="o">=</span> <span class="n">obspy</span><span class="o">.</span><span class="n">UTCDateTime</span><span class="p">(</span><span class="s2">&quot;2021-07-08T00:00&quot;</span><span class="p">)</span>
+        <span class="n">endtime</span> <span class="o">=</span> <span class="n">obspy</span><span class="o">.</span><span class="n">UTCDateTime</span><span class="p">(</span><span class="s2">&quot;2021-07-16T00:00&quot;</span><span class="p">)</span>
+        <span class="n">client</span> <span class="o">=</span> <span class="s2">&quot;NCEDC&quot;</span>
+        <span class="n">network_list</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;*&quot;</span><span class="p">]</span>
+        <span class="n">channel_list</span> <span class="o">=</span> <span class="s2">&quot;HH*,BH*,EH*,HN*&quot;</span>
+
+    <span class="k">if</span> <span class="n">region_name</span> <span class="o">==</span> <span class="s2">&quot;Antilles&quot;</span><span class="p">:</span>
+        <span class="n">center</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mf">61.14867</span><span class="p">,</span> <span class="mf">14.79683</span><span class="p">)</span>
+        <span class="n">horizontal_degree</span> <span class="o">=</span> <span class="mf">0.2</span>
+        <span class="n">vertical_degree</span> <span class="o">=</span> <span class="mf">0.2</span>
+        <span class="n">starttime</span> <span class="o">=</span> <span class="n">obspy</span><span class="o">.</span><span class="n">UTCDateTime</span><span class="p">(</span><span class="s2">&quot;2021-04-10T00&quot;</span><span class="p">)</span>
+        <span class="n">endtime</span> <span class="o">=</span> <span class="n">obspy</span><span class="o">.</span><span class="n">UTCDateTime</span><span class="p">(</span><span class="s2">&quot;2021-04-15T00&quot;</span><span class="p">)</span>
+        <span class="n">client</span> <span class="o">=</span> <span class="s2">&quot;RESIF&quot;</span>
+        <span class="n">network_list</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;*&quot;</span><span class="p">]</span>
+        <span class="n">channel_list</span> <span class="o">=</span> <span class="s2">&quot;HH*,BH*,EH*,HN*&quot;</span>
+
+    <span class="k">if</span> <span class="n">region_name</span> <span class="o">==</span> <span class="s2">&quot;LongValley&quot;</span><span class="p">:</span>
+        <span class="c1"># center = (-118.8, 37.6+0.3)</span>
+        <span class="c1"># horizontal_degree = 3.0</span>
+        <span class="c1"># vertical_degree = 3.0</span>
+        <span class="n">center</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mf">118.8</span> <span class="o">-</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">37.6</span><span class="p">)</span>
+        <span class="n">horizontal_degree</span> <span class="o">=</span> <span class="mf">1.5</span>
+        <span class="n">vertical_degree</span> <span class="o">=</span> <span class="mf">1.5</span>
+        <span class="n">starttime</span> <span class="o">=</span> <span class="n">obspy</span><span class="o">.</span><span class="n">UTCDateTime</span><span class="p">(</span><span class="s2">&quot;2020-01-01T00&quot;</span><span class="p">)</span>
+        <span class="n">endtime</span> <span class="o">=</span> <span class="n">obspy</span><span class="o">.</span><span class="n">UTCDateTime</span><span class="p">(</span><span class="s2">&quot;2022-08-11T00&quot;</span><span class="p">)</span>
+        <span class="n">client</span> <span class="o">=</span> <span class="s2">&quot;NCEDC&quot;</span>
+        <span class="n">network_list</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;*&quot;</span><span class="p">]</span>
+        <span class="n">channel_list</span> <span class="o">=</span> <span class="s2">&quot;HH*,EH*&quot;</span>
+
+    <span class="k">if</span> <span class="n">region_name</span> <span class="o">==</span> <span class="s2">&quot;SierraNegra&quot;</span><span class="p">:</span>
+        <span class="n">center</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mf">91.13</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.81</span><span class="p">)</span>
+        <span class="n">horizontal_degree</span> <span class="o">=</span> <span class="mf">1.5</span>
+        <span class="n">vertical_degree</span> <span class="o">=</span> <span class="mf">1.5</span>
+        <span class="n">starttime</span> <span class="o">=</span> <span class="n">obspy</span><span class="o">.</span><span class="n">UTCDateTime</span><span class="p">(</span><span class="s2">&quot;2009-07-22T00&quot;</span><span class="p">)</span>
+        <span class="n">endtime</span> <span class="o">=</span> <span class="n">obspy</span><span class="o">.</span><span class="n">UTCDateTime</span><span class="p">(</span><span class="s2">&quot;2011-06-20T00&quot;</span><span class="p">)</span>
+        <span class="n">client</span> <span class="o">=</span> <span class="s2">&quot;IRIS&quot;</span>
+        <span class="n">network_list</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;*&quot;</span><span class="p">]</span>
+        <span class="n">channel_list</span> <span class="o">=</span> <span class="s2">&quot;BH*,HH*,EH*&quot;</span>
+
+    <span class="c1">####### save config ########</span>
+    <span class="n">config</span> <span class="o">=</span> <span class="p">{}</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;region&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">region_name</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;center&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">center</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;xlim_degree&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span>
+        <span class="n">center</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">horizontal_degree</span> <span class="o">/</span> <span class="mi">2</span><span class="p">,</span>
+        <span class="n">center</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">horizontal_degree</span> <span class="o">/</span> <span class="mi">2</span><span class="p">,</span>
+    <span class="p">]</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;ylim_degree&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span>
+        <span class="n">center</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">vertical_degree</span> <span class="o">/</span> <span class="mi">2</span><span class="p">,</span>
+        <span class="n">center</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">vertical_degree</span> <span class="o">/</span> <span class="mi">2</span><span class="p">,</span>
+    <span class="p">]</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;min_longitude&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">center</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">horizontal_degree</span> <span class="o">/</span> <span class="mi">2</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;max_longitude&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">center</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">horizontal_degree</span> <span class="o">/</span> <span class="mi">2</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;min_latitude&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">center</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">vertical_degree</span> <span class="o">/</span> <span class="mi">2</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;max_latitude&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">center</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">vertical_degree</span> <span class="o">/</span> <span class="mi">2</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;degree2km&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">degree2km</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;starttime&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">starttime</span><span class="o">.</span><span class="n">datetime</span><span class="o">.</span><span class="n">isoformat</span><span class="p">(</span><span class="n">timespec</span><span class="o">=</span><span class="s2">&quot;milliseconds&quot;</span><span class="p">)</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;endtime&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">endtime</span><span class="o">.</span><span class="n">datetime</span><span class="o">.</span><span class="n">isoformat</span><span class="p">(</span><span class="n">timespec</span><span class="o">=</span><span class="s2">&quot;milliseconds&quot;</span><span class="p">)</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;networks&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">network_list</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;channels&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">channel_list</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;client&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">client</span>
+
+    <span class="c1">## PhaseNet</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;phasenet&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">{}</span>
+    <span class="c1">## GaMMA</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;gamma&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">{}</span>
+    <span class="c1">## HypoDD</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;hypodd&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;MAXEVENT&quot;</span><span class="p">:</span> <span class="mf">1e4</span><span class="p">}</span>
+
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">config_json</span><span class="p">,</span> <span class="s2">&quot;w&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">json</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="n">config</span><span class="p">,</span> <span class="n">fp</span><span class="p">,</span> <span class="n">indent</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+
+    <span class="nb">print</span><span class="p">(</span><span class="n">json</span><span class="o">.</span><span class="n">dumps</span><span class="p">(</span><span class="n">config</span><span class="p">,</span> <span class="n">indent</span><span class="o">=</span><span class="mi">4</span><span class="p">))</span>
+
+    <span class="c1">####### set paraell for cloud ########</span>
+    <span class="c1">## split data by hours</span>
+    <span class="c1"># one_day = datetime.timedelta(days=1)</span>
+    <span class="n">one_hour</span> <span class="o">=</span> <span class="n">datetime</span><span class="o">.</span><span class="n">timedelta</span><span class="p">(</span><span class="n">hours</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">starttimes</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">tmp_start</span> <span class="o">=</span> <span class="n">starttime</span>
+    <span class="k">while</span> <span class="n">tmp_start</span> <span class="o">&lt;</span> <span class="n">endtime</span><span class="p">:</span>
+        <span class="n">starttimes</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp_start</span><span class="o">.</span><span class="n">datetime</span><span class="o">.</span><span class="n">isoformat</span><span class="p">(</span><span class="n">timespec</span><span class="o">=</span><span class="s2">&quot;milliseconds&quot;</span><span class="p">))</span>
+        <span class="n">tmp_start</span> <span class="o">+=</span> <span class="n">one_hour</span>
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">datetime_json</span><span class="p">,</span> <span class="s2">&quot;w&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">json</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span>
+            <span class="p">{</span><span class="s2">&quot;starttimes&quot;</span><span class="p">:</span> <span class="n">starttimes</span><span class="p">,</span> <span class="s2">&quot;interval&quot;</span><span class="p">:</span> <span class="n">one_hour</span><span class="o">.</span><span class="n">total_seconds</span><span class="p">()},</span>
+            <span class="n">fp</span><span class="p">,</span>
+            <span class="n">indent</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
+        <span class="p">)</span>
+
+    <span class="c1">## split stattimes into N parallel process</span>
+    <span class="k">if</span> <span class="n">num_parallel</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="n">num_parallel</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="mi">60</span><span class="p">,</span> <span class="nb">int</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">starttimes</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">//</span> <span class="mi">6</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
+        <span class="c1"># num_parallel = min(30, int((len(starttimes)-1)//6+1))</span>
+        <span class="c1"># num_parallel = min(24, len(starttimes))</span>
+    <span class="n">idx</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">array_split</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">starttimes</span><span class="p">)),</span> <span class="n">num_parallel</span><span class="p">)]</span>
+
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">index_json</span><span class="p">,</span> <span class="s2">&quot;w&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">json</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="n">idx</span><span class="p">,</span> <span class="n">fp</span><span class="p">,</span> <span class="n">indent</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+
+    <span class="c1">## upload to s3 bucket</span>
+    <span class="k">try</span><span class="p">:</span>
+        <span class="kn">from</span> <span class="nn">minio</span> <span class="kn">import</span> <span class="n">Minio</span>
+
+        <span class="n">minioClient</span> <span class="o">=</span> <span class="n">Minio</span><span class="p">(</span><span class="n">s3_url</span><span class="p">,</span> <span class="n">access_key</span><span class="o">=</span><span class="s2">&quot;minio&quot;</span><span class="p">,</span> <span class="n">secret_key</span><span class="o">=</span><span class="s2">&quot;minio123&quot;</span><span class="p">,</span> <span class="n">secure</span><span class="o">=</span><span class="n">secure</span><span class="p">)</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">minioClient</span><span class="o">.</span><span class="n">bucket_exists</span><span class="p">(</span><span class="n">bucket_name</span><span class="p">):</span>
+            <span class="n">minioClient</span><span class="o">.</span><span class="n">make_bucket</span><span class="p">(</span><span class="n">bucket_name</span><span class="p">)</span>
+
+        <span class="n">minioClient</span><span class="o">.</span><span class="n">fput_object</span><span class="p">(</span>
+            <span class="n">bucket_name</span><span class="p">,</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;region&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">/config.json&quot;</span><span class="p">,</span>
+            <span class="n">config_json</span><span class="p">,</span>
+        <span class="p">)</span>
+
+        <span class="n">minioClient</span><span class="o">.</span><span class="n">fput_object</span><span class="p">(</span>
+            <span class="n">bucket_name</span><span class="p">,</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;region&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">/datetime.json&quot;</span><span class="p">,</span>
+            <span class="n">datetime_json</span><span class="p">,</span>
+        <span class="p">)</span>
+
+        <span class="n">minioClient</span><span class="o">.</span><span class="n">fput_object</span><span class="p">(</span>
+            <span class="n">bucket_name</span><span class="p">,</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;region&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">/index.json&quot;</span><span class="p">,</span>
+            <span class="n">index_json</span><span class="p">,</span>
+        <span class="p">)</span>
+
+    <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">err</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;ERROR: can not access minio service! </span><span class="se">\n</span><span class="si">{</span><span class="n">err</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+        <span class="k">pass</span>
+
+    <span class="k">return</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">num_parallel</span><span class="p">))</span>
+</pre></div>
+<div id="cell-3" class="clipboard-copy-txt">def set_config(
+    region_name,
+    index_json: OutputPath("json"),
+    config_json: OutputPath("json"),
+    datetime_json: OutputPath("json"),
+    num_parallel: int = 1,
+    bucket_name: str = "catalogs",
+    s3_url: str = "minio-service:9000",
+    secure: bool = True,
+) -> list:
+
+    import datetime
+    import json
+    import os
+    import pickle
+
+    import numpy as np
+    import obspy
+
+    degree2km = np.pi * 6371 / 180
+
+    if region_name == "Demo":
+        center = (-117.504, 35.705)
+        horizontal_degree = 1.0
+        vertical_degree = 1.0
+        starttime = obspy.UTCDateTime("2019-07-04T17")
+        endtime = obspy.UTCDateTime("2019-07-04T19")
+        client = "SCEDC"
+        network_list = ["CI"]
+        channel_list = "HH*,BH*,EH*,HN*"
+
+    if region_name == "Ridgecrest":
+        center = (-117.504, 35.705)
+        horizontal_degree = 1.0
+        vertical_degree = 1.0
+        starttime = obspy.UTCDateTime("2019-07-04T00")
+        endtime = obspy.UTCDateTime("2019-07-10T00")
+        client = "SCEDC"
+        network_list = ["CI"]
+        channel_list = "HH*,BH*,EH*,HN*"
+
+    if region_name == "Hawaii":
+        center = (-155.32, 19.39)
+        horizontal_degree = 2.0
+        vertical_degree = 2.0
+        starttime = obspy.UTCDateTime("2018-01-01T00")
+        endtime = obspy.UTCDateTime("2022-08-12T00")
+        client = "IRIS"
+        network_list = ["HV", "PT"]
+        channel_list = "HH*,BH*,EH*,HN*"
+
+    if region_name == "Hawaii2":
+        center = (-155.32, 19.39)
+        horizontal_degree = 2.0
+        vertical_degree = 2.0
+        starttime = obspy.UTCDateTime("2022-08-01T00")
+        endtime = obspy.UTCDateTime("2022-08-16T00")
+        client = "IRIS"
+        network_list = ["HV", "PT"]
+        channel_list = "HH*,BH*,EH*,HN*"
+
+    if region_name == "PuertoRico":
+        center = (-66.5, 18)
+        horizontal_degree = 3.0
+        vertical_degree = 2.0
+        starttime = obspy.UTCDateTime("2018-05-01T00")
+        endtime = obspy.UTCDateTime("2021-11-01T00")
+        client = "IRIS"
+        network_list = ["*"]
+        channel_list = "HH*,BH*,HN*"
+
+    if region_name == "SaltonSea":
+        center = (-115.53, 32.98)
+        horizontal_degree = 1.0
+        vertical_degree = 1.0
+        starttime = obspy.UTCDateTime("2020-10-01T00")
+        endtime = obspy.UTCDateTime("2020-10-01T02")
+        client = "SCEDC"
+        network_list = ["CI"]
+        channel_list = "HH*,BH*,EH*,HN*"
+
+    if region_name == "2003SanSimeon":
+        center = (-121.101, 35.701)
+        horizontal_degree = 1.0
+        vertical_degree = 1.0
+        starttime = obspy.UTCDateTime("2003-12-22T00")
+        endtime = obspy.UTCDateTime("2003-12-24T00")
+        client = "NCEDC"
+        network_list = ["*"]
+        channel_list = "HH*,BH*,EH*,HN*"
+
+    if region_name == "Italy":
+        center = (13.188, 42.723)
+        horizontal_degree = 1.0
+        vertical_degree = 1.0
+        starttime = obspy.UTCDateTime("2016-08-24T00")
+        endtime = obspy.UTCDateTime("2016-08-26T00")
+        client = "INGV"
+        network_list = ["*"]
+        channel_list = "HH*,BH*,EH*,HN*"
+
+    if region_name == "SmithValley":
+        center = (-119.5, 38.51)
+        horizontal_degree = 1.0
+        vertical_degree = 1.0
+        starttime = obspy.UTCDateTime("2021-07-08T00:00")
+        endtime = obspy.UTCDateTime("2021-07-16T00:00")
+        client = "NCEDC"
+        network_list = ["*"]
+        channel_list = "HH*,BH*,EH*,HN*"
+
+    if region_name == "Antilles":
+        center = (-61.14867, 14.79683)
+        horizontal_degree = 0.2
+        vertical_degree = 0.2
+        starttime = obspy.UTCDateTime("2021-04-10T00")
+        endtime = obspy.UTCDateTime("2021-04-15T00")
+        client = "RESIF"
+        network_list = ["*"]
+        channel_list = "HH*,BH*,EH*,HN*"
+
+    if region_name == "LongValley":
+        # center = (-118.8, 37.6+0.3)
+        # horizontal_degree = 3.0
+        # vertical_degree = 3.0
+        center = (-118.8 - 0.1, 37.6)
+        horizontal_degree = 1.5
+        vertical_degree = 1.5
+        starttime = obspy.UTCDateTime("2020-01-01T00")
+        endtime = obspy.UTCDateTime("2022-08-11T00")
+        client = "NCEDC"
+        network_list = ["*"]
+        channel_list = "HH*,EH*"
+
+    if region_name == "SierraNegra":
+        center = (-91.13, -0.81)
+        horizontal_degree = 1.5
+        vertical_degree = 1.5
+        starttime = obspy.UTCDateTime("2009-07-22T00")
+        endtime = obspy.UTCDateTime("2011-06-20T00")
+        client = "IRIS"
+        network_list = ["*"]
+        channel_list = "BH*,HH*,EH*"
+
+    ####### save config ########
+    config = {}
+    config["region"] = region_name
+    config["center"] = center
+    config["xlim_degree"] = [
+        center[0] - horizontal_degree / 2,
+        center[0] + horizontal_degree / 2,
+    ]
+    config["ylim_degree"] = [
+        center[1] - vertical_degree / 2,
+        center[1] + vertical_degree / 2,
+    ]
+    config["min_longitude"] = center[0] - horizontal_degree / 2
+    config["max_longitude"] = center[0] + horizontal_degree / 2
+    config["min_latitude"] = center[1] - vertical_degree / 2
+    config["max_latitude"] = center[1] + vertical_degree / 2
+    config["degree2km"] = degree2km
+    config["starttime"] = starttime.datetime.isoformat(timespec="milliseconds")
+    config["endtime"] = endtime.datetime.isoformat(timespec="milliseconds")
+    config["networks"] = network_list
+    config["channels"] = channel_list
+    config["client"] = client
+
+    ## PhaseNet
+    config["phasenet"] = {}
+    ## GaMMA
+    config["gamma"] = {}
+    ## HypoDD
+    config["hypodd"] = {"MAXEVENT": 1e4}
+
+    with open(config_json, "w") as fp:
+        json.dump(config, fp, indent=2)
+
+    print(json.dumps(config, indent=4))
+
+    ####### set paraell for cloud ########
+    ## split data by hours
+    # one_day = datetime.timedelta(days=1)
+    one_hour = datetime.timedelta(hours=1)
+    starttimes = []
+    tmp_start = starttime
+    while tmp_start < endtime:
+        starttimes.append(tmp_start.datetime.isoformat(timespec="milliseconds"))
+        tmp_start += one_hour
+    with open(datetime_json, "w") as fp:
+        json.dump(
+            {"starttimes": starttimes, "interval": one_hour.total_seconds()},
+            fp,
+            indent=2,
+        )
+
+    ## split stattimes into N parallel process
+    if num_parallel == 0:
+        num_parallel = min(60, int((len(starttimes) - 1) // 6 + 1))
+        # num_parallel = min(30, int((len(starttimes)-1)//6+1))
+        # num_parallel = min(24, len(starttimes))
+    idx = [x.tolist() for x in np.array_split(np.arange(len(starttimes)), num_parallel)]
+
+    with open(index_json, "w") as fp:
+        json.dump(idx, fp, indent=2)
+
+    ## upload to s3 bucket
+    try:
+        from minio import Minio
+
+        minioClient = Minio(s3_url, access_key="minio", secret_key="minio123", secure=secure)
+        if not minioClient.bucket_exists(bucket_name):
+            minioClient.make_bucket(bucket_name)
+
+        minioClient.fput_object(
+            bucket_name,
+            f"{config['region']}/config.json",
+            config_json,
+        )
+
+        minioClient.fput_object(
+            bucket_name,
+            f"{config['region']}/datetime.json",
+            datetime_json,
+        )
+
+        minioClient.fput_object(
+            bucket_name,
+            f"{config['region']}/index.json",
+            index_json,
+        )
+
+    except Exception as err:
+        print(f"ERROR: can not access minio service! \n{err}")
+        pass
+
+    return list(range(num_parallel))</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[22]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-4">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">if</span> <span class="n">run_local</span><span class="p">:</span>
+    <span class="n">idx</span> <span class="o">=</span> <span class="n">set_config</span><span class="p">(</span>
+        <span class="n">region_name</span><span class="p">,</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;index.json&quot;</span><span class="p">),</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;config.json&quot;</span><span class="p">),</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;datetimes.json&quot;</span><span class="p">),</span>
+        <span class="n">num_parallel</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+    <span class="p">)</span>
+</pre></div>
+<div id="cell-4" class="clipboard-copy-txt">if run_local:
+    idx = set_config(
+        region_name,
+        root_dir("index.json"),
+        root_dir("config.json"),
+        root_dir("datetimes.json"),
+        num_parallel=1,
+    )</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
+<pre>{
+    &#34;region&#34;: &#34;Demo&#34;,
+    &#34;center&#34;: [
+        -117.504,
+        35.705
+    ],
+    &#34;xlim_degree&#34;: [
+        -118.004,
+        -117.004
+    ],
+    &#34;ylim_degree&#34;: [
+        35.205,
+        36.205
+    ],
+    &#34;min_longitude&#34;: -118.004,
+    &#34;max_longitude&#34;: -117.004,
+    &#34;min_latitude&#34;: 35.205,
+    &#34;max_latitude&#34;: 36.205,
+    &#34;degree2km&#34;: 111.19492664455873,
+    &#34;starttime&#34;: &#34;2019-07-04T17:00:00.000&#34;,
+    &#34;endtime&#34;: &#34;2019-07-04T19:00:00.000&#34;,
+    &#34;networks&#34;: [
+        &#34;CI&#34;
+    ],
+    &#34;channels&#34;: &#34;HH*,BH*,EH*,HN*&#34;,
+    &#34;client&#34;: &#34;SCEDC&#34;,
+    &#34;phasenet&#34;: {},
+    &#34;gamma&#34;: {},
+    &#34;hypodd&#34;: {
+        &#34;MAXEVENT&#34;: 10000.0
+    }
+}
+ERROR: can not access minio service! 
+No module named &#39;minio&#39;
+</pre>
+</div>
+</div>
+
+</div>
+
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[23]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-5">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">config_op</span> <span class="o">=</span> <span class="n">comp</span><span class="o">.</span><span class="n">func_to_container_op</span><span class="p">(</span>
+    <span class="n">set_config</span><span class="p">,</span>
+    <span class="n">base_image</span><span class="o">=</span><span class="s2">&quot;python:3.8&quot;</span><span class="p">,</span>
+    <span class="n">packages_to_install</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;numpy&quot;</span><span class="p">,</span> <span class="s2">&quot;obspy&quot;</span><span class="p">,</span> <span class="s2">&quot;minio&quot;</span><span class="p">],</span>
+<span class="p">)</span>
+</pre></div>
+<div id="cell-5" class="clipboard-copy-txt">config_op = comp.func_to_container_op(
+    set_config,
+    base_image="python:3.8",
+    packages_to_install=["numpy", "obspy", "minio"],
+)</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="2-download-events-of-the-standard-catalog">2. Download events of the standard catalog<a class="anchor-link" href="#2-download-events-of-the-standard-catalog">&#182;</a></h2><p>This catalog is not used by QuakeFolow. It is only used for comparing detection results.</p>
+
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[24]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-6">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">def</span> <span class="nf">download_events</span><span class="p">(</span>
+    <span class="n">config_json</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="s2">&quot;json&quot;</span><span class="p">),</span> 
+    <span class="n">standard_catalog</span><span class="p">:</span> <span class="n">OutputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span>
+    <span class="n">bucket_name</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;catalogs&quot;</span><span class="p">,</span>
+    <span class="n">s3_url</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;minio-service:9000&quot;</span><span class="p">,</span>
+    <span class="n">secure</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+<span class="p">):</span>
+
+    <span class="kn">import</span> <span class="nn">json</span>
+    <span class="kn">import</span> <span class="nn">os</span>
+    <span class="kn">import</span> <span class="nn">pickle</span>
+    <span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">defaultdict</span>
+
+    <span class="kn">import</span> <span class="nn">matplotlib</span>
+    <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+    <span class="kn">import</span> <span class="nn">obspy</span>
+    <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+    <span class="kn">from</span> <span class="nn">obspy.clients.fdsn</span> <span class="kn">import</span> <span class="n">Client</span>
+
+    <span class="c1"># matplotlib.use(&quot;agg&quot;)</span>
+    <span class="c1"># %matplotlib inline</span>
+
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">config_json</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">config</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">fp</span><span class="p">)</span>
+
+    <span class="c1">####### IRIS catalog ########</span>
+    <span class="k">try</span><span class="p">:</span>
+        <span class="n">events</span> <span class="o">=</span> <span class="n">Client</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;client&quot;</span><span class="p">])</span><span class="o">.</span><span class="n">get_events</span><span class="p">(</span>
+            <span class="n">starttime</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;starttime&quot;</span><span class="p">],</span>
+            <span class="n">endtime</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;endtime&quot;</span><span class="p">],</span>
+            <span class="n">minlongitude</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;xlim_degree&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span>
+            <span class="n">maxlongitude</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;xlim_degree&quot;</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span>
+            <span class="n">minlatitude</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;ylim_degree&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span>
+            <span class="n">maxlatitude</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;ylim_degree&quot;</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span>
+            <span class="c1"># filename=&#39;events.xml&#39;,</span>
+        <span class="p">)</span>
+    <span class="k">except</span><span class="p">:</span>
+        <span class="n">events</span> <span class="o">=</span> <span class="n">Client</span><span class="p">(</span><span class="s2">&quot;iris&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">get_events</span><span class="p">(</span>
+            <span class="n">starttime</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;starttime&quot;</span><span class="p">],</span>
+            <span class="n">endtime</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;endtime&quot;</span><span class="p">],</span>
+            <span class="n">minlongitude</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;xlim_degree&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span>
+            <span class="n">maxlongitude</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;xlim_degree&quot;</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span>
+            <span class="n">minlatitude</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;ylim_degree&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span>
+            <span class="n">maxlatitude</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;ylim_degree&quot;</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span>
+            <span class="c1"># filename=&#39;events.xml&#39;,</span>
+        <span class="p">)</span>
+
+    <span class="c1">#     events = obspy.read_events(&#39;events.xml&#39;)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Number of events: </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">events</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+    <span class="c1">#     events.plot(&#39;local&#39;, outfile=&quot;events.png&quot;)</span>
+    <span class="c1">#     events.plot(&#39;local&#39;)</span>
+
+    <span class="c1">####### Save catalog ########</span>
+    <span class="n">catalog</span> <span class="o">=</span> <span class="n">defaultdict</span><span class="p">(</span><span class="nb">list</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">event</span> <span class="ow">in</span> <span class="n">events</span><span class="p">:</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">event</span><span class="o">.</span><span class="n">magnitudes</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="n">catalog</span><span class="p">[</span><span class="s2">&quot;time&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">event</span><span class="o">.</span><span class="n">origins</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">time</span><span class="o">.</span><span class="n">datetime</span><span class="p">)</span>
+            <span class="n">catalog</span><span class="p">[</span><span class="s2">&quot;magnitude&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">event</span><span class="o">.</span><span class="n">magnitudes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">mag</span><span class="p">)</span>
+            <span class="n">catalog</span><span class="p">[</span><span class="s2">&quot;longitude&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">event</span><span class="o">.</span><span class="n">origins</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">longitude</span><span class="p">)</span>
+            <span class="n">catalog</span><span class="p">[</span><span class="s2">&quot;latitude&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">event</span><span class="o">.</span><span class="n">origins</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">latitude</span><span class="p">)</span>
+            <span class="n">catalog</span><span class="p">[</span><span class="s2">&quot;depth(m)&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">event</span><span class="o">.</span><span class="n">origins</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">depth</span><span class="p">)</span>
+    <span class="n">catalog</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">from_dict</span><span class="p">(</span><span class="n">catalog</span><span class="p">)</span><span class="o">.</span><span class="n">sort_values</span><span class="p">([</span><span class="s2">&quot;time&quot;</span><span class="p">])</span>
+    <span class="n">catalog</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span>
+        <span class="n">standard_catalog</span><span class="p">,</span>
+        <span class="c1"># sep=&quot;\t&quot;,</span>
+        <span class="n">index</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+        <span class="n">float_format</span><span class="o">=</span><span class="s2">&quot;</span><span class="si">%.3f</span><span class="s2">&quot;</span><span class="p">,</span>
+        <span class="n">date_format</span><span class="o">=</span><span class="s2">&quot;%Y-%m-</span><span class="si">%d</span><span class="s2">T%H:%M:%S.</span><span class="si">%f</span><span class="s2">&quot;</span><span class="p">,</span>
+        <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;time&quot;</span><span class="p">,</span> <span class="s2">&quot;magnitude&quot;</span><span class="p">,</span> <span class="s2">&quot;longitude&quot;</span><span class="p">,</span> <span class="s2">&quot;latitude&quot;</span><span class="p">,</span> <span class="s2">&quot;depth(m)&quot;</span><span class="p">],</span>
+    <span class="p">)</span>
+
+    <span class="c1">## upload to s3 bucket</span>
+    <span class="k">try</span><span class="p">:</span>
+        <span class="kn">from</span> <span class="nn">minio</span> <span class="kn">import</span> <span class="n">Minio</span>
+
+        <span class="n">minioClient</span> <span class="o">=</span> <span class="n">Minio</span><span class="p">(</span><span class="n">s3_url</span><span class="p">,</span> <span class="n">access_key</span><span class="o">=</span><span class="s2">&quot;minio&quot;</span><span class="p">,</span> <span class="n">secret_key</span><span class="o">=</span><span class="s2">&quot;minio123&quot;</span><span class="p">,</span> <span class="n">secure</span><span class="o">=</span><span class="n">secure</span><span class="p">)</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">minioClient</span><span class="o">.</span><span class="n">bucket_exists</span><span class="p">(</span><span class="n">bucket_name</span><span class="p">):</span>
+            <span class="n">minioClient</span><span class="o">.</span><span class="n">make_bucket</span><span class="p">(</span><span class="n">bucket_name</span><span class="p">)</span>
+
+        <span class="n">minioClient</span><span class="o">.</span><span class="n">fput_object</span><span class="p">(</span>
+            <span class="n">bucket_name</span><span class="p">,</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;region&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">/standard_catalog.csv&quot;</span><span class="p">,</span>
+            <span class="n">standard_catalog</span><span class="p">,</span>
+        <span class="p">)</span>
+
+    <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">err</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;ERROR: can not access minio service! </span><span class="se">\n</span><span class="si">{</span><span class="n">err</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+        <span class="k">pass</span>
+
+    <span class="c1">####### Plot catalog ########</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">catalog</span><span class="p">[</span><span class="s2">&quot;longitude&quot;</span><span class="p">],</span> <span class="n">catalog</span><span class="p">[</span><span class="s2">&quot;latitude&quot;</span><span class="p">],</span> <span class="s2">&quot;.&quot;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">&quot;Longitude&quot;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">&quot;Latitude&quot;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s2">&quot;scaled&quot;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;xlim_degree&quot;</span><span class="p">])</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;ylim_degree&quot;</span><span class="p">])</span>
+    <span class="c1"># plt.savefig(os.path.join(data_path, &quot;events_loc.png&quot;))</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+
+    <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">plot_date</span><span class="p">(</span><span class="n">catalog</span><span class="p">[</span><span class="s2">&quot;time&quot;</span><span class="p">],</span> <span class="n">catalog</span><span class="p">[</span><span class="s2">&quot;magnitude&quot;</span><span class="p">],</span> <span class="s2">&quot;.&quot;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">gcf</span><span class="p">()</span><span class="o">.</span><span class="n">autofmt_xdate</span><span class="p">()</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">&quot;Magnitude&quot;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Number of events: </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">events</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+    <span class="c1"># plt.savefig(os.path.join(data_path, &quot;events_mag_time.png&quot;))</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+<div id="cell-6" class="clipboard-copy-txt">def download_events(
+    config_json: InputPath("json"), 
+    standard_catalog: OutputPath(str),
+    bucket_name: str = "catalogs",
+    s3_url: str = "minio-service:9000",
+    secure: bool = True,
+):
+
+    import json
+    import os
+    import pickle
+    from collections import defaultdict
+
+    import matplotlib
+    import matplotlib.pyplot as plt
+    import obspy
+    import pandas as pd
+    from obspy.clients.fdsn import Client
+
+    # matplotlib.use("agg")
+    # %matplotlib inline
+
+    with open(config_json, "r") as fp:
+        config = json.load(fp)
+
+    ####### IRIS catalog ########
+    try:
+        events = Client(config["client"]).get_events(
+            starttime=config["starttime"],
+            endtime=config["endtime"],
+            minlongitude=config["xlim_degree"][0],
+            maxlongitude=config["xlim_degree"][1],
+            minlatitude=config["ylim_degree"][0],
+            maxlatitude=config["ylim_degree"][1],
+            # filename='events.xml',
+        )
+    except:
+        events = Client("iris").get_events(
+            starttime=config["starttime"],
+            endtime=config["endtime"],
+            minlongitude=config["xlim_degree"][0],
+            maxlongitude=config["xlim_degree"][1],
+            minlatitude=config["ylim_degree"][0],
+            maxlatitude=config["ylim_degree"][1],
+            # filename='events.xml',
+        )
+
+    #     events = obspy.read_events('events.xml')
+    print(f"Number of events: {len(events)}")
+    #     events.plot('local', outfile="events.png")
+    #     events.plot('local')
+
+    ####### Save catalog ########
+    catalog = defaultdict(list)
+    for event in events:
+        if len(event.magnitudes) > 0:
+            catalog["time"].append(event.origins[0].time.datetime)
+            catalog["magnitude"].append(event.magnitudes[0].mag)
+            catalog["longitude"].append(event.origins[0].longitude)
+            catalog["latitude"].append(event.origins[0].latitude)
+            catalog["depth(m)"].append(event.origins[0].depth)
+    catalog = pd.DataFrame.from_dict(catalog).sort_values(["time"])
+    catalog.to_csv(
+        standard_catalog,
+        # sep="\t",
+        index=False,
+        float_format="%.3f",
+        date_format="%Y-%m-%dT%H:%M:%S.%f",
+        columns=["time", "magnitude", "longitude", "latitude", "depth(m)"],
+    )
+
+    ## upload to s3 bucket
+    try:
+        from minio import Minio
+
+        minioClient = Minio(s3_url, access_key="minio", secret_key="minio123", secure=secure)
+        if not minioClient.bucket_exists(bucket_name):
+            minioClient.make_bucket(bucket_name)
+
+        minioClient.fput_object(
+            bucket_name,
+            f"{config['region']}/standard_catalog.csv",
+            standard_catalog,
+        )
+
+    except Exception as err:
+        print(f"ERROR: can not access minio service! \n{err}")
+        pass
+
+    ####### Plot catalog ########
+    plt.figure()
+    plt.plot(catalog["longitude"], catalog["latitude"], ".", markersize=1)
+    plt.xlabel("Longitude")
+    plt.ylabel("Latitude")
+    plt.axis("scaled")
+    plt.xlim(config["xlim_degree"])
+    plt.ylim(config["ylim_degree"])
+    # plt.savefig(os.path.join(data_path, "events_loc.png"))
+    plt.show()
+
+    plt.figure()
+    plt.plot_date(catalog["time"], catalog["magnitude"], ".", markersize=1)
+    plt.gcf().autofmt_xdate()
+    plt.ylabel("Magnitude")
+    plt.title(f"Number of events: {len(events)}")
+    # plt.savefig(os.path.join(data_path, "events_mag_time.png"))
+    plt.show()</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[25]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-7">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">if</span> <span class="n">run_local</span><span class="p">:</span>
+    <span class="n">download_events</span><span class="p">(</span><span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;config.json&quot;</span><span class="p">),</span> <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;standard_catalog.csv&quot;</span><span class="p">))</span>
+</pre></div>
+<div id="cell-7" class="clipboard-copy-txt">if run_local:
+    download_events(root_dir("config.json"), root_dir("standard_catalog.csv"))</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
+<pre>Number of events: 252
+ERROR: can not access minio service! 
+No module named &#39;minio&#39;
+</pre>
+</div>
+</div>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+
+
+<div class="jp-RenderedImage jp-OutputArea-output ">
+<img 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"
+class="
+"
+>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
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+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[26]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-8">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">download_events_op</span> <span class="o">=</span> <span class="n">comp</span><span class="o">.</span><span class="n">func_to_container_op</span><span class="p">(</span>
+    <span class="n">download_events</span><span class="p">,</span>
+    <span class="c1"># base_image=&#39;zhuwq0/quakeflow-env:latest&#39;,</span>
+    <span class="n">base_image</span><span class="o">=</span><span class="s2">&quot;python:3.8&quot;</span><span class="p">,</span>
+    <span class="n">packages_to_install</span><span class="o">=</span><span class="p">[</span>
+        <span class="s2">&quot;obspy&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;pandas&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;matplotlib&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;minio&quot;</span><span class="p">,</span>
+    <span class="p">],</span>
+<span class="p">)</span>
+</pre></div>
+<div id="cell-8" class="clipboard-copy-txt">download_events_op = comp.func_to_container_op(
+    download_events,
+    # base_image='zhuwq0/quakeflow-env:latest',
+    base_image="python:3.8",
+    packages_to_install=[
+        "obspy",
+        "pandas",
+        "matplotlib",
+        "minio",
+    ],
+)</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="3-download-stations">3. Download stations<a class="anchor-link" href="#3-download-stations">&#182;</a></h2>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[27]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-9">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">def</span> <span class="nf">download_stations</span><span class="p">(</span>
+    <span class="n">config_json</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="s2">&quot;json&quot;</span><span class="p">),</span>
+    <span class="n">station_json</span><span class="p">:</span> <span class="n">OutputPath</span><span class="p">(</span><span class="s2">&quot;json&quot;</span><span class="p">),</span>
+    <span class="n">station_pkl</span><span class="p">:</span> <span class="n">OutputPath</span><span class="p">(</span><span class="s2">&quot;pickle&quot;</span><span class="p">),</span>
+    <span class="n">bucket_name</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;catalogs&quot;</span><span class="p">,</span>
+    <span class="n">s3_url</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;minio-service:9000&quot;</span><span class="p">,</span>
+    <span class="n">secure</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+<span class="p">):</span>
+
+    <span class="kn">import</span> <span class="nn">json</span>
+    <span class="kn">import</span> <span class="nn">os</span>
+    <span class="kn">import</span> <span class="nn">pickle</span>
+    <span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">defaultdict</span>
+
+    <span class="kn">import</span> <span class="nn">matplotlib</span>
+    <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+    <span class="kn">import</span> <span class="nn">obspy</span>
+    <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+    <span class="kn">from</span> <span class="nn">obspy.clients.fdsn</span> <span class="kn">import</span> <span class="n">Client</span>
+
+    <span class="c1"># matplotlib.use(&quot;agg&quot;)</span>
+    <span class="c1"># %matplotlib inline</span>
+
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">config_json</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">config</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">fp</span><span class="p">)</span>
+
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Network:&quot;</span><span class="p">,</span> <span class="s2">&quot;,&quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;networks&quot;</span><span class="p">]))</span>
+
+    <span class="c1">####### Download stations ########</span>
+    <span class="n">stations</span> <span class="o">=</span> <span class="n">Client</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;client&quot;</span><span class="p">])</span><span class="o">.</span><span class="n">get_stations</span><span class="p">(</span>
+        <span class="n">network</span><span class="o">=</span><span class="s2">&quot;,&quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;networks&quot;</span><span class="p">]),</span>
+        <span class="n">station</span><span class="o">=</span><span class="s2">&quot;*&quot;</span><span class="p">,</span>
+        <span class="n">starttime</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;starttime&quot;</span><span class="p">],</span>
+        <span class="n">endtime</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;endtime&quot;</span><span class="p">],</span>
+        <span class="n">minlongitude</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;xlim_degree&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span>
+        <span class="n">maxlongitude</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;xlim_degree&quot;</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span>
+        <span class="n">minlatitude</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;ylim_degree&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span>
+        <span class="n">maxlatitude</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;ylim_degree&quot;</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span>
+        <span class="n">channel</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;channels&quot;</span><span class="p">],</span>
+        <span class="n">level</span><span class="o">=</span><span class="s2">&quot;response&quot;</span><span class="p">,</span>
+        <span class="c1"># filename=&quot;stations.xml&quot;</span>
+    <span class="p">)</span>
+
+    <span class="c1">#     stations = obspy.read_inventory(&quot;stations.xml&quot;)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Number of stations: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">sum</span><span class="p">([</span><span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">stations</span><span class="p">])))</span>
+    <span class="c1"># stations.plot(&#39;local&#39;, outfile=&quot;stations.png&quot;)</span>
+    <span class="c1">#     stations.plot(&#39;local&#39;)</span>
+
+    <span class="c1">####### Save stations ########</span>
+    <span class="n">station_locs</span> <span class="o">=</span> <span class="n">defaultdict</span><span class="p">(</span><span class="nb">dict</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">network</span> <span class="ow">in</span> <span class="n">stations</span><span class="p">:</span>
+        <span class="k">for</span> <span class="n">station</span> <span class="ow">in</span> <span class="n">network</span><span class="p">:</span>
+            <span class="k">for</span> <span class="n">chn</span> <span class="ow">in</span> <span class="n">station</span><span class="p">:</span>
+                <span class="n">sid</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">network</span><span class="o">.</span><span class="n">code</span><span class="si">}</span><span class="s2">.</span><span class="si">{</span><span class="n">station</span><span class="o">.</span><span class="n">code</span><span class="si">}</span><span class="s2">.</span><span class="si">{</span><span class="n">chn</span><span class="o">.</span><span class="n">location_code</span><span class="si">}</span><span class="s2">.</span><span class="si">{</span><span class="n">chn</span><span class="o">.</span><span class="n">code</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="si">}</span><span class="s2">&quot;</span>
+                <span class="k">if</span> <span class="n">sid</span> <span class="ow">in</span> <span class="n">station_locs</span><span class="p">:</span>
+                    <span class="k">if</span> <span class="n">chn</span><span class="o">.</span><span class="n">code</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">station_locs</span><span class="p">[</span><span class="n">sid</span><span class="p">][</span><span class="s2">&quot;component&quot;</span><span class="p">]:</span>
+                        <span class="n">station_locs</span><span class="p">[</span><span class="n">sid</span><span class="p">][</span><span class="s2">&quot;component&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">chn</span><span class="o">.</span><span class="n">code</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+                        <span class="n">station_locs</span><span class="p">[</span><span class="n">sid</span><span class="p">][</span><span class="s2">&quot;response&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">chn</span><span class="o">.</span><span class="n">response</span><span class="o">.</span><span class="n">instrument_sensitivity</span><span class="o">.</span><span class="n">value</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">tmp_dict</span> <span class="o">=</span> <span class="p">{</span>
+                        <span class="s2">&quot;longitude&quot;</span><span class="p">:</span> <span class="n">chn</span><span class="o">.</span><span class="n">longitude</span><span class="p">,</span>
+                        <span class="s2">&quot;latitude&quot;</span><span class="p">:</span> <span class="n">chn</span><span class="o">.</span><span class="n">latitude</span><span class="p">,</span>
+                        <span class="s2">&quot;elevation(m)&quot;</span><span class="p">:</span> <span class="n">chn</span><span class="o">.</span><span class="n">elevation</span><span class="p">,</span>
+                        <span class="s2">&quot;component&quot;</span><span class="p">:</span> <span class="p">[</span>
+                            <span class="n">chn</span><span class="o">.</span><span class="n">code</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span>
+                        <span class="p">],</span>
+                        <span class="s2">&quot;response&quot;</span><span class="p">:</span> <span class="p">[</span>
+                            <span class="nb">round</span><span class="p">(</span><span class="n">chn</span><span class="o">.</span><span class="n">response</span><span class="o">.</span><span class="n">instrument_sensitivity</span><span class="o">.</span><span class="n">value</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span>
+                        <span class="p">],</span>
+                        <span class="s2">&quot;unit&quot;</span><span class="p">:</span> <span class="n">chn</span><span class="o">.</span><span class="n">response</span><span class="o">.</span><span class="n">instrument_sensitivity</span><span class="o">.</span><span class="n">input_units</span><span class="o">.</span><span class="n">lower</span><span class="p">(),</span>
+                    <span class="p">}</span>
+                    <span class="n">station_locs</span><span class="p">[</span><span class="n">sid</span><span class="p">]</span> <span class="o">=</span> <span class="n">tmp_dict</span>
+
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">station_json</span><span class="p">,</span> <span class="s2">&quot;w&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">json</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="n">station_locs</span><span class="p">,</span> <span class="n">fp</span><span class="p">,</span> <span class="n">indent</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">station_pkl</span><span class="p">,</span> <span class="s2">&quot;wb&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">pickle</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="n">stations</span><span class="p">,</span> <span class="n">fp</span><span class="p">)</span>
+
+    <span class="c1">## upload to s3 bucket</span>
+    <span class="k">try</span><span class="p">:</span>
+        <span class="kn">from</span> <span class="nn">minio</span> <span class="kn">import</span> <span class="n">Minio</span>
+
+        <span class="n">minioClient</span> <span class="o">=</span> <span class="n">Minio</span><span class="p">(</span><span class="n">s3_url</span><span class="p">,</span> <span class="n">access_key</span><span class="o">=</span><span class="s2">&quot;minio&quot;</span><span class="p">,</span> <span class="n">secret_key</span><span class="o">=</span><span class="s2">&quot;minio123&quot;</span><span class="p">,</span> <span class="n">secure</span><span class="o">=</span><span class="n">secure</span><span class="p">)</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">minioClient</span><span class="o">.</span><span class="n">bucket_exists</span><span class="p">(</span><span class="n">bucket_name</span><span class="p">):</span>
+            <span class="n">minioClient</span><span class="o">.</span><span class="n">make_bucket</span><span class="p">(</span><span class="n">bucket_name</span><span class="p">)</span>
+
+        <span class="n">minioClient</span><span class="o">.</span><span class="n">fput_object</span><span class="p">(</span>
+            <span class="n">bucket_name</span><span class="p">,</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;region&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">/stations.json&quot;</span><span class="p">,</span>
+            <span class="n">station_json</span><span class="p">,</span>
+        <span class="p">)</span>
+
+        <span class="n">minioClient</span><span class="o">.</span><span class="n">fput_object</span><span class="p">(</span>
+            <span class="n">bucket_name</span><span class="p">,</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;region&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">/stations.pkl&quot;</span><span class="p">,</span>
+            <span class="n">station_pkl</span><span class="p">,</span>
+        <span class="p">)</span>
+
+    <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">err</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;ERROR: can not access minio service! </span><span class="se">\n</span><span class="si">{</span><span class="n">err</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+        <span class="k">pass</span>
+    
+    <span class="c1">######## Plot stations ########</span>
+    <span class="n">station_locs</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">from_dict</span><span class="p">(</span><span class="n">station_locs</span><span class="p">,</span> <span class="n">orient</span><span class="o">=</span><span class="s2">&quot;index&quot;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">station_locs</span><span class="p">[</span><span class="s2">&quot;longitude&quot;</span><span class="p">],</span> <span class="n">station_locs</span><span class="p">[</span><span class="s2">&quot;latitude&quot;</span><span class="p">],</span> <span class="s2">&quot;^&quot;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;Stations&quot;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">&quot;X (km)&quot;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">&quot;Y (km)&quot;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s2">&quot;scaled&quot;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;xlim_degree&quot;</span><span class="p">])</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;ylim_degree&quot;</span><span class="p">])</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Number of stations: </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">station_locs</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+    <span class="c1">#     plt.savefig(os.path.join(data_path, &quot;stations_loc.png&quot;))</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+<div id="cell-9" class="clipboard-copy-txt">def download_stations(
+    config_json: InputPath("json"),
+    station_json: OutputPath("json"),
+    station_pkl: OutputPath("pickle"),
+    bucket_name: str = "catalogs",
+    s3_url: str = "minio-service:9000",
+    secure: bool = True,
+):
+
+    import json
+    import os
+    import pickle
+    from collections import defaultdict
+
+    import matplotlib
+    import matplotlib.pyplot as plt
+    import obspy
+    import pandas as pd
+    from obspy.clients.fdsn import Client
+
+    # matplotlib.use("agg")
+    # %matplotlib inline
+
+    with open(config_json, "r") as fp:
+        config = json.load(fp)
+
+    print("Network:", ",".join(config["networks"]))
+
+    ####### Download stations ########
+    stations = Client(config["client"]).get_stations(
+        network=",".join(config["networks"]),
+        station="*",
+        starttime=config["starttime"],
+        endtime=config["endtime"],
+        minlongitude=config["xlim_degree"][0],
+        maxlongitude=config["xlim_degree"][1],
+        minlatitude=config["ylim_degree"][0],
+        maxlatitude=config["ylim_degree"][1],
+        channel=config["channels"],
+        level="response",
+        # filename="stations.xml"
+    )
+
+    #     stations = obspy.read_inventory("stations.xml")
+    print("Number of stations: {}".format(sum([len(x) for x in stations])))
+    # stations.plot('local', outfile="stations.png")
+    #     stations.plot('local')
+
+    ####### Save stations ########
+    station_locs = defaultdict(dict)
+    for network in stations:
+        for station in network:
+            for chn in station:
+                sid = f"{network.code}.{station.code}.{chn.location_code}.{chn.code[:-1]}"
+                if sid in station_locs:
+                    if chn.code[-1] not in station_locs[sid]["component"]:
+                        station_locs[sid]["component"].append(chn.code[-1])
+                        station_locs[sid]["response"].append(round(chn.response.instrument_sensitivity.value, 2))
+                else:
+                    tmp_dict = {
+                        "longitude": chn.longitude,
+                        "latitude": chn.latitude,
+                        "elevation(m)": chn.elevation,
+                        "component": [
+                            chn.code[-1],
+                        ],
+                        "response": [
+                            round(chn.response.instrument_sensitivity.value, 2),
+                        ],
+                        "unit": chn.response.instrument_sensitivity.input_units.lower(),
+                    }
+                    station_locs[sid] = tmp_dict
+
+    with open(station_json, "w") as fp:
+        json.dump(station_locs, fp, indent=2)
+
+    with open(station_pkl, "wb") as fp:
+        pickle.dump(stations, fp)
+
+    ## upload to s3 bucket
+    try:
+        from minio import Minio
+
+        minioClient = Minio(s3_url, access_key="minio", secret_key="minio123", secure=secure)
+        if not minioClient.bucket_exists(bucket_name):
+            minioClient.make_bucket(bucket_name)
+
+        minioClient.fput_object(
+            bucket_name,
+            f"{config['region']}/stations.json",
+            station_json,
+        )
+
+        minioClient.fput_object(
+            bucket_name,
+            f"{config['region']}/stations.pkl",
+            station_pkl,
+        )
+
+    except Exception as err:
+        print(f"ERROR: can not access minio service! \n{err}")
+        pass
+    
+    ######## Plot stations ########
+    station_locs = pd.DataFrame.from_dict(station_locs, orient="index")
+    plt.figure()
+    plt.plot(station_locs["longitude"], station_locs["latitude"], "^", label="Stations")
+    plt.xlabel("X (km)")
+    plt.ylabel("Y (km)")
+    plt.axis("scaled")
+    plt.xlim(config["xlim_degree"])
+    plt.ylim(config["ylim_degree"])
+    plt.legend()
+    plt.title(f"Number of stations: {len(station_locs)}")
+    #     plt.savefig(os.path.join(data_path, "stations_loc.png"))
+    plt.show()</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[28]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-10">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">if</span> <span class="n">run_local</span><span class="p">:</span>
+    <span class="n">download_stations</span><span class="p">(</span><span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;config.json&quot;</span><span class="p">),</span> <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;stations.json&quot;</span><span class="p">),</span> <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;stations.pkl&quot;</span><span class="p">))</span>
+</pre></div>
+<div id="cell-10" class="clipboard-copy-txt">if run_local:
+    download_stations(root_dir("config.json"), root_dir("stations.json"), root_dir("stations.pkl"))</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
+<pre>Network: CI
+Number of stations: 17
+ERROR: can not access minio service! 
+No module named &#39;minio&#39;
+</pre>
+</div>
+</div>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+
+
+<div class="jp-RenderedImage jp-OutputArea-output ">
+<img 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"
+class="
+"
+>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[29]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-11">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">download_stations_op</span> <span class="o">=</span> <span class="n">comp</span><span class="o">.</span><span class="n">func_to_container_op</span><span class="p">(</span>
+    <span class="n">download_stations</span><span class="p">,</span>
+    <span class="c1"># base_image=&#39;zhuwq0/quakeflow-env:latest&#39;,</span>
+    <span class="n">base_image</span><span class="o">=</span><span class="s2">&quot;python:3.8&quot;</span><span class="p">,</span>
+    <span class="n">packages_to_install</span><span class="o">=</span><span class="p">[</span>
+        <span class="s2">&quot;obspy&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;pandas&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;matplotlib&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;minio&quot;</span><span class="p">,</span>
+    <span class="p">],</span>
+<span class="p">)</span>
+</pre></div>
+<div id="cell-11" class="clipboard-copy-txt">download_stations_op = comp.func_to_container_op(
+    download_stations,
+    # base_image='zhuwq0/quakeflow-env:latest',
+    base_image="python:3.8",
+    packages_to_install=[
+        "obspy",
+        "pandas",
+        "matplotlib",
+        "minio",
+    ],
+)</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="4-download-waveform-data">4. Download waveform data<a class="anchor-link" href="#4-download-waveform-data">&#182;</a></h2>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[30]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-12">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">def</span> <span class="nf">download_waveform</span><span class="p">(</span>
+    <span class="n">node_i</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
+    <span class="n">index_json</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="s2">&quot;json&quot;</span><span class="p">),</span>
+    <span class="n">config_json</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="s2">&quot;json&quot;</span><span class="p">),</span>
+    <span class="n">datetime_json</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="s2">&quot;json&quot;</span><span class="p">),</span>
+    <span class="n">station_pkl</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="s2">&quot;pickle&quot;</span><span class="p">),</span>
+    <span class="n">fname_csv</span><span class="p">:</span> <span class="n">OutputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span>
+    <span class="n">data_path</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
+    <span class="n">bucket_name</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;waveforms&quot;</span><span class="p">,</span>
+    <span class="n">s3_url</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;minio-service:9000&quot;</span><span class="p">,</span>
+    <span class="n">secure</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
+
+    <span class="kn">import</span> <span class="nn">json</span>
+    <span class="kn">import</span> <span class="nn">os</span>
+    <span class="kn">import</span> <span class="nn">pickle</span>
+    <span class="kn">import</span> <span class="nn">random</span>
+    <span class="kn">import</span> <span class="nn">threading</span>
+    <span class="kn">import</span> <span class="nn">time</span>
+
+    <span class="kn">import</span> <span class="nn">obspy</span>
+    <span class="kn">from</span> <span class="nn">obspy.clients.fdsn</span> <span class="kn">import</span> <span class="n">Client</span>
+
+    <span class="n">lock</span> <span class="o">=</span> <span class="n">threading</span><span class="o">.</span><span class="n">Lock</span><span class="p">()</span>
+
+    <span class="n">upload_minio</span> <span class="o">=</span> <span class="kc">False</span>
+    <span class="c1"># try:</span>
+    <span class="c1">#     from minio import Minio</span>
+
+    <span class="c1">#     minioClient = Minio(s3_url, access_key=&#39;minio&#39;, secret_key=&#39;minio123&#39;, secure=secure)</span>
+    <span class="c1">#     if not minioClient.bucket_exists(bucket_name):</span>
+    <span class="c1">#         minioClient.make_bucket(bucket_name)</span>
+    <span class="c1">#     upload_minio = True</span>
+    <span class="c1"># except Exception as err:</span>
+    <span class="c1">#     # print(f&quot;ERROR: can not access minio service! \n{err}&quot;)</span>
+    <span class="c1">#     pass</span>
+
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">index_json</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">index</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">fp</span><span class="p">)</span>
+    <span class="n">idx</span> <span class="o">=</span> <span class="n">index</span><span class="p">[</span><span class="n">node_i</span><span class="p">]</span>
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">config_json</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">config</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">fp</span><span class="p">)</span>
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">datetime_json</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">tmp</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">fp</span><span class="p">)</span>
+        <span class="n">starttimes</span> <span class="o">=</span> <span class="n">tmp</span><span class="p">[</span><span class="s2">&quot;starttimes&quot;</span><span class="p">]</span>
+        <span class="n">interval</span> <span class="o">=</span> <span class="n">tmp</span><span class="p">[</span><span class="s2">&quot;interval&quot;</span><span class="p">]</span>
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">station_pkl</span><span class="p">,</span> <span class="s2">&quot;rb&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">stations</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">fp</span><span class="p">)</span>
+
+    <span class="n">waveform_dir</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">data_path</span><span class="p">,</span> <span class="n">config</span><span class="p">[</span><span class="s2">&quot;region&quot;</span><span class="p">],</span> <span class="s2">&quot;waveforms&quot;</span><span class="p">)</span>
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">waveform_dir</span><span class="p">):</span>
+        <span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">waveform_dir</span><span class="p">)</span>
+
+    <span class="c1">####### Download data ########</span>
+    <span class="n">client</span> <span class="o">=</span> <span class="n">Client</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;client&quot;</span><span class="p">])</span>
+    <span class="n">fname_list</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;fname&quot;</span><span class="p">]</span>
+
+    <span class="k">def</span> <span class="nf">download</span><span class="p">(</span><span class="n">i</span><span class="p">):</span>
+        <span class="c1">#     for i in idx:</span>
+        <span class="n">starttime</span> <span class="o">=</span> <span class="n">obspy</span><span class="o">.</span><span class="n">UTCDateTime</span><span class="p">(</span><span class="n">starttimes</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+        <span class="n">endtime</span> <span class="o">=</span> <span class="n">starttime</span> <span class="o">+</span> <span class="n">interval</span>
+        <span class="n">fname</span> <span class="o">=</span> <span class="s2">&quot;</span><span class="si">{}</span><span class="s2">.mseed&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">starttime</span><span class="o">.</span><span class="n">datetime</span><span class="o">.</span><span class="n">strftime</span><span class="p">(</span><span class="s2">&quot;%Y-%m-</span><span class="si">%d</span><span class="s2">T%H:%M:%S&quot;</span><span class="p">))</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">upload_minio</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">waveform_dir</span><span class="p">,</span> <span class="n">fname</span><span class="p">)):</span>
+                <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">fname</span><span class="si">}</span><span class="s2"> exists&quot;</span><span class="p">)</span>
+                <span class="n">fname_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">fname</span><span class="p">)</span>
+                <span class="k">return</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">try</span><span class="p">:</span>
+                <span class="n">minioClient</span><span class="o">.</span><span class="n">fget_object</span><span class="p">(</span>
+                    <span class="n">bucket_name</span><span class="p">,</span>
+                    <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;region&quot;</span><span class="p">],</span> <span class="n">fname</span><span class="p">),</span>
+                    <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">waveform_dir</span><span class="p">,</span> <span class="n">fname</span><span class="p">),</span>
+                <span class="p">)</span>
+                <span class="nb">print</span><span class="p">(</span>
+                    <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">bucket_name</span><span class="si">}</span><span class="s2">/</span><span class="si">{</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;region&#39;</span><span class="p">],</span><span class="w"> </span><span class="n">fname</span><span class="p">)</span><span class="si">}</span><span class="s2"> download to </span><span class="si">{</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">waveform_dir</span><span class="p">,</span><span class="w"> </span><span class="n">fname</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span>
+                <span class="p">)</span>
+                <span class="n">fname_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">fname</span><span class="p">)</span>
+                <span class="k">return</span>
+            <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">err</span><span class="p">:</span>
+                <span class="nb">print</span><span class="p">(</span><span class="n">err</span><span class="p">)</span>
+
+        <span class="n">max_retry</span> <span class="o">=</span> <span class="mi">10</span>
+        <span class="n">stream</span> <span class="o">=</span> <span class="n">obspy</span><span class="o">.</span><span class="n">Stream</span><span class="p">()</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">fname</span><span class="si">}</span><span class="s2"> download starts&quot;</span><span class="p">)</span>
+        <span class="n">num_sta</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="k">for</span> <span class="n">network</span> <span class="ow">in</span> <span class="n">stations</span><span class="p">:</span>
+            <span class="k">for</span> <span class="n">station</span> <span class="ow">in</span> <span class="n">network</span><span class="p">:</span>
+                <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;********</span><span class="si">{</span><span class="n">network</span><span class="o">.</span><span class="n">code</span><span class="si">}</span><span class="s2">.</span><span class="si">{</span><span class="n">station</span><span class="o">.</span><span class="n">code</span><span class="si">}</span><span class="s2">********&quot;</span><span class="p">)</span>
+                <span class="n">retry</span> <span class="o">=</span> <span class="mi">0</span>
+                <span class="k">while</span> <span class="n">retry</span> <span class="o">&lt;</span> <span class="n">max_retry</span><span class="p">:</span>
+                    <span class="k">try</span><span class="p">:</span>
+                        <span class="n">tmp</span> <span class="o">=</span> <span class="n">client</span><span class="o">.</span><span class="n">get_waveforms</span><span class="p">(</span>
+                            <span class="n">network</span><span class="o">.</span><span class="n">code</span><span class="p">,</span>
+                            <span class="n">station</span><span class="o">.</span><span class="n">code</span><span class="p">,</span>
+                            <span class="s2">&quot;*&quot;</span><span class="p">,</span>
+                            <span class="n">config</span><span class="p">[</span><span class="s2">&quot;channels&quot;</span><span class="p">],</span>
+                            <span class="n">starttime</span><span class="p">,</span>
+                            <span class="n">endtime</span><span class="p">,</span>
+                        <span class="p">)</span>
+                        <span class="c1">#  for trace in tmp:</span>
+                        <span class="c1">#      if trace.stats.sampling_rate != 100:</span>
+                        <span class="c1">#          print(trace)</span>
+                        <span class="c1">#          trace = trace.interpolate(100, method=&quot;linear&quot;)</span>
+                        <span class="c1">#      trace = trace.detrend(&quot;spline&quot;, order=2, dspline=5*trace.stats.sampling_rate)</span>
+                        <span class="c1">#      stream.append(trace)</span>
+                        <span class="n">stream</span> <span class="o">+=</span> <span class="n">tmp</span>
+                        <span class="n">num_sta</span> <span class="o">+=</span> <span class="nb">len</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
+                        <span class="k">break</span>
+                    <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">err</span><span class="p">:</span>
+                        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Error </span><span class="si">{}</span><span class="s2">.</span><span class="si">{}</span><span class="s2">: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">network</span><span class="o">.</span><span class="n">code</span><span class="p">,</span> <span class="n">station</span><span class="o">.</span><span class="n">code</span><span class="p">,</span> <span class="n">err</span><span class="p">))</span>
+                        <span class="n">message</span> <span class="o">=</span> <span class="s2">&quot;No data available for request.&quot;</span>
+                        <span class="k">if</span> <span class="nb">str</span><span class="p">(</span><span class="n">err</span><span class="p">)[:</span> <span class="nb">len</span><span class="p">(</span><span class="n">message</span><span class="p">)]</span> <span class="o">==</span> <span class="n">message</span><span class="p">:</span>
+                            <span class="k">break</span>
+                        <span class="n">retry</span> <span class="o">+=</span> <span class="mi">1</span>
+                        <span class="n">time</span><span class="o">.</span><span class="n">sleep</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
+                        <span class="k">continue</span>
+                <span class="k">if</span> <span class="n">retry</span> <span class="o">==</span> <span class="n">max_retry</span><span class="p">:</span>
+                    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">fname</span><span class="si">}</span><span class="s2">: MAX </span><span class="si">{</span><span class="n">max_retry</span><span class="si">}</span><span class="s2"> retries reached : </span><span class="si">{</span><span class="n">network</span><span class="o">.</span><span class="n">code</span><span class="si">}</span><span class="s2">.</span><span class="si">{</span><span class="n">station</span><span class="o">.</span><span class="n">code</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">stream</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="c1"># stream = stream.merge(fill_value=0)</span>
+            <span class="c1"># stream = stream.trim(starttime, endtime, pad=True, fill_value=0)</span>
+            <span class="n">stream</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">waveform_dir</span><span class="p">,</span> <span class="n">fname</span><span class="p">))</span>
+            <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">fname</span><span class="si">}</span><span class="s2"> download succeeds&quot;</span><span class="p">)</span>
+            <span class="c1"># if upload_minio:</span>
+            <span class="c1">#     minioClient.fput_object(bucket_name, os.path.join(config[&#39;region&#39;], fname), os.path.join(waveform_dir, fname))</span>
+            <span class="c1">#     print(f&quot;{fname} upload to minio {os.path.join(config[&#39;region&#39;], fname)}&quot;)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">fname</span><span class="si">}</span><span class="s2"> empty data&quot;</span><span class="p">)</span>
+        <span class="n">lock</span><span class="o">.</span><span class="n">acquire</span><span class="p">()</span>
+        <span class="n">fname_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">fname</span><span class="p">)</span>
+        <span class="n">lock</span><span class="o">.</span><span class="n">release</span><span class="p">()</span>
+
+    <span class="n">threads</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">MAX_THREADS</span> <span class="o">=</span> <span class="mi">2</span>
+    <span class="c1"># MAX_THREADS = 1</span>
+    <span class="k">for</span> <span class="n">ii</span><span class="p">,</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">idx</span><span class="p">):</span>
+        <span class="n">t</span> <span class="o">=</span> <span class="n">threading</span><span class="o">.</span><span class="n">Thread</span><span class="p">(</span><span class="n">target</span><span class="o">=</span><span class="n">download</span><span class="p">,</span> <span class="n">args</span><span class="o">=</span><span class="p">(</span><span class="n">i</span><span class="p">,))</span>
+        <span class="n">t</span><span class="o">.</span><span class="n">start</span><span class="p">()</span>
+        <span class="n">time</span><span class="o">.</span><span class="n">sleep</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+        <span class="n">threads</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">t</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">ii</span> <span class="o">%</span> <span class="n">MAX_THREADS</span> <span class="o">==</span> <span class="n">MAX_THREADS</span> <span class="o">-</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">threads</span><span class="p">:</span>
+                <span class="n">t</span><span class="o">.</span><span class="n">join</span><span class="p">()</span>
+            <span class="n">threads</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">threads</span><span class="p">:</span>
+        <span class="n">t</span><span class="o">.</span><span class="n">join</span><span class="p">()</span>
+
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">fname_csv</span><span class="p">,</span> <span class="s2">&quot;w&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">fp</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">fname_list</span><span class="p">))</span>
+
+    <span class="k">return</span> <span class="n">waveform_dir</span>
+</pre></div>
+<div id="cell-12" class="clipboard-copy-txt">def download_waveform(
+    node_i: int,
+    index_json: InputPath("json"),
+    config_json: InputPath("json"),
+    datetime_json: InputPath("json"),
+    station_pkl: InputPath("pickle"),
+    fname_csv: OutputPath(str),
+    data_path: str,
+    bucket_name: str = "waveforms",
+    s3_url: str = "minio-service:9000",
+    secure: bool = True,
+) -> str:
+
+    import json
+    import os
+    import pickle
+    import random
+    import threading
+    import time
+
+    import obspy
+    from obspy.clients.fdsn import Client
+
+    lock = threading.Lock()
+
+    upload_minio = False
+    # try:
+    #     from minio import Minio
+
+    #     minioClient = Minio(s3_url, access_key='minio', secret_key='minio123', secure=secure)
+    #     if not minioClient.bucket_exists(bucket_name):
+    #         minioClient.make_bucket(bucket_name)
+    #     upload_minio = True
+    # except Exception as err:
+    #     # print(f"ERROR: can not access minio service! \n{err}")
+    #     pass
+
+    with open(index_json, "r") as fp:
+        index = json.load(fp)
+    idx = index[node_i]
+    with open(config_json, "r") as fp:
+        config = json.load(fp)
+    with open(datetime_json, "r") as fp:
+        tmp = json.load(fp)
+        starttimes = tmp["starttimes"]
+        interval = tmp["interval"]
+    with open(station_pkl, "rb") as fp:
+        stations = pickle.load(fp)
+
+    waveform_dir = os.path.join(data_path, config["region"], "waveforms")
+    if not os.path.exists(waveform_dir):
+        os.makedirs(waveform_dir)
+
+    ####### Download data ########
+    client = Client(config["client"])
+    fname_list = ["fname"]
+
+    def download(i):
+        #     for i in idx:
+        starttime = obspy.UTCDateTime(starttimes[i])
+        endtime = starttime + interval
+        fname = "{}.mseed".format(starttime.datetime.strftime("%Y-%m-%dT%H:%M:%S"))
+        if not upload_minio:
+            if os.path.exists(os.path.join(waveform_dir, fname)):
+                print(f"{fname} exists")
+                fname_list.append(fname)
+                return
+        else:
+            try:
+                minioClient.fget_object(
+                    bucket_name,
+                    os.path.join(config["region"], fname),
+                    os.path.join(waveform_dir, fname),
+                )
+                print(
+                    f"{bucket_name}/{os.path.join(config['region'], fname)} download to {os.path.join(waveform_dir, fname)}"
+                )
+                fname_list.append(fname)
+                return
+            except Exception as err:
+                print(err)
+
+        max_retry = 10
+        stream = obspy.Stream()
+        print(f"{fname} download starts")
+        num_sta = 0
+        for network in stations:
+            for station in network:
+                print(f"********{network.code}.{station.code}********")
+                retry = 0
+                while retry < max_retry:
+                    try:
+                        tmp = client.get_waveforms(
+                            network.code,
+                            station.code,
+                            "*",
+                            config["channels"],
+                            starttime,
+                            endtime,
+                        )
+                        #  for trace in tmp:
+                        #      if trace.stats.sampling_rate != 100:
+                        #          print(trace)
+                        #          trace = trace.interpolate(100, method="linear")
+                        #      trace = trace.detrend("spline", order=2, dspline=5*trace.stats.sampling_rate)
+                        #      stream.append(trace)
+                        stream += tmp
+                        num_sta += len(tmp)
+                        break
+                    except Exception as err:
+                        print("Error {}.{}: {}".format(network.code, station.code, err))
+                        message = "No data available for request."
+                        if str(err)[: len(message)] == message:
+                            break
+                        retry += 1
+                        time.sleep(5)
+                        continue
+                if retry == max_retry:
+                    print(f"{fname}: MAX {max_retry} retries reached : {network.code}.{station.code}")
+
+        if len(stream) > 0:
+            # stream = stream.merge(fill_value=0)
+            # stream = stream.trim(starttime, endtime, pad=True, fill_value=0)
+            stream.write(os.path.join(waveform_dir, fname))
+            print(f"{fname} download succeeds")
+            # if upload_minio:
+            #     minioClient.fput_object(bucket_name, os.path.join(config['region'], fname), os.path.join(waveform_dir, fname))
+            #     print(f"{fname} upload to minio {os.path.join(config['region'], fname)}")
+        else:
+            print(f"{fname} empty data")
+        lock.acquire()
+        fname_list.append(fname)
+        lock.release()
+
+    threads = []
+    MAX_THREADS = 2
+    # MAX_THREADS = 1
+    for ii, i in enumerate(idx):
+        t = threading.Thread(target=download, args=(i,))
+        t.start()
+        time.sleep(1)
+        threads.append(t)
+        if ii % MAX_THREADS == MAX_THREADS - 1:
+            for t in threads:
+                t.join()
+            threads = []
+    for t in threads:
+        t.join()
+
+    with open(fname_csv, "w") as fp:
+        fp.write("\n".join(fname_list))
+
+    return waveform_dir</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[31]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-13">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">if</span> <span class="n">run_local</span><span class="p">:</span>
+    <span class="n">waveform_path</span> <span class="o">=</span> <span class="n">download_waveform</span><span class="p">(</span>
+        <span class="mi">0</span><span class="p">,</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;index.json&quot;</span><span class="p">),</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;config.json&quot;</span><span class="p">),</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;datetimes.json&quot;</span><span class="p">),</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;stations.pkl&quot;</span><span class="p">),</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;fname.csv&quot;</span><span class="p">),</span>
+        <span class="n">data_path</span><span class="o">=</span><span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;&quot;</span><span class="p">),</span>
+    <span class="p">)</span>
+</pre></div>
+<div id="cell-13" class="clipboard-copy-txt">if run_local:
+    waveform_path = download_waveform(
+        0,
+        root_dir("index.json"),
+        root_dir("config.json"),
+        root_dir("datetimes.json"),
+        root_dir("stations.pkl"),
+        root_dir("fname.csv"),
+        data_path=root_dir(""),
+    )</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
+<pre>2019-07-04T17:00:00.mseed exists
+2019-07-04T18:00:00.mseed exists
+</pre>
+</div>
+</div>
+
+</div>
+
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[32]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-14">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">download_waveform_op</span> <span class="o">=</span> <span class="n">comp</span><span class="o">.</span><span class="n">func_to_container_op</span><span class="p">(</span>
+    <span class="n">download_waveform</span><span class="p">,</span>
+    <span class="c1"># base_image=&#39;zhuwq0/quakeflow-env:latest&#39;,</span>
+    <span class="n">base_image</span><span class="o">=</span><span class="s2">&quot;python:3.8&quot;</span><span class="p">,</span>
+    <span class="n">packages_to_install</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;obspy&quot;</span><span class="p">,</span> <span class="s2">&quot;minio&quot;</span><span class="p">],</span>
+<span class="p">)</span>
+</pre></div>
+<div id="cell-14" class="clipboard-copy-txt">download_waveform_op = comp.func_to_container_op(
+    download_waveform,
+    # base_image='zhuwq0/quakeflow-env:latest',
+    base_image="python:3.8",
+    packages_to_install=["obspy", "minio"],
+)</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="5-run-phasenet-to-pick-ps-picks">5. Run PhaseNet to pick P/S picks<a class="anchor-link" href="#5-run-phasenet-to-pick-ps-picks">&#182;</a></h2>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[33]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-15">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">def</span> <span class="nf">phasenet_op</span><span class="p">(</span><span class="n">data_path</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">data_list</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">stations</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span>
+
+    <span class="k">return</span> <span class="n">dsl</span><span class="o">.</span><span class="n">ContainerOp</span><span class="p">(</span>
+        <span class="n">name</span><span class="o">=</span><span class="s2">&quot;PhaseNet Picking&quot;</span><span class="p">,</span>
+        <span class="n">image</span><span class="o">=</span><span class="s2">&quot;zhuwq0/phasenet-api:1.0&quot;</span><span class="p">,</span>
+        <span class="n">command</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;python&quot;</span><span class="p">],</span>
+        <span class="n">arguments</span><span class="o">=</span><span class="p">[</span>
+            <span class="s2">&quot;phasenet/predict.py&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;--model&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;model/190703-214543&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;--data_dir&quot;</span><span class="p">,</span>
+            <span class="n">data_path</span><span class="p">,</span>
+            <span class="s2">&quot;--data_list&quot;</span><span class="p">,</span>
+            <span class="n">dsl</span><span class="o">.</span><span class="n">InputArgumentPath</span><span class="p">(</span><span class="n">data_list</span><span class="p">),</span>
+            <span class="s2">&quot;--stations&quot;</span><span class="p">,</span>
+            <span class="n">dsl</span><span class="o">.</span><span class="n">InputArgumentPath</span><span class="p">(</span><span class="n">stations</span><span class="p">),</span>
+            <span class="c1"># &#39;--result_dir&#39;, &quot;results&quot;,</span>
+            <span class="s2">&quot;--format&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;mseed_array&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;--amplitude&quot;</span><span class="p">,</span>
+        <span class="p">],</span>
+        <span class="n">file_outputs</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;picks&quot;</span><span class="p">:</span> <span class="s2">&quot;/opt/results/picks.csv&quot;</span><span class="p">},</span>
+    <span class="p">)</span>
+</pre></div>
+<div id="cell-15" class="clipboard-copy-txt">def phasenet_op(data_path: str, data_list: str, stations: str):
+
+    return dsl.ContainerOp(
+        name="PhaseNet Picking",
+        image="zhuwq0/phasenet-api:1.0",
+        command=["python"],
+        arguments=[
+            "phasenet/predict.py",
+            "--model",
+            "model/190703-214543",
+            "--data_dir",
+            data_path,
+            "--data_list",
+            dsl.InputArgumentPath(data_list),
+            "--stations",
+            dsl.InputArgumentPath(stations),
+            # '--result_dir', "results",
+            "--format",
+            "mseed_array",
+            "--amplitude",
+        ],
+        file_outputs={"picks": "/opt/results/picks.csv"},
+    )</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[37]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-16">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># %%capture</span>
+<span class="k">if</span> <span class="n">run_local</span><span class="p">:</span>
+    <span class="n">command</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;python ../PhaseNet/phasenet/predict.py --model=../PhaseNet/model/190703-214543 --data_dir=</span><span class="si">{</span><span class="n">root_dir</span><span class="p">(</span><span class="n">root_dir</span><span class="p">(</span><span class="s1">&#39;waveforms&#39;</span><span class="p">))</span><span class="si">}</span><span class="s2"> --data_list=</span><span class="si">{</span><span class="n">root_dir</span><span class="p">(</span><span class="s1">&#39;fname.csv&#39;</span><span class="p">)</span><span class="si">}</span><span class="s2"> --stations=</span><span class="si">{</span><span class="n">root_dir</span><span class="p">(</span><span class="s1">&#39;stations.json&#39;</span><span class="p">)</span><span class="si">}</span><span class="s2"> --result_dir=</span><span class="si">{</span><span class="n">root_dir</span><span class="p">(</span><span class="s1">&#39;phasenet&#39;</span><span class="p">)</span><span class="si">}</span><span class="s2"> --format=mseed_array --amplitude&quot;</span><span class="c1"># --upload_waveform&quot;</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">command</span><span class="p">)</span>
+    <span class="err">!</span><span class="p">{</span><span class="n">command</span><span class="p">}</span>
+</pre></div>
+<div id="cell-16" class="clipboard-copy-txt"># %%capture
+if run_local:
+    command = f"python ../PhaseNet/phasenet/predict.py --model=../PhaseNet/model/190703-214543 --data_dir={root_dir(root_dir('waveforms'))} --data_list={root_dir('fname.csv')} --stations={root_dir('stations.json')} --result_dir={root_dir('phasenet')} --format=mseed_array --amplitude"# --upload_waveform"
+    print(command)
+    !{command}</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
+<pre>python ../PhaseNet/phasenet/predict.py --model=../PhaseNet/model/190703-214543 --data_dir=Demo/Demo/waveforms --data_list=Demo/fname.csv --stations=Demo/stations.json --result_dir=Demo/phasenet --format=mseed_array --amplitude
+2023-03-15 14:40:16.834011: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  SSE4.1 SSE4.2 AVX AVX2 FMA
+To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
+                 longitude   latitude  elevation(m)  component                                 response    unit
+CI.CCC..BH     -117.364530  35.524950         670.0  [E, N, Z]  [627368000.0, 627368000.0, 627368000.0]     m/s
+CI.CCC..HH     -117.364530  35.524950         670.0  [E, N, Z]  [627368000.0, 627368000.0, 627368000.0]     m/s
+CI.CCC..HN     -117.364530  35.524950         670.0  [E, N, Z]           [213979.0, 214322.0, 213808.0]  m/s**2
+CI.CLC..BH     -117.597510  35.815740         775.0  [E, N, Z]  [627368000.0, 627368000.0, 627368000.0]     m/s
+CI.CLC..HH     -117.597510  35.815740         775.0  [E, N, Z]  [627368000.0, 627368000.0, 627368000.0]     m/s
+CI.CLC..HN     -117.597510  35.815740         775.0  [E, N, Z]           [213945.0, 213808.0, 213740.0]  m/s**2
+CI.DTP..BH     -117.845810  35.267420         908.0  [E, N, Z]  [627368000.0, 627368000.0, 627368000.0]     m/s
+CI.DTP..HH     -117.845810  35.267420         908.0  [E, N, Z]  [627368000.0, 627368000.0, 627368000.0]     m/s
+CI.DTP..HN     -117.845810  35.267420         908.0  [E, N, Z]           [214399.0, 213971.0, 214484.0]  m/s**2
+CI.JRC2..BH    -117.808850  35.982490        1469.0  [E, N, Z]  [784866000.0, 784866000.0, 790478000.0]     m/s
+CI.JRC2..HH    -117.808850  35.982490        1469.0  [E, N, Z]  [784866000.0, 784866000.0, 790478000.0]     m/s
+CI.JRC2..HN    -117.808850  35.982490        1469.0  [E, N, Z]           [213808.0, 213945.0, 214185.0]  m/s**2
+CI.LRL..BH     -117.682120  35.479540        1340.0  [E, N, Z]  [628306000.0, 629984000.0, 627467000.0]     m/s
+CI.LRL..HH     -117.682120  35.479540        1340.0  [E, N, Z]  [628306000.0, 629984000.0, 627467000.0]     m/s
+CI.LRL..HN     -117.682120  35.479540        1340.0  [E, N, Z]           [213757.0, 213671.0, 213201.0]  m/s**2
+CI.LRL.2C.HN   -117.682120  35.479540        1340.0  [E, N, Z]           [213757.0, 213671.0, 213201.0]  m/s**2
+CI.MPM..BH     -117.489010  36.057990        1839.0  [E, N, Z]  [627368000.0, 627368000.0, 627368000.0]     m/s
+CI.MPM..HH     -117.489010  36.057990        1839.0  [E, N, Z]  [627368000.0, 627368000.0, 627368000.0]     m/s
+CI.MPM..HN     -117.489010  36.057990        1839.0  [E, N, Z]           [213911.0, 214219.0, 213911.0]  m/s**2
+CI.Q0072.01.HN -117.666721  35.609617         695.0  [E, N, Z]           [256354.0, 256354.0, 256354.0]  m/s**2
+CI.SLA..BH     -117.283320  35.890950        1174.0  [E, N, Z]  [622338000.0, 618992000.0, 616482000.0]     m/s
+CI.SLA..HH     -117.283320  35.890950        1174.0  [E, N, Z]  [622338000.0, 618992000.0, 616482000.0]     m/s
+CI.SLA..HN     -117.283320  35.890950        1174.0  [E, N, Z]           [214253.0, 213671.0, 213979.0]  m/s**2
+CI.SRT..BH     -117.750510  35.692350         667.0  [E, N, Z]  [633341000.0, 633341000.0, 633341000.0]     m/s
+CI.SRT..HH     -117.750510  35.692350         667.0  [E, N, Z]  [629865000.0, 629865000.0, 629865000.0]     m/s
+CI.SRT..HN     -117.750510  35.692350         667.0  [E, N, Z]           [214301.0, 213873.0, 214087.0]  m/s**2
+CI.TOW2..BH    -117.764880  35.808560         685.0  [E, N, Z]  [626915000.0, 626915000.0, 626843000.0]     m/s
+CI.TOW2..HH    -117.764880  35.808560         685.0  [E, N, Z]  [626911000.0, 626911000.0, 626839000.0]     m/s
+CI.TOW2..HN    -117.764880  35.808560         685.0  [E, N, Z]           [213800.0, 214142.0, 214356.0]  m/s**2
+CI.WBM..BH     -117.890490  35.608390         892.0  [E, N, Z]  [314573000.0, 314573000.0, 314573000.0]     m/s
+CI.WBM..HH     -117.890490  35.608390         892.0  [E, N, Z]  [314573000.0, 314573000.0, 314573000.0]     m/s
+CI.WBM..HN     -117.890490  35.608390         892.0  [E, N, Z]           [213550.0, 214064.0, 213550.0]  m/s**2
+CI.WBM.2C.HN   -117.890490  35.608390         892.0  [E, N, Z]           [213550.0, 214064.0, 213550.0]  m/s**2
+CI.WCS2..BH    -117.765260  36.025210        1143.0  [E, N, Z]  [626910000.0, 626910000.0, 626838000.0]     m/s
+CI.WCS2..HH    -117.765260  36.025210        1143.0  [E, N, Z]  [626910000.0, 626910000.0, 626838000.0]     m/s
+CI.WCS2..HN    -117.765260  36.025210        1143.0  [E, N, Z]           [213757.0, 213329.0, 213415.0]  m/s**2
+CI.WMF..BH     -117.854860  36.117580        1537.4  [E, N, Z]  [629963000.0, 631652000.0, 629963000.0]     m/s
+CI.WMF..HH     -117.854860  36.117580        1537.4  [E, N, Z]  [626506000.0, 628185000.0, 626506000.0]     m/s
+CI.WMF..HN     -117.854860  36.117580        1537.4  [E, N, Z]           [214087.0, 214087.0, 214087.0]  m/s**2
+CI.WMF.2C.HN   -117.854860  36.117580        1537.4  [E, N, Z]           [213952.0, 213952.0, 213952.0]  m/s**2
+CI.WNM..EH     -117.906160  35.842200         974.3        [Z]                             [69306200.0]     m/s
+CI.WNM..HN     -117.906160  35.842200         974.3  [E, N, Z]           [214054.0, 213926.0, 214054.0]  m/s**2
+CI.WNM.2C.HN   -117.906160  35.842200         974.3  [E, N, Z]           [214048.0, 213920.0, 214048.0]  m/s**2
+CI.WRC2..BH    -117.650380  35.947900         943.0  [E, N, Z]  [629145000.0, 629145000.0, 629145000.0]     m/s
+CI.WRC2..HH    -117.650380  35.947900         943.0  [E, N, Z]  [629145000.0, 629145000.0, 629145000.0]     m/s
+CI.WRC2..HN    -117.650380  35.947900         943.0  [E, N, Z]           [214227.0, 213970.0, 214056.0]  m/s**2
+CI.WRV2..EH    -117.890400  36.007740        1070.0        [Z]                             [71427500.0]     m/s
+CI.WRV2..HN    -117.890400  36.007740        1070.0  [E, N, Z]           [213883.0, 235224.0, 235139.0]  m/s**2
+CI.WRV2.2C.HN  -117.890400  36.007740        1070.0  [E, N, Z]           [213877.0, 235218.0, 235132.0]  m/s**2
+CI.WVP2..EH    -117.817690  35.949390        1465.0        [Z]                             [68041300.0]     m/s
+CI.WVP2..HN    -117.817690  35.949390        1465.0  [E, N, Z]           [213764.0, 213550.0, 213721.0]  m/s**2
+CI.WVP2.2C.HN  -117.817690  35.949390        1465.0  [E, N, Z]           [213782.0, 213569.0, 213740.0]  m/s**2
+2023-03-15 14:40:21,519 Resampling CI.CCC..BHE from 40.0 to 100 Hz
+2023-03-15 14:40:21,618 Resampling CI.CCC..BHN from 40.0 to 100 Hz
+2023-03-15 14:40:21,626 Resampling CI.CCC..BHZ from 40.0 to 100 Hz
+2023-03-15 14:40:21,633 Resampling CI.CLC..BHE from 40.0 to 100 Hz
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+2023-03-15 14:40:21,646 Resampling CI.CLC..BHZ from 40.0 to 100 Hz
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+2023-03-15 14:40:21,660 Resampling CI.DTP..BHN from 40.0 to 100 Hz
+2023-03-15 14:40:21,667 Resampling CI.DTP..BHZ from 40.0 to 100 Hz
+2023-03-15 14:40:21,674 Resampling CI.JRC2..BHE from 40.0 to 100 Hz
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+2023-03-15 14:40:21,817 Resampling CI.WCS2..BHZ from 40.0 to 100 Hz
+2023-03-15 14:40:21,823 Resampling CI.WMF..BHE from 40.0 to 100 Hz
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+2023-03-15 14:40:21,836 Resampling CI.WMF..BHZ from 40.0 to 100 Hz
+2023-03-15 14:40:21,842 Resampling CI.WRC2..BHE from 40.0 to 100 Hz
+2023-03-15 14:40:21,847 Resampling CI.WRC2..BHN from 40.0 to 100 Hz
+2023-03-15 14:40:21,853 Resampling CI.WRC2..BHZ from 40.0 to 100 Hz
+Empty trace: CI.LRL.2C.HNE 2019-07-04T16:59:59.994500Z
+Empty trace: CI.LRL.2C.HNN 2019-07-04T16:59:59.994500Z
+Empty trace: CI.LRL.2C.HNZ 2019-07-04T16:59:59.994500Z
+Empty trace: CI.Q0072.01.HNE 2019-07-04T16:59:59.994500Z
+Empty trace: CI.Q0072.01.HNN 2019-07-04T16:59:59.994500Z
+Empty trace: CI.Q0072.01.HNZ 2019-07-04T16:59:59.994500Z
+Empty trace: CI.WBM.2C.HNE 2019-07-04T16:59:59.994500Z
+Empty trace: CI.WBM.2C.HNN 2019-07-04T16:59:59.994500Z
+Empty trace: CI.WBM.2C.HNZ 2019-07-04T16:59:59.994500Z
+Empty trace: CI.WMF.2C.HNE 2019-07-04T16:59:59.994500Z
+Empty trace: CI.WMF.2C.HNN 2019-07-04T16:59:59.994500Z
+Empty trace: CI.WMF.2C.HNZ 2019-07-04T16:59:59.994500Z
+Empty trace: CI.WNM.2C.HNE 2019-07-04T16:59:59.994500Z
+Empty trace: CI.WNM.2C.HNN 2019-07-04T16:59:59.994500Z
+Empty trace: CI.WNM.2C.HNZ 2019-07-04T16:59:59.994500Z
+Empty trace: CI.WRV2.2C.HNE 2019-07-04T16:59:59.994500Z
+Empty trace: CI.WRV2.2C.HNN 2019-07-04T16:59:59.994500Z
+Empty trace: CI.WRV2.2C.HNZ 2019-07-04T16:59:59.994500Z
+Empty trace: CI.WVP2.2C.HNE 2019-07-04T16:59:59.994500Z
+Empty trace: CI.WVP2.2C.HNN 2019-07-04T16:59:59.994500Z
+Empty trace: CI.WVP2.2C.HNZ 2019-07-04T16:59:59.994500Z
+2023-03-15 14:40:32,078 Pred log: Demo/phasenet
+2023-03-15 14:40:32,078 Dataset size: 2
+2023-03-15 14:40:32.210711: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  SSE4.1 SSE4.2 AVX AVX2 FMA
+To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
+2023-03-15 14:40:32.219257: I tensorflow/core/common_runtime/process_util.cc:146] Creating new thread pool with default inter op setting: 80. Tune using inter_op_parallelism_threads for best performance.
+2023-03-15 14:40:32,265 Model: depths 5, filters 8, filter size 7x1, pool size: 4x1, dilation rate: 1x1
+2023-03-15 14:40:34.157725: I tensorflow/core/common_runtime/process_util.cc:146] Creating new thread pool with default inter op setting: 80. Tune using inter_op_parallelism_threads for best performance.
+2023-03-15 14:40:34.286939: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:357] MLIR V1 optimization pass is not enabled
+2023-03-15 14:40:34,616 restoring model ../PhaseNet/model/190703-214543/model_95.ckpt
+Pred:   0%|                                               | 0/2 [00:00&lt;?, ?it/s]2023-03-15 14:40:35,609 Resampling CI.CCC..BHE from 40.0 to 100 Hz
+2023-03-15 14:40:35,618 Resampling CI.CCC..BHN from 40.0 to 100 Hz
+2023-03-15 14:40:35,624 Resampling CI.CCC..BHZ from 40.0 to 100 Hz
+2023-03-15 14:40:35,630 Resampling CI.CLC..BHE from 40.0 to 100 Hz
+2023-03-15 14:40:35,637 Resampling CI.CLC..BHN from 40.0 to 100 Hz
+2023-03-15 14:40:35,643 Resampling CI.CLC..BHZ from 40.0 to 100 Hz
+2023-03-15 14:40:35,649 Resampling CI.DTP..BHE from 40.0 to 100 Hz
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+2023-03-15 14:40:35,661 Resampling CI.DTP..BHZ from 40.0 to 100 Hz
+2023-03-15 14:40:35,668 Resampling CI.JRC2..BHE from 40.0 to 100 Hz
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+2023-03-15 14:40:35,717 Resampling CI.MPM..BHZ from 40.0 to 100 Hz
+2023-03-15 14:40:35,724 Resampling CI.SLA..BHE from 40.0 to 100 Hz
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+Empty trace: CI.LRL.2C.HNE 2019-07-04T16:59:59.994500Z
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+2023-03-15 14:40:52,818 Resampling CI.CCC..BHE from 40.0 to 100 Hz
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+2023-03-15 14:40:52,898 Resampling CI.LRL..BHE from 40.0 to 100 Hz
+2023-03-15 14:40:52,904 Resampling CI.LRL..BHN from 40.0 to 100 Hz
+2023-03-15 14:40:52,910 Resampling CI.LRL..BHZ from 40.0 to 100 Hz
+2023-03-15 14:40:52,916 Resampling CI.MPM..BHE from 40.0 to 100 Hz
+2023-03-15 14:40:52,923 Resampling CI.MPM..BHN from 40.0 to 100 Hz
+2023-03-15 14:40:52,929 Resampling CI.MPM..BHZ from 40.0 to 100 Hz
+2023-03-15 14:40:52,935 Resampling CI.SLA..BHE from 40.0 to 100 Hz
+2023-03-15 14:40:52,942 Resampling CI.SLA..BHN from 40.0 to 100 Hz
+2023-03-15 14:40:52,948 Resampling CI.SLA..BHZ from 40.0 to 100 Hz
+2023-03-15 14:40:52,954 Resampling CI.SRT..BHE from 40.0 to 100 Hz
+2023-03-15 14:40:52,961 Resampling CI.SRT..BHN from 40.0 to 100 Hz
+2023-03-15 14:40:52,967 Resampling CI.SRT..BHZ from 40.0 to 100 Hz
+2023-03-15 14:40:52,974 Resampling CI.TOW2..BHE from 40.0 to 100 Hz
+2023-03-15 14:40:52,980 Resampling CI.TOW2..BHN from 40.0 to 100 Hz
+2023-03-15 14:40:52,987 Resampling CI.TOW2..BHZ from 40.0 to 100 Hz
+2023-03-15 14:40:52,993 Resampling CI.WBM..BHE from 40.0 to 100 Hz
+2023-03-15 14:40:53,000 Resampling CI.WBM..BHN from 40.0 to 100 Hz
+2023-03-15 14:40:53,006 Resampling CI.WBM..BHZ from 40.0 to 100 Hz
+2023-03-15 14:40:53,013 Resampling CI.WCS2..BHE from 40.0 to 100 Hz
+2023-03-15 14:40:53,019 Resampling CI.WCS2..BHN from 40.0 to 100 Hz
+2023-03-15 14:40:53,026 Resampling CI.WCS2..BHZ from 40.0 to 100 Hz
+2023-03-15 14:40:53,032 Resampling CI.WMF..BHE from 40.0 to 100 Hz
+2023-03-15 14:40:53,038 Resampling CI.WMF..BHN from 40.0 to 100 Hz
+2023-03-15 14:40:53,045 Resampling CI.WMF..BHZ from 40.0 to 100 Hz
+2023-03-15 14:40:53,051 Resampling CI.WRC2..BHE from 40.0 to 100 Hz
+2023-03-15 14:40:53,057 Resampling CI.WRC2..BHN from 40.0 to 100 Hz
+2023-03-15 14:40:53,063 Resampling CI.WRC2..BHZ from 40.0 to 100 Hz
+Empty trace: CI.LRL.2C.HNE 2019-07-04T17:59:59.994500Z
+Empty trace: CI.LRL.2C.HNN 2019-07-04T17:59:59.994500Z
+Empty trace: CI.LRL.2C.HNZ 2019-07-04T17:59:59.994500Z
+Empty trace: CI.Q0072.01.HNE 2019-07-04T17:59:59.994500Z
+Empty trace: CI.Q0072.01.HNN 2019-07-04T17:59:59.994500Z
+Empty trace: CI.Q0072.01.HNZ 2019-07-04T17:59:59.994500Z
+Empty trace: CI.WBM.2C.HNE 2019-07-04T17:59:59.994500Z
+Empty trace: CI.WBM.2C.HNN 2019-07-04T17:59:59.994500Z
+Empty trace: CI.WBM.2C.HNZ 2019-07-04T17:59:59.994500Z
+Empty trace: CI.WMF.2C.HNE 2019-07-04T17:59:59.994500Z
+Empty trace: CI.WMF.2C.HNN 2019-07-04T17:59:59.994500Z
+Empty trace: CI.WMF.2C.HNZ 2019-07-04T17:59:59.994500Z
+Empty trace: CI.WNM.2C.HNE 2019-07-04T17:59:59.994500Z
+Empty trace: CI.WNM.2C.HNN 2019-07-04T17:59:59.994500Z
+Empty trace: CI.WNM.2C.HNZ 2019-07-04T17:59:59.994500Z
+Empty trace: CI.WRV2.2C.HNE 2019-07-04T17:59:59.994500Z
+Empty trace: CI.WRV2.2C.HNN 2019-07-04T17:59:59.994500Z
+Empty trace: CI.WRV2.2C.HNZ 2019-07-04T17:59:59.994500Z
+Empty trace: CI.WVP2.2C.HNE 2019-07-04T17:59:59.994500Z
+Empty trace: CI.WVP2.2C.HNN 2019-07-04T17:59:59.994500Z
+Empty trace: CI.WVP2.2C.HNZ 2019-07-04T17:59:59.994500Z
+Pred: 100%|███████████████████████████████████████| 2/2 [01:06&lt;00:00, 33.49s/it]
+Done with 8856 P-picks and 9294 S-picks
+</pre>
+</div>
+</div>
+
+</div>
+
+</div>
+
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
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+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="6-run-gamma-to-associate-ps-picks">6. Run GaMMA to associate P/S picks<a class="anchor-link" href="#6-run-gamma-to-associate-ps-picks">&#182;</a></h2>
+</div>
+</div>
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+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[38]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
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+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">def</span> <span class="nf">gamma</span><span class="p">(</span>
+    <span class="n">node_i</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
+    <span class="n">index_json</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="s2">&quot;json&quot;</span><span class="p">),</span>
+    <span class="n">config_json</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="s2">&quot;json&quot;</span><span class="p">),</span>
+    <span class="n">pick_csv</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="s2">&quot;csv&quot;</span><span class="p">),</span>
+    <span class="n">station_json</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="s2">&quot;json&quot;</span><span class="p">),</span>
+    <span class="n">gamma_catalog_csv</span><span class="p">:</span> <span class="n">OutputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span>
+    <span class="n">gamma_pick_csv</span><span class="p">:</span> <span class="n">OutputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span>
+    <span class="n">bucket_name</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;catalogs&quot;</span><span class="p">,</span>
+    <span class="n">s3_url</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;minio-service:9000&quot;</span><span class="p">,</span>
+    <span class="n">secure</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
+
+    <span class="kn">import</span> <span class="nn">json</span>
+    <span class="kn">import</span> <span class="nn">os</span>
+    <span class="kn">import</span> <span class="nn">pickle</span>
+    <span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">datetime</span><span class="p">,</span> <span class="n">timedelta</span>
+
+    <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+    <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+    <span class="kn">from</span> <span class="nn">gamma.utils</span> <span class="kn">import</span> <span class="n">association</span><span class="p">,</span> <span class="n">convert_picks_csv</span><span class="p">,</span> <span class="n">from_seconds</span>
+    <span class="kn">from</span> <span class="nn">pyproj</span> <span class="kn">import</span> <span class="n">Proj</span>
+    <span class="kn">from</span> <span class="nn">tqdm</span> <span class="kn">import</span> <span class="n">tqdm</span>
+
+    <span class="n">catalog_dir</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="s2">&quot;/tmp/&quot;</span><span class="p">,</span> <span class="n">bucket_name</span><span class="p">)</span>
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">catalog_dir</span><span class="p">):</span>
+        <span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">catalog_dir</span><span class="p">)</span>
+
+    <span class="c1">## read config</span>
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">index_json</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">index</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">fp</span><span class="p">)</span>
+    <span class="n">idx</span> <span class="o">=</span> <span class="n">index</span><span class="p">[</span><span class="n">node_i</span><span class="p">]</span>
+
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">config_json</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">config</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">fp</span><span class="p">)</span>
+
+    <span class="c1">## read picks</span>
+    <span class="c1"># picks = pd.read_json(pick_json)</span>
+    <span class="n">picks</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">pick_csv</span><span class="p">,</span> <span class="n">parse_dates</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;phase_time&quot;</span><span class="p">])</span>
+    <span class="n">picks</span><span class="p">[</span><span class="s2">&quot;id&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">picks</span><span class="p">[</span><span class="s2">&quot;station_id&quot;</span><span class="p">]</span>
+    <span class="n">picks</span><span class="p">[</span><span class="s2">&quot;timestamp&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">picks</span><span class="p">[</span><span class="s2">&quot;phase_time&quot;</span><span class="p">]</span>
+    <span class="n">picks</span><span class="p">[</span><span class="s2">&quot;amp&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">picks</span><span class="p">[</span><span class="s2">&quot;phase_amp&quot;</span><span class="p">]</span>
+    <span class="n">picks</span><span class="p">[</span><span class="s2">&quot;type&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">picks</span><span class="p">[</span><span class="s2">&quot;phase_type&quot;</span><span class="p">]</span>
+    <span class="n">picks</span><span class="p">[</span><span class="s2">&quot;prob&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">picks</span><span class="p">[</span><span class="s2">&quot;phase_score&quot;</span><span class="p">]</span>
+
+    <span class="c1">## read stations</span>
+    <span class="c1"># stations = pd.read_csv(station_csv, delimiter=&quot;\t&quot;)</span>
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">station_json</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">stations</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">fp</span><span class="p">)</span>
+    <span class="n">stations</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">from_dict</span><span class="p">(</span><span class="n">stations</span><span class="p">,</span> <span class="n">orient</span><span class="o">=</span><span class="s2">&quot;index&quot;</span><span class="p">)</span>
+    <span class="c1"># stations = stations.rename(columns={&quot;station&quot;: &quot;id&quot;})</span>
+    <span class="n">stations</span><span class="p">[</span><span class="s2">&quot;id&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">stations</span><span class="o">.</span><span class="n">index</span>
+    <span class="n">proj</span> <span class="o">=</span> <span class="n">Proj</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;+proj=sterea +lon_0=</span><span class="si">{</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;center&#39;</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s2"> +lat_0=</span><span class="si">{</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;center&#39;</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span><span class="si">}</span><span class="s2"> +units=km&quot;</span><span class="p">)</span>
+    <span class="n">stations</span><span class="p">[[</span><span class="s2">&quot;x(km)&quot;</span><span class="p">,</span> <span class="s2">&quot;y(km)&quot;</span><span class="p">]]</span> <span class="o">=</span> <span class="n">stations</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span>
+        <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">proj</span><span class="p">(</span><span class="n">longitude</span><span class="o">=</span><span class="n">x</span><span class="o">.</span><span class="n">longitude</span><span class="p">,</span> <span class="n">latitude</span><span class="o">=</span><span class="n">x</span><span class="o">.</span><span class="n">latitude</span><span class="p">)),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span>
+    <span class="p">)</span>
+    <span class="n">stations</span><span class="p">[</span><span class="s2">&quot;z(km)&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">stations</span><span class="p">[</span><span class="s2">&quot;elevation(m)&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="o">-</span><span class="n">x</span> <span class="o">/</span> <span class="mf">1e3</span><span class="p">)</span>
+
+    <span class="c1">## setting GMMA configs</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;use_dbscan&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">True</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;use_amplitude&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">True</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;method&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;BGMM&quot;</span>
+    <span class="k">if</span> <span class="n">config</span><span class="p">[</span><span class="s2">&quot;method&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;BGMM&quot;</span><span class="p">:</span>  <span class="c1">## BayesianGaussianMixture</span>
+        <span class="n">config</span><span class="p">[</span><span class="s2">&quot;oversample_factor&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">4</span>
+    <span class="k">if</span> <span class="n">config</span><span class="p">[</span><span class="s2">&quot;method&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;GMM&quot;</span><span class="p">:</span>  <span class="c1">## GaussianMixture</span>
+        <span class="n">config</span><span class="p">[</span><span class="s2">&quot;oversample_factor&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
+
+    <span class="c1"># Earthquake location</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;dims&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;x(km)&quot;</span><span class="p">,</span> <span class="s2">&quot;y(km)&quot;</span><span class="p">,</span> <span class="s2">&quot;z(km)&quot;</span><span class="p">]</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;vel&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;p&quot;</span><span class="p">:</span> <span class="mf">6.0</span><span class="p">,</span> <span class="s2">&quot;s&quot;</span><span class="p">:</span> <span class="mf">6.0</span> <span class="o">/</span> <span class="mf">1.73</span><span class="p">}</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;x(km)&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;xlim_degree&quot;</span><span class="p">])</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;center&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">]))</span> <span class="o">*</span> <span class="n">config</span><span class="p">[</span><span class="s2">&quot;degree2km&quot;</span><span class="p">]</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;y(km)&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;ylim_degree&quot;</span><span class="p">])</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;center&quot;</span><span class="p">][</span><span class="mi">1</span><span class="p">]))</span> <span class="o">*</span> <span class="n">config</span><span class="p">[</span><span class="s2">&quot;degree2km&quot;</span><span class="p">]</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;z(km)&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">60</span><span class="p">)</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;bfgs_bounds&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span>
+        <span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;x(km)&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">config</span><span class="p">[</span><span class="s2">&quot;x(km)&quot;</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="mi">1</span><span class="p">),</span>  <span class="c1"># x</span>
+        <span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;y(km)&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">config</span><span class="p">[</span><span class="s2">&quot;y(km)&quot;</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="mi">1</span><span class="p">),</span>  <span class="c1"># y</span>
+        <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">config</span><span class="p">[</span><span class="s2">&quot;z(km)&quot;</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="mi">1</span><span class="p">),</span>  <span class="c1"># z</span>
+        <span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">),</span>  <span class="c1"># t</span>
+    <span class="p">)</span>
+
+    <span class="c1"># DBSCAN</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;dbscan_eps&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">10</span>  <span class="c1"># second</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;dbscan_min_samples&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">3</span>  <span class="c1">## see DBSCAN</span>
+
+    <span class="c1"># Filtering</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;min_picks_per_eq&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">stations</span><span class="p">)</span> <span class="o">//</span> <span class="mi">2</span><span class="p">)</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;min_p_picks_per_eq&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;min_s_picks_per_eq&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;max_sigma11&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="mf">2.0</span>  <span class="c1"># s</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;max_sigma22&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="mf">2.0</span>  <span class="c1"># m/s</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;max_sigma12&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="mf">1.0</span>  <span class="c1"># covariance</span>
+
+    <span class="c1"># if use amplitude</span>
+    <span class="k">if</span> <span class="n">config</span><span class="p">[</span><span class="s2">&quot;use_amplitude&quot;</span><span class="p">]:</span>
+        <span class="n">picks</span> <span class="o">=</span> <span class="n">picks</span><span class="p">[</span><span class="n">picks</span><span class="p">[</span><span class="s2">&quot;amp&quot;</span><span class="p">]</span> <span class="o">!=</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+
+    <span class="c1"># print(config)</span>
+    <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">config</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">k</span><span class="si">}</span><span class="s2">: </span><span class="si">{</span><span class="n">v</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+
+    <span class="c1">## run GMMA association</span>
+    <span class="n">event_idx0</span> <span class="o">=</span> <span class="mi">1</span>
+    <span class="n">assignments</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">catalogs</span><span class="p">,</span> <span class="n">assignments</span> <span class="o">=</span> <span class="n">association</span><span class="p">(</span><span class="n">picks</span><span class="p">,</span> <span class="n">stations</span><span class="p">,</span> <span class="n">config</span><span class="p">,</span> <span class="n">event_idx0</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;method&quot;</span><span class="p">])</span>
+    <span class="n">event_idx0</span> <span class="o">+=</span> <span class="nb">len</span><span class="p">(</span><span class="n">catalogs</span><span class="p">)</span>
+
+    <span class="c1">## create catalog</span>
+    <span class="n">catalogs</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span>
+        <span class="n">catalogs</span><span class="p">,</span>
+        <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;time&quot;</span><span class="p">]</span>
+        <span class="o">+</span> <span class="n">config</span><span class="p">[</span><span class="s2">&quot;dims&quot;</span><span class="p">]</span>
+        <span class="o">+</span> <span class="p">[</span>
+            <span class="s2">&quot;magnitude&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;sigma_time&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;sigma_amp&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;cov_time_amp&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;event_index&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;gamma_score&quot;</span><span class="p">,</span>
+        <span class="p">],</span>
+    <span class="p">)</span>
+
+    <span class="n">catalogs</span><span class="p">[[</span><span class="s2">&quot;longitude&quot;</span><span class="p">,</span> <span class="s2">&quot;latitude&quot;</span><span class="p">]]</span> <span class="o">=</span> <span class="n">catalogs</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span>
+        <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">proj</span><span class="p">(</span><span class="n">longitude</span><span class="o">=</span><span class="n">x</span><span class="p">[</span><span class="s2">&quot;x(km)&quot;</span><span class="p">],</span> <span class="n">latitude</span><span class="o">=</span><span class="n">x</span><span class="p">[</span><span class="s2">&quot;y(km)&quot;</span><span class="p">],</span> <span class="n">inverse</span><span class="o">=</span><span class="kc">True</span><span class="p">)),</span>
+        <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+    <span class="p">)</span>
+    <span class="n">catalogs</span><span class="p">[</span><span class="s2">&quot;depth(m)&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">catalogs</span><span class="p">[</span><span class="s2">&quot;z(km)&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span> <span class="o">*</span> <span class="mf">1e3</span><span class="p">)</span>
+
+    <span class="n">catalogs</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">by</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;time&quot;</span><span class="p">],</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">gamma_catalog_csv</span><span class="p">,</span> <span class="s2">&quot;w&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">catalogs</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span>
+            <span class="n">fp</span><span class="p">,</span>
+            <span class="c1"># sep=&quot;\t&quot;,</span>
+            <span class="n">index</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+            <span class="n">float_format</span><span class="o">=</span><span class="s2">&quot;</span><span class="si">%.3f</span><span class="s2">&quot;</span><span class="p">,</span>
+            <span class="n">date_format</span><span class="o">=</span><span class="s2">&quot;%Y-%m-</span><span class="si">%d</span><span class="s2">T%H:%M:%S.</span><span class="si">%f</span><span class="s2">&quot;</span><span class="p">,</span>
+            <span class="n">columns</span><span class="o">=</span><span class="p">[</span>
+                <span class="s2">&quot;time&quot;</span><span class="p">,</span>
+                <span class="s2">&quot;magnitude&quot;</span><span class="p">,</span>
+                <span class="s2">&quot;longitude&quot;</span><span class="p">,</span>
+                <span class="s2">&quot;latitude&quot;</span><span class="p">,</span>
+                <span class="s2">&quot;depth(m)&quot;</span><span class="p">,</span>
+                <span class="s2">&quot;sigma_time&quot;</span><span class="p">,</span>
+                <span class="s2">&quot;sigma_amp&quot;</span><span class="p">,</span>
+                <span class="s2">&quot;cov_time_amp&quot;</span><span class="p">,</span>
+                <span class="s2">&quot;gamma_score&quot;</span><span class="p">,</span>
+                <span class="s2">&quot;event_index&quot;</span><span class="p">,</span>
+            <span class="p">],</span>
+        <span class="p">)</span>
+    <span class="c1"># catalogs = catalogs[</span>
+    <span class="c1">#     [&#39;time&#39;, &#39;magnitude&#39;, &#39;longitude&#39;, &#39;latitude&#39;, &#39;depth(m)&#39;, &#39;sigma_time&#39;, &#39;sigma_amp&#39;]</span>
+    <span class="c1"># ]</span>
+
+    <span class="c1">## add assignment to picks</span>
+    <span class="n">assignments</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">assignments</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;pick_index&quot;</span><span class="p">,</span> <span class="s2">&quot;event_index&quot;</span><span class="p">,</span> <span class="s2">&quot;gamma_score&quot;</span><span class="p">])</span>
+    <span class="n">picks</span> <span class="o">=</span> <span class="n">picks</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">assignments</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">&quot;pick_index&quot;</span><span class="p">))</span><span class="o">.</span><span class="n">fillna</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">({</span><span class="s2">&quot;event_index&quot;</span><span class="p">:</span> <span class="nb">int</span><span class="p">})</span>
+    <span class="n">picks</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">by</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;timestamp&quot;</span><span class="p">],</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">gamma_pick_csv</span><span class="p">,</span> <span class="s2">&quot;w&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">picks</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span>
+            <span class="n">fp</span><span class="p">,</span>
+            <span class="c1"># sep=&quot;\t&quot;,</span>
+            <span class="n">index</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+            <span class="n">date_format</span><span class="o">=</span><span class="s2">&quot;%Y-%m-</span><span class="si">%d</span><span class="s2">T%H:%M:%S.</span><span class="si">%f</span><span class="s2">&quot;</span><span class="p">,</span>
+            <span class="n">columns</span><span class="o">=</span><span class="p">[</span>
+                <span class="s2">&quot;station_id&quot;</span><span class="p">,</span>
+                <span class="s2">&quot;phase_time&quot;</span><span class="p">,</span>
+                <span class="s2">&quot;phase_type&quot;</span><span class="p">,</span>
+                <span class="s2">&quot;phase_score&quot;</span><span class="p">,</span>
+                <span class="s2">&quot;phase_amp&quot;</span><span class="p">,</span>
+                <span class="s2">&quot;gamma_score&quot;</span><span class="p">,</span>
+                <span class="s2">&quot;event_index&quot;</span><span class="p">,</span>
+            <span class="p">],</span>
+        <span class="p">)</span>
+
+    <span class="c1">## upload to s3 bucket</span>
+    <span class="k">try</span><span class="p">:</span>
+        <span class="kn">from</span> <span class="nn">minio</span> <span class="kn">import</span> <span class="n">Minio</span>
+
+        <span class="n">minioClient</span> <span class="o">=</span> <span class="n">Minio</span><span class="p">(</span><span class="n">s3_url</span><span class="p">,</span> <span class="n">access_key</span><span class="o">=</span><span class="s2">&quot;minio&quot;</span><span class="p">,</span> <span class="n">secret_key</span><span class="o">=</span><span class="s2">&quot;minio123&quot;</span><span class="p">,</span> <span class="n">secure</span><span class="o">=</span><span class="n">secure</span><span class="p">)</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">minioClient</span><span class="o">.</span><span class="n">bucket_exists</span><span class="p">(</span><span class="n">bucket_name</span><span class="p">):</span>
+            <span class="n">minioClient</span><span class="o">.</span><span class="n">make_bucket</span><span class="p">(</span><span class="n">bucket_name</span><span class="p">)</span>
+
+        <span class="n">minioClient</span><span class="o">.</span><span class="n">fput_object</span><span class="p">(</span>
+            <span class="n">bucket_name</span><span class="p">,</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;region&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">/gamma/catalog_</span><span class="si">{</span><span class="n">node_i</span><span class="si">:</span><span class="s2">03d</span><span class="si">}</span><span class="s2">.csv&quot;</span><span class="p">,</span>
+            <span class="n">gamma_catalog_csv</span><span class="p">,</span>
+        <span class="p">)</span>
+
+        <span class="n">minioClient</span><span class="o">.</span><span class="n">fput_object</span><span class="p">(</span>
+            <span class="n">bucket_name</span><span class="p">,</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;region&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">/gamma/picks_</span><span class="si">{</span><span class="n">node_i</span><span class="si">:</span><span class="s2">03d</span><span class="si">}</span><span class="s2">.csv&quot;</span><span class="p">,</span>
+            <span class="n">gamma_pick_csv</span><span class="p">,</span>
+        <span class="p">)</span>
+
+    <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">err</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;ERROR: can not access minio service! </span><span class="se">\n</span><span class="si">{</span><span class="n">err</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+        <span class="k">pass</span>
+
+    <span class="c1">## upload to mongodb</span>
+    <span class="k">try</span><span class="p">:</span>
+        <span class="kn">from</span> <span class="nn">pymongo</span> <span class="kn">import</span> <span class="n">MongoClient</span>
+
+        <span class="n">username</span> <span class="o">=</span> <span class="s2">&quot;root&quot;</span>
+        <span class="n">password</span> <span class="o">=</span> <span class="s2">&quot;quakeflow123&quot;</span>
+        <span class="n">client</span> <span class="o">=</span> <span class="n">MongoClient</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;mongodb://</span><span class="si">{</span><span class="n">username</span><span class="si">}</span><span class="s2">:</span><span class="si">{</span><span class="n">password</span><span class="si">}</span><span class="s2">@127.0.0.1:27017&quot;</span><span class="p">)</span>
+        <span class="n">db</span> <span class="o">=</span> <span class="n">client</span><span class="p">[</span><span class="s2">&quot;quakeflow&quot;</span><span class="p">]</span>
+        <span class="n">collection</span> <span class="o">=</span> <span class="n">db</span><span class="p">[</span><span class="s2">&quot;waveform&quot;</span><span class="p">]</span>
+        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">picks</span><span class="o">.</span><span class="n">iterrows</span><span class="p">(),</span> <span class="n">desc</span><span class="o">=</span><span class="s2">&quot;Uploading to mongodb&quot;</span><span class="p">):</span>
+            <span class="n">collection</span><span class="o">.</span><span class="n">update</span><span class="p">(</span>
+                <span class="p">{</span><span class="s2">&quot;_id&quot;</span><span class="p">:</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">p</span><span class="p">[</span><span class="s1">&#39;station_id&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">_</span><span class="si">{</span><span class="n">p</span><span class="p">[</span><span class="s1">&#39;timestamp&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">isoformat</span><span class="p">(</span><span class="n">timespec</span><span class="o">=</span><span class="s1">&#39;milliseconds&#39;</span><span class="p">)</span><span class="si">}</span><span class="s2">_</span><span class="si">{</span><span class="n">p</span><span class="p">[</span><span class="s1">&#39;type&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">},</span>
+                <span class="p">{</span><span class="s2">&quot;$set&quot;</span><span class="p">:</span> <span class="p">{</span><span class="s2">&quot;event_index&quot;</span><span class="p">:</span> <span class="n">p</span><span class="p">[</span><span class="s2">&quot;event_index&quot;</span><span class="p">]}},</span>
+            <span class="p">)</span>
+    <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">err</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;ERROR: can not access mongodb service! </span><span class="se">\n</span><span class="si">{</span><span class="n">err</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+        <span class="k">pass</span>
+
+    <span class="k">return</span> <span class="sa">f</span><span class="s2">&quot;catalog_</span><span class="si">{</span><span class="n">node_i</span><span class="si">:</span><span class="s2">03d</span><span class="si">}</span><span class="s2">.csv&quot;</span>
+</pre></div>
+<div id="cell-17" class="clipboard-copy-txt">def gamma(
+    node_i: int,
+    index_json: InputPath("json"),
+    config_json: InputPath("json"),
+    pick_csv: InputPath("csv"),
+    station_json: InputPath("json"),
+    gamma_catalog_csv: OutputPath(str),
+    gamma_pick_csv: OutputPath(str),
+    bucket_name: str = "catalogs",
+    s3_url: str = "minio-service:9000",
+    secure: bool = True,
+) -> str:
+
+    import json
+    import os
+    import pickle
+    from datetime import datetime, timedelta
+
+    import numpy as np
+    import pandas as pd
+    from gamma.utils import association, convert_picks_csv, from_seconds
+    from pyproj import Proj
+    from tqdm import tqdm
+
+    catalog_dir = os.path.join("/tmp/", bucket_name)
+    if not os.path.exists(catalog_dir):
+        os.makedirs(catalog_dir)
+
+    ## read config
+    with open(index_json, "r") as fp:
+        index = json.load(fp)
+    idx = index[node_i]
+
+    with open(config_json, "r") as fp:
+        config = json.load(fp)
+
+    ## read picks
+    # picks = pd.read_json(pick_json)
+    picks = pd.read_csv(pick_csv, parse_dates=["phase_time"])
+    picks["id"] = picks["station_id"]
+    picks["timestamp"] = picks["phase_time"]
+    picks["amp"] = picks["phase_amp"]
+    picks["type"] = picks["phase_type"]
+    picks["prob"] = picks["phase_score"]
+
+    ## read stations
+    # stations = pd.read_csv(station_csv, delimiter="\t")
+    with open(station_json, "r") as fp:
+        stations = json.load(fp)
+    stations = pd.DataFrame.from_dict(stations, orient="index")
+    # stations = stations.rename(columns={"station": "id"})
+    stations["id"] = stations.index
+    proj = Proj(f"+proj=sterea +lon_0={config['center'][0]} +lat_0={config['center'][1]} +units=km")
+    stations[["x(km)", "y(km)"]] = stations.apply(
+        lambda x: pd.Series(proj(longitude=x.longitude, latitude=x.latitude)), axis=1
+    )
+    stations["z(km)"] = stations["elevation(m)"].apply(lambda x: -x / 1e3)
+
+    ## setting GMMA configs
+    config["use_dbscan"] = True
+    config["use_amplitude"] = True
+    config["method"] = "BGMM"
+    if config["method"] == "BGMM":  ## BayesianGaussianMixture
+        config["oversample_factor"] = 4
+    if config["method"] == "GMM":  ## GaussianMixture
+        config["oversample_factor"] = 1
+
+    # Earthquake location
+    config["dims"] = ["x(km)", "y(km)", "z(km)"]
+    config["vel"] = {"p": 6.0, "s": 6.0 / 1.73}
+    config["x(km)"] = (np.array(config["xlim_degree"]) - np.array(config["center"][0])) * config["degree2km"]
+    config["y(km)"] = (np.array(config["ylim_degree"]) - np.array(config["center"][1])) * config["degree2km"]
+    config["z(km)"] = (0, 60)
+    config["bfgs_bounds"] = (
+        (config["x(km)"][0] - 1, config["x(km)"][1] + 1),  # x
+        (config["y(km)"][0] - 1, config["y(km)"][1] + 1),  # y
+        (0, config["z(km)"][1] + 1),  # z
+        (None, None),  # t
+    )
+
+    # DBSCAN
+    config["dbscan_eps"] = 10  # second
+    config["dbscan_min_samples"] = 3  ## see DBSCAN
+
+    # Filtering
+    config["min_picks_per_eq"] = min(10, len(stations) // 2)
+    config["min_p_picks_per_eq"] = 0
+    config["min_s_picks_per_eq"] = 0
+    config["max_sigma11"] = 2.0  # s
+    config["max_sigma22"] = 2.0  # m/s
+    config["max_sigma12"] = 1.0  # covariance
+
+    # if use amplitude
+    if config["use_amplitude"]:
+        picks = picks[picks["amp"] != -1]
+
+    # print(config)
+    for k, v in config.items():
+        print(f"{k}: {v}")
+
+    ## run GMMA association
+    event_idx0 = 1
+    assignments = []
+    catalogs, assignments = association(picks, stations, config, event_idx0, method=config["method"])
+    event_idx0 += len(catalogs)
+
+    ## create catalog
+    catalogs = pd.DataFrame(
+        catalogs,
+        columns=["time"]
+        + config["dims"]
+        + [
+            "magnitude",
+            "sigma_time",
+            "sigma_amp",
+            "cov_time_amp",
+            "event_index",
+            "gamma_score",
+        ],
+    )
+
+    catalogs[["longitude", "latitude"]] = catalogs.apply(
+        lambda x: pd.Series(proj(longitude=x["x(km)"], latitude=x["y(km)"], inverse=True)),
+        axis=1,
+    )
+    catalogs["depth(m)"] = catalogs["z(km)"].apply(lambda x: x * 1e3)
+
+    catalogs.sort_values(by=["time"], inplace=True)
+    with open(gamma_catalog_csv, "w") as fp:
+        catalogs.to_csv(
+            fp,
+            # sep="\t",
+            index=False,
+            float_format="%.3f",
+            date_format="%Y-%m-%dT%H:%M:%S.%f",
+            columns=[
+                "time",
+                "magnitude",
+                "longitude",
+                "latitude",
+                "depth(m)",
+                "sigma_time",
+                "sigma_amp",
+                "cov_time_amp",
+                "gamma_score",
+                "event_index",
+            ],
+        )
+    # catalogs = catalogs[
+    #     ['time', 'magnitude', 'longitude', 'latitude', 'depth(m)', 'sigma_time', 'sigma_amp']
+    # ]
+
+    ## add assignment to picks
+    assignments = pd.DataFrame(assignments, columns=["pick_index", "event_index", "gamma_score"])
+    picks = picks.join(assignments.set_index("pick_index")).fillna(-1).astype({"event_index": int})
+    picks.sort_values(by=["timestamp"], inplace=True)
+    with open(gamma_pick_csv, "w") as fp:
+        picks.to_csv(
+            fp,
+            # sep="\t",
+            index=False,
+            date_format="%Y-%m-%dT%H:%M:%S.%f",
+            columns=[
+                "station_id",
+                "phase_time",
+                "phase_type",
+                "phase_score",
+                "phase_amp",
+                "gamma_score",
+                "event_index",
+            ],
+        )
+
+    ## upload to s3 bucket
+    try:
+        from minio import Minio
+
+        minioClient = Minio(s3_url, access_key="minio", secret_key="minio123", secure=secure)
+        if not minioClient.bucket_exists(bucket_name):
+            minioClient.make_bucket(bucket_name)
+
+        minioClient.fput_object(
+            bucket_name,
+            f"{config['region']}/gamma/catalog_{node_i:03d}.csv",
+            gamma_catalog_csv,
+        )
+
+        minioClient.fput_object(
+            bucket_name,
+            f"{config['region']}/gamma/picks_{node_i:03d}.csv",
+            gamma_pick_csv,
+        )
+
+    except Exception as err:
+        print(f"ERROR: can not access minio service! \n{err}")
+        pass
+
+    ## upload to mongodb
+    try:
+        from pymongo import MongoClient
+
+        username = "root"
+        password = "quakeflow123"
+        client = MongoClient(f"mongodb://{username}:{password}@127.0.0.1:27017")
+        db = client["quakeflow"]
+        collection = db["waveform"]
+        for i, p in tqdm(picks.iterrows(), desc="Uploading to mongodb"):
+            collection.update(
+                {"_id": f"{p['station_id']}_{p['timestamp'].isoformat(timespec='milliseconds')}_{p['type']}"},
+                {"$set": {"event_index": p["event_index"]}},
+            )
+    except Exception as err:
+        print(f"ERROR: can not access mongodb service! \n{err}")
+        pass
+
+    return f"catalog_{node_i:03d}.csv"</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[39]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-18">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">if</span> <span class="n">run_local</span><span class="p">:</span>
+    <span class="n">catalog</span> <span class="o">=</span> <span class="n">gamma</span><span class="p">(</span>
+        <span class="mi">0</span><span class="p">,</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;index.json&quot;</span><span class="p">),</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;config.json&quot;</span><span class="p">),</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;phasenet/picks.csv&quot;</span><span class="p">),</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;stations.json&quot;</span><span class="p">),</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;gamma_catalog.csv&quot;</span><span class="p">),</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;gamma_picks.csv&quot;</span><span class="p">),</span>
+        <span class="n">bucket_name</span><span class="o">=</span><span class="s2">&quot;catalogs&quot;</span><span class="p">,</span>
+        <span class="n">s3_url</span><span class="o">=</span><span class="s2">&quot;localhost:9000&quot;</span><span class="p">,</span>
+        <span class="n">secure</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="p">)</span>
+</pre></div>
+<div id="cell-18" class="clipboard-copy-txt">if run_local:
+    catalog = gamma(
+        0,
+        root_dir("index.json"),
+        root_dir("config.json"),
+        root_dir("phasenet/picks.csv"),
+        root_dir("stations.json"),
+        root_dir("gamma_catalog.csv"),
+        root_dir("gamma_picks.csv"),
+        bucket_name="catalogs",
+        s3_url="localhost:9000",
+        secure=False,
+    )</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
+<pre>region: Demo
+center: [-117.504, 35.705]
+xlim_degree: [-118.004, -117.004]
+ylim_degree: [35.205, 36.205]
+min_longitude: -118.004
+max_longitude: -117.004
+min_latitude: 35.205
+max_latitude: 36.205
+degree2km: 111.19492664455873
+starttime: 2019-07-04T17:00:00.000
+endtime: 2019-07-04T19:00:00.000
+networks: [&#39;CI&#39;]
+channels: HH*,BH*,EH*,HN*
+client: SCEDC
+phasenet: {}
+gamma: {}
+hypodd: {&#39;MAXEVENT&#39;: 10000.0}
+use_dbscan: True
+use_amplitude: True
+method: BGMM
+oversample_factor: 4
+dims: [&#39;x(km)&#39;, &#39;y(km)&#39;, &#39;z(km)&#39;]
+vel: {&#39;p&#39;: 6.0, &#39;s&#39;: 3.468208092485549}
+x(km): [-55.59746332  55.59746332]
+y(km): [-55.59746332  55.59746332]
+z(km): (0, 60)
+bfgs_bounds: ((-56.597463322279367, 56.597463322279367), (-56.597463322279367, 56.597463322279367), (0, 61), (None, None))
+dbscan_eps: 10
+dbscan_min_samples: 3
+min_picks_per_eq: 10
+min_p_picks_per_eq: 0
+min_s_picks_per_eq: 0
+max_sigma11: 2.0
+max_sigma22: 2.0
+max_sigma12: 1.0
+Associating 81 clusters with 79 CPUs
+.................................................................................
+Finish 99 events
+
+Finish 199 events
+
+Finish 299 events
+ERROR: can not access minio service! 
+No module named &#39;minio&#39;
+</pre>
+</div>
+</div>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>Uploading to mongodb: 0it [00:30, ?it/s]</pre>
+</div>
+</div>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
+<pre>ERROR: can not access mongodb service! 
+127.0.0.1:27017: [Errno 111] Connection refused, Timeout: 30s, Topology Description: &lt;TopologyDescription id: 64123c7172f5f960d0d7c589, topology_type: Single, servers: [&lt;ServerDescription (&#39;127.0.0.1&#39;, 27017) server_type: Unknown, rtt: None, error=AutoReconnect(&#39;127.0.0.1:27017: [Errno 111] Connection refused&#39;)&gt;]&gt;
+</pre>
+</div>
+</div>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>
+</pre>
+</div>
+</div>
+
+</div>
+
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[40]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-19">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">gamma_op</span> <span class="o">=</span> <span class="n">comp</span><span class="o">.</span><span class="n">func_to_container_op</span><span class="p">(</span>
+    <span class="n">gamma</span><span class="p">,</span>
+    <span class="c1"># base_image=&#39;zhuwq0/quakeflow-env:latest&#39;,</span>
+    <span class="n">base_image</span><span class="o">=</span><span class="s2">&quot;python:3.8&quot;</span><span class="p">,</span>
+    <span class="n">packages_to_install</span><span class="o">=</span><span class="p">[</span>
+        <span class="s2">&quot;pandas&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;numpy&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;scikit-learn&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;tqdm&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;minio&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;gmma&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;pyproj&quot;</span><span class="p">,</span>
+    <span class="p">],</span>
+<span class="p">)</span>
+</pre></div>
+<div id="cell-19" class="clipboard-copy-txt">gamma_op = comp.func_to_container_op(
+    gamma,
+    # base_image='zhuwq0/quakeflow-env:latest',
+    base_image="python:3.8",
+    packages_to_install=[
+        "pandas",
+        "numpy",
+        "scikit-learn",
+        "tqdm",
+        "minio",
+        "gmma",
+        "pyproj",
+    ],
+)</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h3 id="plot-catalogs">Plot catalogs<a class="anchor-link" href="#plot-catalogs">&#182;</a></h3>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[41]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-20">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">if</span> <span class="n">run_local</span><span class="p">:</span>
+    <span class="o">%</span><span class="n">run</span> <span class="n">plot_catalog</span><span class="o">.</span><span class="n">ipynb</span>
+</pre></div>
+<div id="cell-20" class="clipboard-copy-txt">if run_local:
+    %run plot_catalog.ipynb</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+
+
+<div class="jp-RenderedImage jp-OutputArea-output ">
+<img 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Ud292jQSlzaeaP5jdUekpsSzdkrKcTGnOWCc39COrUxJMPrD8XjdWY5xTF7YOvr+nNb2bPuhJp1ia3Yk+WhP0fZ5j83q5UVxVDvQ4WFaCDSLVYIIYQQ4qaIiAgAo7mUNWFubk7Xrl1lHUtTK+nuqvSAM1vALQC4NSQXYFpoe+NjSneZ9Q6mTa9CPK8qGetxERf/gWBrA3mZTHL6mxPu3bC4kQsuvoZGPuNa6pPlkOY3IPGw/volS6OU6g5bUQxVxiVEEyLJpRBCCCHETdeuXUOhUNC8efNan8PNzY3Dhw/XYVSN2x0vlVEm+aqTGEo6t0Z8qe/cmhINLj6G6mOFQ3K9gyE7VZ9gugXg0u0hJttuJ+dCBL+cTyV45NNYxe7leuR6ervaYd3rNaPlSVS2NxPDPDewtbo1BLbM0igVxVB2myw/IpoqGRYrhBBCCHFTXl4elpaWmJub1/ocNjY25OXl1WFUjdsdN+ip4dIc1Y6hpMGOjdKQ6FU5JNfWGdTxcOkv/XxNAO9gwnLbGuZcbkjy4OeCXuw270eKqj/fXLAle88SyLhsOE3yDRsWX2lN8g0bwzky3HqxPK4FyZp8oxhKhsMCRnHJ8iOiqZLKpRBCCCHETc2bN6ewsJCcnBzs7e1rdY709PQ7qnw2NVVWA6ujdNObuoyh9HlLV0SrqpQGPWP809aZdj2HEfjLT2gylIzt24kwiyfoFagkInwrTrF/kWV+FgcrCxj6HgBf74tlS9Q1sguKmDW6I9g6s/Z6B8IuJFNgEc+0fm6G668/lFLhcNg7fqZCmIgkl0IIIYQQN3Xo0AGAmJgYAgMDa3WO2NhYOnfuXJdhNWollbhaK2l2U9cxVHbeMsNUjbj4GJLEEpcObiFEs52waA1hLs311zm3nVC7i+z37IRjc99bySigKPMTyiSL8eGG64/rPdiwPVmTz9f7YlEALwxoLXMvRZMkyaUQQgghxE2DBw8GIDIyslbJZVpaGhcuXOD//u//6jgyUWdqWCnt26YZick29HR1oV9JJdE7GHtgeAXVzxcGtMbe2sKo6lg6+U1VtyHmyk7aBLQx2r447Dxboq4BYG9tIcmlaJIkuaxnOp2OnJwcsrKy6uR8NjY2KJVKo7W3hBBCCFE3vLy8GDBgAL/88gsvvfRSjY//5ZdfsLS05IknnqiH6ESdKKlo5mXq18As3XinJFm8OXQ2xSWIjenteWrwCwT4DwTy4Vy4fr9Kqq0qJ5ubQ1/DwbJ88nn4rx0ok85w+K8d3D+uneFa4wODyC5ohQIZDiuaLkku60laWhpffPEFP/74I9HR0XV67mbNmjF27FimTJlCUFBQnZ5bCCGEuNdNnTqVJ598koSEBDw89GsW6nQ6o30GDx5cbhvA6tWrefzxx1GpVA0S6z2l9FxJqFmH2YrmWcZHQswfkHAUWnSAxCj9dv+R+s/ObKYgbiEnmUyBRTDTujnrk9Fzv8OJ77ke9H8cjdxNP/skHLy7QseHjM9dydDbvm2akZpqTac2zYz2dQNmjb6z4cFCmJokl/Xg+vXr3HfffcTGxjJ27FhmzpyJs7NznVQb8/LyOHr0KBs2bGDdunX89ttvDBw4sA6iFkIIIQTA2LFj6dWrFwsXLmTp0qXVPm7v3r0cPnyYtWvXVrlfRUmpuI28TNj3KaSeh8Ic/RIgl/ff+vPtksyKkj3vYH1imZcJQIZbL36+YMvYvC24+HSF/YtpdeMK/7TfgmvgCH1i6RZA3t/r0VyNJi1hLh3V59HpNBQmHcHKwc343Dd/ll1WxLnLKJyVLuWH595BQyMhGgtJLuvBlClTSExMJDIykk6dOtX5+R977DFmz57Ngw8+yEMPPURSUhI2NrIGkhBCCHE71tbWgP6XtSV/LkuhUPD111+zZcsWiouLq/3L4bS0NL799ltat25d6T4FBQXodDr53q6p+Eh9YpmXrn9fkogV5pRPGiuqUnoH6/ctzNF/buusf4W8pt/XLYC9u8IojNtH3tkDuAx5Be57H/Ndc+jQugdELYU8NSQc5WftQAJyzpLRzANn3TmysCMGP7qVPffNeFb9cYZfoq6RU1DEeze7x95pAyMhGitJLutYdnY2W7duZf78+fWSWJaws7Nj6dKldOzYkR07djBmzJh6u5YQQghxtyhJ/I4dO8aQIUMq3a9z58417vj6yCOP3HafY8eOGcUhqqkkOQRoe5/xvMmSymWJm1XKjLxCNiR58JR7Ai7+A/X7XdwNqWf1SWXpJPBm99cE2wu0KM6BtPPg0AIcVBD7B9i6gpUd5FnTNe1v4AYttQm4+nXhVKE7bbv21w+rtbIvlzjqyvwsK+PcXmIO76BNr0Jcuj1Uhw9NiIYnyWUd279/P/n5+Tz0UP3/5RAQEEC7du3YtWuXJJdCCCFENfTo0QNvb2/Wrl1bZXJZX7799ltUKhV9+/Zt8Gs3abbO0OXRireXrQLeTDQ3xLUg5XQ4MVdi6WlrBd7BZMcd5MKFS/i67jVO5LyDKcwr5GBRf1rZHMEycCwkRIFTK/AdAPbNwbMnXD2Cl5WK66fzad5pADburenZ9j79OeKdwS3gVpOgm1XTFwe0xsHagtAAFYvDzhuGx5bYkORBiqY1bkkeTKnOs6hqnU4hTExajtax9HT9cI1WrVo1yPU8PDxIS0trkGsJIYQQTZ1CoWD69OmsXbuW2bNnk5mZ2SDX1Wg0LFiwgOXLlzN16lTMzc0b5Lr3pJsJ59i+nXDrNJg2vUaA0gMivmRTfk+2ZHdgQ5JHuWPWXu/AH3F5nEpQ6xPLCztAcw3sm5OhtWT5wetkaC1RpkXRxqEQZdpxfaWypALqHUz2wTUcCvuRDT//wKkENYvDzgMwrZ8bMft/IuJ0DOsPxRtdemzfTii7PsjYvtUc8VYyfzQ+8k6flBB1TiqXday4uBgAC4uGebQWFhaGawohhBDi9qZOnYparWbOnDl8/PHH9O7du84a75Wl0+nIzMzk8OHDFBQU8M477/DOO+/U+XVEeSonG6aM7AH0gD8+gIthPOJdxKqu4xhbwVIf43p7E3z5K7oXHoU0e+h0a5jz2YjfuXDdh1WBQ3ir3XBIPgWqLoYqabImn8jtG2mZGs+JNFiXocIt5wzZBUUATPOKJdTuIrSE4N63KubJmny+3heLoiY3Jg2ARCMmlUsTmT9/Ph07djQkhgqFgrlz5xo+3717Nw4ODiQkJNR7LPPmzcPZ2Zl58+bV+7WEEEKIxuD999/n6tWrfPLJJ/j5+WFubo5Coajzl5mZGT4+Pnz44YdcvnyZhQsXolDUKJUQdSHoGWgbikOfSUwLbW80LBUwdHRtM3wKhX5D+SJrMP8+70x2wilo0Z54pyCiFAEUWjiCgxtYWOvnZAIZUVuYuW4f315tSbR9L8JdnyCwrTetmzsQ3LqZfs1K72Ds2w1izIOPGl17/aF4tkRd45eoa+UqmpUqGQosQ2JFIySVSxO4du0aixYtYs2aNYbfkkZERODp6WnY57777qN37968++67t21pfieKior46quvUKvVrFixgvfee6/Bqq5CCCGEKXl4ePDPf/7T1GGI+nRzfmKKSxDrih9nnLmKilYgXX8onrDoZEAFFuP4X/Rlnitch9rhDADxDiN52e84XQKCmH/UEeKd0akd+WeevhmPZ5Yfucp+nHXy5MqVWNwydnCADgzrXpLI2lTYzXZcb2+yC4pQoK+cCtHUSeXSBJYsWYKzszNjx441bAsODjZKLgFee+01vvvuO65cuVJvscyfP5/U1FTMzc1JSUlh/vz59XYtIYQQQoj6kqzJZ3HYeZI1+bc2xkdCzB/E/voxx0+eIHL7d4Z1LUsb19ub0ACV4aeluRlbze4jRhnC90WDyb34F96aY2zd+jPrT2hYndqBH05msSHJgza9RmDh2xeLQg1+6fvoceMonW6cpMeNo3im7mH59qPlYsq5sIdfft0IwKyhrXivbTwqy/xycQnR1Ehy2cAKCwv55ptvGD9+vNHcjrLDYgEefPBBHBwc+Prrr+sllqKiIr7++muKi4uxsbGhuLiYVatWUVRUVC/XE0IIIYSoLyXVR6Phpd7BYKOkSzN42emAft5jqUY4JQkpYBguGxadjJOtBcpWbXB9YB5JihbYte1PvFMQ+wvb4mhjib+7Iw91a8XYvp0obD2cnbH5NE/eR5eUrQzxs0fr3ZeWzra01RzC8dgK5v9vOxlRWyAvkxSXIL6MVbH2qrs+1pKlU87tLZ8cC9HESHLZwA4ePEhaWlq12p9bWVnRt29ftm3bVi+xxMTEoNFoMDMzIz8/HzMzM9RqNZcuXaqX6wkhhLj37N27lwcffJBWrVqhUCjYvHmz0ec6nY65c+fSqlUrbG1tGTx4MKdPnzZNsKLRqrAqWcb4QCUvqs4zPlB5a6OtM4S8hn3AcILHTsW+3SCjRjgVJaTjenszqnNLljwVRFh0MhGxaVjauzBk2Bj6WV3E07aAkZ3dWTg2EJWTDesPxWNjYYaTjSUdWznxYM925PoO47B5EFprJc3M8whR/0bM4R0QH8m6E2r+KO6OlYOrYT4mPv3YkORRPjmuwf0L0RjI5LoGFhERAUD37t2rtX/37t1ZuHAhOTk52Nvb12ks/v7+rF27lri4OMM2Pz8/2rZtW6fXEUIIce/Kycmha9euPPfcczz6aPl1ChctWsRnn33GmjVraN++PQsWLGDYsGGcO3cOR0dHE0QsGqNbcyL1FcaKuGUcY4zzJcg4Bm6l1r4svRami4/RMSXzHA3zHfMyUSVGMq1fMNjaGH3udnkb/+d6kM7mFvTofZ9h/4nNz2LdyYNHu7+CMuOYfi6ljw3WRVkEuvYBFGxMaUkbT7X+s5Y2hnOWno85tmU+BRbxFc69rM79C9EYSHLZwK5du4ZCoaB58+bV2t/NzY3i4mKSkpJo06ZNncfz2GOP1fk5hRBCiBKjRo1i1KhRFX6m0+lYvHgx7733nqEPwdq1a1GpVKxbt46XX365IUMVjVi5JLAiNVmi42ZTHZV3sHGyVrKGJNxKSEuxtTDn/k4tSQYWh51nYvOzuKQcZoqfFbj1ALeRhs6zE90TcE45Az79mNx7kOEcKtuKE0SVk02liWO17l+IRkCSywaWl5eHpaVltRdPtrGxMRwnhBBC3E3i4uJISkpi+PDhhm3W1tYMGjSIAwcOVJpcFhQUUFBQYHiv0WjqPVZhWlUlXga2zvrE8mYn1iqX6qgsiSyToK4/FE/E6Rj80vcxZlgoGVpLNiR4kBYfS2RsGtbtPPSJZQVDba3beTDF4waZ6gy+/uUgCmCyTzIu/gMrja0kMb1V1azB/QvRCMicywbWvHlzCgsLycnJqdb+6enphuNEE5OXCee2V9iVTgghBCQlJQGgUhkvDqFSqQyfVWThwoUolUrDy8vLq17jFE1ISdJYqmlPhW7OcyxX5bw5hDb5hg2Lw84TGqBifMtrhNpdJOPycWYdKMbx2Aoss67iYG3BgC5ty605WTL389HunmBlz8Xjf5Fx/Hecj6/k0sFfjWPLy4STG/WvvMyKmxIhcy5F0yHJZQPr0KEDoG+mUx2xsbE0a9as3BevaAKq+wUnhBD3OIVCYfRep9OV21bazJkzUavVhld9LtklmpYUlyB+yfQlxSWo6h1L5mGWriCW+qXw+kPxbD+VxIJtZwgZ/AD27Qbx81Ulj6WvIkR7hBD1b+jyMonZ/xMknYQ/PiD1ygUWh53H8tpBxjhfwu38Otj3Ke18/ejh60KQ6w38bPP45oLtrSQxPhJObyLv7/VErpvP8NY2hiVRSqss6RSisZHksoENHjwYgMjI6iUckZGRDBo0qMovWdFIVfZbUSGEEAC4u7sDlKtSpqSkVPlLVWtra5ycnIxe4i5Wg5FA606o+Tq5PetOqG97bLlqYKn1J0MDVDjbWZKRW6g/l/9IQl2vU2TpQDPvjgTc/5qhosm+z+BiGDE7lxMWncyqyyp+yfQl+/gWChOOY3NpF48++DA9VObE55hz9dwRfZKYlwmFOdBuBKcL3UlJSebCkV2GJVFKK70OpxCNmSSXDczLy4sBAwbwyy+/3HbfmJgYTp48ydNPP90AkYk6V9FvRYUQQhj4+fnh7u7Orl27DNsKCwvZs2cPffv2NWFkolGp7kigm51bR7ezu5WEVXFsuWqgdzC/qv1Ycr453x28TGcPJQPbtSA0QMXisPP8kOrJAYtefGv/HLv3/km8uS/XXXvyX8vHyPYeTJvhUwgNUFFo4cjXye35SDueC2Z+HDTvCed+h+sXaWedSYhKp18u5eJuOL0JrOzwHjMbXZuhhAx+oMJbK5lzWTbpFKKxkYY+JjB16lSefPJJEhIS8PDwAPRDgMpas2YN3t7ePPTQQw0dohBCCFEnsrOzuXjxouF9XFwcUVFRuLq64u3tzbRp0/jwww9p164d7dq148MPP8TOzo7x48ebMGrRqFS3C2x8pL5zq8cNMmITWJ7kwaPdg3Are+zNTrFP+7fBL/08IYE3G+XYOhPj3J9sxTXOJWVRqC0mNEBFWHQyYdHJhLRuhrLrg6iSwym4uI84s3RmpA0iu6CItIBxTPNqxzQvfUXU3tqC0IBALu43I9TqNJz6CbKTsMjLpHPxdawueYCtlSEkNzcVwSOfZt2heMb1VkoSKZosSS7rWMnw1eLi4kr3GTt2LL169WLhwoUsXbq0wn0yMzP58ssv+fe//42FReX/mIqLi2XIrBBCiHpTUFCAtbV1rY8/cuQIQ4YMMbyfPn06ABMnTmTNmjW8/fbb5OXl8eqrr5KRkUGfPn3YuXOnrHEpbim9TmVVShLIwhxiDu8gRdOadRYPMi20zLE3q5ktLI4yxjnfaF3MFwa0vpkYqtgclUBOQRFjuukLASUdXFNSlPwBOFt1xsbcHjtrC6PhqirLfKZ5xYKrG50ffBQikkDbFhzcOZ1hQUHaVdTnExje1Q86PQJt9WtmGrrMFmUxxS/19h1vhWiEJLmsYyXzPtLS0rC3t69wH4VCwddff82WLVsoLi7GzKz86OS4uDhmzpx529/cpqWl1cv6l0IIIe5NO3bsYP369ezbt4/4+HiKi4uxs7Oje/fuDB8+nOeee45WrVpV+3yDBw+ucHROCYVCwdy5c5k7d24dRC/uaSVJaMZlOnid4nhRTx6oaI5iSRLqFgAp0UZVzdJLfoRFJ/P7qUROJqhZ8lQQKicbTiWoWbAtlimDHmbznhhSs9JR2lryNbE83M2DsOhkw9qXAPiPJKXzC0SEbyVk6ANYZRewc+vPjHZz4kjkH7TpNQKXmwlkSYL6VPOzcPnW8UI0JQpdVX/j36M0Gg1KpRK1Wl3jJgHJycm0atWKr776ihdffLGeItS7du0anp6e/Oc//2HSpEn1ei0hhBDVcyffIaa0efNmZsyYgVqt5v7776d37954eHhga2tLeno6p06dYt++fURERDBp0iT+3//7f7Ro0cLUYQNN95mLO1fhupDntuvnWfr0u6PkLFmTz5T/HSU+PZdHgjx4b3RHnloZQdSVTFzsrMi/oeWGthgrczN0gLZYh4udJZ0dNHzc+jgOfSaBiw+Lw86z/VQSznaWdPFQEhGbhptlPj45J3DrNJgpI3sYX/jmsF2pXIr6UN9/X0rlso6pVCoGDRrE4sWLGTt2LM2aNauX6+h0OubOnYuFhQVjxoypl2sIIYS4d3z44Yd88sknjB49usIRNU888QQACQkJLFmyhG+//ZY33nijocMUwkjJUFLAUHG83RzNChPSUp99vS8WBfohst19XLiakYfu5md+ze0pLCrG29WOnWeSsbYww83JhtjUbErKNc0UFzl7JYWeftHg4sO43t5ExqaRkVuIDghp3YycgiLMrUcxtm/r8vHcZhhwVfELYWqSXNaDL774giFDhtC3b1+mTJlCaGgoLi4uFX5Z14ROpyMvL4+jR4+yZs0afv/9d1auXImLi0sdRS6EEOJedejQoWrt5+HhwaJFi+o5GiGqp2QoqdESHbdJzipMSEt9tiXqGgD21ha8OKA1DjfnVH69L5bd0Sk81K0VD3fzID49F393Rw7FpVOo1WFrac6/xwVxJq45bdzbG5JblZMNs0Z3ZMG2M4ahsxGxaYS0bsb6Q/HkFBQREZtWYTw1jV8IU5Pksh507tyZP//8k3fffZcZM2ZQWFhY59fo0aMH69ev56mnnqrzcwshhBBCNAWl50hWV4UJaanPsguKuJ5VwN7zqYQGqBgfqCRi+3cUKzoAoEA/H7NQW4zKyYas/CIAHG0sGOTvxiB/N5I1nVi8P55xvW1QOdkQFp1MRm4hC7adYcqgNkTGppFdUMTeC6lYmplhZWFGaICqWlXJquIXwtQkuawnnTt3ZsuWLajVak6cOIFGo6mT89ra2tK6dWt8fX3r5HxCCCFEWTqdjp9++ok///yTlJSUch3Qf/75ZxNFJu5lDTEctKTKOHbZfk5cVfP+L6eY2Pwsipg/GOx6Eo+ujzDW4yI3WvUhRVNA2JlkAlo6Ym6m4KOxXQzn+XpfLFuirpGsyUflZINvMzsSM/MpuFHM8j0xZBcUoQByCrTkFBRgaW5mqEberipZm4RaiIYiyWU9UyqVDBgwwNRhCCGEENU2depUVq5cyZAhQ1CpVLLklTC5ZE0+UzccIzP3BnBnw0ErG1Zaer6ll6sd55Ky8Hd3JKT/A8RmnaZLMxho9xek5JMB7L3gQGJmHtaW5jzdx5t9F6+z43Qydtbm5BYUoS3WEX4uFSdbC3IKtBQUaSkq1jFrdEfCopPJKSjCwdoCN0druvu4MK63N6lZBUTGphEaoLqTxyWEyUhyKYQQQggj//vf//j555+5//77TR2KEIA+IczILcTFzqpWw0FLVz0rG1b69b5Y1h+Mx9bKnEeCPHhxYGvG9fbGzckGt/Hv6zu43ly+ZENcC2wssnBzsmGIvxs6YEvUNbJvJoxjurXCp5kdMak5AMwY6c+Gw1eYNbojnT2UdPZQkqzJRweG5kEqJxvWH4onu6CIsOhkOnsoa3V/0uRHmJIkl0IIIYQwolQqad26tanDEMKgbEK4OOz8bROpkoQrNEDFgm1njKqeFVU+FYCNpTktlbbobl7LcP7STYJcfBjbMp8ruec5m5TF+D7etHC0BiC3QIsOfdtYf3dHziVlUVik4FJaLhteCil3zVMJajJzb2BvbcG00PZG91mThFGa/IjG4s7alwohRCkrVqzA19eXFStWmDoUIcQdmDt3LvPmzSMvL8/UoQgB3JpnWFLdC4tOZv2h+CqPWX8ont9PJfLit0eIvqZBnXejyuGmLwxozSPdPbA0V7DvQipf74tlcdh5kjX5FcYTez2HM4kaFmw7A4CDtQVTQ9uhcrJh34XrnEvKYky3Vozt7mFUJU3W5LM47Dxf74slI7cQZztLxvX25lSCmqkbjhEaoKrRfYI+GQ0NUEmTH2FyklwKIeqEVqtl6dKlJCQk8OWXX6LVak0dkhCilh5//HEyMjJwc3OjS5cudO/e3eglhClVN5EKDVCRU6Alr6CI3BtaCrXFhupeiZJEr6TxjoO1BZq8G+QUaLmeVcC6g/Gs2hdb7twla152bOnErNEdDYngqn2x5BQUYWdlTk5hEdbaLCxjdpJ2PcVwbMm+CmBU55YseSoIlZMN7/9yiiOXMnj/l1M1uk8wTr6FMCUZFiuEqBMrV64kLi4OGxsbYmNjWblyJVOmTDF1WEKIWpg0aRJHjx7lmWeekYY+otGpbrfUsOhkQ8OcDu6O2N1cr7K0ssNJx/X2JjI2jYzcQuLTcwFuDnKl3HEnE9SEBqjo7KE0DIvNLigiMjaNQA9nziZpUJ/dR6vCEyz7NpdXXnyNzh5Ko6GvpZPBkmG0/u6ONbpPIRoThU6nq+i/mXuaRqNBqVSiVqtxcnIydThCNHparZZu3bpx4cIFXF1dSU9Pp3379hw7dgxzc3NThydEg7obvkPs7e3ZsWMH/fv3N3Uo1XI3PHNRt0p3fi1pllPZfmXnNZaeq7k5KqHCc1Q2H7Jke3ZBEZuPJaDLy6Sb7izH8MfX04OB7VtUOodSmvKIhlDff1/KsFghxB1LSEggNTUVc3Nz1Go15ubmpKSkkJSUZOrQhBC14OXlJUmaaNLWH4onMjYNe2v9IL2q5k6WHU5asq2zhxIHawsiYtPKzXssfVzJ0NpTCWpW7YslWZNPboEWdycb1Dp7/tR1p8hKSQd3R8McytLDcauKRYimRobFCiHumLe3N+vWrePq1auGbZ6ennh4eJgwKiFEbX366ae8/fbbfPXVV/j6+po6HCFqrPTQ05p0Ui1bPaxs2ZLSVu2LZdOxBL4/fIXComLyb2ixv7kcSaCnkrNJWcwf05kWjtaGc0t3V3G3kuRSCFEnhg4dauoQhBB15JlnniE3N5c2bdpgZ2eHpaWl0efp6ekmikyI6ik9X7E6CWKJsklf2XmPFQ1d1QF5hVq0Fjr8mtvj7+6Ig7VFhcNxaxOTEE2JJJdCCCGEMPL5559LEx9x16iwMU5eJsRHgncwyTdsDAnjuN7eZBcUkVNQZOggW1pFFccXB+jXhK3p/M5poe0Ny4/MGt2Rzh7KOr1vIUxBkkshhBBCGJk0aVKln8nal+KuEB8Jl/cDsP5Ka6OE0cHagrDoZOytLcolpRVVHFVONswa3bHKyyVr8pm64RgZuYWG49cfimfv+VROX1Pzyv+OsnFK31uJaankF1vnOrhhIRqGNPQRQgghhJHXXnutwu05OTmMGjWqgaMRonqSNfks2HaGd38+wQfbzpRr4GPURMc7GHz6gXdwufUkK1pfsuTY1KwCcgqK+Ppm456qYvlg2xkW3Ixj/aF4MnNv4GJnZUgst59K4oa2GFd7a2wszIybBpUkv/GRdfuQhKhnUrkUQgghhJGdO3cya9YsFixYYNiWk5PDyJEjTRiVEFVbfyieLVHXyCkowt7aolzlsdyQVn/9v88qW+OmOhUNoy05NjI2jdjUHAAcKqhslt7/l6hrhv3Krm0ZGqDip6NXsbYwY3RgS6N9AH3yW/qnEE2EJJdCCCGEMLJz50769+9Ps2bNeP3118nKymLEiBFYWFjw+++/mzo8ISpUMl8y92ZyWbZZTk2a6FTWNTY0QMUvUQlVzsssuUZOQRHZBUWkaPJZtS+WF27OzVwcdv5mAmyOvZUFilIxLQ47f/OazobkV4imRJJLIYQQQhjx8/Njx44dDB48GDMzMzZs2IC1tTXbtm3D3t7e1OEJUaHbzX2ssLFPJarqGtvZQ8mCbWf4JeoaOqjwmionG94b3ZHFYedZd1A/3NXe2oLsgiK2RF3jvgA3RnVuSU5BERGl1uMMi042VF5Ld6QVoqmQ5FIIIYQQ5XTu3JmtW7cSGhpKnz592Lp1K7a2tqYOS4gqJWvy+Xpf7G07t97O7aqcijI/qzpPdkER17MK2Hs+FW9XO+DWkNpTCWpOJqgJDVDRwtEagOyCohqtgVnR8ihCmIokl6LRWbFiBQsXLmTmzJm8/PLLpg5HCCHuCUFBQRUuP2Jtbc21a9fo16+fYdvff//dkKEJUW0l8y6BCru9VtftqpwvDGhd4dDbis4za3RHHvz3X5xKUJNbqGV8H2/DcZujEohNzWFzVAKzRndkWmh7kjX55edgVqGi5VGEMBVJLkWjotVqWbp0KQkJCXz55Ze88MILmJubmzosIYS46z388MOmDkGIO1ZSKSw9j7Eyd1Lxq8kQWwCdTgdAek6h0fUqqoBWdO6qYq3JXFIh6pskl6JRWblyJXFxcdjY2BAbG8vKlSuZMmWKqcMSQoi73pw5c0wdghB3rDprTpaoScXvThLRZE0+XTyVpOUUYm9lzvpD8YbrlTT50d3cr7JzVxVrTRNdIeqTrHMpGg2tVsuyZcsoKirC0dGRoqIili9fjlarNXVoQgghyiipxAjRVFW0nmVlSpI7o7Uoq2n9oXhOJqgZHdiSB7q2MrqeyskGe2sLImPTqjx3TWIVwpQkuRSNRkJCAqmpqZibm6NWqzE3NyclJYWkpCRThyaEEHe9gIAA1q1bR2FhYZX7XbhwgSlTpvDxxx83UGRC1I+Sil91KpFVJXfJmnwWh50nWZNf4bGhASocrC14uJtHhderTuJYk1iFMCUZFisaDW9vb9atW8fVq1cN2zw9PfHw8DBhVEIIcW/48ssvmTFjBq+99hrDhw+nZ8+etGrVChsbGzIyMjhz5gx//fUXZ86c4R//+AevvvqqqUMWosFUNfS07JDVskNow6KTDR1gO3sogfLDbGVYq7hbSHIpGpWhQ4eaOgQhhLgnDR06lMOHD3PgwAG+//571q1bx6VLl8jLy6N58+YEBQXx7LPP8swzz+Ds7GzqcIVoNMo21CmbbFbUcEc6vIq7lSSXdxFZwkMIIcSd6tu3L3379jV1GEI0GWUrj2WTyYoqk9LhVdytTDrncvny5QQGBuLk5ISTkxMhISH8/vvvRvtER0fz0EMPoVQqcXR0JDg4mPj4qidTb9y4kY4dO2JtbU3Hjh3ZtGlTfd5Go1B2CQ9pgiOEEMLUFi5cSK9evXB0dMTNzY2HH36Yc+fOGe2j0+mYO3curVq1wtbWlsGDB3P69GkTRSzEnavO/EiZQynuViZNLj09Pfnoo484cuQIR44cYejQoYwZM8bwpRITE0P//v3p0KED4eHhHD9+nNmzZ2NjU/l/iBERETz55JNMmDCB48ePM2HCBJ544gkOHjzYULdlEhUt4SGEEEKY0p49e3jttdeIjIxk165dFBUVMXz4cHJycgz7LFq0iM8++4ylS5dy+PBh3N3dGTZsGFlZWSaMXAghRG0odI2sl7irqyv/+te/mDx5Mk899RSWlpb897//rfbxTz75JBqNxqgCOnLkSFxcXFi/fn21zqHRaFAqlajVapycnGp8Dw1Nq9XSrVs3Lly4gKurK+np6bRv355jx45hbm5u6vCEEOKe0tS+QxpSamoqbm5u7Nmzh4EDB6LT6WjVqhXTpk1jxowZABQUFKBSqfj444+rPcVDnrkQQlRPff992WiWItFqtWzYsIGcnBxCQkIoLi5m27ZttG/fnhEjRuDm5kafPn3YvHlzleeJiIhg+PDhRttGjBjBgQMHKj2moKAAjUZj9GpKZAkPIYQQTYFarQb0v0gGiIuLIykpyeh729ramkGDBt3V39tCCHG3MnlDn5MnTxISEkJ+fj4ODg5s2rSJjh07kpSURHZ2Nh999BELFizg448/Zvv27YwdO5Y///yTQYMGVXi+pKQkVCqV0TaVSlVlorVw4ULmzZtXp/fVkGQJDyGEEI2dTqdj+vTp9O/fn86dOwMYvpsr+t6+fPlypedq6t/bQghxtzJ5cunv709UVBSZmZls3LiRiRMnsmfPHkOb8zFjxvD6668D0K1bNw4cOMBXX31VaXIJoFAojN7rdLpy20qbOXMm06dPN7zXaDR4eXndwV01PFnCo+6VXYNKCCHuJcXFxVy8eJGUlBSKi4uNPhs4cGCNz/ePf/yDEydO8Ndff5X77F783hZNl/z/gRCVM3lyaWVlRdu2bQHo2bMnhw8fZsmSJfz73//GwsKCjh07Gu0fEBBQ4RdTCXd393JVypSUlHK/FS3N2toaa2vrO7gLcTeSNaiEEPeqyMhIxo8fz+XLlynbmkGhUNS4I/n//d//sWXLFvbu3Yunp6dhu7u7O6CvYLZs2dKwXb63RWMm/38gROUazZzLEjqdjoKCAqysrOjVq1e5luXnz5/Hx8en0uNDQkLYtWuX0badO3fKml2ixsb19iY0QCVrUAkh7jmvvPIKPXv25NSpU6Snp5ORkWF4paenV/s8Op2Of/zjH/z888/88ccf+Pn5GX3u5+eHu7u70fd2YWEhe/bske9t0WjJ/x8IUTmTVi7fffddRo0ahZeXF1lZWWzYsIHw8HC2b98OwFtvvcWTTz7JwIEDGTJkCNu3b+fXX38lPDzccI5nn30WDw8PFi5cCMDUqVMZOHAgH3/8MWPGjOGXX34hLCysymqnEBWpaNFjIYS4F1y4cIGffvrJMLKotl577TXWrVvHL7/8gqOjo2FkkVKpxNbWFoVCwbRp0/jwww9p164d7dq148MPP8TOzo7x48fXxa0IUefk/w+EqJxJK5fJyclMmDABf39/7rvvPg4ePMj27dsZNmwYAI888ghfffUVixYtokuXLqxatYqNGzfSv39/wzni4+NJTEw0vO/bty8bNmxg9erVBAYGsmbNGr7//nv69OnT4Pd3L1qxYgW+vr6sWLHC1KEIIYSopT59+nDx4sU7Ps/y5ctRq9UMHjyYli1bGl7ff/+9YZ+3336badOm8eqrr9KzZ08SEhLYuXMnjo6Od3x9IYQQDavRrXPZGMh6WbVTst7m2bNnCQgIkHU2hRD3pKb6HXLixAnDn2NiYpg1axZvvfUWXbp0wdLS0mjfwMDAhg6vSk31mQshREOr778vTd7QR9w9Vq5cSVxcHDY2NsTGxrJy5UqmTJli6rCEEEJUQ7du3VAoFEYNfJ5//nnDn0s+q01DHyGEEPcGSS5FndBqtSxbtoyioiJcXV1JT09n+fLlvPTSS1K9FEKIJiAuLs7UIQghhGjiJLkUdSIhIYHU1FTMzc1Rq9WYm5uTkpJCUlISHh4epg5PCCHEbZTuxL5371769u2LhYXx/yYUFRVx4MCBKru2C3GvkXUvhbhFkktRJ7y9vVm3bh1Xr141bPP09JTEUgghmqAhQ4aQmJiIm5ub0Xa1Ws2QIUNkWKwQpci6l0LcIsmlqDNDhw41dQhCCCHqQMncyrLS0tKwt7c3QURC1K87qT6WrHcp614KIcmlEEIIIW4aO3YsoG/eM2nSJKytrQ2fabVaTpw4Qd++fU0VnhD15k6qj7LupRC3SHIphBBCCACUSiWgr1w6Ojpia2tr+MzKyorg4GBefPFFU4UnRL2R6qMQdUOSSyGEEEIAsHr1agB8fX158803ZQisuGdI9VGIuiHJpRBCCCGMzJkzB4CUlBTOnTuHQqGgffv25Rr8CCHunHSbFXcTM1MHIIQQQojGRaPRMGHCBDw8PBg0aBADBw7Ew8ODZ555BrVaberwhLirlMz3XH8o3tShCHHHJLkUQgghhJEXXniBgwcPsnXrVjIzM1Gr1WzdupUjR47InEsh6ti43t6EBqhkvqe4K0hyKe7IihUr8PX1ZcWKFaYORQghRB3Ztm0b//nPfxgxYgROTk44OjoyYsQIvv76a7Zt22bq8IS4q5TM95QhseJuIMmlqDWtVsvSpUtJSEjgyy+/vOcX1ZZEWwhxt2jWrJmhc2xpSqUSFxcXE0QkhBCiKZDkUtTaypUriYuLw8bGhtjYWFauXGnqkExGEm0hxN1k1qxZTJ8+ncTERMO2pKQk3nrrLWbPnm3CyIQQQjRmklyKWtFqtSxbtoyioiIcHR0pKipi+fLl92xSJYm2EOJusnz5ciIjI/Hx8aFt27a0bdsWb29vDhw4wIoVK+jevbvhJYQQQpSQpUhErSQkJJCamoq5uTlqtRpzc3NSUlJISkrCw8PD1OE1qNKJtqurK+np6SxfvpyXXnoJc3NzU4cnhBA19vDDD5s6BCGEEE2QJJeiVry9vVm3bh1Xr141bPP09LznEkuQRFsIcfcpWedSCCGEqAlJLkWtDR061NQhNAqSaAsh7kaZmZn89NNPxMTE8NZbb+Hq6srff/+NSqWSv9+EEEJUSJJLIeqAJNpCiLvJiRMnCA0NRalUcunSJV588UVcXV3ZtGkTly9f5ttvvzV1iEIIIRohaegjhBBCCCPTp09n0qRJXLhwARubW2vvjRo1ir1795owMiGEEI2ZJJdCCCGEMHL48GFefvnlcts9PDxISkoyQURCCCGaAkkuhRBCCGHExsYGjUZTbvu5c+do0aKFCSISQgjRFEhyKYQQQggjY8aMYf78+dy4cQMAhUJBfHw877zzDo8++qiJoxNCCNFYSXIphBBCCCOffPIJqampuLm5kZeXx6BBg2jbti2Ojo588MEHpg5PCCFEIyXdYoUQQghhxMnJib/++os//viDv//+m+LiYrp3705oaKipQxNCCNGISXIphBBCiAoNHTpUlloS4i5QXFxMYWGhqcMQDcDS0hJzc3OTXV+SSyGEEEIYFBcXs2bNGn7++WcuXbqEQqHAz8+Pxx57jAkTJqBQKEwdohCiBgoLC4mLi6O4uNjUoYgG4uzsjLu7u0n+vpbkUgghhBAA6HQ6HnroIX777Te6du1Kly5d0Ol0REdHM2nSJH7++Wc2b95s6jCFuKska/JZfyiecb29UTnZ3P6AGtDpdCQmJmJubo6XlxdmZtJu5W6m0+nIzc0lJSUFgJYtWzZ4DJJcCiGEEAKANWvWsHfvXnbv3s2QIUOMPvvjjz94+OGH+fbbb3n22WdNFKEQd5/1h+IJi04GYFpo+zo9d1FREbm5ubRq1Qo7O7s6PbdonGxtbQFISUnBzc2twYfIyq8vhBBCCAHA+vXreffdd8sllqCff/nOO+/w3XffmSAyIe5e43p7ExqgYlxv7zo/t1arBcDKyqrOzy0ar5JfJJQsJ9WQJLkUQgghBAAnTpxg5MiRlX4+atQojh8/Xu3zLV++nMDAQJycnHByciIkJITff//d8LlOp2Pu3Lm0atUKW1tbBg8ezOnTp+/oHoRoKpI1+SwOOw/oK5Z1PSS2NJkrfW8x5T9vSS6FEEIIAUB6ejoqlarSz1UqFRkZGdU+n6enJx999BFHjhzhyJEjDB06lDFjxhgSyEWLFvHZZ5+xdOlSDh8+jLu7O8OGDSMrK+uO70WIxq5kOOz6Q/GmDkWIOiPJpRBCCCEA/RA6C4vK2zGYm5tTVFRU7fM9+OCD3H///bRv35727dvzwQcf4ODgQGRkJDqdjsWLF/Pee+8xduxYOnfuzNq1a8nNzWXdunV1cTtCNGr1ORxWCFORhj5CCCGEAPTDVCdNmoS1tXWFnxcUFNT63Fqtlh9//JGcnBxCQkKIi4sjKSmJ4cOHG/axtrZm0KBBHDhwgJdffrnScxUUFBjFotFoah2XEKaicrKp8wY+onbmzp3L5s2biYqKqpfzr1mzhmnTppGZmVkv529MpHIphBBCCAAmTpyIm5sbSqWywpebm1uNO8WePHkSBwcHrK2teeWVV9i0aRMdO3YkKSkJoNwwXJVKZfisMgsXLjSKy8vLq2Y3KoSokbjrOXy8/Sz/t/4YH28/S9z1nAa57pUrV5g8eTKtWrXCysoKHx8fpk6dSlpaWoNcv7rCw8NRKBTlXrNmzTJ1aA1OKpdCCNHIrFixgoULFzJz5swqqzdC1LXVq1fX+Tn9/f2JiooiMzOTjRs3MnHiRPbs2WP4vGzjCZ1Od9tmFDNnzmT69OmG9xqNRhJMIerJD0eu8M7GEygUCsN/nyv2xPDxo4E83rP+/ruLjY0lJCSE9u3bs379evz8/Dh9+jRvvfUWv//+O5GRkbi6utbb9Wvj3LlzODk5Gd47ODiYMBrTkMqlEEI0IlqtlqVLl5KQkMCXX35paCMvRFNlZWVF27Zt6dmzJwsXLqRr164sWbIEd3d3gHJVypSUlCqbCoF++GxJB9qSlxCi7sVdz+GdjSco1oG2WGf0c8bGE1yqxwrma6+9hpWVFTt37mTQoEF4e3szatQowsLCSEhI4L333gP0v6DavHmz0bHOzs6sWbPG8H7GjBm0b98eOzs7WrduzezZs6tcpiMuLo62bdsyZcoUiouLKSws5O2338bDwwN7e3v69OlDeHh4uePc3Nxwd3c3vKpKLpcvX06bNm2wsrLC39+f//73v4bP3njjDR588EHD+8WLF6NQKNi2bZthm7+/PytWrKj0/KYiyaUQwtAOPVmTb+pQ7nkrV64kLi4OGxsbYmNjWblypalDEqJO6XQ6CgoK8PPzw93dnV27dhk+KywsZM+ePfTt29eEEQohSvxw5EqlIwkUCgXfH7lSL9dNT09nx44dvPrqq9ja2hp95u7uztNPP83333+PTqer1vkcHR1Zs2YNZ86cYcmSJXz99dd8/vnnFe576tQp+vXrx+OPP87y5csxMzPjueeeY//+/WzYsIETJ07w+OOPM3LkSC5cuFCr+9u0aRNTp07ljTfe4NSpU7z88ss899xz/PnnnwAMHjyYffv2UVxcDMCePXto3ry5YdRHUlIS58+fZ9CgQbW6fn2S5FIIIe3QGwmtVsuyZcsoKirC0dGRoqIili9fLtVL0WS9++677Nu3j0uXLnHy5Enee+89wsPDefrpp1EoFEybNo0PP/yQTZs2cerUKSZNmoSdnR3jx483dehCCOBqRl6lCZxOp+NqRl69XPfChQvodDoCAgIq/DwgIICMjAxSU1Ordb5Zs2bRt29ffH19efDBB3njjTf44Ycfyu0XERHBoEGDmD59OgsXLgQgJiaG9evX8+OPPzJgwADatGnDm2++Sf/+/ctNJfD09MTBwcHwqmxu6CeffMKkSZN49dVXad++PdOnT2fs2LF88sknAAwcOJCsrCyOHTuGTqdj3759vPHGG4Zq6Z9//olKpaJDhw7Vuv+GJHMum6BkTT7rD8Uzrrd3vS64K+4dJW3QpR26aSUkJJCamoq5uTlqtRpzc3NSUlJISkrCw8PD1OEJUWPJyclMmDCBxMRElEolgYGBbN++nWHDhgHw9ttvk5eXx6uvvkpGRgZ9+vRh586dODo6mjhyIQSAp4utvnJZQYKpUCjwdLGt4Kj6V5LwWllZVWv/n376icWLF3Px4kWys7MpKioqN5w+Pj6e0NBQFixYwOuvv27Y/vfff6PT6Wjf3rizb0FBAc2aNTPatm/fPqO/v1xcXCqMJzo6mpdeesloW79+/ViyZAkASqWSbt26ER4ejqWlJWZmZrz88svMmTOHrKwswsPDG2XVEiS5bJJKqkyAtLAWdULaoTcO3t7erFu3jqtXrxq2eXp6SmIpmqxvvvmmys8VCgVz585l7ty5DROQEKJGnujpxYo9MRV+ptPpeLKeGvq0bdsWhULBmTNnePjhh8t9fvbsWVq0aIGzs7Oh0VBppedTRkZG8tRTTzFv3jxGjBiBUqlkw4YNfPrpp0bHtGjRglatWrFhwwYmT55sSD6Li4sxNzfn6NGjmJubGx1Tdk6ln58fzs7O1brH2zUzGzx4MOHh4VhZWTFo0CBcXFzo1KkT+/fvJzw8nGnTplXrOg1NkssmSKpMQty9hg4dauoQhBBCCAD8mtvz8aOBzCjTLVan0/Hxo4H4Nrevl+s2a9aMYcOGsWzZMl5//XWjeZdJSUl89913vPbaa4A+KUxMTDR8fuHCBXJzcw3v9+/fj4+Pj6EBEMDly5fLXdPW1patW7dy//33M2LECMMoiqCgILRaLSkpKQwYMKBO7i8gIIC//vrLaGmnAwcOGA0DHjx4MN988w0WFhaEhoYCMGjQIDZs2NBo51uCJJdNklSZhBBCCCFEQ3i8pxe9fF35/sgVrmbk4eliy5M9veotsSyxdOlS+vbty4gRI1iwYIHRUiTt27fn/fffB/S/lF26dCnBwcEUFxczY8YMLC0tDedp27Yt8fHxbNiwgV69erFt2zY2bdpU4TXt7e3Ztm0bo0aNYtSoUWzfvp327dvz9NNP8+yzz/Lpp58SFBTE9evX+eOPP+jSpQv3339/je/trbfe4oknnqB79+7cd999/Prrr/z888+EhYUZ9imZd/nrr7+yYMECQJ9wPvroo7Ro0YKOHTvW+LoNQRr6CCGEEEIIISrl29yeGSM78O9xQcwY2aHeE0uAdu3acfjwYVq3bs0TTzyBj48Po0aNon379uzfv98wJPXTTz/Fy8uLgQMHMn78eN58803s7OwM5xkzZgyvv/46//jHP+jWrRsHDhxg9uzZlV7XwcGB33//HZ1Ox/33309OTg6rV6/m2Wef5Y033sDf35+HHnqIgwcP1np93YcffpglS5bwr3/9i06dOrFixQpWr17N4MGDDfsolUqCgoJwdXU1JJIDBgyguLi40VYtARS66vbwvYdoNBqUSiVqtVrWzhJCCFEj8h3S8OSZC1Gx/Px84uLi8PPzw8am6TeBnDNnDp999hk7d+4kJCTE1OE0WlX9c6/vvy9lWKwQQgghhBCi0Zs3bx6+vr4cPHiQPn36YGYmgzAbG0kuhRBCCCGEEE3Cc889Z+oQRBUk3RdCCCGEEEIIccckuRRCCCGEEEIIccckuRRCCCGEEEIIccckuRSiHiVr8lkcdp5kTb6pQxFCCCGEEKJeSXIp7ogkT1VbfyiesOhk1h+KN3UoQgghhBBC1CvpFivuSEnyBDAttL2Jo2l8xvX2NvophBBCCCHE3UqSS3FHJHmqmsrJ5p5OupM1+aw/FM+43t6onJr+4s1CCCGEEKJyMiy2CVqxYgW+vr6sWLHC1KEYkidJHERFZFiwEEIIIZq6S5cuoVAoiIqKalLnNgVJLpsYrVbL0qVLSUhI4Msvv0Sr1Zo6JCEqNa63N6EBKqlsCyGEEE1ZWgyEzYWfntf/TIup90umpKTw8ssv4+3tjbW1Ne7u7owYMYKIiAgAFAoFmzdvrvc4RM3IsNgmZuXKlcTFxWFjY0NsbCwrV65kypQppg5LiArd68OChRBCiCbv2P9gy/8BCkCn/7l/CTy0FIKerrfLPvroo9y4cYO1a9fSunVrkpOT2b17N+np6fV2zfpUWFiIlZWVqcOod1K5bEK0Wi3Lli2jqKgIR0dHioqKWL58uVQvhRBCCCFE3UuL0SeWumLQaY1/bvlHvVUwMzMz+euvv/j4448ZMmQIPj4+9O7dm5kzZzJ69Gh8fX0BeOSRR1AoFIb3MTExjBkzBpVKhYODA7169SIsLMzo3L6+vnz44Yc8//zzODo64u3tzcqVK432OXToEEFBQdjY2NCzZ0+OHTtm9LlWq2Xy5Mn4+flha2uLv78/S5YsMdpn0qRJPPzwwyxcuJBWrVrRvn37ap27qZPksglJSEggNTUVc3Nz1Go15ubmpKSkkJSUZOrQhBBCCCHE3ebYf9FXLCuiuPl53XNwcMDBwYHNmzdTUFBQ7vPDhw8DsHr1ahITEw3vs7Ozuf/++wkLC+PYsWOMGDGCBx98kPh4494Pn376qSGxe/XVV5kyZQpnz54FICcnhwceeAB/f3+OHj3K3LlzefPNN42OLy4uxtPTkx9++IEzZ87w/vvv8+677/LDDz8Y7bd7926io6PZtWsXW7durda5mzoZFtuEeHt7s27dOq5evWrY5unpiYeHhwmjEkIIIYQQd6XMePRDYSuiu/l53bOwsGDNmjW8+OKLfPXVV3Tv3p1Bgwbx1FNPERgYSIsWLQBwdnbG3d3dcFzXrl3p2rWr4f2CBQvYtGkTW7Zs4R//+Idh+/3338+rr74KwIwZM/j8888JDw+nQ4cOfPfdd2i1Wv7zn/9gZ2dHp06duHr1qtE0NEtLS+bNm2d47+fnx4EDB/jhhx944oknDNvt7e1ZtWqVYTjsypUrb3vupk6SyyZm6NChpg5B1ANZskMIIYQQjY6zN1VWLp3rr2Hfo48+yujRo9m3bx8RERFs376dRYsWsWrVKiZNmlThMTk5OcybN4+tW7dy7do1ioqKyMvLK1e5DAwMvHUXCgXu7u6kpKQAEB0dTdeuXbGzszPsExISUu5aX331FatWreLy5cvk5eVRWFhIt27djPbp0qWL0TzL6p67KZNhsUI0ArJkhxBCCCEanaAJVFm5DJpQr5e3sbFh2LBhvP/++xw4cIBJkyYxZ86cSvd/66232LhxIx988AH79u0jKiqKLl26UFhYaLSfpaWl0XuFQkFxcTEAOl1l93vLDz/8wOuvv87zzz/Pzp07iYqK4rnnnit3HXt7e6P31Tl3UyfJpRCNgCzZIYQQQohGp1kbfVdYhRkozI1/PrRU/3kD6tixIzk5OYA+QSzb1HLfvn1MmjSJRx55hC5duuDu7s6lS5dqfI3jx4+Tl5dn2BYZGVnuOn379uXVV18lKCiItm3bEhNz++ZG1Tl3UyfJpRCNQMmSHTIkVgghhBCNStDT8I8j0O+f0OkR/c9/HKnXZUjS0tIYOnQo//vf/zhx4gRxcXH8+OOPLFq0iDFjxgD6rq+7d+8mKSmJjIwMANq2bcvPP/9MVFQUx48fZ/z48YaKZHWNHz8eMzMzJk+ezJkzZ/jtt9/45JNPjPZp27YtR44cYceOHZw/f57Zs2cbmgrd6bmbOkkuhRBCCCGEEJVr1gZC58Jj/9H/rOeKpYODA3369OHzzz9n4MCBdO7cmdmzZ/Piiy+ydOlSQN/xddeuXXh5eREUFATA559/jouLC3379uXBBx9kxIgRdO/evcbX/vXXXzlz5gxBQUG89957fPzxx0b7vPLKK4wdO5Ynn3ySPn36kJaWZmgQdKfnbuoUunth8G8NaTQalEolarUaJycnU4cj7mErVqxg4cKFzJw5k5dfftnU4QghqkG+QxqePHMhKpafn09cXBx+fn7Y2MjoqHtFVf/c6/vvS6lcCtFIabVali5dSkJCAl9++SVarZZkTT6Lw86TrMk3dXhCCCGEEEIYkaVIhGikVq5cSVxcHDY2NsTGxrJy5UoK2t1HWHQyANNC25s4QiGEEEIIIW6RyqVodFasWIGvry8rVqwwdSgmo9VqWbZsGUVFRTg6OlJUVMTy5ct5ooeHdJUVQgghhBCNkkmTy+XLlxMYGIiTkxNOTk6EhITw+++/Gz6fNGkSCoXC6BUcHHzb8y5evBh/f39sbW3x8vLi9ddfJz9fhhE2BRUNBb0XJSQkkJqairm5OWq1GnNzc1JSUtDlZkpXWSGEEEII0SiZdFisp6cnH330EW3btgVg7dq1jBkzhmPHjtGpUycARo4cyerVqw3HWFlZVXnO7777jnfeeYf//Oc/9O3bl/PnzzNp0iRA30FKNG4VDQWdMmWKqcNqcN7e3qxbt46rV68atnl6euLh4WHCqO4dyZp81h+KZ1xvb0nkhRBCNHnSv/PeUtPlV+qSSZPLBx980Oj9Bx98wPLly4mMjDQkl9bW1ri7u1f7nBEREfTr14/x48cD+jVwxo0bx6FDh+oucFEvSg8FdXV1JT09neXLl/PSSy9hbm5u6vAa3NChQ00dwj1r/aF4mdsqhBCiybO0tEShUJCamkqLFi1QKBSmDknUI51OR2FhIampqZiZmd22KFcfGk1DH61Wy48//khOTg4hISGG7eHh4bi5ueHs7MygQYP44IMPcHNzq/Q8/fv353//+x+HDh2id+/exMbG8ttvvzFx4sRKjykoKKCgoMDwXqPR1M1NiRqpbChoUlKSVOxEgyqZ0ypzW4WoWwsXLuTdd99l6tSpLF68GND/z9C8efNYuXIlGRkZ9OnThy+//NLwS2YhRO2Zm5vj6enJ1atXuXTpkqnDEQ3Ezs4Ob29vzMwafgakyZPLkydPEhISQn5+Pg4ODmzatImOHTsCMGrUKB5//HF8fHyIi4tj9uzZDB06lKNHj2JtbV3h+Z566ilSU1Pp378/Op2OoqIipkyZwjvvvFNpDAsXLmTevHn1cn+i+mQoqGgsVE42UrEUoo4dPnyYlStXEhgYaLR90aJFfPbZZ6xZs4b27duzYMEChg0bxrlz53B0dDRRtELcPRwcHGjXrh03btwwdSiiAZibm2NhYWGyKrVCZ+JB2IWFhcTHx5OZmcnGjRtZtWoVe/bsMSSYpSUmJuLj48OGDRsYO3ZshecLDw/nqaeeYsGCBfTp04eLFy8ydepUXnzxRWbPnl3hMRVVLr28vGQx5ruIzKETQjSU+l6guinKzs6me/fuLFu2jAULFtCtWzcWL16MTqejVatWTJs2jRkzZgD672SVSsXHH3/Myy+/XOH55HtbCCFqp76/o0y+FImVlRVt27alZ8+eLFy4kK5du7JkyZIK923ZsiU+Pj5cuHCh0vPNnj2bCRMm8MILL9ClSxceeeQRPvzwQxYuXFjp5FZra2tDx9qSl7i7lMyhW38o3tSh3DWSNfksDjtPskY6MQshqvbaa68xevRoQkNDjbbHxcWRlJTE8OHDDdusra0ZNGgQBw4cqPR8CxcuRKlUGl5eXl71FrsQQojqM/mw2LJ0Op3RbyNLS0tL48qVK7Rs2bLS43Nzc8uNLzY3N0en00mnrHuYzKGre9L0RghRHRs2bODvv//m8OHD5T5LSkoCQKVSGW1XqVRcvny50nPOnDmT6dOnG96XVC6FEEKYlkmTy3fffZdRo0bh5eVFVlYWGzZsIDw8nO3bt5Odnc3cuXN59NFHadmyJZcuXeLdd9+lefPmPPLII4ZzPPvss3h4eLBw4UJA34H2s88+IygoyDAsdvbs2Tz00EP3ZMdRoSdz6OqeJOxCiNu5cuUKU6dOZefOndjYVD4loezcIJ1OV+V8IWtr60p7LwghhDAdkyaXycnJTJgwgcTERJRKJYGBgWzfvp1hw4aRl5fHyZMn+fbbb8nMzKRly5YMGTKE77//3miCf3x8vFGlctasWSgUCmbNmkVCQgItWrTgwQcf5IMPPjDFLQpx15KEXQhxO0ePHiUlJYUePXoYtmm1Wvbu3cvSpUs5d+4coK9glh6VlJKSUq6aWZWSkUnS7V0IIapW8vdkfY3oNHlDn8ZImjEIUXekmZK418h3yC1ZWVnlhrc+99xzdOjQgRkzZtCpUydatWrF66+/zttvvw3oG/25ublV2dCnrKtXr8qwWCGEqIErV67g6elZ5+dtdHMuhRB3F5mbKcS9y9HRkc6dOxtts7e3p1mzZobt06ZN48MPP6Rdu3a0a9eODz/8EDs7O8aPH1/t67Rq1YozZ87QsWNHrly5ck8n9SXzT+/15wDyLErIc7hFnoW+YpmVlUWrVq3q5fySXAoh6pXMzRRCVOXtt98mLy+PV199lYyMDPr06cPOnTtrtMalmZmZYU1k6fquJ8/hFnkWevIcbrnXn4VSqay3c8uw2ArIkCbRlMkwVCFMS75DTEOeu548h1vkWejJc7hFnkX9M/k6l0KIuiVregohhBBCCFOQ5FKIu8y43t6EBqgY19ubZE0+i8POk6zJv+1xNdm3Nvs3dnfb/Qhxr7G2tmbOnDn3/BIl8hxukWehJ8/hFnkW9U+GxVZASubibrE47Dxh0cmEBqhu20ynJvvWZv/G7m67H2E68h0ihBDiXiUNfYS4i92umU7p+Zml963OvM27rVHP3XY/QgghhBANrdaVy5iYGFavXk1MTAxLlizBzc2N7du34+XlRadOneo6zgYlv3UW94rKqnVSxROi9uQ7RAghxL2qVnMu9+zZQ5cuXTh48CA///wz2dnZAJw4cYI5c+bUaYBC1AWZT1ex0vMz4dZzCg1QGW0XQgghhBDidmqVXL7zzjssWLCAXbt2YWVlZdg+ZMgQIiIi6iw4IeqKdFCtmMrJhmmh7Q1DX0ueU1h0sqFiKUm5EEIIIYSojlrNuTx58iTr1q0rt71FixakpaXdcVBC1LV7dj5dXibER4J3MNg633b3ss+pJNkEjIbHylqaQgghhBCirFpVLp2dnUlMTCy3/dixY3h4eNxxUELUtbIVuntGfCRc3q//WQ1ln9O43t6EtG5GdkGRUfVSKsFCiPr2wQcf0LdvX+zs7HB2dq5wn6lTp9KjRw+sra3p1q1bhfvs2LGD4OBgHB0dadGiBY8++ihxcXFVXjsjI4MJEyagVCpRKpVMmDCBzMzMO7uhWqqL5zB37lwUCkW5l729fZXXPn/+PGPGjKF58+Y4OTnRr18//vzzzzq4q9ox5bMA2LZtG3369MHW1pbmzZszduzYO7yj2jH1cwAoKCigW7duKBQKoqKian8zd8BUz+HSpUtMnjwZPz8/bG1tadOmDXPmzKGwsLCO7qxpq1VyOX78eGbMmEFSUhIKhYLi4mL279/Pm2++ybPPPlvXMQohass7GHz66X/mZcK57ZyJjeeplRGcSlBzKkHN2GX7mbbhGB9sO8OpBDUfbDvDgm1nSNbko3Kywd7agsjYNKNEsuxczbomc2SFEIWFhTz++ONMmTKl0n10Oh3PP/88Tz75ZIWfx8bGMmbMGIYOHUpUVBQ7duzg+vXrt00Kxo8fT1RUFNu3b2f79u1ERUUxYcKEO7qf2qqL5/Dmm2+SmJho9OrYsSOPP/54ldcePXo0RUVF/PHHHxw9epRu3brxwAMPkJSUdEf3VFumfBYbN25kwoQJPPfccxw/fpz9+/czfvz4O7qf2jLlcyjx9ttv06pVq1rFX1dM9RzOnj1LcXExK1as4PTp03z++ed89dVXvPvuu3d8T3cFXS0UFhbqxo8frzMzM9MpFAqdpaWlzszMTPfMM8/oioqKanPKRkWtVusAnVqtNnUoQtSds7/rdDtm6RZ89qmu28wfdP/69APd4s8/1HWb+YOu7bvbdL0W7NI9ueKArsf/26nrOPt33cyNJ3Sf7zqnO3k1U/f5rnO6JHVeg4X6+a5zutFf7NV9vutcg11TiLoi3yF1a/Xq1TqlUlnlPnPmzNF17dq13PYff/xRZ2FhodNqtYZtW7Zs0SkUCl1hYWGF5zpz5owO0EVGRhq2RURE6ADd2bNna3UPdeFOnkNZUVFROkC3d+/eSvdJTU0tt49Go9EBurCwsOqGXS8a+lncuHFD5+HhoVu1alUNI61fDf0cSvz222+6Dh066E6fPq0DdMeOHatewPXEVM+htEWLFun8/PxqdMzdqlZzLi0tLfnuu++YP38+x44do7i4mKCgINq1a1cnCa8Qoh4oPUB9Fddm/ryY8RsDtNdQYEW+ixYXFxc0LXrSq4Mfb/10ArviLCwu7mBLYTtSNPm4NfBw4nt2jqwQok717NkTc3NzVq9ezaRJk8jOzua///0vw4cPx9LSssJjIiIiUCqV9OnTx7AtODgYpVLJgQMH8Pf3b6jw682qVato3749AwYMqHSfZs2aERAQwLfffkv37t2xtrZmxYoVqFQqevTo0YDR1q/qPIu///6bhIQEzMzMCAoKIikpiW7duvHJJ580+eX3SlTnOQAkJyfz4osvsnnzZuzs7BoouoZT3edQllqtxtXVtZ6ialpqlVyWaNOmDW3atKmrWIQQdcio6Y5lPuz7DNIuMklZzFkPK7KK2xCe1QorCzO63DhFBx8v1l5xoVino7fZeboWXyRbV8TZpOacSFADNNialyVzP4UQ4k74+vqyc+dOHn/8cV5++WW0Wi0hISH89ttvlR6TlJSEm5tbue1ubm4mGw5alwoKCvjuu+945513qtxPoVCwa9cuxowZg6OjI2ZmZqhUKrZv317p/LamprrPIjY2FtDPz/vss8/w9fXl008/ZdCgQZw/f77JJxXVfQ46nY5Jkybxyiuv0LNnTy5dutQwATaQ6j6HsmJiYvj3v//Np59+Wk+RNS3VTi6nT59e7ZN+9tlntQpGCFF31h+KJ+J0DH7p+xgT4AwW1mBlj23flwnK15DiEsQXm2NJSEziWmYevf6O4UYzFcM7uuNiHkp7xzZsO9+MqUPacSbuCk81Pwt5btXqOiuEEBWZO3cu8+bNq3Kfw4cP07Nnzzq5XlJSEi+88AITJ05k3LhxZGVl8f777/PYY4+xa9cuFApFhcdVtF2n01W6f0019HMo7eeffyYrK+u2PTJ0Oh2vvvoqbm5u7Nu3D1tbW1atWsUDDzzA4cOHadmyZZ3E0xSeRXFxMQDvvfcejz76KACrV6/G09OTH3/8kZdffvmOY2kKz+Hf//43Go2GmTNn1nkMJZrCcyjt2rVrjBw5kscff5wXXnihzmNqiqqdXB47dszo/dGjR9FqtYbhIefPn8fc3PyuGiohRJN0c/mR8YFB+KVfI9TuIpl5gZxIscG+sAX+0btwGDQV3Q0bungoycgphAzwTtxB8vV0+vj6MmbUoyze70LKjWSOXclkml8qXD4M5jfAyr7aS5uUVlJJDQ1QERadLMuYCHEP+sc//sFTTz1V5T6+vr51dr0vv/wSJycnFi1aZNj2v//9Dy8vLw4ePEhwcHC5Y9zd3UlOTi63PTU1FZVKVSdxNfRzKK0kQXR3d69yvz/++IOtW7eSkZGBk5MTAMuWLWPXrl2sXbu2xtWdyjSFZ1GSSHfs2NGwzdramtatWxMfXzdd05vCc/jjjz+IjIzE2traaHvPnj15+umnWbt27R3H0hSeQ4lr164xZMgQQkJCWLlyZb3E1BRVO7ks3Xr6s88+w9HRkbVr1+Li4gLo23Y/99xzNR6jLIS4M8mafH4+cJqn3BNw8R9oWH7EMq+Qa07diLWyZ3qYHZm5/XjRcjtml68SFB/Jz3EtsD+9mX8Ux5Fvlo+TlY72ukv0yzoN+y4xvts/gJvzHi1vDhErzNEvbQLgP9Jw/eqseVmyfElkbBrZBUXA7YfZynqaQtxdmjdvTvPmzRvserm5uZibmxttK3lfUo0qKyQkBLVazaFDh+jduzcABw8eRK1W07dv3zqJq6GfQ4m4uDj+/PNPtmzZctt9c3NzATAzM15YwMzMrNJnVxtN4VmULGVx7tw5+vfvD8CNGze4dOkSPj4+dRJPU3gOX3zxBQsWLDC8v3btGiNGjOD77783mqN8J5rCcwBISEhgyJAh9OjRg9WrV5f77+ReVqs5l59++ik7d+40JJYALi4uLFiwgOHDh/PGG2/UWYBCiKqtPxSP+nQ4MVdi6Wlrpa8qAhviWrDtQi7f5bqRmJWHQmHP3mZP8HifAvAO5v603eTf2ImPWRrXbZuzX9eNq8XNCUzbRW56Inb2vkzz8tAnlrbO+mQyL1N/0cIc/Z9tnQ1JI1SdLJY05ylduazOvVXn3EKIu098fDzp6enEx8ej1WoNa+m1bdsWBwcHAC5evEh2djZJSUnk5eUZ9unYsSNWVlaMHj2azz//nPnz5xuGxb777rv4+PgQFBQEwKFDh3j22WfZvXs3Hh4eBAQEMHLkSF588UVWrFgBwEsvvcQDDzxgkmY+dfEcSvznP/+hZcuWjBo1qtx1yj6HkJAQXFxcmDhxIu+//z62trZ8/fXXxMXFMXr06Hq/74qY6lk4OTnxyiuvMGfOHLy8vPDx8eFf//oXQLWX7qhLpnoO3t7G39sl12rTpg2enp71cKdVM9VzuHbtGoMHD8bb25tPPvmE1NRUw77VrXre1WrTYtbBwUG3e/fuctt3796tc3BwuLP+tY2AtJEXjVmSOs9oaZAkdZ5u2e9HdOnHftHpcjMM+yUnJ+m2rfu37ujPi3Uz583Rjf10m+7k1UzdyauZuidXHNDN++Ev3ecfz9JFLZuoO/zFM7o5c2foXlyyUffXsim6o/P76RIX9dbplvfXZf71jWFJkmW/H9Fl/T5Xl/rDP3UfL/lcd/Jqpi5Jnaf7f1tP6xZsPV3ny5WUvVchmgL5DqkbEydO1AHlXn/++adhn0GDBlW4T1xcnGGf9evX64KCgnT29va6Fi1a6B566CFddHS04fM///yz3DFpaWm6p59+Wufo6KhzdHTUPf3007qMjIz6v+kK1NVz0Gq1Ok9PT927775b4XUqeg6HDx/WDR8+XOfq6qpzdHTUBQcH63777bd6utPbM+WzKCws1L3xxhs6Nzc3naOjoy40NFR36tSperrTqpnyOZQWFxdn0qVITPUcVq9eXeE5a5lW3XUUOp1OV9OE9Nlnn2XPnj18+umnhvkKkZGRvPXWWwwcOLBOxlybkkajQalUolarDfMMhGgsFoedJyw6mdAAVZXVvMxD68ndu5TiYi3nch35qXgwnn0eIeZKAlYJBylu0ZHR7hkM8rUn8cA6rmXmkeJ9P9YWZnRI303rvJMUFhTyh/UQ/rboxlXHrvjknOAhh7PEZlnwkfo+2nh5sOGlkApjOpWg5v1fTtHB3ZGpoe1lWKu4Z8h3iBBCiHtVrYbFfvXVV7z55ps888wz3LhxQ38iCwsmT55sGCYghKgfla4BebORT0mznQMxaXjm6mhpkUW8fTBn8jvhAcwJ1HA8K45gnwLcbAHz9lj6tiMvWUOOogASTqJxasHvmp5kFMFFbSuCnaKZHOhHWHpP2llkY+v7KG32ZjFrdEdSUpJxTw5nsHdXxvX2NsyT3Hs+lRNX1ZxLysLO2gIHawuZOymEEEIIcRerVXJpZ2fHsmXL+Ne//kVMTAw6nY62bdtib29f1/EJIcqodA3Im418APAOpm+bZuQkKHDV5vOUZwYJTu3JLdDyQ6onT/S7n1/TmzPWTY2L+Q3M1Zdxz0rErUsncrNyMMtPZadVf3637MfItjYMaJeGi/9AJsdHwuUiOiiusuElfUOfX374CcsrEbRrbY7KqY+hihno4QxAB3dHcgu0bIm6RoomHzcnG0kyhRBCCCHuQrVKLkvY29sTGBhYV7EIIe7EzUY+eAdDfCTOmWdw7vMYXNiBbd+XcbhgwfbDZ+lw4zSvN+tOoaUZWTYdmNbPjVORf1KUn4nVpTN0t84kx6wYpbklH43qwrErmSS06Mza/cmMDwzCrfS1gJDBDxBx82eyJp/sgiICPZyxszZn6n3tWL4nBvebieTZpCxOJKgBadAjhBBCCHG3qVVyOWTIkCoXEv7jjz9qHZAQjV6Z4aeNRumOroU50LKbfrtnL8jXMLy1J+Z7fsWBbE5lFtLL34uhge3B1hmfkVOJ2bmcNm1aQ/x1Egq0HNa24cfNp7C3Nmfv+VSuZuSRU9CK90aPNLqsm5uK4JFPs3JfLH9fvkhuoRZnO0uyC4rYfiqJ69kFtHVzoHULe6YMasOO00nsPZ9KkJczx65kShVTCCGEEOIuUavkslu3bkbvb9y4QVRUFKdOnWLixIl1EZcQjVfp4af+I6ve1xTiIyEx6lZy2bIbeAdz4deNtLTKJ73QmmDLi3S//jduyc3A7VFa5MbQwsseHJ3AVomHvT2BCddIwg0Pm0KCLU7xnwJ3inJtuXxgI/NOODFxSFdDcrj+UDxboq5xo6gYexsLZo7qwLErmQR5ObN8Twytm9tzIkHNsSuZ+urlVTXv/HySZg76NuBSxRRCCCGEaPpqlVx+/vnnFW6fO3cu2dnZdxSQEI1e6eGnd6K+KqA348pUZ3Lx+D7a9BqBi60zIYMf4LA2j4et4sm5HEUrm6IKjskgLt2Cdj4tmPTUOKxOqJnY/CxnI07R1zKLDoVp/P1XBObqNrzzsxnNHKxI0eRzNimLvm2aEZOaw9WMXJbsvsD8MZ0Ji07mi4dbk3fxL+alOBEaoDLs72RjQUjrZoQGqFgcdl4qmEIIIYQQTdwdzbks65lnnqF379588skndXlaIRqXkuGnd6q+KqA341u//SgpmgTckjyYgn746ujubeHiZejcR1/RbHtf+WOyr+HGYKa4qZgWqoI8NzoA7ZI86Oprzd9XjxJr5k1XTyUpWQXsPJNMRk4hiep8hvi3IDY1m8tpuXz26yHaag5y9PBJ2hTF0NFyBGHRPkwNbU/s9Rwycguxt7YgLDqZsOhkQCqYQgghhBBNWZ0mlxEREdjYSOVBiGqpqwpoJcb27cR6C0fG9va+VSV1C7h1zQqqpUbHlLB1xqXbQ0wBfvnhG2yKsphkuRv11ZPY5CbyX20oaboWpGjyybt+iekWW7jqPoz+Nw5gX3gat8J4HMhjcOEO/nW0C+qMVD7unMRvGl/G9vYmNauAyNg0QgNU9fIchBBCCFO4dOkSfn5+HDt2rNyUsrqgUCjYtGkTDz/8cJ2fW4jaqlVyOXbsWKP3Op2OxMREjhw5wuzZs+skMCHuenVVAa2E0ZIl58KrVSVVOdkwrZ8bxIeDNgBSog2JaLImnwvWnfGyPEzbGydon7eLfIUl3a0u8F7hJFoprtM54Ry9dKfxTrxGjqULaktvdhV2YpTuL2zJp69mO2dPtOX4pTju73c/6w85kl1QRHZBEWHRyXT2UNbb8xBCCHFvmTRpEpmZmWzevNkk1/fy8iIxMZHmzZsDEB4ezpAhQ8jIyMDZ2dkkMQlR32qVXDo5ORl1izUzM8Pf35/58+czfPjwOgtOCFFHSlVJkzX5rD8UX/kcx/hIiPkDTnwP9i302/xHsmpfLLtPJvFiK3+aZcRimW2Gme4GbezzWGGzlrzCIk44DSa60AGHjsPQHNtCQOeuxJoFsyg6mH5ZO/jdMhSlSzOOFNqw+YQTKTeSDfMux5WulgohhBBNnLm5Oe7u7qYOQ4gGZVabg9asWcPq1asNr2+++YaPPvpIEkshGquSKqmtM+sPxRMWncz6Q/EV7priEsThhBzyszPAwtqQmOqAbrpofAvO0z6gGxqFknPFXsTm2uHuoMDDLI1W2afI7jiOvFPb6FR4guzjm5nsk0zXLl35zu4ZBvTqzhfPDcGq4/24u7kT3LoZLwxozbTQ9tLMRwghRIPZs2cPvXv3xtrampYtW/LOO+9QVHSr0d3gwYP55z//ydtvv42rqyvu7u7MnTvX6Bxnz56lf//+2NjY0LFjR8LCwlAoFIZK6aVLl1AoFERFRXHp0iWGDBkCgIuLCwqFgkmTJgHg6+vL4sWLjc7drVs3o+tduHCBgQMHGq61a9eucveUkJDAk08+iYuLC82aNWPMmDFcunTpTh+VEDVSq+SydevWpKWllduemZlJ69at7zgoIUT9Gdfbm+DWzcgpKCJZk2/YnqzJZ3HYeVYeTuOPzJZcydLpm/7cnJv5Uq9mhLZ1wKNjX74pHk1Wz1e5aNmBL21f5k/nsZj7hODYTMWTuh10LjyBOcWc17ZiQ5IHLwxozfg+3ozp5sH6Q/EogBMJmThYW9QoqSyJsXTcQgghRE0kJCRw//3306tXL44fP87y5cv55ptvWLBggdF+a9euxd7enoMHD7Jo0SLmz59vSOqKi4t5+OGHsbOz4+DBg6xcuZL33nuv0mt6eXmxceNGAM6dO0diYiJLliypVrzFxcWMHTsWc3NzIiMj+eqrr5gxY4bRPrm5uQwZMgQHBwf27t3LX3/9hYODAyNHjqSwsLAmj0eIO1KrYbGXLl1Cq9WW215QUEBCQsIdByVERVasWMHChQuZOXMmL7/8sqnDabJUTjY43OzSCmBvbWFYqzIsWj9M1anLKJq7B0LbgfpmQBd345YYxf3KAn5J9mdTshktm+t41K8QN67Tq7M/tu4jCTzxA+RraO6m4veU5sS2mcy0vp0M8z8Xh50nLDqZ4FoOhS2JEaSzrBBCiNpZtmwZXl5eLF26FIVCQYcOHbh27RozZszg/fffx8xMX3sJDAxkzpw5ALRr146lS5eye/duhg0bxs6dO4mJiSE8PNww9PWDDz5g2LBhFV7T3NwcV1dXANzc3Go05zIsLIzo6GguXbqEp6cnAB9++CGjRo0y7LNhwwbMzMxYtWqVYera6tWrcXZ2Jjw8XEYXigZTo+Ryy5Ythj/v2LEDpfJW8w2tVsvu3bvx9fWts+CEKKHValm6dCkJCQl8+eWXvPDCC5ibm5s6rCarJKkraaRTetutuZg99Duf2w6nN4G2EFp2JaTnA1z7+yo9c+K5dkVNN69UzkbEcsPMmq7WiVxXZxOt8+XzgsEMNHcEMKxjaXQNy3x94yDL26zzWWo90NLHCyGEELURHR1NSEiIUf+Qfv36kZ2dzdWrV/H21n/HBAYGGh3XsmVLUlJSAH310cvLy2hOZe/evestXm9vb0NiCRASEmK0z9GjR7l48SKOjo5G2/Pz84mJiamXuISoSI2Sy5JWxwqFgokTJxp9Zmlpia+vL59++mmdBSdEiZUrVxIXF4eNjQ2xsbGsXLmSKVOmmDqspqFUclaSxJVUEpM1+TQzz+Op5mdxsXSruBroFgCufqjt/fgpow1jrx1kiscNTvyVgianmPDLZiRk6X/RdMHBF2VhCivyQ0jX2nEwLr1ctbGmHWxLrweq8h8pFUshhBB3RKfTGSWWJdsAo+2WlpZG+ygUCoqLiys9R22ZmZkZrl/ixo0b5WIrG0tpxcXF9OjRg++++67cvi1atKiTOIWojhollyX/Qfn5+XH48GFDa2Uh6pNWq2XZsmUUFRXh6upKeno6y5cv56WXXpLqZXWUSs7KJnEqJxum+KXC5cNga0WKSxAR4VsJGfwAbm431528egTS49id1JzTlyPodukMPZpradPSnaSCFLo4xxFl7YJt5kV8lS4kaJ2Z7w1vnbDlg4c708IyH8Wpv3jE0hPy3Ei+YcPX+2KxKXJhsk8vXG42DCrpYhsaoCIsOvlWBbWe1wMVQghxb+nYsSMbN240ShAPHDiAo6MjHh4e1TpHhw4diI+PJzk5GZVK/315+PDhKo+xsrICKDe1rEWLFiQmJhreazQa4uLijOKNj4/n2rVrtGrVCtCvLV9a9+7d+f7773Fzc8PJyala9yBEfahVQ5+4uDhJLEWDSUhIIDU1FXNzc9RqNebm5qSkpJCUlGTq0JoG72Dw6Vd5clbq84jwrRTF/kVE+NZyu+mAv+lATlYGhWmXsLezo819k1H2eIJ8ZRvstRquJF0nC3va9BjGxil9OXYlk5MR2+mZs4eC4z9BfCTrD8WzJeoaP5zKYu31DoZqakmFc8G2M8bdbEt1uhVCCCFqQq1WExUVZfR66aWXuHLlCv/3f//H2bNn+eWXX5gzZw7Tp083zLe8nWHDhtGmTRsmTpzIiRMn2L9/v6GhT2UVTR8fHxQKBVu3biU1NZXs7GwAhg4dyn//+1/27dvHqVOnmDhxotEvz0NDQ/H39+fZZ5/l+PHj7Nu3r1zzoKeffprmzZszZswY9u3bR1xcHHv27GHq1KlcvXq1No9OiFqpduXyiy++4KWXXsLGxoYvvviiyn3/+c9/3nFgQpTw9vZm3bp1Rn85enp6Vvu3i3eFCoa2VltJclaNz0MGP0DEzZ8Gbe8DK3sG2rUhZtM2/lf4KFY2Jwju9QK4+EBeJsEaNfuv+XMwtxXbE3rz0OE07K3VhEUn08u9E0n2aTzS1VM/b7KlDdkFRSgwnjtZ8ucgL2eW74khNEBV5W3ddr1OIYQQ97zw8HCCgoKMtk2cOJHffvuNt956i65du+Lq6srkyZOZNWtWtc9rbm7O5s2beeGFF+jVqxetW7fmX//6Fw8++CA2NhV/J3l4eDBv3jzeeecdnnvuOZ599lnWrFnDzJkziY2N5YEHHkCpVPL//t//M6pcmpmZsWnTJiZPnkzv3r3x9fXliy++YOTIW9/tdnZ27N27lxkzZjB27FiysrLw8PDgvvvuk0qmaFAKXUUDuSvg5+fHkSNHaNasGX5+fpWfUKEgNja2zgI0BY1Gg1KpRK1Wy3+QonE4t10/tNWnX9WJYhWqnYxVlsie207OhT2E5bYleOTT+oY8Z36FCzvIy83ikkbHD0WD2JIfyFNdnHjBJ5lVl1VEJBShyFfzrGciYx589LbJcUlH2dAAVZXzK6u7nxANTb5DhLg37d+/n/79+3Px4kXatGlj6nCEMIlqVy5L/wal9J+FEA2gDuYdVnsZj7JzNEuSTaUH9nYO+Lftw9QNx1jU4SLeUZ9zIyeTI4V+hFsPwj6gPxPsXZjY/CxWVyOwinMioMAcN2stoXYa/XlukxxXpyNssiafnIIigls3Y1xvb6liCiGEaHCbNm3CwcGBdu3acfHiRaZOnUq/fv0ksRT3tFrNuZw/fz65ubnltufl5TF//vw7DkoIUUYN5h0ma/JZHHaeZE2+0fZxvb2rt7Zk2Tma8ZEQ8wd5YR9yPuov/vfjRi7GXyXqYDiY25J6w4rfirqzQ9GfCUO6Mi20PS4+XTmZXIAiN41hBbsZ3t6J8CwPMtWZpKQkG8eXl6mvzOZlArc62VaVJK4/FE9EbBoO1haonGwMibNhnqYQQghRz7Kysnj11Vfp0KEDkyZNolevXvzyyy+mDksIk6r2sNjSzM3NSUxMxM3NzWh7Wloabm5u5bpgNTUypEk0ZSXDRYNbN8PB2qJ8Na+iYa95mWSc28uGJA/G9u2EyjLf8P7R7p64RS0l5dhv5OZls1I7hkLrZkzxusTplELcO/ThqwQ/pj/Ym84eN9e+vTmEdnd0Cs75V9lLd9yKU+jWHGKd+/DZpdY81K0Vs0Z3rNWQ37KVSqlcisZEvkOEEELcq2q0FEmJytb2OX78OK6urncclBCi9koqk+bqKzQ79QM/qJ9Aq/S6lXhd3A2nN0FhDnR5VH9QfCQxh3dQkOFCXMJ3qLoFEXtgJ35p15l1/kVmdWuPmWInBWaO6BS2qLoMZuPlCH7UeNIm0YMNrxgv5ox3MPZASNc2rN60jaz0DHyKs7lh5oqVNg8HXQ6KUvsa/ayGkupmZe+FEEIIIUTDq1Fy6eLigkKhQKFQ0L59e6MEU6vVkp2dzSuvvFLnQQohqq8k0crevp4ssxNcuG7Lomv6JLLCBCwvEwpzaNu1P7rj++mU+zfn98diU1hAQPFFeqdu5N0Tz9KT4fRqcZ2OLXvzgE8yp1s/wKatl1HaWvLBtjO8MKD1rarhzWG8LYCJk17h2z+PY5XtQad2TnRLOY65jQPBA0YY7dtQpMophBBCCFE/apRcLl68GJ1Ox/PPP8+8efNQKpWGz6ysrPD19SUkJKSKMwgh6lKFidLNYa/5bR/kcoIat37PExxjRk5BEcmafFQ3lxZJcQliXdh5JjY/i0tCJM62zvR68GXO/jSP2CwzmimbY5afzvUiN4Y5xNKimYr21gWYp+3GysKatNxUtMU+/HXhOvbWFthbW1SYvKqcbHhrTB+gz83YnBnjHQy2pknsqt3YSAghhBBC1EiNksuJEycC+mVJ+vbti6WlZb0EJcQ9rQZrWpYkSjkFRdiXzK9M1Hd73Z/py9e5jxB61QoHa4g4HUNkToR+ORDvYCJ+3UhEYius/T2YYusM+WpQJ9BszEIydy7Hpdfj/H3yJO21eYxwjcfepye/RNuyWaPkYUc17XoOw/P6Fbxd7WjhaF1poyDjBNi5QauUFalON1ohhBBCCFFztZpzOWjQIMOf8/LyuHHjhtHn0sBAiDtQdimQKpQkSNkFRbeqcf30cxfbte2IQ1gCoQEqWjha45e+j1C7i/rzg/7PLaFvp7YQnQ+ubaAwhxaFJ2jhbs7+/f/leEoL0ly6MiTQjxxVHw5eSEZtnUWbfsPYGZ1MobYY3+b2VVYA66JSWJdDWWV+phBCCCFE/ahVcpmbm8vbb7/NDz/8QFpaWrnPm3q3WCFMqgYNbkoSpWRNvqEzLLY24D+SnWHnDUnntND2+oplSUUUsC/MYQyQffw/JJ3ZjbW9IxE5rfDrHIwiLo7m+WqGaC+wNcOGKYcG07p5Mr9EXcPWypyw6ORqVwBrUimsLImUoaxCCCGEEI1frZLLt956iz///JNly5bx7LPP8uWXX5KQkMCKFSv46KOP6jpGIe4ttWhwo3KyYVxvb6PErFxSV9F5T/zA2Qwb1LlKCnOt0GpT2Xj0KoEW1jRzbke2mz/pmg5k5t7gRIKaYp0Od6WN4RrVSfRqUimsLImUoaxCCCGEEI1frZLLX3/9lW+//ZbBgwfz/PPPM2DAANq2bYuPjw/fffcdTz/9dF3HKYS4jbKJ2e2Susy8QnITr+Kgs+WMoiVW5mYUaPNwcVZyVteLB0aMpV9rbzrExrN1689kuHYnRWNJSOtmgH49zbruuFo2iSxdyZSKpRBCCCFE42ZWm4PS09Px8/MD9PMr09PTAejfvz979+6tu+iEEOUka/JZHHaeZE2+0fZxvb0JDVBVr7qXl8mh8wkcuuGL2s6XLu426Fp147hVdyIUQexV9GBnrP78F47sok3ucTrcOIOniy05BVq+3hdLWHQy6w/F3zaumihJiEsS1pKEufR1hBBCCCFE41SrymXr1q25dOkSPj4+dOzYkR9++IHevXvz66+/Gi1PIoSoe+sPxfP7qUQiY9NY8lSQIRGr9vDTvEyI+JIB2mNctb9Bi14P4Kx0wcMliNjDaVgXFBFQMn8TCBn8ABHAZUUHzsVmEZ+eyyNBHuUS2drMi7xdox4ZDiuEEEII0XTUqnL53HPPcfz4cQBmzpzJsmXLsLa25vXXX+ftt9+u0wCFuKvkZcK57fqftRQaoCKnQMv1rIJKK3oVVRFLtmWe3A6JJ7Bt0Zp2Q56l0Hcoi6+0RmejxN7agpMJauytLQzJnpubijFPTMbMzgUbS3O8Xe14YUBrowoj1KxyWhJLRRXQ0spWMoUQQgghRONVq+Ty9ddf55///CcAQ4YM4ezZs6xfv57w8HBD0imEqEDJMiM3lwMxUibxrGyYaVh0MvbW5jSvYm3Jb/88TnzEz3z7563/Hksqi4fOX4G8dHBtDVb2bPz7KttPJTF1wzFCA1SGBLHs9V8c0JoJIT4sf6aHUbJ3KkHNUysjSM0qqHYiWBKLAqo/lFcIIYQQQjRqtUouy/L29mbs2LE4OTmxdu3aujilEHcn72Dw6VfxMiPxkeRc2MMvv240DBetqKoXGqDCxc6KWaM7VprIeWcfp1vxadqoIw0Ja0llsXd7T3BQgSYBLu/nKfcEnO0sycgtNCxbonKyKXf9yqqIC7adIepKJgu2nan2YyiJpaIKqBBCCCGEaJpqNedSiHvF7eYE1lgVy4ycL1By+e/jbLFpz7UDp5nonoB1Ow/GlqnqhUUnG9av7OyhNMS5al8sOvQVxqEDhxCTfxYPDxuOhW/Ew30/P1s/zLi+nXC2dAOlC7gFQEo0Lt7BLGltY7jPEiV/Dg1QVdkZdtbojizYdoZZoztW+zHUZHkSIYQQQgjRNEhyKUQVatOkprZO7vyWtkWJ9C/cz8PuzXBJOcwUPysok9BV1ORm/aF4fv47gfwbWgBmtY3HTmXNqpPpNMtTkJp5gRbFSzmS0YPRDz+jr5zGR+p/2jqjsi1/fyUJ4OKw81U+g84eSja8FFLpfdV5gi6EEEIIIRqlOhkWK8TdqkbLe9yhTg+8xkWnYEIem4aL/8BKh8+qLPOZ5hWLSptsNOTVp5kdtlbmKAC8gwnLbcsfRV34n+JB2rduTZBdCv0L9vHLrxvJOLfXaAhuVe70GchyIkKI6li2bBl+fn7Y2NjQo0cP9u3bZ+qQhBBC1JBCp9Ppqrvz2LFjq/w8MzOTPXv2oNVq7zgwU9JoNCiVStRqNU5OTqYORwhj57brmwJZ2EBRPhluvVh7vQOhASr2nbzIU+4JuPgPJPmGDVM3HCMjt5CHOzgwxeMS204l8m1KGwb7u9FKE8W6xFaEdGpTYUWyriqOje08QtQ3+Q6pue+//54JEyawbNky+vXrx4oVK1i1ahVnzpzB21safgkhRFNRo+Tyueeeq9Z+q1evrnVAjYH8j4FoVPIyjYawGt7fnDP5zQVbrp47glunwUzxS9Unnj79wH9kuYSs9HugymStZDhsaICqUcyPbGzxCFEZ+Q6puT59+tC9e3eWL19u2BYQEMDDDz/MwoULTRiZEEKImqjRnMumnjQK0SSVLF8C+mZApZsCufgwNm8LMYmxtHFvB94DycgrZENcC8a2zC/XOKfs+6qStIrmdppSY4tHCFE3CgsLOXr0KO+8847R9uHDh3PgwIEKjykoKKCgoMDwvri4mPT0dJo1a4ZCoajXeIUQoinT6XRkZWXRqlUrzMzqfoakNPQRorFzC4CEo6TateGn7UcNw16xdQbAxX8gPW2tDJXNtdc7EHYhmQKL+Duq8DW2jq6NLR4hRN24fv06Wq0WlUpltF2lUpGUlFThMQsXLmTevHkNEZ4QQtyVrly5gqenZ52fV5JLIRq7lGjIV3Np+2JaXM/lkqsFLrZWt6qXZZY3qWmFrz7mMsr8SCFETZWtOOp0ukqrkDNnzmT69OmG92q1Gm9vb65cuXJXDkVO0eTz09GrPNbDE7cm+ndqVfdQnfs7c03Nv3acY3J/P7Ycv8ae86k42Vjg6WLH5P5+LNl9AYCp97XjQGwaeQVabK3Nea67Ky0yj4NnL1Ju2LAs/CIXkrNpp3Lg+NVMnG2t+PjRQNycbEjR5LMm4hIJGbmcuKIm0EuJAjiZoGFyf192nUlhcn+/m+cvws7agokhvlzPLuBfO87x1gh/YlOz+X/bonlMeRbvrBN4BPRmz6Vc9uT6kquwp5OHkosp2VibK1Apbfn40UDD8ZP7+3HiqrrK53C7Z1XTf1fq+nyNSVOOvT5pNBq8vLxwdHSsl/PXaM7lvULmywiTKz3PEiDiS/KvHCMhIxdVhz44DJqq3156LmYt1cdcRpkfKe5l8h1SM4WFhdjZ2fHjjz/yyCOPGLZPnTqVqKgo9uzZc9tzyDO/95T9JWZNv3cq+iXoHX13VdYfoTAHEqMMvRAqu7YQDaW+/76UyqUQjdHNeZYZeYWsvd6B8YEv4NbiL9oAtL1P/8VV0jUWjCqXNVUfcxllfqQQorqsrKzo0aMHu3btMkoud+3axZgxY0wYmWjMyk6VqOn3TkVTLcb19ia7oIicgiKSNfm3T/xKJ5Sl+yOUXksawMreaGkxmeYh7maSXApRQ2fPnuX777/nySefpEOHDrU7SdnfcJZxxrIjW8+eJU/djMNJyQBMC33UaJ8UlyAijifQN6ANLc5tv+MKZl2SL04hRE1Mnz6dCRMm0LNnT0JCQli5ciXx8fG88sorpg5NNBF18b2jcrLBwdqCsOhk7K0tqj5fxmUImwvW/7+9+46rsu4fP/467D2UKbIUQcy9cWu4KnN0lysVMyu1+6eVWabmuB3lnaXfhpmV2nBUjry1HJi4cePECYKiDJU9Ba7fH0eOHJYg44C8n48HDzzX+Fyf66Nen/O+Puthy4+1CyTeUv/OCzSzUiErDWLOQUocNBlQbeppISpLxU8RJHRm7ty52NjYyCQHlUhRFHbs2MHBgwfZuXMnT9yrPK/iiQwucve8wCh+jPPhTBw838iMMXaX1AFpPmvPJrIyxpvDR/aXmNbjrDsWSWBoDOuORT7R+UIIUV5Dhw5l6dKlzJs3j5YtW7J//37++usv3N3ddZ01UZOkJ6h79RSoL8tieHs3nm9kRoDVKTi3sXBa6Qnq7TtnQNxlyExST7x34HO4ew0ublW/7HXvrD7+9E9wYTMcXgbX9jxxvoSoKSS4fEpkZ2fz7bffkpiYyIoVK8jOztZ1lp5Kly5d4uTJk9SvX58TJ05w6dKlJ0sor+LJ100mv5nPN6Glqw3zBjZlgmcctrHHib+8n6WBV4hJygBgRHNrxjteoZNft2LTiknKYMH2i8zfflFzXkHD27vh7+v4VHRhjUnK0CojIUTNMXHiRG7cuEFmZiYnT56kW7duus6SqGke8+JWS1GBaHoCjneCmOByA5uwbXD2NzjytfqYvKDywBL19qxUsPcB/zlw6wSkJ4JVPbB2g4xEdXr120LTf5Fl5khMWi6JSQkFc1EqUreJmkSn3WKXL1/O8uXLuXHjBgDPPPMMH3/8Mf379wcgICCANWvWaJ3ToUMHgoNLfmgkJCQwY8YMNm3aRHx8PJ6enixZsoTnnnuuUu6jOpg3bx5xcXHo6+sTGxvLvHnzmDdvnq6z9VRRFIWdO3eSnJyMr68vly5dYteuXTRu3Ljs66oVmOG1oKYu1qx/w0/9IV0dNK4Ptyfw6sMusp0dcDj/PQPNEyDNpdi01h2L5M+Q2wBYFNPF52nqwprXCgslr+EphBB5srOzCQ8PJzk5ucquqaenh42NDe7u7rIuZ0XKe8la1IvbgsNRCq4hnZ7wKJB0agZ1PCEjCe6cedTieHAZZCZCgx7g2e3RHAi3ToCRmTq4vLoT7l15dF1bd6KyzDHIuEv02QO4G5qxMbYeLzncwcbU8FEaJZC6TdQkOg0u69evzyeffIKXlxcAa9asYeDAgZw+fZpnnnkGgH79+rFq1SrNOUZGRiWmmZWVRe/evXFwcOCPP/6gfv363Lx5s9Km260OsrOzWblyJbm5uZiZmZGWlsb333/Pxx9/jIGBDKutKOnp6dy6dQszMzMiIiIwNTXl1q1bZGRkYGpqWgEXSNCeACCvMvN6Fnz6McQ5g0wD9exyRAap34ya2hTb+gnqVsnUzGwUCk9y8DTOVicTCQkhSisiIoL58+ezefNm7t27p5M8eHp68sorrzBr1izMzc11koea5LH1Vl6dWNScBgWDyYKBaGTwo3rV6OHfhYkVJESqu7W6tIGUaMh9AMl31C2X1/ao62ivZ9XnpMSpu8bGXYHGz2vStvdqQ/KlJHwJJyJ4PdapdUg1uoqNk4v6vMdMyleeuu1prOtF9abTyGPAgAFanxcsWMDy5csJDg7WBJfGxsY4OTmVOs0ff/yR+/fvc/jwYQwNDQEeO2YjMzOTzMxMzeekpKRSX686uH79OklJSejp6ZGRkYGenh6JiYncuHFDE7iL8jMzM2Pq1KmkpKRotllYWJQ7sMx78I+xu4Rt7PFHOy5sVv/OSoXEmzi2evXRG0vDfJViCW88Ha1MmPF8kyL3PY1vQp+mVlghROW5ceMGPXr0ICsri/Hjx+Pv74+NjQ16elUzWignJ4fo6Gj+97//8eWXX3L06FG2bdsmAeZjlKreigyG6/9A1Enwm/SojiwYTBbsQZR/f0aievkQKxd1oHg7BBKjwM4HcjIg5wGc+gksnR4Fhz791JP8nF4DSdFg703MAxPWHYtkRIfJOHu0gaw07HMMsLxyi7o5yWDvXeIL4ryXzo5uHZ+4bnsa63pRvVWbZq2cnBx+//13UlNT8fPz02wPCgrCwcEBGxsbunfvzoIFC3BwcCg2na1bt+Ln58ekSZP4888/sbe3Z8SIEXzwwQfo6+sXec6iRYtq9CQ4Pj4+rFmzhvDwcM02T09PCSwrQVledJRW3oPf0tOacWYm6okBTKzVb0DvXYGokzwI28ep0HDCbdrTq/dAHBwciXHuwbpDkQxvb1Ly28hiZqaVVj4hRG01YcIEVCoVR48exdXVVWf5eOGFFxg9ejS9e/dm8eLFNfq7SFUoVb3l1lEdWObVfXkB5GOGo2jtv7ZHfa6RBegbqX8MTMDCDtqPhwtbIDsTXNo+Cg7TE+D0L2Bgqj62biM2Hb5A4oUgNmb3YEI/9YzvNkDfZglwrcGjaxc3g3zB1tYnIHW9qGoq5Ymnu6wY586dw8/Pj4yMDCwsLFi7dq1mbOSGDRuwsLDA3d2d8PBwZs2aRXZ2NidPnsTY2LjI9Bo3bsyNGzcYOXIkEydO5OrVq0yaNInJkyfz8ccfF3lOUS2Xrq6ushizqBKFWi7zFlrOW8cyO5ObFw7xT5IL2SojLHy6MXTkG6Vf7DkvnXwLOJclX2XpSiPdb4So/AWqRWFlKfP79+/j6OjI0qVLmTRpUhXlsGSjR4/mxIkTXLx4UddZqfnSE7SHlZRl6Y+8IC8lDi5tA31DMKur7h4L8CBDnV5sKORkga0HGBhDu9fV267/o/7s3BK8niX+8n6uH99Jw3Z9sW354sOWzV+gyYvqWWUzEqFhL3XaRdXTj1m2TIgnUdl1lM5bLn18fAgJCSEhIYGNGzcyZswY9u3bR5MmTRg6dKjmuKZNm9K2bVvc3d3Zvn07Q4YMKTK93NxcHBwc+O6779DX16dNmzbcvn2b//73v8UGl8bGxsUGq0JUNk1XznQHMDXS6poTn57FL9fNua9yJAh7WilX6ZyTDukJDG/vhnF2MsPsLj0816boC5Q0wUEJnqQrTUnnSOAphKgODh8+THZ2Ns8//7yus6Lxwgsv8PPPPxMXF4e9vb2us1OzRQaru7S6dy57QJbXUujcEtqOVfckig0Ft47E3Y3j+q7leHXvj519Y/U1wg9CRoK6pbLVq+oW01avgq16OJatTzfa5q/XT/8C1wLh3jUwswNDE/XQl/pt1fsL1tOPa20VohrSeXBpZGSk6b7Ztm1bjh8/zrJly1ixYkWhY52dnXF3d+fq1avFpufs7IyhoaFWF1hfX1+io6PJysp67IRAQuhM/krk4dvK9dEu/HwpnlylOW7OZrS3zqCfdbh6DIZPPyZ4xkHEcXVQWlwFVIrKqajAL39XmtIGhiV1v5FxH0KI6iAhIQGgxCE2VS0vLwkJCRJcltcTvlAtdK6pDTFJGWwKv8uw9P0cD40kPS6emFOnGfjKOHWraF1v9fCVVq+qg9DsDPXvh8Flofq31avq301eVI/hzEpVB6l54zbzlkeRlkpRg+k8uCxIURStLqr53bt3j5s3b+Ls7Fzs+Z07d2bt2rXk5uZqBuZfuXIFZ2dnCSxFzREZTOrVfdgmeuLv64eZsT7juzbA0fAZ7Rlly1OJ5lNU4Jd/cpy8Lrj59xelpAl1ZNyHEKI6yBsNVNw8DLqQl5fc3Fwd5+QpUJ7WvgLnrjsWSeKFIK7fDKNTiy7s0++CX48XHh3bZtSjc02s1b8dfNUBYr5WT02gaOtOTNv31JP8NG+FQ8xBdSvpwzr8UTfaLHU3WiFqoKqZFq0YH330EQcOHODGjRucO3eOGTNmEBQUxMiRI0lJSWHq1KkcOXKEGzduEBQUxIABA7Czs2Pw4MGaNEaPHs306dM1nydMmMC9e/eYPHkyV65cYfv27SxcuLDajKsQolTcOhKY5sXmu644WBkz8/km6hbDvIovr6Iq+PkJDW/vhr+vI/6+jkUu1Jy3vzyBYV7gKV1ihRA1wbx582jSpIkm4FOpVMyZM0ezPygoCJVKRVBQkGbbnDlzCq1b2a1bN6ZMmVIFORZPIiYpo8h6D9R1n8MzPWjYri82zfozcMBLOMSfVrcwFpRXH8eGqrvWnv5F/TtSe232vJe5R4K2qVstQX1MegLro13YntSA9dEuFX6fQlQVnbZcxsTEMGrUKO7cuYO1tTXNmzdnx44d9O7dm/T0dM6dO8dPP/1EQkICzs7O9OzZkw0bNmitWRkZGak1dbirqyu7du3inXfeoXnz5ri4uDB58mQ++OADXdyiEE/G1IaO/UYS/rAralmUtgtrweOm+HsX20IpS3wIIWqT27dvs3jxYlavXq35jnHkyBHq16+vOaZ169YcOXKEJk0eLff0+uuv06+fdqvZf/7zH3r37s2ECRPw8fGpmhuo7cowEU5JQzYcrUyY0K8N0Ea9IW+CPHjUjTV/b6LIYHWLJYC1i3rSnrzPD430UdExYjNerV+FHBd119iHaQ7p1IN1BpYMkR4+ogbTaXD5ww8/FLvP1NSUnTt3PjaN/G8M8/j5+REcHFz4YCGqkccFgfkDurJMhlPasY1FHVearqsyMY8Q4mm3bNkybGxstCYP7NhRe/iBlZVVoW3169fXCkABunfvjo+PD0uWLOG7776rvEwLtfQEOPL1o9bFx3SRLfXkeFB4KEr+pUJAO/C8vKPQGMyYpAzCdy2nddZJjCLrQq8ZkJ5AfI4hP1yyJeNamHoIjNStogbTabdYIZ56eYPz83ehebht0+ELBIbGsO5YZIndcuBRILjuWGShfQXPLdiFtbi0i+rqWpquqyXlRQgharqsrCx++OEHRowYodUz6km7xQKMGjWKtWvXkpycXJlZF/BwKZFYSLtbqNWwKI5WJkzwjFMvBRYZ/Nj6WItbR/WstG4dtf9ccB9AegKHtqwg+GYm4elm6tli0xPA1IY1dxvz2/lktobclrpV1HgSXApRmfLeauYfc/Fw2zCnKM04x8nrT7PjfHSxlUpJYx4LBnsFA8TigsEnHQNZEeMvhRCiujp69Cj37t2jZ8+eFZZmjx49SE1NLbK3lahgbh3BwkEdvN06UfgFb3HnOLeErFStF7+FXNvzcDmRh+to5p/3oMAcCLHJmfx5JorY5IeTVEYG0/TuDgbyDxZZd+H8H5p0/H0dqW9rir+vg9StosaT4FKIypSvwtJUbg/fZtr6dGOKvzdbQqK4GpOCmZF+sZVKSYHg44K9iggG87/JlYl5hBBPsyNHjgDqMZUVpVWrVqhUKg4dOvT4g0WxStWqaGoDfpPUS4UAqVf38ef/Nj7+HCNzuBOiefFb6jqzqB5KwJGgbWSHHVRP3APg1pF6nk0wNbekroMLmNbRHBsYGkNWTi4OViYVUreWqfVViAomwaWoFp7aB2G+CkvTelng7aYK0NdT0cbdtlClUppyeVywVxHBoHSFFULUFrdv30alUmFnZ1dhaRoaGmJjY0NUVFSFpVkbPbYuygv0QF3Pej1LYJoXa+/Ue3z95daReId2rI92KX5OAa9n4ZnBj64FRfdQAvx6vIBBgy40atubBdsvMv+f22S7tMXGuSH7jDqT0GgwZKXBuY2MaG5dquEspVXVdfZT+x1OPJFqt86lqJ1KOwlNjeTgC1Enix378XrXBpgbG5TY5RUKl0tVTqxTEWtUykRAQoiaID09HUNDwwpfB9PExIT09PQKTbO2eWxdlH+CnYcvcUs98/rDsY+BV2PINIgs+rtI3gvjiEPq324d1T2TnFsSa9uKtYFXNHWcg4MjAwe8xJ//28ieK3akqMyp28abekb+rL1Tj4wbt+mbFcjN+2nYdTNkiv+jdS2/PxDGnyG3Sc3MZsbzTQrno7zlVMGe6u9woswkuBTVQlU/CKtSfMQZrl+/TcM6Z7B9OGNcfiUt81FSuVTlw7wiliKRykcIURPY2dmRlZVFamoq5ubmFZZufHx8hbaG1kaPrYvyT6bzcJkQR7eOpa5zSvVdJP81IoPVPZOcW3IkaBtH7tQDHtVx8Zf343DvBGPqNyfWtjXDnKJ40PoFws8m4te8NXv2pHM+MwGraBeG5HsBqzy8lFL46qVS1cuHPc3f4UTZSXApqoWneR3F9dEuxCY1wCHahQklHFdUy96TBp7VUU3LrxCidmrcuDEA169fp3nz5hWS5u3bt8nIyNBaE1NUgrxhJ1B4TcpSKNV3kfzXeBhoJiQm4HDvBIPtWtHUtxPLd5xkmFMUm25ZcyvNCwfP9rzvGQcRx8HUiCn+6jUyO/m6EWnrx5BOz2i9gB3ftQEWxfRoqo6e5u9wouwkuBSikg3p9EypFkUua8teTXuY17T8CiFqpx49egAQHBxcYcFl3trbFTkDrXiMgmtSVoaHgea6HSeJTYvCwbMNgaExJF4I4vrNMIa068t6gx4Mc4oChxaa/MQkZRC8YyP+ZteY4N4W7sQxonkrAEY0t8bhThBTOncEUxlCImoeCS6FqGSlDaqkZU8IIXTP1dWVrl278ueff/LGG29USJpbtmyhWbNmNGvWrELSq5UednPFraNmQrwS5W9hLEl8hHp5kVavQhFDVwrlIW8ZEq9nNfl4qXV9jiTZ4Ne6PoqJNZuye+BVxwUb/QdMsL8Il3aC/gNo9hIAm/adJDEyGkMXF54DuP4PDiYnmdJ5kjr9C5vVYzkfHi9ETSLBpRCVqCyT2FRky55MniOEEE9u8uTJDB06lKioKFxcXABQFO0RcD169Ci0bc6cOcyZM0drW1JSEps2beKLL76o1Dw/9SKD1YFXxCGo6w1GZloBHlD2ABQerlsZSEpWNt8bDC+53ry2B4KXg1mdRxP6RAbjkJXKQJsbEH8afPoxoV8buBz3qFtugfyNsIwi1ugY9g2HQf22cGkbZGeq856VCklRpF4/zE9RHgzp9IzU46JGkaVIhKhEulrCQ5YOEUKIoqlUKgBycnKKPWbIkCG0a9eORYsWlft6X3zxBW5ubowdO7bYY/LyoqcnX8uKlRcwxl2B0z+pW/fylv/IW4IkL/gssCxIsdITwNoNPLqwIbvH45c5uRNChr45x9PsibVtpb7e6V/US4q4d9bugvtwTWvavf5o+ZJre+DaHqxv7qVRXRNsTI1IuHqY67djSdEzU88qH3MeMhK5c/0sV4/vZuWBMECW+xA1h7RcClGJdNXVVbrYCiFE0WxsbACIjY3Fw8OjyGNUKhUrV65k69at5Obmlivos7KyYvXq1RgYFP+VKzY2VitvorCYByZsyurH8EYNsNHPVrdc5gVzeUuQOLdU/6TEwrmNhVs2C4oMhntXwL0zLzh3INmkiCVL8lpDs1IhPZEbaYZ886AnLc4mMsXx4TFGZoW74ObvlhsbCpf/hrS7YOUCZnXBwgHqtyXstwWYJkaz4UozhhqsxuLuNTC2JsbQi5B0X559mJzMuC5qCgkuhahEBbu6VlV3VZk8RwghitapUycMDAzYvn07kyZNKva4pk2b0rRp03Jf75133nnsMdu2bcPX1xd7e/tyX+9pte5YJIFX08j0bVO4fiu4PMiFzerPRuYlj7vMf96DYo7JH7haOuJa34BBWYl0bO8Ghg7E5xiyPsqFIe4ZOBpmFOqWG5OUwaZwewJSEzBNjIQ6DcC1gzrtWyfwNblPpJ4BtumRxFxOxKLRM+DagUaOXXjxbGKhl8Xy0lhUdxJcClGF5M2jEELoVp06dejduzf//e9/efHFF3F1ddVpfg4ePMgff/zBtGnTdJqP6q7E4Krg8iBZqY/+XFDBcZkPz1t36ErR9XP+ANTrWbIu7+d2tIvmumvuNubI5evUSwphoK+Net1LeJTuw6DYyPM1xjkeVE8cFBsK1/8hNTmB+zG3uGbXAxNDM1yz94NjU2j2Eg7pCUxxDQNDB8BEXhqLGkOCSyFKqSJaHeXNoxBC6N4333xDz549ad++PaNHj6Z3797Y2NhU2ZjHnJwc7ty5w//+9z/Wrl1Lx44def/996vk2jVVqYMrU5uiZ1nN3701XwCYV7f7+6r7uBZXP8cmZ7L2bCIpmV4Eh90j0yCSKf7eDG/vhuf9A/ibXQPaPhp7mZ5A/OX9ZKc60rFBXV7o2oAYOrDuWCQjmrfCweQk6We2Y5l+H/20s+T4PI+Rylnd2gqPWkwf5lOImkKCSyFKqSJaHeXNoxBC6J6HhwdBQUEsXLiQH3/8kcWLF+skHw0bNmTy5MnMmDEDc3NzneShpirzC9/83VvzTb7z2Lr94XlHzkQRGOONX4O6+Ps6aoJQRysTBg54qfAstZd3cOnI39y+607d1i/iaGXC0sB8raOdJ3HoaibGtw4TaDmQmQ1twHSwevbYyzvUk/tA5a7TKUQlkOBSiFKSVkchhHh6uLu7s2LFCr7++msiIiJISkqqsmvr6elha2uLq6urZvZaUXoxSRlMXn+a+LQsoPgXvloBaMHusg+DwMfW7Q6+EHWSTn7dOH/2AQrQp4EJwTt+xa/HCzg4OBa9pqZbRyKsIgm550jHzGyWBl7Rbh01NSGkTl9ioozpYn6XuOC/oeMwbGJDS2yxlKXGRHUnwaUQpSStjkII8fQxMDCgYcOGus6GKIN1xyJJSHuArZlRsUFhXgCakKaeqWeKvzcJ6VnEBa/HPj0Lm/bDgVLU7bGhkJ2Bfdp1zI0bcOTCdRpd+BMlPZG9D3IYOvKNos8ztaHngNFEH4skJTNb02I5vL0bKw+EoQKcE0NwJRTTewppD6I4cCGSht07s+3SJV7wakKTYu5d5m4Q1ZkEl0IIIYQQokaIScogJTObro3seL1rg0etdwUm6ll3LJL4tCytAPTw9XtYJ2dy9fo9nmtfygvmm9BnuLMJnvcPYBedyYV0M/bHu9MjKUOdh/gI9ZqXTV4kIuwKc89aMbWPD1Ncw4i1bYWFsQHD27ux7lgkW0NuY5abQhfukmPghaNhNlYPYrkcn8OqHTe5GOfDqcAo1r9ROHD293UkOOyephVUiOpGgkshhBBCCFEjrDsWqQmutLqFFpgAJ39317zj2j77Mkf0TfHr8YLmtPwT+gSGxhTubpqvy6ujKQwc8BLxl+35IdiUuAwjTRfV8E3LaJ11EqN71zhzQw+jJE+2bbtAk8b3cACGt+/BygNhpGXm8KyvAxYRZ2mYFkakZSv6vDCEbdvsOJrtTWMnSwAa2JkTkxe45hMYGqNpCW3qYl2xhVsM6YoryqJqpkUTQogaZMWKFXh4eLBixQpdZ0WIGm///v0MGDCAevXqoVKp2LJli9Z+RVGYM2cO9erVw9TUlB49enDhwgXdZFZUe8Pbu2lNqKPh1lFrop687q75gyEHB0cGvjJOPU7yoU2HL5B45n98/r9jBIbGsO5YpHpHeoJ6Yp30BO3rmNpg2/JFFo3oSv+mzprWyBVJnThl1Aa6vkuLzs+h79yUYS3rqicQcuuoabEMDI3B0cqE8SNHYtmgHW90dKKJsxVjAt6ia3MvJvt707uBCcbhu9l0uPD/g2LvvxLldcXVlI0QJZCWSyGEyCcnJ4evvvqKqKgovv76a15//XX09fV1nS0haqzU1FRatGjB2LFjeemlwktELF68mM8//5zVq1fj7e3N/Pnz6d27N5cvX8bS0lIHORbVWZFjJAuuXVkGw5yiuBFxCRenBDYaD2JIXtD2mKVA8ucjL9DzbP8CWJng7tSMr+vugIiTYNQZTG0Y3t6ElMxs0jJzSMnMRjGx5vnWXuprRNrg6NNPk94wpyiu3wyjoVMjoM3j77+SyYSGoiyk5VIIIfL57rvvCA8Px8TEhLCwML777jtdZ0mIGq1///7Mnz+fIUOGFNqnKApLly5lxowZDBkyhKZNm7JmzRrS0tJYu3ZtsWlmZmaSlJSk9SNqsbxAMDK4zKfa+nSjVSMPHAwymOAZ96ils0BLaEkcrUw0LZgxSRkAxNq24s8ED2JtW2mOmfl8ExysjAkOu6duBcx3jZikDJYGXiEmKQNbn2607TYAW59uZb6fylBUK7AQxZHgUogqlr8CEdVLTk4O33zzDdnZ2VhaWpKdnc3y5cvJycnRddaEeCqFh4cTHR1Nnz59NNuMjY3p3r07hw8fLva8RYsWYW1trflxdXWtiuyK6qoMgWCR7BuDUzMSEuNZvuOkun7Ov7zIw+6xefV3bGxMoS6zBbuOrj2byNo79Ti+53c4t1FzrFa31ofXiHlgwuT1p/n7/B11gPrAhPnX3Fjwz235riBqHOkWK0QVk2nEq6+oqCji4uLQ19cnMTERfX19YmNjiY6OxsXFRdfZE+KpEx0dDYCjo/bMl46OjkRERBR73vTp03n33Xc1n5OSkiTArM2KWmeytCKD4eZRSI0j/L4BsSm3WWdgyRR/b2KSMgjesRF/s2uYA5vC7Um8EMTxOGOerxujXjfTyBwcfBljdwnjRi509XXUrGnZJPpPesaug9MeYGROjHMPzeRB645F0q9+FikHvuU+Fgy5F8M+64EMb99BMz4TwNzYQL4riBpFgkshqpiMXai+3NzcWLt2Lbdu3dJsq1+/vgSWQlQylUql9VlRlELb8jM2NsbY2LiysyWqgUqfqdStI0SdhOxMGrnXwYEedPV1ZMH2i5yMiIcMJ6gPA906Mix9P9dvhtHIrQ1kmkBWGtwJgYhD2N4PZ8Izg1kaaklgaAzG2clMMDgD2RmQlQwOvpqXy8Fh90jJzMY1ZC0dU/fgq0ony8CS5xp4YGE1iOHt3UjJzEaFfFcQNY8El0JUMV0Mxhel16tXL11nQYhaw8nJCVC3YDo7O2u2x8bGFmrNFLVTpfT2SU8g/vJ+fohw5H6OKU5KR14zP4hFhwAm2LqzNPAKf4bcJldR8HKoQ8d+z4KpiXospKkRpMTCzVCwdlV3x42PULeAZqUxvL0bxtnJjHrwB6TeBX0jyEyFsP0Mb/8ygGbZk2fqT+L+X7e4n3YfV+f6WHQIAB6Nz8yTf7mULSFRqEB7jU9R/ZVj0qmaRoJLIYQQQuiEp6cnTk5O7N69m1at1BOfZGVlsW/fPj799FMd505UBxXS26fgF/vIYK4f38n9O/YkZBuCwQMuOcfR1jMUbN0Z3t6N1MxsFGB8/iAur/vtuY3qz0bmjz5bOIKRGY6GGUww2gGxl9VB6INU9c+9Kzi2efRy2d7SmJUHwtiXMZ4GmWd5kNmBH02s1WM5CwQg+Vs8w+JSAekuW+M8Zvbhp4kEl0IIIYSoNCkpKVy7dk3zOTw8nJCQEOrUqYObmxtTpkxh4cKFNGrUiEaNGrFw4ULMzMwYMWKEDnMtqovH9fZ5bLfZ9AQ48jVkJKo/+/QDt440bJdF27Ph2CVdJM6qCQ2bt9ZaI3NGvpbDQryeVQeWeRMIPfwca9uKI//biL/RPcydngE7L4gJBVMraPe6Vgvk/O0XuRqbQm6uCekWnfh2QBuIPFJkAJIXWOdvuZTusjVM3r+VJ510qgaR4FIIIYQQxcrMzCzX+MYTJ07Qs2dPzee8iXjGjBnD6tWrmTZtGunp6UycOJH4+Hg6dOjArl27ZI1LUSqP7TYbGawOME1tHn2xN7XBtuWLDPFJ0GrRjEnKYN2hK8UGqlqBbP7Wp4ctmht3nCQxMhpDlwY81/fVR9d38IXYUDaF3yXwahpnr0XgnXwGA5tm+Hq6Purimt6R+PQs1ofbM8Q5Q5OH/AF2Uxfriik4UbXKM+lUDSPBpRBCCCE0du7cybp16zhw4ACRkZHk5uZiZmZG69at6dOnD2PHjqVevXqlTq9Hjx4oilLsfpVKxZw5c5gzZ04F5F7UNo/tNpu/xajgWLe8L/zpCXB5B5vC7Qm8mqZJ7/sDYVpdY78/EMafIbdJzcwusmVzmFMU12/epqFvs0fX8ukHl3eQGroL79vZBD7oS/2MUDobXsb4gR4DW3bU6na75m5jdlyKZnfYSdq428rYSlHjyDqXQgghhGDLli34+PgwZswY9PT0eP/999m0aRM7d+7khx9+oHv37gQGBtKgQQPeeust4uLidJ1lUUvlXy86r1XP0cqk6HWk8wLIkiZReTgebphTFP6+jvj7OjJ5/Wk2nYpi86koJq8/TUxSBimZ2aRmZpOSmV1kMg/qdSDKujUP6nXQ3uHWkZMxudyJjqHuvdP8L96dP+97sCGmPmNWHWP+9ouaPA9v74aNmSER99L4M+S2Zt1MIWoKabkUQgghBAsXLuSzzz7j+eefR0+v8LvnV155BVCvB7ts2TJ++ukn3nvvvarOphDFdoV94pllH7Zu2rp1ZEpLG5YGXiEh7QHudc0AuJucyeT1p2lgZ4GpkT6Xo5M1gW1+a88mEhjjTfjZRKb4O2p1oz3qNJyomH+4a9McTxMbLqfbkxyXin7aA7aG3Mbi4QQ9jlYmLBvWiqWBV7gcnYy/r8yaLGoWCS6FEEIIwbFjx0p1nIuLC4sXL67k3AhRvOK6wj7xzLL5x8OlJzDG7hLGjV0Y0ukZACavP018WhZmxvp4OVgQ9zDYXDaslabFdOWBMNIyc+jYoK7m+uuORbLjfDTBYfeY+XwTthhYYo96GZFLd5L4cNM5WtS3pn4dM4a3d9Oko0I9G2xWTi6BoTEyzlLUKBJcCiGEEEKIGqO4GWSL2v7Y2WTz5C1XkpWKbWwIEzyN4OHxM59vwvztFxnU0oXxXRtogs28MZmnIuK5dzeONqpLuLbrDcDSwCv4+zoSHHaP+LQsfj0awYGrdzE20MPcWP31u66FEfXrmKECTVC55XQUaVk59GniiIWxgbRcihpHgkshhBBCaFEUhT/++IO9e/cSGxtLbm6u1v5NmzbpKGeiNip1gFiEUneVjQwm9eo+gpJd6OTbDlu3jpqWxFMR8SSlP2D+9ovMfL4JzVysScnM5mREPBH301ABgy3D6GUSQWOnKL45YKqZ+GfZsFasOxbJntAY7iSkY2pkQCtXGw5eu0szF2tORcQTeT8NPZWKF1vWw62OGRH304i8nyYtl6JGkuBSCCGEEFomT57Md999R8+ePXF0dESlUuk6S6IWKRhMPvFYSsrQVdatI4Fnolhzx4mfk+uwrIH6ultDbpOTq2BqpE98WhYf/3meW/Hp1Lc1JSn9AUb6evT0cWB8l+Y4xJ8Gt44oUbcBUFC3pg5v70bgxRhUKhWG+iqW77tOSmY2FsYGpGZl41bHjNbutozv2gC6olkL88+QKFIys4sc3/mkyhOoC1EaElwKIYQQQssvv/zCpk2beO6553SdFVGL5AU+MUkZ7AmNJSUzm5nPNyl1gFhU4FRcF9pCTG3o2G8k69afJiHtgaab6rO+DqhQAQpmxgakZWZzKz6dxk6WXIpOJj79AWbG+jg4OIKDetzm+K7qa6vy5Sk7V6F5fWt8nCw1YyoHtnQhMDSmUKA3xd+bmKQMzkUlkpD2QDPZT0UoT6AuRGlIcCmEEEIILdbW1jRo0EDX2RC1TF7gY6Svnq04r738cQFiTFIG3x8I48j1e9xJzCi0DmVRQWdxgWheN9bUzGyOhN3Dr0FdzkUlEp+WRf+mzozv2gCHh62R3x8I41Z8OgXb9fPS23w6ipMR8cwb2BRQB8d59+jv60hTF+tiu7yuOxZJfFoW5kYGpFZg6+UTT3okRCnJOpdCFGHu3LnY2Ngwd+5cXWdFCCGq3Jw5c5g7dy7p6em6zoqoRYa3d8Pf15F5A5syooMbr3ct3QuOdcci2XQqissxyaRlZaMUsT8wNEZrzciituUPOF/v2gB/X0cUICHtAbZmRvj7OrLyQBixSZksDbxCamYOL7asV2Q+VUBaVg7hd1P5+M/zmnNikzK1ZpQtqSz6N3WmjbstR8LuVdh6l/nXBRWiMkjLpRAFZGdn8+2335KYmMiKFSuYMWMGBgbyX0UIUXu8/PLLrFu3DgcHBzw8PDA0NNTaf+rUKR3lTDzN8rdQlmUSm+Ht3dh/JY4bd1PxsDNXj10ssD//7/x/9vd1ZGngFa1WRVB3GZ3i7835qETORyUy8/kmBIbGsPl0FEnpDzDQ18PZKJPJ3ndxNKwHaLeIDmzpAsDJiHjC76ZyLioRA309LI0NGNHh8eMd88oiJikDc2MDaWkUNYZ8YxaigHnz5hEXF4e+vj6xsbHMmzePefPm6TpbQghRZQICAjh58iSvvvqqTOgjqj1HKxOWv9qm2IlqiupWm7dtaeAVAkNjSM1Ut3j6FWhVDAyNISUzWzM2Mi+Itbc0phsX6WQQrV7CxKcfMUkZmmVKAGY834TzUYmM/+kEiqJQ39YMv4aPb7UUoiaT4FKIfLKzs1m5ciW5ubmYmZmRlpbG999/z8cffyytl0KIWmP79u3s3LmTLl266DorQpRKqSfuyScmKYOUzGz8GtRFAYLD7uHv64ijlYmmBTJvncm8oDUviI1JymDPxYY0TLdgmFtHQN3VNq8LbV4AuSUkiswHOZgaGfDJS801LbJ540QVYHzXBsW2ZMoEPKKmkTGXQuRz/fp1kpKS0NPTIyMjAz09PRITE7lx44ausyaEEFXG1dUVKysrXWdDiDKJScpgaeAVYpIySnX8umORBIfdw9zYgPEPx1jmBYV5Qd2WkCitc/KCWAtjA1JU5ly36QKmNoA6AO3X1Illw1ppgkUVkJGdq1mzMv+1/wy5zdaQ25pgtai8541DldZOUVNIU4wQ+fj4+LBmzRrCw8M12zw9PfHy8tJhroQQomotWbKEadOm8e233+Lh4aHr7AhRKmVt5cs/FrNgy2fevtikTLacjmL/lTiWv9pGEzS+3rVBobGQRbWe5k32o1B4zGdeV9yixnuWlKYQ1ZlKUZSCk2rVeklJSVhbW5OYmChvboUQQpTJ01CH2NrakpaWRnZ2NmZmZoUm9Ll//76Ocla0p6HMRfkVtbxIkdIT1OMk3TpqWh2LSys2KYM/Q25jYqjPKD/3xy6JUqrrV/C5QpRFZT8vpeVSCCGEEFq++OILmcRH1Dj5W/lKDNYigyHikPrPPv2KTGvlgTC2htzmWV8HhndwQ0W+lsdigtPvD4TxZ8jtUq2zWVLey0KCUlHdSHAphBBCCC0BAQHF7pO1L0VNUGIX2YcT8ODWkZikDFYeCEOFugtrXoCWlplNamY2KmBmvkAR0ApOY5x7aIK7vK6A+bsEFpxBtqK7uFanCX8k0BUgE/oIIYQQooBJkyYVuT01NZX+/ftXcW6EKLu8iXDy1rHUmijH1EbdYmlqw8oDYaw7GsmmU1GsOxapOcTc2ABTI30uRScXmmQn1rYVfyZ4EGvbShPcrTsWyfiuDRjRwU1rnc11xyK5m5xJamaOZuZZKPvkQ4+7z+ow4U/+shC1lwSXQgghhNCya9cuZs6cqbUtNTWVfv36kZOTo6NcCVF6ed1MA0NjSgx4VICRgR5GBnpawd/rXRvgVseMiHtpfH8gTOuctWcTWRnjzdqzicUGd3nBo7+vI3aWxpgb6xeaLbYiArG8+6wOLYXVKdAVuiPdYoUQQgihZdeuXXTp0oW6devyzjvvkJycTN++fTEwMODvv//WdfaEKLX8M8IW5fWuDTgXlUh8WhaBoTGadSgdrUxo7W7Lrfh0Cs58mTfTa0pmNnHJmaRkZmvWrAwOu6c5bsf5aPaExqIoCq3dbAvNFltSvmoimdlWgASXQgghhCjA09OTnTt30qNHD/T09Fi/fj3GxsZs374dc3NzXWdPiFJ7XMDjaGXCzOebMH/7Ra2WS4DxXRtgUWC5kbxzzI0NCAyN4XxUIldjUsh4kMPAlvW0Wu6Cw+4RcjOB7BwFCxMDrdZFCcTE00q6xQohhBCikKZNm7Jt2zZmzJiBmZkZf//9twSWosYpzdjGwNAYUjKztbqtQtFdTvPSa+Vqg4WxARO6N8TZ2kTTupl3fF7Q6mJjipGBHsPauVbG7QlR7UjLpRBCCCFo1apVkcuPGBsbc/v2bTp37qzZdurUqarMmhBPrDSzqZali2peesFh90jJzOb0zQT8GtYlNjkTc2Ptr9WBoTHcScwgPSuHn45E4NfQTmZTFU89CS6FEEIIwaBBg3SdBSEq3OMCx7Iun5E33jI1MxuAlMxsBrV0wbyI7rPD27ux4/wdrsWm4lbHrFotGyJEZZHgUgghhBDMnj1b11kQosIVN7YxL6hMyczWTMJTmoAvb7zlkbB7WBgbkJKZjYWxQZHnOlqZ0KWRPfdTH2BnafxUTuIjREESXAohhBCizBRFKbIbrRA1QV4rol+DuqVePiMvIM2b+Mff15HA0JgSz80/KZBM4iNqA5nQRwghhBD4+vqydu1asrKySjzu6tWrTJgwgU8//bSKciZExfP3dcTC2ICBLV1KvU5kXkAaGBrDFH9vmrpYM7y9G+uORRY7YVB1WodSiKogLZdCCCGE4Ouvv+aDDz5g0qRJ9OnTh7Zt21KvXj1MTEyIj4/n4sWLHDx4kIsXL/L2228zceJEXWdZiCeWf4bYvLUtH6eobq1FjaMs6zhOIZ4mElwKIYQQgl69enH8+HEOHz7Mhg0bWLt2LTdu3CA9PR07OztatWrF6NGjefXVV7GxsdF1doUolycZ/1hUt9bSBpxC1BYqRVGUxx9WuyQlJWFtbU1iYiJWVla6zo4QQogaROqQqidlLqoTabkU1VllPy9lzKUQQgghKsWiRYto164dlpaWODg4MGjQIC5fvqx1jKIozJkzh3r16mFqakqPHj24cOGCjnIsRPnJOEtRm0lwKYQQQohKsW/fPiZNmkRwcDC7d+8mOzubPn36kJqaqjlm8eLFfP7553z11VccP34cJycnevfuTXJysg5zLoQQ4klIt9giSPcaIYQQT0rqkOLFxcXh4ODAvn376NatG4qiUK9ePaZMmcIHH3wAQGZmJo6Ojnz66ae8+eabRaaTmZlJZmam5nNSUhKurq5S5kII8RjSLVYIIYQQT4XExEQA6tSpA0B4eDjR0dH06dNHc4yxsTHdu3fn8OHDxaazaNEirK2tNT+urq6Vm3EhhBClIsGlEEIIISqdoii8++67dOnShaZNmwIQHR0NgKOjo9axjo6Omn1FmT59OomJiZqfmzdvVl7GRa0Qk5TB0sArxa5XKYQoHVmKRAghhBCF5Obmcu3aNWJjY8nNzdXa161btzKn9/bbb3P27FkOHjxYaJ9KpdL6rChKoW35GRsbY2xsXOY8CFEcWT5EiIohwaUQQgghtAQHBzNixAgiIiIoODWDSqUiJyenTOn9+9//ZuvWrezfv5/69etrtjs5OQHqFkxnZ2fN9tjY2EKtmUJUpidZ91IIUZh0ixVCCCGElrfeeou2bdty/vx57t+/T3x8vObn/v37pU5HURTefvttNm3axD///IOnp6fWfk9PT5ycnNi9e7dmW1ZWFvv27aNTp04Vdj9CPI4sHyJExdBpcLl8+XKaN2+OlZUVVlZW+Pn58ffff2v2BwQEoFKptH46duxY6vTXr1+PSqVi0KBBlZB7IYQQ4ul09epVFi5ciK+vLzY2NlqT51hbW5c6nUmTJvHLL7+wdu1aLC0tiY6OJjo6mvT0dEDdCjplyhQWLlzI5s2bOX/+PAEBAZiZmTFixIjKuj0hhBCVRKfdYuvXr88nn3yCl5cXAGvWrGHgwIGcPn2aZ555BoB+/fqxatUqzTlGRkalSjsiIoKpU6fStWvXis+4EEII8RTr0KED165d09TPT2r58uUA9OjRQ2v7qlWrCAgIAGDatGmkp6czceJE4uPj6dChA7t27cLS0rJc1xZCCFH1dBpcDhgwQOvzggULWL58OcHBwZrg0tjYWDMmo7RycnIYOXIkc+fO5cCBAyQkJJR4fFHrZQkhhBC1ydmzZzV//ve//817771HdHQ0zZo1w9DQUOvY5s2blyrN0iylrVKpmDNnDnPmzClTfoUQQlQ/1WZCn5ycHH7//XdSU1Px8/PTbA8KCsLBwQEbGxu6d+/OggULcHBwKDGtefPmYW9vz7hx4zhw4MBjr71o0SLmzp1b7nsQQgghaqqWLVuiUqm0AsLXXntN8+e8fU8yoY8QQojaQefB5blz5/Dz8yMjIwMLCws2b95MkyZNAOjfvz8vv/wy7u7uhIeHM2vWLHr16sXJkyeLnYL80KFD/PDDD4SEhJQ6D9OnT+fdd9/VfE5KSpIFmYUQQtQq4eHhus6CEEKIGk7nwaWPjw8hISEkJCSwceNGxowZw759+2jSpAlDhw7VHNe0aVPatm2Lu7s727dvZ8iQIYXSSk5O5tVXX2XlypXY2dmVOg+yXpYQQojazt3dXfPn/fv306lTJwwMtL8mZGdnc/jwYa1jhXgaxCRlsO5YJMPbu8mMsUKUg86DSyMjI82EAW3btuX48eMsW7aMFStWFDrW2dkZd3d3rl69WmRa169f58aNG1pjOfMWfjYwMODy5cs0bNiwEu5CCCGEeHr07NmTO3fuFBqGkpiYSM+ePaVbrHjqrDsWSWBoDABT/L11nBshai6dB5cFKYqiNblOfvfu3ePmzZtaCy3n17hxY86dO6e1bebMmSQnJ7Ns2TLp6irEUyg3N5esrCxdZ0PUMoaGhujr6+s6G5Umb2xlQffu3cPc3FwHORKicg1v76b1u7qTllZRXek0uPzoo4/o378/rq6uJCcns379eoKCgtixYwcpKSnMmTOHl156CWdnZ27cuMFHH32EnZ0dgwcP1qQxevRoXFxcWLRoESYmJjRt2lTrGjY2NgCFtgshar6srCzCw8M1PRSEqEo2NjY4OTkVGYTVVHlDTlQqFQEBAVpDRnJycjh79iydOnXSVfaEqDSOViY1qsVSWlpFdaXT4DImJoZRo0Zx584drK2tad68OTt27KB3796kp6dz7tw5fvrpJxISEnB2dqZnz55s2LBBa+2ryMhI9PT0dHgXQghdUBSFO3fuoK+vj6urqzwHRJVRFIW0tDRiY2MBiu1NUxNZW1sD6nu0tLTE1NRUs8/IyIiOHTsyfvx4XWVPCPFQTWtpFbWHSinNIlS1TFJSEtbW1iQmJmJlZaXr7AghivDgwQOuXbtGvXr1NF+IhahK9+7dIzY2Fm9vb60usk9DHTJ37lymTp1aY7rAPg1lLoQQVaGyn5fVbsylEEKURt6EIkZGRjrOiaitzMzMAPWLjqdt/OXs2bMBiI2N5fLly6hUKry9vR+7zrQQ4hEZFylqI+lHJoSo0WryeLcHObnEJGXwIEfGjNZENfnf3uMkJSUxatQoXFxc6N69O926dcPFxYVXX32VxMREXWdPiBohb1zkumORus6KEFVGgkshhNCR+6lZJKU/4H6qzHYrqpfXX3+do0ePsm3bNhISEkhMTGTbtm2cOHFCxlwKUUrD27vh7+so4yJFrSLBpRBC6EgdcyOsTA2pY155XXtXr16tmTVbiNLavn07P/74I3379sXKygpLS0v69u3LypUr2b59u66zJ0SNkDcDrXSJFbWJBJdCCFHFYmNjefPNN2no6YGbvTWuLvXo27cvR44cAdTdLbds2VLmdD08PFi6dKnWtqFDh3LlypUKyLWoTerWrVvkRFnW1tbY2trqIEdCCCFqApnQRwghqthLL73EgwcPWLNmDQ0aNCAmJoY9e/Zw//79Cr+Wqamp1nISQpTGzJkzeffdd/npp580S61ER0fz/vvvM2vWLB3nTgghRHUlLZdCiFrv0qVLzJ07l0uXLlX6tRISEjh48CCffvopPXv2xN3dnfbt2zN9+nSef/55PDw8ABg8eDAqlUrz+fr16wwcOBBHR0csLCxo164dgYGBmnR79OhBREQE77zzDiqVSjPZTFHdYpcvX07Dhg0xMjLCx8eHn3/+WWu/SqXi+++/Z/DgwZiZmdGoUSO2bt2q2R8fH8/IkSOxt7fH1NSURo0asWrVqoovLKEzy5cvJzg4GHd3d7y8vPDy8sLNzY3Dhw+zYsUKWrdurfkRQggh8kjLpRCiVlMUhR07dnDw4EFsbGzw8fGp1FlALSwssLCwYMuWLXTs2BFjY2Ot/cePH8fBwYFVq1bRr18/zRIXKSkpPPfcc8yfPx8TExPWrFnDgAEDuHz5Mm5ubmzatIkWLVrwxhtvlDjhyubNm5k8eTJLly7F39+fbdu2MXbsWOrXr0/Pnj01x82dO5fFixfz3//+ly+//JKRI0cSERFBnTp1mDVrFhcvXuTvv//Gzs6Oa9eukZ6eXjkFJnRi0KBBus6CEEKIGkiCSyFErXbp0iVOnjxJ/fr1OXHiBJcuXcLX17fSrmdgYMDq1asZP3483377La1bt6Z79+4MGzaM5s2bY29vD4CNjQ1OTk6a81q0aEGLFi00n+fPn8/mzZvZunUrb7/9NnXq1EFfXx9LS0ut8wr67LPPCAgIYOLEiQC8++67BAcH89lnn2kFlwEBAQwfPhyAhQsX8uWXX3Ls2DH69etHZGQkrVq1om3btgCa1lXx9Mhb51IIIYQoC+kWK4SotRRFYefOnSQnJ+Pk5ERKSgq7du1CUZRKve5LL73E7du32bp1K3379iUoKIjWrVuzevXqYs9JTU1l2rRpNGnSBBsbGywsLLh06RKRkWVbPy00NJTOnTtrbevcuTOhoaFa25o3b675s7m5OZaWlsTGxgIwYcIE1q9fT8uWLZk2bRqHDx8uUx5EzZCQkMD333/P9OnTNeOBT506RVRUlI5zJoQQorqS4FIIUWulp6dz69YtzMzMiIiIwNTUlFu3bpGRkVHp1zYxMaF37958/PHHHD58mICAgBJbi95//302btzIggULOHDgACEhITRr1oysrLKvkVmw26+iKIW2GRoaFjonNzcXgP79+xMREcGUKVO4ffs2zz77LFOnTi1zPkT1dfbsWby9vfn000/57LPPSEhIANTdqqdPn67bzAkhhKi2pFusEKLWMjMzY+rUqaSkpGi2WVhY6GR21SZNmmiWHzE0NCQnJ0dr/4EDBwgICGDw4MGAegzmjRs3tI4xMjIqdF5Bvr6+HDx4kNGjR2u2HT58uMxdge3t7QkICCAgIICuXbvy/vvv89lnn5UpDVF9vfvuuwQEBLB48WIsLS012/v378+IESN0mDMhhBDVmQSXQoharaTxiZXh3r17vPzyy7z22ms0b94cS0tLTpw4weLFixk4cCCgHsO4Z88eOnfujLGxMba2tnh5ebFp0yYGDBiASqVi1qxZmpbEPB4eHuzfv59hw4ZhbGyMnZ1doeu///77vPLKK7Ru3Zpnn32W//3vf2zatElr5tnH+fjjj2nTpg3PPPMMmZmZbNu2rVLHqYqqd/z4cVasWFFou4uLC9HR0TrIkRBCiJpAusUKIUQVsrCwoEOHDnzxxRd069aNpk2bMmvWLMaPH89XX30FwJIlS9i9ezeurq60atUKgC+++AJbW1s6derEgAED6Nu3b6FlIObNm8eNGzdo2LChZmKgggYNGsSyZcv473//yzPPPMOKFStYtWoVPXr0KPU9GBkZMX36dJo3b063bt3Q19dn/fr1T1YgoloyMTEhKSmp0PbLly8X+29LCCGEUCmVPXNFDZSUlIS1tTWJiYlYWVnpOjtCiCJkZGQQHh6Op6cnJiYmus6OqIWK+zf4NNQhb7zxBnFxcfz222/UqVOHs2fPoq+vz6BBg+jWrRtLly7VdRa1PA1lLoQQVaGyn5fScimEEEIILZ999hlxcXE4ODiQnp5O9+7d8fLywtLSkgULFug6e0IIIaopGXMphBBCCC1WVlYcPHiQf/75h1OnTpGbm0vr1q3x9/fXddaEEEJUYxJcCiGEEKJIvXr1olevXrrORoXIzc19oqV7RM1jaGiIvr6+rrMhRK0kwaUQQgghNHJzc1m9ejWbNm3ixo0bqFQqPD09+de//sWoUaMKrYlaE2RlZREeHl5ohmXx9LKxscHJyalG/nsVoiaT4FIIIYQQACiKwosvvshff/1FixYtaNasGYqiEBoaSkBAAJs2bdKsx1pTKIrCnTt30NfXx9XVFT09mW7iaaYoCmlpacTGxgLg7Oys4xwVLSYpg3XHIhne3g1HK5mUTjw9JLgUQgghBACrV69m//797Nmzh549e2rt++effxg0aBA//fQTo0eP1lEOyy47O5u0tDTq1auHmZmZrrMjqoCpqSkAsbGxODg4VMsusuuORRIYGgPAFH9vHedGiIojr++EEEIIAcC6dev46KOPCgWWoB5/+eGHH/Lrr7/qIGdPLicnB1Cvzypqj7wXCQ8ePNBxToo2vL0b/r6ODG/vpuusFCkmKYOlgVeIScrQdVZEDSPBpRBCCCEAOHv2LP369St2f//+/Tlz5kyp01u+fDnNmzfHysoKKysr/Pz8+PvvvzX7FUVhzpw51KtXD1NTU3r06MGFCxfKdQ/FkbF3tUt1//t2tDJhir93te0Sm9eyuu5YpK6zImoYCS6FEEIIAcD9+/dxdHQsdr+joyPx8fGlTq9+/fp88sknnDhxghMnTtCrVy8GDhyoCSAXL17M559/zldffcXx48dxcnKid+/eJCcnl/tehBBPrrq3rIrqS4JLIYQQQgDqLqQGBsVPx6Cvr092dnap0xswYADPPfcc3t7eeHt7s2DBAiwsLAgODkZRFJYuXcqMGTMYMmQITZs2Zc2aNaSlpbF27doS083MzCQpKUnrRwhRcap7y6qovmRCHyFErRZ+N5XfTtzkVnw69W1NeaWtK5525rrOVoWZM2cOW7ZsISQkpFLSX716NVOmTCEhIaFS0hdVS1EUAgICMDY2LnJ/ZmbmE6edk5PD77//TmpqKn5+foSHhxMdHU2fPn00xxgbG9O9e3cOHz7Mm2++WWxaixYtYu7cuU+clychz4rykWeFELWDtFwKIWqt307c5NklQXy3P4ztZ2/z3f4wnl0SxO8nblbqdW/evMm4ceOoV68eRkZGuLu7M3nyZO7du1ep130SQUFBqFSqQj8zZ87UddYAuHz5Mj179sTR0RETExMaNGjAzJkzC03ikZmZyYwZM3B3d8fY2JiGDRvy448/avZv2rSJtm3bYmNjg7m5OS1btuTnn3+u6tvRuTFjxuDg4IC1tXWRPw4ODmWeKfbcuXNYWFhgbGzMW2+9xebNm2nSpAnR0dEAhbrhOjo6avYVZ/r06SQmJmp+bt6s3P+zunpWQM15XlT3Z4UQompIy6UQolYKv5vKhxvPkqsAiqLe+PD3BxvP0s6jDh6V0CoRFhaGn58f3t7erFu3Dk9PTy5cuMD777/P33//TXBwMHXq1Knw65bX5cuXsbKy0ny2sLDQYW4eMTQ0ZPTo0bRu3RobGxvOnDnD+PHjyc3NZeHChZrjXnnlFWJiYvjhhx/w8vIiNjZWq3tnnTp1mDFjBo0bN8bIyIht27YxduxYHBwc6Nu3ry5uTSdWrVpV4Wn6+PgQEhJCQkICGzduZMyYMezbt0+zv+DEK4qiPHYyFmNj42JbVyuarp4VUDOfF9X1WSGEqBrScimEqJV+O3Gz2C+wKpWKDZXUIjFp0iSMjIzYtWsX3bt3x83Njf79+xMYGEhUVBQzZszQykfBBettbGxYvXq15vMHH3yAt7c3ZmZmNGjQgFmzZpU49X54eDheXl5MmDCB3NxcsrKymDZtGi4uLpibm9OhQweCgoIKnefg4ICTk5Pmp6QvjMuXL6dhw4YYGRnh4+Oj1QL43nvvMWDAAM3npUuXolKp2L59u2abj48PK1asKDb9/Bo0aMDYsWNp0aIF7u7uvPjii4wcOZIDBw5ojtmxYwf79u3jr7/+wt/fHw8PD9q3b0+nTp00x/To0YPBgwfj6+tLw4YNmTx5Ms2bN+fgwYOlyoconpGREV5eXrRt25ZFixbRokULli1bhpOTE0ChVsrY2NgSJxWqarp6VkDpnxfyrBBCVBcSXAohaqVb8ekoea0QBSiKwq349Aq/5v3799m5cycTJ07ULPKdx8nJiZEjR7Jhw4Zi81UUS0tLVq9ezcWLF1m2bBkrV67kiy++KPLY8+fP07lzZ15++WWWL1+Onp4eY8eO5dChQ6xfv56zZ8/y8ssv069fP65evfpE97h582YmT57Me++9x/nz53nzzTcZO3Yse/fuBdRB3IEDB8jNzQVg37592NnZaVqyoqOjuXLlCt27dwfUX5rzf0F+nGvXrrFjxw7N+QBbt26lbdu2LF68GBcXF7y9vZk6dSrp6UX/HSuKwp49e7h8+TLdunV7kmIQJVAUhczMTDw9PXFycmL37t2afVlZWezbt08r8Nc1XTwroOKfF0/7s0IIUT1It1ghRK1U39ZU3RpRxBczlUpFfVvTIs4qn6tXr6IoCr6+vkXu9/X1JT4+nri4OBwcHEqVZv7xTB4eHrz33nts2LCBadOmaR135MgRXnjhBaZPn87UqVMBuH79OuvWrePWrVvUq1cPgKlTp7Jjxw5WrVql1a20fv36WulFRERQt27dQvn57LPPCAgIYOLEiQC8++67BAcH89lnn9GzZ0+6detGcnIyp0+fpnXr1hw4cICpU6eyadMmAPbu3YujoyONGzcG1C0T1tbWjy2HTp06cerUKTIzM3njjTeYN2+eZl9YWBgHDx7ExMSEzZs3c/fuXSZOnMj9+/e1xl0mJibi4uJCZmYm+vr6fPPNN/Tu3fux1xbF++ijj+jfvz+urq4kJyezfv16goKC2LFjByqViilTprBw4UIaNWpEo0aNWLhwIWZmZowYMULXWdfQxbMCyva8KI2n/VkhhKgeJLgUQtRKr7R1ZcW+60XuUxSFoW1dqzhHaFogjIyMSn3OH3/8wdKlS7l27RopKSlkZ2drjXcCiIyMxN/fn/nz5/POO+9otp86dQpFUfD29tY6PjMzs9CXwQMHDmBpaan5bGtrW2R+QkNDeeONN7S2de7cmWXLlgFgbW1Ny5YtCQoKwtDQED09Pd58801mz55NcnIyQUFBWi0Rly5dKlU5bNiwgeTkZM6cOcP777/PZ599pvnSnJubi0ql4tdff9UEqp9//jn/+te/+PrrrzWtQpaWloSEhJCSksKePXt49913adCgAT169ChVHkRhMTExjBo1ijt37mBtbU3z5s3ZsWOHJmifNm0a6enpTJw4kfj4eDp06MCuXbu0/q3pWnV8VuRdG0r/vHjanxVCiOpBgkshRK3kaWfOpy8154ONZ1GpVJpJRBRF4dOXmlfKBB1eXl6oVCouXrzIoEGDCu2/dOkS9vb22NjYAGjyk1/+MVLBwcEMGzaMuXPn0rdvX6ytrVm/fj1LlizROsfe3p569eqxfv16xo0bp/lCmZubi76+PidPnkRfX1/rnILjpDw9PTX5epzHTdDSo0cPgoKCMDIyonv37tja2vLMM89w6NAhgoKCmDJlSqmuk5+rq/oLfpMmTcjJyeGNN97gvffeQ19fH2dnZ1xcXLRaQH19fdVdGm/dolGjRgDo6enh5eUFQMuWLQkNDWXRokUSXJbDDz/8UOJ+lUrFnDlzmDNnTtVk6Ano4lkBZXteyLNCCFFdSHAphKi1Xm7rSjuPOmzIt3bd0LaulfZlsW7duvTu3ZtvvvmGd955R2scVXR0NL/++iuTJk3SbLO3t+fOnTuaz1evXiUtLU3z+dChQ7i7u2tNAhQREVHouqampmzbto3nnnuOvn37alqGWrVqRU5ODrGxsXTt2rVC7tHX15eDBw9qLVdx+PBhra59PXr04IcffsDAwAB/f38Aunfvzvr16ytkDJWiKDx48EDzZbtz5878/vvvpKSkaL4IX7lyBT09vUJd+AqmU551HcXTo6qfFVC254U8K4QQ1YYiCklMTFQAJTExUddZEUIUIz09Xbl48aKSnp6u66yUyZUrVxQ7Ozula9euyr59+5TIyEjl77//Vpo2baq0bNlSSU5O1hw7bNgwxdfXVzl58qRy/PhxpVevXoqhoaGyatUqRVEUZcuWLYqBgYGybt065dq1a8qyZcuUOnXqKNbW1po0Zs+erbRo0UJRFEVJTk5WunTponTu3FlznZEjRyoeHh7Kxo0blbCwMOXYsWPKJ598omzfvl1RFEXZu3evAijx8fFF3s+qVau0rrd582bF0NBQWb58uXLlyhVlyZIlir6+vrJ3717NMQkJCYqenp6ir6+vnD9/XnMv+vr6ir29vVb6Pj4+yqZNm4otz19++UXZsGGDcvHiReX69evKb7/9pri4uCgjR47UHJOcnKzUr19f+de//qVcuHBB2bdvn9KoUSPl9ddf1xyzcOFCZdeuXcr169eV0NBQZcmSJYqBgYGycuXKYq9d3L9BqUOqXkllXlOfFYpS+ueFPCsKq8l/70JUpsquoyS4LIJ8MRCi+qvJXxzCw8OVMWPGKI6OjopKpVIAZciQIUpqaqrWcVFRUUqfPn0Uc3NzpVGjRspff/2lWFtba74wKoqivP/++0rdunUVCwsLZejQocoXX3xR7BdGRVF/aezUqZPStWtXJSUlRcnKylI+/vhjxcPDQzE0NFScnJyUwYMHK2fPnlUUpexfGBVFUb755hulQYMGiqGhoeLt7a389NNPhc5r06aNYm9vr+Tm5iqKoij37t1TVCqV8q9//UvrOEDrfgtav3690rp1a8XCwkIxNzdXmjRpoixcuLDQv4vQ0FDF399fMTU1VerXr6+8++67Slpammb/jBkzFC8vL8XExESxtbVV/Pz8lPXr1xd7XUWR4LI6eVqDS0Up3fNCnhWF1fS/dyEqS2XXUSpFKcOc97VEUlIS1tbWJCYmFhrsLoSoHjIyMggPD8fT0xMTExNdZ6dcZs+ezeeff86uXbvw8/PTdXZEKRX3b1DqkKpXUpk/Tc8KkOdFaT1tf+9CVJTKrqNkzKUQQujY3Llz8fDw4OjRo3To0AE9PVmCWAhRNHleCCGqMwkuhRCiGhg7dqyusyCEqCHkeSGEqK7kdZcQQgghhBBCiHKT4FIIIYQQQgghRLlJcCmEEEIIIYQQotwkuBRCCCGEEEIIUW4SXAohhBBCCCGEKDcJLkUhK1aswMPDgxUrVug6K0IIIYQQQogaQoJLoSUnJ4evvvqKqKgovv76a3JycnSdJSFEJbpx4wYqlYqQkJAalbYQomrJs0IIURoSXAot3333HeHh4ZiYmBAWFsZ3332n6ywJUbnuXYfAOfDHa+rf965X6uViY2N58803cXNzw9jYGCcnJ/r27cuRI0c0x6hUKrZs2VKp+aiuzpw5w/Dhw3F1dcXU1BRfX1+WLVumdUzeF9GCPzt27NAcs2nTJnr37o29vT1WVlb4+fmxc+fOqr4d8TSp4mcFPP55UZufFUKI6slA1xkQ1UdOTg7ffPMN2dnZ1KlTh/v377N8+XLeeOMN9PX1dZ09ISre6V9g678BFaCofx9aBi9+Ba1GVsolX3rpJR48eMCaNWto0KABMTEx7Nmzh/v371fK9apCVlYWRkZGFZLWyZMnsbe355dffsHV1ZXDhw9rnkFvv/221rGBgYE888wzms916tTR/Hn//v307t2bhQsXYmNjw6pVqxgwYABHjx6lVatWFZJXUYvo4FkBT9/zoiKfFUKI6klaLoVGVFQUcXFx6Ovrk5iYiL6+PrGxsURHR+s6a0JUvHvX1V8WlVxQcrR/b327UlolEhISOHjwIJ9++ik9e/bE3d2d9u3bM336dJ5//nkAPDw8ABg8eDAqlUrz+fr16wwcOBBHR0csLCxo164dgYGBWul7eHiwcOFCXnvtNSwtLXFzcyvU++DYsWO0atUKExMT2rZty+nTp7X25+TkMG7cODw9PTE1NcXHx6dQy2FAQACDBg1i0aJF1KtXD29v71KlXRqvvfYa//d//0f37t1p0KABr776KmPHjmXTpk2Fjq1bty5OTk6an/xfWpcuXcq0adNo164djRo1YuHChTRq1Ij//e9/Zc6TqOV08KyAxz8vavuzQghRPUlwKTTc3NxYu3Yty5cv1/ysXbsWFxcXXWdNiIp3+mfUrRBFUT3cX7EsLCywsLBgy5YtZGZmFnnM8ePHAVi1ahV37tzRfE5JSeG5554jMDCQ06dP07dvXwYMGEBkZKTW+UuWLNF8WZs4cSITJkzg0qVLAKSmpvLCCy/g4+PDyZMnmTNnDlOnTtU6Pzc3l/r16/Pbb79x8eJFPv74Yz766CN+++03reP27NlDaGgou3fvZtu2baVKG9RfaufMmVOmcktMTNRqlczz4osv4uDgQOfOnfnjjz9KTCM3N5fk5OQi0xGiRDp4VsDjnxdP+7NCCFFDKaKQxMREBVASExN1nRUhRDHS09OVixcvKunp6U+WwO9jFWWOjaLMtir8M8dGvb8S/PHHH4qtra1iYmKidOrUSZk+fbpy5swZrWMAZfPmzY9Nq0mTJsqXX36p+ezu7q68+uqrms+5ubmKg4ODsnz5ckVRFGXFihVKnTp1lNTUVM0xy5cvVwDl9OnTxV5n4sSJyksvvaT5PGbMGMXR0VHJzMzUbCtt2r169dLK8+McPnxYMTQ0VHbt2qXZFhcXp3z++efK0aNHlePHjyuzZs1S9PT0lJ9//rnYdBYvXqzUqVNHiYmJKfW1H6e4f4NSh1S9ksq8pj4rFOXxz4un+VlRXuX+exfiKVXZdZS0XAohaicbN0psjbBxq5TLvvTSS9y+fZutW7fSt29fgoKCaN26NatXry7xvNTUVKZNm0aTJk2wsbHBwsKCS5cuFWqNaN68uebPKpUKJycnYmNjAQgNDaVFixaYmZlpjvHz8yt0rW+//Za2bdtib2+PhYUFK1euLHSdZs2aaXVDLW3ae/bsKTR2sjgXLlxg4MCBfPzxx/Tu3Vuz3c7OjnfeeYf27dvTtm1b5s2bx8SJE1m8eHGR6axbt445c+awYcMGHBwcSnVtITR09KyAJ3tePC3PCiFEzSTBpRCidmo1CvXEHEVRHu6vHCYmJvTu3ZuPP/6Yw4cPExAQwOzZs0s85/3332fjxo0sWLCAAwcOEBISQrNmzcjKytI6ztDQUOuzSqUiNzcXAEUp7n4f+e2333jnnXd47bXX2LVrFyEhIYwdO7bQdczNzbU+lybtsrh48SK9evVi/PjxzJw587HHd+zYkatXrxbavmHDBsaNG8dvv/2Gv79/heZR1BI6fFZA2Z8Xte1ZIYSoXiS4FELUTnUbqmd6VOmBSl/794tfqfdXkSZNmpCamqr5bGhoWGiN2QMHDhAQEMDgwYNp1qwZTk5O3Lhxo8zXOXPmDOnp6ZptwcHBha7TqVMnJk6cSKtWrfDy8uL69cdPWFKatEvrwoUL9OzZkzFjxrBgwYJSnXP69GmcnZ21tq1bt46AgADWrl2rmTBJiDKrRs8K0H5e1PZnhRCi+pHgUghRe7UaCW+fgM7/D54ZrP799olKW1rg3r179OrVi19++YWzZ88SHh7O77//zuLFixk4cKDmOA8PD/bs2UN0dDTx8fEAeHl5sWnTJkJCQjhz5gwjRozQtDKU1ogRI9DT02PcuHFcvHiRv/76i88++0zrGC8vL06cOMHOnTu5cuUKs2bN0kwUUt60AZ599lm++uqrYtPJCyx79+7Nu+++S3R0NNHR0cTFxWmOWbNmDWvXriU0NJTLly/z2Wef8X//93/8+9//1hyzbt06Ro8ezZIlS+jYsaMmncTExNIUlRDaqvhZAaV7XjzNzwohRM0kwaUQonar2xD858C/flT/rsRWCAsLCzp06MAXX3xBt27daNq0KbNmzWL8+PFaAdeSJUvYvXs3rq6umjUZv/jiC2xtbenUqRMDBgygb9++tG7duszX/9///sfFixdp1aoVM2bM4NNPP9U65q233mLIkCEMHTqUDh06cO/ePSZOnFghaYN6mYS7d+8Wm87vv/9OXFwcv/76K87Ozpqfdu3aaR03f/582rZtS7t27Vi/fj0//vgj77zzjmb/ihUryM7OZtKkSVrpTJ48+bH3IkSRqvBZAaV7XjzNzwohRM2kUqTzeyFJSUlYW1uTmJiIlZWVrrMjhChCRkYG4eHheHp6YmJiouvsiFqouH+DUodUvZLKXJ4VtZP8vQtRtMquo6TlUgghhBBCCCFEuUlwKYQQQgghhBCi3CS4FEIIIYQQQghRbhJcCiGEEEIIIYQoNwkuhRA1msxJJnRF/u3VLPL3VbvI37cQuiHBpRCiRtLX1wcgKytLxzkRtVVaWhqgXsheVF/yrKid5P+nELphoOsMCCHEkzAwMMDMzIy4uDgMDQ3R05N3ZaJqKIpCWloasbGx2NjYaIIX8XiLFi3io48+YvLkySxduhRQl+fcuXP57rvviI+Pp0OHDnz99dc888wzFXJNeVbULvL/UwjdkuBSCFEjqVQqnJ2dCQ8PJyIiQtfZEbWQjY0NTk5Ous5GjXH8+HG+++47mjdvrrV98eLFfP7556xevRpvb2/mz59P7969uXz5MpaWluW+rjwraif5/ymEbkhwKYSosYyMjGjUqJF0dxNVztDQUFpEyiAlJYWRI0eycuVK5s+fr9muKApLly5lxowZDBkyBIA1a9bg6OjI2rVrefPNNyvk+vKsqF3k/6cQuiPBpRCiRtPT08PExETX2RBClGDSpEk8//zz+Pv7awWX4eHhREdH06dPH802Y2NjunfvzuHDh4sNLjMzM8nMzNR8TkpKemwe5FkhhBCVT4JLIYQQQlSa9evXc+rUKY4fP15oX3R0NACOjo5a2x0dHUvswrpo0SLmzp1bsRkVQghRbjKqXQghhBCV4ubNm0yePJlffvmlxFZDlUql9VlRlELb8ps+fTqJiYman5s3b1ZYnoUQQjw5abkUQgghRKU4efIksbGxtGnTRrMtJyeH/fv389VXX3H58mVA3YLp7OysOSY2NrZQa2Z+xsbGGBsbV17GhRBCPBEJLouQt/BuacZwCCGEEPnl1R1JSUlYWlqW2AL3tHv22Wc5d+6c1raxY8fSuHFjPvjgAxo0aICTkxO7d++mVatWgHo9yn379vHpp5+W+jpSbwshROnkPSfznpsVTYLLIiQnJwPg6uqq45wIIYSoqVxdXUlMTMTKykrXWdEZS0tLmjZtqrXN3NycunXrarZPmTKFhQsX0qhRIxo1asTChQsxMzNjxIgRpb6O1NtCCFE2ycnJWFtbV3i6ElwWoV69ety8eRNFUXBzc+PmzZu1+svBk0pKSsLV1VXK7wlJ+ZWflGH5SPk9GUVRSE5OxtLSskLWaXzaTZs2jfT0dCZOnEh8fDwdOnRg165dZSq7vHq7trYUy//VR6Qs1KQcHpGyUMsrh8jISFQqFfXq1auU66iUymoTfQokJSVhbW1d6988Pykpv/KR8is/KcPykfITomaQ/6uPSFmoSTk8ImWhVlXlILPFCiGEEEIIIYQoNwkuhRBCCCGEEEKUmwSXJTA2Nmb27Nky3fkTkvIrHym/8pMyLB8pPyFqBvm/+oiUhZqUwyNSFmpVVQ4y5lIIIYQQQgghRLlJy6UQQgghhBBCiHKT4FIIIYQQQgghRLlJcCmEEEIIIYQQotwkuBRCCCGEEEIIUW61KrhcsGABnTp1wszMDBsbmyKPmTx5Mm3atMHY2JiWLVsWeczOnTvp2LEjlpaW2Nvb89JLLxEeHl7itePj4xk1ahTW1tZYW1szatQoEhISyndDVawiym/OnDmoVKpCP+bm5iVe+8qVKwwcOBA7OzusrKzo3Lkze/furYC7qjq6LD+A7du306FDB0xNTbGzs2PIkCHlvKOqpevyA8jMzKRly5aoVCpCQkKe/GZ0RFdleOPGDcaNG4enpyempqY0bNiQ2bNnk5WVVUF3JsTTY/ny5TRv3hwrKyusrKzw8/Pj77//1uwPCAgo9P+vY8eOpU5//fr1qFQqBg0aVAm5rziVVQ4JCQlMmjQJZ2dnTExM8PX15a+//qrMWym3yiqLpUuX4uPjg6mpKa6urrzzzjtkZGRU5q2Uy+PKASA0NJQXX3wRa2trLC0t6dixI5GRkSWmu3HjRpo0aYKxsTFNmjRh8+bNlXkbFaIyymLlypV07doVW1tbbG1t8ff359ixY2XOW60KLrOysnj55ZeZMGFCsccoisJrr73G0KFDi9wfFhbGwIED6dWrFyEhIezcuZO7d+8+9ov6iBEjCAkJYceOHezYsYOQkBBGjRpVrvupahVRflOnTuXOnTtaP02aNOHll18u8drPP/882dnZ/PPPP5w8eZKWLVvywgsvEB0dXa57qkq6LL+NGzcyatQoxo4dy5kzZzh06BAjRowo1/1UNV2WX55p06ZRr169J8p/daCrMrx06RK5ubmsWLGCCxcu8MUXX/Dtt9/y0UcflfuehHja1K9fn08++YQTJ05w4sQJevXqxcCBA7lw4YLmmH79+mn9HyxtcBQREcHUqVPp2rVrZWW/wlRGOWRlZdG7d29u3LjBH3/8weXLl1m5ciUuLi6VfTvlUhll8euvv/Lhhx8ye/ZsQkND+eGHH9iwYQPTp0+v7Nt5Yo8rh+vXr9OlSxcaN25MUFAQZ86cYdasWZiYmBSb5pEjRxg6dCijRo3izJkzjBo1ildeeYWjR49W1W09kcooi6CgIIYPH87evXs5cuQIbm5u9OnTh6ioqLJlTqmFVq1apVhbW5d4zOzZs5UWLVoU2v77778rBgYGSk5Ojmbb1q1bFZVKpWRlZRWZ1sWLFxVACQ4O1mw7cuSIAiiXLl16onvQpfKUX0EhISEKoOzfv7/YY+Li4godk5SUpABKYGBgabNdbVR1+T148EBxcXFRvv/++zLmtHqq6vLL89dffymNGzdWLly4oADK6dOnS5fhakhXZZjf4sWLFU9PzzKdI0RtZWtrq3mGjxkzRhk4cGCZ08jOzlY6d+6sfP/990+chq6VtxyWL1+uNGjQoNjvazVJecti0qRJSq9evbS2vfvuu0qXLl0qKotVIn85DB06VHn11VfLdP4rr7yi9OvXT2tb3759lWHDhlVYHqtKecuioOzsbMXS0lJZs2ZNmc6rVS2XFaFt27bo6+uzatUqcnJySExM5Oeff6ZPnz4YGhoWec6RI0ewtramQ4cOmm0dO3bE2tqaw4cPV1XWq6Xvv/8eb2/vEt+i1q1bF19fX3766SdSU1PJzs5mxYoVODo60qZNmyrMbfVTmvI7deoUUVFR6Onp0apVK5ydnenfv7/WG8/aqjTlBxATE8P48eP5+eefMTMzq6Lc1QylLcOCEhMTqVOnTiXlSoinQ05ODuvXryc1NRU/Pz/N9qCgIBwcHPD29mb8+PHExsY+Nq158+Zhb2/PuHHjKjPLlaKiymHr1q34+fkxadIkHB0dadq0KQsXLiQnJ6eyb6HCVFRZdOnShZMnT2q6PYaFhfHXX3/x/PPPV2r+K0rBcsjNzWX79u14e3vTt29fHBwc6NChA1u2bCkxnSNHjtCnTx+tbX379q1R388rqiwKSktL48GDB2WuqyW4LCMPDw927drFRx99hLGxMTY2Nty6dYv169cXe050dDQODg6Ftjs4ONSobp0VLTMzk19//fWxFZ1KpWL37t2cPn0aS0tLTExM+OKLL9ixY0ex48Zqg9KWX1hYGKAeKzdz5ky2bduGra0t3bt35/79+1WR1WqptOWnKAoBAQG89dZbtG3btopyVzOUtgwLun79Ol9++SVvvfVWJeVMiJrt3LlzWFhYYGxszFtvvcXmzZtp0qQJAP379+fXX3/ln3/+YcmSJRw/fpxevXqRmZlZbHqHDh3ihx9+YOXKlVV1CxWiosshLCyMP/74g5ycHP766y9mzpzJkiVLWLBgQVXd0hOr6LIYNmwY//nPf+jSpQuGhoY0bNiQnj178uGHH1bVLT2R4sohNjaWlJQUPvnkE/r168euXbsYPHgwQ4YMYd++fcWmFx0djaOjo9Y2R0fHGvH9vKLLoqAPP/wQFxcX/P39y5axcrWXVgOzZ89WgBJ/jh8/rnVOebqE3blzR2nUqJHy/vvvK6dOnVL27dundO/eXXn22WeV3NzcItNasGCB4u3tXWi7l5eXsmjRolLfa2Wo6vLLb+3atYqBgYFy586dEo/Lzc1VXnzxRaV///7KwYMHlZMnTyoTJkxQXFxclNu3b5fmNitNTSi/X3/9VQGUFStWaLZlZGQodnZ2yrffflviuZWtJpTfsmXLlE6dOinZ2dmKoihKeHh4teoWWxPKML+oqCjFy8tLGTduXKnPEaK2yczMVK5evaocP35c+fDDDxU7OzvlwoULRR57+/ZtxdDQUNm4cWOR+5OSkhQPDw/lr7/+0myrKd1iK7IcFEVRGjVqpLi6umqe54qiKEuWLFGcnJwqPO8VraLLYu/evYqjo6OycuVK5ezZs8qmTZsUV1dXZd68eZV1CxWiuHKIiopSAGX48OFaxw8YMKDELq6GhobK2rVrtbb98ssvirGxcaXkvyJVdFnk9+mnnyq2trbKm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gAAwLRKbNCJjY1VaGioIiIijG4FAAAUkRIbdHiODgDAFU6ePCmLxWL7qqN75dxmwXN0AAAF8smsqfrl0mmX1StVKViDho/N9/Gpqal68803tX79el24cEEVKlRQo0aNNHHiRNtTkS0Wi9asWXPHbzg3u/379+vdd9/Vjh07dPHiRdWsWVNDhw7V8OHDbcecPHlSISEhed67fv16dezYUZK0evVqzZ07V4mJicrMzFT9+vU1ceJEdejQwWWfJT8IOgCAAvnl0mlFRnj+8YFOEru7YKHq6aef1o0bN7Ro0SLVqlVLFy5c0ObNm/XTTz8VUYdFLysrS15eXk451549e1S5cmUtWbJEwcHB2rlzp55//nm5u7vrpZdesjv23//+t+rXr2/brlixou3P27ZtU7t27TRlyhSVL19ecXFx6tq1q/773/+qSZMmTunVGUrs1BUAwHx+/vln7dixQ9OmTVObNm1Uo0YNPfTQQxo7dqy6dOkiSapZs6YkqUePHrJYLLbtY8eOqVu3bqpatarKli2riIgI/fvf/7Y7f82aNTVlyhQNGjRIvr6+ql69uubPn293zLfffqsmTZrIx8dH4eHh2rdvn93rOTk5Gjx4sEJCQlSqVCk98MADmjVrlt0xAwYMUPfu3TV16lQFBgaqbt26+Tp3fgwaNEgffPCBWrVqpVq1aqlv374aOHCgVq9enefYSpUqqVq1araf34atmTNn6rXXXlNERITq1KmjKVOmqE6dOlq3bl2BeypKjOgAKJCK1ssK2DfHpTUzfk52aT3cu8qWLauyZctq7dq1euSRR+Tt7Z3nmN27d6tKlSqKi4tTx44d5e7uLklKT09X586dNXnyZPn4+GjRokXq2rWrDh06pOrVq9veHx0drbffflt/+9vf9I9//EMvvviiHn/8cdWrV08ZGRn605/+pLZt22rJkiU6ceKE3ZSQJOXm5iooKEgrV66Uv7+/bUQlICBAvXr1sh23efNmlStXTvHx8bJarfk6t3QzjA0YMOCu37B+J1euXLEbrbnlqaee0vXr11WnTh2NHDlSf/7zn+96jtzcXF29evWO5zESQQdAgfh75+jlCOcMoefXh+cyXVoP9y4PDw8tXLhQQ4YM0YcffqimTZuqVatWeuaZZ9SwYUNJUuXKlSVJ5cuXV7Vq1WzvbdSokRo1amTbnjx5stasWaPPPvvMbkqnc+fOGjZsmCTp9ddf1/vvv68tW7aoXr16Wrp0qXJycvTJJ5+odOnSql+/vs6cOaMXX3zR9n5PT09NmjTJth0SEqKdO3dq5cqVdkGnTJkyWrBggW0UZf78+X94bkm6//775e/vn+9r9p///EcrV67UF198YdtXtmxZxcTE6NFHH5Wbm5s+++wz9e7dW4sWLVLfvn3veJ7o6GhlZGTYfYbigKADADCVp59+Wl26dNH27dv1n//8Rxs2bNB7772nBQsWaMCAAXd9X0ZGhiZNmqTPP/9c586dU3Z2tn755RclJ9uPKN4KTNLNRc3VqlVTamqqJOngwYNq1KiRSpcubTvm1gLo3/rwww+1YMECnTp1Sr/88ouysrLUuHFju2MaNGhgN1WU33Nv3rz5rp/xdgcOHFC3bt00fvx4tWvXzrbf399fI0eOtG2Hh4fr8uXLeu+99+4YdJYtW6aJEyfqX//6l6pUqZLv+q7AGh0AgOn4+PioXbt2Gj9+vHbu3KkBAwZowoQJv/ueV199Vf/85z/1zjvvaPv27UpMTFSDBg2UlZVld5ynp/1CbIvFotzcXEmS1Wr9w95WrlypkSNHatCgQdq0aZMSExM1cODAPHXKlCljt52fcxdEUlKS2rZtqyFDhuiNN974w+MfeeQRHTlyJM/+FStWaPDgwVq5cqWefPJJp/boDAQdAIDphYaGKiMjw7bt6empnJwcu2O2b9+uAQMGqEePHmrQoIGqVaumkydPFrjO/v379csvv9j27dq1K0+dFi1aaNiwYWrSpIlq166tY8eOOeXc+XXgwAG1adNG/fv31zvvvJOv9+zbt08BAQF2+5YtW6YBAwbo73//u22xd3FTYoMOT0YGAPO5dOmSbbHud999pxMnTmjVqlV677331K1bN9txNWvW1ObNm3X+/HldvnxZklS7dm2tXr1aiYmJ2r9/v/r06WMbqcmvPn36yM3NTYMHD1ZSUpK+/PJLzZgxw+6Y2rVrKyEhQRs3btThw4f15ptv5uvhtfk5tyQ98cQTmj179l3PcyvktGvXTlFRUTp//rzOnz+vH3/80XbMokWL9Pe//10HDx7UoUOHNGPGDH3wwQd6+eWXbccsW7ZM/fr1U3R0tB555BHbea5cuZKfS+UyJTbo8GRkADCfsmXL6uGHH9b777+vxx9/XGFhYXrzzTc1ZMgQu1/+0dHRio+PV3BwsO2ZL++//74qVKigFi1aqGvXrurQoYOaNm1a4Prr1q1TUlKSmjRponHjxmnatGl2xwwdOlQ9e/ZU79699fDDD+vSpUu2xc2FPbd08zb5ixcv3vU8q1at0o8//qilS5cqICDA9nP7P/wnT56s8PBwRUREaPny5frkk0/s1u3MmzdP2dnZioyMtDvPne4EM5LF6uxJv3tMWlqa/Pz8dOXKFZUrV87odoACCWveVumtRrm0ZoUVz+rVv7Zyac0P/5Wgbd+77km8uOn69es6ceKEQkJC5OPjY9tf3J+MDPO4299BKf+/v7nrCgBQIIQO3EtK7NQVAAAwP4IOAAAwLYIOAAAwLYIOAOB3lfB7VmAgZ/zdI+gAAO7o1pdd3v7EXsBVrl27Jinv06gLgruuAAB35OHhodKlS+vHH3+Up6en3Nz4tzFcw2q16tq1a0pNTVX58uVtodsRBB0AwB1ZLBYFBAToxIkTOnXqlNHtoAS6/RvmHVFig05sbKxiY2PzfNcJAOBXXl5eqlOnDtNXcDlPT89CjeTcUmKDTmRkpCIjI21PVgQA3Jmbm1uep9IC9womXAEAgGkRdAAAgGkRdAAAgGkRdAAAgGkRdAAAgGkRdAAAgGkRdAAAgGkRdAAAgGkRdAAAgGkRdAAAgGkRdAAAgGmV2KATGxur0NBQRUREGN0KAAAoIiU26ERGRiopKUm7d+82uhUAAFBESmzQAQAA5kfQAQAApkXQAQAApkXQAQAApkXQAQAApuVhdAMA8EfSr15VWPO2Lq0ZVNVfG9audGlNAM5H0AFQ7FktbkpvNcqlNc9sjXZpPQBFg6krAABgWozoAMAdnD51kukywAQIOgBwB9kWD6bLABNg6goAAJgWQQcAAJgWQQcAAJgWa3SAe5jbz8kK2DfHpTWt1kyX1gOAwiDoAPew8h6ZGhrh5dKa849aXVoPAAqjxE5dxcbGKjQ0VBEREUa3AgAAikiJDTqRkZFKSkrS7t27jW4FAAAUEaauAOAOKlovu3z9U8bPyS6tB5QEBB0AuAN/7xy97OL1Tx+eY6E34GwEHQDFX/Z17i4D4BCCDoBiz89Lep67ywA4oMQuRgYAAOZH0AEAAKZF0AEAAKZF0AEAAKZF0AEAAKZF0AEAAKZF0AEAAKZF0AEAAKZF0AEAAKZF0AEAAKZF0AEAAKZF0AEAAKZF0AEAAKZF0AEAAKZF0AEAAKZVYoNObGysQkNDFRERYXQrAACgiJTYoBMZGamkpCTt3r3b6FYAAEARKbFBBwAAmB9BBwAAmBZBBwAAmBZBBwAAmBZBBwAAmBZBBwAAmBZBBwAAmBZBBwAAmJaH0Q0AAG7KvHZVseOHubxuqUrBGjR8rMvrAq5A0AGAYsLPK1eREZ4urxu7+7TLawKuwtQVAAAwLYIOAAAwLYIOAAAwLYIOAAAwLYIOAAAwLYIOAAAwLYIOAAAwLYIOAAAwLYIOAAAwLYIOAAAwLYIOAAAwLYIOAAAwLYIOAAAwLYIOAAAwLQ+jGwAA3HQj64ZWrv3c5XW/+MFNkW+5vCzgEgQdACgmrLIoO6Chy+umf5vg8pqAqzB1BQAATKvEBp3Y2FiFhoYqIiLC6FYAAEARKbFBJzIyUklJSdq9e7fRrQAAgCJSYoMOAAAwP4IOAAAwLYIOAAAwLYIOAAAwLYIOAAAwLYIOAAAwLZ6MDDhBx+69dObCRZfX9b52zeU1AeBeQtABnODMhYtKbzXK5XU9Vzzr8poAcC9h6goAAJgWQQcAAJgWQQcAAJgWQQcAAJgWQQcAAJgWQQcAAJgWQQcAAJgWQQcAAJgWQQcAAJiWQ0HnxIkTzu4DAADA6RwKOrVr11abNm20ZMkSXb9+3dk9AQAAOIVDQWf//v1q0qSJRo0apWrVqumFF17Qt99+6+zeAAAACsWhoBMWFqaYmBidPXtWcXFxOn/+vB577DHVr19fMTEx+vHHH53dJwAAQIEVajGyh4eHevTooZUrV2ratGk6duyYRo8eraCgIPXr108pKSnO6hMAAKDAChV0EhISNGzYMAUEBCgmJkajR4/WsWPH9NVXX+ns2bPq1q2bs/oEAAAoMA9H3hQTE6O4uDgdOnRInTt31uLFi9W5c2e5ud3MTSEhIZo3b57q1avn1GYBAAAKwqGgM3fuXA0aNEgDBw5UtWrV7nhM9erV9fHHHxeqOQAAgMJwKOgcOXLkD4/x8vJS//79HTk9AACAUzi0RicuLk6rVq3Ks3/VqlVatGhRoZsCAABwBoeCzrvvvit/f/88+6tUqaIpU6YUuikAAABncCjonDp1SiEhIXn216hRQ8nJyYVuCgAAwBkcCjpVqlTRd999l2f//v37ValSpUI3BQAA4AwOLUZ+5pln9Morr8jX11ePP/64JGnr1q0aPny4nnnmGac2CAAoWpnXrip2/DCX1ixVKViDho91aU2UTA4FncmTJ+vUqVN64okn5OFx8xS5ubnq168fa3QA4B7j55WryAhPl9aM3X3apfVQcjkUdLy8vLRixQq9/fbb2r9/v0qVKqUGDRqoRo0azu4PAADAYQ4FnVvq1q2runXrOqsXAAAAp3Io6OTk5GjhwoXavHmzUlNTlZuba/f6V1995ZTmAAAACsOhoDN8+HAtXLhQXbp0UVhYmCwWi7P7AgAAKDSHgs7y5cu1cuVKde7c2dn9AAAAOI1Dz9Hx8vJS7dq1nd0LAACAUzkUdEaNGqVZs2bJarU6ux8AAACncWjqaseOHfr666+1fv161a9fX56e9s9fWL16tVOaAwAAKAyHgk758uXVo0cPZ/cCAADgVA4Fnbi4OGf3AQAA4HQOrdGRpOzsbP373//WvHnzdPXqVUnSuXPnlJ6e7rTmAAAACsOhEZ1Tp06pY8eOSk5OVmZmptq1aydfX1+99957un79uj788ENn9wkAAFBgDo3oDB8+XOHh4bp8+bJKlSpl29+jRw9t3rzZac3lV48ePVShQgX9+c9/dnltAABQfDkUdHbs2KE33nhDXl5edvtr1Kihs2fPOqWxgnjllVe0ePFil9cFAADFm0NBJzc3Vzk5OXn2nzlzRr6+voVuqqDatGljSF0AAFC8ORR02rVrp5kzZ9q2LRaL0tPTNWHChAJ/LcS2bdvUtWtXBQYGymKxaO3atXmOmTNnjkJCQuTj46NmzZpp+/btjrQNAABKGIeCzvvvv6+tW7cqNDRU169fV58+fVSzZk2dPXtW06ZNK9C5MjIy1KhRI82ePfuOr69YsUIjRozQuHHjtG/fPrVs2VKdOnVScnKyI60rMzNTaWlpdj8AAMCcHLrrKjAwUImJiVq2bJn27t2r3NxcDR48WH/961/tFifnR6dOndSpU6e7vh4TE6PBgwfrueeekyTNnDlTGzdu1Ny5czV16tQC9z516lRNmjSpwO+DYzp276UzFy66tGZQVX9tWLvSpTWBe9mNrBtaufZzl9b84gc3Rb7l0pIooRwKOpJUqlQpDRo0SIMGDXJmP3aysrK0Z88ejRkzxm5/+/bttXPnTofOOXbsWEVFRdm209LSFBwcXKg+cXdnLlxUeqtRrq25Ndql9YB7nVUWZQc0dGnN9G8TXFoPJZdDQeeP7nDq16+fQ83c7uLFi8rJyVHVqlXt9letWlXnz5+3bXfo0EF79+5VRkaGgoKCtGbNGkVERNzxnN7e3vL29nZKfwAAoHhzKOgMHz7cbvvGjRu6du2avLy8VLp0aacFnVssFovdttVqtdu3ceNGp9YDAADm4NBi5MuXL9v9pKen69ChQ3rssce0bNkypzXn7+8vd3d3u9EbSUpNTc0zygMAAHA7h7/r6nZ16tTRu+++m2e0pzC8vLzUrFkzxcfH2+2Pj49XixYtnFYHAACYk8OLke/E3d1d586dK9B70tPTdfToUdv2iRMnlJiYqIoVK6p69eqKiorSs88+q/DwcDVv3lzz589XcnKyhg4d6szWAQCACTkUdD777DO7bavVqpSUFM2ePVuPPvpogc6VkJCgNm3a2LZv3RHVv39/LVy4UL1799alS5f01ltvKSUlRWFhYfryyy9Vo0YNR1oHAAAliENBp3v37nbbFotFlStXVtu2bRUdXbBbe1u3bi2r1fq7xwwbNkzDhg0raJu/KzY2VrGxsXf8KgsAAGAODgWd3NxcZ/fhcpGRkYqMjFRaWpr8/PyMbgcAABQBpy1GBgAAKG4cGtH57ZOF/0hMTIwjJQAAAArNoaCzb98+7d27V9nZ2XrggQckSYcPH5a7u7uaNm1qO+72B/0BAAC4kkNBp2vXrvL19dWiRYtUoUIFSTcfIjhw4EC1bNlSo0a59ruNAAAA7sShNTrR0dGaOnWqLeRIUoUKFTR58uQC33UFAABQVBwKOmlpabpw4UKe/ampqbp69WqhmwIAAHAGh6auevTooYEDByo6OlqPPPKIJGnXrl169dVX1bNnT6c2WFR4jo5ruP2crIB9c1xa89zJ/Qpr3talNc+cS1F5l1YEAOSHQ0Hnww8/1OjRo9W3b1/duHHj5ok8PDR48GBNnz7dqQ0WFZ6j4xrlPTI1NMLLpTWnH7bqcivXrhO78elIl9YDAOSPQ0GndOnSmjNnjqZPn65jx47JarWqdu3aKlOmjLP7AwAAcFihHhiYkpKilJQU1a1bV2XKlPnDr3IAAABwJYeCzqVLl/TEE0+obt266ty5s1JSUiRJzz33HLeWAwCAYsOhoDNy5Eh5enoqOTlZpUuXtu3v3bu3NmzY4LTmAAAACsOhNTqbNm3Sxo0bFRQUZLe/Tp06OnXqlFMaAwAAKCyHRnQyMjLsRnJuuXjxory9vQvdFAAAgDM4FHQef/xxLV682LZtsViUm5ur6dOnq02bNk5rDgAAoDAcmrqaPn26WrdurYSEBGVlZem1117TgQMH9NNPP+mbb75xdo8AAAAOcWhEJzQ0VN99950eeughtWvXThkZGerZs6f27dun+++/39k9FonY2FiFhoYqIiLC6FYAAEARKfCIzo0bN9S+fXvNmzdPkyZNKoqeXIInIwMAYH4FHtHx9PTUDz/8IIvFUhT9AAAAOI1DU1f9+vXTxx9/7OxeAAAAnMqhxchZWVlasGCB4uPjFR4enuc7rmJiYpzSHAAAQGEUKOgcP35cNWvW1A8//KCmTZtKkg4fPmx3DFNaAACguChQ0KlTp45SUlL09ddfS7r5lQ8ffPCBqlatWiTNAQAAFEaB1ujc/u3k69evV0ZGhlMbAgAAcBaHFiPfcnvwAQAAKE4KFHQsFkueNTisyQEAAMVVgdboWK1WDRgwwPbFndevX9fQoUPz3HW1evVq53UIAADgoAIFnf79+9tt9+3b16nNuFJsbKxiY2OVk5NjdCsAUOKkX72qsOZtXVozqKq/Nqxd6dKaMF6Bgk5cXFxR9eFyfAUEABjHanFTeqtRLq15Zmu0S+uheCjUYmQAAIDijKADAABMi6ADAABMi6ADAABMi6ADAABMi6ADAABMi6ADAABMi6ADAABMi6ADAABMi6ADAABMi6ADAABMq0DfdWUmfKknABgo+7oC9s1xacmMn5NdWg/FQ4kNOnypJwAYx89Lej7Cy6U1PzyX6dJ6KB6YugIAAKZF0AEAAKZF0AEAAKZF0AEAAKZF0AEAAKZF0AEAAKZF0AEAAKZF0AEAAKZF0AEAAKZF0AEAAKZF0AEAAKZF0AEAAKZF0AEAAKZF0AEAAKZF0AEAAKZVYoNObGysQkNDFRERYXQrAACgiJTYoBMZGamkpCTt3r3b6FYAAEARKbFBBwAAmB9BBwAAmBZBBwAAmBZBBwAAmBZBBwAAmBZBBwAAmBZBBwAAmBZBBwAAmBZBBwAAmBZBBwAAmBZBBwAAmBZBBwAAmBZBBwAAmBZBBwAAmBZBBwAAmBZBBwAAmBZBBwAAmBZBBwAAmBZBBwAAmJaH0Q0YJTY2VrGxscrJyTG6FZfp2L2Xzly46NKa3teuubSeUSpaLytg3xyX17VaM11eEwDuJSU26ERGRioyMlJpaWny8/Mzuh2XOHPhotJbjXJpTc8Vz7q0nlH8vXP0coSXy+vOP2p1eU0AuJcwdQUAAEyLoAMAAEyLoAMAAEyLoAMAAEyLoAMAAEyLoAMAAEyLoAMAAEyLoAMAAEyLoAMAAEyLoAMAAEyLoAMAAEyLoAMAAEyLoAMAAEyLoAMAAEyLoAMAAEyLoAMAAEyLoAMAAEyLoAMAAEyLoAMAAEyLoAMAAEyLoAMAAEyLoAMAAEyLoAMAAEyLoAMAAEyLoAMAAEyLoAMAAEyLoAMAAEzLw+gGjBIbG6vY2Fjl5OQY3QqcLfu6AvbNcWlJqzXTpfUAFFz61asKa97WpTWDqvprw9qVLq0JeyU26ERGRioyMlJpaWny8/Mzuh04kZ+X9HyEl0trzj9qdWk9AAVntbgpvdUol9Y8szXapfWQF1NXAADAtAg6AADAtAg6AADAtAg6AADAtAg6AADAtAg6AADAtAg6AADAtAg6AADAtAg6AADAtAg6AADAtAg6AADAtAg6AADAtAg6AADAtAg6AADAtAg6AADAtAg6AADAtAg6AADAtAg6AADAtAg6AADAtAg6AADAtAg6AADAtAg6AADAtAg6AADAtAg6AADAtAg6AADAtAg6AADAtAg6AADAtAg6AADAtAg6AADAtAg6AADAtAg6AADAtAg6AADAtAg6AADAtAg6AADAtAg6AADAtAg6AADAtAg6AADAtAg6AADAtAg6AADAtAg6AADAtAg6AADAtAg6AADAtAg6AADAtAg6AADAtEwRdD7//HM98MADqlOnjhYsWGB0OwAAoJjwMLqBwsrOzlZUVJS+/vprlStXTk2bNlXPnj1VsWJFo1sDAAAGu+dHdL799lvVr19f9913n3x9fdW5c2dt3LjR6LYAAEAxYHjQ2bZtm7p27arAwEBZLBatXbs2zzFz5sxRSEiIfHx81KxZM23fvt322rlz53TffffZtoOCgnT27FlXtA4AAIo5w4NORkaGGjVqpNmzZ9/x9RUrVmjEiBEaN26c9u3bp5YtW6pTp05KTk6WJFmt1jzvsVgsd62XmZmptLQ0ux8AAGBOhq/R6dSpkzp16nTX12NiYjR48GA999xzkqSZM2dq48aNmjt3rqZOnar77rvPbgTnzJkzevjhh+96vqlTp2rSpEnO+wAAgHtD9nUF7Jvj0pIZPye7tJ4kdezeS2cuXHRpzQrZF/VMp8dcWvOXzKx8HWd40Pk9WVlZ2rNnj8aMGWO3v3379tq5c6ck6aGHHtIPP/ygs2fPqly5cvryyy81fvz4u55z7NixioqKsm2npaUpODi4aD4AAKDY8POSno/wcmnND89lurSeJJ25cFHprUa5tGbFL4YqMsLTpTXTruXq1XwcV6yDzsWLF5WTk6OqVava7a9atarOnz8vSfLw8FB0dLTatGmj3Nxcvfbaa6pUqdJdz+nt7S1vb+8i7RsAABQPxTro3HL7mhur1Wq376mnntJTTz3l6rYAAEAxZ/hi5N/j7+8vd3d32+jNLampqXlGeQAAAG5XrIOOl5eXmjVrpvj4eLv98fHxa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+                <span class="notice" hidden>Copied!</span>
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+                </svg>
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+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">def</span> <span class="nf">merge_catalog</span><span class="p">(</span>
+    <span class="n">index_json</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="s2">&quot;json&quot;</span><span class="p">),</span>
+    <span class="n">config_json</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="s2">&quot;json&quot;</span><span class="p">),</span>
+    <span class="n">catalog_csv</span><span class="p">:</span> <span class="n">OutputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span>
+    <span class="n">picks_csv</span><span class="p">:</span> <span class="n">OutputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span>
+    <span class="n">bucket_name</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;catalogs&quot;</span><span class="p">,</span>
+    <span class="n">s3_url</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;minio-service:9000&quot;</span><span class="p">,</span>
+    <span class="n">secure</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+<span class="p">):</span>
+
+    <span class="kn">import</span> <span class="nn">json</span>
+    <span class="kn">import</span> <span class="nn">os</span>
+    <span class="kn">from</span> <span class="nn">glob</span> <span class="kn">import</span> <span class="n">glob</span>
+
+    <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+    <span class="kn">from</span> <span class="nn">minio</span> <span class="kn">import</span> <span class="n">Minio</span>
+
+    <span class="n">minioClient</span> <span class="o">=</span> <span class="n">Minio</span><span class="p">(</span><span class="n">s3_url</span><span class="p">,</span> <span class="n">access_key</span><span class="o">=</span><span class="s1">&#39;minio&#39;</span><span class="p">,</span> <span class="n">secret_key</span><span class="o">=</span><span class="s1">&#39;minio123&#39;</span><span class="p">,</span> <span class="n">secure</span><span class="o">=</span><span class="n">secure</span><span class="p">)</span>
+
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">index_json</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">index</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">fp</span><span class="p">)</span>
+        
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">config_json</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">config</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">fp</span><span class="p">)</span>
+
+    <span class="n">objects</span> <span class="o">=</span> <span class="n">minioClient</span><span class="o">.</span><span class="n">list_objects</span><span class="p">(</span><span class="n">bucket_name</span><span class="p">,</span> <span class="n">prefix</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;region&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">/gamma&quot;</span><span class="p">,</span> <span class="n">recursive</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+    <span class="n">tmp_path</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="s2">&quot;/tmp/&quot;</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">obj</span> <span class="ow">in</span> <span class="n">objects</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="n">obj</span><span class="o">.</span><span class="n">_object_name</span><span class="p">)</span>
+        <span class="n">minioClient</span><span class="o">.</span><span class="n">fget_object</span><span class="p">(</span><span class="n">bucket_name</span><span class="p">,</span> <span class="n">obj</span><span class="o">.</span><span class="n">_object_name</span><span class="p">,</span> <span class="n">tmp_path</span><span class="p">(</span><span class="n">obj</span><span class="o">.</span><span class="n">_object_name</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;/&quot;</span><span class="p">)[</span><span class="o">-</span><span class="mi">1</span><span class="p">]))</span>
+
+    <span class="c1"># files_catalog = sorted(glob(tmp_path(&quot;catalog_*.csv&quot;)))</span>
+    <span class="c1"># files_picks = sorted(glob(tmp_path(&quot;picks_*.csv&quot;)))</span>
+    <span class="n">files_catalog</span> <span class="o">=</span> <span class="p">[</span><span class="n">tmp_path</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;catalog_</span><span class="si">{</span><span class="n">node_i</span><span class="si">:</span><span class="s2">03d</span><span class="si">}</span><span class="s2">.csv&quot;</span><span class="p">)</span> <span class="k">for</span> <span class="n">node_i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">index</span><span class="p">))]</span>
+    <span class="n">files_picks</span> <span class="o">=</span> <span class="p">[</span><span class="n">tmp_path</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;picks_</span><span class="si">{</span><span class="n">node_i</span><span class="si">:</span><span class="s2">03d</span><span class="si">}</span><span class="s2">.csv&quot;</span><span class="p">)</span> <span class="k">for</span> <span class="n">node_i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">index</span><span class="p">))]</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Merge catalog: </span><span class="si">{</span><span class="n">files_catalog</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Merge picks: </span><span class="si">{</span><span class="n">files_picks</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">files_catalog</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="n">catalog_list</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="n">files_catalog</span><span class="p">:</span>
+            <span class="n">tmp</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">str</span><span class="p">)</span>
+            <span class="n">tmp</span><span class="p">[</span><span class="s2">&quot;file_index&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">rstrip</span><span class="p">(</span><span class="s2">&quot;.csv&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;_&quot;</span><span class="p">)[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+            <span class="n">catalog_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
+        <span class="n">merged_catalog</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">(</span><span class="n">catalog_list</span><span class="p">)</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">by</span><span class="o">=</span><span class="s2">&quot;time&quot;</span><span class="p">)</span>
+        <span class="n">merged_catalog</span><span class="p">[</span><span class="s2">&quot;match_id&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">merged_catalog</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">x</span><span class="p">[</span><span class="s2">&quot;event_index&quot;</span><span class="p">]</span><span class="si">}</span><span class="s1">_</span><span class="si">{</span><span class="n">x</span><span class="p">[</span><span class="s2">&quot;file_index&quot;</span><span class="p">]</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+        <span class="n">merged_catalog</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">by</span><span class="o">=</span><span class="s2">&quot;time&quot;</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">ignore_index</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+        <span class="n">merged_catalog</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;event_index&quot;</span><span class="p">,</span> <span class="s2">&quot;file_index&quot;</span><span class="p">],</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+        <span class="n">merged_catalog</span><span class="p">[</span><span class="s2">&quot;event_index&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">merged_catalog</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">values</span> <span class="o">+</span> <span class="mi">1</span>
+        <span class="n">mapping</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">merged_catalog</span><span class="p">[</span><span class="s2">&quot;match_id&quot;</span><span class="p">],</span> <span class="n">merged_catalog</span><span class="p">[</span><span class="s2">&quot;event_index&quot;</span><span class="p">]))</span>
+        <span class="n">merged_catalog</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;match_id&quot;</span><span class="p">],</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+        <span class="n">merged_catalog</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="n">catalog_csv</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+        <span class="k">del</span> <span class="n">merged_catalog</span>
+
+        <span class="n">pick_list</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="n">files_picks</span><span class="p">:</span>
+            <span class="n">tmp</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">str</span><span class="p">)</span>
+            <span class="n">tmp</span><span class="p">[</span><span class="s2">&quot;file_index&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">rstrip</span><span class="p">(</span><span class="s2">&quot;.csv&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;_&quot;</span><span class="p">)[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+            <span class="n">pick_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
+        <span class="n">merged_picks</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">(</span><span class="n">pick_list</span><span class="p">)</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">by</span><span class="o">=</span><span class="s2">&quot;phase_time&quot;</span><span class="p">)</span>
+        <span class="n">merged_picks</span><span class="p">[</span><span class="s2">&quot;match_id&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">merged_picks</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">x</span><span class="p">[</span><span class="s2">&quot;event_index&quot;</span><span class="p">]</span><span class="si">}</span><span class="s1">_</span><span class="si">{</span><span class="n">x</span><span class="p">[</span><span class="s2">&quot;file_index&quot;</span><span class="p">]</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+        <span class="n">merged_picks</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;event_index&quot;</span><span class="p">,</span> <span class="s2">&quot;file_index&quot;</span><span class="p">],</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+        <span class="n">merged_picks</span><span class="p">[</span><span class="s2">&quot;event_index&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">merged_picks</span><span class="p">[</span><span class="s2">&quot;match_id&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">mapping</span><span class="p">[</span><span class="n">x</span><span class="p">]</span> <span class="k">if</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">mapping</span> <span class="k">else</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+        <span class="n">merged_picks</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;match_id&quot;</span><span class="p">],</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+        <span class="n">merged_picks</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="n">picks_csv</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+        <span class="k">del</span> <span class="n">merged_picks</span>
+        
+        <span class="n">minioClient</span><span class="o">.</span><span class="n">fput_object</span><span class="p">(</span>
+            <span class="n">bucket_name</span><span class="p">,</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;region&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">/gamma_catalog.csv&quot;</span><span class="p">,</span>
+            <span class="n">catalog_csv</span><span class="p">,</span>
+        <span class="p">)</span>
+        
+        <span class="n">minioClient</span><span class="o">.</span><span class="n">fput_object</span><span class="p">(</span>
+            <span class="n">bucket_name</span><span class="p">,</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;region&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">/gamma_picks.csv&quot;</span><span class="p">,</span>
+            <span class="n">picks_csv</span><span class="p">,</span>
+        <span class="p">)</span>
+
+    <span class="c1"># else:</span>
+    <span class="c1">#     with open(catalog_csv, &quot;w&quot;) as fout:</span>
+    <span class="c1">#         pass</span>
+    <span class="c1">#     print(&quot;No catalog.csv found!&quot;)</span>
+    <span class="c1">#     with open(picks_csv, &quot;w&quot;) as fout:</span>
+    <span class="c1">#         pass</span>
+    <span class="c1">#     print(&quot;No picks.csv found!&quot;)</span>
+</pre></div>
+<div id="cell-21" class="clipboard-copy-txt"> def merge_catalog(
+    index_json: InputPath("json"),
+    config_json: InputPath("json"),
+    catalog_csv: OutputPath(str),
+    picks_csv: OutputPath(str),
+    bucket_name: str = "catalogs",
+    s3_url: str = "minio-service:9000",
+    secure: bool = True,
+):
+
+    import json
+    import os
+    from glob import glob
+
+    import pandas as pd
+    from minio import Minio
+
+    minioClient = Minio(s3_url, access_key='minio', secret_key='minio123', secure=secure)
+
+    with open(index_json, "r") as fp:
+        index = json.load(fp)
+        
+    with open(config_json, "r") as fp:
+        config = json.load(fp)
+
+    objects = minioClient.list_objects(bucket_name, prefix=f"{config['region']}/gamma", recursive=True)
+
+    tmp_path = lambda x: os.path.join("/tmp/", x)
+    for obj in objects:
+        print(obj._object_name)
+        minioClient.fget_object(bucket_name, obj._object_name, tmp_path(obj._object_name.split("/")[-1]))
+
+    # files_catalog = sorted(glob(tmp_path("catalog_*.csv")))
+    # files_picks = sorted(glob(tmp_path("picks_*.csv")))
+    files_catalog = [tmp_path(f"catalog_{node_i:03d}.csv") for node_i in range(len(index))]
+    files_picks = [tmp_path(f"picks_{node_i:03d}.csv") for node_i in range(len(index))]
+    print(f"Merge catalog: {files_catalog}")
+    print(f"Merge picks: {files_picks}")
+
+    if len(files_catalog) > 0:
+        catalog_list = []
+        for f in files_catalog:
+            tmp = pd.read_csv(f, dtype=str)
+            tmp["file_index"] = f.rstrip(".csv").split("_")[-1]
+            catalog_list.append(tmp)
+        merged_catalog = pd.concat(catalog_list).sort_values(by="time")
+        merged_catalog["match_id"] = merged_catalog.apply(lambda x: f'{x["event_index"]}_{x["file_index"]}', axis=1)
+        merged_catalog.sort_values(by="time", inplace=True, ignore_index=True)
+        merged_catalog.drop(columns=["event_index", "file_index"], inplace=True)
+        merged_catalog["event_index"] = merged_catalog.index.values + 1
+        mapping = dict(zip(merged_catalog["match_id"], merged_catalog["event_index"]))
+        merged_catalog.drop(columns=["match_id"], inplace=True)
+        merged_catalog.to_csv(catalog_csv, index=False)
+        del merged_catalog
+
+        pick_list = []
+        for f in files_picks:
+            tmp = pd.read_csv(f, dtype=str)
+            tmp["file_index"] = f.rstrip(".csv").split("_")[-1]
+            pick_list.append(tmp)
+        merged_picks = pd.concat(pick_list).sort_values(by="phase_time")
+        merged_picks["match_id"] = merged_picks.apply(lambda x: f'{x["event_index"]}_{x["file_index"]}', axis=1)
+        merged_picks.drop(columns=["event_index", "file_index"], inplace=True)
+        merged_picks["event_index"] = merged_picks["match_id"].apply(lambda x: mapping[x] if x in mapping else -1)
+        merged_picks.drop(columns=["match_id"], inplace=True)
+        merged_picks.to_csv(picks_csv, index=False)
+        del merged_picks
+        
+        minioClient.fput_object(
+            bucket_name,
+            f"{config['region']}/gamma_catalog.csv",
+            catalog_csv,
+        )
+        
+        minioClient.fput_object(
+            bucket_name,
+            f"{config['region']}/gamma_picks.csv",
+            picks_csv,
+        )
+
+    # else:
+    #     with open(catalog_csv, "w") as fout:
+    #         pass
+    #     print("No catalog.csv found!")
+    #     with open(picks_csv, "w") as fout:
+    #         pass
+    #     print("No picks.csv found!")</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[43]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-22">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">merge_op</span> <span class="o">=</span> <span class="n">comp</span><span class="o">.</span><span class="n">func_to_container_op</span><span class="p">(</span>
+    <span class="n">merge_catalog</span><span class="p">,</span>
+    <span class="c1"># base_image=&#39;zhuwq0/quakeflow-env:latest&#39;,</span>
+    <span class="n">base_image</span><span class="o">=</span><span class="s2">&quot;python:3.8&quot;</span><span class="p">,</span>
+    <span class="n">packages_to_install</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;pandas&quot;</span><span class="p">,</span> <span class="s2">&quot;minio&quot;</span><span class="p">],</span>
+<span class="p">)</span>
+</pre></div>
+<div id="cell-22" class="clipboard-copy-txt">merge_op = comp.func_to_container_op(
+    merge_catalog,
+    # base_image='zhuwq0/quakeflow-env:latest',
+    base_image="python:3.8",
+    packages_to_install=["pandas", "minio"],
+)</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="7-hypodd-earthquake-relocation">7. HypoDD earthquake relocation<a class="anchor-link" href="#7-hypodd-earthquake-relocation">&#182;</a></h2>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h3 id="download-and-compile-hypodd">Download and compile HypoDD<a class="anchor-link" href="#download-and-compile-hypodd">&#182;</a></h3>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[44]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-23">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">if</span> <span class="n">run_local</span><span class="p">:</span>
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="s2">&quot;HYPODD&quot;</span><span class="p">):</span>
+        <span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s2">&quot;wget -O HYPODD_1.3.tar.gz http://www.ldeo.columbia.edu/~felixw/HYPODD/HYPODD_1.3.tar.gz&quot;</span><span class="p">)</span>
+        <span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s2">&quot;tar -xf HYPODD_1.3.tar.gz&quot;</span><span class="p">)</span>
+        <span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s2">&quot;ln -s $(which gfortran) ./HYPODD/f77&quot;</span><span class="p">)</span>
+        <span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s2">&quot;ln -s $(which gfortran) ./HYPODD/g77&quot;</span><span class="p">)</span>
+        <span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s2">&quot;PATH&quot;</span><span class="p">]</span> <span class="o">+=</span> <span class="n">os</span><span class="o">.</span><span class="n">pathsep</span> <span class="o">+</span> <span class="n">os</span><span class="o">.</span><span class="n">getcwd</span><span class="p">()</span> <span class="o">+</span> <span class="s2">&quot;/HYPODD/&quot;</span>
+        <span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s2">&quot;make -C HYPODD/src&quot;</span><span class="p">)</span>
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;hypodd&quot;</span><span class="p">)):</span>
+        <span class="n">os</span><span class="o">.</span><span class="n">mkdir</span><span class="p">(</span><span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;hypodd&quot;</span><span class="p">))</span>
+    <span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;cp HYPODD/src/ph2dt/ph2dt </span><span class="si">{</span><span class="n">root_dir</span><span class="p">(</span><span class="s1">&#39;hypodd&#39;</span><span class="p">)</span><span class="si">}</span><span class="s2">/&quot;</span><span class="p">)</span>
+    <span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;cp HYPODD/src/hypoDD/hypoDD </span><span class="si">{</span><span class="n">root_dir</span><span class="p">(</span><span class="s1">&#39;hypodd&#39;</span><span class="p">)</span><span class="si">}</span><span class="s2">/&quot;</span><span class="p">)</span>
+</pre></div>
+<div id="cell-23" class="clipboard-copy-txt">if run_local:
+    if not os.path.exists("HYPODD"):
+        os.system("wget -O HYPODD_1.3.tar.gz http://www.ldeo.columbia.edu/~felixw/HYPODD/HYPODD_1.3.tar.gz")
+        os.system("tar -xf HYPODD_1.3.tar.gz")
+        os.system("ln -s $(which gfortran) ./HYPODD/f77")
+        os.system("ln -s $(which gfortran) ./HYPODD/g77")
+        os.environ["PATH"] += os.pathsep + os.getcwd() + "/HYPODD/"
+        os.system("make -C HYPODD/src")
+    if not os.path.exists(root_dir("hypodd")):
+        os.mkdir(root_dir("hypodd"))
+    os.system(f"cp HYPODD/src/ph2dt/ph2dt {root_dir('hypodd')}/")
+    os.system(f"cp HYPODD/src/hypoDD/hypoDD {root_dir('hypodd')}/")</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>--2023-03-15 14:46:00--  http://www.ldeo.columbia.edu/~felixw/HYPODD/HYPODD_1.3.tar.gz
+Resolving www.ldeo.columbia.edu (www.ldeo.columbia.edu)... 129.236.14.15
+Connecting to www.ldeo.columbia.edu (www.ldeo.columbia.edu)|129.236.14.15|:80... connected.
+HTTP request sent, awaiting response... 302 Found
+Location: https://www.ldeo.columbia.edu/~felixw/HYPODD/HYPODD_1.3.tar.gz [following]
+--2023-03-15 14:46:00--  https://www.ldeo.columbia.edu/~felixw/HYPODD/HYPODD_1.3.tar.gz
+Connecting to www.ldeo.columbia.edu (www.ldeo.columbia.edu)|129.236.14.15|:443... connected.
+HTTP request sent, awaiting response... 200 OK
+Length: 1393039 (1.3M) [application/x-gzip]
+Saving to: ‘HYPODD_1.3.tar.gz’
+
+     0K .......... .......... .......... .......... ..........  3%  384K 3s
+    50K .......... .......... .......... .......... ..........  7%  774K 2s
+   100K .......... .......... .......... .......... .......... 11% 47.0M 2s
+   150K .......... .......... .......... .......... .......... 14% 65.5M 1s
+   200K .......... .......... .......... .......... .......... 18%  783K 1s
+   250K .......... .......... .......... .......... .......... 22% 68.0M 1s
+   300K .......... .......... .......... .......... .......... 25% 41.3M 1s
+   350K .......... .......... .......... .......... .......... 29%  213M 1s
+   400K .......... .......... .......... .......... .......... 33%  799K 1s
+   450K .......... .......... .......... .......... .......... 36% 56.2M 1s
+   500K .......... .......... .......... .......... .......... 40% 64.0M 0s
+   550K .......... .......... .......... .......... .......... 44%  110M 0s
+   600K .......... .......... .......... .......... .......... 47% 86.9M 0s
+   650K .......... .......... .......... .......... .......... 51% 80.3M 0s
+   700K .......... .......... .......... .......... .......... 55%  102M 0s
+   750K .......... .......... .......... .......... .......... 58%  161M 0s
+   800K .......... .......... .......... .......... .......... 62%  186M 0s
+   850K .......... .......... .......... .......... .......... 66%  823K 0s
+   900K .......... .......... .......... .......... .......... 69% 96.5M 0s
+   950K .......... .......... .......... .......... .......... 73% 62.2M 0s
+  1000K .......... .......... .......... .......... .......... 77% 43.8M 0s
+  1050K .......... .......... .......... .......... .......... 80% 50.9M 0s
+  1100K .......... .......... .......... .......... .......... 84% 76.9M 0s
+  1150K .......... .......... .......... .......... .......... 88%  209M 0s
+  1200K .......... .......... .......... .......... .......... 91%  178M 0s
+  1250K .......... .......... .......... .......... .......... 95%  210M 0s
+  1300K .......... .......... .......... .......... .......... 99%  202M 0s
+  1350K ..........                                            100%  135M=0.4s
+
+2023-03-15 14:46:01 (3.36 MB/s) - ‘HYPODD_1.3.tar.gz’ saved [1393039/1393039]
+
+</pre>
+</div>
+</div>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
+<pre>make: Entering directory &#39;/net/han/export/ssd-tmp-nobak2/zhuwq/QuakeFlow/kubeflow/HYPODD/src&#39;
+for d in hypoDD ncsn2pha ph2dt hista2ddsta; do (cd $d; echo $d; make all); done
+hypoDD
+make[1]: Entering directory &#39;/net/han/export/ssd-tmp-nobak2/zhuwq/QuakeFlow/kubeflow/HYPODD/src/hypoDD&#39;
+g77 -I../../include -c hypoDD.f -o hypoDD.o
+g77 -I../../include -c aprod.f -o aprod.o
+g77 -I../../include -c cluster1.f -o cluster1.o
+</pre>
+</div>
+</div>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>hypoDD.f:654:29:
+
+  654 |      &amp; f7.1,f7.1,f7.1,f7.1)&#39;),xav,yav,zav,tav
+      |                             1
+Warning: Legacy Extension: Comma before i/o item list at (1)
+</pre>
+</div>
+</div>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
+<pre>g77 -I../../include -c covar.f -o covar.o
+g77 -I../../include -c datum.f -o datum.o
+g77 -I../../include -c delaz.f -o delaz.o
+g77 -I../../include -c delaz2.f -o delaz2.o
+g77 -I../../include -c direct1.f -o direct1.o
+g77 -I../../include -c dist.f -o dist.o
+g77 -I../../include -c dtres.f -o dtres.o
+g77 -I../../include -c exist.f -o exist.o
+g77 -I../../include -c freeunit.f -o freeunit.o
+g77 -I../../include -c getdata.f -o getdata.o
+g77 -I../../include -c getinp.f -o getinp.o
+g77 -I../../include -c ifindi.f -o ifindi.o
+g77 -I../../include -c indexxi.f -o indexxi.o
+g77 -I../../include -c juliam.f -o juliam.o
+g77 -I../../include -c lsfit_lsqr.f -o lsfit_lsqr.o
+g77 -I../../include -c lsfit_svd.f -o lsfit_svd.o
+g77 -I../../include -c lsqr.f -o lsqr.o
+g77 -I../../include -c matmult1.f -o matmult1.o
+g77 -I../../include -c matmult2.f -o matmult2.o
+g77 -I../../include -c matmult3.f -o matmult3.o
+g77 -I../../include -c mdian1.f -o mdian1.o
+g77 -I../../include -c normlz.f -o normlz.o
+g77 -I../../include -c partials.f -o partials.o
+g77 -I../../include -c ran.f -o ran.o
+g77 -I../../include -c redist.f -o redist.o
+g77 -I../../include -c refract.f -o refract.o
+g77 -I../../include -c resstat.f -o resstat.o
+g77 -I../../include -c scopy.f -o scopy.o
+</pre>
+</div>
+</div>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>refract.f:84:16:
+
+   84 |       do 23151m=j1,nl
+      |                1
+Warning: Deleted feature: Start expression in DO loop at (1) must be integer
+refract.f:126:36:
+
+  126 |       xovmax=tinj(lx)*v(lx)*v(1)/(v(lx)-v(1))
+      |                                    1
+Warning: Legacy Extension: REAL array index at (1)
+refract.f:126:24:
+
+  126 |       xovmax=tinj(lx)*v(lx)*v(1)/(v(lx)-v(1))
+      |                        1
+Warning: Legacy Extension: REAL array index at (1)
+refract.f:126:18:
+
+  126 |       xovmax=tinj(lx)*v(lx)*v(1)/(v(lx)-v(1))
+      |                  1
+Warning: Legacy Extension: REAL array index at (1)
+refract.f:155:53:
+
+  155 |       xovmax=(tinj(lx)-tid(jx))*v(lx)*v(jx)/(v(lx)-v(jx))
+      |                                                     1
+Warning: Legacy Extension: REAL array index at (1)
+refract.f:155:47:
+
+  155 |       xovmax=(tinj(lx)-tid(jx))*v(lx)*v(jx)/(v(lx)-v(jx))
+      |                                               1
+Warning: Legacy Extension: REAL array index at (1)
+refract.f:155:40:
+
+  155 |       xovmax=(tinj(lx)-tid(jx))*v(lx)*v(jx)/(v(lx)-v(jx))
+      |                                        1
+Warning: Legacy Extension: REAL array index at (1)
+refract.f:155:34:
+
+  155 |       xovmax=(tinj(lx)-tid(jx))*v(lx)*v(jx)/(v(lx)-v(jx))
+      |                                  1
+Warning: Legacy Extension: REAL array index at (1)
+refract.f:155:27:
+
+  155 |       xovmax=(tinj(lx)-tid(jx))*v(lx)*v(jx)/(v(lx)-v(jx))
+      |                           1
+Warning: Legacy Extension: REAL array index at (1)
+refract.f:155:19:
+
+  155 |       xovmax=(tinj(lx)-tid(jx))*v(lx)*v(jx)/(v(lx)-v(jx))
+      |                   1
+Warning: Legacy Extension: REAL array index at (1)
+refract.f:161:36:
+
+  161 |       xovmax=tinj(lx)*v(lx)*v(1)/(v(lx)-v(1))
+      |                                    1
+Warning: Legacy Extension: REAL array index at (1)
+refract.f:161:24:
+
+  161 |       xovmax=tinj(lx)*v(lx)*v(1)/(v(lx)-v(1))
+      |                        1
+Warning: Legacy Extension: REAL array index at (1)
+refract.f:161:18:
+
+  161 |       xovmax=tinj(lx)*v(lx)*v(1)/(v(lx)-v(1))
+      |                  1
+Warning: Legacy Extension: REAL array index at (1)
+scopy.f:25:72:
+
+   25 |       if(incx.eq.incy) if(incx-1) 5,20,60
+      |                                                                        1
+Warning: Fortran 2018 deleted feature: Arithmetic IF statement at (1)
+</pre>
+</div>
+</div>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
+<pre>g77 -I../../include -c sdc2.f -o sdc2.o
+g77 -I../../include -c setorg.f -o setorg.o
+g77 -I../../include -c skip.f -o skip.o
+g77 -I../../include -c snrm2.f -o snrm2.o
+</pre>
+</div>
+</div>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>snrm2.f:67:72:
+
+   67 |    10 assign 30 to next
+      |                                                                        1
+Warning: Deleted feature: ASSIGN statement at (1)
+snrm2.f:72:19:
+
+   72 |    20    go to next,(30, 50, 70, 110)
+      |                   1
+Warning: Deleted feature: Assigned GOTO statement at (1)
+snrm2.f:74:72:
+
+   74 |       assign 50 to next
+      |                                                                        1
+Warning: Deleted feature: ASSIGN statement at (1)
+snrm2.f:83:72:
+
+   83 |       assign 70 to next
+      |                                                                        1
+Warning: Deleted feature: ASSIGN statement at (1)
+snrm2.f:89:72:
+
+   89 |       assign 110 to next
+      |                                                                        1
+Warning: Deleted feature: ASSIGN statement at (1)
+snrm2.f:125:72:
+
+  125 |    95    sum = sum + sx(j)**2
+      |                                                                        1
+Warning: Fortran 2018 deleted feature: DO termination statement which is not END DO or CONTINUE with label 95 at (1)
+</pre>
+</div>
+</div>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
+<pre>g77 -I../../include -c sort.f -o sort.o
+g77 -I../../include -c sorti.f -o sorti.o
+g77 -I../../include -c sscal.f -o sscal.o
+g77 -I../../include -c svd.f -o svd.o
+g77 -I../../include -c tiddid.f -o tiddid.o
+g77 -I../../include -c trialsrc.f -o trialsrc.o
+g77 -I../../include -c trimlen.f -o trimlen.o
+g77 -I../../include -c ttime.f -o ttime.o
+</pre>
+</div>
+</div>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>svd.f:49:72:
+
+   49 |       DO 1100 J=1,N
+      |                                                                        1
+Warning: Fortran 2018 deleted feature: Shared DO termination label 1100 at (1)
+svd.f:50:72:
+
+   50 |  1100 U(I,J)=A(I,J)
+      |                                                                        1
+Warning: Fortran 2018 deleted feature: DO termination statement which is not END DO or CONTINUE with label 1100 at (1)
+svd.f:59:72:
+
+   59 |  2100 S=U(J,I)**2 + S
+      |                                                                        1
+Warning: Fortran 2018 deleted feature: DO termination statement which is not END DO or CONTINUE with label 2100 at (1)
+svd.f:69:72:
+
+   69 |  2200 S=U(K,I)*U(K,J) + S
+      |                                                                        1
+Warning: Fortran 2018 deleted feature: DO termination statement which is not END DO or CONTINUE with label 2200 at (1)
+svd.f:72:72:
+
+   72 |  2300 U(K,J)=U(K,J) + F*U(K,I)
+      |                                                                        1
+Warning: Fortran 2018 deleted feature: DO termination statement which is not END DO or CONTINUE with label 2300 at (1)
+svd.f:82:72:
+
+   82 |  2600 S=U(I,J)**2 + S
+      |                                                                        1
+Warning: Fortran 2018 deleted feature: DO termination statement which is not END DO or CONTINUE with label 2600 at (1)
+svd.f:90:72:
+
+   90 |  2650 E(J)=U(I,J)/H
+      |                                                                        1
+Warning: Fortran 2018 deleted feature: DO termination statement which is not END DO or CONTINUE with label 2650 at (1)
+svd.f:97:72:
+
+   97 |  2670 S=U(J,K)*U(I,K) + S
+      |                                                                        1
+Warning: Fortran 2018 deleted feature: DO termination statement which is not END DO or CONTINUE with label 2670 at (1)
+svd.f:99:72:
+
+   99 |  2690 U(J,K)=U(J,K) + S*E(K)
+      |                                                                        1
+Warning: Fortran 2018 deleted feature: DO termination statement which is not END DO or CONTINUE with label 2690 at (1)
+svd.f:117:72:
+
+  117 |  3100 V(J,I)=U(I,J)/H
+      |                                                                        1
+Warning: Fortran 2018 deleted feature: DO termination statement which is not END DO or CONTINUE with label 3100 at (1)
+svd.f:121:72:
+
+  121 |  3200 S=U(I,K)*V(K,J) + S
+      |                                                                        1
+Warning: Fortran 2018 deleted feature: DO termination statement which is not END DO or CONTINUE with label 3200 at (1)
+svd.f:123:72:
+
+  123 |  3300 V(K,J)=V(K,J) + S*V(K,I)
+      |                                                                        1
+Warning: Fortran 2018 deleted feature: DO termination statement which is not END DO or CONTINUE with label 3300 at (1)
+svd.f:129:72:
+
+  129 |  3600 V(I,J)=0.0
+      |                                                                        1
+Warning: Fortran 2018 deleted feature: DO termination statement which is not END DO or CONTINUE with label 3600 at (1)
+svd.f:145:72:
+
+  145 |  4100 U(I,J)=0.0
+      |                                                                        1
+Warning: Fortran 2018 deleted feature: DO termination statement which is not END DO or CONTINUE with label 4100 at (1)
+svd.f:152:72:
+
+  152 |  4200 S=U(K,I)*U(K,J) + S
+      |                                                                        1
+Warning: Fortran 2018 deleted feature: DO termination statement which is not END DO or CONTINUE with label 4200 at (1)
+svd.f:155:72:
+
+  155 |  4300 U(K,J)=U(K,J) + F*U(K,I)
+      |                                                                        1
+Warning: Fortran 2018 deleted feature: DO termination statement which is not END DO or CONTINUE with label 4300 at (1)
+svd.f:159:72:
+
+  159 |  4550 U(J,I)=U(J,I)/G
+      |                                                                        1
+Warning: Fortran 2018 deleted feature: DO termination statement which is not END DO or CONTINUE with label 4550 at (1)
+svd.f:163:72:
+
+  163 |  4600 U(J,I)=0.0
+      |                                                                        1
+Warning: Fortran 2018 deleted feature: DO termination statement which is not END DO or CONTINUE with label 4600 at (1)
+svd.f:164:72:
+
+  164 |  4700 U(I,I)=U(I,I) + 1.0
+      |                                                                        1
+Warning: Fortran 2018 deleted feature: DO termination statement which is not END DO or CONTINUE with label 4700 at (1)
+svd.f:258:72:
+
+  258 |  8200 V(J,K)=-V(J,K)
+      |                                                                        1
+Warning: Fortran 2018 deleted feature: DO termination statement which is not END DO or CONTINUE with label 8200 at (1)
+trimlen.f:11:72:
+
+   11 |     1    if(t(trimlen:trimlen).ne.&#39; &#39;)RETURN
+      |                                                                        1
+Warning: Fortran 2018 deleted feature: DO termination statement which is not END DO or CONTINUE with label 1 at (1)
+vmodel.f:34:72:
+
+   34 |    10 vsq(i)=v(i)*v(i)
+      |                                                                        1
+Warning: Fortran 2018 deleted feature: DO termination statement which is not END DO or CONTINUE with label 10 at (1)
+vmodel.f:53:72:
+
+   53 |    30 thk(i)=top(i+1)-top(i)
+      |                                                                        1
+Warning: Fortran 2018 deleted feature: DO termination statement which is not END DO or CONTINUE with label 30 at (1)
+</pre>
+</div>
+</div>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
+<pre>g77 -I../../include -c vmodel.f -o vmodel.o
+g77 -I../../include -c weighting.f -o weighting.o
+gcc -O -I../../include   -c -o atoangle_.o atoangle_.c
+</pre>
+</div>
+</div>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>atoangle_.c:3:8: warning: extra tokens at end of #endif directive [-Wendif-labels]
+ #endif lint
+        ^~~~
+In file included from atoangle_.c:6:0:
+../../include/f77types.h:10:8: warning: extra tokens at end of #endif directive [-Wendif-labels]
+ #endif F77TYPES_H
+        ^~~~~~~~~~
+</pre>
+</div>
+</div>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
+<pre>gcc -O -I../../include   -c -o atoangle.o atoangle.c
+gcc -O -I../../include   -c -o datetime_.o datetime_.c
+gcc -O -I../../include   -c -o hypot_.o hypot_.c
+gcc -O -I../../include   -c -o rpad_.o rpad_.c
+gcc -O -I../../include   -c -o sscanf3_.o sscanf3_.c
+g77  hypoDD.o aprod.o cluster1.o covar.o datum.o delaz.o delaz2.o direct1.o dist.o dtres.o exist.o freeunit.o getdata.o getinp.o ifindi.o indexxi.o juliam.o lsfit_lsqr.o lsfit_svd.o lsqr.o matmult1.o matmult2.o matmult3.o mdian1.o normlz.o partials.o ran.o redist.o refract.o resstat.o scopy.o sdc2.o setorg.o skip.o snrm2.o sort.o sorti.o sscal.o svd.o tiddid.o trialsrc.o trimlen.o ttime.o vmodel.o weighting.o atoangle_.o atoangle.o datetime_.o hypot_.o rpad_.o sscanf3_.o  -o hypoDD
+make[1]: Leaving directory &#39;/net/han/export/ssd-tmp-nobak2/zhuwq/QuakeFlow/kubeflow/HYPODD/src/hypoDD&#39;
+ncsn2pha
+make[1]: Entering directory &#39;/net/han/export/ssd-tmp-nobak2/zhuwq/QuakeFlow/kubeflow/HYPODD/src/ncsn2pha&#39;
+g77 -O -c ncsn2pha.f -o ncsn2pha.o
+</pre>
+</div>
+</div>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>atoangle.c:3:8: warning: extra tokens at end of #endif directive [-Wendif-labels]
+ #endif lint
+        ^~~~
+atoangle.c:9:8: warning: extra tokens at end of #endif directive [-Wendif-labels]
+ #endif NULL
+        ^~~~
+In file included from datetime_.c:7:0:
+../../include/f77types.h:10:8: warning: extra tokens at end of #endif directive [-Wendif-labels]
+ #endif F77TYPES_H
+        ^~~~~~~~~~
+In file included from rpad_.c:6:0:
+../../include/f77types.h:10:8: warning: extra tokens at end of #endif directive [-Wendif-labels]
+ #endif F77TYPES_H
+        ^~~~~~~~~~
+In file included from sscanf3_.c:7:0:
+../../include/f77types.h:10:8: warning: extra tokens at end of #endif directive [-Wendif-labels]
+ #endif F77TYPES_H
+        ^~~~~~~~~~
+ncsn2pha.f:279:72:
+
+  279 |     1    if(t(trimlen:trimlen).ne.&#39; &#39;)RETURN
+      |                                                                        1
+Warning: Fortran 2018 deleted feature: DO termination statement which is not END DO or CONTINUE with label 1 at (1)
+ncsn2pha.f:62:0:
+
+   62 |       fn0= str30 (1:trimlen(str30))
+      | 
+Warning: ‘__builtin_memcpy’ reading 80 bytes from a region of size 30 [-Wstringop-overflow=]
+</pre>
+</div>
+</div>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
+<pre>g77  ncsn2pha.o   -o ncsn2pha
+make[1]: Leaving directory &#39;/net/han/export/ssd-tmp-nobak2/zhuwq/QuakeFlow/kubeflow/HYPODD/src/ncsn2pha&#39;
+ph2dt
+make[1]: Entering directory &#39;/net/han/export/ssd-tmp-nobak2/zhuwq/QuakeFlow/kubeflow/HYPODD/src/ph2dt&#39;
+g77 -O -I../../include -c ph2dt.f -o ph2dt.o
+g77 -O -I../../include -c cluster.f -o cluster.o
+g77 -O -I../../include -c datetime.f -o datetime.o
+g77 -O -I../../include -c delaz.f -o delaz.o
+g77 -O -I../../include -c ifindi.f -o ifindi.o
+g77 -O -I../../include -c indexx.f -o indexx.o
+g77 -O -I../../include -c indexxi.f -o indexxi.o
+g77 -O -I../../include -c sorti.f -o sorti.o
+g77 -O -I../../include -c trimlen.f -o trimlen.o
+gcc -I../../include -O   -c -o atoangle_.o atoangle_.c
+gcc -I../../include -O   -c -o atoangle.o atoangle.c
+gcc -I../../include -O   -c -o rpad_.o rpad_.c
+gcc -I../../include -O   -c -o sscanf3_.o sscanf3_.c
+g77  ph2dt.o cluster.o datetime.o delaz.o ifindi.o indexx.o indexxi.o sorti.o trimlen.o atoangle_.o atoangle.o rpad_.o sscanf3_.o  -o ph2dt
+</pre>
+</div>
+</div>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>trimlen.f:11:72:
+
+   11 |     1    if(t(trimlen:trimlen).ne.&#39; &#39;)RETURN
+      |                                                                        1
+Warning: Fortran 2018 deleted feature: DO termination statement which is not END DO or CONTINUE with label 1 at (1)
+atoangle_.c:3:8: warning: extra tokens at end of #endif directive [-Wendif-labels]
+ #endif lint
+        ^~~~
+In file included from atoangle_.c:6:0:
+../../include/f77types.h:10:8: warning: extra tokens at end of #endif directive [-Wendif-labels]
+ #endif F77TYPES_H
+        ^~~~~~~~~~
+atoangle.c:3:8: warning: extra tokens at end of #endif directive [-Wendif-labels]
+ #endif lint
+        ^~~~
+atoangle.c:9:8: warning: extra tokens at end of #endif directive [-Wendif-labels]
+ #endif NULL
+        ^~~~
+In file included from rpad_.c:6:0:
+../../include/f77types.h:10:8: warning: extra tokens at end of #endif directive [-Wendif-labels]
+ #endif F77TYPES_H
+        ^~~~~~~~~~
+In file included from sscanf3_.c:7:0:
+../../include/f77types.h:10:8: warning: extra tokens at end of #endif directive [-Wendif-labels]
+ #endif F77TYPES_H
+        ^~~~~~~~~~
+</pre>
+</div>
+</div>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
+<pre>make[1]: Leaving directory &#39;/net/han/export/ssd-tmp-nobak2/zhuwq/QuakeFlow/kubeflow/HYPODD/src/ph2dt&#39;
+hista2ddsta
+make[1]: Entering directory &#39;/net/han/export/ssd-tmp-nobak2/zhuwq/QuakeFlow/kubeflow/HYPODD/src/hista2ddsta&#39;
+f77 -O -I../../include -c hista2ddsta.f -o hista2ddsta.o
+f77  hista2ddsta.o  -o hista2ddsta
+make[1]: Leaving directory &#39;/net/han/export/ssd-tmp-nobak2/zhuwq/QuakeFlow/kubeflow/HYPODD/src/hista2ddsta&#39;
+make: Leaving directory &#39;/net/han/export/ssd-tmp-nobak2/zhuwq/QuakeFlow/kubeflow/HYPODD/src&#39;
+</pre>
+</div>
+</div>
+
+</div>
+
+</div>
+
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h3 id="71-convert-station-format">7.1. Convert station format<a class="anchor-link" href="#71-convert-station-format">&#182;</a></h3>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[45]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-24">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">def</span> <span class="nf">convert_station</span><span class="p">(</span>
+    <span class="n">config_json</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="s2">&quot;json&quot;</span><span class="p">),</span>
+    <span class="n">station_json</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="s2">&quot;json&quot;</span><span class="p">),</span>
+    <span class="n">hypoinverse_station</span><span class="p">:</span> <span class="n">OutputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span>
+    <span class="n">hypodd_station</span><span class="p">:</span> <span class="n">OutputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span>
+    <span class="n">data_path</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;./&quot;</span><span class="p">,</span>
+    <span class="n">bucket_name</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;catalogs&quot;</span><span class="p">,</span>
+    <span class="n">s3_url</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;minio-service:9000&quot;</span><span class="p">,</span>
+    <span class="n">secure</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+<span class="p">):</span>
+
+    <span class="kn">import</span> <span class="nn">os</span>
+    <span class="kn">import</span> <span class="nn">json</span>
+
+    <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+    <span class="kn">from</span> <span class="nn">tqdm</span> <span class="kn">import</span> <span class="n">tqdm</span>
+    
+
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">config_json</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">config</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">fp</span><span class="p">)</span>
+
+    <span class="n">hypodd_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">data_path</span><span class="p">,</span> <span class="s2">&quot;hypodd&quot;</span><span class="p">)</span>
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">hypodd_path</span><span class="p">):</span>
+        <span class="n">os</span><span class="o">.</span><span class="n">mkdir</span><span class="p">(</span><span class="n">hypodd_path</span><span class="p">)</span>
+    <span class="n">hypoinv_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">data_path</span><span class="p">,</span> <span class="s2">&quot;hypoinv&quot;</span><span class="p">)</span>
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">hypoinv_path</span><span class="p">):</span>
+        <span class="n">os</span><span class="o">.</span><span class="n">mkdir</span><span class="p">(</span><span class="n">hypoinv_path</span><span class="p">)</span>
+
+    <span class="n">stations</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_json</span><span class="p">(</span><span class="n">station_json</span><span class="p">,</span> <span class="n">orient</span><span class="o">=</span><span class="s2">&quot;index&quot;</span><span class="p">)</span>
+
+    <span class="n">converted_hypoinverse</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">converted_hypodd</span> <span class="o">=</span> <span class="p">{}</span>
+
+    <span class="k">for</span> <span class="n">sta</span><span class="p">,</span> <span class="n">row</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">stations</span><span class="o">.</span><span class="n">iterrows</span><span class="p">()):</span>
+
+        <span class="n">network_code</span><span class="p">,</span> <span class="n">station_code</span><span class="p">,</span> <span class="n">comp_code</span><span class="p">,</span> <span class="n">channel_code</span> <span class="o">=</span> <span class="n">sta</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;.&quot;</span><span class="p">)</span>
+        <span class="n">station_weight</span> <span class="o">=</span> <span class="s2">&quot; &quot;</span>
+        <span class="n">lat_degree</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">row</span><span class="p">[</span><span class="s2">&quot;latitude&quot;</span><span class="p">])</span>
+        <span class="n">lat_minute</span> <span class="o">=</span> <span class="p">(</span><span class="n">row</span><span class="p">[</span><span class="s2">&quot;latitude&quot;</span><span class="p">]</span> <span class="o">-</span> <span class="n">lat_degree</span><span class="p">)</span> <span class="o">*</span> <span class="mi">60</span>
+        <span class="n">north</span> <span class="o">=</span> <span class="s2">&quot;N&quot;</span> <span class="k">if</span> <span class="n">lat_degree</span> <span class="o">&gt;=</span> <span class="mi">0</span> <span class="k">else</span> <span class="s2">&quot;S&quot;</span>
+        <span class="n">lng_degree</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">row</span><span class="p">[</span><span class="s2">&quot;longitude&quot;</span><span class="p">])</span>
+        <span class="n">lng_minute</span> <span class="o">=</span> <span class="p">(</span><span class="n">row</span><span class="p">[</span><span class="s2">&quot;longitude&quot;</span><span class="p">]</span> <span class="o">-</span> <span class="n">lng_degree</span><span class="p">)</span> <span class="o">*</span> <span class="mi">60</span>
+        <span class="n">west</span> <span class="o">=</span> <span class="s2">&quot;W&quot;</span> <span class="k">if</span> <span class="n">lng_degree</span> <span class="o">&lt;=</span> <span class="mi">0</span> <span class="k">else</span> <span class="s2">&quot;E&quot;</span>
+        <span class="n">elevation</span> <span class="o">=</span> <span class="n">row</span><span class="p">[</span><span class="s2">&quot;elevation(m)&quot;</span><span class="p">]</span>
+        <span class="n">line_hypoinverse</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">station_code</span><span class="si">:</span><span class="s2">&lt;5</span><span class="si">}</span><span class="s2"> </span><span class="si">{</span><span class="n">network_code</span><span class="si">:</span><span class="s2">&lt;2</span><span class="si">}</span><span class="s2"> </span><span class="si">{</span><span class="n">comp_code</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="si">:</span><span class="s2">&lt;1</span><span class="si">}{</span><span class="n">channel_code</span><span class="si">:</span><span class="s2">&lt;3</span><span class="si">}</span><span class="s2"> </span><span class="si">{</span><span class="n">station_weight</span><span class="si">}{</span><span class="nb">abs</span><span class="p">(</span><span class="n">lat_degree</span><span class="p">)</span><span class="si">:</span><span class="s2">2.0f</span><span class="si">}</span><span class="s2"> </span><span class="si">{</span><span class="nb">abs</span><span class="p">(</span><span class="n">lat_minute</span><span class="p">)</span><span class="si">:</span><span class="s2">7.4f</span><span class="si">}{</span><span class="n">north</span><span class="si">}{</span><span class="nb">abs</span><span class="p">(</span><span class="n">lng_degree</span><span class="p">)</span><span class="si">:</span><span class="s2">3.0f</span><span class="si">}</span><span class="s2"> </span><span class="si">{</span><span class="nb">abs</span><span class="p">(</span><span class="n">lng_minute</span><span class="p">)</span><span class="si">:</span><span class="s2">7.4f</span><span class="si">}{</span><span class="n">west</span><span class="si">}{</span><span class="n">elevation</span><span class="si">:</span><span class="s2">4.0f</span><span class="si">}</span><span class="se">\n</span><span class="s2">&quot;</span>
+        <span class="n">converted_hypoinverse</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">line_hypoinverse</span><span class="p">)</span>
+
+        <span class="n">tmp_code</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">station_code</span><span class="si">}{</span><span class="n">channel_code</span><span class="si">}</span><span class="s2">&quot;</span>
+        <span class="n">converted_hypodd</span><span class="p">[</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">station_code</span><span class="si">}{</span><span class="n">channel_code</span><span class="si">}</span><span class="s2">&quot;</span>
+        <span class="p">]</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">tmp_code</span><span class="si">:</span><span class="s2">&lt;8s</span><span class="si">}</span><span class="s2"> </span><span class="si">{</span><span class="n">row</span><span class="p">[</span><span class="s1">&#39;latitude&#39;</span><span class="p">]</span><span class="si">:</span><span class="s2">.3f</span><span class="si">}</span><span class="s2"> </span><span class="si">{</span><span class="n">row</span><span class="p">[</span><span class="s1">&#39;longitude&#39;</span><span class="p">]</span><span class="si">:</span><span class="s2">.3f</span><span class="si">}</span><span class="se">\n</span><span class="s2">&quot;</span>
+
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">hypoinverse_station</span><span class="p">,</span> <span class="s2">&quot;w&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+        <span class="n">f</span><span class="o">.</span><span class="n">writelines</span><span class="p">(</span><span class="n">converted_hypoinverse</span><span class="p">)</span>
+
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">hypodd_station</span><span class="p">,</span> <span class="s2">&quot;w&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+        <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">converted_hypodd</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+            <span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">v</span><span class="p">)</span>
+
+    <span class="c1">## upload to s3 bucket</span>
+    <span class="k">try</span><span class="p">:</span>
+        <span class="kn">from</span> <span class="nn">minio</span> <span class="kn">import</span> <span class="n">Minio</span>
+
+        <span class="n">minioClient</span> <span class="o">=</span> <span class="n">Minio</span><span class="p">(</span><span class="n">s3_url</span><span class="p">,</span> <span class="n">access_key</span><span class="o">=</span><span class="s2">&quot;minio&quot;</span><span class="p">,</span> <span class="n">secret_key</span><span class="o">=</span><span class="s2">&quot;minio123&quot;</span><span class="p">,</span> <span class="n">secure</span><span class="o">=</span><span class="n">secure</span><span class="p">)</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">minioClient</span><span class="o">.</span><span class="n">bucket_exists</span><span class="p">(</span><span class="n">bucket_name</span><span class="p">):</span>
+            <span class="n">minioClient</span><span class="o">.</span><span class="n">make_bucket</span><span class="p">(</span><span class="n">bucket_name</span><span class="p">)</span>
+
+        <span class="n">minioClient</span><span class="o">.</span><span class="n">fput_object</span><span class="p">(</span>
+            <span class="n">bucket_name</span><span class="p">,</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;region&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">/hypodd/stations.dat&quot;</span><span class="p">,</span>
+            <span class="n">hypodd_station</span><span class="p">,</span>
+        <span class="p">)</span>
+
+    <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">err</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;ERROR: can not access minio service! </span><span class="se">\n</span><span class="si">{</span><span class="n">err</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+        <span class="k">pass</span>
+</pre></div>
+<div id="cell-24" class="clipboard-copy-txt">def convert_station(
+    config_json: InputPath("json"),
+    station_json: InputPath("json"),
+    hypoinverse_station: OutputPath(str),
+    hypodd_station: OutputPath(str),
+    data_path: str = "./",
+    bucket_name: str = "catalogs",
+    s3_url: str = "minio-service:9000",
+    secure: bool = True,
+):
+
+    import os
+    import json
+
+    import pandas as pd
+    from tqdm import tqdm
+    
+
+    with open(config_json, "r") as fp:
+        config = json.load(fp)
+
+    hypodd_path = os.path.join(data_path, "hypodd")
+    if not os.path.exists(hypodd_path):
+        os.mkdir(hypodd_path)
+    hypoinv_path = os.path.join(data_path, "hypoinv")
+    if not os.path.exists(hypoinv_path):
+        os.mkdir(hypoinv_path)
+
+    stations = pd.read_json(station_json, orient="index")
+
+    converted_hypoinverse = []
+    converted_hypodd = {}
+
+    for sta, row in tqdm(stations.iterrows()):
+
+        network_code, station_code, comp_code, channel_code = sta.split(".")
+        station_weight = " "
+        lat_degree = int(row["latitude"])
+        lat_minute = (row["latitude"] - lat_degree) * 60
+        north = "N" if lat_degree >= 0 else "S"
+        lng_degree = int(row["longitude"])
+        lng_minute = (row["longitude"] - lng_degree) * 60
+        west = "W" if lng_degree <= 0 else "E"
+        elevation = row["elevation(m)"]
+        line_hypoinverse = f"{station_code:<5} {network_code:<2} {comp_code[:-1]:<1}{channel_code:<3} {station_weight}{abs(lat_degree):2.0f} {abs(lat_minute):7.4f}{north}{abs(lng_degree):3.0f} {abs(lng_minute):7.4f}{west}{elevation:4.0f}\n"
+        converted_hypoinverse.append(line_hypoinverse)
+
+        tmp_code = f"{station_code}{channel_code}"
+        converted_hypodd[
+            f"{station_code}{channel_code}"
+        ] = f"{tmp_code:<8s} {row['latitude']:.3f} {row['longitude']:.3f}\n"
+
+    with open(hypoinverse_station, "w") as f:
+        f.writelines(converted_hypoinverse)
+
+    with open(hypodd_station, "w") as f:
+        for k, v in converted_hypodd.items():
+            f.write(v)
+
+    ## upload to s3 bucket
+    try:
+        from minio import Minio
+
+        minioClient = Minio(s3_url, access_key="minio", secret_key="minio123", secure=secure)
+        if not minioClient.bucket_exists(bucket_name):
+            minioClient.make_bucket(bucket_name)
+
+        minioClient.fput_object(
+            bucket_name,
+            f"{config['region']}/hypodd/stations.dat",
+            hypodd_station,
+        )
+
+    except Exception as err:
+        print(f"ERROR: can not access minio service! \n{err}")
+        pass
+    </div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[46]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-25">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">if</span> <span class="n">run_local</span><span class="p">:</span>
+    <span class="n">convert_station</span><span class="p">(</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;config.json&quot;</span><span class="p">),</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;stations.json&quot;</span><span class="p">),</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;hypoinv/stations.dat&quot;</span><span class="p">),</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;hypodd/stations.dat&quot;</span><span class="p">),</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;&quot;</span><span class="p">),</span>
+    <span class="p">)</span>
+</pre></div>
+<div id="cell-25" class="clipboard-copy-txt">if run_local:
+    convert_station(
+        root_dir("config.json"),
+        root_dir("stations.json"),
+        root_dir("hypoinv/stations.dat"),
+        root_dir("hypodd/stations.dat"),
+        root_dir(""),
+    )</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>52it [00:00, 11423.83it/s]</pre>
+</div>
+</div>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
+<pre>ERROR: can not access minio service! 
+No module named &#39;minio&#39;
+</pre>
+</div>
+</div>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>
+</pre>
+</div>
+</div>
+
+</div>
+
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[47]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-26">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">convert_station_op</span> <span class="o">=</span> <span class="n">comp</span><span class="o">.</span><span class="n">func_to_container_op</span><span class="p">(</span>
+    <span class="n">convert_station</span><span class="p">,</span>
+    <span class="n">base_image</span><span class="o">=</span><span class="s2">&quot;python:3.8&quot;</span><span class="p">,</span>
+    <span class="n">packages_to_install</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;pandas&quot;</span><span class="p">,</span> <span class="s2">&quot;tqdm&quot;</span><span class="p">,</span> <span class="s2">&quot;minio&quot;</span><span class="p">],</span>
+<span class="p">)</span>
+</pre></div>
+<div id="cell-26" class="clipboard-copy-txt">convert_station_op = comp.func_to_container_op(
+    convert_station,
+    base_image="python:3.8",
+    packages_to_install=["pandas", "tqdm", "minio"],
+)</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
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+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h3 id="split-large-catalog-due-to-memory-and-time">Split large catalog due to memory and time<a class="anchor-link" href="#split-large-catalog-due-to-memory-and-time">&#182;</a></h3>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
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+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[48]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-27">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">def</span> <span class="nf">split_hypodd</span><span class="p">(</span>
+    <span class="n">config_json</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="s2">&quot;json&quot;</span><span class="p">),</span>
+    <span class="n">catalog_csv</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">:</span>
+
+    <span class="kn">import</span> <span class="nn">json</span>
+    <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">config_json</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">config</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">fp</span><span class="p">)</span>
+
+    <span class="n">events</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">catalog_csv</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="s2">&quot;MAXEVENT&quot;</span> <span class="ow">in</span> <span class="n">config</span><span class="p">[</span><span class="s2">&quot;hypodd&quot;</span><span class="p">]:</span>
+        <span class="n">MAXEVENT</span> <span class="o">=</span> <span class="n">config</span><span class="p">[</span><span class="s2">&quot;hypodd&quot;</span><span class="p">][</span><span class="s2">&quot;MAXEVENT&quot;</span><span class="p">]</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">MAXEVENT</span> <span class="o">=</span> <span class="mf">1e4</span>  <span class="c1">## segment by time</span>
+
+    <span class="n">MAXEVENT</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">events</span><span class="p">)</span> <span class="o">//</span> <span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">events</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">//</span> <span class="n">MAXEVENT</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span>
+    <span class="n">num_parallel</span> <span class="o">=</span> <span class="nb">int</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">events</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">//</span> <span class="n">MAXEVENT</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">num_parallel</span><span class="p">))</span>
+</pre></div>
+<div id="cell-27" class="clipboard-copy-txt">def split_hypodd(
+    config_json: InputPath("json"),
+    catalog_csv: InputPath(str),
+) -> list:
+
+    import json
+    import pandas as pd
+
+    with open(config_json, "r") as fp:
+        config = json.load(fp)
+
+    events = pd.read_csv(catalog_csv)
+
+    if "MAXEVENT" in config["hypodd"]:
+        MAXEVENT = config["hypodd"]["MAXEVENT"]
+    else:
+        MAXEVENT = 1e4  ## segment by time
+
+    MAXEVENT = len(events) // ((len(events) - 1) // MAXEVENT + 1) + 1
+    num_parallel = int((len(events) - 1) // MAXEVENT + 1)
+
+    return list(range(num_parallel))</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[49]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-28">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">if</span> <span class="n">run_local</span><span class="p">:</span>
+    <span class="n">nodes</span> <span class="o">=</span> <span class="n">split_hypodd</span><span class="p">(</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;config.json&quot;</span><span class="p">),</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;gamma_catalog.csv&quot;</span><span class="p">),</span>
+    <span class="p">)</span>
+</pre></div>
+<div id="cell-28" class="clipboard-copy-txt">if run_local:
+    nodes = split_hypodd(
+        root_dir("config.json"),
+        root_dir("gamma_catalog.csv"),
+    )</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[50]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-29">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">split_hypodd_op</span> <span class="o">=</span> <span class="n">comp</span><span class="o">.</span><span class="n">func_to_container_op</span><span class="p">(</span>
+    <span class="n">split_hypodd</span><span class="p">,</span>
+    <span class="n">base_image</span><span class="o">=</span><span class="s2">&quot;python:3.8&quot;</span><span class="p">,</span>
+    <span class="n">packages_to_install</span><span class="o">=</span><span class="p">[</span>
+        <span class="s2">&quot;pandas&quot;</span><span class="p">,</span>
+    <span class="p">],</span>
+<span class="p">)</span>
+</pre></div>
+<div id="cell-29" class="clipboard-copy-txt">split_hypodd_op = comp.func_to_container_op(
+    split_hypodd,
+    base_image="python:3.8",
+    packages_to_install=[
+        "pandas",
+    ],
+)</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h3 id="72-convert-phase-format">7.2. Convert phase format<a class="anchor-link" href="#72-convert-phase-format">&#182;</a></h3>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[51]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-30">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">def</span> <span class="nf">convert_phase</span><span class="p">(</span>
+    <span class="n">node_i</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
+    <span class="n">config_json</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="s2">&quot;json&quot;</span><span class="p">),</span>
+    <span class="n">picks_csv</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span>
+    <span class="n">catalog_csv</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span>
+    <span class="n">hypodd_phase</span><span class="p">:</span> <span class="n">OutputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span>
+    <span class="n">data_path</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;./&quot;</span><span class="p">,</span>
+    <span class="n">bucket_name</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;catalogs&quot;</span><span class="p">,</span>
+    <span class="n">s3_url</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;minio-service:9000&quot;</span><span class="p">,</span>
+    <span class="n">secure</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+<span class="p">):</span>
+
+    <span class="kn">import</span> <span class="nn">json</span>
+    <span class="kn">import</span> <span class="nn">os</span>
+    <span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">datetime</span>
+
+    <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+    <span class="kn">from</span> <span class="nn">tqdm</span> <span class="kn">import</span> <span class="n">tqdm</span>
+
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">config_json</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">config</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">fp</span><span class="p">)</span>
+    <span class="n">hypodd_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">data_path</span><span class="p">,</span> <span class="s2">&quot;hypodd&quot;</span><span class="p">)</span>
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">hypodd_path</span><span class="p">):</span>
+        <span class="n">os</span><span class="o">.</span><span class="n">mkdir</span><span class="p">(</span><span class="n">hypodd_path</span><span class="p">)</span>
+
+    <span class="n">picks</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">picks_csv</span><span class="p">)</span>
+    <span class="n">events</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">catalog_csv</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="s2">&quot;MAXEVENT&quot;</span> <span class="ow">in</span> <span class="n">config</span><span class="p">[</span><span class="s2">&quot;hypodd&quot;</span><span class="p">]:</span>
+        <span class="n">MAXEVENT</span> <span class="o">=</span> <span class="n">config</span><span class="p">[</span><span class="s2">&quot;hypodd&quot;</span><span class="p">][</span><span class="s2">&quot;MAXEVENT&quot;</span><span class="p">]</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">MAXEVENT</span> <span class="o">=</span> <span class="mf">1e4</span>  <span class="c1">## segment by time</span>
+    <span class="n">MAXEVENT</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">events</span><span class="p">)</span> <span class="o">//</span> <span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">events</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">//</span> <span class="n">MAXEVENT</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span>
+    <span class="n">num_parallel</span> <span class="o">=</span> <span class="nb">int</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">events</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">//</span> <span class="n">MAXEVENT</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
+
+    <span class="n">events</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="s2">&quot;time&quot;</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="n">events</span> <span class="o">=</span> <span class="n">events</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">node_i</span><span class="p">::</span><span class="n">num_parallel</span><span class="p">]</span>
+    <span class="n">picks</span> <span class="o">=</span> <span class="n">picks</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">picks</span><span class="p">[</span><span class="s2">&quot;event_index&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">isin</span><span class="p">(</span><span class="n">events</span><span class="p">[</span><span class="s2">&quot;event_index&quot;</span><span class="p">])]</span>
+    <span class="c1"># output_lines = []</span>
+    <span class="n">output_file</span> <span class="o">=</span> <span class="nb">open</span><span class="p">(</span><span class="n">hypodd_phase</span><span class="p">,</span> <span class="s2">&quot;w&quot;</span><span class="p">)</span>
+
+    <span class="n">picks_by_event</span> <span class="o">=</span> <span class="n">picks</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s2">&quot;event_index&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">groups</span>
+    <span class="c1"># for i in tqdm(range(node_i, len(events), num_parallel)):</span>
+    <span class="c1">#     event = events.iloc[i]</span>
+    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">event</span> <span class="ow">in</span> <span class="n">events</span><span class="o">.</span><span class="n">iterrows</span><span class="p">():</span>
+        <span class="n">event_time</span> <span class="o">=</span> <span class="n">datetime</span><span class="o">.</span><span class="n">strptime</span><span class="p">(</span><span class="n">event</span><span class="p">[</span><span class="s2">&quot;time&quot;</span><span class="p">],</span> <span class="s2">&quot;%Y-%m-</span><span class="si">%d</span><span class="s2">T%H:%M:%S.</span><span class="si">%f</span><span class="s2">&quot;</span><span class="p">)</span>
+        <span class="n">lat</span> <span class="o">=</span> <span class="n">event</span><span class="p">[</span><span class="s2">&quot;latitude&quot;</span><span class="p">]</span>
+        <span class="n">lng</span> <span class="o">=</span> <span class="n">event</span><span class="p">[</span><span class="s2">&quot;longitude&quot;</span><span class="p">]</span>
+        <span class="n">dep</span> <span class="o">=</span> <span class="n">event</span><span class="p">[</span><span class="s2">&quot;depth(m)&quot;</span><span class="p">]</span> <span class="o">/</span> <span class="mf">1e3</span>
+        <span class="n">mag</span> <span class="o">=</span> <span class="n">event</span><span class="p">[</span><span class="s2">&quot;magnitude&quot;</span><span class="p">]</span>
+        <span class="n">EH</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="n">EZ</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="n">RMS</span> <span class="o">=</span> <span class="n">event</span><span class="p">[</span><span class="s2">&quot;sigma_time&quot;</span><span class="p">]</span>
+
+        <span class="n">year</span><span class="p">,</span> <span class="n">month</span><span class="p">,</span> <span class="n">day</span><span class="p">,</span> <span class="n">hour</span><span class="p">,</span> <span class="nb">min</span><span class="p">,</span> <span class="n">sec</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="n">event_time</span><span class="o">.</span><span class="n">year</span><span class="p">,</span>
+            <span class="n">event_time</span><span class="o">.</span><span class="n">month</span><span class="p">,</span>
+            <span class="n">event_time</span><span class="o">.</span><span class="n">day</span><span class="p">,</span>
+            <span class="n">event_time</span><span class="o">.</span><span class="n">hour</span><span class="p">,</span>
+            <span class="n">event_time</span><span class="o">.</span><span class="n">minute</span><span class="p">,</span>
+            <span class="nb">float</span><span class="p">(</span><span class="n">event_time</span><span class="o">.</span><span class="n">strftime</span><span class="p">(</span><span class="s2">&quot;%S.</span><span class="si">%f</span><span class="s2">&quot;</span><span class="p">)),</span>
+        <span class="p">)</span>
+        <span class="n">event_line</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;# </span><span class="si">{</span><span class="n">year</span><span class="si">:</span><span class="s2">4d</span><span class="si">}</span><span class="s2"> </span><span class="si">{</span><span class="n">month</span><span class="si">:</span><span class="s2">2d</span><span class="si">}</span><span class="s2"> </span><span class="si">{</span><span class="n">day</span><span class="si">:</span><span class="s2">2d</span><span class="si">}</span><span class="s2"> </span><span class="si">{</span><span class="n">hour</span><span class="si">:</span><span class="s2">2d</span><span class="si">}</span><span class="s2"> </span><span class="si">{</span><span class="nb">min</span><span class="si">:</span><span class="s2">2d</span><span class="si">}</span><span class="s2"> </span><span class="si">{</span><span class="n">sec</span><span class="si">:</span><span class="s2">5.2f</span><span class="si">}</span><span class="s2">  </span><span class="si">{</span><span class="n">lat</span><span class="si">:</span><span class="s2">7.4f</span><span class="si">}</span><span class="s2"> </span><span class="si">{</span><span class="n">lng</span><span class="si">:</span><span class="s2">9.4f</span><span class="si">}</span><span class="s2">   </span><span class="si">{</span><span class="n">dep</span><span class="si">:</span><span class="s2">5.2f</span><span class="si">}</span><span class="s2"> </span><span class="si">{</span><span class="n">mag</span><span class="si">:</span><span class="s2">5.2f</span><span class="si">}</span><span class="s2"> </span><span class="si">{</span><span class="n">EH</span><span class="si">:</span><span class="s2">5.2f</span><span class="si">}</span><span class="s2"> </span><span class="si">{</span><span class="n">EZ</span><span class="si">:</span><span class="s2">5.2f</span><span class="si">}</span><span class="s2"> </span><span class="si">{</span><span class="n">RMS</span><span class="si">:</span><span class="s2">5.2f</span><span class="si">}</span><span class="s2"> </span><span class="si">{</span><span class="n">event</span><span class="p">[</span><span class="s1">&#39;event_index&#39;</span><span class="p">]</span><span class="si">:</span><span class="s2">9d</span><span class="si">}</span><span class="se">\n</span><span class="s2">&quot;</span>
+
+        <span class="c1"># output_lines.append(event_line)</span>
+        <span class="n">output_file</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">event_line</span><span class="p">)</span>
+
+        <span class="n">picks_idx</span> <span class="o">=</span> <span class="n">picks_by_event</span><span class="p">[</span><span class="n">event</span><span class="p">[</span><span class="s2">&quot;event_index&quot;</span><span class="p">]]</span>
+        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="n">picks_idx</span><span class="p">:</span>
+            <span class="c1"># pick = picks.iloc[j]</span>
+            <span class="n">pick</span> <span class="o">=</span> <span class="n">picks</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">j</span><span class="p">]</span>
+            <span class="n">network_code</span><span class="p">,</span> <span class="n">station_code</span><span class="p">,</span> <span class="n">comp_code</span><span class="p">,</span> <span class="n">channel_code</span> <span class="o">=</span> <span class="n">pick</span><span class="p">[</span><span class="s2">&quot;station_id&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;.&quot;</span><span class="p">)</span>
+            <span class="n">phase_type</span> <span class="o">=</span> <span class="n">pick</span><span class="p">[</span><span class="s2">&quot;phase_type&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">upper</span><span class="p">()</span>
+            <span class="n">phase_score</span> <span class="o">=</span> <span class="n">pick</span><span class="p">[</span><span class="s2">&quot;phase_score&quot;</span><span class="p">]</span>
+            <span class="n">pick_time</span> <span class="o">=</span> <span class="p">(</span><span class="n">datetime</span><span class="o">.</span><span class="n">strptime</span><span class="p">(</span><span class="n">pick</span><span class="p">[</span><span class="s2">&quot;phase_time&quot;</span><span class="p">],</span> <span class="s2">&quot;%Y-%m-</span><span class="si">%d</span><span class="s2">T%H:%M:%S.</span><span class="si">%f</span><span class="s2">&quot;</span><span class="p">)</span> <span class="o">-</span> <span class="n">event_time</span><span class="p">)</span><span class="o">.</span><span class="n">total_seconds</span><span class="p">()</span>
+            <span class="n">tmp_code</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">station_code</span><span class="si">}{</span><span class="n">channel_code</span><span class="si">}</span><span class="s2">&quot;</span>
+            <span class="n">pick_line</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">tmp_code</span><span class="si">:</span><span class="s2">&lt;7s</span><span class="si">}</span><span class="s2">   </span><span class="si">{</span><span class="n">pick_time</span><span class="si">:</span><span class="s2">6.3f</span><span class="si">}</span><span class="s2">   </span><span class="si">{</span><span class="n">phase_score</span><span class="si">:</span><span class="s2">5.4f</span><span class="si">}</span><span class="s2">   </span><span class="si">{</span><span class="n">phase_type</span><span class="si">}</span><span class="se">\n</span><span class="s2">&quot;</span>
+            <span class="c1"># output_lines.append(pick_line)</span>
+            <span class="n">output_file</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">pick_line</span><span class="p">)</span>
+
+    <span class="c1"># with open(hypodd_phase, &quot;w&quot;) as fp:</span>
+    <span class="c1">#     fp.writelines(output_lines)</span>
+
+    <span class="c1">## upload to s3 bucket</span>
+    <span class="k">try</span><span class="p">:</span>
+        <span class="kn">from</span> <span class="nn">minio</span> <span class="kn">import</span> <span class="n">Minio</span>
+
+        <span class="n">minioClient</span> <span class="o">=</span> <span class="n">Minio</span><span class="p">(</span><span class="n">s3_url</span><span class="p">,</span> <span class="n">access_key</span><span class="o">=</span><span class="s2">&quot;minio&quot;</span><span class="p">,</span> <span class="n">secret_key</span><span class="o">=</span><span class="s2">&quot;minio123&quot;</span><span class="p">,</span> <span class="n">secure</span><span class="o">=</span><span class="n">secure</span><span class="p">)</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">minioClient</span><span class="o">.</span><span class="n">bucket_exists</span><span class="p">(</span><span class="n">bucket_name</span><span class="p">):</span>
+            <span class="n">minioClient</span><span class="o">.</span><span class="n">make_bucket</span><span class="p">(</span><span class="n">bucket_name</span><span class="p">)</span>
+
+        <span class="n">minioClient</span><span class="o">.</span><span class="n">fput_object</span><span class="p">(</span>
+            <span class="n">bucket_name</span><span class="p">,</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;region&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">/hypodd/phase_</span><span class="si">{</span><span class="n">node_i</span><span class="si">:</span><span class="s2">03d</span><span class="si">}</span><span class="s2">.pha&quot;</span><span class="p">,</span>
+            <span class="n">hypodd_phase</span><span class="p">,</span>
+        <span class="p">)</span>
+
+    <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">err</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;ERROR: can not access minio service! </span><span class="se">\n</span><span class="si">{</span><span class="n">err</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+        <span class="k">pass</span>
+
+    <span class="k">return</span> <span class="n">hypodd_phase</span>
+</pre></div>
+<div id="cell-30" class="clipboard-copy-txt">def convert_phase(
+    node_i: int,
+    config_json: InputPath("json"),
+    picks_csv: InputPath(str),
+    catalog_csv: InputPath(str),
+    hypodd_phase: OutputPath(str),
+    data_path: str = "./",
+    bucket_name: str = "catalogs",
+    s3_url: str = "minio-service:9000",
+    secure: bool = True,
+):
+
+    import json
+    import os
+    from datetime import datetime
+
+    import pandas as pd
+    from tqdm import tqdm
+
+    with open(config_json, "r") as fp:
+        config = json.load(fp)
+    hypodd_path = os.path.join(data_path, "hypodd")
+    if not os.path.exists(hypodd_path):
+        os.mkdir(hypodd_path)
+
+    picks = pd.read_csv(picks_csv)
+    events = pd.read_csv(catalog_csv)
+
+    if "MAXEVENT" in config["hypodd"]:
+        MAXEVENT = config["hypodd"]["MAXEVENT"]
+    else:
+        MAXEVENT = 1e4  ## segment by time
+    MAXEVENT = len(events) // ((len(events) - 1) // MAXEVENT + 1) + 1
+    num_parallel = int((len(events) - 1) // MAXEVENT + 1)
+
+    events.sort_values("time", inplace=True)
+    events = events.iloc[node_i::num_parallel]
+    picks = picks.loc[picks["event_index"].isin(events["event_index"])]
+    # output_lines = []
+    output_file = open(hypodd_phase, "w")
+
+    picks_by_event = picks.groupby("event_index").groups
+    # for i in tqdm(range(node_i, len(events), num_parallel)):
+    #     event = events.iloc[i]
+    for i, event in events.iterrows():
+        event_time = datetime.strptime(event["time"], "%Y-%m-%dT%H:%M:%S.%f")
+        lat = event["latitude"]
+        lng = event["longitude"]
+        dep = event["depth(m)"] / 1e3
+        mag = event["magnitude"]
+        EH = 0
+        EZ = 0
+        RMS = event["sigma_time"]
+
+        year, month, day, hour, min, sec = (
+            event_time.year,
+            event_time.month,
+            event_time.day,
+            event_time.hour,
+            event_time.minute,
+            float(event_time.strftime("%S.%f")),
+        )
+        event_line = f"# {year:4d} {month:2d} {day:2d} {hour:2d} {min:2d} {sec:5.2f}  {lat:7.4f} {lng:9.4f}   {dep:5.2f} {mag:5.2f} {EH:5.2f} {EZ:5.2f} {RMS:5.2f} {event['event_index']:9d}\n"
+
+        # output_lines.append(event_line)
+        output_file.write(event_line)
+
+        picks_idx = picks_by_event[event["event_index"]]
+        for j in picks_idx:
+            # pick = picks.iloc[j]
+            pick = picks.loc[j]
+            network_code, station_code, comp_code, channel_code = pick["station_id"].split(".")
+            phase_type = pick["phase_type"].upper()
+            phase_score = pick["phase_score"]
+            pick_time = (datetime.strptime(pick["phase_time"], "%Y-%m-%dT%H:%M:%S.%f") - event_time).total_seconds()
+            tmp_code = f"{station_code}{channel_code}"
+            pick_line = f"{tmp_code:<7s}   {pick_time:6.3f}   {phase_score:5.4f}   {phase_type}\n"
+            # output_lines.append(pick_line)
+            output_file.write(pick_line)
+
+    # with open(hypodd_phase, "w") as fp:
+    #     fp.writelines(output_lines)
+
+    ## upload to s3 bucket
+    try:
+        from minio import Minio
+
+        minioClient = Minio(s3_url, access_key="minio", secret_key="minio123", secure=secure)
+        if not minioClient.bucket_exists(bucket_name):
+            minioClient.make_bucket(bucket_name)
+
+        minioClient.fput_object(
+            bucket_name,
+            f"{config['region']}/hypodd/phase_{node_i:03d}.pha",
+            hypodd_phase,
+        )
+
+    except Exception as err:
+        print(f"ERROR: can not access minio service! \n{err}")
+        pass
+
+    return hypodd_phase</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[52]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-31">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">if</span> <span class="n">run_local</span><span class="p">:</span>
+    <span class="k">for</span> <span class="n">node_i</span> <span class="ow">in</span> <span class="n">nodes</span><span class="p">:</span>
+        <span class="n">convert_phase</span><span class="p">(</span>
+            <span class="n">node_i</span><span class="p">,</span>
+            <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;config.json&quot;</span><span class="p">),</span>
+            <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;gamma_picks.csv&quot;</span><span class="p">),</span>
+            <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;gamma_catalog.csv&quot;</span><span class="p">),</span>
+            <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;hypodd/hypodd_phase.pha&quot;</span><span class="p">),</span>
+            <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;&quot;</span><span class="p">),</span>
+        <span class="p">)</span>
+</pre></div>
+<div id="cell-31" class="clipboard-copy-txt">if run_local:
+    for node_i in nodes:
+        convert_phase(
+            node_i,
+            root_dir("config.json"),
+            root_dir("gamma_picks.csv"),
+            root_dir("gamma_catalog.csv"),
+            root_dir("hypodd/hypodd_phase.pha"),
+            root_dir(""),
+        )</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
+<pre>ERROR: can not access minio service! 
+No module named &#39;minio&#39;
+</pre>
+</div>
+</div>
+
+</div>
+
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[53]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-32">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">convert_phase_op</span> <span class="o">=</span> <span class="n">comp</span><span class="o">.</span><span class="n">func_to_container_op</span><span class="p">(</span>
+    <span class="n">convert_phase</span><span class="p">,</span>
+    <span class="n">base_image</span><span class="o">=</span><span class="s2">&quot;python:3.8&quot;</span><span class="p">,</span>
+    <span class="n">packages_to_install</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;pandas&quot;</span><span class="p">,</span> <span class="s2">&quot;tqdm&quot;</span><span class="p">,</span> <span class="s2">&quot;minio&quot;</span><span class="p">],</span>
+<span class="p">)</span>
+</pre></div>
+<div id="cell-32" class="clipboard-copy-txt">convert_phase_op = comp.func_to_container_op(
+    convert_phase,
+    base_image="python:3.8",
+    packages_to_install=["pandas", "tqdm", "minio"],
+)</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h3 id="73-run-ph2dt-to-calculate-differential-time-between-phases">7.3. Run ph2dt to calculate differential time between phases<a class="anchor-link" href="#73-run-ph2dt-to-calculate-differential-time-between-phases">&#182;</a></h3>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[56]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-33">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">def</span> <span class="nf">ph2dt</span><span class="p">(</span>
+    <span class="n">node_i</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
+    <span class="n">config_json</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="s2">&quot;json&quot;</span><span class="p">),</span>
+    <span class="n">hypodd_phase</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span>
+    <span class="n">station_dat</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span>
+    <span class="n">ct_file</span><span class="p">:</span> <span class="n">OutputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span>
+    <span class="n">hypodd_event</span><span class="p">:</span> <span class="n">OutputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span>
+    <span class="n">data_path</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;./&quot;</span><span class="p">,</span>
+    <span class="n">bucket_name</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;catalogs&quot;</span><span class="p">,</span>
+    <span class="n">s3_url</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;minio-service:9000&quot;</span><span class="p">,</span>
+    <span class="n">secure</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+<span class="p">):</span>
+
+    <span class="kn">import</span> <span class="nn">json</span>
+    <span class="kn">import</span> <span class="nn">os</span>
+    <span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">datetime</span>
+
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">config_json</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">config</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">fp</span><span class="p">)</span>
+
+    <span class="n">hypodd_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">data_path</span><span class="p">,</span> <span class="s2">&quot;hypodd&quot;</span><span class="p">)</span>
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">hypodd_path</span><span class="p">):</span>
+        <span class="n">os</span><span class="o">.</span><span class="n">mkdir</span><span class="p">(</span><span class="n">hypodd_path</span><span class="p">)</span>
+
+    <span class="c1"># try:</span>
+    <span class="c1">#     minioClient = Minio(s3_url, access_key=&#39;minio&#39;, secret_key=&#39;minio123&#39;, secure=secure)</span>
+    <span class="c1">#     minioClient.fget_object(bucket_name, f&quot;{config[&#39;region&#39;]}/hypodd_{node_i:03d}.pha&quot;, os.path.join(hypodd_path, f&quot;hypodd_{node_i:03d}.pha&quot;))</span>
+    <span class="c1"># except Exception as err:</span>
+    <span class="c1">#     print(f&quot;ERROR: can not access minio service! \n{err}&quot;)</span>
+    <span class="c1">#     pass</span>
+
+    <span class="n">ph2dt</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;* ph2dt.inp - input control file for program ph2dt</span>
+<span class="s2">* Input station file:</span>
+<span class="s2">stations.dat</span>
+<span class="s2">* Input phase file:</span>
+<span class="s2">hypodd.pha</span>
+<span class="s2">*MINWGHT: min. pick weight allowed [0]</span>
+<span class="s2">*MAXDIST: max. distance in km between event pair and stations [200]</span>
+<span class="s2">*MAXSEP: max. hypocentral separation in km [10]</span>
+<span class="s2">*MAXNGH: max. number of neighbors per event [10]</span>
+<span class="s2">*MINLNK: min. number of links required to define a neighbor [8]</span>
+<span class="s2">*MINOBS: min. number of links per pair saved [8]</span>
+<span class="s2">*MAXOBS: max. number of links per pair saved [20]</span>
+<span class="s2">*MINWGHT MAXDIST MAXSEP MAXNGH MINLNK MINOBS MAXOBS</span>
+<span class="s2">   0      120     10     50     8      8     100</span>
+<span class="s2">&quot;&quot;&quot;</span>
+
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">hypodd_path</span><span class="p">,</span> <span class="s2">&quot;ph2dt.inp&quot;</span><span class="p">),</span> <span class="s2">&quot;w&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">fp</span><span class="o">.</span><span class="n">writelines</span><span class="p">(</span><span class="n">ph2dt</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">copy_file</span><span class="p">(</span><span class="n">fp_from</span><span class="p">,</span> <span class="n">fp_to</span><span class="p">):</span>
+        <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">fp_from</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+            <span class="n">lines</span> <span class="o">=</span> <span class="n">fp</span><span class="o">.</span><span class="n">readlines</span><span class="p">()</span>
+        <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">fp_to</span><span class="p">,</span> <span class="s2">&quot;w&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+            <span class="n">fp</span><span class="o">.</span><span class="n">writelines</span><span class="p">(</span><span class="n">lines</span><span class="p">)</span>
+
+    <span class="n">copy_file</span><span class="p">(</span><span class="n">hypodd_phase</span><span class="p">,</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">hypodd_path</span><span class="p">,</span> <span class="s2">&quot;hypodd.pha&quot;</span><span class="p">))</span>
+    <span class="n">copy_file</span><span class="p">(</span><span class="n">station_dat</span><span class="p">,</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">hypodd_path</span><span class="p">,</span> <span class="s2">&quot;stations.dat&quot;</span><span class="p">))</span>
+
+    <span class="n">PH2DT_CMD</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;cd </span><span class="si">{</span><span class="n">hypodd_path</span><span class="si">}</span><span class="s2"> &amp;&amp; ./ph2dt ph2dt.inp&quot;</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">PH2DT_CMD</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="n">PH2DT_CMD</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="p">(</span><span class="s2">&quot;</span><span class="si">{PH2DT_CMD}</span><span class="s2">&quot;</span> <span class="o">+</span> <span class="s2">&quot; failed!&quot;</span><span class="p">)</span>
+
+    <span class="n">copy_file</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">hypodd_path</span><span class="p">,</span> <span class="s2">&quot;dt.ct&quot;</span><span class="p">),</span> <span class="n">ct_file</span><span class="p">)</span>
+    <span class="n">copy_file</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">hypodd_path</span><span class="p">,</span> <span class="s2">&quot;event.sel&quot;</span><span class="p">),</span> <span class="n">hypodd_event</span><span class="p">)</span>
+
+    <span class="c1">## upload to s3 bucket</span>
+    <span class="k">try</span><span class="p">:</span>
+        <span class="kn">from</span> <span class="nn">minio</span> <span class="kn">import</span> <span class="n">Minio</span>
+
+        <span class="n">minioClient</span> <span class="o">=</span> <span class="n">Minio</span><span class="p">(</span><span class="n">s3_url</span><span class="p">,</span> <span class="n">access_key</span><span class="o">=</span><span class="s2">&quot;minio&quot;</span><span class="p">,</span> <span class="n">secret_key</span><span class="o">=</span><span class="s2">&quot;minio123&quot;</span><span class="p">,</span> <span class="n">secure</span><span class="o">=</span><span class="n">secure</span><span class="p">)</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">minioClient</span><span class="o">.</span><span class="n">bucket_exists</span><span class="p">(</span><span class="n">bucket_name</span><span class="p">):</span>
+            <span class="n">minioClient</span><span class="o">.</span><span class="n">make_bucket</span><span class="p">(</span><span class="n">bucket_name</span><span class="p">)</span>
+
+        <span class="n">minioClient</span><span class="o">.</span><span class="n">fput_object</span><span class="p">(</span>
+            <span class="n">bucket_name</span><span class="p">,</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;region&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">/hypodd/dt_</span><span class="si">{</span><span class="n">node_i</span><span class="si">:</span><span class="s2">03d</span><span class="si">}</span><span class="s2">.ct&quot;</span><span class="p">,</span>
+            <span class="n">ct_file</span><span class="p">,</span>
+        <span class="p">)</span>
+
+        <span class="n">minioClient</span><span class="o">.</span><span class="n">fput_object</span><span class="p">(</span>
+            <span class="n">bucket_name</span><span class="p">,</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;region&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">/hypodd/event_</span><span class="si">{</span><span class="n">node_i</span><span class="si">:</span><span class="s2">03d</span><span class="si">}</span><span class="s2">.sel&quot;</span><span class="p">,</span>
+            <span class="n">hypodd_event</span><span class="p">,</span>
+        <span class="p">)</span>
+
+    <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">err</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;ERROR: can not access minio service! </span><span class="se">\n</span><span class="si">{</span><span class="n">err</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+        <span class="k">pass</span>
+
+    <span class="c1"># try:</span>
+    <span class="c1">#     minioClient.fput_object(</span>
+    <span class="c1">#         bucket_name,</span>
+    <span class="c1">#         f&quot;{config[&#39;region&#39;]}/dt_{node_i:03d}.ct&quot;,</span>
+    <span class="c1">#         f&quot;{os.path.join(hypodd_path, &#39;dt.ct&#39;)}&quot;,</span>
+    <span class="c1">#     )</span>
+    <span class="c1">#     minioClient.fput_object(</span>
+    <span class="c1">#         bucket_name,</span>
+    <span class="c1">#         f&quot;{config[&#39;region&#39;]}/event_{node_i:03d}.dat&quot;,</span>
+    <span class="c1">#         f&quot;{os.path.join(hypodd_path, &#39;event.dat&#39;)}&quot;,</span>
+    <span class="c1">#     )</span>
+    <span class="c1">#     minioClient.fput_object(</span>
+    <span class="c1">#         bucket_name,</span>
+    <span class="c1">#         f&quot;{config[&#39;region&#39;]}/event_{node_i:03d}.sel&quot;,</span>
+    <span class="c1">#         f&quot;{os.path.join(hypodd_path, &#39;event.sel&#39;)}&quot;,</span>
+    <span class="c1">#     )</span>
+    <span class="c1"># except Exception as err:</span>
+    <span class="c1">#     print(f&quot;ERROR: can not access minio service! \n{err}&quot;)</span>
+    <span class="c1">#     pass</span>
+
+    <span class="k">return</span> <span class="mi">0</span>
+</pre></div>
+<div id="cell-33" class="clipboard-copy-txt">def ph2dt(
+    node_i: int,
+    config_json: InputPath("json"),
+    hypodd_phase: InputPath(str),
+    station_dat: InputPath(str),
+    ct_file: OutputPath(str),
+    hypodd_event: OutputPath(str),
+    data_path: str = "./",
+    bucket_name: str = "catalogs",
+    s3_url: str = "minio-service:9000",
+    secure: bool = True,
+):
+
+    import json
+    import os
+    from datetime import datetime
+
+    with open(config_json, "r") as fp:
+        config = json.load(fp)
+
+    hypodd_path = os.path.join(data_path, "hypodd")
+    if not os.path.exists(hypodd_path):
+        os.mkdir(hypodd_path)
+
+    # try:
+    #     minioClient = Minio(s3_url, access_key='minio', secret_key='minio123', secure=secure)
+    #     minioClient.fget_object(bucket_name, f"{config['region']}/hypodd_{node_i:03d}.pha", os.path.join(hypodd_path, f"hypodd_{node_i:03d}.pha"))
+    # except Exception as err:
+    #     print(f"ERROR: can not access minio service! \n{err}")
+    #     pass
+
+    ph2dt = """* ph2dt.inp - input control file for program ph2dt
+* Input station file:
+stations.dat
+* Input phase file:
+hypodd.pha
+*MINWGHT: min. pick weight allowed [0]
+*MAXDIST: max. distance in km between event pair and stations [200]
+*MAXSEP: max. hypocentral separation in km [10]
+*MAXNGH: max. number of neighbors per event [10]
+*MINLNK: min. number of links required to define a neighbor [8]
+*MINOBS: min. number of links per pair saved [8]
+*MAXOBS: max. number of links per pair saved [20]
+*MINWGHT MAXDIST MAXSEP MAXNGH MINLNK MINOBS MAXOBS
+   0      120     10     50     8      8     100
+"""
+
+    with open(os.path.join(hypodd_path, "ph2dt.inp"), "w") as fp:
+        fp.writelines(ph2dt)
+
+    def copy_file(fp_from, fp_to):
+        with open(fp_from, "r") as fp:
+            lines = fp.readlines()
+        with open(fp_to, "w") as fp:
+            fp.writelines(lines)
+
+    copy_file(hypodd_phase, os.path.join(hypodd_path, "hypodd.pha"))
+    copy_file(station_dat, os.path.join(hypodd_path, "stations.dat"))
+
+    PH2DT_CMD = f"cd {hypodd_path} && ./ph2dt ph2dt.inp"
+    print(PH2DT_CMD)
+    if os.system(PH2DT_CMD) != 0:
+        raise ("{PH2DT_CMD}" + " failed!")
+
+    copy_file(os.path.join(hypodd_path, "dt.ct"), ct_file)
+    copy_file(os.path.join(hypodd_path, "event.sel"), hypodd_event)
+
+    ## upload to s3 bucket
+    try:
+        from minio import Minio
+
+        minioClient = Minio(s3_url, access_key="minio", secret_key="minio123", secure=secure)
+        if not minioClient.bucket_exists(bucket_name):
+            minioClient.make_bucket(bucket_name)
+
+        minioClient.fput_object(
+            bucket_name,
+            f"{config['region']}/hypodd/dt_{node_i:03d}.ct",
+            ct_file,
+        )
+
+        minioClient.fput_object(
+            bucket_name,
+            f"{config['region']}/hypodd/event_{node_i:03d}.sel",
+            hypodd_event,
+        )
+
+    except Exception as err:
+        print(f"ERROR: can not access minio service! \n{err}")
+        pass
+
+    # try:
+    #     minioClient.fput_object(
+    #         bucket_name,
+    #         f"{config['region']}/dt_{node_i:03d}.ct",
+    #         f"{os.path.join(hypodd_path, 'dt.ct')}",
+    #     )
+    #     minioClient.fput_object(
+    #         bucket_name,
+    #         f"{config['region']}/event_{node_i:03d}.dat",
+    #         f"{os.path.join(hypodd_path, 'event.dat')}",
+    #     )
+    #     minioClient.fput_object(
+    #         bucket_name,
+    #         f"{config['region']}/event_{node_i:03d}.sel",
+    #         f"{os.path.join(hypodd_path, 'event.sel')}",
+    #     )
+    # except Exception as err:
+    #     print(f"ERROR: can not access minio service! \n{err}")
+    #     pass
+
+    return 0</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[57]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-34">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">if</span> <span class="n">run_local</span><span class="p">:</span>
+    <span class="n">ph2dt</span><span class="p">(</span>
+        <span class="mi">0</span><span class="p">,</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;config.json&quot;</span><span class="p">),</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;hypodd/hypodd_phase.pha&quot;</span><span class="p">),</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;hypodd/stations.dat&quot;</span><span class="p">),</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;hypodd/dt.ct&quot;</span><span class="p">),</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;hypodd/event.sel&quot;</span><span class="p">),</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;&quot;</span><span class="p">),</span>
+    <span class="p">)</span>
+</pre></div>
+<div id="cell-34" class="clipboard-copy-txt">if run_local:
+    ph2dt(
+        0,
+        root_dir("config.json"),
+        root_dir("hypodd/hypodd_phase.pha"),
+        root_dir("hypodd/stations.dat"),
+        root_dir("hypodd/dt.ct"),
+        root_dir("hypodd/event.sel"),
+        root_dir(""),
+    )</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
+<pre>cd Demo/hypodd &amp;&amp; ./ph2dt ph2dt.inp
+starting ph2dt (v1.3 - 08/2010)...                          
+
+reading data ...
+ &gt; stations =           46
+ &gt; events total =          363
+ &gt; events selected =          363
+ &gt; phases =        16187
+forming dtimes...
+ &gt; P-phase pairs total =       145432
+ &gt; S-phase pairs total =       173413
+ &gt; outliers =         3467  (           1 %)
+ &gt; phases at stations not in station list =            0
+ &gt; phases at distances larger than MAXDIST =            0
+ &gt; P-phase pairs selected =       138381  (          95 %)
+ &gt; S-phase pairs selected =       166806  (          96 %)
+ &gt; weakly linked events =           86  (          23 %)
+ &gt; linked event pairs =         9869
+ &gt; average links per pair =           30
+ &gt; average offset (km) betw. linked events =    4.81116629    
+ &gt; average offset (km) betw. strongly linked events =    4.81116629    
+ &gt; maximum offset (km) betw. strongly linked events =    9.99739456    
+
+Done.                      
+
+Output files: dt.ct; event.dat; event.sel; ph2dt.log
+ph2dt parameters were: 
+ (minwght,maxdist,maxsep,maxngh,minlnk,minobs,maxobs)
+   0.00000000       120.000000       10.0000000              50           8           8         100
+ERROR: can not access minio service! 
+No module named &#39;minio&#39;
+</pre>
+</div>
+</div>
+
+</div>
+
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[58]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-35">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">ph2dt_op</span> <span class="o">=</span> <span class="n">comp</span><span class="o">.</span><span class="n">func_to_container_op</span><span class="p">(</span><span class="n">ph2dt</span><span class="p">,</span> <span class="n">base_image</span><span class="o">=</span><span class="s2">&quot;zhuwq0/hypodd-api:1.0&quot;</span><span class="p">)</span>
+</pre></div>
+<div id="cell-35" class="clipboard-copy-txt">ph2dt_op = comp.func_to_container_op(ph2dt, base_image="zhuwq0/hypodd-api:1.0")</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h3 id="74-run-hypodd-re-location">7.4. Run HypoDD re-location<a class="anchor-link" href="#74-run-hypodd-re-location">&#182;</a></h3>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[59]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-36">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">def</span> <span class="nf">hypodd_ct</span><span class="p">(</span>
+    <span class="n">node_i</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
+    <span class="n">config_json</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="s2">&quot;json&quot;</span><span class="p">),</span>
+    <span class="n">ct_file</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span>
+    <span class="n">event</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span>
+    <span class="n">station</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span>
+    <span class="n">catalog_txt</span><span class="p">:</span> <span class="n">OutputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span>
+    <span class="n">data_path</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;./&quot;</span><span class="p">,</span>
+    <span class="n">bucket_name</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;catalogs&quot;</span><span class="p">,</span>
+    <span class="n">s3_url</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;minio-service:9000&quot;</span><span class="p">,</span>
+    <span class="n">secure</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+<span class="p">):</span>
+    <span class="kn">import</span> <span class="nn">json</span>
+    <span class="kn">import</span> <span class="nn">os</span>
+    <span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">datetime</span>
+
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">config_json</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">config</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">fp</span><span class="p">)</span>
+    <span class="n">hypodd_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">data_path</span><span class="p">,</span> <span class="s2">&quot;hypodd&quot;</span><span class="p">)</span>
+
+    <span class="n">ct_inp</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;* RELOC.INP:</span>
+<span class="s2">*--- input file selection</span>
+<span class="s2">* cross correlation diff times:</span>
+
+<span class="s2">*</span>
+<span class="s2">*catalog P diff times:</span>
+<span class="s2">dt.ct</span>
+<span class="s2">*</span>
+<span class="s2">* event file:</span>
+<span class="s2">event.sel</span>
+<span class="s2">*</span>
+<span class="s2">* station file:</span>
+<span class="s2">stations.dat</span>
+<span class="s2">*</span>
+<span class="s2">*--- output file selection</span>
+<span class="s2">* original locations:</span>
+<span class="s2">hypodd.loc</span>
+<span class="s2">* relocations:</span>
+<span class="s2">hypodd.reloc</span>
+<span class="s2">* station information:</span>
+<span class="s2">hypodd.sta</span>
+<span class="s2">* residual information:</span>
+<span class="s2">hypodd.res</span>
+<span class="s2">* source paramater information:</span>
+<span class="s2">hypodd.src</span>
+<span class="s2">*</span>
+<span class="s2">*--- data type selection: </span>
+<span class="s2">* IDAT:  0 = synthetics; 1= cross corr; 2= catalog; 3= cross &amp; cat </span>
+<span class="s2">* IPHA: 1= P; 2= S; 3= P&amp;S</span>
+<span class="s2">* DIST:max dist [km] between cluster centroid and station </span>
+<span class="s2">* IDAT   IPHA   DIST</span>
+<span class="s2">    2     3     120</span>
+<span class="s2">*</span>
+<span class="s2">*--- event clustering:</span>
+<span class="s2">* OBSCC:    min # of obs/pair for crosstime data (0= no clustering)</span>
+<span class="s2">* OBSCT:    min # of obs/pair for network data (0= no clustering)</span>
+<span class="s2">* OBSCC  OBSCT    </span>
+<span class="s2">     0     8        </span>
+<span class="s2">*</span>
+<span class="s2">*--- solution control:</span>
+<span class="s2">* ISTART:  	1 = from single source; 2 = from network sources</span>
+<span class="s2">* ISOLV:	1 = SVD, 2=lsqr</span>
+<span class="s2">* NSET:      	number of sets of iteration with specifications following</span>
+<span class="s2">*  ISTART  ISOLV  NSET</span>
+<span class="s2">    2        2      4</span>
+<span class="s2">*</span>
+<span class="s2">*--- data weighting and re-weighting: </span>
+<span class="s2">* NITER: 		last iteration to used the following weights</span>
+<span class="s2">* WTCCP, WTCCS:		weight cross P, S </span>
+<span class="s2">* WTCTP, WTCTS:		weight catalog P, S </span>
+<span class="s2">* WRCC, WRCT:		residual threshold in sec for cross, catalog data </span>
+<span class="s2">* WDCC, WDCT:  		max dist [km] between cross, catalog linked pairs</span>
+<span class="s2">* DAMP:    		damping (for lsqr only) </span>
+<span class="s2">*       ---  CROSS DATA ----- ----CATALOG DATA ----</span>
+<span class="s2">* NITER WTCCP WTCCS WRCC WDCC WTCTP WTCTS WRCT WDCT DAMP</span>
+<span class="s2">   4     -9     -9   -9    -9   1     1      8   -9  70 </span>
+<span class="s2">   4     -9     -9   -9    -9   1     1      6    4  70 </span>
+<span class="s2">   4     -9     -9   -9    -9   1    0.8     4    2  70 </span>
+<span class="s2">   4     -9     -9   -9    -9   1    0.8     3    2  70 </span>
+<span class="s2">*</span>
+<span class="s2">*--- 1D model:</span>
+<span class="s2">* NLAY:		number of model layers  </span>
+<span class="s2">* RATIO:	vp/vs ratio </span>
+<span class="s2">* TOP:		depths of top of layer (km) </span>
+<span class="s2">* VEL: 		layer velocities (km/s)</span>
+<span class="s2">* NLAY  RATIO </span>
+<span class="s2">   12     1.82</span>
+<span class="s2">* TOP </span>
+<span class="s2">0.0 1.0 3.0 5.0 7.0 9.0 11.0 13.0 17.0 21.0 31.00 31.10</span>
+<span class="s2">* VEL</span>
+<span class="s2">5.30 5.65 5.93 6.20 6.20 6.20 6.20 6.20 6.20 6.20 7.50 8.11</span>
+<span class="s2">*</span>
+<span class="s2">*--- event selection:</span>
+<span class="s2">* CID: 	cluster to be relocated (0 = all)</span>
+<span class="s2">* ID:	cuspids of event to be relocated (8 per line)</span>
+<span class="s2">* CID    </span>
+<span class="s2">    0      </span>
+<span class="s2">* ID</span>
+<span class="s2">&quot;&quot;&quot;</span>
+
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">hypodd_path</span><span class="p">,</span> <span class="s2">&quot;ct.inp&quot;</span><span class="p">),</span> <span class="s2">&quot;w&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">fp</span><span class="o">.</span><span class="n">writelines</span><span class="p">(</span><span class="n">ct_inp</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">copy_file</span><span class="p">(</span><span class="n">fp_from</span><span class="p">,</span> <span class="n">fp_to</span><span class="p">):</span>
+        <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">fp_from</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+            <span class="n">lines</span> <span class="o">=</span> <span class="n">fp</span><span class="o">.</span><span class="n">readlines</span><span class="p">()</span>
+        <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">fp_to</span><span class="p">,</span> <span class="s2">&quot;w&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+            <span class="n">fp</span><span class="o">.</span><span class="n">writelines</span><span class="p">(</span><span class="n">lines</span><span class="p">)</span>
+
+    <span class="n">copy_file</span><span class="p">(</span><span class="n">ct_file</span><span class="p">,</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">hypodd_path</span><span class="p">,</span> <span class="s2">&quot;dt.ct&quot;</span><span class="p">))</span>
+    <span class="n">copy_file</span><span class="p">(</span><span class="n">event</span><span class="p">,</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">hypodd_path</span><span class="p">,</span> <span class="s2">&quot;event.sel&quot;</span><span class="p">))</span>
+    <span class="n">copy_file</span><span class="p">(</span><span class="n">station</span><span class="p">,</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">hypodd_path</span><span class="p">,</span> <span class="s2">&quot;stations.dat&quot;</span><span class="p">))</span>
+
+    <span class="c1"># os.system(f&quot;cat {ct_file}&quot;)</span>
+    <span class="c1"># os.system(f&quot;cat {event}&quot;)</span>
+    <span class="c1"># os.system(f&quot;cat {station}&quot;)</span>
+
+    <span class="n">HYPODD_CMD</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;cd </span><span class="si">{</span><span class="n">hypodd_path</span><span class="si">}</span><span class="s2"> &amp;&amp; ./hypoDD ct.inp&quot;</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">HYPODD_CMD</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="n">HYPODD_CMD</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="p">(</span><span class="s2">&quot;</span><span class="si">{HYPODD_CMD}</span><span class="s2">&quot;</span> <span class="o">+</span> <span class="s2">&quot; failed!&quot;</span><span class="p">)</span>
+
+    <span class="n">copy_file</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">hypodd_path</span><span class="p">,</span> <span class="s2">&quot;hypodd.reloc&quot;</span><span class="p">),</span> <span class="n">catalog_txt</span><span class="p">)</span>
+
+    <span class="k">try</span><span class="p">:</span>
+        <span class="kn">from</span> <span class="nn">minio</span> <span class="kn">import</span> <span class="n">Minio</span>
+
+        <span class="n">minioClient</span> <span class="o">=</span> <span class="n">Minio</span><span class="p">(</span><span class="n">s3_url</span><span class="p">,</span> <span class="n">access_key</span><span class="o">=</span><span class="s2">&quot;minio&quot;</span><span class="p">,</span> <span class="n">secret_key</span><span class="o">=</span><span class="s2">&quot;minio123&quot;</span><span class="p">,</span> <span class="n">secure</span><span class="o">=</span><span class="n">secure</span><span class="p">)</span>
+        <span class="n">minioClient</span><span class="o">.</span><span class="n">fput_object</span><span class="p">(</span>
+            <span class="n">bucket_name</span><span class="p">,</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;region&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">/hypodd/hypodd_ct_</span><span class="si">{</span><span class="n">node_i</span><span class="si">:</span><span class="s2">03d</span><span class="si">}</span><span class="s2">.reloc&quot;</span><span class="p">,</span>
+            <span class="n">catalog_txt</span><span class="p">,</span>
+        <span class="p">)</span>
+    <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">err</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;ERROR: can not access minio service! </span><span class="se">\n</span><span class="si">{</span><span class="n">err</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+        <span class="k">pass</span>
+</pre></div>
+<div id="cell-36" class="clipboard-copy-txt">def hypodd_ct(
+    node_i: int,
+    config_json: InputPath("json"),
+    ct_file: InputPath(str),
+    event: InputPath(str),
+    station: InputPath(str),
+    catalog_txt: OutputPath(str),
+    data_path: str = "./",
+    bucket_name: str = "catalogs",
+    s3_url: str = "minio-service:9000",
+    secure: bool = True,
+):
+    import json
+    import os
+    from datetime import datetime
+
+    with open(config_json, "r") as fp:
+        config = json.load(fp)
+    hypodd_path = os.path.join(data_path, "hypodd")
+
+    ct_inp = """* RELOC.INP:
+*--- input file selection
+* cross correlation diff times:
+
+*
+*catalog P diff times:
+dt.ct
+*
+* event file:
+event.sel
+*
+* station file:
+stations.dat
+*
+*--- output file selection
+* original locations:
+hypodd.loc
+* relocations:
+hypodd.reloc
+* station information:
+hypodd.sta
+* residual information:
+hypodd.res
+* source paramater information:
+hypodd.src
+*
+*--- data type selection: 
+* IDAT:  0 = synthetics; 1= cross corr; 2= catalog; 3= cross & cat 
+* IPHA: 1= P; 2= S; 3= P&S
+* DIST:max dist [km] between cluster centroid and station 
+* IDAT   IPHA   DIST
+    2     3     120
+*
+*--- event clustering:
+* OBSCC:    min # of obs/pair for crosstime data (0= no clustering)
+* OBSCT:    min # of obs/pair for network data (0= no clustering)
+* OBSCC  OBSCT    
+     0     8        
+*
+*--- solution control:
+* ISTART:  	1 = from single source; 2 = from network sources
+* ISOLV:	1 = SVD, 2=lsqr
+* NSET:      	number of sets of iteration with specifications following
+*  ISTART  ISOLV  NSET
+    2        2      4
+*
+*--- data weighting and re-weighting: 
+* NITER: 		last iteration to used the following weights
+* WTCCP, WTCCS:		weight cross P, S 
+* WTCTP, WTCTS:		weight catalog P, S 
+* WRCC, WRCT:		residual threshold in sec for cross, catalog data 
+* WDCC, WDCT:  		max dist [km] between cross, catalog linked pairs
+* DAMP:    		damping (for lsqr only) 
+*       ---  CROSS DATA ----- ----CATALOG DATA ----
+* NITER WTCCP WTCCS WRCC WDCC WTCTP WTCTS WRCT WDCT DAMP
+   4     -9     -9   -9    -9   1     1      8   -9  70 
+   4     -9     -9   -9    -9   1     1      6    4  70 
+   4     -9     -9   -9    -9   1    0.8     4    2  70 
+   4     -9     -9   -9    -9   1    0.8     3    2  70 
+*
+*--- 1D model:
+* NLAY:		number of model layers  
+* RATIO:	vp/vs ratio 
+* TOP:		depths of top of layer (km) 
+* VEL: 		layer velocities (km/s)
+* NLAY  RATIO 
+   12     1.82
+* TOP 
+0.0 1.0 3.0 5.0 7.0 9.0 11.0 13.0 17.0 21.0 31.00 31.10
+* VEL
+5.30 5.65 5.93 6.20 6.20 6.20 6.20 6.20 6.20 6.20 7.50 8.11
+*
+*--- event selection:
+* CID: 	cluster to be relocated (0 = all)
+* ID:	cuspids of event to be relocated (8 per line)
+* CID    
+    0      
+* ID
+"""
+
+    with open(os.path.join(hypodd_path, "ct.inp"), "w") as fp:
+        fp.writelines(ct_inp)
+
+    def copy_file(fp_from, fp_to):
+        with open(fp_from, "r") as fp:
+            lines = fp.readlines()
+        with open(fp_to, "w") as fp:
+            fp.writelines(lines)
+
+    copy_file(ct_file, os.path.join(hypodd_path, "dt.ct"))
+    copy_file(event, os.path.join(hypodd_path, "event.sel"))
+    copy_file(station, os.path.join(hypodd_path, "stations.dat"))
+
+    # os.system(f"cat {ct_file}")
+    # os.system(f"cat {event}")
+    # os.system(f"cat {station}")
+
+    HYPODD_CMD = f"cd {hypodd_path} && ./hypoDD ct.inp"
+    print(HYPODD_CMD)
+    if os.system(HYPODD_CMD) != 0:
+        raise ("{HYPODD_CMD}" + " failed!")
+
+    copy_file(os.path.join(hypodd_path, "hypodd.reloc"), catalog_txt)
+
+    try:
+        from minio import Minio
+
+        minioClient = Minio(s3_url, access_key="minio", secret_key="minio123", secure=secure)
+        minioClient.fput_object(
+            bucket_name,
+            f"{config['region']}/hypodd/hypodd_ct_{node_i:03d}.reloc",
+            catalog_txt,
+        )
+    except Exception as err:
+        print(f"ERROR: can not access minio service! \n{err}")
+        pass</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[60]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-37">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">if</span> <span class="n">run_local</span><span class="p">:</span>
+    <span class="n">hypodd_ct</span><span class="p">(</span>
+        <span class="mi">0</span><span class="p">,</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;config.json&quot;</span><span class="p">),</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;hypodd/dt.ct&quot;</span><span class="p">),</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;hypodd/event.sel&quot;</span><span class="p">),</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;hypodd/stations.dat&quot;</span><span class="p">),</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;hypodd_ct_catalog.txt&quot;</span><span class="p">),</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;&quot;</span><span class="p">),</span>
+    <span class="p">)</span>
+</pre></div>
+<div id="cell-37" class="clipboard-copy-txt">if run_local:
+    hypodd_ct(
+        0,
+        root_dir("config.json"),
+        root_dir("hypodd/dt.ct"),
+        root_dir("hypodd/event.sel"),
+        root_dir("hypodd/stations.dat"),
+        root_dir("hypodd_ct_catalog.txt"),
+        root_dir(""),
+    )</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
+<pre>cd Demo/hypodd &amp;&amp; ./hypoDD ct.inp
+starting hypoDD (v1.3 - 11/2010)...          Wed Mar 15 14:47:00 2023
+INPUT FILES:
+cross dtime data:  
+catalog dtime data: dt.ct
+events: event.sel
+stations: stations.dat
+OUTPUT FILES:
+initial locations: hypodd.loc
+relocated events: hypodd.reloc
+event pair residuals: hypodd.res
+station residuals: hypodd.sta
+source parameters: hypodd.src
+ Relocate all clusters
+ Relocate all events
+Reading data ...   Wed Mar 15 14:47:00 2023 
+# events =   363
+# stations &lt; maxdist =     46
+# catalog P dtimes =  138381
+# catalog S dtimes =  166806
+# dtimes total =   305187
+# events after dtime match =        351
+# stations =     45
+clustering ...  
+Clustered events:   351
+Isolated events:     0
+# clusters:    1
+Cluster   1:   351 events
+
+RELOCATION OF CLUSTER: 1     Wed Mar 15 14:47:01 2023
+----------------------
+Initial trial sources =   351
+
+  IT   EV  CT    RMSCT   RMSST   DX   DY   DZ   DT   OS  AQ  CND
+        %   %   ms     %    ms    m    m    m   ms    m 
+ 1    100 100  230 -21.4     0  798  660 2348  177    0   8  205
+ 2     98  99  229  -0.4     0  773  631 2302  168    0   1  202
+ 3  1  97  99  228  -0.4   410  770  629 2301  167 1128   0  202
+ 4     97  98  187 -18.0   410  407  252 2664   77 1128  13  192
+ 5     93  97  185  -1.5   410  392  230 1314   71 1128   1  189
+ 6  2  93  97  184  -0.5   383  389  229 1287   71  958   0  189
+ 7     93  96  163 -11.1   383  254  135  666   44  958   3  180
+ 8  3  92  95  162  -1.0   335  248  134  645   43  551   0  177
+ 9     92  95  152  -6.0   335  188   89  912   28  551   2  175
+10  4  91  94  151  -0.5   327  189   90  526   27  697   0  174
+11  5  87  49  135 -10.4   269  167  116  379   30  735   0  135
+12  6  86  46  115 -14.9   236   92   64  245   17  771   0  131
+13  7  85  45  109  -5.8   222   60   42  158   12  750   0  129
+14  8  85  44  106  -2.7   219   39   26   99    9  728   0  121
+15  9  79  14   76 -27.8   146   43   35   83    7  568   0   66
+16 10  78  13   64 -15.9   120   30   24   59    5  573   0   64
+17 11  78  13   60  -7.0   112   22   18   47    4  563   0   60
+18 12  77  12   57  -4.2   108   16   14   34    3  561   0   60
+19 13  77  12   47 -17.6    86   17   15   33    3  558   0   59
+20 14  77  11   43  -7.6    78   16   13   28    2  557   0   57
+21 15  77  11   41  -5.1    75   14   11   26    2  560   0   54
+22 16  77  11   40  -3.5    72   12    9   21    2  557   0   55
+
+writing out results ...
+ERROR: can not access minio service! 
+No module named &#39;minio&#39;
+</pre>
+</div>
+</div>
+
+</div>
+
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[61]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-38">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">hypodd_ct_op</span> <span class="o">=</span> <span class="n">comp</span><span class="o">.</span><span class="n">func_to_container_op</span><span class="p">(</span><span class="n">hypodd_ct</span><span class="p">,</span> <span class="n">base_image</span><span class="o">=</span><span class="s2">&quot;zhuwq0/hypodd-api:1.0&quot;</span><span class="p">)</span>
+</pre></div>
+<div id="cell-38" class="clipboard-copy-txt">hypodd_ct_op = comp.func_to_container_op(hypodd_ct, base_image="zhuwq0/hypodd-api:1.0")</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[62]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-39">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">if</span> <span class="n">run_local</span><span class="p">:</span>
+    <span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">datetime</span>
+
+    <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+    <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+
+    <span class="n">catalog_hypodd</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;hypodd/hypodd.reloc&quot;</span><span class="p">),</span>
+        <span class="n">sep</span><span class="o">=</span><span class="s2">&quot;\s+&quot;</span><span class="p">,</span>
+        <span class="n">names</span><span class="o">=</span><span class="p">[</span>
+            <span class="s2">&quot;ID&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;LAT&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;LON&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;DEPTH&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;X&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;Y&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;Z&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;EX&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;EY&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;EZ&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;YR&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;MO&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;DY&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;HR&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;MI&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;SC&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;MAG&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;NCCP&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;NCCS&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;NCTP&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;NCTS&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;RCC&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;RCT&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;CID&quot;</span><span class="p">,</span>
+        <span class="p">],</span>
+    <span class="p">)</span>
+    <span class="n">catalog_hypodd</span><span class="p">[</span><span class="s2">&quot;time&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">catalog_hypodd</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span>
+        <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">x</span><span class="p">[</span><span class="s2">&quot;YR&quot;</span><span class="p">]</span><span class="si">:</span><span class="s1">04.0f</span><span class="si">}</span><span class="s1">-</span><span class="si">{</span><span class="n">x</span><span class="p">[</span><span class="s2">&quot;MO&quot;</span><span class="p">]</span><span class="si">:</span><span class="s1">02.0f</span><span class="si">}</span><span class="s1">-</span><span class="si">{</span><span class="n">x</span><span class="p">[</span><span class="s2">&quot;DY&quot;</span><span class="p">]</span><span class="si">:</span><span class="s1">02.0f</span><span class="si">}</span><span class="s1">T</span><span class="si">{</span><span class="n">x</span><span class="p">[</span><span class="s2">&quot;HR&quot;</span><span class="p">]</span><span class="si">:</span><span class="s1">02.0f</span><span class="si">}</span><span class="s1">:</span><span class="si">{</span><span class="n">x</span><span class="p">[</span><span class="s2">&quot;MI&quot;</span><span class="p">]</span><span class="si">:</span><span class="s1">02.0f</span><span class="si">}</span><span class="s1">:</span><span class="si">{</span><span class="nb">min</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="s2">&quot;SC&quot;</span><span class="p">],</span><span class="w"> </span><span class="mf">59.999</span><span class="p">)</span><span class="si">:</span><span class="s1">05.3f</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">,</span>
+        <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+    <span class="p">)</span>
+    <span class="n">catalog_hypodd</span><span class="p">[</span><span class="s2">&quot;time&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">catalog_hypodd</span><span class="p">[</span><span class="s2">&quot;time&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">datetime</span><span class="o">.</span><span class="n">strptime</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="s2">&quot;%Y-%m-</span><span class="si">%d</span><span class="s2">T%H:%M:%S.</span><span class="si">%f</span><span class="s2">&quot;</span><span class="p">))</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">catalog_hypodd</span><span class="p">[</span><span class="s2">&quot;LON&quot;</span><span class="p">],</span> <span class="n">catalog_hypodd</span><span class="p">[</span><span class="s2">&quot;LAT&quot;</span><span class="p">],</span> <span class="s2">&quot;.&quot;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+<div id="cell-39" class="clipboard-copy-txt">if run_local:
+    from datetime import datetime
+
+    import matplotlib.pyplot as plt
+    import pandas as pd
+
+    catalog_hypodd = pd.read_csv(
+        root_dir(f"hypodd/hypodd.reloc"),
+        sep="\s+",
+        names=[
+            "ID",
+            "LAT",
+            "LON",
+            "DEPTH",
+            "X",
+            "Y",
+            "Z",
+            "EX",
+            "EY",
+            "EZ",
+            "YR",
+            "MO",
+            "DY",
+            "HR",
+            "MI",
+            "SC",
+            "MAG",
+            "NCCP",
+            "NCCS",
+            "NCTP",
+            "NCTS",
+            "RCC",
+            "RCT",
+            "CID",
+        ],
+    )
+    catalog_hypodd["time"] = catalog_hypodd.apply(
+        lambda x: f'{x["YR"]:04.0f}-{x["MO"]:02.0f}-{x["DY"]:02.0f}T{x["HR"]:02.0f}:{x["MI"]:02.0f}:{min(x["SC"], 59.999):05.3f}',
+        axis=1,
+    )
+    catalog_hypodd["time"] = catalog_hypodd["time"].apply(lambda x: datetime.strptime(x, "%Y-%m-%dT%H:%M:%S.%f"))
+    plt.figure()
+    plt.plot(catalog_hypodd["LON"], catalog_hypodd["LAT"], ".")
+    plt.show()</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+
+
+<div class="jp-RenderedImage jp-OutputArea-output ">
+<img 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lLXx1lmRNIYDBGRQ2wFPLYeytY+ZxwIAcDuo03YfbQpYB7SjgQWtmaxWUuQlhOkOdqbpGvrRNWpFpOK35xlRnSVrJyhoqIiZGRkIDY2FrGxsdBqtdizZ4/4/tKlS6FQKEz+TJw40WabU6ZMsfiMQqHA3XffLe5TUFCA8ePHIyYmBvHx8bj33ntx7Ngxk3acOTYROUbX1onzHXqLPBjjXghry0dI5cLYqqsYCEtLSAU2a3fV4KvGFqufcXRpELnmj78OB9dMxdvLJuLgmqkWAZkhl2vljmqL6f7uOD5RMJDVM5SSkoLCwkIMHToUALBt2zbMmjUL1dXVGDlyJABgxowZKC4uFj8TERFhs81du3ZBr9eLr8+dO4cxY8Zg3rx54rYDBw4gJycH48ePx5UrV7Bu3TpMnz4d3377LaKjo8X95B6biOwz7gFR4GoNHfNeCGtDPFK9F4smXoc3Pztl9Zj+PhVcclV5Abj3TxUonCvdq+VI1WhnWetNcndNJaJgJSsYmjlzpsnrDRs2oKioCIcOHRKDIaVSicRE60XFzA0YMMDkdUlJCaKiokyCobKyMpN9iouLER8fj6qqKkyaNEncLvfYRGSb+cNUQO+Q0CsLbsLYwZbT0K09lA2BUlV9C6AABEGwGQz5+0NaKrABeu+PtaEnZxKkXeXumkpEwcrpnKHu7m787W9/Q0dHB7Rarbi9vLwc8fHxuPbaazF58mRs2LAB8fHxDre7detWLFiwwKTHx1xbW++K0OaBlNxjd3V1oaurS3zd3t7u8HkShQJrPSAD+yllP0Q/Of4vkxwba1WaA+EhbQhspHpdbPVqOTOLzZXZX5I1lRTAH60Es0ShSiEI5msf21ZTUwOtVovLly+jX79+2LFjB+666y4AQGlpKfr16weNRoO6ujo8++yzuHLlCqqqqqBUKu22/fnnn2PChAk4fPgwbrnlFsl9BEHArFmz0NLSgk8//VTc7syx8/LysH79eovtbW1tiI2NdeR2EAU1XVsnMgv3WwztHFwzVdaDVKodhQJQCEDPz20+nX0DMpKv9Volanf4qrEF9/6pwiSoc+b+SNG1daL4YB02f1on9sgZJ2lLBUlS20orGyx6o/w9OZ1Irvb2dqhUKqef37KDIb1ej4aGBrS2tmLnzp3YsmULDhw4gBEjRljsq9PpoNFoUFJSgjlz5thte/ny5aioqEBNTY3VfXJycvDBBx/g4MGDSElJsbqfI8eW6hlKTU1lMERkxB0P04oTzVi4+bDF9j8tvAkDopUBFQCZ80SwYW0hWUOgZd7LVjBnNABYnd2ma+v06pInRN7majAke5gsIiJCTKAeN24cKisrsWnTJrz22msW+6rVamg0GtTW1tpt99KlSygpKcFzzz1ndZ9HH30U77//Pj755BObgZCjx1YqlQ71WBGFMmcLFBr3UlhLHnZ2+Qt/4uj9cXS4y1bSc7cgoKq+RXImmyDA6rR5by95QhRoXK4zJAiCSe+KsXPnzqGxsRFqtdpuO++88w66urrwwAMPSB7j0UcfxXvvvYfy8nKkpaXZbU/OsYnINrkPU6kaPN5OHvYme/dHTrFDe0nPkEjcthY4+fOMPGtYIZt8QVYwlJubi+zsbKSmpuLChQsoKSlBeXk5ysrKcPHiReTl5WHu3LlQq9Wor69Hbm4u4uLiMHv2bLGNxYsXIzk5GQUFBSZtb926Fffeey8GDhxocdycnBzs2LEDf//73xETE4Ompt5ibSqVCpGRkQ4fm4g8z1pxwYNrplpdIsIaXy3X4U5yV6O3NlMtDED+nFEYq+kvmRRt3DME+P+MPCmskE2+IisYOnPmDBYtWgSdTgeVSoWMjAyUlZUhKysLnZ2dqKmpwfbt29Ha2gq1Wo2pU6eitLQUMTExYhsNDQ0ICzOt9Xj8+HEcPHgQe/fulTxuUVERgN4CjcaKi4uxdOlShIeHO3RsIvI8W8UFDQuZOuLJd45g55c/iq/n3pyMP/zHjW48U++Quxq9+RT8MAXw8G2/wK9uGyzuL9XLBiCge97kBo1E7iQrGNq6davV9yIjI/Hhhx/abaO8vNxiW3p6OmzlcdvL8Xb02ETkee4oLvhVY4tJIAQAO7/8EYu1moDrIXLmftjLQ7L2vicXn/U0uUEjkTvJWo6DiMgeW0tzOOrz+vOS29/94ge/XqZDirP3Q62KtNmTJvW+vc9I0bV1ouJEs8/vq9SyLYE41EeBSfbU+mDm6tQ8IrrKlencXzW2YNafKiTfC9RcEm9Mb5ebfOxvOTqsiUTO8nqdoWDGYIjIf5jnDBlzV2HDQGWtuKKcwMZdBTXdjTWRyBlerzNEROQNf/iPG7FYq8G7X/yAvxxuMHnPH3NJXJ0S7ujnpYKeSemDZCcf+2uODmsikS8wGCIivzUmtT/iY/virc8bLKaSN1+8DF1bp188OF0dbnL089ZmXL28YIzswMYdie5EwYIJ1ETk18wTkBU/19R59O0jyCzcj9LKBjsteJa1AMXRhGQ5n7fWmxOmUMhOPnZHojtRsGDPEBH5PcNU8i9PtWDljmqry074gqvDTXI+b2tZE2cqfDu71ApRsGEwREQBQa2KRP/oDpjP+PB1nourw01yPm9ekNE46HE2sGGOTmDgMiWexWCIiAKGP+a52ApQPPF5W0EPA5vg5G8lEIIRp9Yb4dR6Iv9nrxaNr/4F7eqU8GCZUs4eDPfy1xII/oZT64kopNjqGfHlv6Bd7ZUJhl4df+nBCKaAzNWctGC6F57EYIiIAo5U4MCFPn3LX+6/vwRk7pIWFw0FYJIr5+jQcLDdC0/i1HoiCgq2/gVNnucP99/VMgf+6JPj/zJ5rQAcykkLxnvhSQyGiCgocKFP3/KH++8PAZk7GQIa40tSKIBJ6YPsfjbY7oWnMRgioqDAIoK+5en7r2vrRMWJZps9G/4QkLmTVEDTI8ChgCbY7oWnMWeIiIIGiwj6lqfuv3nuy0O3peHB29Is2ne1zIE/ME54dqWURDDcC2/i1HojnFpPRORfpKaWA725M4Vzra/h5q8Bsa3ZXVIJzwBslpJw5Hj+ei/cydXnN4MhIwyGiIj8S8WJZizcfFjyvUCrt2NtdpeurRNVp1rwqNFSM8DV6wMQEgGNK1hniIiIAoajdW8M+0VHhFsMFRn4eikWOazN7mq99BNeKPvO5vVphwwMiGsMZAyGiIjIKxyte2O8nwLALWn98Xl9C8zHMcIUCJiEYGuzuwr2fGf1MwoEzvUFOs4mIyIij3O07o35fgKAw3WWgRAACIJlHR5nz83eTDVXSc3uUkjvKmMHchcGQ0RE5HGO1r2R2s8aAcCanTX459ennQ5kSisbkFm4Hws3H0Zm4X6UVjY41Y495qUHHHn4Cg5OoyfXcZiMiIg8ztFp4lLLT9giAFi5o9qp5Sa8vYSIcemBcx1dWLmj2ub+rAvkPewZIiIij5NTlNGZKc7OLDfhiyrNalUktEMGYqymv+SwmeGhzLpA3sWeISIi8gpHijJ+UX/e6fblzi5zpaihqwzBoXlCOYuG+gZ7hoiIyGsMPSPWHvQKhWtZw1//2CrrXHyxhIshYbu18ycxMdzwX3v3hzyDPUNEROQ3xmr6y8oZMvfinmO4Z0ySw8GEp5dw+aqxBZ/Xn8ctgwdgTGp/k7IBxgRY5is5WpPJHne1E8wYDBERkcc5+kBWqyJROHe0ZMDgCGcKMapVkR4JEp585wh2fvmj+Pqu0YkoO9pk9bqMz93Rmkz2uKudYMdgiIiITLi7J0HuA9m4t+brH1vx4p5j6HZw5Shv5fzYu0dfNbaYBEIAsLumyWabhnN31yw3b8+WC2QMhoiISOTungRnH8iG3hrtkIG4Z0xSb2D0QysK93xnMoQ26fo4HKxtRg+8l/PjyD36XGYiuPG5V5xotjrLTc612Zotx2DIFIMhIiIC4JmehOKDdS4/kE0CoxuTUFXfAoUCuFnTH2pVpNMrszvTA+boPbpl8ADJz+dMGYI/HzgprkL/9IwbkJFyrcm5R0eEW+RNOdPj5cvZcoGGwRAREQFwf0+Crq0Tmz+ts9juyppialUkfjkm0mKb3PNzpHdHKlhy9B6NSe2PuTcnmwyVzb05Gf/fjGF4QKuxGrwZzss8EHKmx8swWy5311Ex+GLtImkMhoiICID7exLqmjskZ4U9fNsvnH4gGwcohmPIzW1ypHfHWrAk5x794T9uxGKtBl/Ut2Dc4P4Yk9ofgPXgzfy8gN5j71qhFT8rl6dnywULBkNERATA/T0JUoFDGIBf3TbYqfbMV7MHeoeS5OY22evdsRcsOXqPdG2d6NB3464MtUP3UOq8egTgkr7HoeuyxlOz5YIJgyEiIhK5syfBncGV1Gr2BnJzm+z17tgLlhy5R84kojPHx3cYDBERkQl39iS4K7iyt5q9nNwme0GatR6tqIgwkzasFUd0ZQYdc3x8g8EQERF5lDuCK6kAxZjcHhRbQZp5UAIAPQBmv1ph0cMj1QOUOiDK7jCctVwn5vj4hqy1yYqKipCRkYHY2FjExsZCq9Viz5494vtLly6FQqEw+TNx4kSbbU6ZMsXiMwqFAnfffbfJfq+++irS0tLQt29fjB07Fp9++qnJ+4IgIC8vD0lJSYiMjMSUKVPwzTffyLk8IiLyU+briCkUV/OGXJltZW0dsPnjr8OuFVoYL5Vm6OHRtXUCsJ6IHR0RbrEivSFYK61sQGbhfizcfBiZhftRWtkg67zIM2T1DKWkpKCwsBBDhw4FAGzbtg2zZs1CdXU1Ro4cCQCYMWMGiouLxc9ERETYbHPXrl3Q6/Xi63PnzmHMmDGYN2+euK20tBSPP/44Xn31VWRmZuK1115DdnY2vv32W1x3XW+E/uKLL2Ljxo148803kZ6ejueffx5ZWVk4duwYYmJi5FwmERH5IfNeEwAe7UHp0HfDvPC1cQ+PtdyiS/oeyeEuAKwI7adkBUMzZ840eb1hwwYUFRXh0KFDYjCkVCqRmJjocJsDBpgWpiopKUFUVJRJMLRx40Y89NBDePjhhwEAL7/8Mj788EMUFRWhoKAAgiDg5Zdfxrp16zBnzhwAvYFaQkICduzYgeXLl8u5TCIi8gB3LPNhPuTmySDCXkKzrfe1QwZaDHe5q7I0uZ+sYTJj3d3dKCkpQUdHB7Rarbi9vLwc8fHxSE9Px7Jly3D27FlZ7W7duhULFixAdHRvDQm9Xo+qqipMnz7dZL/p06ejoqICAFBXV4empiaTfZRKJSZPnizuI6Wrqwvt7e0mf4iIyP0cGR7yN+ZDc8Y9PBUnmgFA8n1DYGM+3GUInoxxtph/kJ1AXVNTA61Wi8uXL6Nfv3547733MGLECABAdnY25s2bB41Gg7q6Ojz77LOYNm0aqqqqoFQq7bb9+eef4+jRo9i6dau4rbm5Gd3d3UhISDDZNyEhAU1NvYveGf4rtc+pU6esHq+goADr16937MKJiMgpgbxgqPnQ3CfH/4XMwv1iwvTq7GF4ecEYhCkU4vIg1nC2mP+SHQzdcMMNOHLkCFpbW7Fz504sWbIEBw4cwIgRIzB//nxxv1GjRmHcuHHQaDT44IMPxOErW7Zu3YpRo0bhlltusXhPoTANpwVBsNjmyD7G1q5diyeeeEJ83d7ejtTUVLvnSUREjgvUBUONh/W0QwZKBnUFu78D4HgtIc4W80+yg6GIiAgxgXrcuHGorKzEpk2b8Nprr1nsq1arodFoUFtba7fdS5cuoaSkBM8995zJ9ri4OISHh4u9PwZnz54Ve4IMOUpNTU1Qq9WS+0hRKpUO9VgREZHzArGYoKNT5g3k9HaxIrT/cTpnyEAQBHR1dUm+d+7cOTQ2NpoEKNa888476OrqwgMPPGCyPSIiAmPHjsVHH31ksv2jjz7CrbfeCgBIS0tDYmKiyT56vR4HDhwQ9yEiIs/QtXWi4kSzOOXcnLXcG38JCMzPX86UeWPdgoCq+hYvnDG5m6yeodzcXGRnZyM1NRUXLlxASUkJysvLUVZWhosXLyIvLw9z586FWq1GfX09cnNzERcXh9mzZ4ttLF68GMnJySgoKDBpe+vWrbj33nsxcOBAi+M+8cQTWLRoEcaNGwetVovXX38dDQ0NeOSRRwD0Do89/vjjyM/Px/XXX4/rr78e+fn5iIqKwsKFC525L0RE5ABby04YDzP56/CQnKKJ5lPmpTxWUo0O/RWH10kj/yArGDpz5gwWLVoEnU4HlUqFjIwMlJWVISsrC52dnaipqcH27dvR2toKtVqNqVOnorS01KTOT0NDA8LCTDukjh8/joMHD2Lv3r2Sx50/fz7OnTuH5557DjqdDqNGjcLu3buh0WjEfZ5++ml0dnZixYoVaGlpwYQJE7B3717WGCIi8hBbidGfHP+XZJDkL0EQYP38d63QOjRl/usfWvHCnu9gvIxqICWH01UKQbAS3oag9vZ2qFQqtLW1ITY21tenQ0Tk1ypONGPh5sMW2//7vpvwWEm1RTBxcM1UjwUIztQwsnb+by+biIbzHRazvqR6e/759Wms3FEt2YZ2iOVIB3mGq89vrk1GREQA5AcU1hKjIbGGmL3ZY64UZHRmhXhb52+taKLUOQuCEHDJ4WSJwRARETkVUFirmzNW019WgCD32MaBE+D8Ehf26v7YmvVlfM4K9K6VJgj+lxxOjmEwREQU4lwpimgtMdrR4oJyj20eOD10W5pLNYycSew2P2cBQJgA/PfCm+wWXiT/xGCIiCjEuVoUUaoHxdEgQ86xpQKnrQfroEBvQGIg1QtlaxhObt0fqXPuATAgWslAKEAxGCIiCnGeKoroSJAh59iSQYgA/HpSGrZ+Wm/RC2UIgGp+aMMLZd/JzikyMA+kpM4ZAL7+oZVJ0wGKwRARUYjz5ZpZco5tLXD6VWYafpWZZtILZTycZkxqGM5Wr5G1fKbV2cPEpTgMXiw7hntuTGLvUABiMERERD4tiujosR1JeAYsh9PMGQ/D2SsaaS2faXSyyma7FFgYDBEREQDfrpnl6LEdCZykhtOMGYbh7CVv28pnCsT11sg6l9cmIyIi8ia1KhLaIQOtBk+GQEWKcW+SrWDHWjthCqD54mUA8Ov11kge9gwREVFQkRpOezr7BmQkX2vSm2Svd8e8HUMtoUffPiIOqR1cM9Xv1lsj+bgchxEux0FEFDx0bZ12A5XSyga7y27o2jrx5akWrNxRbTGF3xNLjLhSjTuYzkEOLsdBREQkwZE8JEdykNSqSPSP7oB5z4EnEqadXVrEnfzhHLyNOUNERBSQvmpsweZPT+CrxhaX2rGXgwRI5w+5O2HaWkK3rq3TbccIhHPwBfYMERFRwHnynSPY+eWP4uu5NyfjD/9xo8eOZ8gfWruzBj3o7Ulwd8K0q5XAg+UcfIE9Q0REFFC+amwxCYQAYOeXPzrUQ6Rr60TFiWbnezoUZv91I2/0PgXCOfgCgyEiIgoon9efl9z+Rb3tYKi0sgGZhfuxcPNhZBbuR2llg8PH9MbwkaH3yZfT9f3hHHyBw2RERBRQbhk8QHL7uMH9TV4bz4gCYLPAor3ZU94aPvJlJXB/OgdvYzBERER+x1ZwMia1P+benGyRMzQm9WowZD4j6qHb0qwGM58c/5fd2VOeqDht7Rp9WQncn87Bm1hnyAjrDBER+Z6jU7u/amzBF/UtGDe4v0kgpGvrRGbhfpPAJezngonmdYJ2rdBi9qsVFkGOVP0gR2oSufsayTGsM0REREHD3nphxsakmgZBBlJDWj0C8OtJadj6ab1JMNOh73Z4+Mtdw0dyrpG8g8EQERH5DXfk5lgb0vpVZhp+lZlmEszo2jplDX+5Y/goVKev+zPOJiMiIr/hjqndtmZEmRdY9MXsqVCdvu7PmDNkhDlDRES+567cHEfWJnNmX3dwZ/4Ruf78ZjBkhMEQEZF1nly807xtbwcnvhAK1+gtTKAmIiKP8+TsJ2ttB3uAEGrT1/0Zc4aIiMgmT1Vf1rV14p9fn8aanaG3MCj5F/YMEREFEU8MZXli9pNxb5A5zqwib2MwREQUJDw1lOXu6svmPU3mOLOKvI3DZEREQcCTC4m6e/q5VE+TQagsDEr+hT1DRERBwF1DWdaG2VytvmzcrlRPU5gC+OOCmzB2cH8GQuR1DIaIiIKAO4ay7A2zOTv7ybzd24bGmZyn4udj/XJMkuy2A4UnyxKQ6zhMRkQUBFwdyvLkjDHzdj+pbTbZRyEAk9IHuXQcf1Za2YDMwv1YuPkwMgv3o7SywdenRGbYM0REFCRcGcry1HpZtvKDDHqAoJk9JlU8kouy+j8GQ0REQcTZoSx3zxiz1a65YJk9JjXMmDogSjLIrKpvwS/HMBjyFxwmIyIijy1YamjXfGFSgzAgKGaPWesBio4Il7z2x0qqOVzmR9gzRETkh3yRcOvqjDF77Rb/Tx22fFKHHvT2nDx82y/wq9sGB3wgBFgfZryk70HBnNFYu7MGPUbvcbjMv8jqGSoqKkJGRgZiY2MRGxsLrVaLPXv2iO8vXboUCoXC5M/EiRPtttva2oqcnByo1Wr07dsXw4cPx+7du8X3Bw8ebNGuQqFATk6Oy8cmIvI3vky4VasioR0y0OIBrWvrRMWJZqcTqtWqSOTeNQL/s3Ya3l42Ef+zZhpy7x4eNIGAYTjQmGH4b/746/DHhTdZfMaQkxWIXP374G9k9QylpKSgsLAQQ4cOBQBs27YNs2bNQnV1NUaOHAkAmDFjBoqLi8XPRERE2GxTr9cjKysL8fHxePfdd5GSkoLGxkbExMSI+1RWVqK7u1t8ffToUWRlZWHevHkmbck9NhGRv/HHhFt3VrYO1sVJDcOBubuOolsQLIYZx2r6eyQnyxc8uWivr8gKhmbOnGnyesOGDSgqKsKhQ4fEYEipVCIxMdHhNt944w2cP38eFRUV6NOnDwBAo9GY7DNokOmUy8LCQgwZMgSTJ0822S732ETkWaytIp+nZnU5y13BmSN/FwL974utYUZ7wZJcvrpX/hisu4PTOUPd3d3429/+ho6ODmi1WnF7eXk54uPjce2112Ly5MnYsGED4uPjrbbz/vvvQ6vVIicnB3//+98xaNAgLFy4EKtXr0Z4eLjF/nq9Hn/961/xxBNPQKEw7ZOUe+yuri50dXWJr9vb2+XcAiKywdV/PQb6g9FZ7pzV5Y57aC04+/JUC/pHdyA6Ihwd+m6bx5D6uzApfZDJuQVLb4Otni935WT58l75W7DuLgpBEOxUgDBVU1MDrVaLy5cvo1+/ftixYwfuuusuAEBpaSn69esHjUaDuro6PPvss7hy5QqqqqqgVCol2xs2bBjq6+tx//33Y8WKFaitrUVOTg5WrVqF3/3udxb7v/POO1i4cCEaGhqQlHS1Wqkzx87Ly8P69esttre1tSE2NlbObSEiI7q2TmQW7rd4oB9cM1WsvWLrIR0sD0ZnlVY2WPQgyL1+d91Dqe9SoQAgAMYPD2vHkPz8z20Yzm119jC8sOc7q39f6Cp7v61gP7417e3tUKlUTj+/ZQdDer0eDQ0NaG1txc6dO7FlyxYcOHAAI0aMsNhXp9NBo9GgpKQEc+bMkWwvPT0dly9fRl1dndgTtHHjRrz00kvQ6XQW+995552IiIjAP/7xD5vn6cixpXqGUlNTGQwRuajiRDMWbj5ssf3tZRPRcL7D5kPaX//P1tt0bZ0urQMm5x4aglNrvTzGwVmYAhDMAiFbx7D2d8GYtTpEby+bCO2QgXavN5TY+m3Zulfu7Gl1R7Dubq4GQ7KHySIiIsQE6nHjxqGyshKbNm3Ca6+9ZrGvWq2GRqNBbW2t1fbUajX69OljMiQ2fPhwNDU1Qa/XmyRBnzp1Cvv27cOuXbvsnqcjx1YqlVZ7jYjIedaGeqIiwuzmGwRrN7xcriQay7mHxj1IBuZBqvHwTvPFy3j07SOSx5U6hiNFF3uE3t4i410CNbnY05wZRnV3T6unSjD4kstFFwVBMOldMXbu3Dk0NjZCrVZb/XxmZia+//579PRcrcBw/PhxqNVqi9lgxcXFiI+Px9133233vBw5NhF5hrUCfh36bqsPaQNbU5TJMY7eQ/NkWAOpdckMU+7HDR5gtYCi1DHM/y6EoTfwMf/cmuxhbi/4GIzkFsf01Jpz1kowBCpZPUO5ubnIzs5GamoqLly4gJKSEpSXl6OsrAwXL15EXl4e5s6dC7Vajfr6euTm5iIuLg6zZ88W21i8eDGSk5NRUFAAAPjNb36DV155BatWrcKjjz6K2tpa5Ofn47HHHjM5dk9PD4qLi7FkyRJcc43paTt6bCLyHql/PeraOu3+q9bds25CkaP30Na6YdZ6kszbNrD1PZn/Xfjk+L8kh1nuuTEpqHobPEVOzwx7Wh0jKxg6c+YMFi1aBJ1OB5VKhYyMDJSVlSErKwudnZ2oqanB9u3b0draCrVajalTp6K0tNSkZlBDQwPCwq52SKWmpmLv3r347W9/i4yMDCQnJ2PVqlVYvXq1ybH37duHhoYGPPjggxbnFR4e7tCxicg9HM0/MB/qcfQhHYzd8N7myD20NYRlqzfOuO2oiDBc0vfY/Z6M/y5YO7dgrUHkCY7eK0+tORdsZCdQBzNXE7CIQoE78g9cSQ4m9zJOhjUIQ+8Mr+WTh/juxMht/DHh2d28PpssmDEYIrKNM72C02sHTqBwz3cOTZWnwBTs/wBx9fnNVeuJyGG28g/Ie9y5LpSurRMvlH1nMVXeXYm25B+CLeHZ3bhqPRE5jPkHvufuadLOJFETBRv2DBGRw+RO6yX38sQ0aalp+AZyAt1gW8WcQgt7hohIFs708h1PTJN2Zqq8OWu9VaG6vhwFHgZDRCSb1LRePvg8z1Zl74oTzU6vCO/MVHnjdqV6q1o7fxLXG2MyNvk7ziYzwtlkRM4J9YVVvcl8mvS9NyXhveofbd57T34/1tbKUvy8hpkBZx2SJ3l9bTIiImPWegaM1xsj9zHvxZn9aoXNe+/p70eqtyoMloUcmYxN/owJ1ETkEk639z7DNGlH1nrz9PcjlVS/OnsY15ejgMKeISJySVpctMWK4woF+ODzAkdKHXijHIJUUv21UX24vhwFDAZDROR+zET0CkfWevPWwrfmSfWcdUiBhMEQEbmkrrnDIvYRAOaHWOHuWXeOBB2uBCaunC8XXiVz/jrrlMEQEbmEVakdZz6ra/WMYRidonL5weBI0GFtH11bJ76oPw+FQoGxmv4m+3CWILmTP/994tR6I5xaT+ScUFgV21VSi9wamD8YvPWv59LKBqzZWSP27CkAFM69WjDR2qK8APzyX/fkvzy9yDOn1hORzzE/xD5ba4AZT3f/5Pi/vPKvZ11bp0kgBPQOb67dVYNJ6YOszkIrPliPLQdP+uW/7sl/eaJ6ujtxaj0RuQVXxbbN1hpgQO+Doaq+xe1rj1kjletlOGZ98yXJ8w0DxEDI0+dHwUXq75M/DaczGCIi8gLzejzmwhUKQGG9WKG7GUoiSPn6x1bJ+kEP357GmlLkFH9f5JnDZEQUUnw5m8V4OPHrH1vx4p5jJnlWYzX9vZaMrlZFonDuaKzeWWPx3ot7juGeMUkWw58AsOVgHZPlySn+PJzOYIiIQoa7Z7M4E1gZZnVphwzEPWOSLB4M3qgJZDB//HWIVl6DlTuqTbYb53KYz0Lz5vlR8PHXcgsMhogoJLh7jS53BFZSDwZv/+tZbm+UP//rnshZzBkiopDgzjW6rAVW7kgk/qqxBf/8+jSiIsK8Emg4k8vBZHkKNuwZIqKQ4M7ikO6cJqxr60TVqRYIgoA9NU3YfbRJfG/uzcn4w3/cKPv85GJvD4U6BkNEFBLcuUaXq4GVIdeo5sc2FO7+zupSbju//BF3jU5EZMQ1Hk/4tlWhmgUWKdixArURVqAmCn66tk639IA4W3XbONdIDl8UOPTn5ROIjLn6/GYwZITBEBHJITewsrUkhyPcuXyBPZ5ePoHInbgcBxGRj8idJmxrSQ5HeHP5An9fPoHInRgMERF5SXREuM33FQBWTB2CAVERGBwXhWXbq3xW4NCdCedE/o7BEBGRl3TouyW3PzZtKNITYjB2cH+/KXDozoRzIn/HYIiIyEukelsAILrvNfjlmCSL/X095d3XxyfyFhZdJCLyErUqEqtnDLPY/uKeY1YLNvq6wKGvj0/kDQyGiIi8aHSKymIbV34n8i0GQ0REXmQYKjPGxGQi32IwRER+T9fWiYoTzW5Z+8vXnFkLjIg8iwnUROTXArEKsr0lLJiYTORfGAwRkd+ytjr8pPRBfhtAOBq8yS3YSESew2EyIvJbtqog+yNrwVswDO8RBTMGQ0TktwIt2TjQgjci6iUrGCoqKkJGRgZiY2MRGxsLrVaLPXv2iO8vXboUCoXC5M/EiRPtttva2oqcnByo1Wr07dsXw4cPx+7du8X38/LyLNpNTEw0aUMQBOTl5SEpKQmRkZGYMmUKvvnmGzmXR0R+JtCSjQMteCOiXrJyhlJSUlBYWIihQ4cCALZt24ZZs2ahuroaI0eOBADMmDEDxcXF4mciIiJstqnX65GVlYX4+Hi8++67SElJQWNjI2JiYkz2GzlyJPbt2ye+Dg83XePnxRdfxMaNG/Hmm28iPT0dzz//PLKysnDs2DGLtogocARSsjGXsCAKTLKCoZkzZ5q83rBhA4qKinDo0CExGFIqlRa9Nra88cYbOH/+PCoqKtCnTx8AgEajsTzRa66x2q4gCHj55Zexbt06zJkzB0BvoJaQkIAdO3Zg+fLlDp8PEfmfQEo2DqTgjYh6OZ0z1N3djZKSEnR0dECr1Yrby8vLER8fj/T0dCxbtgxnz5612c77778PrVaLnJwcJCQkYNSoUcjPz0d3t+mChrW1tUhKSkJaWhoWLFiAkydPiu/V1dWhqakJ06dPF7cplUpMnjwZFRUVVo/d1dWF9vZ2kz9ERK7iEhZEgUV2MFRTU4N+/fpBqVTikUcewXvvvYcRI0YAALKzs/HWW29h//79+MMf/oDKykpMmzYNXV1dVts7efIk3n33XXR3d2P37t145pln8Ic//AEbNmwQ95kwYQK2b9+ODz/8EJs3b0ZTUxNuvfVWnDt3DgDQ1NQEAEhISDBpOyEhQXxPSkFBAVQqlfgnNTVV7u0gIiuCqVAiEQU3hSAIgv3drtLr9WhoaEBrayt27tyJLVu24MCBA2JAZEyn00Gj0aCkpEQcvjKXnp6Oy5cvo66uTswD2rhxI1566SXodDrJz3R0dGDIkCF4+umn8cQTT6CiogKZmZk4ffo01Gq1uN+yZcvQ2NiIsrIyyXa6urpMArX29nakpqaira0NsbGxDt8TomBhr1igowKxUKIj3HV/iMi92tvboVKpnH5+yy66GBERISZQjxs3DpWVldi0aRNee+01i33VajU0Gg1qa2uttqdWq9GnTx+ThOjhw4ejqakJer1eMgE7Ojoao0ePFts15BI1NTWZBENnz5616C0yplQqoVQq7VwxUWhwVwAjVWtn7c4avyyUKCe4CdYAj4jcUGdIEASrw2Dnzp1DY2OjSYBiLjMzE99//z16enrEbcePH4darbY6E62rqwv/+7//K7ablpaGxMREfPTRR+I+er0eBw4cwK233urMZZEPcXjF+9xZLFCq1k4PgOKD9S6fpzuVVjYgs3A/Fm4+jMzC/SitbLC6L4spEgU3WcFQbm4uPv30U9TX16Ompgbr1q1DeXk57r//fly8eBFPPfUUPvvsM9TX16O8vBwzZ85EXFwcZs+eLbaxePFirF27Vnz9m9/8BufOncOqVatw/PhxfPDBB8jPz0dOTo64z1NPPYUDBw6grq4Ohw8fxr//+7+jvb0dS5YsAQAoFAo8/vjjyM/Px3vvvYejR49i6dKliIqKwsKFC129R+RFch5Q5D7uLBaYFhcNhcT2LQdP+k3wIDe4YTFFouAma5jszJkzWLRoEXQ6HVQqFTIyMlBWVoasrCx0dnaipqYG27dvR2trK9RqNaZOnYrS0lKTOj8NDQ0IC7sag6WmpmLv3r347W9/i4yMDCQnJ2PVqlVYvXq1uM8PP/yA++67D83NzRg0aBAmTpyIQ4cOmUzBf/rpp9HZ2YkVK1agpaUFEyZMwN69e1ljKIAE4jpUwcJQLND4ge9ssUC1KhLLbk/D65/WmWzvEYD65ktWv0tv5uNYC26+PNWC/tGW5+DO+0NE/kd2AnUwczUBi1xTcaIZCzcfttj+9rKJ0A4Z6IMzCi2llQ0WxQKdzYnRtXUis3C/RfBwcM1UyUDHPB9n9YxhGJ2i8lhgJHV+CgUAARAgnRPkzvtDRO7l6vObwZARBkO+JfcBSu6na+t0W7FAR4MHqe/dwJOJysbnF6YAhJ8DIQOpv3vuvD9E5D5en01G5CmGpQzW7qxBD3oT2riUgXe5s9Kzo5WYpYasDDw5VGp8fs0XL+PRt4+YvG/ICTI+biBVwiYixzEYIv/z83CFZBYuBRRHggepfBxjUkGJu89P19bJnCCiEOby1Hoid5GsT7Orxm9mIJFnmK9Mb84bQYn5OXCBVaLQwp4h8huS9WkEoPh/6pB7l2WFcwoexkNWX//Yihf3HLO66ru9WWfOzkrjAqtEoYvBEPkNa8MlWz6pw68y0wCASyFICJYlIgxDVtohA3HPmCTJoMR41plCAazJHoblk4ZIvu9M8jVzgohCE4Mh8htqVSQeui0Nm83r06C3evGWgye5FIKZYF0iQiooMR9GFQSgYPd3gAAsnzyEdaqIyGnMGSK/8uBtaRZ502GAGAgBvl8KwV+WC3F2iQh/OX+5rM06e2HPd2LvGKtEE5EzGAyRX1GrIlE41zSR9eHb0/zmIedPy4U48/D3p/OXKy0uGlI51j3orWxtGGY1xhlhROQIBkPkd+aPvw4H10zF28sm4uCaqfjVbWl+8ZDzxWKdtnpx5D78A32xUbUqEmuyh1lsN1wzZ4QRkbOYM0R+yTxnpGDOaItqxt5+yNnqifHEudjLBzI8/B29L94+f09YPmkIIPQOjfXAMuDhjDAicgaDIQoI/vCQ8+ZinY4mA8u5L8Gy2OjyyUNwz43Ss80AzggjIvk4TEYBwzDt2lcPOm8Ow8jJB3L0vgTTMJKv/y4QUXBhzxCRDN7qofJUL44/9LD5m2Cp00REzmMwRCSTp4dhDA/n1TOG4cUy65WYncVhpKuCtU4TEcnDYIhCjj/3BJg/nFdnD0NG8rXsxfEAFmkkIgMGQxRS/LknQOrh/OKeYzi4Ziofzh4QDLPriMg9mEBNIcPf6+ywgrJ3sUgjERkwGKKQ4e/BBh/O3hVMs+uIyDUcJqOQ4e91duQWUSTXcXYdEQEMhiiEBEKwwYez93F2HRExGKKQEgjBBh/ORETexWCIQg6DDSIiMsYEaiIiIgppDIaIiIgopDEYIiIiopDGYIiIiIhCGoMhIiIiCmkMhoiIiCikMRgiIrJB19aJihPNfrOGHRG5H+sMERFZUVrZIC7uG6YACuaMxvzx1/n6tIjIzdgzREQkQdfWKQZCQO+adrm7jrKHiCgIMRiikMdhEJJS19xhsqgvAHQLAuqbL/nmhIjIYzhMRiGNwyBkTVpcNMIUMAmIwhUKDI6L8t1JEZFHsGeIQhaHQcgWtSoSBXNGI1yhANAbCOXPGcV17YiCEHuGKGTZGgbhA48AYP746zApfRDqmy9hcFwU/14QBSlZPUNFRUXIyMhAbGwsYmNjodVqsWfPHvH9pUuXQqFQmPyZOHGi3XZbW1uRk5MDtVqNvn37Yvjw4di9e7f4fkFBAcaPH4+YmBjEx8fj3nvvxbFjx0zacPbYFLoMwyDGOAxC5tSqSGiHDGQgRBTEZPUMpaSkoLCwEEOHDgUAbNu2DbNmzUJ1dTVGjhwJAJgxYwaKi4vFz0RERNhsU6/XIysrC/Hx8Xj33XeRkpKCxsZGxMTEiPscOHAAOTk5GD9+PK5cuYJ169Zh+vTp+PbbbxEdHS3uJ/fYFNoMwyC5u46iWxA4DEJEFKJkBUMzZ840eb1hwwYUFRXh0KFDYjCkVCqRmJjocJtvvPEGzp8/j4qKCvTp0wcAoNFoTPYpKyszeV1cXIz4+HhUVVVh0qRJ4na5xyYKpmEQXVsn6po7kBYXHdDXQUTkbU4nUHd3d6OkpAQdHR3QarXi9vLycsTHxyM9PR3Lli3D2bNnbbbz/vvvQ6vVIicnBwkJCRg1ahTy8/PR3d1t9TNtbW0AgAEDBphsl3tsIiA4hkFKKxuQWbgfCzcfRmbhfpRWNvj6lIiIAoZCEATB/m5X1dTUQKvV4vLly+jXrx927NiBu+66CwBQWlqKfv36QaPRoK6uDs8++yyuXLmCqqoqKJVKyfaGDRuG+vp63H///VixYgVqa2uRk5ODVatW4Xe/+53F/oIgYNasWWhpacGnn34qbnfm2F1dXejq6hJft7e3IzU1FW1tbYiNjZVzW4h8RtfWiczC/RZTwA+umRrQAR4RkaPa29uhUqmcfn7LDob0ej0aGhrQ2tqKnTt3YsuWLThw4ABGjBhhsa9Op4NGo0FJSQnmzJkj2V56ejouX76Muro6hIeHAwA2btyIl156CTqdzmL/nJwcfPDBBzh48CBSUlKsnqcjx87Ly8P69esttjMYokBScaIZCzcfttj+9rKJ0A4Z6IMzIiLyLleDIdnDZBERERg6dCjGjRuHgoICjBkzBps2bZLcV61WQ6PRoLa21mp7arUa6enpYiAEAMOHD0dTUxP0er3Jvo8++ijef/99fPzxxzYDIUePvXbtWrS1tYl/GhsbbbZJ5I84K46IyDUuF10UBMFkqMnYuXPn0NjYCLVabfXzmZmZ+P7779HT0yNuO378ONRqtTgbTBAErFy5Ert27cL+/fuRlpZm97wcObZSqRTLBBj+UODgMhq9WByQiMg1smaT5ebmIjs7G6mpqbhw4QJKSkpQXl6OsrIyXLx4EXl5eZg7dy7UajXq6+uRm5uLuLg4zJ49W2xj8eLFSE5ORkFBAQDgN7/5DV555RWsWrUKjz76KGpra5Gfn4/HHntM/ExOTg527NiBv//974iJiUFTUxMAQKVSITIy0uFjU/DgMhqmgmlWHBGRt8kKhs6cOYNFixZBp9NBpVIhIyMDZWVlyMrKQmdnJ2pqarB9+3a0trZCrVZj6tSpKC0tNakZ1NDQgLCwqx1Sqamp2Lt3L377298iIyMDycnJWLVqFVavXi3uU1RUBACYMmWKyfkUFxdj6dKlCA8Pd+jYFBysLaMxKX1QSAcBalVkSF8/EZGzZCdQBzNXE7DIO5gwTERExryeQE3ka0wYJiIid2IwRAGHCcNEROROXLWeAhIThomIyF0YDFHAYsIwERG5A4fJiIiIKKQxGCIiIqKQxmCIiIiIQhqDISIiIgppDIaIiIgopDEYIiIiopDGYIiIiIhCGoMhIiIiCmkMhoiIiCikMRgiIiKikMZgiMgDdG2dqDjRDF1bp69PhYiI7ODaZERuVlrZgLW7atAjAGEKoGDOaMwff52vT4uIiKxgzxCRG+naOsVACAB6BCB311H2EBER+TEGQ0RuVNfcIQZCBt2CgPrmS745ISIisovBEJEbRUeEQ2G2LVyhwOC4KJ+cDxER2cdgiMhNSisbMPvVChh3DIUrFMifMwpqVaS4jcnVRET+hQnURG5gnisE9CZP71qhxZjU/uI2JlcTEfkf9gwRuYFUrlCPAFzS94ivmVxNROSfGAwRuUFaXDTCzJKFzHOFmFxNROSfGAwRuYFaFYmCOaMRruiNiKRyhRwJmIiIyPuYM0TkJvPHX4dJ6YNQVd8CKICxmv4m7xsCptxdR9EtCJIBk4GurRN1zR1Ii4uWfJ+IiNyHwRCRE6wFK58c/5fNBGlDwFTffAmD46IkAx0mWRMReReDISKZrAUr1hKkJ6UPMgl61KpIq709jrZBRETuw5whIhlszQhzR4I0k6yJiLyPwRCRDLaClbS4aIvq0woFZCVIM8maiMj7GAwRySA7WBGkN1vjyKw0IiJyL+YMEclga0ZYxYlmi9hHAFDffElWMONIkjUREbkPgyEimawFK4ZeI+NhNGeHuGwlWRMRkXtxmIzICWpVJLRDBlrMEuMQFxFR4GHPEIUUTxcz5BAXEVHgYTBEIcNbxQw5xEVEFFgYDFFIkKoPtHZXDaIiwjFu8ACPBi9cWoOIyL8xGKKQIFUfqEcAHn37CMIUwEO3peHB29LcHqxwaQ0iIv8nK4G6qKgIGRkZiI2NRWxsLLRaLfbs2SO+v3TpUigUCpM/EydOtNtua2srcnJyoFar0bdvXwwfPhy7d+822efVV19FWloa+vbti7Fjx+LTTz81eV8QBOTl5SEpKQmRkZGYMmUKvvnmGzmXR0EsOiLc6ns9ArD50zrcWrAfpZUNJu/p2jpRcaIZurZO2ce0Va2aiIj8h6xgKCUlBYWFhfjiiy/wxRdfYNq0aZg1a5ZJ0DFjxgzodDrxj3lQY06v1yMrKwv19fV49913cezYMWzevBnJycniPqWlpXj88cexbt06VFdX4/bbb0d2djYaGq4+uF588UVs3LgR//3f/43KykokJiYiKysLFy5ckHOJFKQ69N129xFgGqyUVjYgs3A/Fm4+jMxCy0DJHleW1nAlCCMiInlkDZPNnDnT5PWGDRtQVFSEQ4cOYeTIkQAApVKJxMREh9t84403cP78eVRUVKBPnz4AAI1GY7LPxo0b8dBDD+Hhhx8GALz88sv48MMPUVRUhIKCAgiCgJdffhnr1q3DnDlzAADbtm1DQkICduzYgeXLl8u5TAoQ5rk4tnJzpGoASTEOVqRyjIYlxmBMan+Hzs/ZukMcWiMi8i6n6wx1d3ejpKQEHR0d0Gq14vby8nLEx8cjPT0dy5Ytw9mzZ2228/7770Or1SInJwcJCQkYNWoU8vPz0d3d+y95vV6PqqoqTJ8+3eRz06dPR0VFBQCgrq4OTU1NJvsolUpMnjxZ3EdKV1cX2tvbTf5QYDDvtXnynSM2e3HMawApfv5jzhCsWMsxuvdPFQ73EDlTd4hDa0RE3ic7gbqmpgZarRaXL19Gv3798N5772HEiBEAgOzsbMybNw8ajQZ1dXV49tlnMW3aNFRVVUGpVEq2d/LkSezfvx/3338/du/ejdraWuTk5ODKlSv43e9+h+bmZnR3dyMhIcHkcwkJCWhqagIA8b9S+5w6dcrqtRQUFGD9+vVybwH5mFTAsPPLH8X3DQHEpPRBJoGHeQ0gACj+nzps+aQOPbAMVqR6kgxDaYa27c0Uk1t3yNbQGmeiERF5huxg6IYbbsCRI0fQ2tqKnTt3YsmSJThw4ABGjBiB+fPni/uNGjUK48aNg0ajwQcffCAOX5nr6elBfHw8Xn/9dYSHh2Ps2LE4ffo0XnrpJfzud78T91MoTP8dLwiCxTZH9jG2du1aPPHEE+Lr9vZ2pKam2r8J5FNSAYM5awGEeQ2g3LtG4FeZaSbBiiHAWT1jGF4o+85qcPLJ8X85NJwlp+6QO5f0ICIix8gOhiIiIjB06FAAwLhx41BZWYlNmzbhtddes9hXrVZDo9GgtrbWantqtRp9+vRBePjV2T7Dhw9HU1MT9Ho94uLiEB4eLvb+GJw9e1bsCTLkKDU1NUGtVkvuI0WpVFrtsSL/5Uj+j5wAwjhYMc/XWTFlCP708QmTBVjDFQpERYRJDmeZ90bJZWshWCIi8gyX1yYTBAFdXV2S7507dw6NjY0mAYq5zMxMfP/99+jp6RG3HT9+HGq1GhEREYiIiMDYsWPx0UcfmXzuo48+wq233goASEtLQ2Jiosk+er0eBw4cEPeh4CGVizP35mSX1wSTGn4rKj+JNdnDLNru0Hc7PVPMnvnjr8PBNVPx9rKJOLhmKpOniYg8TFbPUG5uLrKzs5GamooLFy6gpKQE5eXlKCsrw8WLF5GXl4e5c+dCrVajvr4eubm5iIuLw+zZs8U2Fi9ejOTkZBQUFAAAfvOb3+CVV17BqlWr8Oijj6K2thb5+fl47LHHxM888cQTWLRoEcaNGwetVovXX38dDQ0NeOSRRwD0Do89/vjjyM/Px/XXX4/rr78e+fn5iIqKwsKFC91xn8jPSOXiPHXnDS6tCWYtXycj5VocXDPVYijNk8NZXNKDiMh7ZAVDZ86cwaJFi6DT6aBSqZCRkYGysjJkZWWhs7MTNTU12L59O1pbW6FWqzF16lSUlpYiJiZGbKOhoQFhYVc7pFJTU7F371789re/RUZGBpKTk7Fq1SqsXr1a3Gf+/Pk4d+4cnnvuOeh0OowaNQq7d+82mYL/9NNPo7OzEytWrEBLSwsmTJiAvXv3mhybgot5wOBqAGErX0fqWBzOIiIKDgpBEOykooaO9vZ2qFQqtLW1ITY21tenQz5QWtlgEeDYGqbStXVyhXoiIh9z9fnNtcmIjMidCs/hLCKiwMdgiMgMAxwiotDi8mwyIiIiokDGYIiIiIhCGoMhIiIiCmkMhoiIiCikMRgiIiKikMZgiIiIiEIagyEiIiIKaQyGiIiIKKQxGCIiIqKQxmCIiIiIQhqDISIiIgppDIaIiIgopDEYIiIiopDGYIiIiIhCGoMhIiIiCmkMhoiIiCikMRgiIiKikMZgiIiIiEIagyEiIiIKaQyGiIiIKKQxGCIiIqKQxmCIiIiIQhqDISIiIgppDIaIiIgopDEYIiIiopDGYIiIiIhCGoMhIiIiCmkMhkKcrq0TFSeaoWvr9PWpEBER+cQ1vj4B8p3Sygas3VWDHgEIUwAFc0Zj/vjrfH1aREREXsWeoRCla+sUAyEA6BGA3F1H2UNEREQhh8GQl/jbcFRdc4cYCBl0CwLqmy/55oSIiIh8hMNkXuCPw1FpcdEIU8AkIApXKDA4Lsp3J0VEROQD7BnyMH8djlKrIlEwZzTCFQoAvYFQ/pxRUKsifXpeRERE3saeIQ+zNRzl68Bj/vjrMCl9EOqbL2FwXJTPz4eIiMgXZPUMFRUVISMjA7GxsYiNjYVWq8WePXvE95cuXQqFQmHyZ+LEiTbbfPPNNy0+o1AocPnyZXGfwYMHS+6Tk5Pj0rG9wTAcZcyfhqPUqkhohwxkIERERCFLVs9QSkoKCgsLMXToUADAtm3bMGvWLFRXV2PkyJEAgBkzZqC4uFj8TEREhN12Y2NjcezYMZNtffv2Ff93ZWUluru7xddHjx5FVlYW5s2bZ/IZZ47taYbhqNxdR9EtCByOIiIi8jOygqGZM2eavN6wYQOKiopw6NAhMRhSKpVITEyUdRIKhcLmZwYNGmTyurCwEEOGDMHkyZNNtjtzbG/gcBQREZH/cjqBuru7GyUlJejo6IBWqxW3l5eXIz4+Hunp6Vi2bBnOnj1rt62LFy9Co9EgJSUFv/zlL1FdXW11X71ej7/+9a948MEHoVCYjj85c2xv4XAUERGRf1IIgiDY3+2qmpoaaLVaXL58Gf369cOOHTtw1113AQBKS0vRr18/aDQa1NXV4dlnn8WVK1dQVVUFpVIp2d6hQ4fw/fffY/To0Whvb8emTZuwe/dufPXVV7j++ust9n/nnXewcOFCNDQ0ICkpSdzuzLG7urrQ1dUlvm5vb0dqaira2toQGxsr57YQERGRj7S3t0OlUjn9/JYdDOn1ejQ0NKC1tRU7d+7Eli1bcODAAYwYMcJiX51OB41Gg5KSEsyZM8eh9nt6enDzzTdj0qRJ+OMf/2jx/p133omIiAj84x//sNmOI8fOy8vD+vXrLbYzGCIiIgocrgZDsofJIiIiMHToUIwbNw4FBQUYM2YMNm3aJLmvWq2GRqNBbW2t4ycUFobx48dLfubUqVPYt28fHn74YbvtOHLstWvXoq2tTfzT2Njo8HkSERFRcHC5zpAgCCZDTcbOnTuHxsZGqNVqWe0dOXIEo0ePtnivuLgY8fHxuPvuu+2248ixlUql1SE0IiIiCg2yeoZyc3Px6aefor6+HjU1NVi3bh3Ky8tx//334+LFi3jqqafw2Wefob6+HuXl5Zg5cybi4uIwe/ZssY3Fixdj7dq14uv169fjww8/xMmTJ3HkyBE89NBDOHLkCB555BGTY/f09KC4uBhLlizBNdeYxnCOHpuIiIjInKyeoTNnzmDRokXQ6XRQqVTIyMhAWVkZsrKy0NnZiZqaGmzfvh2tra1Qq9WYOnUqSktLERMTI7bR0NCAsLCrMVhrayt+/etfo6mpCSqVCjfddBM++eQT3HLLLSbH3rdvHxoaGvDggw9anFd4eLhDxyYiIiIyJzuBOpi5moBFRERE3uf1BGoiIiKiYMJgiIiIiEIagyEiIiIKaQyGiIiIKKS5XGcomBhyydvb2318JkREROQow3Pb2TlhDIaMXLhwAQCQmprq4zMhIiIiuS5cuACVSiX7c5xab6SnpwenT59GTEwMLly4gNTUVDQ2Ngb1NHvD4rS8zuDA6wwuoXCdoXCNAK/T0wRBwIULF5CUlGRSy9BR7BkyEhYWhpSUFACAQqEAAMTGxgb1X1wDXmdw4XUGl1C4zlC4RoDX6UnO9AgZMIGaiIiIQhqDISIiIgppDIasUCqV+P3vfx/0q9rzOoMLrzO4hMJ1hsI1ArxOf8cEaiIiIgpp7BkiIiKikMZgiIiIiEIagyEiIiIKaQyGiIiIKKQFRTC0YcMG3HrrrYiKisK1114ruc+qVaswduxYKJVK3HjjjRbv5+XlQaFQWPyJjo62e/wPPvgAEyZMQGRkJOLi4jBnzhyT96Xa/fOf/2yyT01NDSZPnozIyEgkJyfjueees1hjxZ+v86uvvsJ9992H1NRUREZGYvjw4di0aZPJ5+vr6yWPXVZWFjDXCQANDQ2YOXMmoqOjERcXh8ceewx6vd5kH3vfpy+vcfDgwRafWbNmjfj+m2++KdmuQqHA2bNnAQTGd2nvOoHg+G3au85g+W068n2647fp6+s06Orqwo033giFQoEjR46I24Pl92nvOgH3/T4dERQVqPV6PebNmwetVoutW7dK7iMIAh588EEcPnwYX3/9tcX7Tz31FB555BGTbXfccQfGjx9v89g7d+7EsmXLkJ+fj2nTpkEQBNTU1FjsV1xcjBkzZoivjStltre3IysrC1OnTkVlZSWOHz+OpUuXIjo6Gk8++WRAXGdVVRUGDRqEv/71r0hNTUVFRQV+/etfIzw8HCtXrjRpa9++fRg5cqT4esCAASbv+/N1dnd34+6778agQYNw8OBBnDt3DkuWLIEgCHjllVcAOPZ9+vIaAeC5557DsmXLxNf9+vUT//f8+fNN/q4CwNKlS3H58mXEx8ebbPfn7xKwfZ0Ggf7btHedwfLbtHed7vpt+sN1AsDTTz+NpKQkfPXVVybbg+n3CVi/TgN3/D4dIgSR4uJiQaVS2dzn97//vTBmzBi7bR05ckQAIHzyySdW9/npp5+E5ORkYcuWLTbbAiC89957Vt9/9dVXBZVKJVy+fFncVlBQICQlJQk9PT0W+/vrdZpbsWKFMHXqVPF1XV2dAECorq526PP+eJ27d+8WwsLChB9//FHc9vbbbwtKpVJoa2sTBEHe9+ntaxQEQdBoNML/+T//x257BmfPnhX69OkjbN++Xdzm79+lIDh2nYH+2xQE+d+nIATeb1MQ7F+nu3+bguCb6zRcy7Bhw4RvvvnG7vcSqL9PQbB/ne7+fdoSFMNknrBlyxakp6fj9ttvt7rPl19+iR9//BFhYWG46aaboFarkZ2djW+++cZi35UrVyIuLg7jx4/Hn//8Z/T09IjvffbZZ5g8ebJJkao777wTp0+fRn19vVuvy5y7r9NYW1ubxb9EAOCee+5BfHw8MjMz8e6777p8DY5w13V+9tlnGDVqFJKSksRtd955J7q6ulBVVSXu44vv05FrNHjhhRcwcOBA3HjjjdiwYYPFUIKx7du3IyoqCv/+7/9u8Z6/fpcGjlxnIP82DeR8n0Dg/TYNbF2nP/82Acev88yZM1i2bBn+8pe/ICoqym67gfr7dPQ6vfX7ZDAkoaurC2+99RYeeughm/udPHkSQO+Y6TPPPIN//vOf6N+/PyZPnozz58+L+/3nf/4n/va3v2Hfvn1YsGABnnzySeTn54vvNzU1ISEhwaRtw+umpiZ3XZYFd1+nsc8++wzvvPMOli9fLm7r168fNm7ciHfffRe7d+/GHXfcgfnz5+Ovf/2r+y5KgjuvU+q76t+/PyIiIsTvyhffp6PXCPTmAJSUlODjjz/GypUr8fLLL2PFihVW93/jjTewcOFCREZGitv8/bsEHLvOQP9tAvK/z0D8bQL2r9Nff5uA49cpCAKWLl2KRx55BOPGjXOo7UD8fTp6nV79fcrqR/Ki3//+9wIAm38qKytNPuOurr4dO3YI11xzjaDT6Wzu99ZbbwkAhNdee03cdvnyZSEuLk7485//bPVz//Vf/yXExsYG7XUePXpUGDRokPCf//mfJucTDNe5bNkyYfr06Raf7dOnjzB37ly71/jGG2/49BqlvPvuuwIAobm52eK9iooKAYDwxRdfmJyPv3+XUmxdp0Gg/Tal2LrOQP1tSjG/Tlu/zbfffjsgrnPTpk3CrbfeKly5ckUQBPvDXYH6+5R7nQbGv09BEISsrCzh17/+tck+P/zwgwBA+Oyzz2y2Zc5vE6hXrlyJBQsW2Nxn8ODBHjn2li1b8Mtf/hKJiYk291Or1QCAESNGiNuUSiV+8YtfoKGhwernJk6ciPb2dpw5cwYrV65EdXU1Lly4gFdffVXc59tvv8XcuXPx0UcfYdSoUS5ekTRPXOe3336LadOmYdmyZXjmmWfE7da+z3/84x945pln8NVXXwXE95mYmIjDhw+bfK6lpQU//fQTHnjgATz//PNYvXq11e9Tq9W667JMOHqNUiZOnAgA+P777zFw4ECLdm+88UaMHTtW3BYI36UUW9dpvE8g/TalWLvOQP5tSjG/Tlu/zYSEBPzbv/2b33+f+/fvx6FDhyzW9Ro3bhzuv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+class="
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+>
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+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-40">
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+                <span class="notice" hidden>Copied!</span>
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+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">def</span> <span class="nf">cross_correlation</span><span class="p">(</span>
+    <span class="n">ct_file</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span>
+    <span class="n">catalog_file</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span>
+    <span class="n">picks_file</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span>
+    <span class="n">cc_file</span><span class="p">:</span> <span class="n">OutputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span>
+<span class="p">):</span>
+
+    <span class="kn">import</span> <span class="nn">time</span>
+    <span class="kn">from</span> <span class="nn">multiprocessing</span> <span class="kn">import</span> <span class="n">Manager</span><span class="p">,</span> <span class="n">Process</span>
+
+    <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+    <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+    <span class="kn">from</span> <span class="nn">pymongo</span> <span class="kn">import</span> <span class="n">MongoClient</span>
+    <span class="kn">from</span> <span class="nn">tqdm</span> <span class="kn">import</span> <span class="n">tqdm</span>
+
+    <span class="n">catalog</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span>
+        <span class="n">catalog_file</span><span class="p">,</span>
+        <span class="n">sep</span><span class="o">=</span><span class="s2">&quot;</span><span class="se">\t</span><span class="s2">&quot;</span><span class="p">,</span>
+        <span class="n">parse_dates</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;time&quot;</span><span class="p">],</span>
+        <span class="n">index_col</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;event_idx&quot;</span><span class="p">],</span>
+        <span class="n">dtype</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;event_idx&quot;</span><span class="p">:</span> <span class="nb">str</span><span class="p">},</span>
+    <span class="p">)</span>
+    <span class="n">picks</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">picks_file</span><span class="p">,</span> <span class="n">sep</span><span class="o">=</span><span class="s2">&quot;</span><span class="se">\t</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">parse_dates</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;timestamp&quot;</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;event_idx&quot;</span><span class="p">:</span> <span class="nb">str</span><span class="p">})</span>
+    <span class="n">picks</span><span class="p">[</span><span class="s2">&quot;station&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">picks</span><span class="p">[</span><span class="s2">&quot;id&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;.&quot;</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">x</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;.&quot;</span><span class="p">)[</span><span class="mi">3</span><span class="p">])</span>
+    <span class="n">picks</span> <span class="o">=</span> <span class="n">picks</span><span class="o">.</span><span class="n">set_index</span><span class="p">([</span><span class="s2">&quot;event_idx&quot;</span><span class="p">,</span> <span class="s2">&quot;station&quot;</span><span class="p">,</span> <span class="s2">&quot;type&quot;</span><span class="p">])</span>
+    <span class="n">picks</span> <span class="o">=</span> <span class="n">picks</span><span class="o">.</span><span class="n">sort_index</span><span class="p">()</span>
+
+    <span class="n">pick_index</span> <span class="o">=</span> <span class="mi">100</span>
+    <span class="n">lo</span> <span class="o">=</span> <span class="n">pick_index</span> <span class="o">-</span> <span class="mi">50</span>
+    <span class="n">hi</span> <span class="o">=</span> <span class="n">pick_index</span> <span class="o">+</span> <span class="mi">100</span>
+    <span class="n">dt</span> <span class="o">=</span> <span class="mf">0.01</span>
+
+    <span class="n">ct_dict</span> <span class="o">=</span> <span class="n">Manager</span><span class="p">()</span><span class="o">.</span><span class="n">dict</span><span class="p">()</span>
+    <span class="n">cc_dict</span> <span class="o">=</span> <span class="n">Manager</span><span class="p">()</span><span class="o">.</span><span class="n">dict</span><span class="p">()</span>
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">ct_file</span><span class="p">)</span> <span class="k">as</span> <span class="n">fct</span><span class="p">:</span>
+        <span class="n">meta</span> <span class="o">=</span> <span class="n">fct</span><span class="o">.</span><span class="n">readlines</span><span class="p">()</span>
+        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">line</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">meta</span><span class="p">):</span>
+            <span class="k">if</span> <span class="n">line</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;#&quot;</span><span class="p">:</span>
+                <span class="k">if</span> <span class="n">i</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+                    <span class="n">ct_dict</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
+                <span class="n">key</span> <span class="o">=</span> <span class="n">line</span>
+                <span class="n">value</span> <span class="o">=</span> <span class="p">[]</span>
+                <span class="k">continue</span>
+            <span class="n">value</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">line</span><span class="p">)</span>
+        <span class="n">ct_dict</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
+    <span class="n">keys</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">ct_dict</span><span class="o">.</span><span class="n">keys</span><span class="p">()))</span>
+
+    <span class="k">def</span> <span class="nf">calc_cross_correlation</span><span class="p">(</span><span class="n">keys</span><span class="p">,</span> <span class="n">ct_dict</span><span class="p">,</span> <span class="n">cc_dict</span><span class="p">):</span>
+        <span class="n">username</span> <span class="o">=</span> <span class="s2">&quot;root&quot;</span>
+        <span class="n">password</span> <span class="o">=</span> <span class="s2">&quot;quakeflow123&quot;</span>
+        <span class="c1"># client = MongoClient(f&quot;mongodb://{username}:{password}@127.0.0.1:27017&quot;)</span>
+        <span class="n">client</span> <span class="o">=</span> <span class="n">MongoClient</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;mongodb://</span><span class="si">{</span><span class="n">username</span><span class="si">}</span><span class="s2">:</span><span class="si">{</span><span class="n">password</span><span class="si">}</span><span class="s2">@quakeflow-mongodb.default.svc.cluster.local:27017&quot;</span><span class="p">)</span>
+        <span class="n">db</span> <span class="o">=</span> <span class="n">client</span><span class="p">[</span><span class="s2">&quot;quakeflow&quot;</span><span class="p">]</span>
+        <span class="n">collection</span> <span class="o">=</span> <span class="n">db</span><span class="p">[</span><span class="s2">&quot;waveform&quot;</span><span class="p">]</span>
+        <span class="c1"># normalize = lambda x: (x - np.mean(x, axis=0, keepdims=True)) / np.std(x, axis=0, keepdims=True)</span>
+
+        <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">keys</span><span class="p">:</span>
+            <span class="n">tmp</span> <span class="o">=</span> <span class="n">key</span><span class="o">.</span><span class="n">split</span><span class="p">()</span>
+            <span class="n">ID1</span><span class="p">,</span> <span class="n">ID2</span> <span class="o">=</span> <span class="n">tmp</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">tmp</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
+            <span class="n">key_cc</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;#    </span><span class="si">{</span><span class="n">ID1</span><span class="si">}</span><span class="s2">    </span><span class="si">{</span><span class="n">ID2</span><span class="si">}</span><span class="s2">    0.0</span><span class="se">\n</span><span class="s2">&quot;</span>
+            <span class="n">lines_cc</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">ct_dict</span><span class="p">[</span><span class="n">key</span><span class="p">]:</span>
+                <span class="n">tmp</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">split</span><span class="p">()</span>
+                <span class="n">STA</span><span class="p">,</span> <span class="n">TT1</span><span class="p">,</span> <span class="n">TT2</span><span class="p">,</span> <span class="n">WGT</span><span class="p">,</span> <span class="n">PHA</span> <span class="o">=</span> <span class="p">(</span>
+                    <span class="n">tmp</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
+                    <span class="n">tmp</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
+                    <span class="n">tmp</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span>
+                    <span class="n">tmp</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span>
+                    <span class="n">tmp</span><span class="p">[</span><span class="mi">4</span><span class="p">],</span>
+                <span class="p">)</span>  <span class="c1">##HypoDD format</span>
+
+                <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">row1</span> <span class="ow">in</span> <span class="n">picks</span><span class="o">.</span><span class="n">loc</span><span class="p">[(</span><span class="n">ID1</span><span class="p">,</span> <span class="n">STA</span><span class="p">,</span> <span class="n">PHA</span><span class="p">)]</span><span class="o">.</span><span class="n">iterrows</span><span class="p">():</span>
+
+                    <span class="n">data1</span> <span class="o">=</span> <span class="n">collection</span><span class="o">.</span><span class="n">find_one</span><span class="p">(</span>
+                        <span class="p">{</span><span class="s2">&quot;_id&quot;</span><span class="p">:</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">row1</span><span class="p">[</span><span class="s1">&#39;id&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">_</span><span class="si">{</span><span class="n">row1</span><span class="p">[</span><span class="s1">&#39;timestamp&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">isoformat</span><span class="p">(</span><span class="n">timespec</span><span class="o">=</span><span class="s1">&#39;milliseconds&#39;</span><span class="p">)</span><span class="si">}</span><span class="s2">_</span><span class="si">{</span><span class="n">PHA</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">}</span>
+                    <span class="p">)</span>
+
+                    <span class="k">for</span> <span class="n">j</span><span class="p">,</span> <span class="n">row2</span> <span class="ow">in</span> <span class="n">picks</span><span class="o">.</span><span class="n">loc</span><span class="p">[(</span><span class="n">ID2</span><span class="p">,</span> <span class="n">STA</span><span class="p">,</span> <span class="n">PHA</span><span class="p">)]</span><span class="o">.</span><span class="n">iterrows</span><span class="p">():</span>
+
+                        <span class="n">data2</span> <span class="o">=</span> <span class="n">collection</span><span class="o">.</span><span class="n">find_one</span><span class="p">(</span>
+                            <span class="p">{</span><span class="s2">&quot;_id&quot;</span><span class="p">:</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">row2</span><span class="p">[</span><span class="s1">&#39;id&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">_</span><span class="si">{</span><span class="n">row2</span><span class="p">[</span><span class="s1">&#39;timestamp&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">isoformat</span><span class="p">(</span><span class="n">timespec</span><span class="o">=</span><span class="s1">&#39;milliseconds&#39;</span><span class="p">)</span><span class="si">}</span><span class="s2">_</span><span class="si">{</span><span class="n">PHA</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">}</span>
+                        <span class="p">)</span>
+
+                        <span class="c1"># if PHA == &quot;P&quot;:  # Z</span>
+                        <span class="c1">#     waveform1 = np.array(data1[&quot;waveform&quot;])[lo:hi, -1:]</span>
+                        <span class="c1">#     waveform2 = np.array(data2[&quot;waveform&quot;])[lo:hi, -1:]</span>
+                        <span class="c1"># elif PHA == &quot;S&quot;:  # E, N</span>
+                        <span class="c1">#     waveform1 = np.array(data1[&quot;waveform&quot;])[lo:hi, :-1]</span>
+                        <span class="c1">#     waveform2 = np.array(data2[&quot;waveform&quot;])[lo:hi, :-1]</span>
+                        <span class="c1"># else:</span>
+                        <span class="c1">#     raise (Exception(&quot;PHA must be P or S&quot;))</span>
+                        <span class="n">waveform1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">data1</span><span class="p">[</span><span class="s2">&quot;waveform&quot;</span><span class="p">])[</span><span class="n">lo</span><span class="p">:</span><span class="n">hi</span><span class="p">,</span> <span class="p">:]</span>
+                        <span class="n">waveform2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">data2</span><span class="p">[</span><span class="s2">&quot;waveform&quot;</span><span class="p">])[</span><span class="n">lo</span><span class="p">:</span><span class="n">hi</span><span class="p">,</span> <span class="p">:]</span>
+
+                        <span class="n">cc</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">waveform1</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+                        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">waveform1</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]):</span>
+                            <span class="n">cc</span> <span class="o">+=</span> <span class="n">np</span><span class="o">.</span><span class="n">correlate</span><span class="p">(</span><span class="n">waveform1</span><span class="p">[:,</span> <span class="n">k</span><span class="p">],</span> <span class="n">waveform2</span><span class="p">[:,</span> <span class="n">k</span><span class="p">],</span> <span class="n">mode</span><span class="o">=</span><span class="s2">&quot;same&quot;</span><span class="p">)</span>
+                        <span class="n">norm</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">waveform1</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">waveform2</span><span class="o">**</span><span class="mi">2</span><span class="p">))</span>
+                        <span class="k">if</span> <span class="n">norm</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+                            <span class="k">continue</span>
+                        <span class="k">else</span><span class="p">:</span>
+                            <span class="n">cc</span> <span class="o">/=</span> <span class="n">norm</span>
+                        <span class="n">shift</span> <span class="o">=</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">cc</span><span class="p">))</span> <span class="o">-</span> <span class="n">waveform1</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">//</span> <span class="mi">2</span><span class="p">)</span> <span class="o">*</span> <span class="n">dt</span> <span class="o">+</span> <span class="nb">float</span><span class="p">(</span><span class="n">TT1</span><span class="p">)</span> <span class="o">-</span> <span class="nb">float</span><span class="p">(</span><span class="n">TT2</span><span class="p">)</span>
+                        <span class="n">coeff</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">cc</span><span class="p">))</span>
+
+                        <span class="k">if</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">coeff</span><span class="p">):</span>
+                            <span class="n">lines_cc</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">STA</span><span class="si">:</span><span class="s2">&lt;7s</span><span class="si">}</span><span class="s2">    </span><span class="si">{</span><span class="n">shift</span><span class="si">:</span><span class="s2">.5f</span><span class="si">}</span><span class="s2">    </span><span class="si">{</span><span class="n">coeff</span><span class="si">:</span><span class="s2">.3f</span><span class="si">}</span><span class="s2">    </span><span class="si">{</span><span class="n">PHA</span><span class="si">}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
+
+                <span class="n">cc_dict</span><span class="p">[</span><span class="n">key_cc</span><span class="p">]</span> <span class="o">=</span> <span class="n">lines_cc</span>
+
+        <span class="k">return</span> <span class="mi">0</span>
+
+    <span class="n">t0</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+    <span class="n">processes</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">num_process</span> <span class="o">=</span> <span class="mi">16</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_process</span><span class="p">):</span>
+        <span class="n">p</span> <span class="o">=</span> <span class="n">Process</span><span class="p">(</span><span class="n">target</span><span class="o">=</span><span class="n">calc_cross_correlation</span><span class="p">,</span> <span class="n">args</span><span class="o">=</span><span class="p">(</span><span class="n">keys</span><span class="p">[</span><span class="n">i</span><span class="p">::</span><span class="n">num_process</span><span class="p">],</span> <span class="n">ct_dict</span><span class="p">,</span> <span class="n">cc_dict</span><span class="p">))</span>
+        <span class="n">p</span><span class="o">.</span><span class="n">start</span><span class="p">()</span>
+        <span class="n">processes</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">processes</span><span class="p">:</span>
+        <span class="n">p</span><span class="o">.</span><span class="n">join</span><span class="p">()</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">num_process</span><span class="si">}</span><span class="s2"> process: time = </span><span class="si">{</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span><span class="o">-</span><span class="n">t0</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">cc_file</span><span class="p">,</span> <span class="s2">&quot;w&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fcc</span><span class="p">:</span>
+        <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">cc_dict</span><span class="p">:</span>
+            <span class="n">fcc</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
+            <span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">cc_dict</span><span class="p">[</span><span class="n">key</span><span class="p">]:</span>
+                <span class="n">fcc</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">line</span><span class="p">)</span>
+</pre></div>
+<div id="cell-40" class="clipboard-copy-txt">def cross_correlation(
+    ct_file: InputPath(str),
+    catalog_file: InputPath(str),
+    picks_file: InputPath(str),
+    cc_file: OutputPath(str),
+):
+
+    import time
+    from multiprocessing import Manager, Process
+
+    import numpy as np
+    import pandas as pd
+    from pymongo import MongoClient
+    from tqdm import tqdm
+
+    catalog = pd.read_csv(
+        catalog_file,
+        sep="\t",
+        parse_dates=["time"],
+        index_col=["event_idx"],
+        dtype={"event_idx": str},
+    )
+    picks = pd.read_csv(picks_file, sep="\t", parse_dates=["timestamp"], dtype={"event_idx": str})
+    picks["station"] = picks["id"].apply(lambda x: x.split(".")[1] + x.split(".")[3])
+    picks = picks.set_index(["event_idx", "station", "type"])
+    picks = picks.sort_index()
+
+    pick_index = 100
+    lo = pick_index - 50
+    hi = pick_index + 100
+    dt = 0.01
+
+    ct_dict = Manager().dict()
+    cc_dict = Manager().dict()
+    with open(ct_file) as fct:
+        meta = fct.readlines()
+        for i, line in enumerate(meta):
+            if line[0] == "#":
+                if i > 0:
+                    ct_dict[key] = value
+                key = line
+                value = []
+                continue
+            value.append(line)
+        ct_dict[key] = value
+    keys = sorted(list(ct_dict.keys()))
+
+    def calc_cross_correlation(keys, ct_dict, cc_dict):
+        username = "root"
+        password = "quakeflow123"
+        # client = MongoClient(f"mongodb://{username}:{password}@127.0.0.1:27017")
+        client = MongoClient(f"mongodb://{username}:{password}@quakeflow-mongodb.default.svc.cluster.local:27017")
+        db = client["quakeflow"]
+        collection = db["waveform"]
+        # normalize = lambda x: (x - np.mean(x, axis=0, keepdims=True)) / np.std(x, axis=0, keepdims=True)
+
+        for key in keys:
+            tmp = key.split()
+            ID1, ID2 = tmp[1], tmp[2]
+            key_cc = f"#    {ID1}    {ID2}    0.0\n"
+            lines_cc = []
+            for line in ct_dict[key]:
+                tmp = line.split()
+                STA, TT1, TT2, WGT, PHA = (
+                    tmp[0],
+                    tmp[1],
+                    tmp[2],
+                    tmp[3],
+                    tmp[4],
+                )  ##HypoDD format
+
+                for i, row1 in picks.loc[(ID1, STA, PHA)].iterrows():
+
+                    data1 = collection.find_one(
+                        {"_id": f"{row1['id']}_{row1['timestamp'].isoformat(timespec='milliseconds')}_{PHA}"}
+                    )
+
+                    for j, row2 in picks.loc[(ID2, STA, PHA)].iterrows():
+
+                        data2 = collection.find_one(
+                            {"_id": f"{row2['id']}_{row2['timestamp'].isoformat(timespec='milliseconds')}_{PHA}"}
+                        )
+
+                        # if PHA == "P":  # Z
+                        #     waveform1 = np.array(data1["waveform"])[lo:hi, -1:]
+                        #     waveform2 = np.array(data2["waveform"])[lo:hi, -1:]
+                        # elif PHA == "S":  # E, N
+                        #     waveform1 = np.array(data1["waveform"])[lo:hi, :-1]
+                        #     waveform2 = np.array(data2["waveform"])[lo:hi, :-1]
+                        # else:
+                        #     raise (Exception("PHA must be P or S"))
+                        waveform1 = np.array(data1["waveform"])[lo:hi, :]
+                        waveform2 = np.array(data2["waveform"])[lo:hi, :]
+
+                        cc = np.zeros(waveform1.shape[0])
+                        for k in range(waveform1.shape[1]):
+                            cc += np.correlate(waveform1[:, k], waveform2[:, k], mode="same")
+                        norm = np.sqrt(np.sum(waveform1**2) * np.sum(waveform2**2))
+                        if norm == 0:
+                            continue
+                        else:
+                            cc /= norm
+                        shift = (np.argmax(np.abs(cc)) - waveform1.shape[0] // 2) * dt + float(TT1) - float(TT2)
+                        coeff = np.max(np.abs(cc))
+
+                        if not np.isnan(coeff):
+                            lines_cc.append(f"{STA:<7s}    {shift:.5f}    {coeff:.3f}    {PHA}\n")
+
+                cc_dict[key_cc] = lines_cc
+
+        return 0
+
+    t0 = time.time()
+    processes = []
+    num_process = 16
+    for i in range(num_process):
+        p = Process(target=calc_cross_correlation, args=(keys[i::num_process], ct_dict, cc_dict))
+        p.start()
+        processes.append(p)
+    for p in processes:
+        p.join()
+    print(f"{num_process} process: time = {time.time()-t0:.1f}")
+
+    with open(cc_file, "w") as fcc:
+        for key in cc_dict:
+            fcc.write(key)
+            for line in cc_dict[key]:
+                fcc.write(line)</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
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+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">cc_op</span> <span class="o">=</span> <span class="n">comp</span><span class="o">.</span><span class="n">func_to_container_op</span><span class="p">(</span>
+    <span class="n">cross_correlation</span><span class="p">,</span>
+    <span class="n">base_image</span><span class="o">=</span><span class="s2">&quot;python:3.8&quot;</span><span class="p">,</span>
+    <span class="n">packages_to_install</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;pandas&quot;</span><span class="p">,</span> <span class="s2">&quot;tqdm&quot;</span><span class="p">,</span> <span class="s2">&quot;minio&quot;</span><span class="p">,</span> <span class="s2">&quot;pymongo&quot;</span><span class="p">],</span>
+<span class="p">)</span>
+</pre></div>
+<div id="cell-41" class="clipboard-copy-txt">cc_op = comp.func_to_container_op(
+    cross_correlation,
+    base_image="python:3.8",
+    packages_to_install=["pandas", "tqdm", "minio", "pymongo"],
+)</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-42">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">def</span> <span class="nf">hypodd_cc</span><span class="p">(</span>
+    <span class="n">i</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
+    <span class="n">config_json</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="s2">&quot;json&quot;</span><span class="p">),</span>
+    <span class="n">ct_file</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span>
+    <span class="n">cc_file</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span>
+    <span class="n">event</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span>
+    <span class="n">station</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span>
+    <span class="n">inp_file</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;hypodd_cc.inp&quot;</span><span class="p">,</span>
+    <span class="n">bucket_name</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;catalogs&quot;</span><span class="p">,</span>
+    <span class="n">s3_url</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;minio-service:9000&quot;</span><span class="p">,</span>
+    <span class="n">secure</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+<span class="p">):</span>
+    <span class="kn">import</span> <span class="nn">json</span>
+    <span class="kn">import</span> <span class="nn">os</span>
+
+    <span class="kn">from</span> <span class="nn">minio</span> <span class="kn">import</span> <span class="n">Minio</span>
+
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">config_json</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">config</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">fp</span><span class="p">)</span>
+
+    <span class="n">minioClient</span> <span class="o">=</span> <span class="n">Minio</span><span class="p">(</span><span class="n">s3_url</span><span class="p">,</span> <span class="n">access_key</span><span class="o">=</span><span class="s2">&quot;minio&quot;</span><span class="p">,</span> <span class="n">secret_key</span><span class="o">=</span><span class="s2">&quot;minio123&quot;</span><span class="p">,</span> <span class="n">secure</span><span class="o">=</span><span class="n">secure</span><span class="p">)</span>
+
+    <span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;cat </span><span class="si">{</span><span class="n">ct_file</span><span class="si">}</span><span class="s2"> &gt; dt.ct&quot;</span><span class="p">)</span>
+    <span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;cat </span><span class="si">{</span><span class="n">cc_file</span><span class="si">}</span><span class="s2"> &gt; dt.cc&quot;</span><span class="p">)</span>
+    <span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;cat </span><span class="si">{</span><span class="n">event</span><span class="si">}</span><span class="s2"> &gt; event.sel&quot;</span><span class="p">)</span>
+    <span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;cat </span><span class="si">{</span><span class="n">station</span><span class="si">}</span><span class="s2"> &gt; stations_hypodd.dat &quot;</span><span class="p">)</span>
+
+    <span class="n">HYPODD_CMD</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;HYPODD/src/hypodd/hypodd </span><span class="si">{</span><span class="n">inp_file</span><span class="si">}</span><span class="s2">&quot;</span>
+    <span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="n">HYPODD_CMD</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="p">(</span><span class="s2">&quot;</span><span class="si">{HYPODD_CMD}</span><span class="s2">&quot;</span> <span class="o">+</span> <span class="s2">&quot; failed!&quot;</span><span class="p">)</span>
+    <span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;mv hypodd.reloc hypodd_cc_</span><span class="si">{</span><span class="n">i</span><span class="si">:</span><span class="s2">03d</span><span class="si">}</span><span class="s2">.reloc&quot;</span><span class="p">)</span>
+
+    <span class="n">minioClient</span><span class="o">.</span><span class="n">fput_object</span><span class="p">(</span>
+        <span class="n">bucket_name</span><span class="p">,</span>
+        <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;region&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">/hypodd_cc_</span><span class="si">{</span><span class="n">i</span><span class="si">:</span><span class="s2">03d</span><span class="si">}</span><span class="s2">.reloc&quot;</span><span class="p">,</span>
+        <span class="sa">f</span><span class="s2">&quot;hypodd_cc_</span><span class="si">{</span><span class="n">i</span><span class="si">:</span><span class="s2">03d</span><span class="si">}</span><span class="s2">.reloc&quot;</span><span class="p">,</span>
+    <span class="p">)</span>
+</pre></div>
+<div id="cell-42" class="clipboard-copy-txt">def hypodd_cc(
+    i: int,
+    config_json: InputPath("json"),
+    ct_file: InputPath(str),
+    cc_file: InputPath(str),
+    event: InputPath(str),
+    station: InputPath(str),
+    inp_file: str = "hypodd_cc.inp",
+    bucket_name: str = "catalogs",
+    s3_url: str = "minio-service:9000",
+    secure: bool = True,
+):
+    import json
+    import os
+
+    from minio import Minio
+
+    with open(config_json, "r") as fp:
+        config = json.load(fp)
+
+    minioClient = Minio(s3_url, access_key="minio", secret_key="minio123", secure=secure)
+
+    os.system(f"cat {ct_file} > dt.ct")
+    os.system(f"cat {cc_file} > dt.cc")
+    os.system(f"cat {event} > event.sel")
+    os.system(f"cat {station} > stations_hypodd.dat ")
+
+    HYPODD_CMD = f"HYPODD/src/hypodd/hypodd {inp_file}"
+    if os.system(HYPODD_CMD) != 0:
+        raise ("{HYPODD_CMD}" + " failed!")
+    os.system(f"mv hypodd.reloc hypodd_cc_{i:03d}.reloc")
+
+    minioClient.fput_object(
+        bucket_name,
+        f"{config['region']}/hypodd_cc_{i:03d}.reloc",
+        f"hypodd_cc_{i:03d}.reloc",
+    )</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-43">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">hypodd_cc_op</span> <span class="o">=</span> <span class="n">comp</span><span class="o">.</span><span class="n">func_to_container_op</span><span class="p">(</span><span class="n">hypodd_cc</span><span class="p">,</span> <span class="n">base_image</span><span class="o">=</span><span class="s2">&quot;zhuwq0/hypodd-api:1.0&quot;</span><span class="p">)</span>
+</pre></div>
+<div id="cell-43" class="clipboard-copy-txt">hypodd_cc_op = comp.func_to_container_op(hypodd_cc, base_image="zhuwq0/hypodd-api:1.0")</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h3 id="merge-hypodd-results">Merge hypodd results<a class="anchor-link" href="#merge-hypodd-results">&#182;</a></h3>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-44">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">def</span> <span class="nf">merge_hypodd</span><span class="p">(</span>
+    <span class="n">index</span><span class="p">:</span> <span class="nb">list</span><span class="p">,</span>
+    <span class="n">config_json</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="s2">&quot;json&quot;</span><span class="p">),</span>
+    <span class="n">catalog_ct</span><span class="p">:</span> <span class="n">OutputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span>
+    <span class="n">catalog_cc</span><span class="p">:</span> <span class="n">OutputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span>
+    <span class="n">bucket_name</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;catalogs&quot;</span><span class="p">,</span>
+    <span class="n">s3_url</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;minio-service:9000&quot;</span><span class="p">,</span>
+    <span class="n">secure</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+<span class="p">):</span>
+    <span class="kn">import</span> <span class="nn">json</span>
+    <span class="kn">import</span> <span class="nn">os</span>
+    <span class="kn">from</span> <span class="nn">glob</span> <span class="kn">import</span> <span class="n">glob</span>
+
+    <span class="kn">from</span> <span class="nn">minio</span> <span class="kn">import</span> <span class="n">Minio</span>
+
+    <span class="n">minioClient</span> <span class="o">=</span> <span class="n">Minio</span><span class="p">(</span><span class="n">s3_url</span><span class="p">,</span> <span class="n">access_key</span><span class="o">=</span><span class="s2">&quot;minio&quot;</span><span class="p">,</span> <span class="n">secret_key</span><span class="o">=</span><span class="s2">&quot;minio123&quot;</span><span class="p">,</span> <span class="n">secure</span><span class="o">=</span><span class="n">secure</span><span class="p">)</span>
+
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">config_json</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">config</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">fp</span><span class="p">)</span>
+
+    <span class="n">objects</span> <span class="o">=</span> <span class="n">minioClient</span><span class="o">.</span><span class="n">list_objects</span><span class="p">(</span><span class="n">bucket_name</span><span class="p">,</span> <span class="n">prefix</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;region&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">/hypodd/hypodd_&quot;</span><span class="p">,</span> <span class="n">recursive</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+    <span class="n">tmp_path</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="s2">&quot;/tmp/&quot;</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">obj</span> <span class="ow">in</span> <span class="n">objects</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="n">obj</span><span class="o">.</span><span class="n">_object_name</span><span class="p">)</span>
+        <span class="n">minioClient</span><span class="o">.</span><span class="n">fget_object</span><span class="p">(</span><span class="n">bucket_name</span><span class="p">,</span> <span class="n">obj</span><span class="o">.</span><span class="n">_object_name</span><span class="p">,</span> <span class="n">tmp_path</span><span class="p">(</span><span class="n">obj</span><span class="o">.</span><span class="n">_object_name</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;/&quot;</span><span class="p">)[</span><span class="o">-</span><span class="mi">1</span><span class="p">]))</span>
+
+    <span class="c1"># tmp_ct_catalogs = sorted(glob(tmp_path(&quot;hypodd_ct_*.reloc&quot;)))</span>
+    <span class="n">hypodd_ct_catalogs</span> <span class="o">=</span> <span class="p">[</span><span class="n">tmp_path</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;hypodd_ct_</span><span class="si">{</span><span class="n">i</span><span class="si">:</span><span class="s2">03d</span><span class="si">}</span><span class="s2">.reloc&quot;</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">index</span><span class="p">]</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;cat </span><span class="si">{</span><span class="s1">&#39; &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">hypodd_ct_catalogs</span><span class="p">)</span><span class="si">}</span><span class="s2"> &gt; </span><span class="si">{</span><span class="n">tmp_path</span><span class="p">(</span><span class="s1">&#39;hypodd_ct_catalog.txt&#39;</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+    <span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;cat </span><span class="si">{</span><span class="s1">&#39; &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">hypodd_ct_catalogs</span><span class="p">)</span><span class="si">}</span><span class="s2"> &gt; </span><span class="si">{</span><span class="n">tmp_path</span><span class="p">(</span><span class="s1">&#39;hypodd_ct_catalog.txt&#39;</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+    <span class="n">minioClient</span><span class="o">.</span><span class="n">fput_object</span><span class="p">(</span>
+        <span class="n">bucket_name</span><span class="p">,</span>
+        <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;region&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">/hypodd_ct_catalog.txt&quot;</span><span class="p">,</span>
+        <span class="n">tmp_path</span><span class="p">(</span><span class="s2">&quot;hypodd_ct_catalog.txt&quot;</span><span class="p">),</span>
+    <span class="p">)</span>
+    <span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;cat </span><span class="si">{</span><span class="n">tmp_path</span><span class="p">(</span><span class="s1">&#39;hypodd_ct_catalog.txt&#39;</span><span class="p">)</span><span class="si">}</span><span class="s2"> &gt; </span><span class="si">{</span><span class="n">catalog_ct</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+
+    <span class="c1"># hypodd_cc_catalogs = sorted(glob(tmp_path(&quot;hypodd_cc_*.reloc&quot;)))</span>
+    <span class="n">hypodd_cc_catalogs</span> <span class="o">=</span> <span class="p">[</span><span class="n">tmp_path</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;hypodd_cc_</span><span class="si">{</span><span class="n">i</span><span class="si">:</span><span class="s2">03d</span><span class="si">}</span><span class="s2">.reloc&quot;</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">index</span><span class="p">]</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;cat </span><span class="si">{</span><span class="s1">&#39; &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">hypodd_cc_catalogs</span><span class="p">)</span><span class="si">}</span><span class="s2"> &gt; </span><span class="si">{</span><span class="n">tmp_path</span><span class="p">(</span><span class="s1">&#39;hypodd_cc_catalog.txt&#39;</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+    <span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;cat </span><span class="si">{</span><span class="s1">&#39; &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">hypodd_cc_catalogs</span><span class="p">)</span><span class="si">}</span><span class="s2"> &gt; </span><span class="si">{</span><span class="n">tmp_path</span><span class="p">(</span><span class="s1">&#39;hypodd_cc_catalog.txt&#39;</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+    <span class="n">minioClient</span><span class="o">.</span><span class="n">fput_object</span><span class="p">(</span>
+        <span class="n">bucket_name</span><span class="p">,</span>
+        <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;region&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">/hypodd_cc_catalog.txt&quot;</span><span class="p">,</span>
+        <span class="n">tmp_path</span><span class="p">(</span><span class="s2">&quot;hypodd_cc_catalog.txt&quot;</span><span class="p">),</span>
+    <span class="p">)</span>
+    <span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;cat </span><span class="si">{</span><span class="n">tmp_path</span><span class="p">(</span><span class="s1">&#39;hypodd_cc_catalog.txt&#39;</span><span class="p">)</span><span class="si">}</span><span class="s2"> &gt; </span><span class="si">{</span><span class="n">catalog_cc</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="mi">0</span>
+</pre></div>
+<div id="cell-44" class="clipboard-copy-txt">def merge_hypodd(
+    index: list,
+    config_json: InputPath("json"),
+    catalog_ct: OutputPath(str),
+    catalog_cc: OutputPath(str),
+    bucket_name: str = "catalogs",
+    s3_url: str = "minio-service:9000",
+    secure: bool = True,
+):
+    import json
+    import os
+    from glob import glob
+
+    from minio import Minio
+
+    minioClient = Minio(s3_url, access_key="minio", secret_key="minio123", secure=secure)
+
+    with open(config_json, "r") as fp:
+        config = json.load(fp)
+
+    objects = minioClient.list_objects(bucket_name, prefix=f"{config['region']}/hypodd/hypodd_", recursive=True)
+
+    tmp_path = lambda x: os.path.join("/tmp/", x)
+    for obj in objects:
+        print(obj._object_name)
+        minioClient.fget_object(bucket_name, obj._object_name, tmp_path(obj._object_name.split("/")[-1]))
+
+    # tmp_ct_catalogs = sorted(glob(tmp_path("hypodd_ct_*.reloc")))
+    hypodd_ct_catalogs = [tmp_path(f"hypodd_ct_{i:03d}.reloc") for i in index]
+    print(f"cat {' '.join(hypodd_ct_catalogs)} > {tmp_path('hypodd_ct_catalog.txt')}")
+    os.system(f"cat {' '.join(hypodd_ct_catalogs)} > {tmp_path('hypodd_ct_catalog.txt')}")
+    minioClient.fput_object(
+        bucket_name,
+        f"{config['region']}/hypodd_ct_catalog.txt",
+        tmp_path("hypodd_ct_catalog.txt"),
+    )
+    os.system(f"cat {tmp_path('hypodd_ct_catalog.txt')} > {catalog_ct}")
+
+    # hypodd_cc_catalogs = sorted(glob(tmp_path("hypodd_cc_*.reloc")))
+    hypodd_cc_catalogs = [tmp_path(f"hypodd_cc_{i:03d}.reloc") for i in index]
+    print(f"cat {' '.join(hypodd_cc_catalogs)} > {tmp_path('hypodd_cc_catalog.txt')}")
+    os.system(f"cat {' '.join(hypodd_cc_catalogs)} > {tmp_path('hypodd_cc_catalog.txt')}")
+    minioClient.fput_object(
+        bucket_name,
+        f"{config['region']}/hypodd_cc_catalog.txt",
+        tmp_path("hypodd_cc_catalog.txt"),
+    )
+    os.system(f"cat {tmp_path('hypodd_cc_catalog.txt')} > {catalog_cc}")
+
+    return 0</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-45">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">merge_hypodd_op</span> <span class="o">=</span> <span class="n">comp</span><span class="o">.</span><span class="n">func_to_container_op</span><span class="p">(</span>
+    <span class="n">merge_hypodd</span><span class="p">,</span>
+    <span class="n">base_image</span><span class="o">=</span><span class="s2">&quot;python:3.8&quot;</span><span class="p">,</span>
+    <span class="n">packages_to_install</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;pandas&quot;</span><span class="p">,</span> <span class="s2">&quot;tqdm&quot;</span><span class="p">,</span> <span class="s2">&quot;minio&quot;</span><span class="p">],</span>
+<span class="p">)</span>
+</pre></div>
+<div id="cell-45" class="clipboard-copy-txt">merge_hypodd_op = comp.func_to_container_op(
+    merge_hypodd,
+    base_image="python:3.8",
+    packages_to_install=["pandas", "tqdm", "minio"],
+)</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="8-visulization">8. Visulization<a class="anchor-link" href="#8-visulization">&#182;</a></h2>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-46">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">def</span> <span class="nf">visulization</span><span class="p">(</span>
+    <span class="n">config_json</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="s2">&quot;json&quot;</span><span class="p">),</span>
+    <span class="n">hypodd_catalog_ct</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span>
+    <span class="n">hypodd_catalog_cc</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span>
+    <span class="n">gamma_catalog</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span>
+    <span class="n">standard_catalog</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span>
+    <span class="n">data_path</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;./&quot;</span><span class="p">,</span>
+    <span class="n">bucket_name</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;catalogs&quot;</span><span class="p">,</span>
+    <span class="n">s3_url</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;minio-service:9000&quot;</span><span class="p">,</span>
+    <span class="n">secure</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+<span class="p">):</span>
+    <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+    <span class="kn">import</span> <span class="nn">plotly.graph_objects</span> <span class="k">as</span> <span class="nn">go</span>
+    <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+    <span class="kn">import</span> <span class="nn">json</span>
+    <span class="kn">import</span> <span class="nn">os</span>
+    <span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">datetime</span>
+    <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+    <span class="kn">import</span> <span class="nn">matplotlib.dates</span> <span class="k">as</span> <span class="nn">mdates</span>
+
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">config_json</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">config</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">fp</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">plot3d</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">z</span><span class="p">,</span> <span class="n">fig_name</span><span class="p">):</span>
+        <span class="n">fig</span> <span class="o">=</span> <span class="n">go</span><span class="o">.</span><span class="n">Figure</span><span class="p">(</span>
+            <span class="n">data</span><span class="o">=</span><span class="p">[</span>
+                <span class="n">go</span><span class="o">.</span><span class="n">Scatter3d</span><span class="p">(</span>
+                    <span class="n">x</span><span class="o">=</span><span class="n">x</span><span class="p">,</span>
+                    <span class="n">y</span><span class="o">=</span><span class="n">y</span><span class="p">,</span>
+                    <span class="n">z</span><span class="o">=</span><span class="n">z</span><span class="p">,</span>
+                    <span class="n">mode</span><span class="o">=</span><span class="s2">&quot;markers&quot;</span><span class="p">,</span>
+                    <span class="n">marker</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="mf">3.0</span><span class="p">,</span> <span class="n">color</span><span class="o">=-</span><span class="n">z</span><span class="p">,</span> <span class="n">cmin</span><span class="o">=-</span><span class="mi">60</span><span class="p">,</span> <span class="n">cmax</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">colorscale</span><span class="o">=</span><span class="s2">&quot;Viridis&quot;</span><span class="p">,</span> <span class="n">opacity</span><span class="o">=</span><span class="mf">0.8</span><span class="p">),</span>
+                <span class="p">)</span>
+            <span class="p">],</span>
+        <span class="p">)</span>
+
+        <span class="n">fig</span><span class="o">.</span><span class="n">update_layout</span><span class="p">(</span>
+            <span class="n">scene</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span>
+                <span class="n">xaxis</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span>
+                    <span class="n">nticks</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span>
+                    <span class="nb">range</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;xlim_degree&quot;</span><span class="p">],</span>
+                <span class="p">),</span>
+                <span class="n">yaxis</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span>
+                    <span class="n">nticks</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span>
+                    <span class="nb">range</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;ylim_degree&quot;</span><span class="p">],</span>
+                <span class="p">),</span>
+                <span class="n">zaxis</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span>
+                    <span class="n">nticks</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span>
+                    <span class="c1"># range=[z.max(), z.min()],</span>
+                    <span class="nb">range</span><span class="o">=</span><span class="p">[</span><span class="mi">60</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">],</span>
+                <span class="p">),</span>
+                <span class="c1">#         aspectratio = dict(x=(xlim[1]-xlim[0])/2, y=(ylim[1]-ylim[0])/2, z=1),</span>
+                <span class="n">aspectratio</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">z</span><span class="o">=</span><span class="mf">0.5</span><span class="p">),</span>
+            <span class="p">),</span>
+            <span class="n">margin</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">r</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">l</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">b</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">t</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span>
+        <span class="p">)</span>
+        <span class="n">fig</span><span class="o">.</span><span class="n">write_html</span><span class="p">(</span><span class="n">fig_name</span><span class="p">)</span>
+
+    <span class="n">hypodd_ct_catalog</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span>
+        <span class="n">hypodd_catalog_ct</span><span class="p">,</span>
+        <span class="n">sep</span><span class="o">=</span><span class="s2">&quot;\s+&quot;</span><span class="p">,</span>
+        <span class="n">names</span><span class="o">=</span><span class="p">[</span>
+            <span class="s2">&quot;ID&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;LAT&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;LON&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;DEPTH&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;X&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;Y&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;Z&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;EX&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;EY&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;EZ&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;YR&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;MO&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;DY&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;HR&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;MI&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;SC&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;MAG&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;NCCP&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;NCCS&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;NCTP&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;NCTS&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;RCC&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;RCT&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;CID&quot;</span><span class="p">,</span>
+        <span class="p">],</span>
+    <span class="p">)</span>
+    <span class="n">hypodd_ct_catalog</span><span class="p">[</span><span class="s2">&quot;time&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">hypodd_ct_catalog</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span>
+        <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">x</span><span class="p">[</span><span class="s2">&quot;YR&quot;</span><span class="p">]</span><span class="si">:</span><span class="s1">04.0f</span><span class="si">}</span><span class="s1">-</span><span class="si">{</span><span class="n">x</span><span class="p">[</span><span class="s2">&quot;MO&quot;</span><span class="p">]</span><span class="si">:</span><span class="s1">02.0f</span><span class="si">}</span><span class="s1">-</span><span class="si">{</span><span class="n">x</span><span class="p">[</span><span class="s2">&quot;DY&quot;</span><span class="p">]</span><span class="si">:</span><span class="s1">02.0f</span><span class="si">}</span><span class="s1">T</span><span class="si">{</span><span class="n">x</span><span class="p">[</span><span class="s2">&quot;HR&quot;</span><span class="p">]</span><span class="si">:</span><span class="s1">02.0f</span><span class="si">}</span><span class="s1">:</span><span class="si">{</span><span class="n">x</span><span class="p">[</span><span class="s2">&quot;MI&quot;</span><span class="p">]</span><span class="si">:</span><span class="s1">02.0f</span><span class="si">}</span><span class="s1">:</span><span class="si">{</span><span class="nb">min</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="s2">&quot;SC&quot;</span><span class="p">],</span><span class="w"> </span><span class="mf">59.999</span><span class="p">)</span><span class="si">:</span><span class="s1">05.3f</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">,</span>
+        <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+    <span class="p">)</span>
+    <span class="n">hypodd_ct_catalog</span><span class="p">[</span><span class="s2">&quot;magnitude&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">hypodd_ct_catalog</span><span class="p">[</span><span class="s2">&quot;MAG&quot;</span><span class="p">]</span>
+    <span class="n">hypodd_ct_catalog</span><span class="p">[</span><span class="s2">&quot;time&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">hypodd_ct_catalog</span><span class="p">[</span><span class="s2">&quot;time&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">datetime</span><span class="o">.</span><span class="n">strptime</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="s2">&quot;%Y-%m-</span><span class="si">%d</span><span class="s2">T%H:%M:%S.</span><span class="si">%f</span><span class="s2">&quot;</span><span class="p">))</span>
+    <span class="n">plot3d</span><span class="p">(</span>
+        <span class="n">hypodd_ct_catalog</span><span class="p">[</span><span class="s2">&quot;LON&quot;</span><span class="p">],</span>
+        <span class="n">hypodd_ct_catalog</span><span class="p">[</span><span class="s2">&quot;LAT&quot;</span><span class="p">],</span>
+        <span class="n">hypodd_ct_catalog</span><span class="p">[</span><span class="s2">&quot;DEPTH&quot;</span><span class="p">],</span>
+        <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">data_path</span><span class="si">}</span><span class="s2">/hypodd_ct_catalog.html&quot;</span><span class="p">,</span>
+    <span class="p">)</span>
+
+    <span class="n">gamma_catalog</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">gamma_catalog</span><span class="p">,</span> <span class="n">parse_dates</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;time&quot;</span><span class="p">])</span>
+    <span class="n">gamma_catalog</span><span class="p">[</span><span class="s2">&quot;depth_km&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">gamma_catalog</span><span class="p">[</span><span class="s2">&quot;depth(m)&quot;</span><span class="p">]</span> <span class="o">/</span> <span class="mf">1e3</span>
+    <span class="n">plot3d</span><span class="p">(</span>
+        <span class="n">gamma_catalog</span><span class="p">[</span><span class="s2">&quot;longitude&quot;</span><span class="p">],</span>
+        <span class="n">gamma_catalog</span><span class="p">[</span><span class="s2">&quot;latitude&quot;</span><span class="p">],</span>
+        <span class="n">gamma_catalog</span><span class="p">[</span><span class="s2">&quot;depth_km&quot;</span><span class="p">],</span>
+        <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">data_path</span><span class="si">}</span><span class="s2">/gamma_catalog.html&quot;</span><span class="p">,</span>
+    <span class="p">)</span>
+
+    <span class="n">standard_catalog</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">standard_catalog</span><span class="p">,</span> <span class="n">parse_dates</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;time&quot;</span><span class="p">])</span>
+    <span class="n">standard_catalog</span><span class="p">[</span><span class="s2">&quot;depth_km&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">standard_catalog</span><span class="p">[</span><span class="s2">&quot;depth(m)&quot;</span><span class="p">]</span> <span class="o">/</span> <span class="mf">1e3</span>
+    <span class="n">plot3d</span><span class="p">(</span>
+        <span class="n">standard_catalog</span><span class="p">[</span><span class="s2">&quot;longitude&quot;</span><span class="p">],</span>
+        <span class="n">standard_catalog</span><span class="p">[</span><span class="s2">&quot;latitude&quot;</span><span class="p">],</span>
+        <span class="n">standard_catalog</span><span class="p">[</span><span class="s2">&quot;depth_km&quot;</span><span class="p">],</span>
+        <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">data_path</span><span class="si">}</span><span class="s2">/standard_catalog.html&quot;</span><span class="p">,</span>
+    <span class="p">)</span>
+
+    <span class="c1">## histogram</span>
+    <span class="n">bins</span> <span class="o">=</span> <span class="mi">30</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;starttime&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">datetime</span><span class="o">.</span><span class="n">fromisoformat</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;starttime&quot;</span><span class="p">])</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;endtime&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">datetime</span><span class="o">.</span><span class="n">fromisoformat</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;endtime&quot;</span><span class="p">])</span>
+    <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">gamma_catalog</span><span class="p">[</span><span class="s2">&quot;time&quot;</span><span class="p">],</span> <span class="nb">range</span><span class="o">=</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;starttime&quot;</span><span class="p">],</span> <span class="n">config</span><span class="p">[</span><span class="s2">&quot;endtime&quot;</span><span class="p">]),</span> <span class="n">bins</span><span class="o">=</span><span class="n">bins</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s2">&quot;k&quot;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;GaMMA: </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">gamma_catalog</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">hypodd_ct_catalog</span><span class="p">[</span><span class="s2">&quot;time&quot;</span><span class="p">],</span> <span class="nb">range</span><span class="o">=</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;starttime&quot;</span><span class="p">],</span> <span class="n">config</span><span class="p">[</span><span class="s2">&quot;endtime&quot;</span><span class="p">]),</span> <span class="n">bins</span><span class="o">=</span><span class="n">bins</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s2">&quot;k&quot;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.8</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;HypoDD: </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">hypodd_ct_catalog</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">standard_catalog</span><span class="p">[</span><span class="s2">&quot;time&quot;</span><span class="p">],</span> <span class="nb">range</span><span class="o">=</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;starttime&quot;</span><span class="p">],</span> <span class="n">config</span><span class="p">[</span><span class="s2">&quot;endtime&quot;</span><span class="p">]),</span> <span class="n">bins</span><span class="o">=</span><span class="n">bins</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s2">&quot;k&quot;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.6</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;Standard: </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">standard_catalog</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+    <span class="c1"># ax.set_xlabel(&quot;Date&quot;)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;Frequency&quot;</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">autoscale</span><span class="p">(</span><span class="n">enable</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="s1">&#39;x&#39;</span><span class="p">,</span> <span class="n">tight</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="c1"># ax.xaxis.set_major_formatter(mdates.DateFormatter(&#39;%Y-%m-%d&#39;))</span>
+    <span class="n">fig</span><span class="o">.</span><span class="n">autofmt_xdate</span><span class="p">()</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+    <span class="n">fig</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">data_path</span><span class="si">}</span><span class="s2">/earthquake_frequency_time.png&quot;</span><span class="p">,</span> <span class="n">bbox_inches</span><span class="o">=</span><span class="s2">&quot;tight&quot;</span><span class="p">,</span> <span class="n">dpi</span><span class="o">=</span><span class="mi">300</span><span class="p">)</span>
+    <span class="c1"># fig.savefig(f&quot;{data_path}/earthquake_number.pdf&quot;, bbox_inches=&quot;tight&quot;)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+
+    <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
+    <span class="n">xrange</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mf">1.</span><span class="p">,</span> <span class="nb">max</span><span class="p">(</span><span class="n">standard_catalog</span><span class="p">[</span><span class="s2">&quot;magnitude&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">(),</span> <span class="n">gamma_catalog</span><span class="p">[</span><span class="s2">&quot;magnitude&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">()))</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">gamma_catalog</span><span class="p">[</span><span class="s2">&quot;magnitude&quot;</span><span class="p">],</span> <span class="nb">range</span><span class="o">=</span><span class="n">xrange</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="n">bins</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span>  <span class="n">edgecolor</span><span class="o">=</span><span class="s2">&quot;k&quot;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;GaMMA: </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">gamma_catalog</span><span class="p">[</span><span class="s1">&#39;magnitude&#39;</span><span class="p">])</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">hypodd_ct_catalog</span><span class="p">[</span><span class="s2">&quot;magnitude&quot;</span><span class="p">],</span> <span class="nb">range</span><span class="o">=</span><span class="n">xrange</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="n">bins</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.6</span><span class="p">,</span>  <span class="n">edgecolor</span><span class="o">=</span><span class="s2">&quot;k&quot;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;HypoDD: </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">hypodd_ct_catalog</span><span class="p">[</span><span class="s1">&#39;magnitude&#39;</span><span class="p">])</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">standard_catalog</span><span class="p">[</span><span class="s2">&quot;magnitude&quot;</span><span class="p">],</span> <span class="nb">range</span><span class="o">=</span><span class="n">xrange</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="n">bins</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.6</span><span class="p">,</span>  <span class="n">edgecolor</span><span class="o">=</span><span class="s2">&quot;k&quot;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;Standard: </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">standard_catalog</span><span class="p">[</span><span class="s1">&#39;magnitude&#39;</span><span class="p">])</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">autoscale</span><span class="p">(</span><span class="n">enable</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="s1">&#39;x&#39;</span><span class="p">,</span> <span class="n">tight</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;Magnitude&quot;</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;Frequency&quot;</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_yscale</span><span class="p">(</span><span class="s1">&#39;log&#39;</span><span class="p">)</span>
+    <span class="n">fig</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">data_path</span><span class="si">}</span><span class="s2">/earthquake_magnitude_frequency.png&quot;</span><span class="p">,</span> <span class="n">bbox_inches</span><span class="o">=</span><span class="s2">&quot;tight&quot;</span><span class="p">,</span> <span class="n">dpi</span><span class="o">=</span><span class="mi">300</span><span class="p">)</span>
+    <span class="c1"># fig.savefig(f&quot;{data_path}/earthquake_magnitude_frequency.pdf&quot;, bbox_inches=&quot;tight&quot;)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+
+    <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">gamma_catalog</span><span class="p">[</span><span class="s2">&quot;time&quot;</span><span class="p">],</span> <span class="n">gamma_catalog</span><span class="p">[</span><span class="s2">&quot;magnitude&quot;</span><span class="p">],</span> <span class="s1">&#39;.&#39;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mf">5.0</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">rasterized</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;GaMMA: </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">gamma_catalog</span><span class="p">[</span><span class="s1">&#39;magnitude&#39;</span><span class="p">])</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">hypodd_ct_catalog</span><span class="p">[</span><span class="s2">&quot;time&quot;</span><span class="p">],</span> <span class="n">hypodd_ct_catalog</span><span class="p">[</span><span class="s2">&quot;magnitude&quot;</span><span class="p">],</span> <span class="s1">&#39;.&#39;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mf">5.0</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">rasterized</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;HypoDD: </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">hypodd_ct_catalog</span><span class="p">[</span><span class="s1">&#39;magnitude&#39;</span><span class="p">])</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">standard_catalog</span><span class="p">[</span><span class="s2">&quot;time&quot;</span><span class="p">],</span> <span class="n">standard_catalog</span><span class="p">[</span><span class="s2">&quot;magnitude&quot;</span><span class="p">],</span> <span class="s1">&#39;.&#39;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mf">5.0</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">rasterized</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;Standard: </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">standard_catalog</span><span class="p">[</span><span class="s1">&#39;magnitude&#39;</span><span class="p">])</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;starttime&quot;</span><span class="p">],</span> <span class="n">config</span><span class="p">[</span><span class="s2">&quot;endtime&quot;</span><span class="p">])</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;Magnitude&quot;</span><span class="p">)</span>
+    <span class="c1"># ax.set_xlabel(&quot;Date&quot;)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="n">bottom</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">markerscale</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">grid</span><span class="p">()</span>
+    <span class="c1"># ax.xaxis.set_major_formatter(mdates.DateFormatter(&#39;%Y-%m-%d&#39;))</span>
+    <span class="n">fig</span><span class="o">.</span><span class="n">autofmt_xdate</span><span class="p">()</span>
+    <span class="n">fig</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">data_path</span><span class="si">}</span><span class="s2">/earthquake_magnitude_time.png&quot;</span><span class="p">,</span> <span class="n">bbox_inches</span><span class="o">=</span><span class="s2">&quot;tight&quot;</span><span class="p">,</span> <span class="n">dpi</span><span class="o">=</span><span class="mi">300</span><span class="p">)</span>
+    <span class="c1"># fig.savefig(f&quot;{data_path}/earthquake_magnitude_time.pdf&quot;, bbox_inches=&quot;tight&quot;, dpi=300)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+
+    <span class="k">try</span><span class="p">:</span>
+        <span class="n">hypodd_cc_catalog</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span>
+            <span class="n">hypodd_catalog_cc</span><span class="p">,</span>
+            <span class="n">sep</span><span class="o">=</span><span class="s2">&quot;\s+&quot;</span><span class="p">,</span>
+            <span class="n">names</span><span class="o">=</span><span class="p">[</span>
+                <span class="s2">&quot;ID&quot;</span><span class="p">,</span>
+                <span class="s2">&quot;LAT&quot;</span><span class="p">,</span>
+                <span class="s2">&quot;LON&quot;</span><span class="p">,</span>
+                <span class="s2">&quot;DEPTH&quot;</span><span class="p">,</span>
+                <span class="s2">&quot;X&quot;</span><span class="p">,</span>
+                <span class="s2">&quot;Y&quot;</span><span class="p">,</span>
+                <span class="s2">&quot;Z&quot;</span><span class="p">,</span>
+                <span class="s2">&quot;EX&quot;</span><span class="p">,</span>
+                <span class="s2">&quot;EY&quot;</span><span class="p">,</span>
+                <span class="s2">&quot;EZ&quot;</span><span class="p">,</span>
+                <span class="s2">&quot;YR&quot;</span><span class="p">,</span>
+                <span class="s2">&quot;MO&quot;</span><span class="p">,</span>
+                <span class="s2">&quot;DY&quot;</span><span class="p">,</span>
+                <span class="s2">&quot;HR&quot;</span><span class="p">,</span>
+                <span class="s2">&quot;MI&quot;</span><span class="p">,</span>
+                <span class="s2">&quot;SC&quot;</span><span class="p">,</span>
+                <span class="s2">&quot;MAG&quot;</span><span class="p">,</span>
+                <span class="s2">&quot;NCCP&quot;</span><span class="p">,</span>
+                <span class="s2">&quot;NCCS&quot;</span><span class="p">,</span>
+                <span class="s2">&quot;NCTP&quot;</span><span class="p">,</span>
+                <span class="s2">&quot;NCTS&quot;</span><span class="p">,</span>
+                <span class="s2">&quot;RCC&quot;</span><span class="p">,</span>
+                <span class="s2">&quot;RCT&quot;</span><span class="p">,</span>
+                <span class="s2">&quot;CID&quot;</span><span class="p">,</span>
+            <span class="p">],</span>
+        <span class="p">)</span>
+        <span class="n">hypodd_cc_catalog</span><span class="p">[</span><span class="s2">&quot;time&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">hypodd_cc_catalog</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span>
+            <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">x</span><span class="p">[</span><span class="s2">&quot;YR&quot;</span><span class="p">]</span><span class="si">:</span><span class="s1">04.0f</span><span class="si">}</span><span class="s1">-</span><span class="si">{</span><span class="n">x</span><span class="p">[</span><span class="s2">&quot;MO&quot;</span><span class="p">]</span><span class="si">:</span><span class="s1">02.0f</span><span class="si">}</span><span class="s1">-</span><span class="si">{</span><span class="n">x</span><span class="p">[</span><span class="s2">&quot;DY&quot;</span><span class="p">]</span><span class="si">:</span><span class="s1">02.0f</span><span class="si">}</span><span class="s1">T</span><span class="si">{</span><span class="n">x</span><span class="p">[</span><span class="s2">&quot;HR&quot;</span><span class="p">]</span><span class="si">:</span><span class="s1">02.0f</span><span class="si">}</span><span class="s1">:</span><span class="si">{</span><span class="n">x</span><span class="p">[</span><span class="s2">&quot;MI&quot;</span><span class="p">]</span><span class="si">:</span><span class="s1">02.0f</span><span class="si">}</span><span class="s1">:</span><span class="si">{</span><span class="nb">min</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="s2">&quot;SC&quot;</span><span class="p">],</span><span class="w"> </span><span class="mf">59.999</span><span class="p">)</span><span class="si">:</span><span class="s1">05.3f</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">,</span>
+            <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="n">hypodd_cc_catalog</span><span class="p">[</span><span class="s2">&quot;time&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">hypodd_cc_catalog</span><span class="p">[</span><span class="s2">&quot;time&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span>
+            <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">datetime</span><span class="o">.</span><span class="n">strptime</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="s2">&quot;%Y-%m-</span><span class="si">%d</span><span class="s2">T%H:%M:%S.</span><span class="si">%f</span><span class="s2">&quot;</span><span class="p">)</span>
+        <span class="p">)</span>
+        <span class="n">plot3d</span><span class="p">(</span>
+            <span class="n">hypodd_cc_catalog</span><span class="p">[</span><span class="s2">&quot;LON&quot;</span><span class="p">],</span>
+            <span class="n">hypodd_cc_catalog</span><span class="p">[</span><span class="s2">&quot;LAT&quot;</span><span class="p">],</span>
+            <span class="n">hypodd_cc_catalog</span><span class="p">[</span><span class="s2">&quot;DEPTH&quot;</span><span class="p">],</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">data_path</span><span class="si">}</span><span class="s2">/hypodd_cc_catalog.html&quot;</span><span class="p">,</span>
+        <span class="p">)</span>
+    <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">err</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">err</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+
+    <span class="c1">## upload to s3 bucket</span>
+    <span class="k">try</span><span class="p">:</span>
+        <span class="kn">from</span> <span class="nn">minio</span> <span class="kn">import</span> <span class="n">Minio</span>
+
+        <span class="n">minioClient</span> <span class="o">=</span> <span class="n">Minio</span><span class="p">(</span><span class="n">s3_url</span><span class="p">,</span> <span class="n">access_key</span><span class="o">=</span><span class="s2">&quot;minio&quot;</span><span class="p">,</span> <span class="n">secret_key</span><span class="o">=</span><span class="s2">&quot;minio123&quot;</span><span class="p">,</span> <span class="n">secure</span><span class="o">=</span><span class="n">secure</span><span class="p">)</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">minioClient</span><span class="o">.</span><span class="n">bucket_exists</span><span class="p">(</span><span class="n">bucket_name</span><span class="p">):</span>
+            <span class="n">minioClient</span><span class="o">.</span><span class="n">make_bucket</span><span class="p">(</span><span class="n">bucket_name</span><span class="p">)</span>
+
+        <span class="n">minioClient</span><span class="o">.</span><span class="n">fput_object</span><span class="p">(</span>
+            <span class="n">bucket_name</span><span class="p">,</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;region&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">/hypodd_ct_catalog.html&quot;</span><span class="p">,</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">data_path</span><span class="si">}</span><span class="s2">/hypodd_ct_catalog.html&quot;</span><span class="p">,</span>
+        <span class="p">)</span>
+
+        <span class="n">minioClient</span><span class="o">.</span><span class="n">fput_object</span><span class="p">(</span>
+            <span class="n">bucket_name</span><span class="p">,</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;region&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">/gamma_catalog.html&quot;</span><span class="p">,</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">data_path</span><span class="si">}</span><span class="s2">/gamma_catalog.html&quot;</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="n">minioClient</span><span class="o">.</span><span class="n">fput_object</span><span class="p">(</span>
+            <span class="n">bucket_name</span><span class="p">,</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;region&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">/standard_catalog.html&quot;</span><span class="p">,</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">data_path</span><span class="si">}</span><span class="s2">/standard_catalog.html&quot;</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="n">minioClient</span><span class="o">.</span><span class="n">fput_object</span><span class="p">(</span>
+            <span class="n">bucket_name</span><span class="p">,</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;region&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">/earthquake_frequency_time.png&quot;</span><span class="p">,</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">data_path</span><span class="si">}</span><span class="s2">/earthquake_frequency_time.png&quot;</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="n">minioClient</span><span class="o">.</span><span class="n">fput_object</span><span class="p">(</span>
+            <span class="n">bucket_name</span><span class="p">,</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;region&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">/earthquake_magnitude_frequency.png&quot;</span><span class="p">,</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">data_path</span><span class="si">}</span><span class="s2">/earthquake_magnitude_frequency.png&quot;</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="n">minioClient</span><span class="o">.</span><span class="n">fput_object</span><span class="p">(</span>
+            <span class="n">bucket_name</span><span class="p">,</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;region&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">/earthquake_magnitude_time.png&quot;</span><span class="p">,</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">data_path</span><span class="si">}</span><span class="s2">/earthquake_magnitude_time.png&quot;</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">data_path</span><span class="si">}</span><span class="s2">/hypodd_cc_catalog.html&quot;</span><span class="p">):</span>
+            <span class="n">minioClient</span><span class="o">.</span><span class="n">fput_object</span><span class="p">(</span>
+                <span class="n">bucket_name</span><span class="p">,</span>
+                <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;region&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">/hypodd_cc_catalog.html&quot;</span><span class="p">,</span>
+                <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">data_path</span><span class="si">}</span><span class="s2">/hypodd_cc_catalog.html&quot;</span><span class="p">,</span>
+            <span class="p">)</span>
+
+    <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">err</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;ERROR: can not access minio service! </span><span class="se">\n</span><span class="si">{</span><span class="n">err</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+        <span class="k">pass</span>
+</pre></div>
+<div id="cell-46" class="clipboard-copy-txt">def visulization(
+    config_json: InputPath("json"),
+    hypodd_catalog_ct: InputPath(str),
+    hypodd_catalog_cc: InputPath(str),
+    gamma_catalog: InputPath(str),
+    standard_catalog: InputPath(str),
+    data_path: str = "./",
+    bucket_name: str = "catalogs",
+    s3_url: str = "minio-service:9000",
+    secure: bool = True,
+):
+    import pandas as pd
+    import plotly.graph_objects as go
+    import numpy as np
+    import json
+    import os
+    from datetime import datetime
+    import matplotlib.pyplot as plt
+    import matplotlib.dates as mdates
+
+    with open(config_json, "r") as fp:
+        config = json.load(fp)
+
+    def plot3d(x, y, z, fig_name):
+        fig = go.Figure(
+            data=[
+                go.Scatter3d(
+                    x=x,
+                    y=y,
+                    z=z,
+                    mode="markers",
+                    marker=dict(size=3.0, color=-z, cmin=-60, cmax=2, colorscale="Viridis", opacity=0.8),
+                )
+            ],
+        )
+
+        fig.update_layout(
+            scene=dict(
+                xaxis=dict(
+                    nticks=4,
+                    range=config["xlim_degree"],
+                ),
+                yaxis=dict(
+                    nticks=4,
+                    range=config["ylim_degree"],
+                ),
+                zaxis=dict(
+                    nticks=4,
+                    # range=[z.max(), z.min()],
+                    range=[60, -2],
+                ),
+                #         aspectratio = dict(x=(xlim[1]-xlim[0])/2, y=(ylim[1]-ylim[0])/2, z=1),
+                aspectratio=dict(x=1, y=1, z=0.5),
+            ),
+            margin=dict(r=0, l=0, b=0, t=0),
+        )
+        fig.write_html(fig_name)
+
+    hypodd_ct_catalog = pd.read_csv(
+        hypodd_catalog_ct,
+        sep="\s+",
+        names=[
+            "ID",
+            "LAT",
+            "LON",
+            "DEPTH",
+            "X",
+            "Y",
+            "Z",
+            "EX",
+            "EY",
+            "EZ",
+            "YR",
+            "MO",
+            "DY",
+            "HR",
+            "MI",
+            "SC",
+            "MAG",
+            "NCCP",
+            "NCCS",
+            "NCTP",
+            "NCTS",
+            "RCC",
+            "RCT",
+            "CID",
+        ],
+    )
+    hypodd_ct_catalog["time"] = hypodd_ct_catalog.apply(
+        lambda x: f'{x["YR"]:04.0f}-{x["MO"]:02.0f}-{x["DY"]:02.0f}T{x["HR"]:02.0f}:{x["MI"]:02.0f}:{min(x["SC"], 59.999):05.3f}',
+        axis=1,
+    )
+    hypodd_ct_catalog["magnitude"] = hypodd_ct_catalog["MAG"]
+    hypodd_ct_catalog["time"] = hypodd_ct_catalog["time"].apply(lambda x: datetime.strptime(x, "%Y-%m-%dT%H:%M:%S.%f"))
+    plot3d(
+        hypodd_ct_catalog["LON"],
+        hypodd_ct_catalog["LAT"],
+        hypodd_ct_catalog["DEPTH"],
+        f"{data_path}/hypodd_ct_catalog.html",
+    )
+
+    gamma_catalog = pd.read_csv(gamma_catalog, parse_dates=["time"])
+    gamma_catalog["depth_km"] = gamma_catalog["depth(m)"] / 1e3
+    plot3d(
+        gamma_catalog["longitude"],
+        gamma_catalog["latitude"],
+        gamma_catalog["depth_km"],
+        f"{data_path}/gamma_catalog.html",
+    )
+
+    standard_catalog = pd.read_csv(standard_catalog, parse_dates=["time"])
+    standard_catalog["depth_km"] = standard_catalog["depth(m)"] / 1e3
+    plot3d(
+        standard_catalog["longitude"],
+        standard_catalog["latitude"],
+        standard_catalog["depth_km"],
+        f"{data_path}/standard_catalog.html",
+    )
+
+    ## histogram
+    bins = 30
+    config["starttime"] = datetime.fromisoformat(config["starttime"])
+    config["endtime"] = datetime.fromisoformat(config["endtime"])
+    fig, ax = plt.subplots()
+    ax.hist(gamma_catalog["time"], range=(config["starttime"], config["endtime"]), bins=bins, edgecolor="k", alpha=1.0, linewidth=0.5, label=f"GaMMA: {len(gamma_catalog)}")
+    ax.hist(hypodd_ct_catalog["time"], range=(config["starttime"], config["endtime"]), bins=bins, edgecolor="k", alpha=0.8, linewidth=0.5, label=f"HypoDD: {len(hypodd_ct_catalog)}")
+    ax.hist(standard_catalog["time"], range=(config["starttime"], config["endtime"]), bins=bins, edgecolor="k", alpha=0.6, linewidth=0.5, label=f"Standard: {len(standard_catalog)}")
+    # ax.set_xlabel("Date")
+    ax.set_ylabel("Frequency")
+    ax.autoscale(enable=True, axis='x', tight=True)
+    # ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
+    fig.autofmt_xdate()
+    ax.legend()
+    fig.savefig(f"{data_path}/earthquake_frequency_time.png", bbox_inches="tight", dpi=300)
+    # fig.savefig(f"{data_path}/earthquake_number.pdf", bbox_inches="tight")
+    plt.show()
+
+    fig, ax = plt.subplots()
+    xrange = (-1., max(standard_catalog["magnitude"].max(), gamma_catalog["magnitude"].max()))
+    ax.hist(gamma_catalog["magnitude"], range=xrange, bins=bins, alpha=1.0,  edgecolor="k", linewidth=0.5, label=f"GaMMA: {len(gamma_catalog['magnitude'])}")
+    ax.hist(hypodd_ct_catalog["magnitude"], range=xrange, bins=bins, alpha=0.6,  edgecolor="k", linewidth=0.5, label=f"HypoDD: {len(hypodd_ct_catalog['magnitude'])}")
+    ax.hist(standard_catalog["magnitude"], range=xrange, bins=bins, alpha=0.6,  edgecolor="k", linewidth=0.5, label=f"Standard: {len(standard_catalog['magnitude'])}")
+    ax.legend()
+    ax.autoscale(enable=True, axis='x', tight=True)
+    ax.set_xlabel("Magnitude")
+    ax.set_ylabel("Frequency")
+    ax.set_yscale('log')
+    fig.savefig(f"{data_path}/earthquake_magnitude_frequency.png", bbox_inches="tight", dpi=300)
+    # fig.savefig(f"{data_path}/earthquake_magnitude_frequency.pdf", bbox_inches="tight")
+    plt.show()
+
+    fig, ax = plt.subplots()
+    ax.plot(gamma_catalog["time"], gamma_catalog["magnitude"], '.', markersize=5.0, alpha=1.0, rasterized=True, label=f"GaMMA: {len(gamma_catalog['magnitude'])}")
+    ax.plot(hypodd_ct_catalog["time"], hypodd_ct_catalog["magnitude"], '.', markersize=5.0, alpha=1.0, rasterized=True, label=f"HypoDD: {len(hypodd_ct_catalog['magnitude'])}")
+    ax.plot(standard_catalog["time"], standard_catalog["magnitude"], '.', markersize=5.0, alpha=1.0, rasterized=True, label=f"Standard: {len(standard_catalog['magnitude'])}")
+    ax.set_xlim(config["starttime"], config["endtime"])
+    ax.set_ylabel("Magnitude")
+    # ax.set_xlabel("Date")
+    ax.set_ylim(bottom=-1)
+    ax.legend(markerscale=2)
+    ax.grid()
+    # ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
+    fig.autofmt_xdate()
+    fig.savefig(f"{data_path}/earthquake_magnitude_time.png", bbox_inches="tight", dpi=300)
+    # fig.savefig(f"{data_path}/earthquake_magnitude_time.pdf", bbox_inches="tight", dpi=300)
+    plt.show()
+
+    try:
+        hypodd_cc_catalog = pd.read_csv(
+            hypodd_catalog_cc,
+            sep="\s+",
+            names=[
+                "ID",
+                "LAT",
+                "LON",
+                "DEPTH",
+                "X",
+                "Y",
+                "Z",
+                "EX",
+                "EY",
+                "EZ",
+                "YR",
+                "MO",
+                "DY",
+                "HR",
+                "MI",
+                "SC",
+                "MAG",
+                "NCCP",
+                "NCCS",
+                "NCTP",
+                "NCTS",
+                "RCC",
+                "RCT",
+                "CID",
+            ],
+        )
+        hypodd_cc_catalog["time"] = hypodd_cc_catalog.apply(
+            lambda x: f'{x["YR"]:04.0f}-{x["MO"]:02.0f}-{x["DY"]:02.0f}T{x["HR"]:02.0f}:{x["MI"]:02.0f}:{min(x["SC"], 59.999):05.3f}',
+            axis=1,
+        )
+        hypodd_cc_catalog["time"] = hypodd_cc_catalog["time"].apply(
+            lambda x: datetime.strptime(x, "%Y-%m-%dT%H:%M:%S.%f")
+        )
+        plot3d(
+            hypodd_cc_catalog["LON"],
+            hypodd_cc_catalog["LAT"],
+            hypodd_cc_catalog["DEPTH"],
+            f"{data_path}/hypodd_cc_catalog.html",
+        )
+    except Exception as err:
+        print(f"{err}")
+
+    ## upload to s3 bucket
+    try:
+        from minio import Minio
+
+        minioClient = Minio(s3_url, access_key="minio", secret_key="minio123", secure=secure)
+        if not minioClient.bucket_exists(bucket_name):
+            minioClient.make_bucket(bucket_name)
+
+        minioClient.fput_object(
+            bucket_name,
+            f"{config['region']}/hypodd_ct_catalog.html",
+            f"{data_path}/hypodd_ct_catalog.html",
+        )
+
+        minioClient.fput_object(
+            bucket_name,
+            f"{config['region']}/gamma_catalog.html",
+            f"{data_path}/gamma_catalog.html",
+        )
+        minioClient.fput_object(
+            bucket_name,
+            f"{config['region']}/standard_catalog.html",
+            f"{data_path}/standard_catalog.html",
+        )
+        minioClient.fput_object(
+            bucket_name,
+            f"{config['region']}/earthquake_frequency_time.png",
+            f"{data_path}/earthquake_frequency_time.png",
+        )
+        minioClient.fput_object(
+            bucket_name,
+            f"{config['region']}/earthquake_magnitude_frequency.png",
+            f"{data_path}/earthquake_magnitude_frequency.png",
+        )
+        minioClient.fput_object(
+            bucket_name,
+            f"{config['region']}/earthquake_magnitude_time.png",
+            f"{data_path}/earthquake_magnitude_time.png",
+        )
+        if os.path.exists(f"{data_path}/hypodd_cc_catalog.html"):
+            minioClient.fput_object(
+                bucket_name,
+                f"{config['region']}/hypodd_cc_catalog.html",
+                f"{data_path}/hypodd_cc_catalog.html",
+            )
+
+    except Exception as err:
+        print(f"ERROR: can not access minio service! \n{err}")
+        pass</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-47">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">if</span> <span class="n">run_local</span><span class="p">:</span>
+    <span class="n">visulization</span><span class="p">(</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;config.json&quot;</span><span class="p">),</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;hypodd_ct_catalog.txt&quot;</span><span class="p">),</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;hypodd_cc_catalog.txt&quot;</span><span class="p">),</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;gamma_catalog.csv&quot;</span><span class="p">),</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;standard_catalog.csv&quot;</span><span class="p">),</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;&quot;</span><span class="p">),</span>
+    <span class="p">)</span>
+</pre></div>
+<div id="cell-47" class="clipboard-copy-txt">if run_local:
+    visulization(
+        root_dir("config.json"),
+        root_dir("hypodd_ct_catalog.txt"),
+        root_dir("hypodd_cc_catalog.txt"),
+        root_dir("gamma_catalog.csv"),
+        root_dir("standard_catalog.csv"),
+        root_dir(""),
+    )</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-48">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">visulization_op</span> <span class="o">=</span> <span class="n">comp</span><span class="o">.</span><span class="n">func_to_container_op</span><span class="p">(</span>
+    <span class="n">visulization</span><span class="p">,</span>
+    <span class="n">base_image</span><span class="o">=</span><span class="s2">&quot;python:3.8&quot;</span><span class="p">,</span>
+    <span class="n">packages_to_install</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;matplotlib&quot;</span><span class="p">,</span> <span class="s2">&quot;pandas&quot;</span><span class="p">,</span> <span class="s2">&quot;plotly&quot;</span><span class="p">,</span> <span class="s2">&quot;minio&quot;</span><span class="p">],</span>
+<span class="p">)</span>
+</pre></div>
+<div id="cell-48" class="clipboard-copy-txt">visulization_op = comp.func_to_container_op(
+    visulization,
+    base_image="python:3.8",
+    packages_to_install=["matplotlib", "pandas", "plotly", "minio"],
+)</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="9-parallel-process-on-cloud">9. Parallel process on cloud<a class="anchor-link" href="#9-parallel-process-on-cloud">&#182;</a></h2>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-49">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="nd">@dsl</span><span class="o">.</span><span class="n">pipeline</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;QuakeFlow&quot;</span><span class="p">,</span> <span class="n">description</span><span class="o">=</span><span class="s2">&quot;&quot;</span><span class="p">)</span>
+<span class="k">def</span> <span class="nf">quakeflow_pipeline</span><span class="p">(</span>
+    <span class="n">data_path</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;/tmp/&quot;</span><span class="p">,</span>
+    <span class="n">num_parallel</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+    <span class="n">bucket_catalog</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;catalogs&quot;</span><span class="p">,</span>
+    <span class="n">s3_url</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;minio-service:9000&quot;</span><span class="p">,</span>
+    <span class="n">secure</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+<span class="p">):</span>
+
+    <span class="n">config</span> <span class="o">=</span> <span class="n">config_op</span><span class="p">(</span>
+        <span class="n">region_name</span><span class="p">,</span> <span class="n">num_parallel</span><span class="p">,</span> <span class="n">bucket_name</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;catalogs&quot;</span><span class="p">,</span> <span class="n">s3_url</span><span class="o">=</span><span class="n">s3_url</span><span class="p">,</span> <span class="n">secure</span><span class="o">=</span><span class="n">secure</span>
+    <span class="p">)</span><span class="o">.</span><span class="n">add_node_selector_constraint</span><span class="p">(</span><span class="s2">&quot;cloud.google.com/gke-nodepool&quot;</span><span class="p">,</span> <span class="s2">&quot;spot-2g&quot;</span><span class="p">)</span>
+
+    <span class="n">events</span> <span class="o">=</span> <span class="p">(</span>
+        <span class="n">download_events_op</span><span class="p">(</span><span class="n">config</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;config_json&quot;</span><span class="p">],</span> <span class="n">bucket_name</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;catalogs&quot;</span><span class="p">,</span> <span class="n">s3_url</span><span class="o">=</span><span class="n">s3_url</span><span class="p">,</span> <span class="n">secure</span><span class="o">=</span><span class="n">secure</span><span class="p">)</span>
+        <span class="o">.</span><span class="n">add_node_selector_constraint</span><span class="p">(</span><span class="s2">&quot;cloud.google.com/gke-nodepool&quot;</span><span class="p">,</span> <span class="s2">&quot;spot-2g&quot;</span><span class="p">)</span>
+        <span class="o">.</span><span class="n">set_retry</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
+        <span class="o">.</span><span class="n">set_memory_request</span><span class="p">(</span><span class="s2">&quot;1G&quot;</span><span class="p">)</span>
+        <span class="o">.</span><span class="n">set_display_name</span><span class="p">(</span><span class="s2">&quot;Download Events&quot;</span><span class="p">)</span>
+    <span class="p">)</span>
+
+    <span class="n">stations</span> <span class="o">=</span> <span class="p">(</span>
+        <span class="n">download_stations_op</span><span class="p">(</span><span class="n">config</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;config_json&quot;</span><span class="p">],</span> <span class="n">bucket_name</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;catalogs&quot;</span><span class="p">,</span> <span class="n">s3_url</span><span class="o">=</span><span class="n">s3_url</span><span class="p">,</span> <span class="n">secure</span><span class="o">=</span><span class="n">secure</span><span class="p">)</span>
+        <span class="o">.</span><span class="n">add_node_selector_constraint</span><span class="p">(</span><span class="s2">&quot;cloud.google.com/gke-nodepool&quot;</span><span class="p">,</span> <span class="s2">&quot;spot-2g&quot;</span><span class="p">)</span>
+        <span class="o">.</span><span class="n">set_retry</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
+        <span class="o">.</span><span class="n">set_display_name</span><span class="p">(</span><span class="s2">&quot;Download Stations&quot;</span><span class="p">)</span>
+    <span class="p">)</span>
+
+    <span class="k">with</span> <span class="n">kfp</span><span class="o">.</span><span class="n">dsl</span><span class="o">.</span><span class="n">ParallelFor</span><span class="p">(</span><span class="n">config</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;output&quot;</span><span class="p">])</span> <span class="k">as</span> <span class="n">i</span><span class="p">:</span>
+
+        <span class="n">vop_</span> <span class="o">=</span> <span class="n">dsl</span><span class="o">.</span><span class="n">VolumeOp</span><span class="p">(</span>
+            <span class="n">name</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;Create volume </span><span class="si">{</span><span class="n">region_name</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span>
+            <span class="n">resource_name</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;data-volume-</span><span class="si">{</span><span class="nb">str</span><span class="p">(</span><span class="n">i</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span>
+            <span class="n">size</span><span class="o">=</span><span class="s2">&quot;100Gi&quot;</span><span class="p">,</span>
+            <span class="n">modes</span><span class="o">=</span><span class="n">dsl</span><span class="o">.</span><span class="n">VOLUME_MODE_RWO</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="c1"># ).set_retry(3)</span>
+
+        <span class="n">download_op_</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="n">download_waveform_op</span><span class="p">(</span>
+                <span class="n">i</span><span class="p">,</span>
+                <span class="n">config</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;index_json&quot;</span><span class="p">],</span>
+                <span class="n">config</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;config_json&quot;</span><span class="p">],</span>
+                <span class="n">config</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;datetime_json&quot;</span><span class="p">],</span>
+                <span class="n">stations</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;station_pkl&quot;</span><span class="p">],</span>
+                <span class="n">data_path</span><span class="o">=</span><span class="n">data_path</span><span class="p">,</span>
+                <span class="n">bucket_name</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;waveforms&quot;</span><span class="p">,</span>
+                <span class="n">s3_url</span><span class="o">=</span><span class="n">s3_url</span><span class="p">,</span>
+                <span class="n">secure</span><span class="o">=</span><span class="n">secure</span><span class="p">,</span>
+            <span class="p">)</span>
+            <span class="o">.</span><span class="n">add_pvolumes</span><span class="p">({</span><span class="n">data_path</span><span class="p">:</span> <span class="n">vop_</span><span class="o">.</span><span class="n">volume</span><span class="p">})</span>
+            <span class="o">.</span><span class="n">set_cpu_request</span><span class="p">(</span><span class="s2">&quot;700m&quot;</span><span class="p">)</span>
+            <span class="c1"># .set_cpu_request(&quot;350m&quot;)</span>
+            <span class="o">.</span><span class="n">add_node_selector_constraint</span><span class="p">(</span><span class="s2">&quot;cloud.google.com/gke-nodepool&quot;</span><span class="p">,</span> <span class="s2">&quot;spot-2g&quot;</span><span class="p">)</span>
+            <span class="o">.</span><span class="n">set_retry</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
+            <span class="o">.</span><span class="n">set_display_name</span><span class="p">(</span><span class="s2">&quot;Download Waveforms&quot;</span><span class="p">)</span>
+        <span class="p">)</span>
+        <span class="n">download_op_</span><span class="o">.</span><span class="n">execution_options</span><span class="o">.</span><span class="n">caching_strategy</span><span class="o">.</span><span class="n">max_cache_staleness</span> <span class="o">=</span> <span class="s2">&quot;P30D&quot;</span>
+
+        <span class="n">phasenet_op_</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="n">phasenet_op</span><span class="p">(</span>
+                <span class="n">download_op_</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;Output&quot;</span><span class="p">],</span>
+                <span class="n">download_op_</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;fname_csv&quot;</span><span class="p">],</span>
+                <span class="n">stations</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;station_json&quot;</span><span class="p">],</span>
+            <span class="p">)</span>
+            <span class="o">.</span><span class="n">add_pvolumes</span><span class="p">({</span><span class="n">data_path</span><span class="p">:</span> <span class="n">download_op_</span><span class="o">.</span><span class="n">pvolume</span><span class="p">})</span>
+            <span class="o">.</span><span class="n">set_memory_request</span><span class="p">(</span><span class="s2">&quot;9G&quot;</span><span class="p">)</span>
+            <span class="o">.</span><span class="n">set_retry</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
+            <span class="o">.</span><span class="n">set_display_name</span><span class="p">(</span><span class="s2">&quot;PhaseNet Picking&quot;</span><span class="p">)</span>
+        <span class="p">)</span>
+        <span class="n">phasenet_op_</span><span class="o">.</span><span class="n">execution_options</span><span class="o">.</span><span class="n">caching_strategy</span><span class="o">.</span><span class="n">max_cache_staleness</span> <span class="o">=</span> <span class="s2">&quot;P30D&quot;</span>
+        <span class="n">phasenet_op_</span><span class="o">.</span><span class="n">set_image_pull_policy</span><span class="p">(</span><span class="s2">&quot;Always&quot;</span><span class="p">)</span>
+
+        <span class="n">gamma_op_</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="n">gamma_op</span><span class="p">(</span>
+                <span class="n">i</span><span class="p">,</span>
+                <span class="n">config</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;index_json&quot;</span><span class="p">],</span>
+                <span class="n">config</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;config_json&quot;</span><span class="p">],</span>
+                <span class="n">phasenet_op_</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;picks&quot;</span><span class="p">],</span>
+                <span class="n">stations</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;station_json&quot;</span><span class="p">],</span>
+                <span class="n">bucket_name</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;catalogs&quot;</span><span class="p">,</span>
+                <span class="n">s3_url</span><span class="o">=</span><span class="n">s3_url</span><span class="p">,</span>
+                <span class="n">secure</span><span class="o">=</span><span class="n">secure</span><span class="p">,</span>
+            <span class="p">)</span>
+            <span class="o">.</span><span class="n">set_cpu_request</span><span class="p">(</span><span class="s2">&quot;800m&quot;</span><span class="p">)</span>
+            <span class="o">.</span><span class="n">set_retry</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
+            <span class="o">.</span><span class="n">set_display_name</span><span class="p">(</span><span class="s2">&quot;GaMMA Association&quot;</span><span class="p">)</span>
+        <span class="p">)</span>
+        <span class="n">gamma_op_</span><span class="o">.</span><span class="n">execution_options</span><span class="o">.</span><span class="n">caching_strategy</span><span class="o">.</span><span class="n">max_cache_staleness</span> <span class="o">=</span> <span class="s2">&quot;P30D&quot;</span>
+
+    <span class="n">merge_op_</span> <span class="o">=</span> <span class="p">(</span>
+        <span class="n">merge_op</span><span class="p">(</span>
+            <span class="n">config</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;index_json&quot;</span><span class="p">],</span>
+            <span class="n">config</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;config_json&quot;</span><span class="p">],</span>
+            <span class="n">bucket_name</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;catalogs&quot;</span><span class="p">,</span>
+            <span class="n">s3_url</span><span class="o">=</span><span class="n">s3_url</span><span class="p">,</span>
+            <span class="n">secure</span><span class="o">=</span><span class="n">secure</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="o">.</span><span class="n">after</span><span class="p">(</span><span class="n">gamma_op_</span><span class="p">)</span>
+        <span class="o">.</span><span class="n">set_memory_request</span><span class="p">(</span><span class="s2">&quot;12G&quot;</span><span class="p">)</span>
+        <span class="o">.</span><span class="n">add_node_selector_constraint</span><span class="p">(</span><span class="s2">&quot;cloud.google.com/gke-nodepool&quot;</span><span class="p">,</span> <span class="s2">&quot;spot-64g&quot;</span><span class="p">)</span>
+        <span class="o">.</span><span class="n">set_display_name</span><span class="p">(</span><span class="s2">&quot;Merge Catalog&quot;</span><span class="p">)</span>
+    <span class="p">)</span>
+    <span class="n">merge_op_</span><span class="o">.</span><span class="n">execution_options</span><span class="o">.</span><span class="n">caching_strategy</span><span class="o">.</span><span class="n">max_cache_staleness</span> <span class="o">=</span> <span class="s2">&quot;P30D&quot;</span>
+
+    <span class="n">convert_station_op_</span> <span class="o">=</span> <span class="p">(</span>
+        <span class="n">convert_station_op</span><span class="p">(</span>
+            <span class="n">config_json</span><span class="o">=</span><span class="n">config</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;config_json&quot;</span><span class="p">],</span>
+            <span class="n">station_json</span><span class="o">=</span><span class="n">stations</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;station_json&quot;</span><span class="p">],</span>
+            <span class="n">bucket_name</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;catalogs&quot;</span><span class="p">,</span> <span class="n">s3_url</span><span class="o">=</span><span class="n">s3_url</span><span class="p">,</span> <span class="n">secure</span><span class="o">=</span><span class="n">secure</span><span class="p">)</span>
+        <span class="o">.</span><span class="n">add_node_selector_constraint</span><span class="p">(</span><span class="s2">&quot;cloud.google.com/gke-nodepool&quot;</span><span class="p">,</span> <span class="s2">&quot;spot-2g&quot;</span><span class="p">)</span>
+        <span class="o">.</span><span class="n">set_display_name</span><span class="p">(</span><span class="s2">&quot;Convert Station Format&quot;</span><span class="p">)</span>
+    <span class="p">)</span>
+    <span class="n">split_hypodd_op_</span> <span class="o">=</span> <span class="p">(</span>
+        <span class="n">split_hypodd_op</span><span class="p">(</span>
+            <span class="n">config</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;config_json&quot;</span><span class="p">],</span>
+            <span class="n">catalog_csv</span><span class="o">=</span><span class="n">merge_op_</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;catalog_csv&quot;</span><span class="p">],</span>
+        <span class="p">)</span>
+        <span class="o">.</span><span class="n">after</span><span class="p">(</span><span class="n">merge_op_</span><span class="p">)</span>
+        <span class="o">.</span><span class="n">set_display_name</span><span class="p">(</span><span class="s2">&quot;Split Catalog&quot;</span><span class="p">)</span>
+    <span class="p">)</span>
+    <span class="n">split_hypodd_op_</span><span class="o">.</span><span class="n">execution_options</span><span class="o">.</span><span class="n">caching_strategy</span><span class="o">.</span><span class="n">max_cache_staleness</span> <span class="o">=</span> <span class="s2">&quot;P30D&quot;</span>
+    <span class="n">split_hypodd_op_</span><span class="o">.</span><span class="n">set_image_pull_policy</span><span class="p">(</span><span class="s2">&quot;Always&quot;</span><span class="p">)</span>
+
+    <span class="k">with</span> <span class="n">kfp</span><span class="o">.</span><span class="n">dsl</span><span class="o">.</span><span class="n">ParallelFor</span><span class="p">(</span><span class="n">split_hypodd_op_</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;output&quot;</span><span class="p">])</span> <span class="k">as</span> <span class="n">i</span><span class="p">:</span>
+
+        <span class="n">convert_phase_op_</span> <span class="o">=</span> <span class="n">convert_phase_op</span><span class="p">(</span>
+            <span class="n">i</span><span class="p">,</span>
+            <span class="n">config_json</span><span class="o">=</span><span class="n">config</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;config_json&quot;</span><span class="p">],</span>
+            <span class="n">picks_csv</span><span class="o">=</span><span class="n">merge_op_</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;picks_csv&quot;</span><span class="p">],</span>
+            <span class="n">catalog_csv</span><span class="o">=</span><span class="n">merge_op_</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;catalog_csv&quot;</span><span class="p">],</span>
+            <span class="n">bucket_name</span><span class="o">=</span><span class="s2">&quot;catalogs&quot;</span><span class="p">,</span>
+            <span class="n">s3_url</span><span class="o">=</span><span class="n">s3_url</span><span class="p">,</span>
+            <span class="n">secure</span><span class="o">=</span><span class="n">secure</span><span class="p">,</span>
+        <span class="p">)</span><span class="o">.</span><span class="n">set_display_name</span><span class="p">(</span><span class="s2">&quot;Convert Phase Format&quot;</span><span class="p">)</span>
+        <span class="n">convert_phase_op_</span><span class="o">.</span><span class="n">execution_options</span><span class="o">.</span><span class="n">caching_strategy</span><span class="o">.</span><span class="n">max_cache_staleness</span> <span class="o">=</span> <span class="s2">&quot;P30D&quot;</span>
+        <span class="n">convert_phase_op_</span><span class="o">.</span><span class="n">set_image_pull_policy</span><span class="p">(</span><span class="s2">&quot;Always&quot;</span><span class="p">)</span>
+
+        <span class="n">ph2dt_op_</span> <span class="o">=</span> <span class="n">ph2dt_op</span><span class="p">(</span>
+            <span class="n">i</span><span class="p">,</span>
+            <span class="n">config_json</span><span class="o">=</span><span class="n">config</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;config_json&quot;</span><span class="p">],</span>
+            <span class="n">hypodd_phase</span><span class="o">=</span><span class="n">convert_phase_op_</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;hypodd_phase&quot;</span><span class="p">],</span>
+            <span class="n">station_dat</span><span class="o">=</span><span class="n">convert_station_op_</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;hypodd_station&quot;</span><span class="p">],</span>
+            <span class="n">bucket_name</span><span class="o">=</span><span class="s2">&quot;catalogs&quot;</span><span class="p">,</span>
+            <span class="n">s3_url</span><span class="o">=</span><span class="n">s3_url</span><span class="p">,</span>
+            <span class="n">secure</span><span class="o">=</span><span class="n">secure</span><span class="p">,</span>
+        <span class="p">)</span><span class="o">.</span><span class="n">set_display_name</span><span class="p">(</span><span class="s2">&quot;PH2DT&quot;</span><span class="p">)</span>
+        <span class="n">ph2dt_op_</span><span class="o">.</span><span class="n">execution_options</span><span class="o">.</span><span class="n">caching_strategy</span><span class="o">.</span><span class="n">max_cache_staleness</span> <span class="o">=</span> <span class="s2">&quot;P30D&quot;</span>
+        <span class="n">ph2dt_op_</span><span class="o">.</span><span class="n">set_image_pull_policy</span><span class="p">(</span><span class="s2">&quot;Always&quot;</span><span class="p">)</span>
+
+        <span class="n">hypodd_ct_op_</span> <span class="o">=</span> <span class="n">hypodd_ct_op</span><span class="p">(</span>
+            <span class="n">i</span><span class="p">,</span>
+            <span class="n">config_json</span><span class="o">=</span><span class="n">config</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;config_json&quot;</span><span class="p">],</span>
+            <span class="n">ct</span><span class="o">=</span><span class="n">ph2dt_op_</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;ct&quot;</span><span class="p">],</span>
+            <span class="n">event</span><span class="o">=</span><span class="n">ph2dt_op_</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;hypodd_event&quot;</span><span class="p">],</span>
+            <span class="n">station</span><span class="o">=</span><span class="n">convert_station_op_</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;hypodd_station&quot;</span><span class="p">],</span>
+            <span class="n">bucket_name</span><span class="o">=</span><span class="s2">&quot;catalogs&quot;</span><span class="p">,</span>
+            <span class="n">s3_url</span><span class="o">=</span><span class="n">s3_url</span><span class="p">,</span>
+            <span class="n">secure</span><span class="o">=</span><span class="n">secure</span><span class="p">,</span>
+        <span class="p">)</span><span class="o">.</span><span class="n">set_display_name</span><span class="p">(</span><span class="s2">&quot;HypoDD&quot;</span><span class="p">)</span>
+        <span class="n">hypodd_ct_op_</span><span class="o">.</span><span class="n">execution_options</span><span class="o">.</span><span class="n">caching_strategy</span><span class="o">.</span><span class="n">max_cache_staleness</span> <span class="o">=</span> <span class="s2">&quot;P30D&quot;</span>
+        <span class="n">hypodd_ct_op_</span><span class="o">.</span><span class="n">set_image_pull_policy</span><span class="p">(</span><span class="s2">&quot;Always&quot;</span><span class="p">)</span>
+
+        <span class="c1"># cc_op_ = cc_op(</span>
+        <span class="c1">#     ct=ph2dt_op_.outputs[&quot;ct&quot;],</span>
+        <span class="c1">#     picks=merge_op_.outputs[&quot;picks_csv&quot;],</span>
+        <span class="c1">#     catalog=merge_op_.outputs[&quot;catalog_csv&quot;],</span>
+        <span class="c1"># ).set_display_name(&#39;Cross Correlation&#39;)</span>
+        <span class="c1"># cc_op_.execution_options.caching_strategy.max_cache_staleness = &quot;P30D&quot;</span>
+        <span class="c1"># cc_op_.set_image_pull_policy(&quot;Always&quot;)</span>
+
+        <span class="c1"># hypodd_cc_op_ = hypodd_cc_op(</span>
+        <span class="c1">#     i,</span>
+        <span class="c1">#     config_json=config.outputs[&quot;config_json&quot;],</span>
+        <span class="c1">#     ct=ph2dt_op_.outputs[&quot;ct&quot;],</span>
+        <span class="c1">#     cc=cc_op_.outputs[&quot;cc&quot;],</span>
+        <span class="c1">#     event=ph2dt_op_.outputs[&quot;hypodd_event&quot;],</span>
+        <span class="c1">#     station=convert_station_op_.outputs[&quot;hypodd_station&quot;],</span>
+        <span class="c1">#     bucket_name=&quot;catalogs&quot;,</span>
+        <span class="c1">#     s3_url=s3_url,</span>
+        <span class="c1">#     secure=secure,</span>
+        <span class="c1"># ).set_display_name(&#39;HypoDD + CC&#39;)</span>
+        <span class="c1"># hypodd_cc_op_.execution_options.caching_strategy.max_cache_staleness = &quot;P30D&quot;</span>
+        <span class="c1"># hypodd_cc_op_.set_image_pull_policy(&quot;Always&quot;)</span>
+
+    <span class="n">merge_hypodd_op_</span> <span class="o">=</span> <span class="p">(</span>
+        <span class="n">merge_hypodd_op</span><span class="p">(</span>
+            <span class="n">split_hypodd_op_</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;output&quot;</span><span class="p">],</span>
+            <span class="n">config_json</span><span class="o">=</span><span class="n">config</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;config_json&quot;</span><span class="p">],</span>
+            <span class="n">bucket_name</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;catalogs&quot;</span><span class="p">,</span>
+            <span class="n">s3_url</span><span class="o">=</span><span class="n">s3_url</span><span class="p">,</span>
+            <span class="n">secure</span><span class="o">=</span><span class="n">secure</span><span class="p">,</span>
+        <span class="p">)</span><span class="o">.</span><span class="n">after</span><span class="p">(</span><span class="n">hypodd_ct_op_</span><span class="p">)</span>
+        <span class="c1"># .after(hypodd_cc_op_)</span>
+        <span class="o">.</span><span class="n">set_display_name</span><span class="p">(</span><span class="s2">&quot;Merge Catalog&quot;</span><span class="p">)</span>
+    <span class="p">)</span>
+    <span class="n">merge_hypodd_op_</span><span class="o">.</span><span class="n">execution_options</span><span class="o">.</span><span class="n">caching_strategy</span><span class="o">.</span><span class="n">max_cache_staleness</span> <span class="o">=</span> <span class="s2">&quot;P0D&quot;</span>
+    <span class="n">merge_hypodd_op_</span><span class="o">.</span><span class="n">set_image_pull_policy</span><span class="p">(</span><span class="s2">&quot;Always&quot;</span><span class="p">)</span>
+
+    <span class="n">visulization_op_</span> <span class="o">=</span> <span class="p">(</span>
+        <span class="n">visulization_op</span><span class="p">(</span>
+            <span class="n">config_json</span><span class="o">=</span><span class="n">config</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;config_json&quot;</span><span class="p">],</span>
+            <span class="n">hypodd_catalog_ct</span><span class="o">=</span><span class="n">merge_hypodd_op_</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;catalog_ct&quot;</span><span class="p">],</span>
+            <span class="n">hypodd_catalog_cc</span><span class="o">=</span><span class="n">merge_hypodd_op_</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;catalog_cc&quot;</span><span class="p">],</span>
+            <span class="n">gamma_catalog</span><span class="o">=</span><span class="n">merge_op_</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;catalog_csv&quot;</span><span class="p">],</span>
+            <span class="n">standard_catalog</span><span class="o">=</span><span class="n">events</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;standard_catalog&quot;</span><span class="p">],</span>
+            <span class="n">bucket_name</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;catalogs&quot;</span><span class="p">,</span>
+            <span class="n">s3_url</span><span class="o">=</span><span class="n">s3_url</span><span class="p">,</span>
+            <span class="n">secure</span><span class="o">=</span><span class="n">secure</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="o">.</span><span class="n">after</span><span class="p">(</span><span class="n">merge_hypodd_op_</span><span class="p">)</span>
+        <span class="o">.</span><span class="n">set_display_name</span><span class="p">(</span><span class="s2">&quot;Visulization&quot;</span><span class="p">)</span>
+    <span class="p">)</span>
+    <span class="n">visulization_op_</span><span class="o">.</span><span class="n">execution_options</span><span class="o">.</span><span class="n">caching_strategy</span><span class="o">.</span><span class="n">max_cache_staleness</span> <span class="o">=</span> <span class="s2">&quot;P0D&quot;</span>
+    <span class="n">visulization_op_</span><span class="o">.</span><span class="n">set_image_pull_policy</span><span class="p">(</span><span class="s2">&quot;Always&quot;</span><span class="p">)</span>
+
+    <span class="c1"># vop_.delete().after(merge_hypodd_op_)</span>
+</pre></div>
+<div id="cell-49" class="clipboard-copy-txt">@dsl.pipeline(name="QuakeFlow", description="")
+def quakeflow_pipeline(
+    data_path: str = "/tmp/",
+    num_parallel=0,
+    bucket_catalog: str = "catalogs",
+    s3_url: str = "minio-service:9000",
+    secure: bool = False,
+):
+
+    config = config_op(
+        region_name, num_parallel, bucket_name=f"catalogs", s3_url=s3_url, secure=secure
+    ).add_node_selector_constraint("cloud.google.com/gke-nodepool", "spot-2g")
+
+    events = (
+        download_events_op(config.outputs["config_json"], bucket_name=f"catalogs", s3_url=s3_url, secure=secure)
+        .add_node_selector_constraint("cloud.google.com/gke-nodepool", "spot-2g")
+        .set_retry(3)
+        .set_memory_request("1G")
+        .set_display_name("Download Events")
+    )
+
+    stations = (
+        download_stations_op(config.outputs["config_json"], bucket_name=f"catalogs", s3_url=s3_url, secure=secure)
+        .add_node_selector_constraint("cloud.google.com/gke-nodepool", "spot-2g")
+        .set_retry(3)
+        .set_display_name("Download Stations")
+    )
+
+    with kfp.dsl.ParallelFor(config.outputs["output"]) as i:
+
+        vop_ = dsl.VolumeOp(
+            name=f"Create volume {region_name}",
+            resource_name=f"data-volume-{str(i)}",
+            size="100Gi",
+            modes=dsl.VOLUME_MODE_RWO,
+        )
+        # ).set_retry(3)
+
+        download_op_ = (
+            download_waveform_op(
+                i,
+                config.outputs["index_json"],
+                config.outputs["config_json"],
+                config.outputs["datetime_json"],
+                stations.outputs["station_pkl"],
+                data_path=data_path,
+                bucket_name=f"waveforms",
+                s3_url=s3_url,
+                secure=secure,
+            )
+            .add_pvolumes({data_path: vop_.volume})
+            .set_cpu_request("700m")
+            # .set_cpu_request("350m")
+            .add_node_selector_constraint("cloud.google.com/gke-nodepool", "spot-2g")
+            .set_retry(5)
+            .set_display_name("Download Waveforms")
+        )
+        download_op_.execution_options.caching_strategy.max_cache_staleness = "P30D"
+
+        phasenet_op_ = (
+            phasenet_op(
+                download_op_.outputs["Output"],
+                download_op_.outputs["fname_csv"],
+                stations.outputs["station_json"],
+            )
+            .add_pvolumes({data_path: download_op_.pvolume})
+            .set_memory_request("9G")
+            .set_retry(5)
+            .set_display_name("PhaseNet Picking")
+        )
+        phasenet_op_.execution_options.caching_strategy.max_cache_staleness = "P30D"
+        phasenet_op_.set_image_pull_policy("Always")
+
+        gamma_op_ = (
+            gamma_op(
+                i,
+                config.outputs["index_json"],
+                config.outputs["config_json"],
+                phasenet_op_.outputs["picks"],
+                stations.outputs["station_json"],
+                bucket_name=f"catalogs",
+                s3_url=s3_url,
+                secure=secure,
+            )
+            .set_cpu_request("800m")
+            .set_retry(3)
+            .set_display_name("GaMMA Association")
+        )
+        gamma_op_.execution_options.caching_strategy.max_cache_staleness = "P30D"
+
+    merge_op_ = (
+        merge_op(
+            config.outputs["index_json"],
+            config.outputs["config_json"],
+            bucket_name=f"catalogs",
+            s3_url=s3_url,
+            secure=secure,
+        )
+        .after(gamma_op_)
+        .set_memory_request("12G")
+        .add_node_selector_constraint("cloud.google.com/gke-nodepool", "spot-64g")
+        .set_display_name("Merge Catalog")
+    )
+    merge_op_.execution_options.caching_strategy.max_cache_staleness = "P30D"
+
+    convert_station_op_ = (
+        convert_station_op(
+            config_json=config.outputs["config_json"],
+            station_json=stations.outputs["station_json"],
+            bucket_name=f"catalogs", s3_url=s3_url, secure=secure)
+        .add_node_selector_constraint("cloud.google.com/gke-nodepool", "spot-2g")
+        .set_display_name("Convert Station Format")
+    )
+    split_hypodd_op_ = (
+        split_hypodd_op(
+            config.outputs["config_json"],
+            catalog_csv=merge_op_.outputs["catalog_csv"],
+        )
+        .after(merge_op_)
+        .set_display_name("Split Catalog")
+    )
+    split_hypodd_op_.execution_options.caching_strategy.max_cache_staleness = "P30D"
+    split_hypodd_op_.set_image_pull_policy("Always")
+
+    with kfp.dsl.ParallelFor(split_hypodd_op_.outputs["output"]) as i:
+
+        convert_phase_op_ = convert_phase_op(
+            i,
+            config_json=config.outputs["config_json"],
+            picks_csv=merge_op_.outputs["picks_csv"],
+            catalog_csv=merge_op_.outputs["catalog_csv"],
+            bucket_name="catalogs",
+            s3_url=s3_url,
+            secure=secure,
+        ).set_display_name("Convert Phase Format")
+        convert_phase_op_.execution_options.caching_strategy.max_cache_staleness = "P30D"
+        convert_phase_op_.set_image_pull_policy("Always")
+
+        ph2dt_op_ = ph2dt_op(
+            i,
+            config_json=config.outputs["config_json"],
+            hypodd_phase=convert_phase_op_.outputs["hypodd_phase"],
+            station_dat=convert_station_op_.outputs["hypodd_station"],
+            bucket_name="catalogs",
+            s3_url=s3_url,
+            secure=secure,
+        ).set_display_name("PH2DT")
+        ph2dt_op_.execution_options.caching_strategy.max_cache_staleness = "P30D"
+        ph2dt_op_.set_image_pull_policy("Always")
+
+        hypodd_ct_op_ = hypodd_ct_op(
+            i,
+            config_json=config.outputs["config_json"],
+            ct=ph2dt_op_.outputs["ct"],
+            event=ph2dt_op_.outputs["hypodd_event"],
+            station=convert_station_op_.outputs["hypodd_station"],
+            bucket_name="catalogs",
+            s3_url=s3_url,
+            secure=secure,
+        ).set_display_name("HypoDD")
+        hypodd_ct_op_.execution_options.caching_strategy.max_cache_staleness = "P30D"
+        hypodd_ct_op_.set_image_pull_policy("Always")
+
+        # cc_op_ = cc_op(
+        #     ct=ph2dt_op_.outputs["ct"],
+        #     picks=merge_op_.outputs["picks_csv"],
+        #     catalog=merge_op_.outputs["catalog_csv"],
+        # ).set_display_name('Cross Correlation')
+        # cc_op_.execution_options.caching_strategy.max_cache_staleness = "P30D"
+        # cc_op_.set_image_pull_policy("Always")
+
+        # hypodd_cc_op_ = hypodd_cc_op(
+        #     i,
+        #     config_json=config.outputs["config_json"],
+        #     ct=ph2dt_op_.outputs["ct"],
+        #     cc=cc_op_.outputs["cc"],
+        #     event=ph2dt_op_.outputs["hypodd_event"],
+        #     station=convert_station_op_.outputs["hypodd_station"],
+        #     bucket_name="catalogs",
+        #     s3_url=s3_url,
+        #     secure=secure,
+        # ).set_display_name('HypoDD + CC')
+        # hypodd_cc_op_.execution_options.caching_strategy.max_cache_staleness = "P30D"
+        # hypodd_cc_op_.set_image_pull_policy("Always")
+
+    merge_hypodd_op_ = (
+        merge_hypodd_op(
+            split_hypodd_op_.outputs["output"],
+            config_json=config.outputs["config_json"],
+            bucket_name=f"catalogs",
+            s3_url=s3_url,
+            secure=secure,
+        ).after(hypodd_ct_op_)
+        # .after(hypodd_cc_op_)
+        .set_display_name("Merge Catalog")
+    )
+    merge_hypodd_op_.execution_options.caching_strategy.max_cache_staleness = "P0D"
+    merge_hypodd_op_.set_image_pull_policy("Always")
+
+    visulization_op_ = (
+        visulization_op(
+            config_json=config.outputs["config_json"],
+            hypodd_catalog_ct=merge_hypodd_op_.outputs["catalog_ct"],
+            hypodd_catalog_cc=merge_hypodd_op_.outputs["catalog_cc"],
+            gamma_catalog=merge_op_.outputs["catalog_csv"],
+            standard_catalog=events.outputs["standard_catalog"],
+            bucket_name=f"catalogs",
+            s3_url=s3_url,
+            secure=secure,
+        )
+        .after(merge_hypodd_op_)
+        .set_display_name("Visulization")
+    )
+    visulization_op_.execution_options.caching_strategy.max_cache_staleness = "P0D"
+    visulization_op_.set_image_pull_policy("Always")
+
+    # vop_.delete().after(merge_hypodd_op_)</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-50">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
+
+<span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s2">&quot;GOOGLE_APPLICATION_CREDENTIALS&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;/home/weiqiang/.dotbot/cloud/quakeflow_zhuwq.json&quot;</span>
+<span class="n">experiment_name</span> <span class="o">=</span> <span class="s2">&quot;QuakeFlow&quot;</span>
+<span class="n">pipeline_func</span> <span class="o">=</span> <span class="n">quakeflow_pipeline</span>
+<span class="n">run_name</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">pipeline_func</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s2">_</span><span class="si">{</span><span class="n">region_name</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span><span class="si">}</span><span class="s2">&quot;</span>
+
+<span class="n">arguments</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;data_path&quot;</span><span class="p">:</span> <span class="s2">&quot;/tmp&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;num_parallel&quot;</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span>
+    <span class="s2">&quot;bucket_catalog&quot;</span><span class="p">:</span> <span class="s2">&quot;catalogs&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;s3_url&quot;</span><span class="p">:</span> <span class="s2">&quot;minio-service:9000&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;secure&quot;</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span>
+<span class="p">}</span>
+
+<span class="k">if</span> <span class="ow">not</span> <span class="n">run_local</span><span class="p">:</span>
+    <span class="n">pipeline_conf</span> <span class="o">=</span> <span class="n">kfp</span><span class="o">.</span><span class="n">dsl</span><span class="o">.</span><span class="n">PipelineConf</span><span class="p">()</span>
+    <span class="n">pipeline_conf</span><span class="o">.</span><span class="n">set_image_pull_policy</span><span class="p">(</span><span class="s2">&quot;Always&quot;</span><span class="p">)</span>
+    <span class="n">pipeline_conf</span><span class="o">.</span><span class="n">ttl_seconds_after_finished</span> <span class="o">=</span> <span class="mi">60</span> <span class="o">*</span> <span class="mi">10</span>
+    <span class="n">client</span> <span class="o">=</span> <span class="n">kfp</span><span class="o">.</span><span class="n">Client</span><span class="p">(</span><span class="n">host</span><span class="o">=</span><span class="s2">&quot;670838cea8a18858-dot-us-west1.pipelines.googleusercontent.com&quot;</span><span class="p">)</span>
+    <span class="c1"># client = kfp.Client(host=&quot;http://localhost:8080&quot;)</span>
+    <span class="n">kfp</span><span class="o">.</span><span class="n">compiler</span><span class="o">.</span><span class="n">Compiler</span><span class="p">()</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">pipeline_func</span><span class="p">,</span> <span class="s2">&quot;</span><span class="si">{}</span><span class="s2">.zip&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">experiment_name</span><span class="p">),</span> <span class="n">pipeline_conf</span><span class="o">=</span><span class="n">pipeline_conf</span><span class="p">)</span>
+    <span class="n">results</span> <span class="o">=</span> <span class="n">client</span><span class="o">.</span><span class="n">create_run_from_pipeline_func</span><span class="p">(</span>
+        <span class="n">pipeline_func</span><span class="p">,</span>
+        <span class="n">experiment_name</span><span class="o">=</span><span class="n">experiment_name</span><span class="p">,</span>
+        <span class="n">run_name</span><span class="o">=</span><span class="n">run_name</span><span class="p">,</span>
+        <span class="n">arguments</span><span class="o">=</span><span class="n">arguments</span><span class="p">,</span>
+    <span class="p">)</span>
+</pre></div>
+<div id="cell-50" class="clipboard-copy-txt">import os
+
+os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "/home/weiqiang/.dotbot/cloud/quakeflow_zhuwq.json"
+experiment_name = "QuakeFlow"
+pipeline_func = quakeflow_pipeline
+run_name = f"{pipeline_func.__name__}_{region_name.lower()}"
+
+arguments = {
+    "data_path": "/tmp",
+    "num_parallel": 0,
+    "bucket_catalog": "catalogs",
+    "s3_url": "minio-service:9000",
+    "secure": False,
+}
+
+if not run_local:
+    pipeline_conf = kfp.dsl.PipelineConf()
+    pipeline_conf.set_image_pull_policy("Always")
+    pipeline_conf.ttl_seconds_after_finished = 60 * 10
+    client = kfp.Client(host="670838cea8a18858-dot-us-west1.pipelines.googleusercontent.com")
+    # client = kfp.Client(host="http://localhost:8080")
+    kfp.compiler.Compiler().compile(pipeline_func, "{}.zip".format(experiment_name), pipeline_conf=pipeline_conf)
+    results = client.create_run_from_pipeline_func(
+        pipeline_func,
+        experiment_name=experiment_name,
+        run_name=run_name,
+        arguments=arguments,
+    )</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-51">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span>
+</pre></div>
+<div id="cell-51" class="clipboard-copy-txt"></div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+</div>
+
+
+
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+      
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+  
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+          </div>
+        </a>
+      
+    </nav>
+  
+  <div class="md-footer-meta md-typeset">
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+      Material for MkDocs
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+    <script id="__config" type="application/json">{"base": "../..", "features": [], "search": "../../assets/javascripts/workers/search.ecf98df9.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}}</script>
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+      <script src="../../assets/javascripts/bundle.9c69f0bc.min.js"></script>
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+  </body>
+</html>
\ No newline at end of file
diff --git a/kubeflow/workflow_debug/index.html b/kubeflow/workflow_debug/index.html
new file mode 100644
index 0000000..92b543c
--- /dev/null
+++ b/kubeflow/workflow_debug/index.html
@@ -0,0 +1,10336 @@
+
+<!doctype html>
+<html lang="en" class="no-js">
+  <head>
+    
+      <meta charset="utf-8">
+      <meta name="viewport" content="width=device-width,initial-scale=1">
+      
+        <meta name="description" content="QuakeFlow: a Scalable Deep-learning-based Earthquake Monitoring Workflow with Cloud Computing">
+      
+      
+        <meta name="author" content="Weiqiang Zhu">
+      
+      
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+        <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
+      
+    
+    
+    <script>__md_scope=new URL("../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
+    
+      
+  
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+  
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+  <script id="__analytics">function __md_analytics(){function n(){dataLayer.push(arguments)}window.dataLayer=window.dataLayer||[],n("js",new Date),n("config","G-69DX3B35RK"),document.addEventListener("DOMContentLoaded",function(){if(document.forms.search&&document.forms.search.query.addEventListener("blur",function(){this.value&&n("event","search",{search_term:this.value})}),document.forms.feedback){var e,a=document.forms.feedback;for(e of a.querySelectorAll("[type=submit]"))e.addEventListener("click",function(e){e.preventDefault();var t=document.location.pathname,e=this.getAttribute("data-md-value");n("event","feedback",{page:t,data:e}),a.firstElementChild.disabled=!0;e=a.querySelector(".md-feedback__note [data-md-value='"+e+"']");e&&(e.hidden=!1)}),a.hidden=!1}"undefined"!=typeof location$&&location$.subscribe(function(e){n("config","G-69DX3B35RK",{page_path:e.pathname})})});var e=document.createElement("script");e.async=!0,e.src="https://www.googletagmanager.com/gtag/js?id=G-69DX3B35RK",document.getElementById("__analytics").insertAdjacentElement("afterEnd",e)}</script>
+
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+    <script>"undefined"!=typeof __md_analytics&&__md_analytics()</script>
+  
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+  </head>
+  
+  
+    
+    
+    
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+    
+    <body dir="ltr" data-md-color-scheme="" data-md-color-primary="none" data-md-color-accent="none">
+  
+    
+    
+    <input class="md-toggle" data-md-toggle="drawer" type="checkbox" id="__drawer" autocomplete="off">
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+    <label class="md-overlay" for="__drawer"></label>
+    <div data-md-component="skip">
+      
+        
+        <a href="#earthquake-detection-workflow-using-quakeflow" class="md-skip">
+          Skip to content
+        </a>
+      
+    </div>
+    <div data-md-component="announce">
+      
+    </div>
+    
+    
+      
+
+<header class="md-header" data-md-component="header">
+  <nav class="md-header__inner md-grid" aria-label="Header">
+    <a href="../.." title="QuakeFlow" class="md-header__button md-logo" aria-label="QuakeFlow" data-md-component="logo">
+      
+  
+  <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54Z"/></svg>
+
+    </a>
+    <label class="md-header__button md-icon" for="__drawer">
+      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M3 6h18v2H3V6m0 5h18v2H3v-2m0 5h18v2H3v-2Z"/></svg>
+    </label>
+    <div class="md-header__title" data-md-component="header-title">
+      <div class="md-header__ellipsis">
+        <div class="md-header__topic">
+          <span class="md-ellipsis">
+            QuakeFlow
+          </span>
+        </div>
+        <div class="md-header__topic" data-md-component="header-topic">
+          <span class="md-ellipsis">
+            
+              Workflow debug
+            
+          </span>
+        </div>
+      </div>
+    </div>
+    
+    
+    
+    
+      <div class="md-header__source">
+        <a href="https://github.com/ai4eps/QuakeFlow" title="Go to repository" class="md-source" data-md-component="source">
+  <div class="md-source__icon md-icon">
+    
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.1.2 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+  </div>
+  <div class="md-source__repository">
+    AI4EPS/QuakeFlow
+  </div>
+</a>
+      </div>
+    
+  </nav>
+  
+</header>
+    
+    <div class="md-container" data-md-component="container">
+      
+      
+        
+          
+        
+      
+      <main class="md-main" data-md-component="main">
+        <div class="md-main__inner md-grid">
+          
+            
+              
+              <div class="md-sidebar md-sidebar--primary" data-md-component="sidebar" data-md-type="navigation" >
+                <div class="md-sidebar__scrollwrap">
+                  <div class="md-sidebar__inner">
+                    
+
+
+<nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
+  <label class="md-nav__title" for="__drawer">
+    <a href="../.." title="QuakeFlow" class="md-nav__button md-logo" aria-label="QuakeFlow" data-md-component="logo">
+      
+  
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+
+    </a>
+    QuakeFlow
+  </label>
+  
+    <div class="md-nav__source">
+      <a href="https://github.com/ai4eps/QuakeFlow" title="Go to repository" class="md-source" data-md-component="source">
+  <div class="md-source__icon md-icon">
+    
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.1.2 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+  </div>
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+    AI4EPS/QuakeFlow
+  </div>
+</a>
+    </div>
+  
+  <ul class="md-nav__list" data-md-scrollfix>
+    
+      
+      
+      
+
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../.." class="md-nav__link">
+        Overview
+      </a>
+    </li>
+  
+
+    
+      
+      
+      
+
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../workflow/" class="md-nav__link">
+        Pipeline
+      </a>
+    </li>
+  
+
+    
+      
+      
+      
+
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../data_format/" class="md-nav__link">
+        Standard Formats
+      </a>
+    </li>
+  
+
+    
+  </ul>
+</nav>
+                  </div>
+                </div>
+              </div>
+            
+            
+              
+              <div class="md-sidebar md-sidebar--secondary" data-md-component="sidebar" data-md-type="toc" >
+                <div class="md-sidebar__scrollwrap">
+                  <div class="md-sidebar__inner">
+                    
+
+<nav class="md-nav md-nav--secondary" aria-label="Table of contents">
+  
+  
+  
+    
+  
+  
+    <label class="md-nav__title" for="__toc">
+      <span class="md-nav__icon md-icon"></span>
+      Table of contents
+    </label>
+    <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
+      
+        <li class="md-nav__item">
+  <a href="#outline" class="md-nav__link">
+    Outline
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#0-install-miniconda-and-download-packages" class="md-nav__link">
+    0. Install miniconda and download packages
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#1-set-configurations" class="md-nav__link">
+    1. Set configurations
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#2-download-events-of-the-standard-catalog" class="md-nav__link">
+    2. Download events of the standard catalog
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#3-download-stations" class="md-nav__link">
+    3. Download stations
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#4-download-waveform-data" class="md-nav__link">
+    4. Download waveform data
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#5-run-phasenet-to-pick-ps-picks" class="md-nav__link">
+    5. Run PhaseNet to pick P/S picks
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#6-run-gamma-to-associate-ps-picks" class="md-nav__link">
+    6. Run GaMMA to associate P/S picks
+  </a>
+  
+    <nav class="md-nav" aria-label="6. Run GaMMA to associate P/S picks">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#plot-catalogs" class="md-nav__link">
+    Plot catalogs
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#merge-parallel-processing-on-cloud" class="md-nav__link">
+    Merge parallel processing on cloud
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#7-hypodd-earthquake-relocation" class="md-nav__link">
+    7. HypoDD earthquake relocation
+  </a>
+  
+    <nav class="md-nav" aria-label="7. HypoDD earthquake relocation">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#download-and-compile-hypodd" class="md-nav__link">
+    Download and compile HypoDD
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#71-convert-station-format" class="md-nav__link">
+    7.1. Convert station format
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#split-large-catalog-due-to-memory-and-time" class="md-nav__link">
+    Split large catalog due to memory and time
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#72-convert-phase-format" class="md-nav__link">
+    7.2. Convert phase format
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#73-run-ph2dt-to-calculate-differential-time-between-phases" class="md-nav__link">
+    7.3. Run ph2dt to calculate differential time between phases
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#74-run-hypodd-re-location" class="md-nav__link">
+    7.4. Run HypoDD re-location
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#merge-hypodd-results" class="md-nav__link">
+    Merge hypodd results
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#8-visulization" class="md-nav__link">
+    8. Visulization
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#9-parallel-process-on-cloud" class="md-nav__link">
+    9. Parallel process on cloud
+  </a>
+  
+</li>
+      
+    </ul>
+  
+</nav>
+                  </div>
+                </div>
+              </div>
+            
+          
+          <div class="md-content" data-md-component="content">
+            <article class="md-content__inner md-typeset">
+              
+                
+  <a href="https://github.com/ai4eps/QuakeFlow/edit/master/docs/kubeflow/workflow_debug.ipynb" title="Edit this page" class="md-content__button md-icon">
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
+  </a>
+
+
+
+<script>
+(function (global, factory) {
+    typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() :
+    typeof define === 'function' && define.amd ? define(factory) :
+    (global = global || self, global.ClipboardCopyElement = factory());
+  }(this, function () { 'use strict';
+
+    function createNode(text) {
+      const node = document.createElement('pre');
+      node.style.width = '1px';
+      node.style.height = '1px';
+      node.style.position = 'fixed';
+      node.style.top = '5px';
+      node.textContent = text;
+      return node;
+    }
+
+    function copyNode(node) {
+      if ('clipboard' in navigator) {
+        // eslint-disable-next-line flowtype/no-flow-fix-me-comments
+        // $FlowFixMe Clipboard is not defined in Flow yet.
+        return navigator.clipboard.writeText(node.textContent);
+      }
+
+      const selection = getSelection();
+
+      if (selection == null) {
+        return Promise.reject(new Error());
+      }
+
+      selection.removeAllRanges();
+      const range = document.createRange();
+      range.selectNodeContents(node);
+      selection.addRange(range);
+      document.execCommand('copy');
+      selection.removeAllRanges();
+      return Promise.resolve();
+    }
+    function copyText(text) {
+      if ('clipboard' in navigator) {
+        // eslint-disable-next-line flowtype/no-flow-fix-me-comments
+        // $FlowFixMe Clipboard is not defined in Flow yet.
+        return navigator.clipboard.writeText(text);
+      }
+
+      const body = document.body;
+
+      if (!body) {
+        return Promise.reject(new Error());
+      }
+
+      const node = createNode(text);
+      body.appendChild(node);
+      copyNode(node);
+      body.removeChild(node);
+      return Promise.resolve();
+    }
+
+    function copy(button) {
+      const id = button.getAttribute('for');
+      const text = button.getAttribute('value');
+
+      function trigger() {
+        button.dispatchEvent(new CustomEvent('clipboard-copy', {
+          bubbles: true
+        }));
+      }
+
+      if (text) {
+        copyText(text).then(trigger);
+      } else if (id) {
+        const root = 'getRootNode' in Element.prototype ? button.getRootNode() : button.ownerDocument;
+        if (!(root instanceof Document || 'ShadowRoot' in window && root instanceof ShadowRoot)) return;
+        const node = root.getElementById(id);
+        if (node) copyTarget(node).then(trigger);
+      }
+    }
+
+    function copyTarget(content) {
+      if (content instanceof HTMLInputElement || content instanceof HTMLTextAreaElement) {
+        return copyText(content.value);
+      } else if (content instanceof HTMLAnchorElement && content.hasAttribute('href')) {
+        return copyText(content.href);
+      } else {
+        return copyNode(content);
+      }
+    }
+
+    function clicked(event) {
+      const button = event.currentTarget;
+
+      if (button instanceof HTMLElement) {
+        copy(button);
+      }
+    }
+
+    function keydown(event) {
+      if (event.key === ' ' || event.key === 'Enter') {
+        const button = event.currentTarget;
+
+        if (button instanceof HTMLElement) {
+          event.preventDefault();
+          copy(button);
+        }
+      }
+    }
+
+    function focused(event) {
+      event.currentTarget.addEventListener('keydown', keydown);
+    }
+
+    function blurred(event) {
+      event.currentTarget.removeEventListener('keydown', keydown);
+    }
+
+    class ClipboardCopyElement extends HTMLElement {
+      constructor() {
+        super();
+        this.addEventListener('click', clicked);
+        this.addEventListener('focus', focused);
+        this.addEventListener('blur', blurred);
+      }
+
+      connectedCallback() {
+        if (!this.hasAttribute('tabindex')) {
+          this.setAttribute('tabindex', '0');
+        }
+
+        if (!this.hasAttribute('role')) {
+          this.setAttribute('role', 'button');
+        }
+      }
+
+      get value() {
+        return this.getAttribute('value') || '';
+      }
+
+      set value(text) {
+        this.setAttribute('value', text);
+      }
+
+    }
+
+    if (!window.customElements.get('clipboard-copy')) {
+      window.ClipboardCopyElement = ClipboardCopyElement;
+      window.customElements.define('clipboard-copy', ClipboardCopyElement);
+    }
+
+    return ClipboardCopyElement;
+
+  }));
+</script>
+<script>
+      document.addEventListener('clipboard-copy', function(event) {
+        const notice = event.target.querySelector('.notice')
+        notice.hidden = false
+        setTimeout(function() {
+          notice.hidden = true
+        }, 1000)
+      })
+</script>
+
+
+
+
+<style type="text/css">
+    
+    pre { line-height: 125%; }
+td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+.highlight-ipynb .hll { background-color: var(--jp-cell-editor-active-background) }
+.highlight-ipynb { background: var(--jp-cell-editor-background); color: var(--jp-mirror-editor-variable-color) }
+.highlight-ipynb .c { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment */
+.highlight-ipynb .err { color: var(--jp-mirror-editor-error-color) } /* Error */
+.highlight-ipynb .k { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword */
+.highlight-ipynb .o { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator */
+.highlight-ipynb .p { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation */
+.highlight-ipynb .ch { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Hashbang */
+.highlight-ipynb .cm { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Multiline */
+.highlight-ipynb .cp { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Preproc */
+.highlight-ipynb .cpf { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.PreprocFile */
+.highlight-ipynb .c1 { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Single */
+.highlight-ipynb .cs { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Special */
+.highlight-ipynb .kc { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Constant */
+.highlight-ipynb .kd { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Declaration */
+.highlight-ipynb .kn { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Namespace */
+.highlight-ipynb .kp { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Pseudo */
+.highlight-ipynb .kr { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Reserved */
+.highlight-ipynb .kt { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Type */
+.highlight-ipynb .m { color: var(--jp-mirror-editor-number-color) } /* Literal.Number */
+.highlight-ipynb .s { color: var(--jp-mirror-editor-string-color) } /* Literal.String */
+.highlight-ipynb .ow { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator.Word */
+.highlight-ipynb .pm { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation.Marker */
+.highlight-ipynb .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */
+.highlight-ipynb .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */
+.highlight-ipynb .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */
+.highlight-ipynb .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */
+.highlight-ipynb .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */
+.highlight-ipynb .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */
+.highlight-ipynb .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */
+.highlight-ipynb .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */
+.highlight-ipynb .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */
+.highlight-ipynb .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */
+.highlight-ipynb .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */
+.highlight-ipynb .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */
+.highlight-ipynb .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */
+.highlight-ipynb .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */
+.highlight-ipynb .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */
+.highlight-ipynb .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */
+.highlight-ipynb .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */
+.highlight-ipynb .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */
+.highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */
+.highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */
+  </style>
+
+
+
+
+<style type="text/css">
+/*
+This file is taken from the built JupyterLab theme.css
+Found on share/nbconvert/templates/lab/static
+Some changes have been made and marked with CHANGE
+*/
+
+.jupyter-wrapper {
+    /* Elevation
+   *
+   * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here:
+   *
+   * https://github.com/material-components/material-components-web
+   * https://material-components-web.appspot.com/elevation.html
+   */
+
+    --jp-shadow-base-lightness: 0;
+    --jp-shadow-umbra-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.2
+    );
+    --jp-shadow-penumbra-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.14
+    );
+    --jp-shadow-ambient-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.12
+    );
+    --jp-elevation-z0: none;
+    --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color),
+        0px 1px 1px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 3px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color),
+        0px 2px 2px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 5px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color),
+        0px 4px 5px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 10px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color),
+        0px 6px 10px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 18px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color),
+        0px 8px 10px 1px var(--jp-shadow-penumbra-color),
+        0px 3px 14px 2px var(--jp-shadow-ambient-color);
+    --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color),
+        0px 12px 17px 2px var(--jp-shadow-penumbra-color),
+        0px 5px 22px 4px var(--jp-shadow-ambient-color);
+    --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color),
+        0px 16px 24px 2px var(--jp-shadow-penumbra-color),
+        0px 6px 30px 5px var(--jp-shadow-ambient-color);
+    --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color),
+        0px 20px 31px 3px var(--jp-shadow-penumbra-color),
+        0px 8px 38px 7px var(--jp-shadow-ambient-color);
+    --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color),
+        0px 24px 38px 3px var(--jp-shadow-penumbra-color),
+        0px 9px 46px 8px var(--jp-shadow-ambient-color);
+
+    /* Borders
+   *
+   * The following variables, specify the visual styling of borders in JupyterLab.
+   */
+
+    --jp-border-width: 1px;
+    --jp-border-color0: var(--md-grey-400);
+    --jp-border-color1: var(--md-grey-400);
+    --jp-border-color2: var(--md-grey-300);
+    --jp-border-color3: var(--md-grey-200);
+    --jp-border-radius: 2px;
+
+    /* UI Fonts
+   *
+   * The UI font CSS variables are used for the typography all of the JupyterLab
+   * user interface elements that are not directly user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-ui-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+    --jp-ui-font-scale-factor: 1.2;
+    --jp-ui-font-size0: 0.83333em;
+    --jp-ui-font-size1: 13px; /* Base font size */
+    --jp-ui-font-size2: 1.2em;
+    --jp-ui-font-size3: 1.44em;
+
+    --jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+
+    /*
+   * Use these font colors against the corresponding main layout colors.
+   * In a light theme, these go from dark to light.
+   */
+
+    /* Defaults use Material Design specification */
+    --jp-ui-font-color0: rgba(0, 0, 0, 1);
+    --jp-ui-font-color1: rgba(0, 0, 0, 0.87);
+    --jp-ui-font-color2: rgba(0, 0, 0, 0.54);
+    --jp-ui-font-color3: rgba(0, 0, 0, 0.38);
+
+    /*
+   * Use these against the brand/accent/warn/error colors.
+   * These will typically go from light to darker, in both a dark and light theme.
+   */
+
+    --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1);
+    --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1);
+    --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7);
+    --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5);
+
+    /* Content Fonts
+   *
+   * Content font variables are used for typography of user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-content-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+    --jp-content-line-height: 1.6;
+    --jp-content-font-scale-factor: 1.2;
+    --jp-content-font-size0: 0.83333em;
+    --jp-content-font-size1: 14px; /* Base font size */
+    --jp-content-font-size2: 1.2em;
+    --jp-content-font-size3: 1.44em;
+    --jp-content-font-size4: 1.728em;
+    --jp-content-font-size5: 2.0736em;
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-content-presentation-font-size1: 17px;
+
+    --jp-content-heading-line-height: 1;
+    --jp-content-heading-margin-top: 1.2em;
+    --jp-content-heading-margin-bottom: 0.8em;
+    --jp-content-heading-font-weight: 500;
+
+    /* Defaults use Material Design specification */
+    --jp-content-font-color0: rgba(0, 0, 0, 1);
+    --jp-content-font-color1: rgba(0, 0, 0, 0.87);
+    --jp-content-font-color2: rgba(0, 0, 0, 0.54);
+    --jp-content-font-color3: rgba(0, 0, 0, 0.38);
+
+    --jp-content-link-color: var(--md-blue-700);
+
+    --jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+
+    /*
+   * Code Fonts
+   *
+   * Code font variables are used for typography of code and other monospaces content.
+   */
+
+    --jp-code-font-size: 13px;
+    --jp-code-line-height: 1.3077; /* 17px for 13px base */
+    --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */
+    --jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono",
+        monospace;
+    --jp-code-font-family: var(--jp-code-font-family-default);
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-code-presentation-font-size: 16px;
+
+    /* may need to tweak cursor width if you change font size */
+    --jp-code-cursor-width0: 1.4px;
+    --jp-code-cursor-width1: 2px;
+    --jp-code-cursor-width2: 4px;
+
+    /* Layout
+   *
+   * The following are the main layout colors use in JupyterLab. In a light
+   * theme these would go from light to dark.
+   */
+
+    --jp-layout-color0: white;
+    --jp-layout-color1: white;
+    --jp-layout-color2: var(--md-grey-200);
+    --jp-layout-color3: var(--md-grey-400);
+    --jp-layout-color4: var(--md-grey-600);
+
+    /* Inverse Layout
+   *
+   * The following are the inverse layout colors use in JupyterLab. In a light
+   * theme these would go from dark to light.
+   */
+
+    --jp-inverse-layout-color0: #111111;
+    --jp-inverse-layout-color1: var(--md-grey-900);
+    --jp-inverse-layout-color2: var(--md-grey-800);
+    --jp-inverse-layout-color3: var(--md-grey-700);
+    --jp-inverse-layout-color4: var(--md-grey-600);
+
+    /* Brand/accent */
+
+    --jp-brand-color0: var(--md-blue-900);
+    --jp-brand-color1: var(--md-blue-700);
+    --jp-brand-color2: var(--md-blue-300);
+    --jp-brand-color3: var(--md-blue-100);
+    --jp-brand-color4: var(--md-blue-50);
+
+    --jp-accent-color0: var(--md-green-900);
+    --jp-accent-color1: var(--md-green-700);
+    --jp-accent-color2: var(--md-green-300);
+    --jp-accent-color3: var(--md-green-100);
+
+    /* State colors (warn, error, success, info) */
+
+    --jp-warn-color0: var(--md-orange-900);
+    --jp-warn-color1: var(--md-orange-700);
+    --jp-warn-color2: var(--md-orange-300);
+    --jp-warn-color3: var(--md-orange-100);
+
+    --jp-error-color0: var(--md-red-900);
+    --jp-error-color1: var(--md-red-700);
+    --jp-error-color2: var(--md-red-300);
+    --jp-error-color3: var(--md-red-100);
+
+    --jp-success-color0: var(--md-green-900);
+    --jp-success-color1: var(--md-green-700);
+    --jp-success-color2: var(--md-green-300);
+    --jp-success-color3: var(--md-green-100);
+
+    --jp-info-color0: var(--md-cyan-900);
+    --jp-info-color1: var(--md-cyan-700);
+    --jp-info-color2: var(--md-cyan-300);
+    --jp-info-color3: var(--md-cyan-100);
+
+    /* Cell specific styles */
+
+    --jp-cell-padding: 5px;
+
+    --jp-cell-collapser-width: 8px;
+    --jp-cell-collapser-min-height: 20px;
+    --jp-cell-collapser-not-active-hover-opacity: 0.6;
+
+    --jp-cell-editor-background: var(--md-grey-100);
+    --jp-cell-editor-border-color: var(--md-grey-300);
+    --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-cell-editor-active-background: var(--jp-layout-color0);
+    --jp-cell-editor-active-border-color: var(--jp-brand-color1);
+
+    --jp-cell-prompt-width: 64px;
+    --jp-cell-prompt-font-family: var(--jp-code-font-family-default);
+    --jp-cell-prompt-letter-spacing: 0px;
+    --jp-cell-prompt-opacity: 1;
+    --jp-cell-prompt-not-active-opacity: 0.5;
+    --jp-cell-prompt-not-active-font-color: var(--md-grey-700);
+    /* A custom blend of MD grey and blue 600
+   * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */
+    --jp-cell-inprompt-font-color: #307fc1;
+    /* A custom blend of MD grey and orange 600
+   * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */
+    --jp-cell-outprompt-font-color: #bf5b3d;
+
+    /* Notebook specific styles */
+
+    --jp-notebook-padding: 10px;
+    --jp-notebook-select-background: var(--jp-layout-color1);
+    --jp-notebook-multiselected-color: var(--md-blue-50);
+
+    /* The scroll padding is calculated to fill enough space at the bottom of the
+  notebook to show one single-line cell (with appropriate padding) at the top
+  when the notebook is scrolled all the way to the bottom. We also subtract one
+  pixel so that no scrollbar appears if we have just one single-line cell in the
+  notebook. This padding is to enable a 'scroll past end' feature in a notebook.
+  */
+    --jp-notebook-scroll-padding: calc(
+        100% - var(--jp-code-font-size) * var(--jp-code-line-height) -
+            var(--jp-code-padding) - var(--jp-cell-padding) - 1px
+    );
+
+    /* Rendermime styles */
+
+    --jp-rendermime-error-background: #fdd;
+    --jp-rendermime-table-row-background: var(--md-grey-100);
+    --jp-rendermime-table-row-hover-background: var(--md-light-blue-50);
+
+    /* Dialog specific styles */
+
+    --jp-dialog-background: rgba(0, 0, 0, 0.25);
+
+    /* Console specific styles */
+
+    --jp-console-padding: 10px;
+
+    /* Toolbar specific styles */
+
+    --jp-toolbar-border-color: var(--jp-border-color1);
+    --jp-toolbar-micro-height: 8px;
+    --jp-toolbar-background: var(--jp-layout-color1);
+    --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24);
+    --jp-toolbar-header-margin: 4px 4px 0px 4px;
+    --jp-toolbar-active-background: var(--md-grey-300);
+
+    /* Statusbar specific styles */
+
+    --jp-statusbar-height: 24px;
+
+    /* Input field styles */
+
+    --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-input-active-background: var(--jp-layout-color1);
+    --jp-input-hover-background: var(--jp-layout-color1);
+    --jp-input-background: var(--md-grey-100);
+    --jp-input-border-color: var(--jp-border-color1);
+    --jp-input-active-border-color: var(--jp-brand-color1);
+    --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3);
+
+    /* General editor styles */
+
+    --jp-editor-selected-background: #d9d9d9;
+    --jp-editor-selected-focused-background: #d7d4f0;
+    --jp-editor-cursor-color: var(--jp-ui-font-color0);
+
+    /* Code mirror specific styles */
+
+    --jp-mirror-editor-keyword-color: #008000;
+    --jp-mirror-editor-atom-color: #88f;
+    --jp-mirror-editor-number-color: #080;
+    --jp-mirror-editor-def-color: #00f;
+    --jp-mirror-editor-variable-color: var(--md-grey-900);
+    --jp-mirror-editor-variable-2-color: #05a;
+    --jp-mirror-editor-variable-3-color: #085;
+    --jp-mirror-editor-punctuation-color: #05a;
+    --jp-mirror-editor-property-color: #05a;
+    --jp-mirror-editor-operator-color: #aa22ff;
+    --jp-mirror-editor-comment-color: #408080;
+    --jp-mirror-editor-string-color: #ba2121;
+    --jp-mirror-editor-string-2-color: #708;
+    --jp-mirror-editor-meta-color: #aa22ff;
+    --jp-mirror-editor-qualifier-color: #555;
+    --jp-mirror-editor-builtin-color: #008000;
+    --jp-mirror-editor-bracket-color: #997;
+    --jp-mirror-editor-tag-color: #170;
+    --jp-mirror-editor-attribute-color: #00c;
+    --jp-mirror-editor-header-color: blue;
+    --jp-mirror-editor-quote-color: #090;
+    --jp-mirror-editor-link-color: #00c;
+    --jp-mirror-editor-error-color: #f00;
+    --jp-mirror-editor-hr-color: #999;
+
+    /* Vega extension styles */
+
+    --jp-vega-background: white;
+
+    /* Sidebar-related styles */
+
+    --jp-sidebar-min-width: 250px;
+
+    /* Search-related styles */
+
+    --jp-search-toggle-off-opacity: 0.5;
+    --jp-search-toggle-hover-opacity: 0.8;
+    --jp-search-toggle-on-opacity: 1;
+    --jp-search-selected-match-background-color: rgb(245, 200, 0);
+    --jp-search-selected-match-color: black;
+    --jp-search-unselected-match-background-color: var(
+        --jp-inverse-layout-color0
+    );
+    --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);
+
+    /* Icon colors that work well with light or dark backgrounds */
+    --jp-icon-contrast-color0: var(--md-purple-600);
+    --jp-icon-contrast-color1: var(--md-green-600);
+    --jp-icon-contrast-color2: var(--md-pink-600);
+    --jp-icon-contrast-color3: var(--md-blue-600);
+}
+
+[data-md-color-scheme="slate"] .jupyter-wrapper {
+    /* Elevation
+   *
+   * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here:
+   *
+   * https://github.com/material-components/material-components-web
+   * https://material-components-web.appspot.com/elevation.html
+   */
+
+    /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout
+   * colors used in the theme as well. */
+    --jp-shadow-base-lightness: 32;
+    --jp-shadow-umbra-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.2
+    );
+    --jp-shadow-penumbra-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.14
+    );
+    --jp-shadow-ambient-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.12
+    );
+    --jp-elevation-z0: none;
+    --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color),
+        0px 1px 1px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 3px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color),
+        0px 2px 2px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 5px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color),
+        0px 4px 5px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 10px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color),
+        0px 6px 10px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 18px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color),
+        0px 8px 10px 1px var(--jp-shadow-penumbra-color),
+        0px 3px 14px 2px var(--jp-shadow-ambient-color);
+    --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color),
+        0px 12px 17px 2px var(--jp-shadow-penumbra-color),
+        0px 5px 22px 4px var(--jp-shadow-ambient-color);
+    --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color),
+        0px 16px 24px 2px var(--jp-shadow-penumbra-color),
+        0px 6px 30px 5px var(--jp-shadow-ambient-color);
+    --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color),
+        0px 20px 31px 3px var(--jp-shadow-penumbra-color),
+        0px 8px 38px 7px var(--jp-shadow-ambient-color);
+    --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color),
+        0px 24px 38px 3px var(--jp-shadow-penumbra-color),
+        0px 9px 46px 8px var(--jp-shadow-ambient-color);
+
+    /* Borders
+   *
+   * The following variables, specify the visual styling of borders in JupyterLab.
+   */
+
+    --jp-border-width: 1px;
+    --jp-border-color0: var(--md-grey-700);
+    --jp-border-color1: var(--md-grey-700);
+    --jp-border-color2: var(--md-grey-800);
+    --jp-border-color3: var(--md-grey-900);
+    --jp-border-radius: 2px;
+
+    /* UI Fonts
+   *
+   * The UI font CSS variables are used for the typography all of the JupyterLab
+   * user interface elements that are not directly user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-ui-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+    --jp-ui-font-scale-factor: 1.2;
+    --jp-ui-font-size0: 0.83333em;
+    --jp-ui-font-size1: 13px; /* Base font size */
+    --jp-ui-font-size2: 1.2em;
+    --jp-ui-font-size3: 1.44em;
+
+    --jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+
+    /*
+   * Use these font colors against the corresponding main layout colors.
+   * In a light theme, these go from dark to light.
+   */
+
+    /* Defaults use Material Design specification */
+    --jp-ui-font-color0: rgba(255, 255, 255, 1);
+    --jp-ui-font-color1: rgba(255, 255, 255, 0.87);
+    --jp-ui-font-color2: rgba(255, 255, 255, 0.54);
+    --jp-ui-font-color3: rgba(255, 255, 255, 0.38);
+
+    /*
+   * Use these against the brand/accent/warn/error colors.
+   * These will typically go from light to darker, in both a dark and light theme.
+   */
+
+    --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1);
+    --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8);
+    --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5);
+    --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3);
+
+    /* Content Fonts
+   *
+   * Content font variables are used for typography of user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-content-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+    --jp-content-line-height: 1.6;
+    --jp-content-font-scale-factor: 1.2;
+    --jp-content-font-size0: 0.83333em;
+    --jp-content-font-size1: 14px; /* Base font size */
+    --jp-content-font-size2: 1.2em;
+    --jp-content-font-size3: 1.44em;
+    --jp-content-font-size4: 1.728em;
+    --jp-content-font-size5: 2.0736em;
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-content-presentation-font-size1: 17px;
+
+    --jp-content-heading-line-height: 1;
+    --jp-content-heading-margin-top: 1.2em;
+    --jp-content-heading-margin-bottom: 0.8em;
+    --jp-content-heading-font-weight: 500;
+
+    /* Defaults use Material Design specification */
+    --jp-content-font-color0: rgba(255, 255, 255, 1);
+    --jp-content-font-color1: rgba(255, 255, 255, 1);
+    --jp-content-font-color2: rgba(255, 255, 255, 0.7);
+    --jp-content-font-color3: rgba(255, 255, 255, 0.5);
+
+    --jp-content-link-color: var(--md-blue-300);
+
+    --jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+
+    /*
+   * Code Fonts
+   *
+   * Code font variables are used for typography of code and other monospaces content.
+   */
+
+    --jp-code-font-size: 13px;
+    --jp-code-line-height: 1.3077; /* 17px for 13px base */
+    --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */
+    --jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono",
+        monospace;
+    --jp-code-font-family: var(--jp-code-font-family-default);
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-code-presentation-font-size: 16px;
+
+    /* may need to tweak cursor width if you change font size */
+    --jp-code-cursor-width0: 1.4px;
+    --jp-code-cursor-width1: 2px;
+    --jp-code-cursor-width2: 4px;
+
+    /* Layout
+   *
+   * The following are the main layout colors use in JupyterLab. In a light
+   * theme these would go from light to dark.
+   */
+
+    --jp-layout-color0: #111111;
+    --jp-layout-color1: var(--md-grey-900);
+    --jp-layout-color2: var(--md-grey-800);
+    --jp-layout-color3: var(--md-grey-700);
+    --jp-layout-color4: var(--md-grey-600);
+
+    /* Inverse Layout
+   *
+   * The following are the inverse layout colors use in JupyterLab. In a light
+   * theme these would go from dark to light.
+   */
+
+    --jp-inverse-layout-color0: white;
+    --jp-inverse-layout-color1: white;
+    --jp-inverse-layout-color2: var(--md-grey-200);
+    --jp-inverse-layout-color3: var(--md-grey-400);
+    --jp-inverse-layout-color4: var(--md-grey-600);
+
+    /* Brand/accent */
+
+    --jp-brand-color0: var(--md-blue-700);
+    --jp-brand-color1: var(--md-blue-500);
+    --jp-brand-color2: var(--md-blue-300);
+    --jp-brand-color3: var(--md-blue-100);
+    --jp-brand-color4: var(--md-blue-50);
+
+    --jp-accent-color0: var(--md-green-700);
+    --jp-accent-color1: var(--md-green-500);
+    --jp-accent-color2: var(--md-green-300);
+    --jp-accent-color3: var(--md-green-100);
+
+    /* State colors (warn, error, success, info) */
+
+    --jp-warn-color0: var(--md-orange-700);
+    --jp-warn-color1: var(--md-orange-500);
+    --jp-warn-color2: var(--md-orange-300);
+    --jp-warn-color3: var(--md-orange-100);
+
+    --jp-error-color0: var(--md-red-700);
+    --jp-error-color1: var(--md-red-500);
+    --jp-error-color2: var(--md-red-300);
+    --jp-error-color3: var(--md-red-100);
+
+    --jp-success-color0: var(--md-green-700);
+    --jp-success-color1: var(--md-green-500);
+    --jp-success-color2: var(--md-green-300);
+    --jp-success-color3: var(--md-green-100);
+
+    --jp-info-color0: var(--md-cyan-700);
+    --jp-info-color1: var(--md-cyan-500);
+    --jp-info-color2: var(--md-cyan-300);
+    --jp-info-color3: var(--md-cyan-100);
+
+    /* Cell specific styles */
+
+    --jp-cell-padding: 5px;
+
+    --jp-cell-collapser-width: 8px;
+    --jp-cell-collapser-min-height: 20px;
+    --jp-cell-collapser-not-active-hover-opacity: 0.6;
+
+    --jp-cell-editor-background: var(--jp-layout-color1);
+    --jp-cell-editor-border-color: var(--md-grey-700);
+    --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-cell-editor-active-background: var(--jp-layout-color0);
+    --jp-cell-editor-active-border-color: var(--jp-brand-color1);
+
+    --jp-cell-prompt-width: 64px;
+    --jp-cell-prompt-font-family: var(--jp-code-font-family-default);
+    --jp-cell-prompt-letter-spacing: 0px;
+    --jp-cell-prompt-opacity: 1;
+    --jp-cell-prompt-not-active-opacity: 1;
+    --jp-cell-prompt-not-active-font-color: var(--md-grey-300);
+
+    /* A custom blend of MD grey and blue 600
+   * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */
+    --jp-cell-inprompt-font-color: #307fc1;
+    /* A custom blend of MD grey and orange 600
+   * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */
+    --jp-cell-outprompt-font-color: #bf5b3d;
+
+    /* Notebook specific styles */
+
+    --jp-notebook-padding: 10px;
+    --jp-notebook-select-background: var(--jp-layout-color1);
+    --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24);
+
+    /* The scroll padding is calculated to fill enough space at the bottom of the
+  notebook to show one single-line cell (with appropriate padding) at the top
+  when the notebook is scrolled all the way to the bottom. We also subtract one
+  pixel so that no scrollbar appears if we have just one single-line cell in the
+  notebook. This padding is to enable a 'scroll past end' feature in a notebook.
+  */
+    --jp-notebook-scroll-padding: calc(
+        100% - var(--jp-code-font-size) * var(--jp-code-line-height) -
+            var(--jp-code-padding) - var(--jp-cell-padding) - 1px
+    );
+
+    /* Rendermime styles */
+
+    --jp-rendermime-error-background: rgba(244, 67, 54, 0.28);
+    --jp-rendermime-table-row-background: var(--md-grey-900);
+    --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2);
+
+    /* Dialog specific styles */
+
+    --jp-dialog-background: rgba(0, 0, 0, 0.6);
+
+    /* Console specific styles */
+
+    --jp-console-padding: 10px;
+
+    /* Toolbar specific styles */
+
+    --jp-toolbar-border-color: var(--jp-border-color2);
+    --jp-toolbar-micro-height: 8px;
+    --jp-toolbar-background: var(--jp-layout-color1);
+    --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8);
+    --jp-toolbar-header-margin: 4px 4px 0px 4px;
+    --jp-toolbar-active-background: var(--jp-layout-color0);
+
+    /* Statusbar specific styles */
+
+    --jp-statusbar-height: 24px;
+
+    /* Input field styles */
+
+    --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-input-active-background: var(--jp-layout-color0);
+    --jp-input-hover-background: var(--jp-layout-color2);
+    --jp-input-background: var(--md-grey-800);
+    --jp-input-border-color: var(--jp-border-color1);
+    --jp-input-active-border-color: var(--jp-brand-color1);
+    --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3);
+
+    /* General editor styles */
+
+    --jp-editor-selected-background: var(--jp-layout-color2);
+    --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24);
+    --jp-editor-cursor-color: var(--jp-ui-font-color0);
+
+    /* Code mirror specific styles */
+
+    --jp-mirror-editor-keyword-color: var(--md-green-500);
+    --jp-mirror-editor-atom-color: var(--md-blue-300);
+    --jp-mirror-editor-number-color: var(--md-green-400);
+    --jp-mirror-editor-def-color: var(--md-blue-600);
+    --jp-mirror-editor-variable-color: var(--md-grey-300);
+    --jp-mirror-editor-variable-2-color: var(--md-blue-400);
+    --jp-mirror-editor-variable-3-color: var(--md-green-600);
+    --jp-mirror-editor-punctuation-color: var(--md-blue-400);
+    --jp-mirror-editor-property-color: var(--md-blue-400);
+    --jp-mirror-editor-operator-color: #aa22ff;
+    --jp-mirror-editor-comment-color: #408080;
+    --jp-mirror-editor-string-color: #ff7070;
+    --jp-mirror-editor-string-2-color: var(--md-purple-300);
+    --jp-mirror-editor-meta-color: #aa22ff;
+    --jp-mirror-editor-qualifier-color: #555;
+    --jp-mirror-editor-builtin-color: var(--md-green-600);
+    --jp-mirror-editor-bracket-color: #997;
+    --jp-mirror-editor-tag-color: var(--md-green-700);
+    --jp-mirror-editor-attribute-color: var(--md-blue-700);
+    --jp-mirror-editor-header-color: var(--md-blue-500);
+    --jp-mirror-editor-quote-color: var(--md-green-300);
+    --jp-mirror-editor-link-color: var(--md-blue-700);
+    --jp-mirror-editor-error-color: #f00;
+    --jp-mirror-editor-hr-color: #999;
+
+    /* Vega extension styles */
+
+    --jp-vega-background: var(--md-grey-400);
+
+    /* Sidebar-related styles */
+
+    --jp-sidebar-min-width: 250px;
+
+    /* Search-related styles */
+
+    --jp-search-toggle-off-opacity: 0.6;
+    --jp-search-toggle-hover-opacity: 0.8;
+    --jp-search-toggle-on-opacity: 1;
+    --jp-search-selected-match-background-color: rgb(255, 225, 0);
+    --jp-search-selected-match-color: black;
+    --jp-search-unselected-match-background-color: var(
+        --jp-inverse-layout-color0
+    );
+    --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);
+
+    /* scrollbar related styles. Supports every browser except Edge. */
+
+    /* colors based on JetBrain's Darcula theme */
+
+    --jp-scrollbar-background-color: #3f4244;
+    --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */
+
+    --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */
+
+    /* hacks for setting the thumb shape. These do nothing in Firefox */
+
+    --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */
+    --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */
+
+    /* Icon colors that work well with light or dark backgrounds */
+    --jp-icon-contrast-color0: var(--md-purple-600);
+    --jp-icon-contrast-color1: var(--md-green-600);
+    --jp-icon-contrast-color2: var(--md-pink-600);
+    --jp-icon-contrast-color3: var(--md-blue-600);
+}
+
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+Copyright 2015-present Palantir Technologies, Inc. All rights reserved.
+Licensed under the Apache License, Version 2.0.
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+
+Copyright 2017-present Palantir Technologies, Inc. All rights reserved.
+Licensed under the Apache License, Version 2.0.
+
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+
+/*# sourceMappingURL=mkdocs-jupyter.css.map*/</style>
+
+
+<!-- Load mathjax -->
+    <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.7/latest.js?config=TeX-AMS_CHTML-full,Safe"> </script>
+    <!-- MathJax configuration -->
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+    }
+    init_mathjax();
+    </script>
+    <!-- End of mathjax configuration --><div class="jupyter-wrapper">
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="earthquake-detection-workflow-using-quakeflow">Earthquake Detection Workflow using QuakeFlow<a class="anchor-link" href="#earthquake-detection-workflow-using-quakeflow">&#182;</a></h1><h2 id="outline">Outline<a class="anchor-link" href="#outline">&#182;</a></h2><p>Here we show an example of the current modules in QuakeFlow</p>
+<ol>
+<li><p>Download data using Obpsy:</p>
+<p><a href="https://docs.obspy.org/packages/obspy.clients.fdsn.html#module-obspy.clients.fdsn">FDSN web service client for ObsPy</a></p>
+<p><a href="https://docs.obspy.org/packages/autogen/obspy.clients.fdsn.mass_downloader.html#module-obspy.clients.fdsn.mass_downloader">Mass Downloader for FDSN Compliant Web Services</a></p>
+</li>
+<li><p>PhaseNet for picking P/S phases</p>
+<p>Find more details in <a href="https://wayneweiqiang.github.io/PhaseNet/">PhaseNet github page</a></p>
+</li>
+<li><p>GaMMA for associating picking and estimate approximate location and magnitude</p>
+<p>Find more details in <a href="https://wayneweiqiang.github.io/GaMMA/">GaMMA github page</a></p>
+</li>
+<li><p>Earthquake location, magnitude estimation, etc.</p>
+</li>
+</ol>
+
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="0-install-miniconda-and-download-packages">0. Install <a href="https://docs.conda.io/en/latest/miniconda.html">miniconda</a> and download packages<a class="anchor-link" href="#0-install-miniconda-and-download-packages">&#182;</a></h2>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+
+<pre><code>git clone https://github.com/wayneweiqiang/PhaseNet.git
+git clone https://github.com/wayneweiqiang/GaMMA.git
+conda env update -f=env.yml -n base</code></pre>
+<p><strong>Second option: install to quakeflow environment, but need to select jupyter notebook kernel to quakeflow</strong></p>
+
+<pre><code>conda env create -f=env.yml -n quakeflow
+python -m ipykernel install --user --name=quakeflow</code></pre>
+
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[23]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-1">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">warnings</span>
+
+<span class="kn">import</span> <span class="nn">kfp</span>
+<span class="kn">import</span> <span class="nn">kfp.components</span> <span class="k">as</span> <span class="nn">comp</span>
+<span class="kn">import</span> <span class="nn">kfp.dsl</span> <span class="k">as</span> <span class="nn">dsl</span>
+<span class="kn">from</span> <span class="nn">kfp.components</span> <span class="kn">import</span> <span class="n">InputPath</span><span class="p">,</span> <span class="n">OutputPath</span>
+
+<span class="n">warnings</span><span class="o">.</span><span class="n">filterwarnings</span><span class="p">(</span><span class="s2">&quot;ignore&quot;</span><span class="p">)</span>
+</pre></div>
+<div id="cell-1" class="clipboard-copy-txt">import warnings
+
+import kfp
+import kfp.components as comp
+import kfp.dsl as dsl
+from kfp.components import InputPath, OutputPath
+
+warnings.filterwarnings("ignore")</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="1-set-configurations">1. Set configurations<a class="anchor-link" href="#1-set-configurations">&#182;</a></h2>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[24]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-2">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
+
+<span class="kn">import</span> <span class="nn">matplotlib</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+
+<span class="n">matplotlib</span><span class="o">.</span><span class="n">use</span><span class="p">(</span><span class="s2">&quot;agg&quot;</span><span class="p">)</span>
+<span class="o">%</span><span class="n">matplotlib</span> <span class="n">inline</span>
+
+<span class="n">region_name</span> <span class="o">=</span> <span class="s2">&quot;Demo&quot;</span>
+<span class="c1"># region_name = &quot;Ridgecrest&quot;</span>
+<span class="c1"># region_name = &quot;SaltonSea&quot;</span>
+<span class="c1"># region_name = &quot;Ridgecrest&quot;</span>
+<span class="c1"># region_name = &quot;SanSimeon&quot;</span>
+<span class="c1"># region_name = &quot;Italy&quot;</span>
+<span class="c1"># region_name = &quot;PNSN&quot;</span>
+<span class="c1"># region_name = &quot;Hawaii3&quot;</span>
+<span class="c1"># region_name = &quot;Hawaii4&quot;</span>
+<span class="c1"># region_name = &quot;Hawaii_Loa&quot;</span>
+<span class="c1"># region_name = &quot;SierraNegra&quot;</span>
+<span class="c1"># region_name = &quot;PuertoRico&quot;</span>
+<span class="c1"># region_name = &quot;SmithValley&quot;</span>
+<span class="c1"># region_name = &quot;Antilles&quot;</span>
+<span class="c1"># region_name = &quot;LongValley&quot;</span>
+
+<span class="n">dir_name</span> <span class="o">=</span> <span class="n">region_name</span>
+<span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">dir_name</span><span class="p">):</span>
+    <span class="n">os</span><span class="o">.</span><span class="n">mkdir</span><span class="p">(</span><span class="n">dir_name</span><span class="p">)</span>
+<span class="n">root_dir</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">dir_name</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span>
+
+<span class="n">run_local</span> <span class="o">=</span> <span class="kc">True</span>
+</pre></div>
+<div id="cell-2" class="clipboard-copy-txt">import os
+
+import matplotlib
+import matplotlib.pyplot as plt
+
+matplotlib.use("agg")
+%matplotlib inline
+
+region_name = "Demo"
+# region_name = "Ridgecrest"
+# region_name = "SaltonSea"
+# region_name = "Ridgecrest"
+# region_name = "SanSimeon"
+# region_name = "Italy"
+# region_name = "PNSN"
+# region_name = "Hawaii3"
+# region_name = "Hawaii4"
+# region_name = "Hawaii_Loa"
+# region_name = "SierraNegra"
+# region_name = "PuertoRico"
+# region_name = "SmithValley"
+# region_name = "Antilles"
+# region_name = "LongValley"
+
+dir_name = region_name
+if not os.path.exists(dir_name):
+    os.mkdir(dir_name)
+root_dir = lambda x: os.path.join(dir_name, x)
+
+run_local = True</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[25]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-3">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">def</span> <span class="nf">set_config</span><span class="p">(</span>
+    <span class="n">region_name</span><span class="p">,</span>
+    <span class="n">index_json</span><span class="p">:</span> <span class="n">OutputPath</span><span class="p">(</span><span class="s2">&quot;json&quot;</span><span class="p">),</span>
+    <span class="n">config_json</span><span class="p">:</span> <span class="n">OutputPath</span><span class="p">(</span><span class="s2">&quot;json&quot;</span><span class="p">),</span>
+    <span class="n">datetime_json</span><span class="p">:</span> <span class="n">OutputPath</span><span class="p">(</span><span class="s2">&quot;json&quot;</span><span class="p">),</span>
+    <span class="n">num_parallel</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span>
+    <span class="n">bucket_name</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;catalogs&quot;</span><span class="p">,</span>
+    <span class="n">s3_url</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;minio-service:9000&quot;</span><span class="p">,</span>
+    <span class="n">secure</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">:</span>
+
+    <span class="kn">import</span> <span class="nn">datetime</span>
+    <span class="kn">import</span> <span class="nn">json</span>
+    <span class="kn">import</span> <span class="nn">os</span>
+    <span class="kn">import</span> <span class="nn">pickle</span>
+
+    <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+    <span class="kn">import</span> <span class="nn">obspy</span>
+
+    <span class="n">degree2km</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="mi">6371</span> <span class="o">/</span> <span class="mi">180</span>
+
+    <span class="k">if</span> <span class="n">region_name</span> <span class="o">==</span> <span class="s2">&quot;Demo&quot;</span><span class="p">:</span>
+        <span class="n">center</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mf">117.504</span><span class="p">,</span> <span class="mf">35.705</span><span class="p">)</span>
+        <span class="n">horizontal_degree</span> <span class="o">=</span> <span class="mf">1.0</span>
+        <span class="n">vertical_degree</span> <span class="o">=</span> <span class="mf">1.0</span>
+        <span class="n">starttime</span> <span class="o">=</span> <span class="n">obspy</span><span class="o">.</span><span class="n">UTCDateTime</span><span class="p">(</span><span class="s2">&quot;2019-07-04T17&quot;</span><span class="p">)</span>
+        <span class="n">endtime</span> <span class="o">=</span> <span class="n">obspy</span><span class="o">.</span><span class="n">UTCDateTime</span><span class="p">(</span><span class="s2">&quot;2019-07-04T19&quot;</span><span class="p">)</span>
+        <span class="n">client</span> <span class="o">=</span> <span class="s2">&quot;SCEDC&quot;</span>
+        <span class="n">network_list</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;CI&quot;</span><span class="p">]</span>
+        <span class="n">channel_list</span> <span class="o">=</span> <span class="s2">&quot;HH*,BH*,EH*,HN*&quot;</span>
+
+    <span class="k">if</span> <span class="n">region_name</span> <span class="o">==</span> <span class="s2">&quot;Ridgecrest&quot;</span><span class="p">:</span>
+        <span class="n">center</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mf">117.504</span><span class="p">,</span> <span class="mf">35.705</span><span class="p">)</span>
+        <span class="n">horizontal_degree</span> <span class="o">=</span> <span class="mf">1.0</span>
+        <span class="n">vertical_degree</span> <span class="o">=</span> <span class="mf">1.0</span>
+        <span class="n">starttime</span> <span class="o">=</span> <span class="n">obspy</span><span class="o">.</span><span class="n">UTCDateTime</span><span class="p">(</span><span class="s2">&quot;2019-07-04T00&quot;</span><span class="p">)</span>
+        <span class="n">endtime</span> <span class="o">=</span> <span class="n">obspy</span><span class="o">.</span><span class="n">UTCDateTime</span><span class="p">(</span><span class="s2">&quot;2019-07-10T00&quot;</span><span class="p">)</span>
+        <span class="n">client</span> <span class="o">=</span> <span class="s2">&quot;SCEDC&quot;</span>
+        <span class="n">network_list</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;CI&quot;</span><span class="p">]</span>
+        <span class="n">channel_list</span> <span class="o">=</span> <span class="s2">&quot;HH*,BH*,EH*,HN*&quot;</span>
+
+    <span class="k">if</span> <span class="n">region_name</span> <span class="o">==</span> <span class="s2">&quot;Hawaii&quot;</span><span class="p">:</span>
+        <span class="n">center</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mf">155.32</span><span class="p">,</span> <span class="mf">19.39</span><span class="p">)</span>
+        <span class="n">horizontal_degree</span> <span class="o">=</span> <span class="mf">2.0</span>
+        <span class="n">vertical_degree</span> <span class="o">=</span> <span class="mf">2.0</span>
+        <span class="n">starttime</span> <span class="o">=</span> <span class="n">obspy</span><span class="o">.</span><span class="n">UTCDateTime</span><span class="p">(</span><span class="s2">&quot;2018-01-01T00&quot;</span><span class="p">)</span>
+        <span class="n">endtime</span> <span class="o">=</span> <span class="n">obspy</span><span class="o">.</span><span class="n">UTCDateTime</span><span class="p">(</span><span class="s2">&quot;2022-08-12T00&quot;</span><span class="p">)</span>
+        <span class="n">client</span> <span class="o">=</span> <span class="s2">&quot;IRIS&quot;</span>
+        <span class="n">network_list</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;HV&quot;</span><span class="p">,</span> <span class="s2">&quot;PT&quot;</span><span class="p">]</span>
+        <span class="n">channel_list</span> <span class="o">=</span> <span class="s2">&quot;HH*,BH*,EH*,HN*&quot;</span>
+
+    <span class="k">if</span> <span class="n">region_name</span> <span class="o">==</span> <span class="s2">&quot;Hawaii2&quot;</span><span class="p">:</span>
+        <span class="n">center</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mf">155.32</span><span class="p">,</span> <span class="mf">19.39</span><span class="p">)</span>
+        <span class="n">horizontal_degree</span> <span class="o">=</span> <span class="mf">2.0</span>
+        <span class="n">vertical_degree</span> <span class="o">=</span> <span class="mf">2.0</span>
+        <span class="n">starttime</span> <span class="o">=</span> <span class="n">obspy</span><span class="o">.</span><span class="n">UTCDateTime</span><span class="p">(</span><span class="s2">&quot;2022-08-01T00&quot;</span><span class="p">)</span>
+        <span class="n">endtime</span> <span class="o">=</span> <span class="n">obspy</span><span class="o">.</span><span class="n">UTCDateTime</span><span class="p">(</span><span class="s2">&quot;2022-08-16T00&quot;</span><span class="p">)</span>
+        <span class="n">client</span> <span class="o">=</span> <span class="s2">&quot;IRIS&quot;</span>
+        <span class="n">network_list</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;HV&quot;</span><span class="p">,</span> <span class="s2">&quot;PT&quot;</span><span class="p">]</span>
+        <span class="n">channel_list</span> <span class="o">=</span> <span class="s2">&quot;HH*,BH*,EH*,HN*&quot;</span>
+
+    <span class="k">if</span> <span class="n">region_name</span> <span class="o">==</span> <span class="s2">&quot;Hawaii3&quot;</span><span class="p">:</span>
+        <span class="n">center</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mf">155.32</span><span class="p">,</span> <span class="mf">19.39</span><span class="p">)</span>
+        <span class="n">horizontal_degree</span> <span class="o">=</span> <span class="mf">2.0</span>
+        <span class="n">vertical_degree</span> <span class="o">=</span> <span class="mf">2.0</span>
+        <span class="c1"># starttime = obspy.UTCDateTime(&quot;2010-01-01T00&quot;)</span>
+        <span class="n">starttime</span> <span class="o">=</span> <span class="n">obspy</span><span class="o">.</span><span class="n">UTCDateTime</span><span class="p">(</span><span class="s2">&quot;2022-08-01T00&quot;</span><span class="p">)</span>
+        <span class="n">endtime</span> <span class="o">=</span> <span class="n">obspy</span><span class="o">.</span><span class="n">UTCDateTime</span><span class="p">(</span><span class="s2">&quot;2022-11-09T00&quot;</span><span class="p">)</span>
+        <span class="n">client</span> <span class="o">=</span> <span class="s2">&quot;IRIS&quot;</span>
+        <span class="n">network_list</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;HV&quot;</span><span class="p">]</span>
+        <span class="n">channel_list</span> <span class="o">=</span> <span class="s2">&quot;HH*,BH*,EH*&quot;</span>
+
+    <span class="k">if</span> <span class="n">region_name</span> <span class="o">==</span> <span class="s2">&quot;Hawaii4&quot;</span><span class="p">:</span>
+        <span class="n">center</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mf">155.32</span><span class="p">,</span> <span class="mf">19.39</span><span class="p">)</span>
+        <span class="n">horizontal_degree</span> <span class="o">=</span> <span class="mf">2.0</span>
+        <span class="n">vertical_degree</span> <span class="o">=</span> <span class="mf">2.0</span>
+        <span class="n">starttime</span> <span class="o">=</span> <span class="n">obspy</span><span class="o">.</span><span class="n">UTCDateTime</span><span class="p">(</span><span class="s2">&quot;2010-01-01T00&quot;</span><span class="p">)</span>
+        <span class="c1"># starttime = obspy.UTCDateTime(&quot;2022-08-01T00&quot;)</span>
+        <span class="n">endtime</span> <span class="o">=</span> <span class="n">obspy</span><span class="o">.</span><span class="n">UTCDateTime</span><span class="p">(</span><span class="s2">&quot;2022-11-09T00&quot;</span><span class="p">)</span>
+        <span class="n">client</span> <span class="o">=</span> <span class="s2">&quot;IRIS&quot;</span>
+        <span class="n">network_list</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;HV&quot;</span><span class="p">]</span>
+        <span class="n">channel_list</span> <span class="o">=</span> <span class="s2">&quot;HH*,BH*,EH*&quot;</span>
+
+    <span class="k">if</span> <span class="n">region_name</span> <span class="o">==</span> <span class="s2">&quot;Hawaii_Loa&quot;</span><span class="p">:</span>
+        <span class="n">center</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mf">155.602737</span><span class="p">,</span> <span class="mf">19.451827</span><span class="p">)</span>
+        <span class="n">horizontal_degree</span> <span class="o">=</span> <span class="mf">0.3</span>
+        <span class="n">vertical_degree</span> <span class="o">=</span> <span class="mf">0.3</span>
+        <span class="n">starttime</span> <span class="o">=</span> <span class="n">obspy</span><span class="o">.</span><span class="n">UTCDateTime</span><span class="p">(</span><span class="s2">&quot;2010-01-01T00&quot;</span><span class="p">)</span>
+        <span class="c1"># starttime = obspy.UTCDateTime(&quot;2022-08-01T00&quot;)</span>
+        <span class="n">endtime</span> <span class="o">=</span> <span class="n">obspy</span><span class="o">.</span><span class="n">UTCDateTime</span><span class="p">(</span><span class="s2">&quot;2022-11-09T00&quot;</span><span class="p">)</span>
+        <span class="n">client</span> <span class="o">=</span> <span class="s2">&quot;IRIS&quot;</span>
+        <span class="n">network_list</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;HV&quot;</span><span class="p">]</span>
+        <span class="n">channel_list</span> <span class="o">=</span> <span class="s2">&quot;HH*,BH*,EH*&quot;</span>
+
+    <span class="k">if</span> <span class="n">region_name</span> <span class="o">==</span> <span class="s2">&quot;PuertoRico&quot;</span><span class="p">:</span>
+        <span class="n">center</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mf">66.5</span><span class="p">,</span> <span class="mi">18</span><span class="p">)</span>
+        <span class="n">horizontal_degree</span> <span class="o">=</span> <span class="mf">3.0</span>
+        <span class="n">vertical_degree</span> <span class="o">=</span> <span class="mf">2.0</span>
+        <span class="n">starttime</span> <span class="o">=</span> <span class="n">obspy</span><span class="o">.</span><span class="n">UTCDateTime</span><span class="p">(</span><span class="s2">&quot;2018-05-01T00&quot;</span><span class="p">)</span>
+        <span class="n">endtime</span> <span class="o">=</span> <span class="n">obspy</span><span class="o">.</span><span class="n">UTCDateTime</span><span class="p">(</span><span class="s2">&quot;2021-11-01T00&quot;</span><span class="p">)</span>
+        <span class="n">client</span> <span class="o">=</span> <span class="s2">&quot;IRIS&quot;</span>
+        <span class="n">network_list</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;*&quot;</span><span class="p">]</span>
+        <span class="n">channel_list</span> <span class="o">=</span> <span class="s2">&quot;HH*,BH*,HN*&quot;</span>
+
+    <span class="k">if</span> <span class="n">region_name</span> <span class="o">==</span> <span class="s2">&quot;SaltonSea&quot;</span><span class="p">:</span>
+        <span class="n">center</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mf">115.53</span><span class="p">,</span> <span class="mf">32.98</span><span class="p">)</span>
+        <span class="n">horizontal_degree</span> <span class="o">=</span> <span class="mf">1.0</span>
+        <span class="n">vertical_degree</span> <span class="o">=</span> <span class="mf">1.0</span>
+        <span class="n">starttime</span> <span class="o">=</span> <span class="n">obspy</span><span class="o">.</span><span class="n">UTCDateTime</span><span class="p">(</span><span class="s2">&quot;2020-10-01T00&quot;</span><span class="p">)</span>
+        <span class="n">endtime</span> <span class="o">=</span> <span class="n">obspy</span><span class="o">.</span><span class="n">UTCDateTime</span><span class="p">(</span><span class="s2">&quot;2020-10-01T02&quot;</span><span class="p">)</span>
+        <span class="n">client</span> <span class="o">=</span> <span class="s2">&quot;SCEDC&quot;</span>
+        <span class="n">network_list</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;CI&quot;</span><span class="p">]</span>
+        <span class="n">channel_list</span> <span class="o">=</span> <span class="s2">&quot;HH*,BH*,EH*,HN*&quot;</span>
+
+    <span class="k">if</span> <span class="n">region_name</span> <span class="o">==</span> <span class="s2">&quot;2003SanSimeon&quot;</span><span class="p">:</span>
+        <span class="n">center</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mf">121.101</span><span class="p">,</span> <span class="mf">35.701</span><span class="p">)</span>
+        <span class="n">horizontal_degree</span> <span class="o">=</span> <span class="mf">1.0</span>
+        <span class="n">vertical_degree</span> <span class="o">=</span> <span class="mf">1.0</span>
+        <span class="n">starttime</span> <span class="o">=</span> <span class="n">obspy</span><span class="o">.</span><span class="n">UTCDateTime</span><span class="p">(</span><span class="s2">&quot;2003-12-22T00&quot;</span><span class="p">)</span>
+        <span class="n">endtime</span> <span class="o">=</span> <span class="n">obspy</span><span class="o">.</span><span class="n">UTCDateTime</span><span class="p">(</span><span class="s2">&quot;2003-12-24T00&quot;</span><span class="p">)</span>
+        <span class="n">client</span> <span class="o">=</span> <span class="s2">&quot;NCEDC&quot;</span>
+        <span class="n">network_list</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;*&quot;</span><span class="p">]</span>
+        <span class="n">channel_list</span> <span class="o">=</span> <span class="s2">&quot;HH*,BH*,EH*,HN*&quot;</span>
+
+    <span class="k">if</span> <span class="n">region_name</span> <span class="o">==</span> <span class="s2">&quot;Italy&quot;</span><span class="p">:</span>
+        <span class="n">center</span> <span class="o">=</span> <span class="p">(</span><span class="mf">13.188</span><span class="p">,</span> <span class="mf">42.723</span><span class="p">)</span>
+        <span class="n">horizontal_degree</span> <span class="o">=</span> <span class="mf">1.0</span>
+        <span class="n">vertical_degree</span> <span class="o">=</span> <span class="mf">1.0</span>
+        <span class="n">starttime</span> <span class="o">=</span> <span class="n">obspy</span><span class="o">.</span><span class="n">UTCDateTime</span><span class="p">(</span><span class="s2">&quot;2016-08-24T00&quot;</span><span class="p">)</span>
+        <span class="n">endtime</span> <span class="o">=</span> <span class="n">obspy</span><span class="o">.</span><span class="n">UTCDateTime</span><span class="p">(</span><span class="s2">&quot;2016-08-26T00&quot;</span><span class="p">)</span>
+        <span class="n">client</span> <span class="o">=</span> <span class="s2">&quot;INGV&quot;</span>
+        <span class="n">network_list</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;*&quot;</span><span class="p">]</span>
+        <span class="n">channel_list</span> <span class="o">=</span> <span class="s2">&quot;HH*,BH*,EH*,HN*&quot;</span>
+
+    <span class="k">if</span> <span class="n">region_name</span> <span class="o">==</span> <span class="s2">&quot;SmithValley&quot;</span><span class="p">:</span>
+        <span class="n">center</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mf">119.5</span><span class="p">,</span> <span class="mf">38.51</span><span class="p">)</span>
+        <span class="n">horizontal_degree</span> <span class="o">=</span> <span class="mf">1.0</span>
+        <span class="n">vertical_degree</span> <span class="o">=</span> <span class="mf">1.0</span>
+        <span class="n">starttime</span> <span class="o">=</span> <span class="n">obspy</span><span class="o">.</span><span class="n">UTCDateTime</span><span class="p">(</span><span class="s2">&quot;2021-07-08T00:00&quot;</span><span class="p">)</span>
+        <span class="n">endtime</span> <span class="o">=</span> <span class="n">obspy</span><span class="o">.</span><span class="n">UTCDateTime</span><span class="p">(</span><span class="s2">&quot;2021-07-16T00:00&quot;</span><span class="p">)</span>
+        <span class="n">client</span> <span class="o">=</span> <span class="s2">&quot;NCEDC&quot;</span>
+        <span class="n">network_list</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;*&quot;</span><span class="p">]</span>
+        <span class="n">channel_list</span> <span class="o">=</span> <span class="s2">&quot;HH*,BH*,EH*,HN*&quot;</span>
+
+    <span class="k">if</span> <span class="n">region_name</span> <span class="o">==</span> <span class="s2">&quot;Antilles&quot;</span><span class="p">:</span>
+        <span class="n">center</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mf">61.14867</span><span class="p">,</span> <span class="mf">14.79683</span><span class="p">)</span>
+        <span class="n">horizontal_degree</span> <span class="o">=</span> <span class="mf">0.2</span>
+        <span class="n">vertical_degree</span> <span class="o">=</span> <span class="mf">0.2</span>
+        <span class="n">starttime</span> <span class="o">=</span> <span class="n">obspy</span><span class="o">.</span><span class="n">UTCDateTime</span><span class="p">(</span><span class="s2">&quot;2021-04-10T00&quot;</span><span class="p">)</span>
+        <span class="n">endtime</span> <span class="o">=</span> <span class="n">obspy</span><span class="o">.</span><span class="n">UTCDateTime</span><span class="p">(</span><span class="s2">&quot;2021-04-15T00&quot;</span><span class="p">)</span>
+        <span class="n">client</span> <span class="o">=</span> <span class="s2">&quot;RESIF&quot;</span>
+        <span class="n">network_list</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;*&quot;</span><span class="p">]</span>
+        <span class="n">channel_list</span> <span class="o">=</span> <span class="s2">&quot;HH*,BH*,EH*,HN*&quot;</span>
+
+    <span class="k">if</span> <span class="n">region_name</span> <span class="o">==</span> <span class="s2">&quot;LongValley&quot;</span><span class="p">:</span>
+        <span class="c1"># center = (-118.8, 37.6+0.3)</span>
+        <span class="c1"># horizontal_degree = 3.0</span>
+        <span class="c1"># vertical_degree = 3.0</span>
+        <span class="n">center</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mf">118.8</span> <span class="o">-</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">37.6</span><span class="p">)</span>
+        <span class="n">horizontal_degree</span> <span class="o">=</span> <span class="mf">1.5</span>
+        <span class="n">vertical_degree</span> <span class="o">=</span> <span class="mf">1.5</span>
+        <span class="n">starttime</span> <span class="o">=</span> <span class="n">obspy</span><span class="o">.</span><span class="n">UTCDateTime</span><span class="p">(</span><span class="s2">&quot;2020-01-01T00&quot;</span><span class="p">)</span>
+        <span class="n">endtime</span> <span class="o">=</span> <span class="n">obspy</span><span class="o">.</span><span class="n">UTCDateTime</span><span class="p">(</span><span class="s2">&quot;2022-08-11T00&quot;</span><span class="p">)</span>
+        <span class="n">client</span> <span class="o">=</span> <span class="s2">&quot;NCEDC&quot;</span>
+        <span class="n">network_list</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;*&quot;</span><span class="p">]</span>
+        <span class="n">channel_list</span> <span class="o">=</span> <span class="s2">&quot;HH*,EH*&quot;</span>
+
+    <span class="k">if</span> <span class="n">region_name</span> <span class="o">==</span> <span class="s2">&quot;SierraNegra&quot;</span><span class="p">:</span>
+        <span class="n">center</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mf">91.13</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.81</span><span class="p">)</span>
+        <span class="n">horizontal_degree</span> <span class="o">=</span> <span class="mf">1.5</span>
+        <span class="n">vertical_degree</span> <span class="o">=</span> <span class="mf">1.5</span>
+        <span class="n">starttime</span> <span class="o">=</span> <span class="n">obspy</span><span class="o">.</span><span class="n">UTCDateTime</span><span class="p">(</span><span class="s2">&quot;2009-07-22T00&quot;</span><span class="p">)</span>
+        <span class="n">endtime</span> <span class="o">=</span> <span class="n">obspy</span><span class="o">.</span><span class="n">UTCDateTime</span><span class="p">(</span><span class="s2">&quot;2011-06-20T00&quot;</span><span class="p">)</span>
+        <span class="n">client</span> <span class="o">=</span> <span class="s2">&quot;IRIS&quot;</span>
+        <span class="n">network_list</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;*&quot;</span><span class="p">]</span>
+        <span class="n">channel_list</span> <span class="o">=</span> <span class="s2">&quot;BH*,HH*,EH*&quot;</span>
+
+    <span class="c1">####### save config ########</span>
+    <span class="n">config</span> <span class="o">=</span> <span class="p">{}</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;region&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">region_name</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;center&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">center</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;xlim_degree&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span>
+        <span class="n">center</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">horizontal_degree</span> <span class="o">/</span> <span class="mi">2</span><span class="p">,</span>
+        <span class="n">center</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">horizontal_degree</span> <span class="o">/</span> <span class="mi">2</span><span class="p">,</span>
+    <span class="p">]</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;ylim_degree&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span>
+        <span class="n">center</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">vertical_degree</span> <span class="o">/</span> <span class="mi">2</span><span class="p">,</span>
+        <span class="n">center</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">vertical_degree</span> <span class="o">/</span> <span class="mi">2</span><span class="p">,</span>
+    <span class="p">]</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;min_longitude&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">center</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">horizontal_degree</span> <span class="o">/</span> <span class="mi">2</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;max_longitude&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">center</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">horizontal_degree</span> <span class="o">/</span> <span class="mi">2</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;min_latitude&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">center</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">vertical_degree</span> <span class="o">/</span> <span class="mi">2</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;max_latitude&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">center</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">vertical_degree</span> <span class="o">/</span> <span class="mi">2</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;degree2km&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">degree2km</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;starttime&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">starttime</span><span class="o">.</span><span class="n">datetime</span><span class="o">.</span><span class="n">isoformat</span><span class="p">(</span><span class="n">timespec</span><span class="o">=</span><span class="s2">&quot;milliseconds&quot;</span><span class="p">)</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;endtime&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">endtime</span><span class="o">.</span><span class="n">datetime</span><span class="o">.</span><span class="n">isoformat</span><span class="p">(</span><span class="n">timespec</span><span class="o">=</span><span class="s2">&quot;milliseconds&quot;</span><span class="p">)</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;networks&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">network_list</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;channels&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">channel_list</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;client&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">client</span>
+
+    <span class="c1">## PhaseNet</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;phasenet&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">{}</span>
+    <span class="c1">## GaMMA</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;gamma&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">{}</span>
+    <span class="c1">## HypoDD</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;hypodd&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;MAXEVENT&quot;</span><span class="p">:</span> <span class="mf">1e4</span><span class="p">}</span>
+
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">config_json</span><span class="p">,</span> <span class="s2">&quot;w&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">json</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="n">config</span><span class="p">,</span> <span class="n">fp</span><span class="p">,</span> <span class="n">indent</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+
+    <span class="nb">print</span><span class="p">(</span><span class="n">json</span><span class="o">.</span><span class="n">dumps</span><span class="p">(</span><span class="n">config</span><span class="p">,</span> <span class="n">indent</span><span class="o">=</span><span class="mi">4</span><span class="p">))</span>
+
+    <span class="c1">####### set paraell for cloud ########</span>
+    <span class="c1">## split data by hours</span>
+    <span class="c1"># one_day = datetime.timedelta(days=1)</span>
+    <span class="n">one_hour</span> <span class="o">=</span> <span class="n">datetime</span><span class="o">.</span><span class="n">timedelta</span><span class="p">(</span><span class="n">hours</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">starttimes</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">tmp_start</span> <span class="o">=</span> <span class="n">starttime</span>
+    <span class="k">while</span> <span class="n">tmp_start</span> <span class="o">&lt;</span> <span class="n">endtime</span><span class="p">:</span>
+        <span class="n">starttimes</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp_start</span><span class="o">.</span><span class="n">datetime</span><span class="o">.</span><span class="n">isoformat</span><span class="p">(</span><span class="n">timespec</span><span class="o">=</span><span class="s2">&quot;milliseconds&quot;</span><span class="p">))</span>
+        <span class="n">tmp_start</span> <span class="o">+=</span> <span class="n">one_hour</span>
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">datetime_json</span><span class="p">,</span> <span class="s2">&quot;w&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">json</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span>
+            <span class="p">{</span><span class="s2">&quot;starttimes&quot;</span><span class="p">:</span> <span class="n">starttimes</span><span class="p">,</span> <span class="s2">&quot;interval&quot;</span><span class="p">:</span> <span class="n">one_hour</span><span class="o">.</span><span class="n">total_seconds</span><span class="p">()},</span>
+            <span class="n">fp</span><span class="p">,</span>
+            <span class="n">indent</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
+        <span class="p">)</span>
+
+    <span class="c1">## split stattimes into N parallel process</span>
+    <span class="k">if</span> <span class="n">num_parallel</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="n">num_parallel</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="mi">60</span><span class="p">,</span> <span class="nb">int</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">starttimes</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">//</span> <span class="mi">6</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
+        <span class="c1"># num_parallel = min(30, int((len(starttimes)-1)//6+1))</span>
+        <span class="c1"># num_parallel = min(24, len(starttimes))</span>
+    <span class="n">idx</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">array_split</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">starttimes</span><span class="p">)),</span> <span class="n">num_parallel</span><span class="p">)]</span>
+
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">index_json</span><span class="p">,</span> <span class="s2">&quot;w&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">json</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="n">idx</span><span class="p">,</span> <span class="n">fp</span><span class="p">,</span> <span class="n">indent</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+
+    <span class="c1">## upload to s3 bucket</span>
+    <span class="k">try</span><span class="p">:</span>
+        <span class="kn">from</span> <span class="nn">minio</span> <span class="kn">import</span> <span class="n">Minio</span>
+
+        <span class="n">minioClient</span> <span class="o">=</span> <span class="n">Minio</span><span class="p">(</span><span class="n">s3_url</span><span class="p">,</span> <span class="n">access_key</span><span class="o">=</span><span class="s2">&quot;minio&quot;</span><span class="p">,</span> <span class="n">secret_key</span><span class="o">=</span><span class="s2">&quot;minio123&quot;</span><span class="p">,</span> <span class="n">secure</span><span class="o">=</span><span class="n">secure</span><span class="p">)</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">minioClient</span><span class="o">.</span><span class="n">bucket_exists</span><span class="p">(</span><span class="n">bucket_name</span><span class="p">):</span>
+            <span class="n">minioClient</span><span class="o">.</span><span class="n">make_bucket</span><span class="p">(</span><span class="n">bucket_name</span><span class="p">)</span>
+
+        <span class="n">minioClient</span><span class="o">.</span><span class="n">fput_object</span><span class="p">(</span>
+            <span class="n">bucket_name</span><span class="p">,</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;region&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">/config.json&quot;</span><span class="p">,</span>
+            <span class="n">config_json</span><span class="p">,</span>
+        <span class="p">)</span>
+
+        <span class="n">minioClient</span><span class="o">.</span><span class="n">fput_object</span><span class="p">(</span>
+            <span class="n">bucket_name</span><span class="p">,</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;region&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">/datetime.json&quot;</span><span class="p">,</span>
+            <span class="n">datetime_json</span><span class="p">,</span>
+        <span class="p">)</span>
+
+        <span class="n">minioClient</span><span class="o">.</span><span class="n">fput_object</span><span class="p">(</span>
+            <span class="n">bucket_name</span><span class="p">,</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;region&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">/index.json&quot;</span><span class="p">,</span>
+            <span class="n">index_json</span><span class="p">,</span>
+        <span class="p">)</span>
+
+    <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">err</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;ERROR: can not access minio service! </span><span class="se">\n</span><span class="si">{</span><span class="n">err</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+        <span class="k">pass</span>
+
+    <span class="k">return</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">num_parallel</span><span class="p">))</span>
+</pre></div>
+<div id="cell-3" class="clipboard-copy-txt">def set_config(
+    region_name,
+    index_json: OutputPath("json"),
+    config_json: OutputPath("json"),
+    datetime_json: OutputPath("json"),
+    num_parallel: int = 1,
+    bucket_name: str = "catalogs",
+    s3_url: str = "minio-service:9000",
+    secure: bool = True,
+) -> list:
+
+    import datetime
+    import json
+    import os
+    import pickle
+
+    import numpy as np
+    import obspy
+
+    degree2km = np.pi * 6371 / 180
+
+    if region_name == "Demo":
+        center = (-117.504, 35.705)
+        horizontal_degree = 1.0
+        vertical_degree = 1.0
+        starttime = obspy.UTCDateTime("2019-07-04T17")
+        endtime = obspy.UTCDateTime("2019-07-04T19")
+        client = "SCEDC"
+        network_list = ["CI"]
+        channel_list = "HH*,BH*,EH*,HN*"
+
+    if region_name == "Ridgecrest":
+        center = (-117.504, 35.705)
+        horizontal_degree = 1.0
+        vertical_degree = 1.0
+        starttime = obspy.UTCDateTime("2019-07-04T00")
+        endtime = obspy.UTCDateTime("2019-07-10T00")
+        client = "SCEDC"
+        network_list = ["CI"]
+        channel_list = "HH*,BH*,EH*,HN*"
+
+    if region_name == "Hawaii":
+        center = (-155.32, 19.39)
+        horizontal_degree = 2.0
+        vertical_degree = 2.0
+        starttime = obspy.UTCDateTime("2018-01-01T00")
+        endtime = obspy.UTCDateTime("2022-08-12T00")
+        client = "IRIS"
+        network_list = ["HV", "PT"]
+        channel_list = "HH*,BH*,EH*,HN*"
+
+    if region_name == "Hawaii2":
+        center = (-155.32, 19.39)
+        horizontal_degree = 2.0
+        vertical_degree = 2.0
+        starttime = obspy.UTCDateTime("2022-08-01T00")
+        endtime = obspy.UTCDateTime("2022-08-16T00")
+        client = "IRIS"
+        network_list = ["HV", "PT"]
+        channel_list = "HH*,BH*,EH*,HN*"
+
+    if region_name == "Hawaii3":
+        center = (-155.32, 19.39)
+        horizontal_degree = 2.0
+        vertical_degree = 2.0
+        # starttime = obspy.UTCDateTime("2010-01-01T00")
+        starttime = obspy.UTCDateTime("2022-08-01T00")
+        endtime = obspy.UTCDateTime("2022-11-09T00")
+        client = "IRIS"
+        network_list = ["HV"]
+        channel_list = "HH*,BH*,EH*"
+
+    if region_name == "Hawaii4":
+        center = (-155.32, 19.39)
+        horizontal_degree = 2.0
+        vertical_degree = 2.0
+        starttime = obspy.UTCDateTime("2010-01-01T00")
+        # starttime = obspy.UTCDateTime("2022-08-01T00")
+        endtime = obspy.UTCDateTime("2022-11-09T00")
+        client = "IRIS"
+        network_list = ["HV"]
+        channel_list = "HH*,BH*,EH*"
+
+    if region_name == "Hawaii_Loa":
+        center = (-155.602737, 19.451827)
+        horizontal_degree = 0.3
+        vertical_degree = 0.3
+        starttime = obspy.UTCDateTime("2010-01-01T00")
+        # starttime = obspy.UTCDateTime("2022-08-01T00")
+        endtime = obspy.UTCDateTime("2022-11-09T00")
+        client = "IRIS"
+        network_list = ["HV"]
+        channel_list = "HH*,BH*,EH*"
+
+    if region_name == "PuertoRico":
+        center = (-66.5, 18)
+        horizontal_degree = 3.0
+        vertical_degree = 2.0
+        starttime = obspy.UTCDateTime("2018-05-01T00")
+        endtime = obspy.UTCDateTime("2021-11-01T00")
+        client = "IRIS"
+        network_list = ["*"]
+        channel_list = "HH*,BH*,HN*"
+
+    if region_name == "SaltonSea":
+        center = (-115.53, 32.98)
+        horizontal_degree = 1.0
+        vertical_degree = 1.0
+        starttime = obspy.UTCDateTime("2020-10-01T00")
+        endtime = obspy.UTCDateTime("2020-10-01T02")
+        client = "SCEDC"
+        network_list = ["CI"]
+        channel_list = "HH*,BH*,EH*,HN*"
+
+    if region_name == "2003SanSimeon":
+        center = (-121.101, 35.701)
+        horizontal_degree = 1.0
+        vertical_degree = 1.0
+        starttime = obspy.UTCDateTime("2003-12-22T00")
+        endtime = obspy.UTCDateTime("2003-12-24T00")
+        client = "NCEDC"
+        network_list = ["*"]
+        channel_list = "HH*,BH*,EH*,HN*"
+
+    if region_name == "Italy":
+        center = (13.188, 42.723)
+        horizontal_degree = 1.0
+        vertical_degree = 1.0
+        starttime = obspy.UTCDateTime("2016-08-24T00")
+        endtime = obspy.UTCDateTime("2016-08-26T00")
+        client = "INGV"
+        network_list = ["*"]
+        channel_list = "HH*,BH*,EH*,HN*"
+
+    if region_name == "SmithValley":
+        center = (-119.5, 38.51)
+        horizontal_degree = 1.0
+        vertical_degree = 1.0
+        starttime = obspy.UTCDateTime("2021-07-08T00:00")
+        endtime = obspy.UTCDateTime("2021-07-16T00:00")
+        client = "NCEDC"
+        network_list = ["*"]
+        channel_list = "HH*,BH*,EH*,HN*"
+
+    if region_name == "Antilles":
+        center = (-61.14867, 14.79683)
+        horizontal_degree = 0.2
+        vertical_degree = 0.2
+        starttime = obspy.UTCDateTime("2021-04-10T00")
+        endtime = obspy.UTCDateTime("2021-04-15T00")
+        client = "RESIF"
+        network_list = ["*"]
+        channel_list = "HH*,BH*,EH*,HN*"
+
+    if region_name == "LongValley":
+        # center = (-118.8, 37.6+0.3)
+        # horizontal_degree = 3.0
+        # vertical_degree = 3.0
+        center = (-118.8 - 0.1, 37.6)
+        horizontal_degree = 1.5
+        vertical_degree = 1.5
+        starttime = obspy.UTCDateTime("2020-01-01T00")
+        endtime = obspy.UTCDateTime("2022-08-11T00")
+        client = "NCEDC"
+        network_list = ["*"]
+        channel_list = "HH*,EH*"
+
+    if region_name == "SierraNegra":
+        center = (-91.13, -0.81)
+        horizontal_degree = 1.5
+        vertical_degree = 1.5
+        starttime = obspy.UTCDateTime("2009-07-22T00")
+        endtime = obspy.UTCDateTime("2011-06-20T00")
+        client = "IRIS"
+        network_list = ["*"]
+        channel_list = "BH*,HH*,EH*"
+
+    ####### save config ########
+    config = {}
+    config["region"] = region_name
+    config["center"] = center
+    config["xlim_degree"] = [
+        center[0] - horizontal_degree / 2,
+        center[0] + horizontal_degree / 2,
+    ]
+    config["ylim_degree"] = [
+        center[1] - vertical_degree / 2,
+        center[1] + vertical_degree / 2,
+    ]
+    config["min_longitude"] = center[0] - horizontal_degree / 2
+    config["max_longitude"] = center[0] + horizontal_degree / 2
+    config["min_latitude"] = center[1] - vertical_degree / 2
+    config["max_latitude"] = center[1] + vertical_degree / 2
+    config["degree2km"] = degree2km
+    config["starttime"] = starttime.datetime.isoformat(timespec="milliseconds")
+    config["endtime"] = endtime.datetime.isoformat(timespec="milliseconds")
+    config["networks"] = network_list
+    config["channels"] = channel_list
+    config["client"] = client
+
+    ## PhaseNet
+    config["phasenet"] = {}
+    ## GaMMA
+    config["gamma"] = {}
+    ## HypoDD
+    config["hypodd"] = {"MAXEVENT": 1e4}
+
+    with open(config_json, "w") as fp:
+        json.dump(config, fp, indent=2)
+
+    print(json.dumps(config, indent=4))
+
+    ####### set paraell for cloud ########
+    ## split data by hours
+    # one_day = datetime.timedelta(days=1)
+    one_hour = datetime.timedelta(hours=1)
+    starttimes = []
+    tmp_start = starttime
+    while tmp_start < endtime:
+        starttimes.append(tmp_start.datetime.isoformat(timespec="milliseconds"))
+        tmp_start += one_hour
+    with open(datetime_json, "w") as fp:
+        json.dump(
+            {"starttimes": starttimes, "interval": one_hour.total_seconds()},
+            fp,
+            indent=2,
+        )
+
+    ## split stattimes into N parallel process
+    if num_parallel == 0:
+        num_parallel = min(60, int((len(starttimes) - 1) // 6 + 1))
+        # num_parallel = min(30, int((len(starttimes)-1)//6+1))
+        # num_parallel = min(24, len(starttimes))
+    idx = [x.tolist() for x in np.array_split(np.arange(len(starttimes)), num_parallel)]
+
+    with open(index_json, "w") as fp:
+        json.dump(idx, fp, indent=2)
+
+    ## upload to s3 bucket
+    try:
+        from minio import Minio
+
+        minioClient = Minio(s3_url, access_key="minio", secret_key="minio123", secure=secure)
+        if not minioClient.bucket_exists(bucket_name):
+            minioClient.make_bucket(bucket_name)
+
+        minioClient.fput_object(
+            bucket_name,
+            f"{config['region']}/config.json",
+            config_json,
+        )
+
+        minioClient.fput_object(
+            bucket_name,
+            f"{config['region']}/datetime.json",
+            datetime_json,
+        )
+
+        minioClient.fput_object(
+            bucket_name,
+            f"{config['region']}/index.json",
+            index_json,
+        )
+
+    except Exception as err:
+        print(f"ERROR: can not access minio service! \n{err}")
+        pass
+
+    return list(range(num_parallel))</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[26]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-4">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">if</span> <span class="n">run_local</span><span class="p">:</span>
+    <span class="n">idx</span> <span class="o">=</span> <span class="n">set_config</span><span class="p">(</span>
+        <span class="n">region_name</span><span class="p">,</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;index.json&quot;</span><span class="p">),</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;config.json&quot;</span><span class="p">),</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;datetimes.json&quot;</span><span class="p">),</span>
+        <span class="n">num_parallel</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+    <span class="p">)</span>
+</pre></div>
+<div id="cell-4" class="clipboard-copy-txt">if run_local:
+    idx = set_config(
+        region_name,
+        root_dir("index.json"),
+        root_dir("config.json"),
+        root_dir("datetimes.json"),
+        num_parallel=1,
+    )</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
+<pre>{
+    &#34;region&#34;: &#34;Demo&#34;,
+    &#34;center&#34;: [
+        -117.504,
+        35.705
+    ],
+    &#34;xlim_degree&#34;: [
+        -118.004,
+        -117.004
+    ],
+    &#34;ylim_degree&#34;: [
+        35.205,
+        36.205
+    ],
+    &#34;min_longitude&#34;: -118.004,
+    &#34;max_longitude&#34;: -117.004,
+    &#34;min_latitude&#34;: 35.205,
+    &#34;max_latitude&#34;: 36.205,
+    &#34;degree2km&#34;: 111.19492664455873,
+    &#34;starttime&#34;: &#34;2019-07-04T17:00:00.000&#34;,
+    &#34;endtime&#34;: &#34;2019-07-04T19:00:00.000&#34;,
+    &#34;networks&#34;: [
+        &#34;CI&#34;
+    ],
+    &#34;channels&#34;: &#34;HH*,BH*,EH*,HN*&#34;,
+    &#34;client&#34;: &#34;SCEDC&#34;,
+    &#34;phasenet&#34;: {},
+    &#34;gamma&#34;: {},
+    &#34;hypodd&#34;: {
+        &#34;MAXEVENT&#34;: 10000.0
+    }
+}
+ERROR: can not access minio service! 
+HTTPSConnectionPool(host=&#39;minio-service&#39;, port=9000): Max retries exceeded with url: /catalogs?location= (Caused by NewConnectionError(&#39;&lt;urllib3.connection.HTTPSConnection object at 0x2919b2d30&gt;: Failed to establish a new connection: [Errno 8] nodename nor servname provided, or not known&#39;))
+</pre>
+</div>
+</div>
+
+</div>
+
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[27]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-5">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">config_op</span> <span class="o">=</span> <span class="n">comp</span><span class="o">.</span><span class="n">func_to_container_op</span><span class="p">(</span>
+    <span class="n">set_config</span><span class="p">,</span>
+    <span class="n">base_image</span><span class="o">=</span><span class="s2">&quot;python:3.8&quot;</span><span class="p">,</span>
+    <span class="n">packages_to_install</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;numpy&quot;</span><span class="p">,</span> <span class="s2">&quot;obspy&quot;</span><span class="p">,</span> <span class="s2">&quot;minio&quot;</span><span class="p">],</span>
+<span class="p">)</span>
+</pre></div>
+<div id="cell-5" class="clipboard-copy-txt">config_op = comp.func_to_container_op(
+    set_config,
+    base_image="python:3.8",
+    packages_to_install=["numpy", "obspy", "minio"],
+)</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="2-download-events-of-the-standard-catalog">2. Download events of the standard catalog<a class="anchor-link" href="#2-download-events-of-the-standard-catalog">&#182;</a></h2><p>This catalog is not used by QuakeFolow. It is only used for comparing detection results.</p>
+
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[28]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-6">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">def</span> <span class="nf">download_events</span><span class="p">(</span>
+    <span class="n">config_json</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="s2">&quot;json&quot;</span><span class="p">),</span>
+    <span class="n">standard_catalog</span><span class="p">:</span> <span class="n">OutputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span>
+    <span class="n">bucket_name</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;catalogs&quot;</span><span class="p">,</span>
+    <span class="n">s3_url</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;minio-service:9000&quot;</span><span class="p">,</span>
+    <span class="n">secure</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+<span class="p">):</span>
+
+    <span class="kn">import</span> <span class="nn">json</span>
+    <span class="kn">import</span> <span class="nn">os</span>
+    <span class="kn">import</span> <span class="nn">pickle</span>
+    <span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">defaultdict</span>
+
+    <span class="kn">import</span> <span class="nn">matplotlib</span>
+    <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+    <span class="kn">import</span> <span class="nn">obspy</span>
+    <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+    <span class="kn">from</span> <span class="nn">obspy.clients.fdsn</span> <span class="kn">import</span> <span class="n">Client</span>
+
+    <span class="c1"># matplotlib.use(&quot;agg&quot;)</span>
+    <span class="c1"># %matplotlib inline</span>
+
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">config_json</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">config</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">fp</span><span class="p">)</span>
+
+    <span class="c1">####### IRIS catalog ########</span>
+    <span class="k">try</span><span class="p">:</span>
+        <span class="n">events</span> <span class="o">=</span> <span class="n">Client</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;client&quot;</span><span class="p">])</span><span class="o">.</span><span class="n">get_events</span><span class="p">(</span>
+            <span class="n">starttime</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;starttime&quot;</span><span class="p">],</span>
+            <span class="n">endtime</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;endtime&quot;</span><span class="p">],</span>
+            <span class="n">minlongitude</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;xlim_degree&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span>
+            <span class="n">maxlongitude</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;xlim_degree&quot;</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span>
+            <span class="n">minlatitude</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;ylim_degree&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span>
+            <span class="n">maxlatitude</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;ylim_degree&quot;</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span>
+            <span class="c1"># filename=&#39;events.xml&#39;,</span>
+        <span class="p">)</span>
+    <span class="k">except</span><span class="p">:</span>
+        <span class="n">events</span> <span class="o">=</span> <span class="n">Client</span><span class="p">(</span><span class="s2">&quot;iris&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">get_events</span><span class="p">(</span>
+            <span class="n">starttime</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;starttime&quot;</span><span class="p">],</span>
+            <span class="n">endtime</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;endtime&quot;</span><span class="p">],</span>
+            <span class="n">minlongitude</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;xlim_degree&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span>
+            <span class="n">maxlongitude</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;xlim_degree&quot;</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span>
+            <span class="n">minlatitude</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;ylim_degree&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span>
+            <span class="n">maxlatitude</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;ylim_degree&quot;</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span>
+            <span class="c1"># filename=&#39;events.xml&#39;,</span>
+        <span class="p">)</span>
+
+    <span class="c1">#     events = obspy.read_events(&#39;events.xml&#39;)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Number of events: </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">events</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+    <span class="c1">#     events.plot(&#39;local&#39;, outfile=&quot;events.png&quot;)</span>
+    <span class="c1">#     events.plot(&#39;local&#39;)</span>
+
+    <span class="c1">####### Save catalog ########</span>
+    <span class="n">catalog</span> <span class="o">=</span> <span class="n">defaultdict</span><span class="p">(</span><span class="nb">list</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">event</span> <span class="ow">in</span> <span class="n">events</span><span class="p">:</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">event</span><span class="o">.</span><span class="n">magnitudes</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="n">catalog</span><span class="p">[</span><span class="s2">&quot;time&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">event</span><span class="o">.</span><span class="n">origins</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">time</span><span class="o">.</span><span class="n">datetime</span><span class="p">)</span>
+            <span class="n">catalog</span><span class="p">[</span><span class="s2">&quot;magnitude&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">event</span><span class="o">.</span><span class="n">magnitudes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">mag</span><span class="p">)</span>
+            <span class="n">catalog</span><span class="p">[</span><span class="s2">&quot;longitude&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">event</span><span class="o">.</span><span class="n">origins</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">longitude</span><span class="p">)</span>
+            <span class="n">catalog</span><span class="p">[</span><span class="s2">&quot;latitude&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">event</span><span class="o">.</span><span class="n">origins</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">latitude</span><span class="p">)</span>
+            <span class="n">catalog</span><span class="p">[</span><span class="s2">&quot;depth(m)&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">event</span><span class="o">.</span><span class="n">origins</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">depth</span><span class="p">)</span>
+    <span class="n">catalog</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">from_dict</span><span class="p">(</span><span class="n">catalog</span><span class="p">)</span><span class="o">.</span><span class="n">sort_values</span><span class="p">([</span><span class="s2">&quot;time&quot;</span><span class="p">])</span>
+    <span class="n">catalog</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span>
+        <span class="n">standard_catalog</span><span class="p">,</span>
+        <span class="c1"># sep=&quot;\t&quot;,</span>
+        <span class="n">index</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+        <span class="n">float_format</span><span class="o">=</span><span class="s2">&quot;</span><span class="si">%.3f</span><span class="s2">&quot;</span><span class="p">,</span>
+        <span class="n">date_format</span><span class="o">=</span><span class="s2">&quot;%Y-%m-</span><span class="si">%d</span><span class="s2">T%H:%M:%S.</span><span class="si">%f</span><span class="s2">&quot;</span><span class="p">,</span>
+        <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;time&quot;</span><span class="p">,</span> <span class="s2">&quot;magnitude&quot;</span><span class="p">,</span> <span class="s2">&quot;longitude&quot;</span><span class="p">,</span> <span class="s2">&quot;latitude&quot;</span><span class="p">,</span> <span class="s2">&quot;depth(m)&quot;</span><span class="p">],</span>
+    <span class="p">)</span>
+
+    <span class="c1">## upload to s3 bucket</span>
+    <span class="k">try</span><span class="p">:</span>
+        <span class="kn">from</span> <span class="nn">minio</span> <span class="kn">import</span> <span class="n">Minio</span>
+
+        <span class="n">minioClient</span> <span class="o">=</span> <span class="n">Minio</span><span class="p">(</span><span class="n">s3_url</span><span class="p">,</span> <span class="n">access_key</span><span class="o">=</span><span class="s2">&quot;minio&quot;</span><span class="p">,</span> <span class="n">secret_key</span><span class="o">=</span><span class="s2">&quot;minio123&quot;</span><span class="p">,</span> <span class="n">secure</span><span class="o">=</span><span class="n">secure</span><span class="p">)</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">minioClient</span><span class="o">.</span><span class="n">bucket_exists</span><span class="p">(</span><span class="n">bucket_name</span><span class="p">):</span>
+            <span class="n">minioClient</span><span class="o">.</span><span class="n">make_bucket</span><span class="p">(</span><span class="n">bucket_name</span><span class="p">)</span>
+
+        <span class="n">minioClient</span><span class="o">.</span><span class="n">fput_object</span><span class="p">(</span>
+            <span class="n">bucket_name</span><span class="p">,</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;region&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">/standard_catalog.csv&quot;</span><span class="p">,</span>
+            <span class="n">standard_catalog</span><span class="p">,</span>
+        <span class="p">)</span>
+
+    <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">err</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;ERROR: can not access minio service! </span><span class="se">\n</span><span class="si">{</span><span class="n">err</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+        <span class="k">pass</span>
+
+    <span class="c1">####### Plot catalog ########</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">catalog</span><span class="p">[</span><span class="s2">&quot;longitude&quot;</span><span class="p">],</span> <span class="n">catalog</span><span class="p">[</span><span class="s2">&quot;latitude&quot;</span><span class="p">],</span> <span class="s2">&quot;.&quot;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">&quot;Longitude&quot;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">&quot;Latitude&quot;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s2">&quot;scaled&quot;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;xlim_degree&quot;</span><span class="p">])</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;ylim_degree&quot;</span><span class="p">])</span>
+    <span class="c1"># plt.savefig(os.path.join(data_path, &quot;events_loc.png&quot;))</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+
+    <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">plot_date</span><span class="p">(</span><span class="n">catalog</span><span class="p">[</span><span class="s2">&quot;time&quot;</span><span class="p">],</span> <span class="n">catalog</span><span class="p">[</span><span class="s2">&quot;magnitude&quot;</span><span class="p">],</span> <span class="s2">&quot;.&quot;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">gcf</span><span class="p">()</span><span class="o">.</span><span class="n">autofmt_xdate</span><span class="p">()</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">&quot;Magnitude&quot;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Number of events: </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">events</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+    <span class="c1"># plt.savefig(os.path.join(data_path, &quot;events_mag_time.png&quot;))</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+<div id="cell-6" class="clipboard-copy-txt">def download_events(
+    config_json: InputPath("json"),
+    standard_catalog: OutputPath(str),
+    bucket_name: str = "catalogs",
+    s3_url: str = "minio-service:9000",
+    secure: bool = True,
+):
+
+    import json
+    import os
+    import pickle
+    from collections import defaultdict
+
+    import matplotlib
+    import matplotlib.pyplot as plt
+    import obspy
+    import pandas as pd
+    from obspy.clients.fdsn import Client
+
+    # matplotlib.use("agg")
+    # %matplotlib inline
+
+    with open(config_json, "r") as fp:
+        config = json.load(fp)
+
+    ####### IRIS catalog ########
+    try:
+        events = Client(config["client"]).get_events(
+            starttime=config["starttime"],
+            endtime=config["endtime"],
+            minlongitude=config["xlim_degree"][0],
+            maxlongitude=config["xlim_degree"][1],
+            minlatitude=config["ylim_degree"][0],
+            maxlatitude=config["ylim_degree"][1],
+            # filename='events.xml',
+        )
+    except:
+        events = Client("iris").get_events(
+            starttime=config["starttime"],
+            endtime=config["endtime"],
+            minlongitude=config["xlim_degree"][0],
+            maxlongitude=config["xlim_degree"][1],
+            minlatitude=config["ylim_degree"][0],
+            maxlatitude=config["ylim_degree"][1],
+            # filename='events.xml',
+        )
+
+    #     events = obspy.read_events('events.xml')
+    print(f"Number of events: {len(events)}")
+    #     events.plot('local', outfile="events.png")
+    #     events.plot('local')
+
+    ####### Save catalog ########
+    catalog = defaultdict(list)
+    for event in events:
+        if len(event.magnitudes) > 0:
+            catalog["time"].append(event.origins[0].time.datetime)
+            catalog["magnitude"].append(event.magnitudes[0].mag)
+            catalog["longitude"].append(event.origins[0].longitude)
+            catalog["latitude"].append(event.origins[0].latitude)
+            catalog["depth(m)"].append(event.origins[0].depth)
+    catalog = pd.DataFrame.from_dict(catalog).sort_values(["time"])
+    catalog.to_csv(
+        standard_catalog,
+        # sep="\t",
+        index=False,
+        float_format="%.3f",
+        date_format="%Y-%m-%dT%H:%M:%S.%f",
+        columns=["time", "magnitude", "longitude", "latitude", "depth(m)"],
+    )
+
+    ## upload to s3 bucket
+    try:
+        from minio import Minio
+
+        minioClient = Minio(s3_url, access_key="minio", secret_key="minio123", secure=secure)
+        if not minioClient.bucket_exists(bucket_name):
+            minioClient.make_bucket(bucket_name)
+
+        minioClient.fput_object(
+            bucket_name,
+            f"{config['region']}/standard_catalog.csv",
+            standard_catalog,
+        )
+
+    except Exception as err:
+        print(f"ERROR: can not access minio service! \n{err}")
+        pass
+
+    ####### Plot catalog ########
+    plt.figure()
+    plt.plot(catalog["longitude"], catalog["latitude"], ".", markersize=1)
+    plt.xlabel("Longitude")
+    plt.ylabel("Latitude")
+    plt.axis("scaled")
+    plt.xlim(config["xlim_degree"])
+    plt.ylim(config["ylim_degree"])
+    # plt.savefig(os.path.join(data_path, "events_loc.png"))
+    plt.show()
+
+    plt.figure()
+    plt.plot_date(catalog["time"], catalog["magnitude"], ".", markersize=1)
+    plt.gcf().autofmt_xdate()
+    plt.ylabel("Magnitude")
+    plt.title(f"Number of events: {len(events)}")
+    # plt.savefig(os.path.join(data_path, "events_mag_time.png"))
+    plt.show()</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[29]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-7">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">if</span> <span class="n">run_local</span><span class="p">:</span>
+    <span class="n">download_events</span><span class="p">(</span><span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;config.json&quot;</span><span class="p">),</span> <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;standard_catalog.csv&quot;</span><span class="p">))</span>
+</pre></div>
+<div id="cell-7" class="clipboard-copy-txt">if run_local:
+    download_events(root_dir("config.json"), root_dir("standard_catalog.csv"))</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
+<pre>Number of events: 252
+ERROR: can not access minio service! 
+HTTPSConnectionPool(host=&#39;minio-service&#39;, port=9000): Max retries exceeded with url: /catalogs?location= (Caused by NewConnectionError(&#39;&lt;urllib3.connection.HTTPSConnection object at 0x2919bd910&gt;: Failed to establish a new connection: [Errno 8] nodename nor servname provided, or not known&#39;))
+</pre>
+</div>
+</div>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+
+
+<div class="jp-RenderedImage jp-OutputArea-output ">
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"
+class="
+jp-needs-light-background
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+>
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+
+</div>
+
+</div>
+
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
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+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[30]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-8">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">download_events_op</span> <span class="o">=</span> <span class="n">comp</span><span class="o">.</span><span class="n">func_to_container_op</span><span class="p">(</span>
+    <span class="n">download_events</span><span class="p">,</span>
+    <span class="c1"># base_image=&#39;zhuwq0/quakeflow-env:latest&#39;,</span>
+    <span class="n">base_image</span><span class="o">=</span><span class="s2">&quot;python:3.8&quot;</span><span class="p">,</span>
+    <span class="n">packages_to_install</span><span class="o">=</span><span class="p">[</span>
+        <span class="s2">&quot;obspy&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;pandas&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;matplotlib&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;minio&quot;</span><span class="p">,</span>
+    <span class="p">],</span>
+<span class="p">)</span>
+</pre></div>
+<div id="cell-8" class="clipboard-copy-txt">download_events_op = comp.func_to_container_op(
+    download_events,
+    # base_image='zhuwq0/quakeflow-env:latest',
+    base_image="python:3.8",
+    packages_to_install=[
+        "obspy",
+        "pandas",
+        "matplotlib",
+        "minio",
+    ],
+)</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
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+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="3-download-stations">3. Download stations<a class="anchor-link" href="#3-download-stations">&#182;</a></h2>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[31]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-9">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
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+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">def</span> <span class="nf">download_stations</span><span class="p">(</span>
+    <span class="n">config_json</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="s2">&quot;json&quot;</span><span class="p">),</span>
+    <span class="n">station_json</span><span class="p">:</span> <span class="n">OutputPath</span><span class="p">(</span><span class="s2">&quot;json&quot;</span><span class="p">),</span>
+    <span class="n">station_pkl</span><span class="p">:</span> <span class="n">OutputPath</span><span class="p">(</span><span class="s2">&quot;pickle&quot;</span><span class="p">),</span>
+    <span class="n">bucket_name</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;catalogs&quot;</span><span class="p">,</span>
+    <span class="n">s3_url</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;minio-service:9000&quot;</span><span class="p">,</span>
+    <span class="n">secure</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+<span class="p">):</span>
+
+    <span class="kn">import</span> <span class="nn">json</span>
+    <span class="kn">import</span> <span class="nn">os</span>
+    <span class="kn">import</span> <span class="nn">pickle</span>
+    <span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">defaultdict</span>
+
+    <span class="kn">import</span> <span class="nn">matplotlib</span>
+    <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+    <span class="kn">import</span> <span class="nn">obspy</span>
+    <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+    <span class="kn">from</span> <span class="nn">obspy.clients.fdsn</span> <span class="kn">import</span> <span class="n">Client</span>
+
+    <span class="c1"># matplotlib.use(&quot;agg&quot;)</span>
+    <span class="c1"># %matplotlib inline</span>
+
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">config_json</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">config</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">fp</span><span class="p">)</span>
+
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Network:&quot;</span><span class="p">,</span> <span class="s2">&quot;,&quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;networks&quot;</span><span class="p">]))</span>
+
+    <span class="c1">####### Download stations ########</span>
+    <span class="n">stations</span> <span class="o">=</span> <span class="n">Client</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;client&quot;</span><span class="p">])</span><span class="o">.</span><span class="n">get_stations</span><span class="p">(</span>
+        <span class="n">network</span><span class="o">=</span><span class="s2">&quot;,&quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;networks&quot;</span><span class="p">]),</span>
+        <span class="n">station</span><span class="o">=</span><span class="s2">&quot;*&quot;</span><span class="p">,</span>
+        <span class="n">starttime</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;starttime&quot;</span><span class="p">],</span>
+        <span class="n">endtime</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;endtime&quot;</span><span class="p">],</span>
+        <span class="n">minlongitude</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;xlim_degree&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span>
+        <span class="n">maxlongitude</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;xlim_degree&quot;</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span>
+        <span class="n">minlatitude</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;ylim_degree&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span>
+        <span class="n">maxlatitude</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;ylim_degree&quot;</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span>
+        <span class="n">channel</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;channels&quot;</span><span class="p">],</span>
+        <span class="n">level</span><span class="o">=</span><span class="s2">&quot;response&quot;</span><span class="p">,</span>
+        <span class="c1"># filename=&quot;stations.xml&quot;</span>
+    <span class="p">)</span>
+
+    <span class="c1">#     stations = obspy.read_inventory(&quot;stations.xml&quot;)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Number of stations: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">sum</span><span class="p">([</span><span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">stations</span><span class="p">])))</span>
+    <span class="c1"># stations.plot(&#39;local&#39;, outfile=&quot;stations.png&quot;)</span>
+    <span class="c1">#     stations.plot(&#39;local&#39;)</span>
+
+    <span class="c1">####### Save stations ########</span>
+    <span class="n">station_locs</span> <span class="o">=</span> <span class="n">defaultdict</span><span class="p">(</span><span class="nb">dict</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">network</span> <span class="ow">in</span> <span class="n">stations</span><span class="p">:</span>
+        <span class="k">for</span> <span class="n">station</span> <span class="ow">in</span> <span class="n">network</span><span class="p">:</span>
+            <span class="k">for</span> <span class="n">chn</span> <span class="ow">in</span> <span class="n">station</span><span class="p">:</span>
+                <span class="n">sid</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">network</span><span class="o">.</span><span class="n">code</span><span class="si">}</span><span class="s2">.</span><span class="si">{</span><span class="n">station</span><span class="o">.</span><span class="n">code</span><span class="si">}</span><span class="s2">.</span><span class="si">{</span><span class="n">chn</span><span class="o">.</span><span class="n">location_code</span><span class="si">}</span><span class="s2">.</span><span class="si">{</span><span class="n">chn</span><span class="o">.</span><span class="n">code</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="si">}</span><span class="s2">&quot;</span>
+                <span class="k">if</span> <span class="n">sid</span> <span class="ow">in</span> <span class="n">station_locs</span><span class="p">:</span>
+                    <span class="k">if</span> <span class="n">chn</span><span class="o">.</span><span class="n">code</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">station_locs</span><span class="p">[</span><span class="n">sid</span><span class="p">][</span><span class="s2">&quot;component&quot;</span><span class="p">]:</span>
+                        <span class="n">station_locs</span><span class="p">[</span><span class="n">sid</span><span class="p">][</span><span class="s2">&quot;component&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">chn</span><span class="o">.</span><span class="n">code</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+                        <span class="n">station_locs</span><span class="p">[</span><span class="n">sid</span><span class="p">][</span><span class="s2">&quot;response&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">chn</span><span class="o">.</span><span class="n">response</span><span class="o">.</span><span class="n">instrument_sensitivity</span><span class="o">.</span><span class="n">value</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">tmp_dict</span> <span class="o">=</span> <span class="p">{</span>
+                        <span class="s2">&quot;longitude&quot;</span><span class="p">:</span> <span class="n">chn</span><span class="o">.</span><span class="n">longitude</span><span class="p">,</span>
+                        <span class="s2">&quot;latitude&quot;</span><span class="p">:</span> <span class="n">chn</span><span class="o">.</span><span class="n">latitude</span><span class="p">,</span>
+                        <span class="s2">&quot;elevation(m)&quot;</span><span class="p">:</span> <span class="n">chn</span><span class="o">.</span><span class="n">elevation</span><span class="p">,</span>
+                        <span class="s2">&quot;component&quot;</span><span class="p">:</span> <span class="p">[</span>
+                            <span class="n">chn</span><span class="o">.</span><span class="n">code</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span>
+                        <span class="p">],</span>
+                        <span class="s2">&quot;response&quot;</span><span class="p">:</span> <span class="p">[</span>
+                            <span class="nb">round</span><span class="p">(</span><span class="n">chn</span><span class="o">.</span><span class="n">response</span><span class="o">.</span><span class="n">instrument_sensitivity</span><span class="o">.</span><span class="n">value</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span>
+                        <span class="p">],</span>
+                        <span class="s2">&quot;unit&quot;</span><span class="p">:</span> <span class="n">chn</span><span class="o">.</span><span class="n">response</span><span class="o">.</span><span class="n">instrument_sensitivity</span><span class="o">.</span><span class="n">input_units</span><span class="o">.</span><span class="n">lower</span><span class="p">(),</span>
+                    <span class="p">}</span>
+                    <span class="n">station_locs</span><span class="p">[</span><span class="n">sid</span><span class="p">]</span> <span class="o">=</span> <span class="n">tmp_dict</span>
+
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">station_json</span><span class="p">,</span> <span class="s2">&quot;w&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">json</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="n">station_locs</span><span class="p">,</span> <span class="n">fp</span><span class="p">,</span> <span class="n">indent</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">station_pkl</span><span class="p">,</span> <span class="s2">&quot;wb&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">pickle</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="n">stations</span><span class="p">,</span> <span class="n">fp</span><span class="p">)</span>
+
+    <span class="c1">## upload to s3 bucket</span>
+    <span class="k">try</span><span class="p">:</span>
+        <span class="kn">from</span> <span class="nn">minio</span> <span class="kn">import</span> <span class="n">Minio</span>
+
+        <span class="n">minioClient</span> <span class="o">=</span> <span class="n">Minio</span><span class="p">(</span><span class="n">s3_url</span><span class="p">,</span> <span class="n">access_key</span><span class="o">=</span><span class="s2">&quot;minio&quot;</span><span class="p">,</span> <span class="n">secret_key</span><span class="o">=</span><span class="s2">&quot;minio123&quot;</span><span class="p">,</span> <span class="n">secure</span><span class="o">=</span><span class="n">secure</span><span class="p">)</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">minioClient</span><span class="o">.</span><span class="n">bucket_exists</span><span class="p">(</span><span class="n">bucket_name</span><span class="p">):</span>
+            <span class="n">minioClient</span><span class="o">.</span><span class="n">make_bucket</span><span class="p">(</span><span class="n">bucket_name</span><span class="p">)</span>
+
+        <span class="n">minioClient</span><span class="o">.</span><span class="n">fput_object</span><span class="p">(</span>
+            <span class="n">bucket_name</span><span class="p">,</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;region&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">/stations.json&quot;</span><span class="p">,</span>
+            <span class="n">station_json</span><span class="p">,</span>
+        <span class="p">)</span>
+
+        <span class="n">minioClient</span><span class="o">.</span><span class="n">fput_object</span><span class="p">(</span>
+            <span class="n">bucket_name</span><span class="p">,</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;region&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">/stations.pkl&quot;</span><span class="p">,</span>
+            <span class="n">station_pkl</span><span class="p">,</span>
+        <span class="p">)</span>
+
+    <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">err</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;ERROR: can not access minio service! </span><span class="se">\n</span><span class="si">{</span><span class="n">err</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+        <span class="k">pass</span>
+
+    <span class="c1">######## Plot stations ########</span>
+    <span class="n">station_locs</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">from_dict</span><span class="p">(</span><span class="n">station_locs</span><span class="p">,</span> <span class="n">orient</span><span class="o">=</span><span class="s2">&quot;index&quot;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">station_locs</span><span class="p">[</span><span class="s2">&quot;longitude&quot;</span><span class="p">],</span> <span class="n">station_locs</span><span class="p">[</span><span class="s2">&quot;latitude&quot;</span><span class="p">],</span> <span class="s2">&quot;^&quot;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;Stations&quot;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">&quot;X (km)&quot;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">&quot;Y (km)&quot;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s2">&quot;scaled&quot;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;xlim_degree&quot;</span><span class="p">])</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;ylim_degree&quot;</span><span class="p">])</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Number of stations: </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">station_locs</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+    <span class="c1">#     plt.savefig(os.path.join(data_path, &quot;stations_loc.png&quot;))</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+<div id="cell-9" class="clipboard-copy-txt">def download_stations(
+    config_json: InputPath("json"),
+    station_json: OutputPath("json"),
+    station_pkl: OutputPath("pickle"),
+    bucket_name: str = "catalogs",
+    s3_url: str = "minio-service:9000",
+    secure: bool = True,
+):
+
+    import json
+    import os
+    import pickle
+    from collections import defaultdict
+
+    import matplotlib
+    import matplotlib.pyplot as plt
+    import obspy
+    import pandas as pd
+    from obspy.clients.fdsn import Client
+
+    # matplotlib.use("agg")
+    # %matplotlib inline
+
+    with open(config_json, "r") as fp:
+        config = json.load(fp)
+
+    print("Network:", ",".join(config["networks"]))
+
+    ####### Download stations ########
+    stations = Client(config["client"]).get_stations(
+        network=",".join(config["networks"]),
+        station="*",
+        starttime=config["starttime"],
+        endtime=config["endtime"],
+        minlongitude=config["xlim_degree"][0],
+        maxlongitude=config["xlim_degree"][1],
+        minlatitude=config["ylim_degree"][0],
+        maxlatitude=config["ylim_degree"][1],
+        channel=config["channels"],
+        level="response",
+        # filename="stations.xml"
+    )
+
+    #     stations = obspy.read_inventory("stations.xml")
+    print("Number of stations: {}".format(sum([len(x) for x in stations])))
+    # stations.plot('local', outfile="stations.png")
+    #     stations.plot('local')
+
+    ####### Save stations ########
+    station_locs = defaultdict(dict)
+    for network in stations:
+        for station in network:
+            for chn in station:
+                sid = f"{network.code}.{station.code}.{chn.location_code}.{chn.code[:-1]}"
+                if sid in station_locs:
+                    if chn.code[-1] not in station_locs[sid]["component"]:
+                        station_locs[sid]["component"].append(chn.code[-1])
+                        station_locs[sid]["response"].append(round(chn.response.instrument_sensitivity.value, 2))
+                else:
+                    tmp_dict = {
+                        "longitude": chn.longitude,
+                        "latitude": chn.latitude,
+                        "elevation(m)": chn.elevation,
+                        "component": [
+                            chn.code[-1],
+                        ],
+                        "response": [
+                            round(chn.response.instrument_sensitivity.value, 2),
+                        ],
+                        "unit": chn.response.instrument_sensitivity.input_units.lower(),
+                    }
+                    station_locs[sid] = tmp_dict
+
+    with open(station_json, "w") as fp:
+        json.dump(station_locs, fp, indent=2)
+
+    with open(station_pkl, "wb") as fp:
+        pickle.dump(stations, fp)
+
+    ## upload to s3 bucket
+    try:
+        from minio import Minio
+
+        minioClient = Minio(s3_url, access_key="minio", secret_key="minio123", secure=secure)
+        if not minioClient.bucket_exists(bucket_name):
+            minioClient.make_bucket(bucket_name)
+
+        minioClient.fput_object(
+            bucket_name,
+            f"{config['region']}/stations.json",
+            station_json,
+        )
+
+        minioClient.fput_object(
+            bucket_name,
+            f"{config['region']}/stations.pkl",
+            station_pkl,
+        )
+
+    except Exception as err:
+        print(f"ERROR: can not access minio service! \n{err}")
+        pass
+
+    ######## Plot stations ########
+    station_locs = pd.DataFrame.from_dict(station_locs, orient="index")
+    plt.figure()
+    plt.plot(station_locs["longitude"], station_locs["latitude"], "^", label="Stations")
+    plt.xlabel("X (km)")
+    plt.ylabel("Y (km)")
+    plt.axis("scaled")
+    plt.xlim(config["xlim_degree"])
+    plt.ylim(config["ylim_degree"])
+    plt.legend()
+    plt.title(f"Number of stations: {len(station_locs)}")
+    #     plt.savefig(os.path.join(data_path, "stations_loc.png"))
+    plt.show()</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[32]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-10">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">if</span> <span class="n">run_local</span><span class="p">:</span>
+    <span class="n">download_stations</span><span class="p">(</span><span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;config.json&quot;</span><span class="p">),</span> <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;stations.json&quot;</span><span class="p">),</span> <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;stations.pkl&quot;</span><span class="p">))</span>
+</pre></div>
+<div id="cell-10" class="clipboard-copy-txt">if run_local:
+    download_stations(root_dir("config.json"), root_dir("stations.json"), root_dir("stations.pkl"))</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
+<pre>Network: CI
+Number of stations: 17
+ERROR: can not access minio service! 
+HTTPSConnectionPool(host=&#39;minio-service&#39;, port=9000): Max retries exceeded with url: /catalogs?location= (Caused by NewConnectionError(&#39;&lt;urllib3.connection.HTTPSConnection object at 0x292125f10&gt;: Failed to establish a new connection: [Errno 8] nodename nor servname provided, or not known&#39;))
+</pre>
+</div>
+</div>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+
+
+<div class="jp-RenderedImage jp-OutputArea-output ">
+<img 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"
+class="
+jp-needs-light-background
+"
+>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[33]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-11">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">download_stations_op</span> <span class="o">=</span> <span class="n">comp</span><span class="o">.</span><span class="n">func_to_container_op</span><span class="p">(</span>
+    <span class="n">download_stations</span><span class="p">,</span>
+    <span class="c1"># base_image=&#39;zhuwq0/quakeflow-env:latest&#39;,</span>
+    <span class="n">base_image</span><span class="o">=</span><span class="s2">&quot;python:3.8&quot;</span><span class="p">,</span>
+    <span class="n">packages_to_install</span><span class="o">=</span><span class="p">[</span>
+        <span class="s2">&quot;obspy&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;pandas&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;matplotlib&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;minio&quot;</span><span class="p">,</span>
+    <span class="p">],</span>
+<span class="p">)</span>
+</pre></div>
+<div id="cell-11" class="clipboard-copy-txt">download_stations_op = comp.func_to_container_op(
+    download_stations,
+    # base_image='zhuwq0/quakeflow-env:latest',
+    base_image="python:3.8",
+    packages_to_install=[
+        "obspy",
+        "pandas",
+        "matplotlib",
+        "minio",
+    ],
+)</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="4-download-waveform-data">4. Download waveform data<a class="anchor-link" href="#4-download-waveform-data">&#182;</a></h2>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[34]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-12">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">def</span> <span class="nf">download_waveform</span><span class="p">(</span>
+    <span class="n">node_i</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
+    <span class="n">index_json</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="s2">&quot;json&quot;</span><span class="p">),</span>
+    <span class="n">config_json</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="s2">&quot;json&quot;</span><span class="p">),</span>
+    <span class="n">datetime_json</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="s2">&quot;json&quot;</span><span class="p">),</span>
+    <span class="n">station_pkl</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="s2">&quot;pickle&quot;</span><span class="p">),</span>
+    <span class="n">fname_csv</span><span class="p">:</span> <span class="n">OutputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span>
+    <span class="n">data_path</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
+    <span class="n">bucket_name</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;waveforms&quot;</span><span class="p">,</span>
+    <span class="n">s3_url</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;minio-service:9000&quot;</span><span class="p">,</span>
+    <span class="n">secure</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
+
+    <span class="kn">import</span> <span class="nn">json</span>
+    <span class="kn">import</span> <span class="nn">os</span>
+    <span class="kn">import</span> <span class="nn">pickle</span>
+    <span class="kn">import</span> <span class="nn">random</span>
+    <span class="kn">import</span> <span class="nn">threading</span>
+    <span class="kn">import</span> <span class="nn">time</span>
+
+    <span class="kn">import</span> <span class="nn">obspy</span>
+    <span class="kn">from</span> <span class="nn">obspy.clients.fdsn</span> <span class="kn">import</span> <span class="n">Client</span>
+
+    <span class="n">lock</span> <span class="o">=</span> <span class="n">threading</span><span class="o">.</span><span class="n">Lock</span><span class="p">()</span>
+
+    <span class="n">upload_minio</span> <span class="o">=</span> <span class="kc">False</span>
+    <span class="c1"># try:</span>
+    <span class="c1">#     from minio import Minio</span>
+
+    <span class="c1">#     minioClient = Minio(s3_url, access_key=&#39;minio&#39;, secret_key=&#39;minio123&#39;, secure=secure)</span>
+    <span class="c1">#     if not minioClient.bucket_exists(bucket_name):</span>
+    <span class="c1">#         minioClient.make_bucket(bucket_name)</span>
+    <span class="c1">#     upload_minio = True</span>
+    <span class="c1"># except Exception as err:</span>
+    <span class="c1">#     # print(f&quot;ERROR: can not access minio service! \n{err}&quot;)</span>
+    <span class="c1">#     pass</span>
+
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">index_json</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">index</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">fp</span><span class="p">)</span>
+    <span class="n">idx</span> <span class="o">=</span> <span class="n">index</span><span class="p">[</span><span class="n">node_i</span><span class="p">]</span>
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">config_json</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">config</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">fp</span><span class="p">)</span>
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">datetime_json</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">tmp</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">fp</span><span class="p">)</span>
+        <span class="n">starttimes</span> <span class="o">=</span> <span class="n">tmp</span><span class="p">[</span><span class="s2">&quot;starttimes&quot;</span><span class="p">]</span>
+        <span class="n">interval</span> <span class="o">=</span> <span class="n">tmp</span><span class="p">[</span><span class="s2">&quot;interval&quot;</span><span class="p">]</span>
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">station_pkl</span><span class="p">,</span> <span class="s2">&quot;rb&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">stations</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">fp</span><span class="p">)</span>
+
+    <span class="n">waveform_dir</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">data_path</span><span class="p">,</span> <span class="n">config</span><span class="p">[</span><span class="s2">&quot;region&quot;</span><span class="p">],</span> <span class="s2">&quot;waveforms&quot;</span><span class="p">)</span>
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">waveform_dir</span><span class="p">):</span>
+        <span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">waveform_dir</span><span class="p">)</span>
+
+    <span class="c1">####### Download data ########</span>
+    <span class="n">client</span> <span class="o">=</span> <span class="n">Client</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;client&quot;</span><span class="p">])</span>
+    <span class="n">fname_list</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;fname&quot;</span><span class="p">]</span>
+
+    <span class="k">def</span> <span class="nf">download</span><span class="p">(</span><span class="n">i</span><span class="p">):</span>
+        <span class="c1">#     for i in idx:</span>
+        <span class="n">starttime</span> <span class="o">=</span> <span class="n">obspy</span><span class="o">.</span><span class="n">UTCDateTime</span><span class="p">(</span><span class="n">starttimes</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+        <span class="n">endtime</span> <span class="o">=</span> <span class="n">starttime</span> <span class="o">+</span> <span class="n">interval</span>
+        <span class="n">fname</span> <span class="o">=</span> <span class="s2">&quot;</span><span class="si">{}</span><span class="s2">.mseed&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">starttime</span><span class="o">.</span><span class="n">datetime</span><span class="o">.</span><span class="n">strftime</span><span class="p">(</span><span class="s2">&quot;%Y-%m-</span><span class="si">%d</span><span class="s2">T%H:%M:%S&quot;</span><span class="p">))</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">upload_minio</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">waveform_dir</span><span class="p">,</span> <span class="n">fname</span><span class="p">)):</span>
+                <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">fname</span><span class="si">}</span><span class="s2"> exists&quot;</span><span class="p">)</span>
+                <span class="n">fname_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">fname</span><span class="p">)</span>
+                <span class="k">return</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">try</span><span class="p">:</span>
+                <span class="n">minioClient</span><span class="o">.</span><span class="n">fget_object</span><span class="p">(</span>
+                    <span class="n">bucket_name</span><span class="p">,</span>
+                    <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;region&quot;</span><span class="p">],</span> <span class="n">fname</span><span class="p">),</span>
+                    <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">waveform_dir</span><span class="p">,</span> <span class="n">fname</span><span class="p">),</span>
+                <span class="p">)</span>
+                <span class="nb">print</span><span class="p">(</span>
+                    <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">bucket_name</span><span class="si">}</span><span class="s2">/</span><span class="si">{</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;region&#39;</span><span class="p">],</span><span class="w"> </span><span class="n">fname</span><span class="p">)</span><span class="si">}</span><span class="s2"> download to </span><span class="si">{</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">waveform_dir</span><span class="p">,</span><span class="w"> </span><span class="n">fname</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span>
+                <span class="p">)</span>
+                <span class="n">fname_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">fname</span><span class="p">)</span>
+                <span class="k">return</span>
+            <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">err</span><span class="p">:</span>
+                <span class="nb">print</span><span class="p">(</span><span class="n">err</span><span class="p">)</span>
+
+        <span class="n">max_retry</span> <span class="o">=</span> <span class="mi">10</span>
+        <span class="n">stream</span> <span class="o">=</span> <span class="n">obspy</span><span class="o">.</span><span class="n">Stream</span><span class="p">()</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">fname</span><span class="si">}</span><span class="s2"> download starts&quot;</span><span class="p">)</span>
+        <span class="n">num_sta</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="k">for</span> <span class="n">network</span> <span class="ow">in</span> <span class="n">stations</span><span class="p">:</span>
+            <span class="k">for</span> <span class="n">station</span> <span class="ow">in</span> <span class="n">network</span><span class="p">:</span>
+                <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;********</span><span class="si">{</span><span class="n">network</span><span class="o">.</span><span class="n">code</span><span class="si">}</span><span class="s2">.</span><span class="si">{</span><span class="n">station</span><span class="o">.</span><span class="n">code</span><span class="si">}</span><span class="s2">********&quot;</span><span class="p">)</span>
+                <span class="n">retry</span> <span class="o">=</span> <span class="mi">0</span>
+                <span class="k">while</span> <span class="n">retry</span> <span class="o">&lt;</span> <span class="n">max_retry</span><span class="p">:</span>
+                    <span class="k">try</span><span class="p">:</span>
+                        <span class="n">tmp</span> <span class="o">=</span> <span class="n">client</span><span class="o">.</span><span class="n">get_waveforms</span><span class="p">(</span>
+                            <span class="n">network</span><span class="o">.</span><span class="n">code</span><span class="p">,</span>
+                            <span class="n">station</span><span class="o">.</span><span class="n">code</span><span class="p">,</span>
+                            <span class="s2">&quot;*&quot;</span><span class="p">,</span>
+                            <span class="n">config</span><span class="p">[</span><span class="s2">&quot;channels&quot;</span><span class="p">],</span>
+                            <span class="n">starttime</span><span class="p">,</span>
+                            <span class="n">endtime</span><span class="p">,</span>
+                        <span class="p">)</span>
+                        <span class="c1">#  for trace in tmp:</span>
+                        <span class="c1">#      if trace.stats.sampling_rate != 100:</span>
+                        <span class="c1">#          print(trace)</span>
+                        <span class="c1">#          trace = trace.interpolate(100, method=&quot;linear&quot;)</span>
+                        <span class="c1">#      trace = trace.detrend(&quot;spline&quot;, order=2, dspline=5*trace.stats.sampling_rate)</span>
+                        <span class="c1">#      stream.append(trace)</span>
+                        <span class="n">stream</span> <span class="o">+=</span> <span class="n">tmp</span>
+                        <span class="n">num_sta</span> <span class="o">+=</span> <span class="nb">len</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
+                        <span class="k">break</span>
+                    <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">err</span><span class="p">:</span>
+                        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Error </span><span class="si">{}</span><span class="s2">.</span><span class="si">{}</span><span class="s2">: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">network</span><span class="o">.</span><span class="n">code</span><span class="p">,</span> <span class="n">station</span><span class="o">.</span><span class="n">code</span><span class="p">,</span> <span class="n">err</span><span class="p">))</span>
+                        <span class="n">message</span> <span class="o">=</span> <span class="s2">&quot;No data available for request.&quot;</span>
+                        <span class="k">if</span> <span class="nb">str</span><span class="p">(</span><span class="n">err</span><span class="p">)[:</span> <span class="nb">len</span><span class="p">(</span><span class="n">message</span><span class="p">)]</span> <span class="o">==</span> <span class="n">message</span><span class="p">:</span>
+                            <span class="k">break</span>
+                        <span class="n">retry</span> <span class="o">+=</span> <span class="mi">1</span>
+                        <span class="n">time</span><span class="o">.</span><span class="n">sleep</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
+                        <span class="k">continue</span>
+                <span class="k">if</span> <span class="n">retry</span> <span class="o">==</span> <span class="n">max_retry</span><span class="p">:</span>
+                    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">fname</span><span class="si">}</span><span class="s2">: MAX </span><span class="si">{</span><span class="n">max_retry</span><span class="si">}</span><span class="s2"> retries reached : </span><span class="si">{</span><span class="n">network</span><span class="o">.</span><span class="n">code</span><span class="si">}</span><span class="s2">.</span><span class="si">{</span><span class="n">station</span><span class="o">.</span><span class="n">code</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">stream</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="c1"># stream = stream.merge(fill_value=0)</span>
+            <span class="c1"># stream = stream.trim(starttime, endtime, pad=True, fill_value=0)</span>
+            <span class="n">stream</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">waveform_dir</span><span class="p">,</span> <span class="n">fname</span><span class="p">))</span>
+            <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">fname</span><span class="si">}</span><span class="s2"> download succeeds&quot;</span><span class="p">)</span>
+            <span class="c1"># if upload_minio:</span>
+            <span class="c1">#     minioClient.fput_object(bucket_name, os.path.join(config[&#39;region&#39;], fname), os.path.join(waveform_dir, fname))</span>
+            <span class="c1">#     print(f&quot;{fname} upload to minio {os.path.join(config[&#39;region&#39;], fname)}&quot;)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">fname</span><span class="si">}</span><span class="s2"> empty data&quot;</span><span class="p">)</span>
+        <span class="n">lock</span><span class="o">.</span><span class="n">acquire</span><span class="p">()</span>
+        <span class="n">fname_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">fname</span><span class="p">)</span>
+        <span class="n">lock</span><span class="o">.</span><span class="n">release</span><span class="p">()</span>
+
+    <span class="n">threads</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">MAX_THREADS</span> <span class="o">=</span> <span class="mi">2</span>
+    <span class="c1"># MAX_THREADS = 1</span>
+    <span class="k">for</span> <span class="n">ii</span><span class="p">,</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">idx</span><span class="p">):</span>
+        <span class="n">t</span> <span class="o">=</span> <span class="n">threading</span><span class="o">.</span><span class="n">Thread</span><span class="p">(</span><span class="n">target</span><span class="o">=</span><span class="n">download</span><span class="p">,</span> <span class="n">args</span><span class="o">=</span><span class="p">(</span><span class="n">i</span><span class="p">,))</span>
+        <span class="n">t</span><span class="o">.</span><span class="n">start</span><span class="p">()</span>
+        <span class="n">time</span><span class="o">.</span><span class="n">sleep</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+        <span class="n">threads</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">t</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">ii</span> <span class="o">%</span> <span class="n">MAX_THREADS</span> <span class="o">==</span> <span class="n">MAX_THREADS</span> <span class="o">-</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">threads</span><span class="p">:</span>
+                <span class="n">t</span><span class="o">.</span><span class="n">join</span><span class="p">()</span>
+            <span class="n">threads</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">threads</span><span class="p">:</span>
+        <span class="n">t</span><span class="o">.</span><span class="n">join</span><span class="p">()</span>
+
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">fname_csv</span><span class="p">,</span> <span class="s2">&quot;w&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">fp</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">fname_list</span><span class="p">))</span>
+
+    <span class="k">return</span> <span class="n">waveform_dir</span>
+</pre></div>
+<div id="cell-12" class="clipboard-copy-txt">def download_waveform(
+    node_i: int,
+    index_json: InputPath("json"),
+    config_json: InputPath("json"),
+    datetime_json: InputPath("json"),
+    station_pkl: InputPath("pickle"),
+    fname_csv: OutputPath(str),
+    data_path: str,
+    bucket_name: str = "waveforms",
+    s3_url: str = "minio-service:9000",
+    secure: bool = True,
+) -> str:
+
+    import json
+    import os
+    import pickle
+    import random
+    import threading
+    import time
+
+    import obspy
+    from obspy.clients.fdsn import Client
+
+    lock = threading.Lock()
+
+    upload_minio = False
+    # try:
+    #     from minio import Minio
+
+    #     minioClient = Minio(s3_url, access_key='minio', secret_key='minio123', secure=secure)
+    #     if not minioClient.bucket_exists(bucket_name):
+    #         minioClient.make_bucket(bucket_name)
+    #     upload_minio = True
+    # except Exception as err:
+    #     # print(f"ERROR: can not access minio service! \n{err}")
+    #     pass
+
+    with open(index_json, "r") as fp:
+        index = json.load(fp)
+    idx = index[node_i]
+    with open(config_json, "r") as fp:
+        config = json.load(fp)
+    with open(datetime_json, "r") as fp:
+        tmp = json.load(fp)
+        starttimes = tmp["starttimes"]
+        interval = tmp["interval"]
+    with open(station_pkl, "rb") as fp:
+        stations = pickle.load(fp)
+
+    waveform_dir = os.path.join(data_path, config["region"], "waveforms")
+    if not os.path.exists(waveform_dir):
+        os.makedirs(waveform_dir)
+
+    ####### Download data ########
+    client = Client(config["client"])
+    fname_list = ["fname"]
+
+    def download(i):
+        #     for i in idx:
+        starttime = obspy.UTCDateTime(starttimes[i])
+        endtime = starttime + interval
+        fname = "{}.mseed".format(starttime.datetime.strftime("%Y-%m-%dT%H:%M:%S"))
+        if not upload_minio:
+            if os.path.exists(os.path.join(waveform_dir, fname)):
+                print(f"{fname} exists")
+                fname_list.append(fname)
+                return
+        else:
+            try:
+                minioClient.fget_object(
+                    bucket_name,
+                    os.path.join(config["region"], fname),
+                    os.path.join(waveform_dir, fname),
+                )
+                print(
+                    f"{bucket_name}/{os.path.join(config['region'], fname)} download to {os.path.join(waveform_dir, fname)}"
+                )
+                fname_list.append(fname)
+                return
+            except Exception as err:
+                print(err)
+
+        max_retry = 10
+        stream = obspy.Stream()
+        print(f"{fname} download starts")
+        num_sta = 0
+        for network in stations:
+            for station in network:
+                print(f"********{network.code}.{station.code}********")
+                retry = 0
+                while retry < max_retry:
+                    try:
+                        tmp = client.get_waveforms(
+                            network.code,
+                            station.code,
+                            "*",
+                            config["channels"],
+                            starttime,
+                            endtime,
+                        )
+                        #  for trace in tmp:
+                        #      if trace.stats.sampling_rate != 100:
+                        #          print(trace)
+                        #          trace = trace.interpolate(100, method="linear")
+                        #      trace = trace.detrend("spline", order=2, dspline=5*trace.stats.sampling_rate)
+                        #      stream.append(trace)
+                        stream += tmp
+                        num_sta += len(tmp)
+                        break
+                    except Exception as err:
+                        print("Error {}.{}: {}".format(network.code, station.code, err))
+                        message = "No data available for request."
+                        if str(err)[: len(message)] == message:
+                            break
+                        retry += 1
+                        time.sleep(5)
+                        continue
+                if retry == max_retry:
+                    print(f"{fname}: MAX {max_retry} retries reached : {network.code}.{station.code}")
+
+        if len(stream) > 0:
+            # stream = stream.merge(fill_value=0)
+            # stream = stream.trim(starttime, endtime, pad=True, fill_value=0)
+            stream.write(os.path.join(waveform_dir, fname))
+            print(f"{fname} download succeeds")
+            # if upload_minio:
+            #     minioClient.fput_object(bucket_name, os.path.join(config['region'], fname), os.path.join(waveform_dir, fname))
+            #     print(f"{fname} upload to minio {os.path.join(config['region'], fname)}")
+        else:
+            print(f"{fname} empty data")
+        lock.acquire()
+        fname_list.append(fname)
+        lock.release()
+
+    threads = []
+    MAX_THREADS = 2
+    # MAX_THREADS = 1
+    for ii, i in enumerate(idx):
+        t = threading.Thread(target=download, args=(i,))
+        t.start()
+        time.sleep(1)
+        threads.append(t)
+        if ii % MAX_THREADS == MAX_THREADS - 1:
+            for t in threads:
+                t.join()
+            threads = []
+    for t in threads:
+        t.join()
+
+    with open(fname_csv, "w") as fp:
+        fp.write("\n".join(fname_list))
+
+    return waveform_dir</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[35]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-13">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">if</span> <span class="n">run_local</span><span class="p">:</span>
+    <span class="n">waveform_path</span> <span class="o">=</span> <span class="n">download_waveform</span><span class="p">(</span>
+        <span class="mi">0</span><span class="p">,</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;index.json&quot;</span><span class="p">),</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;config.json&quot;</span><span class="p">),</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;datetimes.json&quot;</span><span class="p">),</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;stations.pkl&quot;</span><span class="p">),</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;fname.csv&quot;</span><span class="p">),</span>
+        <span class="n">data_path</span><span class="o">=</span><span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;&quot;</span><span class="p">),</span>
+    <span class="p">)</span>
+</pre></div>
+<div id="cell-13" class="clipboard-copy-txt">if run_local:
+    waveform_path = download_waveform(
+        0,
+        root_dir("index.json"),
+        root_dir("config.json"),
+        root_dir("datetimes.json"),
+        root_dir("stations.pkl"),
+        root_dir("fname.csv"),
+        data_path=root_dir(""),
+    )</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
+<pre>2019-07-04T17:00:00.mseed exists
+2019-07-04T18:00:00.mseed exists
+</pre>
+</div>
+</div>
+
+</div>
+
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[36]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-14">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">download_waveform_op</span> <span class="o">=</span> <span class="n">comp</span><span class="o">.</span><span class="n">func_to_container_op</span><span class="p">(</span>
+    <span class="n">download_waveform</span><span class="p">,</span>
+    <span class="c1"># base_image=&#39;zhuwq0/quakeflow-env:latest&#39;,</span>
+    <span class="n">base_image</span><span class="o">=</span><span class="s2">&quot;python:3.8&quot;</span><span class="p">,</span>
+    <span class="n">packages_to_install</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;obspy&quot;</span><span class="p">,</span> <span class="s2">&quot;minio&quot;</span><span class="p">],</span>
+<span class="p">)</span>
+</pre></div>
+<div id="cell-14" class="clipboard-copy-txt">download_waveform_op = comp.func_to_container_op(
+    download_waveform,
+    # base_image='zhuwq0/quakeflow-env:latest',
+    base_image="python:3.8",
+    packages_to_install=["obspy", "minio"],
+)</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[37]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-15">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">def</span> <span class="nf">resume_waveform</span><span class="p">(</span>
+    <span class="n">i</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
+    <span class="n">index_json</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="s2">&quot;json&quot;</span><span class="p">),</span>
+    <span class="n">config_json</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="s2">&quot;json&quot;</span><span class="p">),</span>
+    <span class="n">datetime_json</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="s2">&quot;json&quot;</span><span class="p">),</span>
+    <span class="n">data_path</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
+    <span class="n">bucket_name</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;waveforms&quot;</span><span class="p">,</span>
+    <span class="n">s3_url</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;minio-service:9000&quot;</span><span class="p">,</span>
+    <span class="n">secure</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
+
+    <span class="kn">import</span> <span class="nn">json</span>
+    <span class="kn">import</span> <span class="nn">pickle</span>
+    <span class="kn">import</span> <span class="nn">time</span>
+    <span class="kn">import</span> <span class="nn">threading</span>
+    <span class="kn">import</span> <span class="nn">os</span>
+    <span class="kn">from</span> <span class="nn">pathlib</span> <span class="kn">import</span> <span class="n">Path</span>
+
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">index_json</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">index</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">fp</span><span class="p">)</span>
+    <span class="n">idx</span> <span class="o">=</span> <span class="n">index</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">config_json</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">config</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">fp</span><span class="p">)</span>
+
+    <span class="n">waveform_dir</span> <span class="o">=</span> <span class="n">Path</span><span class="p">(</span><span class="n">data_path</span><span class="p">,</span> <span class="n">config</span><span class="p">[</span><span class="s2">&quot;region&quot;</span><span class="p">],</span> <span class="s2">&quot;waveforms&quot;</span><span class="p">)</span>
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">waveform_dir</span><span class="o">.</span><span class="n">exists</span><span class="p">():</span>
+        <span class="n">waveform_dir</span><span class="o">.</span><span class="n">mkdir</span><span class="p">(</span><span class="n">parents</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+    <span class="n">status</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
+    <span class="n">max_retry</span> <span class="o">=</span> <span class="mi">10</span>
+    <span class="n">retry_rsync</span> <span class="o">=</span> <span class="mi">0</span>
+
+    <span class="k">while</span> <span class="p">(</span><span class="n">status</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="n">retry_rsync</span> <span class="o">&lt;</span> <span class="n">max_retry</span><span class="p">):</span>
+        <span class="n">status</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span>
+            <span class="sa">f</span><span class="s2">&quot;rsync -av zhuwq@wintermute:/atomic-data/zhuwq/</span><span class="si">{</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;region&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">/</span><span class="si">{</span><span class="n">i</span><span class="si">:</span><span class="s2">03d</span><span class="si">}</span><span class="s2">/ </span><span class="si">{</span><span class="n">data_path</span><span class="si">}</span><span class="s2">/</span><span class="si">{</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;region&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">/waveforms/&quot;</span>
+        <span class="p">)</span>
+        <span class="n">retry_rsync</span> <span class="o">+=</span> <span class="mi">1</span>
+        <span class="k">if</span> <span class="n">status</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="n">time</span><span class="o">.</span><span class="n">sleep</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">status</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span>
+            <span class="sa">f</span><span class="s2">&quot;Failed: rsync -av zhuwq@wintermute:/atomic-data/zhuwq/</span><span class="si">{</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;region&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">/</span><span class="si">{</span><span class="n">i</span><span class="si">:</span><span class="s2">03d</span><span class="si">}</span><span class="s2">/ </span><span class="si">{</span><span class="n">data_path</span><span class="si">}</span><span class="s2">/</span><span class="si">{</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;region&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">/waveforms/&quot;</span>
+        <span class="p">)</span>
+
+    <span class="k">return</span> <span class="mi">0</span>
+</pre></div>
+<div id="cell-15" class="clipboard-copy-txt">def resume_waveform(
+    i: int,
+    index_json: InputPath("json"),
+    config_json: InputPath("json"),
+    datetime_json: InputPath("json"),
+    data_path: str,
+    bucket_name: str = "waveforms",
+    s3_url: str = "minio-service:9000",
+    secure: bool = True,
+) -> int:
+
+    import json
+    import pickle
+    import time
+    import threading
+    import os
+    from pathlib import Path
+
+    with open(index_json, "r") as fp:
+        index = json.load(fp)
+    idx = index[i]
+    with open(config_json, "r") as fp:
+        config = json.load(fp)
+
+    waveform_dir = Path(data_path, config["region"], "waveforms")
+    if not waveform_dir.exists():
+        waveform_dir.mkdir(parents=True)
+
+    status = -1
+    max_retry = 10
+    retry_rsync = 0
+
+    while (status != 0) and (retry_rsync < max_retry):
+        status = os.system(
+            f"rsync -av zhuwq@wintermute:/atomic-data/zhuwq/{config['region']}/{i:03d}/ {data_path}/{config['region']}/waveforms/"
+        )
+        retry_rsync += 1
+        if status != 0:
+            time.sleep(5)
+
+    if status != 0:
+        print(
+            f"Failed: rsync -av zhuwq@wintermute:/atomic-data/zhuwq/{config['region']}/{i:03d}/ {data_path}/{config['region']}/waveforms/"
+        )
+
+    return 0</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[38]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-16">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">resume_waveform_op</span> <span class="o">=</span> <span class="n">comp</span><span class="o">.</span><span class="n">func_to_container_op</span><span class="p">(</span>
+    <span class="n">resume_waveform</span><span class="p">,</span>
+    <span class="n">base_image</span><span class="o">=</span><span class="s2">&quot;zhuwq0/waveform-env:1.1&quot;</span><span class="p">,</span>
+    <span class="c1"># base_image=&#39;python:3.8&#39;,</span>
+    <span class="c1"># packages_to_install=[&quot;obspy&quot;, &quot;minio&quot;],</span>
+<span class="p">)</span>
+</pre></div>
+<div id="cell-16" class="clipboard-copy-txt">resume_waveform_op = comp.func_to_container_op(
+    resume_waveform,
+    base_image="zhuwq0/waveform-env:1.1",
+    # base_image='python:3.8',
+    # packages_to_install=["obspy", "minio"],
+)</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[39]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-17">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">def</span> <span class="nf">save_waveform</span><span class="p">(</span>
+    <span class="n">i</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
+    <span class="n">index_json</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="s2">&quot;json&quot;</span><span class="p">),</span>
+    <span class="n">config_json</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="s2">&quot;json&quot;</span><span class="p">),</span>
+    <span class="n">datetime_json</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="s2">&quot;json&quot;</span><span class="p">),</span>
+    <span class="n">data_path</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
+    <span class="n">bucket_name</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;waveforms&quot;</span><span class="p">,</span>
+    <span class="n">s3_url</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;minio-service:9000&quot;</span><span class="p">,</span>
+    <span class="n">secure</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
+
+    <span class="kn">import</span> <span class="nn">json</span>
+    <span class="kn">import</span> <span class="nn">pickle</span>
+    <span class="kn">import</span> <span class="nn">time</span>
+    <span class="kn">import</span> <span class="nn">threading</span>
+    <span class="kn">import</span> <span class="nn">os</span>
+    <span class="kn">from</span> <span class="nn">pathlib</span> <span class="kn">import</span> <span class="n">Path</span>
+
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">index_json</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">index</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">fp</span><span class="p">)</span>
+    <span class="n">idx</span> <span class="o">=</span> <span class="n">index</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">config_json</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">config</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">fp</span><span class="p">)</span>
+
+    <span class="n">waveform_dir</span> <span class="o">=</span> <span class="n">Path</span><span class="p">(</span><span class="n">data_path</span><span class="p">,</span> <span class="n">config</span><span class="p">[</span><span class="s2">&quot;region&quot;</span><span class="p">],</span> <span class="s2">&quot;waveforms&quot;</span><span class="p">)</span>
+    <span class="n">mseeds</span> <span class="o">=</span> <span class="n">waveform_dir</span><span class="o">.</span><span class="n">glob</span><span class="p">(</span><span class="s2">&quot;*.mseed&quot;</span><span class="p">)</span>
+
+    <span class="n">status</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
+    <span class="n">max_retry</span> <span class="o">=</span> <span class="mi">10</span>
+    <span class="n">retry_rsync</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="k">while</span> <span class="p">(</span><span class="n">status</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="n">retry_rsync</span> <span class="o">&lt;</span> <span class="n">max_retry</span><span class="p">):</span>
+        <span class="n">status</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;ssh zhuwq@wintermute mkdir -p /atomic-data/zhuwq/</span><span class="si">{</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;region&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">/</span><span class="si">{</span><span class="n">i</span><span class="si">:</span><span class="s2">03d</span><span class="si">}</span><span class="s2">/&quot;</span><span class="p">)</span>
+        <span class="n">retry_rsync</span> <span class="o">+=</span> <span class="mi">1</span>
+        <span class="k">if</span> <span class="n">status</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="n">time</span><span class="o">.</span><span class="n">sleep</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">status</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Failed: ssh zhuwq@wintermute mkdir -p /atomic-data/zhuwq/</span><span class="si">{</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;region&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">/</span><span class="si">{</span><span class="n">i</span><span class="si">:</span><span class="s2">03d</span><span class="si">}</span><span class="s2">/&quot;</span><span class="p">)</span>
+
+    <span class="n">status</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
+    <span class="n">max_retry</span> <span class="o">=</span> <span class="mi">10</span>
+    <span class="n">retry_rsync</span> <span class="o">=</span> <span class="mi">0</span>
+
+    <span class="k">while</span> <span class="p">(</span><span class="n">status</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="n">retry_rsync</span> <span class="o">&lt;</span> <span class="n">max_retry</span><span class="p">):</span>
+        <span class="n">status</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span>
+            <span class="sa">f</span><span class="s2">&quot;rsync -av </span><span class="si">{</span><span class="n">data_path</span><span class="si">}</span><span class="s2">/</span><span class="si">{</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;region&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">/waveforms/ zhuwq@wintermute:/atomic-data/zhuwq/</span><span class="si">{</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;region&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">/</span><span class="si">{</span><span class="n">i</span><span class="si">:</span><span class="s2">03d</span><span class="si">}</span><span class="s2">/&quot;</span>
+        <span class="p">)</span>
+        <span class="n">retry_rsync</span> <span class="o">+=</span> <span class="mi">1</span>
+        <span class="k">if</span> <span class="n">status</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="n">time</span><span class="o">.</span><span class="n">sleep</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">status</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span>
+            <span class="sa">f</span><span class="s2">&quot;Failed: rsync -av </span><span class="si">{</span><span class="n">data_path</span><span class="si">}</span><span class="s2">/</span><span class="si">{</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;region&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">/waveforms/ zhuwq@wintermute:/atomic-data/zhuwq/</span><span class="si">{</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;region&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">/</span><span class="si">{</span><span class="n">i</span><span class="si">:</span><span class="s2">03d</span><span class="si">}</span><span class="s2">/&quot;</span>
+        <span class="p">)</span>
+
+    <span class="k">return</span> <span class="mi">0</span>
+</pre></div>
+<div id="cell-17" class="clipboard-copy-txt">def save_waveform(
+    i: int,
+    index_json: InputPath("json"),
+    config_json: InputPath("json"),
+    datetime_json: InputPath("json"),
+    data_path: str,
+    bucket_name: str = "waveforms",
+    s3_url: str = "minio-service:9000",
+    secure: bool = True,
+) -> int:
+
+    import json
+    import pickle
+    import time
+    import threading
+    import os
+    from pathlib import Path
+
+    with open(index_json, "r") as fp:
+        index = json.load(fp)
+    idx = index[i]
+    with open(config_json, "r") as fp:
+        config = json.load(fp)
+
+    waveform_dir = Path(data_path, config["region"], "waveforms")
+    mseeds = waveform_dir.glob("*.mseed")
+
+    status = -1
+    max_retry = 10
+    retry_rsync = 0
+    while (status != 0) and (retry_rsync < max_retry):
+        status = os.system(f"ssh zhuwq@wintermute mkdir -p /atomic-data/zhuwq/{config['region']}/{i:03d}/")
+        retry_rsync += 1
+        if status != 0:
+            time.sleep(5)
+
+    if status != 0:
+        print(f"Failed: ssh zhuwq@wintermute mkdir -p /atomic-data/zhuwq/{config['region']}/{i:03d}/")
+
+    status = -1
+    max_retry = 10
+    retry_rsync = 0
+
+    while (status != 0) and (retry_rsync < max_retry):
+        status = os.system(
+            f"rsync -av {data_path}/{config['region']}/waveforms/ zhuwq@wintermute:/atomic-data/zhuwq/{config['region']}/{i:03d}/"
+        )
+        retry_rsync += 1
+        if status != 0:
+            time.sleep(5)
+
+    if status != 0:
+        print(
+            f"Failed: rsync -av {data_path}/{config['region']}/waveforms/ zhuwq@wintermute:/atomic-data/zhuwq/{config['region']}/{i:03d}/"
+        )
+
+    return 0</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[40]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-18">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">save_waveform_op</span> <span class="o">=</span> <span class="n">comp</span><span class="o">.</span><span class="n">func_to_container_op</span><span class="p">(</span>
+    <span class="n">save_waveform</span><span class="p">,</span>
+    <span class="n">base_image</span><span class="o">=</span><span class="s2">&quot;zhuwq0/waveform-env:1.1&quot;</span><span class="p">,</span>
+    <span class="c1"># base_image=&#39;python:3.8&#39;,</span>
+    <span class="c1"># packages_to_install=[&quot;obspy&quot;, &quot;minio&quot;],</span>
+<span class="p">)</span>
+</pre></div>
+<div id="cell-18" class="clipboard-copy-txt">save_waveform_op = comp.func_to_container_op(
+    save_waveform,
+    base_image="zhuwq0/waveform-env:1.1",
+    # base_image='python:3.8',
+    # packages_to_install=["obspy", "minio"],
+)</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="5-run-phasenet-to-pick-ps-picks">5. Run PhaseNet to pick P/S picks<a class="anchor-link" href="#5-run-phasenet-to-pick-ps-picks">&#182;</a></h2>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[41]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-19">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">def</span> <span class="nf">phasenet_op</span><span class="p">(</span><span class="n">data_path</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">data_list</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">stations</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span>
+
+    <span class="k">return</span> <span class="n">dsl</span><span class="o">.</span><span class="n">ContainerOp</span><span class="p">(</span>
+        <span class="n">name</span><span class="o">=</span><span class="s2">&quot;PhaseNet Picking&quot;</span><span class="p">,</span>
+        <span class="n">image</span><span class="o">=</span><span class="s2">&quot;zhuwq0/phasenet-api:1.0&quot;</span><span class="p">,</span>
+        <span class="n">command</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;python&quot;</span><span class="p">],</span>
+        <span class="n">arguments</span><span class="o">=</span><span class="p">[</span>
+            <span class="s2">&quot;phasenet/predict.py&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;--model&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;model/190703-214543&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;--data_dir&quot;</span><span class="p">,</span>
+            <span class="n">data_path</span><span class="p">,</span>
+            <span class="s2">&quot;--data_list&quot;</span><span class="p">,</span>
+            <span class="n">dsl</span><span class="o">.</span><span class="n">InputArgumentPath</span><span class="p">(</span><span class="n">data_list</span><span class="p">),</span>
+            <span class="s2">&quot;--stations&quot;</span><span class="p">,</span>
+            <span class="n">dsl</span><span class="o">.</span><span class="n">InputArgumentPath</span><span class="p">(</span><span class="n">stations</span><span class="p">),</span>
+            <span class="c1"># &#39;--result_dir&#39;, &quot;results&quot;,</span>
+            <span class="s2">&quot;--format&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;mseed_array&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;--amplitude&quot;</span><span class="p">,</span>
+        <span class="p">],</span>
+        <span class="n">file_outputs</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;picks&quot;</span><span class="p">:</span> <span class="s2">&quot;/opt/results/picks.csv&quot;</span><span class="p">},</span>
+    <span class="p">)</span>
+</pre></div>
+<div id="cell-19" class="clipboard-copy-txt">def phasenet_op(data_path: str, data_list: str, stations: str):
+
+    return dsl.ContainerOp(
+        name="PhaseNet Picking",
+        image="zhuwq0/phasenet-api:1.0",
+        command=["python"],
+        arguments=[
+            "phasenet/predict.py",
+            "--model",
+            "model/190703-214543",
+            "--data_dir",
+            data_path,
+            "--data_list",
+            dsl.InputArgumentPath(data_list),
+            "--stations",
+            dsl.InputArgumentPath(stations),
+            # '--result_dir', "results",
+            "--format",
+            "mseed_array",
+            "--amplitude",
+        ],
+        file_outputs={"picks": "/opt/results/picks.csv"},
+    )</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[42]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-20">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># %%capture</span>
+<span class="k">if</span> <span class="n">run_local</span><span class="p">:</span>
+    <span class="n">command</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;python ../PhaseNet/phasenet/predict.py --model=../PhaseNet/model/190703-214543 --data_dir=</span><span class="si">{</span><span class="n">root_dir</span><span class="p">(</span><span class="n">root_dir</span><span class="p">(</span><span class="s1">&#39;waveforms&#39;</span><span class="p">))</span><span class="si">}</span><span class="s2"> --data_list=</span><span class="si">{</span><span class="n">root_dir</span><span class="p">(</span><span class="s1">&#39;fname.csv&#39;</span><span class="p">)</span><span class="si">}</span><span class="s2"> --stations=</span><span class="si">{</span><span class="n">root_dir</span><span class="p">(</span><span class="s1">&#39;stations.json&#39;</span><span class="p">)</span><span class="si">}</span><span class="s2"> --result_dir=</span><span class="si">{</span><span class="n">root_dir</span><span class="p">(</span><span class="s1">&#39;phasenet&#39;</span><span class="p">)</span><span class="si">}</span><span class="s2"> --format=mseed_array --amplitude&quot;</span><span class="c1"># --upload_waveform&quot;</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">command</span><span class="p">)</span>
+    <span class="err">!</span><span class="p">{</span><span class="n">command</span><span class="p">}</span>
+</pre></div>
+<div id="cell-20" class="clipboard-copy-txt"># %%capture
+if run_local:
+    command = f"python ../PhaseNet/phasenet/predict.py --model=../PhaseNet/model/190703-214543 --data_dir={root_dir(root_dir('waveforms'))} --data_list={root_dir('fname.csv')} --stations={root_dir('stations.json')} --result_dir={root_dir('phasenet')} --format=mseed_array --amplitude"# --upload_waveform"
+    print(command)
+    !{command}</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
+<pre>python ../PhaseNet/phasenet/predict.py --model=../PhaseNet/model/190703-214543 --data_dir=Demo/Demo/waveforms --data_list=Demo/fname.csv --stations=Demo/stations.json --result_dir=Demo/phasenet --format=mseed_array --amplitude
+                 longitude   latitude  elevation(m)  component                                 response    unit
+CI.CCC..BH     -117.364530  35.524950         670.0  [E, N, Z]  [627368000.0, 627368000.0, 627368000.0]     m/s
+CI.CCC..HH     -117.364530  35.524950         670.0  [E, N, Z]  [627368000.0, 627368000.0, 627368000.0]     m/s
+CI.CCC..HN     -117.364530  35.524950         670.0  [E, N, Z]           [213979.0, 214322.0, 213808.0]  m/s**2
+CI.CLC..BH     -117.597510  35.815740         775.0  [E, N, Z]  [627368000.0, 627368000.0, 627368000.0]     m/s
+CI.CLC..HH     -117.597510  35.815740         775.0  [E, N, Z]  [627368000.0, 627368000.0, 627368000.0]     m/s
+CI.CLC..HN     -117.597510  35.815740         775.0  [E, N, Z]           [213945.0, 213808.0, 213740.0]  m/s**2
+CI.DTP..BH     -117.845810  35.267420         908.0  [E, N, Z]  [627368000.0, 627368000.0, 627368000.0]     m/s
+CI.DTP..HH     -117.845810  35.267420         908.0  [E, N, Z]  [627368000.0, 627368000.0, 627368000.0]     m/s
+CI.DTP..HN     -117.845810  35.267420         908.0  [E, N, Z]           [214399.0, 213971.0, 214484.0]  m/s**2
+CI.JRC2..BH    -117.808850  35.982490        1469.0  [E, N, Z]  [784866000.0, 784866000.0, 790478000.0]     m/s
+CI.JRC2..HH    -117.808850  35.982490        1469.0  [E, N, Z]  [784866000.0, 784866000.0, 790478000.0]     m/s
+CI.JRC2..HN    -117.808850  35.982490        1469.0  [E, N, Z]           [213808.0, 213945.0, 214185.0]  m/s**2
+CI.LRL..BH     -117.682120  35.479540        1340.0  [E, N, Z]  [628306000.0, 629984000.0, 627467000.0]     m/s
+CI.LRL..HH     -117.682120  35.479540        1340.0  [E, N, Z]  [628306000.0, 629984000.0, 627467000.0]     m/s
+CI.LRL..HN     -117.682120  35.479540        1340.0  [E, N, Z]           [213757.0, 213671.0, 213201.0]  m/s**2
+CI.LRL.2C.HN   -117.682120  35.479540        1340.0  [E, N, Z]           [213757.0, 213671.0, 213201.0]  m/s**2
+CI.MPM..BH     -117.489010  36.057990        1839.0  [E, N, Z]  [627368000.0, 627368000.0, 627368000.0]     m/s
+CI.MPM..HH     -117.489010  36.057990        1839.0  [E, N, Z]  [627368000.0, 627368000.0, 627368000.0]     m/s
+CI.MPM..HN     -117.489010  36.057990        1839.0  [E, N, Z]           [213911.0, 214219.0, 213911.0]  m/s**2
+CI.Q0072.01.HN -117.666721  35.609617         695.0  [E, N, Z]           [256354.0, 256354.0, 256354.0]  m/s**2
+CI.SLA..BH     -117.283320  35.890950        1174.0  [E, N, Z]  [622338000.0, 618992000.0, 616482000.0]     m/s
+CI.SLA..HH     -117.283320  35.890950        1174.0  [E, N, Z]  [622338000.0, 618992000.0, 616482000.0]     m/s
+CI.SLA..HN     -117.283320  35.890950        1174.0  [E, N, Z]           [214253.0, 213671.0, 213979.0]  m/s**2
+CI.SRT..BH     -117.750510  35.692350         667.0  [E, N, Z]  [633341000.0, 633341000.0, 633341000.0]     m/s
+CI.SRT..HH     -117.750510  35.692350         667.0  [E, N, Z]  [629865000.0, 629865000.0, 629865000.0]     m/s
+CI.SRT..HN     -117.750510  35.692350         667.0  [E, N, Z]           [214301.0, 213873.0, 214087.0]  m/s**2
+CI.TOW2..BH    -117.764880  35.808560         685.0  [E, N, Z]  [626915000.0, 626915000.0, 626843000.0]     m/s
+CI.TOW2..HH    -117.764880  35.808560         685.0  [E, N, Z]  [626911000.0, 626911000.0, 626839000.0]     m/s
+CI.TOW2..HN    -117.764880  35.808560         685.0  [E, N, Z]           [213800.0, 214142.0, 214356.0]  m/s**2
+CI.WBM..BH     -117.890490  35.608390         892.0  [E, N, Z]  [314573000.0, 314573000.0, 314573000.0]     m/s
+CI.WBM..HH     -117.890490  35.608390         892.0  [E, N, Z]  [314573000.0, 314573000.0, 314573000.0]     m/s
+CI.WBM..HN     -117.890490  35.608390         892.0  [E, N, Z]           [213550.0, 214064.0, 213550.0]  m/s**2
+CI.WBM.2C.HN   -117.890490  35.608390         892.0  [E, N, Z]           [213550.0, 214064.0, 213550.0]  m/s**2
+CI.WCS2..BH    -117.765260  36.025210        1143.0  [E, N, Z]  [626910000.0, 626910000.0, 626838000.0]     m/s
+CI.WCS2..HH    -117.765260  36.025210        1143.0  [E, N, Z]  [626910000.0, 626910000.0, 626838000.0]     m/s
+CI.WCS2..HN    -117.765260  36.025210        1143.0  [E, N, Z]           [213757.0, 213329.0, 213415.0]  m/s**2
+CI.WMF..BH     -117.854860  36.117580        1537.4  [E, N, Z]  [629963000.0, 631652000.0, 629963000.0]     m/s
+CI.WMF..HH     -117.854860  36.117580        1537.4  [E, N, Z]  [626506000.0, 628185000.0, 626506000.0]     m/s
+CI.WMF..HN     -117.854860  36.117580        1537.4  [E, N, Z]           [214087.0, 214087.0, 214087.0]  m/s**2
+CI.WMF.2C.HN   -117.854860  36.117580        1537.4  [E, N, Z]           [213952.0, 213952.0, 213952.0]  m/s**2
+CI.WNM..EH     -117.906160  35.842200         974.3        [Z]                             [69306200.0]     m/s
+CI.WNM..HN     -117.906160  35.842200         974.3  [E, N, Z]           [214054.0, 213926.0, 214054.0]  m/s**2
+CI.WNM.2C.HN   -117.906160  35.842200         974.3  [E, N, Z]           [214048.0, 213920.0, 214048.0]  m/s**2
+CI.WRC2..BH    -117.650380  35.947900         943.0  [E, N, Z]  [629145000.0, 629145000.0, 629145000.0]     m/s
+CI.WRC2..HH    -117.650380  35.947900         943.0  [E, N, Z]  [629145000.0, 629145000.0, 629145000.0]     m/s
+CI.WRC2..HN    -117.650380  35.947900         943.0  [E, N, Z]           [214227.0, 213970.0, 214056.0]  m/s**2
+CI.WRV2..EH    -117.890400  36.007740        1070.0        [Z]                             [71427500.0]     m/s
+CI.WRV2..HN    -117.890400  36.007740        1070.0  [E, N, Z]           [213883.0, 235224.0, 235139.0]  m/s**2
+CI.WRV2.2C.HN  -117.890400  36.007740        1070.0  [E, N, Z]           [213877.0, 235218.0, 235132.0]  m/s**2
+CI.WVP2..EH    -117.817690  35.949390        1465.0        [Z]                             [68041300.0]     m/s
+CI.WVP2..HN    -117.817690  35.949390        1465.0  [E, N, Z]           [213764.0, 213550.0, 213721.0]  m/s**2
+CI.WVP2.2C.HN  -117.817690  35.949390        1465.0  [E, N, Z]           [213782.0, 213569.0, 213740.0]  m/s**2
+2022-11-29 12:10:36,186 Resampling CI.CCC..BHE from 40.0 to 100 Hz
+2022-11-29 12:10:36,529 Resampling CI.CCC..BHN from 40.0 to 100 Hz
+2022-11-29 12:10:36,534 Resampling CI.CCC..BHZ from 40.0 to 100 Hz
+2022-11-29 12:10:36,540 Resampling CI.CLC..BHE from 40.0 to 100 Hz
+2022-11-29 12:10:36,545 Resampling CI.CLC..BHN from 40.0 to 100 Hz
+2022-11-29 12:10:36,550 Resampling CI.CLC..BHZ from 40.0 to 100 Hz
+2022-11-29 12:10:36,554 Resampling CI.DTP..BHE from 40.0 to 100 Hz
+2022-11-29 12:10:36,558 Resampling CI.DTP..BHN from 40.0 to 100 Hz
+2022-11-29 12:10:36,562 Resampling CI.DTP..BHZ from 40.0 to 100 Hz
+2022-11-29 12:10:36,565 Resampling CI.JRC2..BHE from 40.0 to 100 Hz
+2022-11-29 12:10:36,568 Resampling CI.JRC2..BHN from 40.0 to 100 Hz
+2022-11-29 12:10:36,572 Resampling CI.JRC2..BHZ from 40.0 to 100 Hz
+2022-11-29 12:10:36,575 Resampling CI.LRL..BHE from 40.0 to 100 Hz
+2022-11-29 12:10:36,578 Resampling CI.LRL..BHN from 40.0 to 100 Hz
+2022-11-29 12:10:36,583 Resampling CI.LRL..BHZ from 40.0 to 100 Hz
+2022-11-29 12:10:36,588 Resampling CI.MPM..BHE from 40.0 to 100 Hz
+2022-11-29 12:10:36,592 Resampling CI.MPM..BHN from 40.0 to 100 Hz
+2022-11-29 12:10:36,596 Resampling CI.MPM..BHZ from 40.0 to 100 Hz
+2022-11-29 12:10:36,602 Resampling CI.SLA..BHE from 40.0 to 100 Hz
+2022-11-29 12:10:36,606 Resampling CI.SLA..BHN from 40.0 to 100 Hz
+2022-11-29 12:10:36,610 Resampling CI.SLA..BHZ from 40.0 to 100 Hz
+2022-11-29 12:10:36,613 Resampling CI.SRT..BHE from 40.0 to 100 Hz
+2022-11-29 12:10:36,617 Resampling CI.SRT..BHN from 40.0 to 100 Hz
+2022-11-29 12:10:36,620 Resampling CI.SRT..BHZ from 40.0 to 100 Hz
+2022-11-29 12:10:36,623 Resampling CI.TOW2..BHE from 40.0 to 100 Hz
+2022-11-29 12:10:36,627 Resampling CI.TOW2..BHN from 40.0 to 100 Hz
+2022-11-29 12:10:36,631 Resampling CI.TOW2..BHZ from 40.0 to 100 Hz
+2022-11-29 12:10:36,635 Resampling CI.WBM..BHE from 40.0 to 100 Hz
+2022-11-29 12:10:36,638 Resampling CI.WBM..BHN from 40.0 to 100 Hz
+2022-11-29 12:10:36,644 Resampling CI.WBM..BHZ from 40.0 to 100 Hz
+2022-11-29 12:10:36,648 Resampling CI.WCS2..BHE from 40.0 to 100 Hz
+2022-11-29 12:10:36,652 Resampling CI.WCS2..BHN from 40.0 to 100 Hz
+2022-11-29 12:10:36,655 Resampling CI.WCS2..BHZ from 40.0 to 100 Hz
+2022-11-29 12:10:36,659 Resampling CI.WMF..BHE from 40.0 to 100 Hz
+2022-11-29 12:10:36,663 Resampling CI.WMF..BHN from 40.0 to 100 Hz
+2022-11-29 12:10:36,668 Resampling CI.WMF..BHZ from 40.0 to 100 Hz
+2022-11-29 12:10:36,671 Resampling CI.WRC2..BHE from 40.0 to 100 Hz
+2022-11-29 12:10:36,675 Resampling CI.WRC2..BHN from 40.0 to 100 Hz
+2022-11-29 12:10:36,679 Resampling CI.WRC2..BHZ from 40.0 to 100 Hz
+Empty trace: CI.LRL.2C.HNE 2019-07-04T16:59:59.994500Z
+Empty trace: CI.LRL.2C.HNN 2019-07-04T16:59:59.994500Z
+Empty trace: CI.LRL.2C.HNZ 2019-07-04T16:59:59.994500Z
+Empty trace: CI.Q0072.01.HNE 2019-07-04T16:59:59.994500Z
+Empty trace: CI.Q0072.01.HNN 2019-07-04T16:59:59.994500Z
+Empty trace: CI.Q0072.01.HNZ 2019-07-04T16:59:59.994500Z
+Empty trace: CI.WBM.2C.HNE 2019-07-04T16:59:59.994500Z
+Empty trace: CI.WBM.2C.HNN 2019-07-04T16:59:59.994500Z
+Empty trace: CI.WBM.2C.HNZ 2019-07-04T16:59:59.994500Z
+Empty trace: CI.WMF.2C.HNE 2019-07-04T16:59:59.994500Z
+Empty trace: CI.WMF.2C.HNN 2019-07-04T16:59:59.994500Z
+Empty trace: CI.WMF.2C.HNZ 2019-07-04T16:59:59.994500Z
+Empty trace: CI.WNM.2C.HNE 2019-07-04T16:59:59.994500Z
+Empty trace: CI.WNM.2C.HNN 2019-07-04T16:59:59.994500Z
+Empty trace: CI.WNM.2C.HNZ 2019-07-04T16:59:59.994500Z
+Empty trace: CI.WRV2.2C.HNE 2019-07-04T16:59:59.994500Z
+Empty trace: CI.WRV2.2C.HNN 2019-07-04T16:59:59.994500Z
+Empty trace: CI.WRV2.2C.HNZ 2019-07-04T16:59:59.994500Z
+Empty trace: CI.WVP2.2C.HNE 2019-07-04T16:59:59.994500Z
+Empty trace: CI.WVP2.2C.HNN 2019-07-04T16:59:59.994500Z
+Empty trace: CI.WVP2.2C.HNZ 2019-07-04T16:59:59.994500Z
+2022-11-29 12:10:41,120 Pred log: Demo/phasenet
+2022-11-29 12:10:41,120 Dataset size: 2
+2022-11-29 12:10:41,174 Model: depths 5, filters 8, filter size 7x1, pool size: 4x1, dilation rate: 1x1
+2022-11-29 12:10:41.993942: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:354] MLIR V1 optimization pass is not enabled
+2022-11-29 12:10:42.045252: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz
+2022-11-29 12:10:42,143 restoring model ../PhaseNet/model/190703-214543/model_95.ckpt
+Pred:   0%|                                               | 0/2 [00:00&lt;?, ?it/s]2022-11-29 12:10:42,670 Resampling CI.CCC..BHE from 40.0 to 100 Hz
+2022-11-29 12:10:42,676 Resampling CI.CCC..BHN from 40.0 to 100 Hz
+2022-11-29 12:10:42,680 Resampling CI.CCC..BHZ from 40.0 to 100 Hz
+2022-11-29 12:10:42,684 Resampling CI.CLC..BHE from 40.0 to 100 Hz
+2022-11-29 12:10:42,689 Resampling CI.CLC..BHN from 40.0 to 100 Hz
+2022-11-29 12:10:42,694 Resampling CI.CLC..BHZ from 40.0 to 100 Hz
+2022-11-29 12:10:42,698 Resampling CI.DTP..BHE from 40.0 to 100 Hz
+2022-11-29 12:10:42,702 Resampling CI.DTP..BHN from 40.0 to 100 Hz
+2022-11-29 12:10:42,707 Resampling CI.DTP..BHZ from 40.0 to 100 Hz
+2022-11-29 12:10:42,711 Resampling CI.JRC2..BHE from 40.0 to 100 Hz
+2022-11-29 12:10:42,716 Resampling CI.JRC2..BHN from 40.0 to 100 Hz
+2022-11-29 12:10:42,722 Resampling CI.JRC2..BHZ from 40.0 to 100 Hz
+2022-11-29 12:10:42,727 Resampling CI.LRL..BHE from 40.0 to 100 Hz
+2022-11-29 12:10:42,732 Resampling CI.LRL..BHN from 40.0 to 100 Hz
+2022-11-29 12:10:42,736 Resampling CI.LRL..BHZ from 40.0 to 100 Hz
+2022-11-29 12:10:42,741 Resampling CI.MPM..BHE from 40.0 to 100 Hz
+2022-11-29 12:10:42,746 Resampling CI.MPM..BHN from 40.0 to 100 Hz
+2022-11-29 12:10:42,751 Resampling CI.MPM..BHZ from 40.0 to 100 Hz
+2022-11-29 12:10:42,756 Resampling CI.SLA..BHE from 40.0 to 100 Hz
+2022-11-29 12:10:42,761 Resampling CI.SLA..BHN from 40.0 to 100 Hz
+2022-11-29 12:10:42,765 Resampling CI.SLA..BHZ from 40.0 to 100 Hz
+2022-11-29 12:10:42,770 Resampling CI.SRT..BHE from 40.0 to 100 Hz
+2022-11-29 12:10:42,776 Resampling CI.SRT..BHN from 40.0 to 100 Hz
+2022-11-29 12:10:42,780 Resampling CI.SRT..BHZ from 40.0 to 100 Hz
+2022-11-29 12:10:42,784 Resampling CI.TOW2..BHE from 40.0 to 100 Hz
+2022-11-29 12:10:42,788 Resampling CI.TOW2..BHN from 40.0 to 100 Hz
+2022-11-29 12:10:42,792 Resampling CI.TOW2..BHZ from 40.0 to 100 Hz
+2022-11-29 12:10:42,797 Resampling CI.WBM..BHE from 40.0 to 100 Hz
+2022-11-29 12:10:42,802 Resampling CI.WBM..BHN from 40.0 to 100 Hz
+2022-11-29 12:10:42,806 Resampling CI.WBM..BHZ from 40.0 to 100 Hz
+2022-11-29 12:10:42,811 Resampling CI.WCS2..BHE from 40.0 to 100 Hz
+2022-11-29 12:10:42,816 Resampling CI.WCS2..BHN from 40.0 to 100 Hz
+2022-11-29 12:10:42,820 Resampling CI.WCS2..BHZ from 40.0 to 100 Hz
+2022-11-29 12:10:42,824 Resampling CI.WMF..BHE from 40.0 to 100 Hz
+2022-11-29 12:10:42,829 Resampling CI.WMF..BHN from 40.0 to 100 Hz
+2022-11-29 12:10:42,834 Resampling CI.WMF..BHZ from 40.0 to 100 Hz
+2022-11-29 12:10:42,841 Resampling CI.WRC2..BHE from 40.0 to 100 Hz
+2022-11-29 12:10:42,845 Resampling CI.WRC2..BHN from 40.0 to 100 Hz
+2022-11-29 12:10:42,850 Resampling CI.WRC2..BHZ from 40.0 to 100 Hz
+Empty trace: CI.LRL.2C.HNE 2019-07-04T16:59:59.994500Z
+Empty trace: CI.LRL.2C.HNN 2019-07-04T16:59:59.994500Z
+Empty trace: CI.LRL.2C.HNZ 2019-07-04T16:59:59.994500Z
+Empty trace: CI.Q0072.01.HNE 2019-07-04T16:59:59.994500Z
+Empty trace: CI.Q0072.01.HNN 2019-07-04T16:59:59.994500Z
+Empty trace: CI.Q0072.01.HNZ 2019-07-04T16:59:59.994500Z
+Empty trace: CI.WBM.2C.HNE 2019-07-04T16:59:59.994500Z
+Empty trace: CI.WBM.2C.HNN 2019-07-04T16:59:59.994500Z
+Empty trace: CI.WBM.2C.HNZ 2019-07-04T16:59:59.994500Z
+Empty trace: CI.WMF.2C.HNE 2019-07-04T16:59:59.994500Z
+Empty trace: CI.WMF.2C.HNN 2019-07-04T16:59:59.994500Z
+Empty trace: CI.WMF.2C.HNZ 2019-07-04T16:59:59.994500Z
+Empty trace: CI.WNM.2C.HNE 2019-07-04T16:59:59.994500Z
+Empty trace: CI.WNM.2C.HNN 2019-07-04T16:59:59.994500Z
+Empty trace: CI.WNM.2C.HNZ 2019-07-04T16:59:59.994500Z
+Empty trace: CI.WRV2.2C.HNE 2019-07-04T16:59:59.994500Z
+Empty trace: CI.WRV2.2C.HNN 2019-07-04T16:59:59.994500Z
+Empty trace: CI.WRV2.2C.HNZ 2019-07-04T16:59:59.994500Z
+Empty trace: CI.WVP2.2C.HNE 2019-07-04T16:59:59.994500Z
+Empty trace: CI.WVP2.2C.HNN 2019-07-04T16:59:59.994500Z
+Empty trace: CI.WVP2.2C.HNZ 2019-07-04T16:59:59.994500Z
+2022-11-29 12:10:51,004 Resampling CI.CCC..BHE from 40.0 to 100 Hz
+2022-11-29 12:10:51,013 Resampling CI.CCC..BHN from 40.0 to 100 Hz
+2022-11-29 12:10:51,024 Resampling CI.CCC..BHZ from 40.0 to 100 Hz
+2022-11-29 12:10:51,032 Resampling CI.CLC..BHE from 40.0 to 100 Hz
+2022-11-29 12:10:51,037 Resampling CI.CLC..BHN from 40.0 to 100 Hz
+2022-11-29 12:10:51,042 Resampling CI.CLC..BHZ from 40.0 to 100 Hz
+2022-11-29 12:10:51,047 Resampling CI.DTP..BHE from 40.0 to 100 Hz
+2022-11-29 12:10:51,056 Resampling CI.DTP..BHN from 40.0 to 100 Hz
+2022-11-29 12:10:51,062 Resampling CI.DTP..BHZ from 40.0 to 100 Hz
+2022-11-29 12:10:51,068 Resampling CI.JRC2..BHE from 40.0 to 100 Hz
+2022-11-29 12:10:51,074 Resampling CI.JRC2..BHN from 40.0 to 100 Hz
+2022-11-29 12:10:51,080 Resampling CI.JRC2..BHZ from 40.0 to 100 Hz
+2022-11-29 12:10:51,097 Resampling CI.LRL..BHE from 40.0 to 100 Hz
+2022-11-29 12:10:51,104 Resampling CI.LRL..BHN from 40.0 to 100 Hz
+2022-11-29 12:10:51,110 Resampling CI.LRL..BHZ from 40.0 to 100 Hz
+2022-11-29 12:10:51,124 Resampling CI.MPM..BHE from 40.0 to 100 Hz
+2022-11-29 12:10:51,133 Resampling CI.MPM..BHN from 40.0 to 100 Hz
+2022-11-29 12:10:51,147 Resampling CI.MPM..BHZ from 40.0 to 100 Hz
+2022-11-29 12:10:51,153 Resampling CI.SLA..BHE from 40.0 to 100 Hz
+2022-11-29 12:10:51,166 Resampling CI.SLA..BHN from 40.0 to 100 Hz
+2022-11-29 12:10:51,184 Resampling CI.SLA..BHZ from 40.0 to 100 Hz
+2022-11-29 12:10:51,189 Resampling CI.SRT..BHE from 40.0 to 100 Hz
+2022-11-29 12:10:51,202 Resampling CI.SRT..BHN from 40.0 to 100 Hz
+2022-11-29 12:10:51,208 Resampling CI.SRT..BHZ from 40.0 to 100 Hz
+2022-11-29 12:10:51,214 Resampling CI.TOW2..BHE from 40.0 to 100 Hz
+2022-11-29 12:10:51,225 Resampling CI.TOW2..BHN from 40.0 to 100 Hz
+2022-11-29 12:10:51,228 Resampling CI.TOW2..BHZ from 40.0 to 100 Hz
+2022-11-29 12:10:51,236 Resampling CI.WBM..BHE from 40.0 to 100 Hz
+2022-11-29 12:10:51,242 Resampling CI.WBM..BHN from 40.0 to 100 Hz
+2022-11-29 12:10:51,292 Resampling CI.WBM..BHZ from 40.0 to 100 Hz
+2022-11-29 12:10:51,313 Resampling CI.WCS2..BHE from 40.0 to 100 Hz
+2022-11-29 12:10:51,379 Resampling CI.WCS2..BHN from 40.0 to 100 Hz
+2022-11-29 12:10:51,400 Resampling CI.WCS2..BHZ from 40.0 to 100 Hz
+2022-11-29 12:10:51,410 Resampling CI.WMF..BHE from 40.0 to 100 Hz
+2022-11-29 12:10:51,415 Resampling CI.WMF..BHN from 40.0 to 100 Hz
+2022-11-29 12:10:51,419 Resampling CI.WMF..BHZ from 40.0 to 100 Hz
+2022-11-29 12:10:51,424 Resampling CI.WRC2..BHE from 40.0 to 100 Hz
+2022-11-29 12:10:51,431 Resampling CI.WRC2..BHN from 40.0 to 100 Hz
+2022-11-29 12:10:51,435 Resampling CI.WRC2..BHZ from 40.0 to 100 Hz
+Pred:  50%|███████████████████▌                   | 1/2 [00:11&lt;00:11, 11.79s/it]Empty trace: CI.LRL.2C.HNE 2019-07-04T17:59:59.994500Z
+Empty trace: CI.LRL.2C.HNN 2019-07-04T17:59:59.994500Z
+Empty trace: CI.LRL.2C.HNZ 2019-07-04T17:59:59.994500Z
+Empty trace: CI.Q0072.01.HNE 2019-07-04T17:59:59.994500Z
+Empty trace: CI.Q0072.01.HNN 2019-07-04T17:59:59.994500Z
+Empty trace: CI.Q0072.01.HNZ 2019-07-04T17:59:59.994500Z
+Empty trace: CI.WBM.2C.HNE 2019-07-04T17:59:59.994500Z
+Empty trace: CI.WBM.2C.HNN 2019-07-04T17:59:59.994500Z
+Empty trace: CI.WBM.2C.HNZ 2019-07-04T17:59:59.994500Z
+Empty trace: CI.WMF.2C.HNE 2019-07-04T17:59:59.994500Z
+Empty trace: CI.WMF.2C.HNN 2019-07-04T17:59:59.994500Z
+Empty trace: CI.WMF.2C.HNZ 2019-07-04T17:59:59.994500Z
+Empty trace: CI.WNM.2C.HNE 2019-07-04T17:59:59.994500Z
+Empty trace: CI.WNM.2C.HNN 2019-07-04T17:59:59.994500Z
+Empty trace: CI.WNM.2C.HNZ 2019-07-04T17:59:59.994500Z
+Empty trace: CI.WRV2.2C.HNE 2019-07-04T17:59:59.994500Z
+Empty trace: CI.WRV2.2C.HNN 2019-07-04T17:59:59.994500Z
+Empty trace: CI.WRV2.2C.HNZ 2019-07-04T17:59:59.994500Z
+Empty trace: CI.WVP2.2C.HNE 2019-07-04T17:59:59.994500Z
+Empty trace: CI.WVP2.2C.HNN 2019-07-04T17:59:59.994500Z
+Empty trace: CI.WVP2.2C.HNZ 2019-07-04T17:59:59.994500Z
+Pred: 100%|███████████████████████████████████████| 2/2 [00:20&lt;00:00, 10.17s/it]
+Done with 8856 P-picks and 9294 S-picks
+</pre>
+</div>
+</div>
+
+</div>
+
+</div>
+
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="6-run-gamma-to-associate-ps-picks">6. Run GaMMA to associate P/S picks<a class="anchor-link" href="#6-run-gamma-to-associate-ps-picks">&#182;</a></h2>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[43]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-21">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
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+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">def</span> <span class="nf">gamma</span><span class="p">(</span>
+    <span class="n">node_i</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
+    <span class="n">index_json</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="s2">&quot;json&quot;</span><span class="p">),</span>
+    <span class="n">config_json</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="s2">&quot;json&quot;</span><span class="p">),</span>
+    <span class="n">pick_csv</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="s2">&quot;csv&quot;</span><span class="p">),</span>
+    <span class="n">station_json</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="s2">&quot;json&quot;</span><span class="p">),</span>
+    <span class="n">gamma_catalog_csv</span><span class="p">:</span> <span class="n">OutputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span>
+    <span class="n">gamma_pick_csv</span><span class="p">:</span> <span class="n">OutputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span>
+    <span class="n">bucket_name</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;catalogs&quot;</span><span class="p">,</span>
+    <span class="n">s3_url</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;minio-service:9000&quot;</span><span class="p">,</span>
+    <span class="n">secure</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
+
+    <span class="kn">import</span> <span class="nn">json</span>
+    <span class="kn">import</span> <span class="nn">os</span>
+    <span class="kn">import</span> <span class="nn">pickle</span>
+    <span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">datetime</span><span class="p">,</span> <span class="n">timedelta</span>
+
+    <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+    <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+    <span class="kn">from</span> <span class="nn">gamma.utils</span> <span class="kn">import</span> <span class="n">association</span><span class="p">,</span> <span class="n">convert_picks_csv</span><span class="p">,</span> <span class="n">from_seconds</span>
+    <span class="kn">from</span> <span class="nn">pyproj</span> <span class="kn">import</span> <span class="n">Proj</span>
+    <span class="kn">from</span> <span class="nn">tqdm</span> <span class="kn">import</span> <span class="n">tqdm</span>
+
+    <span class="n">catalog_dir</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="s2">&quot;/tmp/&quot;</span><span class="p">,</span> <span class="n">bucket_name</span><span class="p">)</span>
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">catalog_dir</span><span class="p">):</span>
+        <span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">catalog_dir</span><span class="p">)</span>
+
+    <span class="c1">## read config</span>
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">index_json</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">index</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">fp</span><span class="p">)</span>
+    <span class="n">idx</span> <span class="o">=</span> <span class="n">index</span><span class="p">[</span><span class="n">node_i</span><span class="p">]</span>
+
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">config_json</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">config</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">fp</span><span class="p">)</span>
+
+    <span class="c1">## read picks</span>
+    <span class="c1"># picks = pd.read_json(pick_json)</span>
+    <span class="n">picks</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">pick_csv</span><span class="p">,</span> <span class="n">parse_dates</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;begin_time&quot;</span><span class="p">,</span> <span class="s2">&quot;phase_time&quot;</span><span class="p">])</span>
+    <span class="n">picks</span><span class="p">[</span><span class="s2">&quot;id&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">picks</span><span class="p">[</span><span class="s2">&quot;station_id&quot;</span><span class="p">]</span>
+    <span class="n">picks</span><span class="p">[</span><span class="s2">&quot;timestamp&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">picks</span><span class="p">[</span><span class="s2">&quot;phase_time&quot;</span><span class="p">]</span>
+    <span class="n">picks</span><span class="p">[</span><span class="s2">&quot;amp&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">picks</span><span class="p">[</span><span class="s2">&quot;phase_amp&quot;</span><span class="p">]</span>
+    <span class="n">picks</span><span class="p">[</span><span class="s2">&quot;type&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">picks</span><span class="p">[</span><span class="s2">&quot;phase_type&quot;</span><span class="p">]</span>
+    <span class="n">picks</span><span class="p">[</span><span class="s2">&quot;prob&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">picks</span><span class="p">[</span><span class="s2">&quot;phase_score&quot;</span><span class="p">]</span>
+
+    <span class="c1">## read stations</span>
+    <span class="c1"># stations = pd.read_csv(station_csv, delimiter=&quot;\t&quot;)</span>
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">station_json</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">stations</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">fp</span><span class="p">)</span>
+    <span class="n">stations</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">from_dict</span><span class="p">(</span><span class="n">stations</span><span class="p">,</span> <span class="n">orient</span><span class="o">=</span><span class="s2">&quot;index&quot;</span><span class="p">)</span>
+    <span class="c1"># stations = stations.rename(columns={&quot;station&quot;: &quot;id&quot;})</span>
+    <span class="n">stations</span><span class="p">[</span><span class="s2">&quot;id&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">stations</span><span class="o">.</span><span class="n">index</span>
+    <span class="n">proj</span> <span class="o">=</span> <span class="n">Proj</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;+proj=sterea +lon_0=</span><span class="si">{</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;center&#39;</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s2"> +lat_0=</span><span class="si">{</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;center&#39;</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span><span class="si">}</span><span class="s2"> +units=km&quot;</span><span class="p">)</span>
+    <span class="n">stations</span><span class="p">[[</span><span class="s2">&quot;x(km)&quot;</span><span class="p">,</span> <span class="s2">&quot;y(km)&quot;</span><span class="p">]]</span> <span class="o">=</span> <span class="n">stations</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span>
+        <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">proj</span><span class="p">(</span><span class="n">longitude</span><span class="o">=</span><span class="n">x</span><span class="o">.</span><span class="n">longitude</span><span class="p">,</span> <span class="n">latitude</span><span class="o">=</span><span class="n">x</span><span class="o">.</span><span class="n">latitude</span><span class="p">)),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span>
+    <span class="p">)</span>
+    <span class="n">stations</span><span class="p">[</span><span class="s2">&quot;z(km)&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">stations</span><span class="p">[</span><span class="s2">&quot;elevation(m)&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="o">-</span><span class="n">x</span> <span class="o">/</span> <span class="mf">1e3</span><span class="p">)</span>
+
+    <span class="c1">## setting GMMA configs</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;use_dbscan&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">True</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;use_amplitude&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">True</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;method&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;BGMM&quot;</span>
+    <span class="k">if</span> <span class="n">config</span><span class="p">[</span><span class="s2">&quot;method&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;BGMM&quot;</span><span class="p">:</span>  <span class="c1">## BayesianGaussianMixture</span>
+        <span class="n">config</span><span class="p">[</span><span class="s2">&quot;oversample_factor&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">4</span>
+    <span class="k">if</span> <span class="n">config</span><span class="p">[</span><span class="s2">&quot;method&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;GMM&quot;</span><span class="p">:</span>  <span class="c1">## GaussianMixture</span>
+        <span class="n">config</span><span class="p">[</span><span class="s2">&quot;oversample_factor&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
+
+    <span class="c1"># Earthquake location</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;dims&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;x(km)&quot;</span><span class="p">,</span> <span class="s2">&quot;y(km)&quot;</span><span class="p">,</span> <span class="s2">&quot;z(km)&quot;</span><span class="p">]</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;vel&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;p&quot;</span><span class="p">:</span> <span class="mf">6.0</span><span class="p">,</span> <span class="s2">&quot;s&quot;</span><span class="p">:</span> <span class="mf">6.0</span> <span class="o">/</span> <span class="mf">1.73</span><span class="p">}</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;x(km)&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;xlim_degree&quot;</span><span class="p">])</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;center&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">]))</span> <span class="o">*</span> <span class="n">config</span><span class="p">[</span><span class="s2">&quot;degree2km&quot;</span><span class="p">]</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;y(km)&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;ylim_degree&quot;</span><span class="p">])</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;center&quot;</span><span class="p">][</span><span class="mi">1</span><span class="p">]))</span> <span class="o">*</span> <span class="n">config</span><span class="p">[</span><span class="s2">&quot;degree2km&quot;</span><span class="p">]</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;z(km)&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">60</span><span class="p">)</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;bfgs_bounds&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span>
+        <span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;x(km)&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">config</span><span class="p">[</span><span class="s2">&quot;x(km)&quot;</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="mi">1</span><span class="p">),</span>  <span class="c1"># x</span>
+        <span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;y(km)&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">config</span><span class="p">[</span><span class="s2">&quot;y(km)&quot;</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="mi">1</span><span class="p">),</span>  <span class="c1"># y</span>
+        <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">config</span><span class="p">[</span><span class="s2">&quot;z(km)&quot;</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="mi">1</span><span class="p">),</span>  <span class="c1"># z</span>
+        <span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">),</span>  <span class="c1"># t</span>
+    <span class="p">)</span>
+
+    <span class="c1"># DBSCAN</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;dbscan_eps&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">10</span>  <span class="c1"># second</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;dbscan_min_samples&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">3</span>  <span class="c1">## see DBSCAN</span>
+
+    <span class="c1"># Filtering</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;min_picks_per_eq&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">stations</span><span class="p">)</span> <span class="o">//</span> <span class="mi">2</span><span class="p">)</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;min_p_picks_per_eq&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;min_s_picks_per_eq&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;max_sigma11&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="mf">2.0</span>  <span class="c1"># s</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;max_sigma22&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="mf">2.0</span>  <span class="c1"># m/s</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;max_sigma12&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="mf">1.0</span>  <span class="c1"># covariance</span>
+
+    <span class="c1"># if use amplitude</span>
+    <span class="k">if</span> <span class="n">config</span><span class="p">[</span><span class="s2">&quot;use_amplitude&quot;</span><span class="p">]:</span>
+        <span class="n">picks</span> <span class="o">=</span> <span class="n">picks</span><span class="p">[</span><span class="n">picks</span><span class="p">[</span><span class="s2">&quot;amp&quot;</span><span class="p">]</span> <span class="o">!=</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+
+    <span class="c1"># print(config)</span>
+    <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">config</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">k</span><span class="si">}</span><span class="s2">: </span><span class="si">{</span><span class="n">v</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+
+    <span class="c1">## run GMMA association</span>
+    <span class="n">event_idx0</span> <span class="o">=</span> <span class="mi">1</span>
+    <span class="n">assignments</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">catalogs</span><span class="p">,</span> <span class="n">assignments</span> <span class="o">=</span> <span class="n">association</span><span class="p">(</span><span class="n">picks</span><span class="p">,</span> <span class="n">stations</span><span class="p">,</span> <span class="n">config</span><span class="p">,</span> <span class="n">event_idx0</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;method&quot;</span><span class="p">])</span>
+    <span class="n">event_idx0</span> <span class="o">+=</span> <span class="nb">len</span><span class="p">(</span><span class="n">catalogs</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">catalogs</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">gamma_catalog_csv</span><span class="p">,</span> <span class="s2">&quot;w&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+            <span class="n">fp</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s2">&quot;time,magnitude,longitude,latitude,depth(m),sigma_time,sigma_amp,cov_time_amp,gamma_score,event_index</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
+        <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">gamma_pick_csv</span><span class="p">,</span> <span class="s2">&quot;w&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+            <span class="n">fp</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s2">&quot;station_id,phase_time,phase_type,phase_score,phase_amp,gamma_score,event_index</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
+
+    <span class="k">else</span><span class="p">:</span>
+        <span class="c1">## create catalog</span>
+        <span class="n">catalogs</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span>
+            <span class="n">catalogs</span><span class="p">,</span>
+            <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;time&quot;</span><span class="p">]</span>
+            <span class="o">+</span> <span class="n">config</span><span class="p">[</span><span class="s2">&quot;dims&quot;</span><span class="p">]</span>
+            <span class="o">+</span> <span class="p">[</span>
+                <span class="s2">&quot;magnitude&quot;</span><span class="p">,</span>
+                <span class="s2">&quot;sigma_time&quot;</span><span class="p">,</span>
+                <span class="s2">&quot;sigma_amp&quot;</span><span class="p">,</span>
+                <span class="s2">&quot;cov_time_amp&quot;</span><span class="p">,</span>
+                <span class="s2">&quot;event_index&quot;</span><span class="p">,</span>
+                <span class="s2">&quot;gamma_score&quot;</span><span class="p">,</span>
+            <span class="p">],</span>
+        <span class="p">)</span>
+
+        <span class="n">catalogs</span><span class="p">[[</span><span class="s2">&quot;longitude&quot;</span><span class="p">,</span> <span class="s2">&quot;latitude&quot;</span><span class="p">]]</span> <span class="o">=</span> <span class="n">catalogs</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span>
+            <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">proj</span><span class="p">(</span><span class="n">longitude</span><span class="o">=</span><span class="n">x</span><span class="p">[</span><span class="s2">&quot;x(km)&quot;</span><span class="p">],</span> <span class="n">latitude</span><span class="o">=</span><span class="n">x</span><span class="p">[</span><span class="s2">&quot;y(km)&quot;</span><span class="p">],</span> <span class="n">inverse</span><span class="o">=</span><span class="kc">True</span><span class="p">)),</span>
+            <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="n">catalogs</span><span class="p">[</span><span class="s2">&quot;depth(m)&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">catalogs</span><span class="p">[</span><span class="s2">&quot;z(km)&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span> <span class="o">*</span> <span class="mf">1e3</span><span class="p">)</span>
+
+        <span class="n">catalogs</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">by</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;time&quot;</span><span class="p">],</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+        <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">gamma_catalog_csv</span><span class="p">,</span> <span class="s2">&quot;w&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+            <span class="n">catalogs</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span>
+                <span class="n">fp</span><span class="p">,</span>
+                <span class="c1"># sep=&quot;\t&quot;,</span>
+                <span class="n">index</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                <span class="n">float_format</span><span class="o">=</span><span class="s2">&quot;</span><span class="si">%.3f</span><span class="s2">&quot;</span><span class="p">,</span>
+                <span class="n">date_format</span><span class="o">=</span><span class="s2">&quot;%Y-%m-</span><span class="si">%d</span><span class="s2">T%H:%M:%S.</span><span class="si">%f</span><span class="s2">&quot;</span><span class="p">,</span>
+                <span class="n">columns</span><span class="o">=</span><span class="p">[</span>
+                    <span class="s2">&quot;time&quot;</span><span class="p">,</span>
+                    <span class="s2">&quot;magnitude&quot;</span><span class="p">,</span>
+                    <span class="s2">&quot;longitude&quot;</span><span class="p">,</span>
+                    <span class="s2">&quot;latitude&quot;</span><span class="p">,</span>
+                    <span class="s2">&quot;depth(m)&quot;</span><span class="p">,</span>
+                    <span class="s2">&quot;sigma_time&quot;</span><span class="p">,</span>
+                    <span class="s2">&quot;sigma_amp&quot;</span><span class="p">,</span>
+                    <span class="s2">&quot;cov_time_amp&quot;</span><span class="p">,</span>
+                    <span class="s2">&quot;gamma_score&quot;</span><span class="p">,</span>
+                    <span class="s2">&quot;event_index&quot;</span><span class="p">,</span>
+                <span class="p">],</span>
+            <span class="p">)</span>
+        <span class="c1"># catalogs = catalogs[</span>
+        <span class="c1">#     [&#39;time&#39;, &#39;magnitude&#39;, &#39;longitude&#39;, &#39;latitude&#39;, &#39;depth(m)&#39;, &#39;sigma_time&#39;, &#39;sigma_amp&#39;]</span>
+        <span class="c1"># ]</span>
+
+        <span class="c1">## add assignment to picks</span>
+        <span class="n">assignments</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">assignments</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;pick_index&quot;</span><span class="p">,</span> <span class="s2">&quot;event_index&quot;</span><span class="p">,</span> <span class="s2">&quot;gamma_score&quot;</span><span class="p">])</span>
+        <span class="n">picks</span> <span class="o">=</span> <span class="n">picks</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">assignments</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">&quot;pick_index&quot;</span><span class="p">))</span><span class="o">.</span><span class="n">fillna</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">({</span><span class="s2">&quot;event_index&quot;</span><span class="p">:</span> <span class="nb">int</span><span class="p">})</span>
+        <span class="n">picks</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">by</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;timestamp&quot;</span><span class="p">],</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+        <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">gamma_pick_csv</span><span class="p">,</span> <span class="s2">&quot;w&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+            <span class="n">picks</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span>
+                <span class="n">fp</span><span class="p">,</span>
+                <span class="c1"># sep=&quot;\t&quot;,</span>
+                <span class="n">index</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                <span class="n">date_format</span><span class="o">=</span><span class="s2">&quot;%Y-%m-</span><span class="si">%d</span><span class="s2">T%H:%M:%S.</span><span class="si">%f</span><span class="s2">&quot;</span><span class="p">,</span>
+                <span class="n">columns</span><span class="o">=</span><span class="p">[</span>
+                    <span class="s2">&quot;station_id&quot;</span><span class="p">,</span>
+                    <span class="s2">&quot;phase_time&quot;</span><span class="p">,</span>
+                    <span class="s2">&quot;phase_type&quot;</span><span class="p">,</span>
+                    <span class="s2">&quot;phase_score&quot;</span><span class="p">,</span>
+                    <span class="s2">&quot;phase_amp&quot;</span><span class="p">,</span>
+                    <span class="s2">&quot;gamma_score&quot;</span><span class="p">,</span>
+                    <span class="s2">&quot;event_index&quot;</span><span class="p">,</span>
+                <span class="p">],</span>
+            <span class="p">)</span>
+
+    <span class="c1">## upload to s3 bucket</span>
+    <span class="k">try</span><span class="p">:</span>
+        <span class="kn">from</span> <span class="nn">minio</span> <span class="kn">import</span> <span class="n">Minio</span>
+
+        <span class="n">minioClient</span> <span class="o">=</span> <span class="n">Minio</span><span class="p">(</span><span class="n">s3_url</span><span class="p">,</span> <span class="n">access_key</span><span class="o">=</span><span class="s2">&quot;minio&quot;</span><span class="p">,</span> <span class="n">secret_key</span><span class="o">=</span><span class="s2">&quot;minio123&quot;</span><span class="p">,</span> <span class="n">secure</span><span class="o">=</span><span class="n">secure</span><span class="p">)</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">minioClient</span><span class="o">.</span><span class="n">bucket_exists</span><span class="p">(</span><span class="n">bucket_name</span><span class="p">):</span>
+            <span class="n">minioClient</span><span class="o">.</span><span class="n">make_bucket</span><span class="p">(</span><span class="n">bucket_name</span><span class="p">)</span>
+
+        <span class="n">minioClient</span><span class="o">.</span><span class="n">fput_object</span><span class="p">(</span>
+            <span class="n">bucket_name</span><span class="p">,</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;region&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">/gamma/catalog_</span><span class="si">{</span><span class="n">node_i</span><span class="si">:</span><span class="s2">03d</span><span class="si">}</span><span class="s2">.csv&quot;</span><span class="p">,</span>
+            <span class="n">gamma_catalog_csv</span><span class="p">,</span>
+        <span class="p">)</span>
+
+        <span class="n">minioClient</span><span class="o">.</span><span class="n">fput_object</span><span class="p">(</span>
+            <span class="n">bucket_name</span><span class="p">,</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;region&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">/gamma/picks_</span><span class="si">{</span><span class="n">node_i</span><span class="si">:</span><span class="s2">03d</span><span class="si">}</span><span class="s2">.csv&quot;</span><span class="p">,</span>
+            <span class="n">gamma_pick_csv</span><span class="p">,</span>
+        <span class="p">)</span>
+
+    <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">err</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;ERROR: can not access minio service! </span><span class="se">\n</span><span class="si">{</span><span class="n">err</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+        <span class="k">pass</span>
+
+    <span class="c1">## upload to mongodb</span>
+    <span class="k">try</span><span class="p">:</span>
+        <span class="kn">from</span> <span class="nn">pymongo</span> <span class="kn">import</span> <span class="n">MongoClient</span>
+
+        <span class="n">username</span> <span class="o">=</span> <span class="s2">&quot;root&quot;</span>
+        <span class="n">password</span> <span class="o">=</span> <span class="s2">&quot;quakeflow123&quot;</span>
+        <span class="n">client</span> <span class="o">=</span> <span class="n">MongoClient</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;mongodb://</span><span class="si">{</span><span class="n">username</span><span class="si">}</span><span class="s2">:</span><span class="si">{</span><span class="n">password</span><span class="si">}</span><span class="s2">@127.0.0.1:27017&quot;</span><span class="p">)</span>
+        <span class="n">db</span> <span class="o">=</span> <span class="n">client</span><span class="p">[</span><span class="s2">&quot;quakeflow&quot;</span><span class="p">]</span>
+        <span class="n">collection</span> <span class="o">=</span> <span class="n">db</span><span class="p">[</span><span class="s2">&quot;waveform&quot;</span><span class="p">]</span>
+        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">picks</span><span class="o">.</span><span class="n">iterrows</span><span class="p">(),</span> <span class="n">desc</span><span class="o">=</span><span class="s2">&quot;Uploading to mongodb&quot;</span><span class="p">):</span>
+            <span class="n">collection</span><span class="o">.</span><span class="n">update</span><span class="p">(</span>
+                <span class="p">{</span><span class="s2">&quot;_id&quot;</span><span class="p">:</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">p</span><span class="p">[</span><span class="s1">&#39;station_id&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">_</span><span class="si">{</span><span class="n">p</span><span class="p">[</span><span class="s1">&#39;timestamp&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">isoformat</span><span class="p">(</span><span class="n">timespec</span><span class="o">=</span><span class="s1">&#39;milliseconds&#39;</span><span class="p">)</span><span class="si">}</span><span class="s2">_</span><span class="si">{</span><span class="n">p</span><span class="p">[</span><span class="s1">&#39;type&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">},</span>
+                <span class="p">{</span><span class="s2">&quot;$set&quot;</span><span class="p">:</span> <span class="p">{</span><span class="s2">&quot;event_index&quot;</span><span class="p">:</span> <span class="n">p</span><span class="p">[</span><span class="s2">&quot;event_index&quot;</span><span class="p">]}},</span>
+            <span class="p">)</span>
+    <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">err</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;ERROR: can not access mongodb service! </span><span class="se">\n</span><span class="si">{</span><span class="n">err</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+        <span class="k">pass</span>
+
+    <span class="k">return</span> <span class="sa">f</span><span class="s2">&quot;catalog_</span><span class="si">{</span><span class="n">node_i</span><span class="si">:</span><span class="s2">03d</span><span class="si">}</span><span class="s2">.csv&quot;</span>
+</pre></div>
+<div id="cell-21" class="clipboard-copy-txt">def gamma(
+    node_i: int,
+    index_json: InputPath("json"),
+    config_json: InputPath("json"),
+    pick_csv: InputPath("csv"),
+    station_json: InputPath("json"),
+    gamma_catalog_csv: OutputPath(str),
+    gamma_pick_csv: OutputPath(str),
+    bucket_name: str = "catalogs",
+    s3_url: str = "minio-service:9000",
+    secure: bool = True,
+) -> str:
+
+    import json
+    import os
+    import pickle
+    from datetime import datetime, timedelta
+
+    import numpy as np
+    import pandas as pd
+    from gamma.utils import association, convert_picks_csv, from_seconds
+    from pyproj import Proj
+    from tqdm import tqdm
+
+    catalog_dir = os.path.join("/tmp/", bucket_name)
+    if not os.path.exists(catalog_dir):
+        os.makedirs(catalog_dir)
+
+    ## read config
+    with open(index_json, "r") as fp:
+        index = json.load(fp)
+    idx = index[node_i]
+
+    with open(config_json, "r") as fp:
+        config = json.load(fp)
+
+    ## read picks
+    # picks = pd.read_json(pick_json)
+    picks = pd.read_csv(pick_csv, parse_dates=["begin_time", "phase_time"])
+    picks["id"] = picks["station_id"]
+    picks["timestamp"] = picks["phase_time"]
+    picks["amp"] = picks["phase_amp"]
+    picks["type"] = picks["phase_type"]
+    picks["prob"] = picks["phase_score"]
+
+    ## read stations
+    # stations = pd.read_csv(station_csv, delimiter="\t")
+    with open(station_json, "r") as fp:
+        stations = json.load(fp)
+    stations = pd.DataFrame.from_dict(stations, orient="index")
+    # stations = stations.rename(columns={"station": "id"})
+    stations["id"] = stations.index
+    proj = Proj(f"+proj=sterea +lon_0={config['center'][0]} +lat_0={config['center'][1]} +units=km")
+    stations[["x(km)", "y(km)"]] = stations.apply(
+        lambda x: pd.Series(proj(longitude=x.longitude, latitude=x.latitude)), axis=1
+    )
+    stations["z(km)"] = stations["elevation(m)"].apply(lambda x: -x / 1e3)
+
+    ## setting GMMA configs
+    config["use_dbscan"] = True
+    config["use_amplitude"] = True
+    config["method"] = "BGMM"
+    if config["method"] == "BGMM":  ## BayesianGaussianMixture
+        config["oversample_factor"] = 4
+    if config["method"] == "GMM":  ## GaussianMixture
+        config["oversample_factor"] = 1
+
+    # Earthquake location
+    config["dims"] = ["x(km)", "y(km)", "z(km)"]
+    config["vel"] = {"p": 6.0, "s": 6.0 / 1.73}
+    config["x(km)"] = (np.array(config["xlim_degree"]) - np.array(config["center"][0])) * config["degree2km"]
+    config["y(km)"] = (np.array(config["ylim_degree"]) - np.array(config["center"][1])) * config["degree2km"]
+    config["z(km)"] = (0, 60)
+    config["bfgs_bounds"] = (
+        (config["x(km)"][0] - 1, config["x(km)"][1] + 1),  # x
+        (config["y(km)"][0] - 1, config["y(km)"][1] + 1),  # y
+        (0, config["z(km)"][1] + 1),  # z
+        (None, None),  # t
+    )
+
+    # DBSCAN
+    config["dbscan_eps"] = 10  # second
+    config["dbscan_min_samples"] = 3  ## see DBSCAN
+
+    # Filtering
+    config["min_picks_per_eq"] = min(10, len(stations) // 2)
+    config["min_p_picks_per_eq"] = 0
+    config["min_s_picks_per_eq"] = 0
+    config["max_sigma11"] = 2.0  # s
+    config["max_sigma22"] = 2.0  # m/s
+    config["max_sigma12"] = 1.0  # covariance
+
+    # if use amplitude
+    if config["use_amplitude"]:
+        picks = picks[picks["amp"] != -1]
+
+    # print(config)
+    for k, v in config.items():
+        print(f"{k}: {v}")
+
+    ## run GMMA association
+    event_idx0 = 1
+    assignments = []
+    catalogs, assignments = association(picks, stations, config, event_idx0, method=config["method"])
+    event_idx0 += len(catalogs)
+
+    if len(catalogs) == 0:
+        with open(gamma_catalog_csv, "w") as fp:
+            fp.write("time,magnitude,longitude,latitude,depth(m),sigma_time,sigma_amp,cov_time_amp,gamma_score,event_index\n")
+        with open(gamma_pick_csv, "w") as fp:
+            fp.write("station_id,phase_time,phase_type,phase_score,phase_amp,gamma_score,event_index\n")
+
+    else:
+        ## create catalog
+        catalogs = pd.DataFrame(
+            catalogs,
+            columns=["time"]
+            + config["dims"]
+            + [
+                "magnitude",
+                "sigma_time",
+                "sigma_amp",
+                "cov_time_amp",
+                "event_index",
+                "gamma_score",
+            ],
+        )
+
+        catalogs[["longitude", "latitude"]] = catalogs.apply(
+            lambda x: pd.Series(proj(longitude=x["x(km)"], latitude=x["y(km)"], inverse=True)),
+            axis=1,
+        )
+        catalogs["depth(m)"] = catalogs["z(km)"].apply(lambda x: x * 1e3)
+
+        catalogs.sort_values(by=["time"], inplace=True)
+        with open(gamma_catalog_csv, "w") as fp:
+            catalogs.to_csv(
+                fp,
+                # sep="\t",
+                index=False,
+                float_format="%.3f",
+                date_format="%Y-%m-%dT%H:%M:%S.%f",
+                columns=[
+                    "time",
+                    "magnitude",
+                    "longitude",
+                    "latitude",
+                    "depth(m)",
+                    "sigma_time",
+                    "sigma_amp",
+                    "cov_time_amp",
+                    "gamma_score",
+                    "event_index",
+                ],
+            )
+        # catalogs = catalogs[
+        #     ['time', 'magnitude', 'longitude', 'latitude', 'depth(m)', 'sigma_time', 'sigma_amp']
+        # ]
+
+        ## add assignment to picks
+        assignments = pd.DataFrame(assignments, columns=["pick_index", "event_index", "gamma_score"])
+        picks = picks.join(assignments.set_index("pick_index")).fillna(-1).astype({"event_index": int})
+        picks.sort_values(by=["timestamp"], inplace=True)
+        with open(gamma_pick_csv, "w") as fp:
+            picks.to_csv(
+                fp,
+                # sep="\t",
+                index=False,
+                date_format="%Y-%m-%dT%H:%M:%S.%f",
+                columns=[
+                    "station_id",
+                    "phase_time",
+                    "phase_type",
+                    "phase_score",
+                    "phase_amp",
+                    "gamma_score",
+                    "event_index",
+                ],
+            )
+
+    ## upload to s3 bucket
+    try:
+        from minio import Minio
+
+        minioClient = Minio(s3_url, access_key="minio", secret_key="minio123", secure=secure)
+        if not minioClient.bucket_exists(bucket_name):
+            minioClient.make_bucket(bucket_name)
+
+        minioClient.fput_object(
+            bucket_name,
+            f"{config['region']}/gamma/catalog_{node_i:03d}.csv",
+            gamma_catalog_csv,
+        )
+
+        minioClient.fput_object(
+            bucket_name,
+            f"{config['region']}/gamma/picks_{node_i:03d}.csv",
+            gamma_pick_csv,
+        )
+
+    except Exception as err:
+        print(f"ERROR: can not access minio service! \n{err}")
+        pass
+
+    ## upload to mongodb
+    try:
+        from pymongo import MongoClient
+
+        username = "root"
+        password = "quakeflow123"
+        client = MongoClient(f"mongodb://{username}:{password}@127.0.0.1:27017")
+        db = client["quakeflow"]
+        collection = db["waveform"]
+        for i, p in tqdm(picks.iterrows(), desc="Uploading to mongodb"):
+            collection.update(
+                {"_id": f"{p['station_id']}_{p['timestamp'].isoformat(timespec='milliseconds')}_{p['type']}"},
+                {"$set": {"event_index": p["event_index"]}},
+            )
+    except Exception as err:
+        print(f"ERROR: can not access mongodb service! \n{err}")
+        pass
+
+    return f"catalog_{node_i:03d}.csv"</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[44]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-22">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">if</span> <span class="n">run_local</span><span class="p">:</span>
+    <span class="n">catalog</span> <span class="o">=</span> <span class="n">gamma</span><span class="p">(</span>
+        <span class="mi">0</span><span class="p">,</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;index.json&quot;</span><span class="p">),</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;config.json&quot;</span><span class="p">),</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;phasenet/picks.csv&quot;</span><span class="p">),</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;stations.json&quot;</span><span class="p">),</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;gamma_catalog.csv&quot;</span><span class="p">),</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;gamma_picks.csv&quot;</span><span class="p">),</span>
+        <span class="n">bucket_name</span><span class="o">=</span><span class="s2">&quot;catalogs&quot;</span><span class="p">,</span>
+        <span class="n">s3_url</span><span class="o">=</span><span class="s2">&quot;localhost:9000&quot;</span><span class="p">,</span>
+        <span class="n">secure</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="p">)</span>
+</pre></div>
+<div id="cell-22" class="clipboard-copy-txt">if run_local:
+    catalog = gamma(
+        0,
+        root_dir("index.json"),
+        root_dir("config.json"),
+        root_dir("phasenet/picks.csv"),
+        root_dir("stations.json"),
+        root_dir("gamma_catalog.csv"),
+        root_dir("gamma_picks.csv"),
+        bucket_name="catalogs",
+        s3_url="localhost:9000",
+        secure=False,
+    )</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
+<pre>region: Demo
+center: [-117.504, 35.705]
+xlim_degree: [-118.004, -117.004]
+ylim_degree: [35.205, 36.205]
+min_longitude: -118.004
+max_longitude: -117.004
+min_latitude: 35.205
+max_latitude: 36.205
+degree2km: 111.19492664455873
+starttime: 2019-07-04T17:00:00.000
+endtime: 2019-07-04T19:00:00.000
+networks: [&#39;CI&#39;]
+channels: HH*,BH*,EH*,HN*
+client: SCEDC
+phasenet: {}
+gamma: {}
+hypodd: {&#39;MAXEVENT&#39;: 10000.0}
+use_dbscan: True
+use_amplitude: True
+method: BGMM
+oversample_factor: 4
+dims: [&#39;x(km)&#39;, &#39;y(km)&#39;, &#39;z(km)&#39;]
+vel: {&#39;p&#39;: 6.0, &#39;s&#39;: 3.468208092485549}
+x(km): [-55.59746332  55.59746332]
+y(km): [-55.59746332  55.59746332]
+z(km): (0, 60)
+bfgs_bounds: ((-56.59746332227937, 56.59746332227937), (-56.59746332227937, 56.59746332227937), (0, 61), (None, None))
+dbscan_eps: 10
+dbscan_min_samples: 3
+min_picks_per_eq: 10
+min_p_picks_per_eq: 0
+min_s_picks_per_eq: 0
+max_sigma11: 2.0
+max_sigma22: 2.0
+max_sigma12: 1.0
+</pre>
+</div>
+</div>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>Skip 34 picks: 100%|██████████| 18150/18150 [01:38&lt;00:00, 184.29it/s]     
+</pre>
+</div>
+</div>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
+<pre>ERROR: can not access minio service! 
+HTTPConnectionPool(host=&#39;localhost&#39;, port=9000): Max retries exceeded with url: /catalogs?location= (Caused by NewConnectionError(&#39;&lt;urllib3.connection.HTTPConnection object at 0x13fe9d2b0&gt;: Failed to establish a new connection: [Errno 61] Connection refused&#39;))
+</pre>
+</div>
+</div>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>Uploading to mongodb: 0it [00:30, ?it/s]</pre>
+</div>
+</div>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
+<pre>ERROR: can not access mongodb service! 
+127.0.0.1:27017: [Errno 61] Connection refused, Timeout: 30s, Topology Description: &lt;TopologyDescription id: 638667c1d805e8e312dbade7, topology_type: Single, servers: [&lt;ServerDescription (&#39;127.0.0.1&#39;, 27017) server_type: Unknown, rtt: None, error=AutoReconnect(&#39;127.0.0.1:27017: [Errno 61] Connection refused&#39;)&gt;]&gt;
+</pre>
+</div>
+</div>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>
+</pre>
+</div>
+</div>
+
+</div>
+
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[45]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-23">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">gamma_op</span> <span class="o">=</span> <span class="n">comp</span><span class="o">.</span><span class="n">func_to_container_op</span><span class="p">(</span>
+    <span class="n">gamma</span><span class="p">,</span>
+    <span class="c1"># base_image=&#39;zhuwq0/quakeflow-env:latest&#39;,</span>
+    <span class="n">base_image</span><span class="o">=</span><span class="s2">&quot;python:3.8&quot;</span><span class="p">,</span>
+    <span class="n">packages_to_install</span><span class="o">=</span><span class="p">[</span>
+        <span class="s2">&quot;pandas&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;numpy&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;scikit-learn&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;tqdm&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;minio&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;gmma&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;pyproj&quot;</span><span class="p">,</span>
+    <span class="p">],</span>
+<span class="p">)</span>
+</pre></div>
+<div id="cell-23" class="clipboard-copy-txt">gamma_op = comp.func_to_container_op(
+    gamma,
+    # base_image='zhuwq0/quakeflow-env:latest',
+    base_image="python:3.8",
+    packages_to_install=[
+        "pandas",
+        "numpy",
+        "scikit-learn",
+        "tqdm",
+        "minio",
+        "gmma",
+        "pyproj",
+    ],
+)</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h3 id="plot-catalogs">Plot catalogs<a class="anchor-link" href="#plot-catalogs">&#182;</a></h3>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[46]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-24">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">if</span> <span class="n">run_local</span><span class="p">:</span>
+    <span class="o">%</span><span class="n">run</span> <span class="n">plot_catalog</span><span class="o">.</span><span class="n">ipynb</span>
+</pre></div>
+<div id="cell-24" class="clipboard-copy-txt">if run_local:
+    %run plot_catalog.ipynb</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+
+
+<div class="jp-RenderedImage jp-OutputArea-output ">
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"
+class="
+jp-needs-light-background
+"
+>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h3 id="merge-parallel-processing-on-cloud">Merge parallel processing on cloud<a class="anchor-link" href="#merge-parallel-processing-on-cloud">&#182;</a></h3>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[47]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-25">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">def</span> <span class="nf">merge_catalog</span><span class="p">(</span>
+    <span class="n">index_json</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="s2">&quot;json&quot;</span><span class="p">),</span>
+    <span class="n">config_json</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="s2">&quot;json&quot;</span><span class="p">),</span>
+    <span class="n">catalog_csv</span><span class="p">:</span> <span class="n">OutputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span>
+    <span class="n">picks_csv</span><span class="p">:</span> <span class="n">OutputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span>
+    <span class="n">bucket_name</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;catalogs&quot;</span><span class="p">,</span>
+    <span class="n">s3_url</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;minio-service:9000&quot;</span><span class="p">,</span>
+    <span class="n">secure</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+<span class="p">):</span>
+
+    <span class="kn">import</span> <span class="nn">json</span>
+    <span class="kn">import</span> <span class="nn">os</span>
+    <span class="kn">from</span> <span class="nn">glob</span> <span class="kn">import</span> <span class="n">glob</span>
+
+    <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+    <span class="kn">from</span> <span class="nn">minio</span> <span class="kn">import</span> <span class="n">Minio</span>
+
+    <span class="n">minioClient</span> <span class="o">=</span> <span class="n">Minio</span><span class="p">(</span><span class="n">s3_url</span><span class="p">,</span> <span class="n">access_key</span><span class="o">=</span><span class="s1">&#39;minio&#39;</span><span class="p">,</span> <span class="n">secret_key</span><span class="o">=</span><span class="s1">&#39;minio123&#39;</span><span class="p">,</span> <span class="n">secure</span><span class="o">=</span><span class="n">secure</span><span class="p">)</span>
+
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">index_json</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">index</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">fp</span><span class="p">)</span>
+        
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">config_json</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">config</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">fp</span><span class="p">)</span>
+
+    <span class="n">objects</span> <span class="o">=</span> <span class="n">minioClient</span><span class="o">.</span><span class="n">list_objects</span><span class="p">(</span><span class="n">bucket_name</span><span class="p">,</span> <span class="n">prefix</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;region&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">/gamma&quot;</span><span class="p">,</span> <span class="n">recursive</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+    <span class="n">tmp_path</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="s2">&quot;/tmp/&quot;</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">obj</span> <span class="ow">in</span> <span class="n">objects</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="n">obj</span><span class="o">.</span><span class="n">_object_name</span><span class="p">)</span>
+        <span class="n">minioClient</span><span class="o">.</span><span class="n">fget_object</span><span class="p">(</span><span class="n">bucket_name</span><span class="p">,</span> <span class="n">obj</span><span class="o">.</span><span class="n">_object_name</span><span class="p">,</span> <span class="n">tmp_path</span><span class="p">(</span><span class="n">obj</span><span class="o">.</span><span class="n">_object_name</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;/&quot;</span><span class="p">)[</span><span class="o">-</span><span class="mi">1</span><span class="p">]))</span>
+
+    <span class="c1"># files_catalog = sorted(glob(tmp_path(&quot;catalog_*.csv&quot;)))</span>
+    <span class="c1"># files_picks = sorted(glob(tmp_path(&quot;picks_*.csv&quot;)))</span>
+    <span class="n">files_catalog</span> <span class="o">=</span> <span class="p">[</span><span class="n">tmp_path</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;catalog_</span><span class="si">{</span><span class="n">node_i</span><span class="si">:</span><span class="s2">03d</span><span class="si">}</span><span class="s2">.csv&quot;</span><span class="p">)</span> <span class="k">for</span> <span class="n">node_i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">index</span><span class="p">))]</span>
+    <span class="n">files_picks</span> <span class="o">=</span> <span class="p">[</span><span class="n">tmp_path</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;picks_</span><span class="si">{</span><span class="n">node_i</span><span class="si">:</span><span class="s2">03d</span><span class="si">}</span><span class="s2">.csv&quot;</span><span class="p">)</span> <span class="k">for</span> <span class="n">node_i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">index</span><span class="p">))]</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Merge catalog: </span><span class="si">{</span><span class="n">files_catalog</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Merge picks: </span><span class="si">{</span><span class="n">files_picks</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">files_catalog</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="n">catalog_list</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="n">files_catalog</span><span class="p">:</span>
+            <span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">f</span><span class="p">):</span>
+                <span class="k">continue</span>
+            <span class="n">tmp</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">str</span><span class="p">)</span>
+            <span class="n">tmp</span><span class="p">[</span><span class="s2">&quot;file_index&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">rstrip</span><span class="p">(</span><span class="s2">&quot;.csv&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;_&quot;</span><span class="p">)[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+            <span class="n">catalog_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
+        <span class="n">merged_catalog</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">(</span><span class="n">catalog_list</span><span class="p">)</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">by</span><span class="o">=</span><span class="s2">&quot;time&quot;</span><span class="p">)</span>
+        <span class="n">merged_catalog</span><span class="p">[</span><span class="s2">&quot;match_id&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">merged_catalog</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">x</span><span class="p">[</span><span class="s2">&quot;event_index&quot;</span><span class="p">]</span><span class="si">}</span><span class="s1">_</span><span class="si">{</span><span class="n">x</span><span class="p">[</span><span class="s2">&quot;file_index&quot;</span><span class="p">]</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+        <span class="n">merged_catalog</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">by</span><span class="o">=</span><span class="s2">&quot;time&quot;</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">ignore_index</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+        <span class="n">merged_catalog</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;event_index&quot;</span><span class="p">,</span> <span class="s2">&quot;file_index&quot;</span><span class="p">],</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+        <span class="n">merged_catalog</span><span class="p">[</span><span class="s2">&quot;event_index&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">merged_catalog</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">values</span> <span class="o">+</span> <span class="mi">1</span>
+        <span class="n">mapping</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">merged_catalog</span><span class="p">[</span><span class="s2">&quot;match_id&quot;</span><span class="p">],</span> <span class="n">merged_catalog</span><span class="p">[</span><span class="s2">&quot;event_index&quot;</span><span class="p">]))</span>
+        <span class="n">merged_catalog</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;match_id&quot;</span><span class="p">],</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+        <span class="n">merged_catalog</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="n">catalog_csv</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+        <span class="k">del</span> <span class="n">merged_catalog</span>
+
+        <span class="n">pick_list</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="n">files_picks</span><span class="p">:</span>
+            <span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">f</span><span class="p">):</span>
+                <span class="k">continue</span>
+            <span class="n">tmp</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">str</span><span class="p">)</span>
+            <span class="n">tmp</span><span class="p">[</span><span class="s2">&quot;file_index&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">rstrip</span><span class="p">(</span><span class="s2">&quot;.csv&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;_&quot;</span><span class="p">)[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+            <span class="n">pick_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
+        <span class="n">merged_picks</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">(</span><span class="n">pick_list</span><span class="p">)</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">by</span><span class="o">=</span><span class="s2">&quot;phase_time&quot;</span><span class="p">)</span>
+        <span class="n">merged_picks</span><span class="p">[</span><span class="s2">&quot;match_id&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">merged_picks</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">x</span><span class="p">[</span><span class="s2">&quot;event_index&quot;</span><span class="p">]</span><span class="si">}</span><span class="s1">_</span><span class="si">{</span><span class="n">x</span><span class="p">[</span><span class="s2">&quot;file_index&quot;</span><span class="p">]</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+        <span class="n">merged_picks</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;event_index&quot;</span><span class="p">,</span> <span class="s2">&quot;file_index&quot;</span><span class="p">],</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+        <span class="n">merged_picks</span><span class="p">[</span><span class="s2">&quot;event_index&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">merged_picks</span><span class="p">[</span><span class="s2">&quot;match_id&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">mapping</span><span class="p">[</span><span class="n">x</span><span class="p">]</span> <span class="k">if</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">mapping</span> <span class="k">else</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+        <span class="n">merged_picks</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;match_id&quot;</span><span class="p">],</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+        <span class="n">merged_picks</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="n">picks_csv</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+        <span class="k">del</span> <span class="n">merged_picks</span>
+        
+        <span class="n">minioClient</span><span class="o">.</span><span class="n">fput_object</span><span class="p">(</span>
+            <span class="n">bucket_name</span><span class="p">,</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;region&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">/gamma_catalog.csv&quot;</span><span class="p">,</span>
+            <span class="n">catalog_csv</span><span class="p">,</span>
+        <span class="p">)</span>
+        
+        <span class="n">minioClient</span><span class="o">.</span><span class="n">fput_object</span><span class="p">(</span>
+            <span class="n">bucket_name</span><span class="p">,</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;region&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">/gamma_picks.csv&quot;</span><span class="p">,</span>
+            <span class="n">picks_csv</span><span class="p">,</span>
+        <span class="p">)</span>
+
+    <span class="c1"># else:</span>
+    <span class="c1">#     with open(catalog_csv, &quot;w&quot;) as fout:</span>
+    <span class="c1">#         pass</span>
+    <span class="c1">#     print(&quot;No catalog.csv found!&quot;)</span>
+    <span class="c1">#     with open(picks_csv, &quot;w&quot;) as fout:</span>
+    <span class="c1">#         pass</span>
+    <span class="c1">#     print(&quot;No picks.csv found!&quot;)</span>
+</pre></div>
+<div id="cell-25" class="clipboard-copy-txt"> def merge_catalog(
+    index_json: InputPath("json"),
+    config_json: InputPath("json"),
+    catalog_csv: OutputPath(str),
+    picks_csv: OutputPath(str),
+    bucket_name: str = "catalogs",
+    s3_url: str = "minio-service:9000",
+    secure: bool = True,
+):
+
+    import json
+    import os
+    from glob import glob
+
+    import pandas as pd
+    from minio import Minio
+
+    minioClient = Minio(s3_url, access_key='minio', secret_key='minio123', secure=secure)
+
+    with open(index_json, "r") as fp:
+        index = json.load(fp)
+        
+    with open(config_json, "r") as fp:
+        config = json.load(fp)
+
+    objects = minioClient.list_objects(bucket_name, prefix=f"{config['region']}/gamma", recursive=True)
+
+    tmp_path = lambda x: os.path.join("/tmp/", x)
+    for obj in objects:
+        print(obj._object_name)
+        minioClient.fget_object(bucket_name, obj._object_name, tmp_path(obj._object_name.split("/")[-1]))
+
+    # files_catalog = sorted(glob(tmp_path("catalog_*.csv")))
+    # files_picks = sorted(glob(tmp_path("picks_*.csv")))
+    files_catalog = [tmp_path(f"catalog_{node_i:03d}.csv") for node_i in range(len(index))]
+    files_picks = [tmp_path(f"picks_{node_i:03d}.csv") for node_i in range(len(index))]
+    print(f"Merge catalog: {files_catalog}")
+    print(f"Merge picks: {files_picks}")
+
+    if len(files_catalog) > 0:
+        catalog_list = []
+        for f in files_catalog:
+            if not os.path.exists(f):
+                continue
+            tmp = pd.read_csv(f, dtype=str)
+            tmp["file_index"] = f.rstrip(".csv").split("_")[-1]
+            catalog_list.append(tmp)
+        merged_catalog = pd.concat(catalog_list).sort_values(by="time")
+        merged_catalog["match_id"] = merged_catalog.apply(lambda x: f'{x["event_index"]}_{x["file_index"]}', axis=1)
+        merged_catalog.sort_values(by="time", inplace=True, ignore_index=True)
+        merged_catalog.drop(columns=["event_index", "file_index"], inplace=True)
+        merged_catalog["event_index"] = merged_catalog.index.values + 1
+        mapping = dict(zip(merged_catalog["match_id"], merged_catalog["event_index"]))
+        merged_catalog.drop(columns=["match_id"], inplace=True)
+        merged_catalog.to_csv(catalog_csv, index=False)
+        del merged_catalog
+
+        pick_list = []
+        for f in files_picks:
+            if not os.path.exists(f):
+                continue
+            tmp = pd.read_csv(f, dtype=str)
+            tmp["file_index"] = f.rstrip(".csv").split("_")[-1]
+            pick_list.append(tmp)
+        merged_picks = pd.concat(pick_list).sort_values(by="phase_time")
+        merged_picks["match_id"] = merged_picks.apply(lambda x: f'{x["event_index"]}_{x["file_index"]}', axis=1)
+        merged_picks.drop(columns=["event_index", "file_index"], inplace=True)
+        merged_picks["event_index"] = merged_picks["match_id"].apply(lambda x: mapping[x] if x in mapping else -1)
+        merged_picks.drop(columns=["match_id"], inplace=True)
+        merged_picks.to_csv(picks_csv, index=False)
+        del merged_picks
+        
+        minioClient.fput_object(
+            bucket_name,
+            f"{config['region']}/gamma_catalog.csv",
+            catalog_csv,
+        )
+        
+        minioClient.fput_object(
+            bucket_name,
+            f"{config['region']}/gamma_picks.csv",
+            picks_csv,
+        )
+
+    # else:
+    #     with open(catalog_csv, "w") as fout:
+    #         pass
+    #     print("No catalog.csv found!")
+    #     with open(picks_csv, "w") as fout:
+    #         pass
+    #     print("No picks.csv found!")</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[48]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-26">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">merge_op</span> <span class="o">=</span> <span class="n">comp</span><span class="o">.</span><span class="n">func_to_container_op</span><span class="p">(</span>
+    <span class="n">merge_catalog</span><span class="p">,</span>
+    <span class="c1"># base_image=&#39;zhuwq0/quakeflow-env:latest&#39;,</span>
+    <span class="n">base_image</span><span class="o">=</span><span class="s2">&quot;python:3.8&quot;</span><span class="p">,</span>
+    <span class="n">packages_to_install</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;pandas&quot;</span><span class="p">,</span> <span class="s2">&quot;minio&quot;</span><span class="p">],</span>
+<span class="p">)</span>
+</pre></div>
+<div id="cell-26" class="clipboard-copy-txt">merge_op = comp.func_to_container_op(
+    merge_catalog,
+    # base_image='zhuwq0/quakeflow-env:latest',
+    base_image="python:3.8",
+    packages_to_install=["pandas", "minio"],
+)</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="7-hypodd-earthquake-relocation">7. HypoDD earthquake relocation<a class="anchor-link" href="#7-hypodd-earthquake-relocation">&#182;</a></h2>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h3 id="download-and-compile-hypodd">Download and compile HypoDD<a class="anchor-link" href="#download-and-compile-hypodd">&#182;</a></h3>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[49]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-27">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">if</span> <span class="n">run_local</span><span class="p">:</span>
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="s2">&quot;HYPODD&quot;</span><span class="p">):</span>
+        <span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s2">&quot;wget -O HYPODD_1.3.tar.gz http://www.ldeo.columbia.edu/~felixw/HYPODD/HYPODD_1.3.tar.gz&quot;</span><span class="p">)</span>
+        <span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s2">&quot;tar -xf HYPODD_1.3.tar.gz&quot;</span><span class="p">)</span>
+        <span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s2">&quot;ln -s $(which gfortran) ./HYPODD/f77&quot;</span><span class="p">)</span>
+        <span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s2">&quot;ln -s $(which gfortran) ./HYPODD/g77&quot;</span><span class="p">)</span>
+        <span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s2">&quot;PATH&quot;</span><span class="p">]</span> <span class="o">+=</span> <span class="n">os</span><span class="o">.</span><span class="n">pathsep</span> <span class="o">+</span> <span class="n">os</span><span class="o">.</span><span class="n">getcwd</span><span class="p">()</span> <span class="o">+</span> <span class="s2">&quot;/HYPODD/&quot;</span>
+        <span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s2">&quot;make -C HYPODD/src&quot;</span><span class="p">)</span>
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;hypodd&quot;</span><span class="p">)):</span>
+        <span class="n">os</span><span class="o">.</span><span class="n">mkdir</span><span class="p">(</span><span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;hypodd&quot;</span><span class="p">))</span>
+    <span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;cp HYPODD/src/ph2dt/ph2dt </span><span class="si">{</span><span class="n">root_dir</span><span class="p">(</span><span class="s1">&#39;hypodd&#39;</span><span class="p">)</span><span class="si">}</span><span class="s2">/&quot;</span><span class="p">)</span>
+    <span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;cp HYPODD/src/hypoDD/hypoDD </span><span class="si">{</span><span class="n">root_dir</span><span class="p">(</span><span class="s1">&#39;hypodd&#39;</span><span class="p">)</span><span class="si">}</span><span class="s2">/&quot;</span><span class="p">)</span>
+</pre></div>
+<div id="cell-27" class="clipboard-copy-txt">if run_local:
+    if not os.path.exists("HYPODD"):
+        os.system("wget -O HYPODD_1.3.tar.gz http://www.ldeo.columbia.edu/~felixw/HYPODD/HYPODD_1.3.tar.gz")
+        os.system("tar -xf HYPODD_1.3.tar.gz")
+        os.system("ln -s $(which gfortran) ./HYPODD/f77")
+        os.system("ln -s $(which gfortran) ./HYPODD/g77")
+        os.environ["PATH"] += os.pathsep + os.getcwd() + "/HYPODD/"
+        os.system("make -C HYPODD/src")
+    if not os.path.exists(root_dir("hypodd")):
+        os.mkdir(root_dir("hypodd"))
+    os.system(f"cp HYPODD/src/ph2dt/ph2dt {root_dir('hypodd')}/")
+    os.system(f"cp HYPODD/src/hypoDD/hypoDD {root_dir('hypodd')}/")</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>URL transformed to HTTPS due to an HSTS policy
+--2022-11-29 12:13:21--  https://www.ldeo.columbia.edu/~felixw/HYPODD/HYPODD_1.3.tar.gz
+Resolving www.ldeo.columbia.edu (www.ldeo.columbia.edu)... 129.236.14.15
+Connecting to www.ldeo.columbia.edu (www.ldeo.columbia.edu)|129.236.14.15|:443... connected.
+HTTP request sent, awaiting response... 200 OK
+Length: 1393039 (1.3M) [application/x-gzip]
+Saving to: ‘HYPODD_1.3.tar.gz’
+
+     0K .......... .......... .......... .......... ..........  3%  377K 3s
+    50K .......... .......... .......... .......... ..........  7%  754K 3s
+   100K .......... .......... .......... .......... .......... 11% 94.6M 2s
+   150K .......... .......... .......... .......... .......... 14%  104M 1s
+   200K .......... .......... .......... .......... .......... 18%  735K 1s
+   250K .......... .......... .......... .......... .......... 22% 61.7M 1s
+   300K .......... .......... .......... .......... .......... 25% 83.9M 1s
+   350K .......... .......... .......... .......... .......... 29%  134M 1s
+   400K .......... .......... .......... .......... .......... 33%  780K 1s
+   450K .......... .......... .......... .......... .......... 36% 69.8M 1s
+   500K .......... .......... .......... .......... .......... 40% 41.9M 0s
+   550K .......... .......... .......... .......... .......... 44% 39.0M 0s
+   600K .......... .......... .......... .......... .......... 47% 55.2M 0s
+   650K .......... .......... .......... .......... .......... 51% 57.6M 0s
+   700K .......... .......... .......... .......... .......... 55% 75.9M 0s
+   750K .......... .......... .......... .......... .......... 58%  387K 0s
+   800K .......... .......... .......... .......... .......... 62% 91.4M 0s
+   850K .......... .......... .......... .......... .......... 66%  313M 0s
+   900K .......... .......... .......... .......... .......... 69%  313M 0s
+   950K .......... .......... .......... .......... .......... 73%  313M 0s
+  1000K .......... .......... .......... .......... .......... 77%  274M 0s
+  1050K .......... .......... .......... .......... .......... 80%  328M 0s
+  1100K .......... .......... .......... .......... .......... 84%  289M 0s
+  1150K .......... .......... .......... .......... .......... 88%  274M 0s
+  1200K .......... .......... .......... .......... .......... 91% 11.3M 0s
+  1250K .......... .......... .......... .......... .......... 95%  279M 0s
+  1300K .......... .......... .......... .......... .......... 99% 75.3K 0s
+  1350K ..........                                            100% 72.0M=1.1s
+
+2022-11-29 12:13:23 (1.17 MB/s) - ‘HYPODD_1.3.tar.gz’ saved [1393039/1393039]
+
+</pre>
+</div>
+</div>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
+<pre>for d in hypoDD ncsn2pha ph2dt hista2ddsta; do (cd $d; echo $d; make all); done
+hypoDD
+g77 -I../../include -c hypoDD.f -o hypoDD.o
+</pre>
+</div>
+</div>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>hypoDD.f:654:29:
+
+  654 |      &amp; f7.1,f7.1,f7.1,f7.1)&#39;),xav,yav,zav,tav
+      |                             1
+Warning: Legacy Extension: Comma before i/o item list at (1)
+</pre>
+</div>
+</div>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
+<pre>g77 -I../../include -c aprod.f -o aprod.o
+g77 -I../../include -c cluster1.f -o cluster1.o
+g77 -I../../include -c covar.f -o covar.o
+g77 -I../../include -c datum.f -o datum.o
+g77 -I../../include -c delaz.f -o delaz.o
+g77 -I../../include -c delaz2.f -o delaz2.o
+g77 -I../../include -c direct1.f -o direct1.o
+g77 -I../../include -c dist.f -o dist.o
+g77 -I../../include -c dtres.f -o dtres.o
+g77 -I../../include -c exist.f -o exist.o
+g77 -I../../include -c freeunit.f -o freeunit.o
+g77 -I../../include -c getdata.f -o getdata.o
+g77 -I../../include -c getinp.f -o getinp.o
+g77 -I../../include -c ifindi.f -o ifindi.o
+g77 -I../../include -c indexxi.f -o indexxi.o
+g77 -I../../include -c juliam.f -o juliam.o
+g77 -I../../include -c lsfit_lsqr.f -o lsfit_lsqr.o
+g77 -I../../include -c lsfit_svd.f -o lsfit_svd.o
+g77 -I../../include -c lsqr.f -o lsqr.o
+g77 -I../../include -c matmult1.f -o matmult1.o
+g77 -I../../include -c matmult2.f -o matmult2.o
+g77 -I../../include -c matmult3.f -o matmult3.o
+g77 -I../../include -c mdian1.f -o mdian1.o
+g77 -I../../include -c normlz.f -o normlz.o
+g77 -I../../include -c partials.f -o partials.o
+g77 -I../../include -c ran.f -o ran.o
+g77 -I../../include -c redist.f -o redist.o
+g77 -I../../include -c refract.f -o refract.o
+g77 -I../../include -c resstat.f -o resstat.o
+g77 -I../../include -c scopy.f -o scopy.o
+g77 -I../../include -c sdc2.f -o sdc2.o
+</pre>
+</div>
+</div>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>refract.f:84:16:
+
+   84 |       do 23151m=j1,nl
+      |                1
+Warning: Deleted feature: Start expression in DO loop at (1) must be integer
+refract.f:126:36:
+
+  126 |       xovmax=tinj(lx)*v(lx)*v(1)/(v(lx)-v(1))
+      |                                    1
+Warning: Legacy Extension: REAL array index at (1)
+refract.f:126:24:
+
+  126 |       xovmax=tinj(lx)*v(lx)*v(1)/(v(lx)-v(1))
+      |                        1
+Warning: Legacy Extension: REAL array index at (1)
+refract.f:126:18:
+
+  126 |       xovmax=tinj(lx)*v(lx)*v(1)/(v(lx)-v(1))
+      |                  1
+Warning: Legacy Extension: REAL array index at (1)
+refract.f:155:53:
+
+  155 |       xovmax=(tinj(lx)-tid(jx))*v(lx)*v(jx)/(v(lx)-v(jx))
+      |                                                     1
+Warning: Legacy Extension: REAL array index at (1)
+refract.f:155:47:
+
+  155 |       xovmax=(tinj(lx)-tid(jx))*v(lx)*v(jx)/(v(lx)-v(jx))
+      |                                               1
+Warning: Legacy Extension: REAL array index at (1)
+refract.f:155:40:
+
+  155 |       xovmax=(tinj(lx)-tid(jx))*v(lx)*v(jx)/(v(lx)-v(jx))
+      |                                        1
+Warning: Legacy Extension: REAL array index at (1)
+refract.f:155:34:
+
+  155 |       xovmax=(tinj(lx)-tid(jx))*v(lx)*v(jx)/(v(lx)-v(jx))
+      |                                  1
+Warning: Legacy Extension: REAL array index at (1)
+refract.f:155:27:
+
+  155 |       xovmax=(tinj(lx)-tid(jx))*v(lx)*v(jx)/(v(lx)-v(jx))
+      |                           1
+Warning: Legacy Extension: REAL array index at (1)
+refract.f:155:19:
+
+  155 |       xovmax=(tinj(lx)-tid(jx))*v(lx)*v(jx)/(v(lx)-v(jx))
+      |                   1
+Warning: Legacy Extension: REAL array index at (1)
+refract.f:161:36:
+
+  161 |       xovmax=tinj(lx)*v(lx)*v(1)/(v(lx)-v(1))
+      |                                    1
+Warning: Legacy Extension: REAL array index at (1)
+refract.f:161:24:
+
+  161 |       xovmax=tinj(lx)*v(lx)*v(1)/(v(lx)-v(1))
+      |                        1
+Warning: Legacy Extension: REAL array index at (1)
+refract.f:161:18:
+
+  161 |       xovmax=tinj(lx)*v(lx)*v(1)/(v(lx)-v(1))
+      |                  1
+Warning: Legacy Extension: REAL array index at (1)
+scopy.f:25:72:
+
+   25 |       if(incx.eq.incy) if(incx-1) 5,20,60
+      |                                                                        1
+Warning: Fortran 2018 deleted feature: Arithmetic IF statement at (1)
+</pre>
+</div>
+</div>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
+<pre>g77 -I../../include -c setorg.f -o setorg.o
+g77 -I../../include -c skip.f -o skip.o
+g77 -I../../include -c snrm2.f -o snrm2.o
+g77 -I../../include -c sort.f -o sort.o
+g77 -I../../include -c sorti.f -o sorti.o
+</pre>
+</div>
+</div>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>snrm2.f:67:72:
+
+   67 |    10 assign 30 to next
+      |                                                                        1
+Warning: Deleted feature: ASSIGN statement at (1)
+snrm2.f:72:20:
+
+   72 |    20    go to next,(30, 50, 70, 110)
+      |                    1
+Warning: Deleted feature: Assigned GOTO statement at (1)
+snrm2.f:74:72:
+
+   74 |       assign 50 to next
+      |                                                                        1
+Warning: Deleted feature: ASSIGN statement at (1)
+snrm2.f:83:72:
+
+   83 |       assign 70 to next
+      |                                                                        1
+Warning: Deleted feature: ASSIGN statement at (1)
+snrm2.f:89:72:
+
+   89 |       assign 110 to next
+      |                                                                        1
+Warning: Deleted feature: ASSIGN statement at (1)
+snrm2.f:125:72:
+
+  125 |    95    sum = sum + sx(j)**2
+      |                                                                        1
+Warning: Fortran 2018 deleted feature: DO termination statement which is not END DO or CONTINUE with label 95 at (1)
+svd.f:49:72:
+
+   49 |       DO 1100 J=1,N
+      |                                                                        1
+Warning: Fortran 2018 deleted feature: Shared DO termination label 1100 at (1)
+svd.f:50:72:
+
+   50 |  1100 U(I,J)=A(I,J)
+      |                                                                        1
+Warning: Fortran 2018 deleted feature: DO termination statement which is not END DO or CONTINUE with label 1100 at (1)
+svd.f:59:72:
+
+   59 |  2100 S=U(J,I)**2 + S
+      |                                                                        1
+Warning: Fortran 2018 deleted feature: DO termination statement which is not END DO or CONTINUE with label 2100 at (1)
+svd.f:69:72:
+
+   69 |  2200 S=U(K,I)*U(K,J) + S
+      |                                                                        1
+Warning: Fortran 2018 deleted feature: DO termination statement which is not END DO or CONTINUE with label 2200 at (1)
+svd.f:72:72:
+
+   72 |  2300 U(K,J)=U(K,J) + F*U(K,I)
+      |                                                                        1
+Warning: Fortran 2018 deleted feature: DO termination statement which is not END DO or CONTINUE with label 2300 at (1)
+svd.f:82:72:
+
+   82 |  2600 S=U(I,J)**2 + S
+      |                                                                        1
+Warning: Fortran 2018 deleted feature: DO termination statement which is not END DO or CONTINUE with label 2600 at (1)
+svd.f:90:72:
+
+   90 |  2650 E(J)=U(I,J)/H
+      |                                                                        1
+Warning: Fortran 2018 deleted feature: DO termination statement which is not END DO or CONTINUE with label 2650 at (1)
+svd.f:97:72:
+
+   97 |  2670 S=U(J,K)*U(I,K) + S
+      |                                                                        1
+Warning: Fortran 2018 deleted feature: DO termination statement which is not END DO or CONTINUE with label 2670 at (1)
+svd.f:99:72:
+
+   99 |  2690 U(J,K)=U(J,K) + S*E(K)
+      |                                                                        1
+Warning: Fortran 2018 deleted feature: DO termination statement which is not END DO or CONTINUE with label 2690 at (1)
+svd.f:117:72:
+
+  117 |  3100 V(J,I)=U(I,J)/H
+      |                                                                        1
+Warning: Fortran 2018 deleted feature: DO termination statement which is not END DO or CONTINUE with label 3100 at (1)
+svd.f:121:72:
+
+  121 |  3200 S=U(I,K)*V(K,J) + S
+      |                                                                        1
+Warning: Fortran 2018 deleted feature: DO termination statement which is not END DO or CONTINUE with label 3200 at (1)
+svd.f:123:72:
+
+  123 |  3300 V(K,J)=V(K,J) + S*V(K,I)
+      |                                                                        1
+Warning: Fortran 2018 deleted feature: DO termination statement which is not END DO or CONTINUE with label 3300 at (1)
+svd.f:129:72:
+
+  129 |  3600 V(I,J)=0.0
+      |                                                                        1
+Warning: Fortran 2018 deleted feature: DO termination statement which is not END DO or CONTINUE with label 3600 at (1)
+svd.f:145:72:
+
+  145 |  4100 U(I,J)=0.0
+      |                                                                        1
+Warning: Fortran 2018 deleted feature: DO termination statement which is not END DO or CONTINUE with label 4100 at (1)
+svd.f:152:72:
+
+  152 |  4200 S=U(K,I)*U(K,J) + S
+      |                                                                        1
+Warning: Fortran 2018 deleted feature: DO termination statement which is not END DO or CONTINUE with label 4200 at (1)
+svd.f:155:72:
+
+  155 |  4300 U(K,J)=U(K,J) + F*U(K,I)
+      |                                                                        1
+Warning: Fortran 2018 deleted feature: DO termination statement which is not END DO or CONTINUE with label 4300 at (1)
+svd.f:159:72:
+
+  159 |  4550 U(J,I)=U(J,I)/G
+      |                                                                        1
+Warning: Fortran 2018 deleted feature: DO termination statement which is not END DO or CONTINUE with label 4550 at (1)
+svd.f:163:72:
+
+  163 |  4600 U(J,I)=0.0
+      |                                                                        1
+Warning: Fortran 2018 deleted feature: DO termination statement which is not END DO or CONTINUE with label 4600 at (1)
+svd.f:164:72:
+
+  164 |  4700 U(I,I)=U(I,I) + 1.0
+      |                                                                        1
+Warning: Fortran 2018 deleted feature: DO termination statement which is not END DO or CONTINUE with label 4700 at (1)
+svd.f:258:72:
+
+  258 |  8200 V(J,K)=-V(J,K)
+      |                                                                        1
+Warning: Fortran 2018 deleted feature: DO termination statement which is not END DO or CONTINUE with label 8200 at (1)
+</pre>
+</div>
+</div>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
+<pre>g77 -I../../include -c sscal.f -o sscal.o
+g77 -I../../include -c svd.f -o svd.o
+g77 -I../../include -c tiddid.f -o tiddid.o
+g77 -I../../include -c trialsrc.f -o trialsrc.o
+g77 -I../../include -c trimlen.f -o trimlen.o
+</pre>
+</div>
+</div>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>trimlen.f:11:72:
+
+   11 |     1    if(t(trimlen:trimlen).ne.&#39; &#39;)RETURN
+      |                                                                        1
+Warning: Fortran 2018 deleted feature: DO termination statement which is not END DO or CONTINUE with label 1 at (1)
+vmodel.f:34:72:
+
+   34 |    10   vsq(i)=v(i)*v(i)
+      |                                                                          1
+Warning: Fortran 2018 deleted feature: DO termination statement which is not END DO or CONTINUE with label 10 at (1)
+vmodel.f:53:72:
+
+   53 |    30   thk(i)=top(i+1)-top(i)
+      |                                                                          1
+Warning: Fortran 2018 deleted feature: DO termination statement which is not END DO or CONTINUE with label 30 at (1)
+</pre>
+</div>
+</div>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
+<pre>g77 -I../../include -c ttime.f -o ttime.o
+g77 -I../../include -c vmodel.f -o vmodel.o
+g77 -I../../include -c weighting.f -o weighting.o
+gcc -O -I../../include   -c -o atoangle_.o atoangle_.c
+</pre>
+</div>
+</div>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>atoangle_.c:3:8: warning: extra tokens at end of #endif directive [-Wextra-tokens]
+#endif lint
+       ^
+       //
+In file included from atoangle_.c:6:
+../../include/f77types.h:10:8: warning: extra tokens at end of #endif directive [-Wextra-tokens]
+#endif F77TYPES_H
+       ^
+       //
+2 warnings generated.
+atoangle.c:3:8: warning: extra tokens at end of #endif directive [-Wextra-tokens]
+#endif lint
+       ^
+       //
+atoangle.c:9:8: warning: extra tokens at end of #endif directive [-Wextra-tokens]
+#endif NULL
+       ^
+       //
+2 warnings generated.
+In file included from datetime_.c:7:
+../../include/f77types.h:10:8: warning: extra tokens at end of #endif directive [-Wextra-tokens]
+#endif F77TYPES_H
+       ^
+       //
+1 warning generated.
+In file included from rpad_.c:6:
+../../include/f77types.h:10:8: warning: extra tokens at end of #endif directive [-Wextra-tokens]
+#endif F77TYPES_H
+       ^
+       //
+1 warning generated.
+</pre>
+</div>
+</div>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
+<pre>gcc -O -I../../include   -c -o atoangle.o atoangle.c
+gcc -O -I../../include   -c -o datetime_.o datetime_.c
+gcc -O -I../../include   -c -o hypot_.o hypot_.c
+gcc -O -I../../include   -c -o rpad_.o rpad_.c
+gcc -O -I../../include   -c -o sscanf3_.o sscanf3_.c
+g77  hypoDD.o aprod.o cluster1.o covar.o datum.o delaz.o delaz2.o direct1.o dist.o dtres.o exist.o freeunit.o getdata.o getinp.o ifindi.o indexxi.o juliam.o lsfit_lsqr.o lsfit_svd.o lsqr.o matmult1.o matmult2.o matmult3.o mdian1.o normlz.o partials.o ran.o redist.o refract.o resstat.o scopy.o sdc2.o setorg.o skip.o snrm2.o sort.o sorti.o sscal.o svd.o tiddid.o trialsrc.o trimlen.o ttime.o vmodel.o weighting.o atoangle_.o atoangle.o datetime_.o hypot_.o rpad_.o sscanf3_.o  -o hypoDD
+</pre>
+</div>
+</div>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>In file included from sscanf3_.c:7:
+../../include/f77types.h:10:8: warning: extra tokens at end of #endif directive [-Wextra-tokens]
+#endif F77TYPES_H
+       ^
+       //
+1 warning generated.
+</pre>
+</div>
+</div>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
+<pre>ncsn2pha
+g77 -O -c ncsn2pha.f -o ncsn2pha.o
+g77  ncsn2pha.o   -o ncsn2pha
+ph2dt
+g77 -O -I../../include -c ph2dt.f -o ph2dt.o
+</pre>
+</div>
+</div>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>ncsn2pha.f:279:72:
+
+  279 |     1    if(t(trimlen:trimlen).ne.&#39; &#39;)RETURN
+      |                                                                        1
+Warning: Fortran 2018 deleted feature: DO termination statement which is not END DO or CONTINUE with label 1 at (1)
+</pre>
+</div>
+</div>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
+<pre>g77 -O -I../../include -c cluster.f -o cluster.o
+g77 -O -I../../include -c datetime.f -o datetime.o
+g77 -O -I../../include -c delaz.f -o delaz.o
+g77 -O -I../../include -c ifindi.f -o ifindi.o
+g77 -O -I../../include -c indexx.f -o indexx.o
+g77 -O -I../../include -c indexxi.f -o indexxi.o
+g77 -O -I../../include -c sorti.f -o sorti.o
+g77 -O -I../../include -c trimlen.f -o trimlen.o
+gcc -I../../include -O   -c -o atoangle_.o atoangle_.c
+gcc -I../../include -O   -c -o atoangle.o atoangle.c
+gcc -I../../include -O   -c -o rpad_.o rpad_.c
+</pre>
+</div>
+</div>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>trimlen.f:11:72:
+
+   11 |     1    if(t(trimlen:trimlen).ne.&#39; &#39;)RETURN
+      |                                                                        1
+Warning: Fortran 2018 deleted feature: DO termination statement which is not END DO or CONTINUE with label 1 at (1)
+atoangle_.c:3:8: warning: extra tokens at end of #endif directive [-Wextra-tokens]
+#endif lint
+       ^
+       //
+In file included from atoangle_.c:6:
+../../include/f77types.h:10:8: warning: extra tokens at end of #endif directive [-Wextra-tokens]
+#endif F77TYPES_H
+       ^
+       //
+2 warnings generated.
+atoangle.c:3:8: warning: extra tokens at end of #endif directive [-Wextra-tokens]
+#endif lint
+       ^
+       //
+atoangle.c:9:8: warning: extra tokens at end of #endif directive [-Wextra-tokens]
+#endif NULL
+       ^
+       //
+2 warnings generated.
+In file included from rpad_.c:6:
+../../include/f77types.h:10:8: warning: extra tokens at end of #endif directive [-Wextra-tokens]
+#endif F77TYPES_H
+       ^
+       //
+1 warning generated.
+In file included from sscanf3_.c:7:
+../../include/f77types.h:10:8: warning: extra tokens at end of #endif directive [-Wextra-tokens]
+#endif F77TYPES_H
+       ^
+       //
+1 warning generated.
+</pre>
+</div>
+</div>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
+<pre>gcc -I../../include -O   -c -o sscanf3_.o sscanf3_.c
+g77  ph2dt.o cluster.o datetime.o delaz.o ifindi.o indexx.o indexxi.o sorti.o trimlen.o atoangle_.o atoangle.o rpad_.o sscanf3_.o  -o ph2dt
+hista2ddsta
+f77 -O -I../../include -c hista2ddsta.f -o hista2ddsta.o
+f77  hista2ddsta.o  -o hista2ddsta
+</pre>
+</div>
+</div>
+
+</div>
+
+</div>
+
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h3 id="71-convert-station-format">7.1. Convert station format<a class="anchor-link" href="#71-convert-station-format">&#182;</a></h3>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[50]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-28">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">def</span> <span class="nf">convert_station</span><span class="p">(</span>
+    <span class="n">config_json</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="s2">&quot;json&quot;</span><span class="p">),</span>
+    <span class="n">station_json</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="s2">&quot;json&quot;</span><span class="p">),</span>
+    <span class="n">hypoinverse_station</span><span class="p">:</span> <span class="n">OutputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span>
+    <span class="n">hypodd_station</span><span class="p">:</span> <span class="n">OutputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span>
+    <span class="n">data_path</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;./&quot;</span><span class="p">,</span>
+    <span class="n">bucket_name</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;catalogs&quot;</span><span class="p">,</span>
+    <span class="n">s3_url</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;minio-service:9000&quot;</span><span class="p">,</span>
+    <span class="n">secure</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+<span class="p">):</span>
+
+    <span class="kn">import</span> <span class="nn">os</span>
+    <span class="kn">import</span> <span class="nn">json</span>
+
+    <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+    <span class="kn">from</span> <span class="nn">tqdm</span> <span class="kn">import</span> <span class="n">tqdm</span>
+
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">config_json</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">config</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">fp</span><span class="p">)</span>
+
+    <span class="n">hypodd_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">data_path</span><span class="p">,</span> <span class="s2">&quot;hypodd&quot;</span><span class="p">)</span>
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">hypodd_path</span><span class="p">):</span>
+        <span class="n">os</span><span class="o">.</span><span class="n">mkdir</span><span class="p">(</span><span class="n">hypodd_path</span><span class="p">)</span>
+    <span class="n">hypoinv_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">data_path</span><span class="p">,</span> <span class="s2">&quot;hypoinv&quot;</span><span class="p">)</span>
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">hypoinv_path</span><span class="p">):</span>
+        <span class="n">os</span><span class="o">.</span><span class="n">mkdir</span><span class="p">(</span><span class="n">hypoinv_path</span><span class="p">)</span>
+
+    <span class="n">stations</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_json</span><span class="p">(</span><span class="n">station_json</span><span class="p">,</span> <span class="n">orient</span><span class="o">=</span><span class="s2">&quot;index&quot;</span><span class="p">)</span>
+
+    <span class="n">converted_hypoinverse</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">converted_hypodd</span> <span class="o">=</span> <span class="p">{}</span>
+
+    <span class="k">for</span> <span class="n">sta</span><span class="p">,</span> <span class="n">row</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">stations</span><span class="o">.</span><span class="n">iterrows</span><span class="p">()):</span>
+
+        <span class="n">network_code</span><span class="p">,</span> <span class="n">station_code</span><span class="p">,</span> <span class="n">comp_code</span><span class="p">,</span> <span class="n">channel_code</span> <span class="o">=</span> <span class="n">sta</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;.&quot;</span><span class="p">)</span>
+        <span class="n">station_weight</span> <span class="o">=</span> <span class="s2">&quot; &quot;</span>
+        <span class="n">lat_degree</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">row</span><span class="p">[</span><span class="s2">&quot;latitude&quot;</span><span class="p">])</span>
+        <span class="n">lat_minute</span> <span class="o">=</span> <span class="p">(</span><span class="n">row</span><span class="p">[</span><span class="s2">&quot;latitude&quot;</span><span class="p">]</span> <span class="o">-</span> <span class="n">lat_degree</span><span class="p">)</span> <span class="o">*</span> <span class="mi">60</span>
+        <span class="n">north</span> <span class="o">=</span> <span class="s2">&quot;N&quot;</span> <span class="k">if</span> <span class="n">lat_degree</span> <span class="o">&gt;=</span> <span class="mi">0</span> <span class="k">else</span> <span class="s2">&quot;S&quot;</span>
+        <span class="n">lng_degree</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">row</span><span class="p">[</span><span class="s2">&quot;longitude&quot;</span><span class="p">])</span>
+        <span class="n">lng_minute</span> <span class="o">=</span> <span class="p">(</span><span class="n">row</span><span class="p">[</span><span class="s2">&quot;longitude&quot;</span><span class="p">]</span> <span class="o">-</span> <span class="n">lng_degree</span><span class="p">)</span> <span class="o">*</span> <span class="mi">60</span>
+        <span class="n">west</span> <span class="o">=</span> <span class="s2">&quot;W&quot;</span> <span class="k">if</span> <span class="n">lng_degree</span> <span class="o">&lt;=</span> <span class="mi">0</span> <span class="k">else</span> <span class="s2">&quot;E&quot;</span>
+        <span class="n">elevation</span> <span class="o">=</span> <span class="n">row</span><span class="p">[</span><span class="s2">&quot;elevation(m)&quot;</span><span class="p">]</span>
+        <span class="n">line_hypoinverse</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">station_code</span><span class="si">:</span><span class="s2">&lt;5</span><span class="si">}</span><span class="s2"> </span><span class="si">{</span><span class="n">network_code</span><span class="si">:</span><span class="s2">&lt;2</span><span class="si">}</span><span class="s2"> </span><span class="si">{</span><span class="n">comp_code</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="si">:</span><span class="s2">&lt;1</span><span class="si">}{</span><span class="n">channel_code</span><span class="si">:</span><span class="s2">&lt;3</span><span class="si">}</span><span class="s2"> </span><span class="si">{</span><span class="n">station_weight</span><span class="si">}{</span><span class="nb">abs</span><span class="p">(</span><span class="n">lat_degree</span><span class="p">)</span><span class="si">:</span><span class="s2">2.0f</span><span class="si">}</span><span class="s2"> </span><span class="si">{</span><span class="nb">abs</span><span class="p">(</span><span class="n">lat_minute</span><span class="p">)</span><span class="si">:</span><span class="s2">7.4f</span><span class="si">}{</span><span class="n">north</span><span class="si">}{</span><span class="nb">abs</span><span class="p">(</span><span class="n">lng_degree</span><span class="p">)</span><span class="si">:</span><span class="s2">3.0f</span><span class="si">}</span><span class="s2"> </span><span class="si">{</span><span class="nb">abs</span><span class="p">(</span><span class="n">lng_minute</span><span class="p">)</span><span class="si">:</span><span class="s2">7.4f</span><span class="si">}{</span><span class="n">west</span><span class="si">}{</span><span class="n">elevation</span><span class="si">:</span><span class="s2">4.0f</span><span class="si">}</span><span class="se">\n</span><span class="s2">&quot;</span>
+        <span class="n">converted_hypoinverse</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">line_hypoinverse</span><span class="p">)</span>
+
+        <span class="n">tmp_code</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">station_code</span><span class="si">}{</span><span class="n">channel_code</span><span class="si">}</span><span class="s2">&quot;</span>
+        <span class="n">converted_hypodd</span><span class="p">[</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">station_code</span><span class="si">}{</span><span class="n">channel_code</span><span class="si">}</span><span class="s2">&quot;</span>
+        <span class="p">]</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">tmp_code</span><span class="si">:</span><span class="s2">&lt;8s</span><span class="si">}</span><span class="s2"> </span><span class="si">{</span><span class="n">row</span><span class="p">[</span><span class="s1">&#39;latitude&#39;</span><span class="p">]</span><span class="si">:</span><span class="s2">.3f</span><span class="si">}</span><span class="s2"> </span><span class="si">{</span><span class="n">row</span><span class="p">[</span><span class="s1">&#39;longitude&#39;</span><span class="p">]</span><span class="si">:</span><span class="s2">.3f</span><span class="si">}</span><span class="se">\n</span><span class="s2">&quot;</span>
+
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">hypoinverse_station</span><span class="p">,</span> <span class="s2">&quot;w&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+        <span class="n">f</span><span class="o">.</span><span class="n">writelines</span><span class="p">(</span><span class="n">converted_hypoinverse</span><span class="p">)</span>
+
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">hypodd_station</span><span class="p">,</span> <span class="s2">&quot;w&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+        <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">converted_hypodd</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+            <span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">v</span><span class="p">)</span>
+
+    <span class="c1">## upload to s3 bucket</span>
+    <span class="k">try</span><span class="p">:</span>
+        <span class="kn">from</span> <span class="nn">minio</span> <span class="kn">import</span> <span class="n">Minio</span>
+
+        <span class="n">minioClient</span> <span class="o">=</span> <span class="n">Minio</span><span class="p">(</span><span class="n">s3_url</span><span class="p">,</span> <span class="n">access_key</span><span class="o">=</span><span class="s2">&quot;minio&quot;</span><span class="p">,</span> <span class="n">secret_key</span><span class="o">=</span><span class="s2">&quot;minio123&quot;</span><span class="p">,</span> <span class="n">secure</span><span class="o">=</span><span class="n">secure</span><span class="p">)</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">minioClient</span><span class="o">.</span><span class="n">bucket_exists</span><span class="p">(</span><span class="n">bucket_name</span><span class="p">):</span>
+            <span class="n">minioClient</span><span class="o">.</span><span class="n">make_bucket</span><span class="p">(</span><span class="n">bucket_name</span><span class="p">)</span>
+
+        <span class="n">minioClient</span><span class="o">.</span><span class="n">fput_object</span><span class="p">(</span>
+            <span class="n">bucket_name</span><span class="p">,</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;region&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">/hypodd/stations.dat&quot;</span><span class="p">,</span>
+            <span class="n">hypodd_station</span><span class="p">,</span>
+        <span class="p">)</span>
+
+    <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">err</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;ERROR: can not access minio service! </span><span class="se">\n</span><span class="si">{</span><span class="n">err</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+        <span class="k">pass</span>
+</pre></div>
+<div id="cell-28" class="clipboard-copy-txt">def convert_station(
+    config_json: InputPath("json"),
+    station_json: InputPath("json"),
+    hypoinverse_station: OutputPath(str),
+    hypodd_station: OutputPath(str),
+    data_path: str = "./",
+    bucket_name: str = "catalogs",
+    s3_url: str = "minio-service:9000",
+    secure: bool = True,
+):
+
+    import os
+    import json
+
+    import pandas as pd
+    from tqdm import tqdm
+
+    with open(config_json, "r") as fp:
+        config = json.load(fp)
+
+    hypodd_path = os.path.join(data_path, "hypodd")
+    if not os.path.exists(hypodd_path):
+        os.mkdir(hypodd_path)
+    hypoinv_path = os.path.join(data_path, "hypoinv")
+    if not os.path.exists(hypoinv_path):
+        os.mkdir(hypoinv_path)
+
+    stations = pd.read_json(station_json, orient="index")
+
+    converted_hypoinverse = []
+    converted_hypodd = {}
+
+    for sta, row in tqdm(stations.iterrows()):
+
+        network_code, station_code, comp_code, channel_code = sta.split(".")
+        station_weight = " "
+        lat_degree = int(row["latitude"])
+        lat_minute = (row["latitude"] - lat_degree) * 60
+        north = "N" if lat_degree >= 0 else "S"
+        lng_degree = int(row["longitude"])
+        lng_minute = (row["longitude"] - lng_degree) * 60
+        west = "W" if lng_degree <= 0 else "E"
+        elevation = row["elevation(m)"]
+        line_hypoinverse = f"{station_code:<5} {network_code:<2} {comp_code[:-1]:<1}{channel_code:<3} {station_weight}{abs(lat_degree):2.0f} {abs(lat_minute):7.4f}{north}{abs(lng_degree):3.0f} {abs(lng_minute):7.4f}{west}{elevation:4.0f}\n"
+        converted_hypoinverse.append(line_hypoinverse)
+
+        tmp_code = f"{station_code}{channel_code}"
+        converted_hypodd[
+            f"{station_code}{channel_code}"
+        ] = f"{tmp_code:<8s} {row['latitude']:.3f} {row['longitude']:.3f}\n"
+
+    with open(hypoinverse_station, "w") as f:
+        f.writelines(converted_hypoinverse)
+
+    with open(hypodd_station, "w") as f:
+        for k, v in converted_hypodd.items():
+            f.write(v)
+
+    ## upload to s3 bucket
+    try:
+        from minio import Minio
+
+        minioClient = Minio(s3_url, access_key="minio", secret_key="minio123", secure=secure)
+        if not minioClient.bucket_exists(bucket_name):
+            minioClient.make_bucket(bucket_name)
+
+        minioClient.fput_object(
+            bucket_name,
+            f"{config['region']}/hypodd/stations.dat",
+            hypodd_station,
+        )
+
+    except Exception as err:
+        print(f"ERROR: can not access minio service! \n{err}")
+        pass</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[51]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-29">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">if</span> <span class="n">run_local</span><span class="p">:</span>
+    <span class="n">convert_station</span><span class="p">(</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;config.json&quot;</span><span class="p">),</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;stations.json&quot;</span><span class="p">),</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;hypoinv/stations.dat&quot;</span><span class="p">),</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;hypodd/stations.dat&quot;</span><span class="p">),</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;&quot;</span><span class="p">),</span>
+    <span class="p">)</span>
+</pre></div>
+<div id="cell-29" class="clipboard-copy-txt">if run_local:
+    convert_station(
+        root_dir("config.json"),
+        root_dir("stations.json"),
+        root_dir("hypoinv/stations.dat"),
+        root_dir("hypodd/stations.dat"),
+        root_dir(""),
+    )</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>52it [00:00, 30932.32it/s]
+</pre>
+</div>
+</div>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
+<pre>ERROR: can not access minio service! 
+HTTPSConnectionPool(host=&#39;minio-service&#39;, port=9000): Max retries exceeded with url: /catalogs?location= (Caused by NewConnectionError(&#39;&lt;urllib3.connection.HTTPSConnection object at 0x2974ef4f0&gt;: Failed to establish a new connection: [Errno 8] nodename nor servname provided, or not known&#39;))
+</pre>
+</div>
+</div>
+
+</div>
+
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[52]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-30">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">convert_station_op</span> <span class="o">=</span> <span class="n">comp</span><span class="o">.</span><span class="n">func_to_container_op</span><span class="p">(</span>
+    <span class="n">convert_station</span><span class="p">,</span>
+    <span class="n">base_image</span><span class="o">=</span><span class="s2">&quot;python:3.8&quot;</span><span class="p">,</span>
+    <span class="n">packages_to_install</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;pandas&quot;</span><span class="p">,</span> <span class="s2">&quot;tqdm&quot;</span><span class="p">,</span> <span class="s2">&quot;minio&quot;</span><span class="p">],</span>
+<span class="p">)</span>
+</pre></div>
+<div id="cell-30" class="clipboard-copy-txt">convert_station_op = comp.func_to_container_op(
+    convert_station,
+    base_image="python:3.8",
+    packages_to_install=["pandas", "tqdm", "minio"],
+)</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h3 id="split-large-catalog-due-to-memory-and-time">Split large catalog due to memory and time<a class="anchor-link" href="#split-large-catalog-due-to-memory-and-time">&#182;</a></h3>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[53]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-31">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">def</span> <span class="nf">split_hypodd</span><span class="p">(</span>
+    <span class="n">config_json</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="s2">&quot;json&quot;</span><span class="p">),</span>
+    <span class="n">catalog_csv</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">:</span>
+
+    <span class="kn">import</span> <span class="nn">json</span>
+    <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">config_json</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">config</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">fp</span><span class="p">)</span>
+
+    <span class="n">events</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">catalog_csv</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="s2">&quot;MAXEVENT&quot;</span> <span class="ow">in</span> <span class="n">config</span><span class="p">[</span><span class="s2">&quot;hypodd&quot;</span><span class="p">]:</span>
+        <span class="n">MAXEVENT</span> <span class="o">=</span> <span class="n">config</span><span class="p">[</span><span class="s2">&quot;hypodd&quot;</span><span class="p">][</span><span class="s2">&quot;MAXEVENT&quot;</span><span class="p">]</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">MAXEVENT</span> <span class="o">=</span> <span class="mf">1e4</span>  <span class="c1">## segment by time</span>
+
+    <span class="n">MAXEVENT</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">events</span><span class="p">)</span> <span class="o">//</span> <span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">events</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">//</span> <span class="n">MAXEVENT</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span>
+    <span class="n">num_parallel</span> <span class="o">=</span> <span class="nb">int</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">events</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">//</span> <span class="n">MAXEVENT</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">num_parallel</span><span class="p">))</span>
+</pre></div>
+<div id="cell-31" class="clipboard-copy-txt">def split_hypodd(
+    config_json: InputPath("json"),
+    catalog_csv: InputPath(str),
+) -> list:
+
+    import json
+    import pandas as pd
+
+    with open(config_json, "r") as fp:
+        config = json.load(fp)
+
+    events = pd.read_csv(catalog_csv)
+
+    if "MAXEVENT" in config["hypodd"]:
+        MAXEVENT = config["hypodd"]["MAXEVENT"]
+    else:
+        MAXEVENT = 1e4  ## segment by time
+
+    MAXEVENT = len(events) // ((len(events) - 1) // MAXEVENT + 1) + 1
+    num_parallel = int((len(events) - 1) // MAXEVENT + 1)
+
+    return list(range(num_parallel))</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[54]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-32">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">if</span> <span class="n">run_local</span><span class="p">:</span>
+    <span class="n">nodes</span> <span class="o">=</span> <span class="n">split_hypodd</span><span class="p">(</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;config.json&quot;</span><span class="p">),</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;gamma_catalog.csv&quot;</span><span class="p">),</span>
+    <span class="p">)</span>
+</pre></div>
+<div id="cell-32" class="clipboard-copy-txt">if run_local:
+    nodes = split_hypodd(
+        root_dir("config.json"),
+        root_dir("gamma_catalog.csv"),
+    )</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[55]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-33">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">split_hypodd_op</span> <span class="o">=</span> <span class="n">comp</span><span class="o">.</span><span class="n">func_to_container_op</span><span class="p">(</span>
+    <span class="n">split_hypodd</span><span class="p">,</span>
+    <span class="n">base_image</span><span class="o">=</span><span class="s2">&quot;python:3.8&quot;</span><span class="p">,</span>
+    <span class="n">packages_to_install</span><span class="o">=</span><span class="p">[</span>
+        <span class="s2">&quot;pandas&quot;</span><span class="p">,</span>
+    <span class="p">],</span>
+<span class="p">)</span>
+</pre></div>
+<div id="cell-33" class="clipboard-copy-txt">split_hypodd_op = comp.func_to_container_op(
+    split_hypodd,
+    base_image="python:3.8",
+    packages_to_install=[
+        "pandas",
+    ],
+)</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h3 id="72-convert-phase-format">7.2. Convert phase format<a class="anchor-link" href="#72-convert-phase-format">&#182;</a></h3>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[56]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-34">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">def</span> <span class="nf">convert_phase</span><span class="p">(</span>
+    <span class="n">node_i</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
+    <span class="n">config_json</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="s2">&quot;json&quot;</span><span class="p">),</span>
+    <span class="n">picks_csv</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span>
+    <span class="n">catalog_csv</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span>
+    <span class="n">hypodd_phase</span><span class="p">:</span> <span class="n">OutputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span>
+    <span class="n">data_path</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;./&quot;</span><span class="p">,</span>
+    <span class="n">bucket_name</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;catalogs&quot;</span><span class="p">,</span>
+    <span class="n">s3_url</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;minio-service:9000&quot;</span><span class="p">,</span>
+    <span class="n">secure</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+<span class="p">):</span>
+
+    <span class="kn">import</span> <span class="nn">json</span>
+    <span class="kn">import</span> <span class="nn">os</span>
+    <span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">datetime</span>
+
+    <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+    <span class="kn">from</span> <span class="nn">tqdm</span> <span class="kn">import</span> <span class="n">tqdm</span>
+
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">config_json</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">config</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">fp</span><span class="p">)</span>
+    <span class="n">hypodd_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">data_path</span><span class="p">,</span> <span class="s2">&quot;hypodd&quot;</span><span class="p">)</span>
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">hypodd_path</span><span class="p">):</span>
+        <span class="n">os</span><span class="o">.</span><span class="n">mkdir</span><span class="p">(</span><span class="n">hypodd_path</span><span class="p">)</span>
+
+    <span class="n">picks</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">picks_csv</span><span class="p">)</span>
+    <span class="n">events</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">catalog_csv</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="s2">&quot;MAXEVENT&quot;</span> <span class="ow">in</span> <span class="n">config</span><span class="p">[</span><span class="s2">&quot;hypodd&quot;</span><span class="p">]:</span>
+        <span class="n">MAXEVENT</span> <span class="o">=</span> <span class="n">config</span><span class="p">[</span><span class="s2">&quot;hypodd&quot;</span><span class="p">][</span><span class="s2">&quot;MAXEVENT&quot;</span><span class="p">]</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">MAXEVENT</span> <span class="o">=</span> <span class="mf">1e4</span>  <span class="c1">## segment by time</span>
+    <span class="n">MAXEVENT</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">events</span><span class="p">)</span> <span class="o">//</span> <span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">events</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">//</span> <span class="n">MAXEVENT</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span>
+    <span class="n">num_parallel</span> <span class="o">=</span> <span class="nb">int</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">events</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">//</span> <span class="n">MAXEVENT</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
+
+    <span class="n">events</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="s2">&quot;time&quot;</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="n">events</span> <span class="o">=</span> <span class="n">events</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">node_i</span><span class="p">::</span><span class="n">num_parallel</span><span class="p">]</span>
+    <span class="n">picks</span> <span class="o">=</span> <span class="n">picks</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">picks</span><span class="p">[</span><span class="s2">&quot;event_index&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">isin</span><span class="p">(</span><span class="n">events</span><span class="p">[</span><span class="s2">&quot;event_index&quot;</span><span class="p">])]</span>
+    <span class="c1"># output_lines = []</span>
+    <span class="n">output_file</span> <span class="o">=</span> <span class="nb">open</span><span class="p">(</span><span class="n">hypodd_phase</span><span class="p">,</span> <span class="s2">&quot;w&quot;</span><span class="p">)</span>
+
+    <span class="n">picks_by_event</span> <span class="o">=</span> <span class="n">picks</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s2">&quot;event_index&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">groups</span>
+    <span class="c1"># for i in tqdm(range(node_i, len(events), num_parallel)):</span>
+    <span class="c1">#     event = events.iloc[i]</span>
+    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">event</span> <span class="ow">in</span> <span class="n">events</span><span class="o">.</span><span class="n">iterrows</span><span class="p">():</span>
+        <span class="n">event_time</span> <span class="o">=</span> <span class="n">datetime</span><span class="o">.</span><span class="n">strptime</span><span class="p">(</span><span class="n">event</span><span class="p">[</span><span class="s2">&quot;time&quot;</span><span class="p">],</span> <span class="s2">&quot;%Y-%m-</span><span class="si">%d</span><span class="s2">T%H:%M:%S.</span><span class="si">%f</span><span class="s2">&quot;</span><span class="p">)</span>
+        <span class="n">lat</span> <span class="o">=</span> <span class="n">event</span><span class="p">[</span><span class="s2">&quot;latitude&quot;</span><span class="p">]</span>
+        <span class="n">lng</span> <span class="o">=</span> <span class="n">event</span><span class="p">[</span><span class="s2">&quot;longitude&quot;</span><span class="p">]</span>
+        <span class="n">dep</span> <span class="o">=</span> <span class="n">event</span><span class="p">[</span><span class="s2">&quot;depth(m)&quot;</span><span class="p">]</span> <span class="o">/</span> <span class="mf">1e3</span>
+        <span class="n">mag</span> <span class="o">=</span> <span class="n">event</span><span class="p">[</span><span class="s2">&quot;magnitude&quot;</span><span class="p">]</span>
+        <span class="n">EH</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="n">EZ</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="n">RMS</span> <span class="o">=</span> <span class="n">event</span><span class="p">[</span><span class="s2">&quot;sigma_time&quot;</span><span class="p">]</span>
+
+        <span class="n">year</span><span class="p">,</span> <span class="n">month</span><span class="p">,</span> <span class="n">day</span><span class="p">,</span> <span class="n">hour</span><span class="p">,</span> <span class="nb">min</span><span class="p">,</span> <span class="n">sec</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="n">event_time</span><span class="o">.</span><span class="n">year</span><span class="p">,</span>
+            <span class="n">event_time</span><span class="o">.</span><span class="n">month</span><span class="p">,</span>
+            <span class="n">event_time</span><span class="o">.</span><span class="n">day</span><span class="p">,</span>
+            <span class="n">event_time</span><span class="o">.</span><span class="n">hour</span><span class="p">,</span>
+            <span class="n">event_time</span><span class="o">.</span><span class="n">minute</span><span class="p">,</span>
+            <span class="nb">float</span><span class="p">(</span><span class="n">event_time</span><span class="o">.</span><span class="n">strftime</span><span class="p">(</span><span class="s2">&quot;%S.</span><span class="si">%f</span><span class="s2">&quot;</span><span class="p">)),</span>
+        <span class="p">)</span>
+        <span class="n">event_line</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;# </span><span class="si">{</span><span class="n">year</span><span class="si">:</span><span class="s2">4d</span><span class="si">}</span><span class="s2"> </span><span class="si">{</span><span class="n">month</span><span class="si">:</span><span class="s2">2d</span><span class="si">}</span><span class="s2"> </span><span class="si">{</span><span class="n">day</span><span class="si">:</span><span class="s2">2d</span><span class="si">}</span><span class="s2"> </span><span class="si">{</span><span class="n">hour</span><span class="si">:</span><span class="s2">2d</span><span class="si">}</span><span class="s2"> </span><span class="si">{</span><span class="nb">min</span><span class="si">:</span><span class="s2">2d</span><span class="si">}</span><span class="s2"> </span><span class="si">{</span><span class="n">sec</span><span class="si">:</span><span class="s2">5.2f</span><span class="si">}</span><span class="s2">  </span><span class="si">{</span><span class="n">lat</span><span class="si">:</span><span class="s2">7.4f</span><span class="si">}</span><span class="s2"> </span><span class="si">{</span><span class="n">lng</span><span class="si">:</span><span class="s2">9.4f</span><span class="si">}</span><span class="s2">   </span><span class="si">{</span><span class="n">dep</span><span class="si">:</span><span class="s2">5.2f</span><span class="si">}</span><span class="s2"> </span><span class="si">{</span><span class="n">mag</span><span class="si">:</span><span class="s2">5.2f</span><span class="si">}</span><span class="s2"> </span><span class="si">{</span><span class="n">EH</span><span class="si">:</span><span class="s2">5.2f</span><span class="si">}</span><span class="s2"> </span><span class="si">{</span><span class="n">EZ</span><span class="si">:</span><span class="s2">5.2f</span><span class="si">}</span><span class="s2"> </span><span class="si">{</span><span class="n">RMS</span><span class="si">:</span><span class="s2">5.2f</span><span class="si">}</span><span class="s2"> </span><span class="si">{</span><span class="n">event</span><span class="p">[</span><span class="s1">&#39;event_index&#39;</span><span class="p">]</span><span class="si">:</span><span class="s2">9d</span><span class="si">}</span><span class="se">\n</span><span class="s2">&quot;</span>
+
+        <span class="c1"># output_lines.append(event_line)</span>
+        <span class="n">output_file</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">event_line</span><span class="p">)</span>
+
+        <span class="n">picks_idx</span> <span class="o">=</span> <span class="n">picks_by_event</span><span class="p">[</span><span class="n">event</span><span class="p">[</span><span class="s2">&quot;event_index&quot;</span><span class="p">]]</span>
+        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="n">picks_idx</span><span class="p">:</span>
+            <span class="c1"># pick = picks.iloc[j]</span>
+            <span class="n">pick</span> <span class="o">=</span> <span class="n">picks</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">j</span><span class="p">]</span>
+            <span class="n">network_code</span><span class="p">,</span> <span class="n">station_code</span><span class="p">,</span> <span class="n">comp_code</span><span class="p">,</span> <span class="n">channel_code</span> <span class="o">=</span> <span class="n">pick</span><span class="p">[</span><span class="s2">&quot;station_id&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;.&quot;</span><span class="p">)</span>
+            <span class="n">phase_type</span> <span class="o">=</span> <span class="n">pick</span><span class="p">[</span><span class="s2">&quot;phase_type&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">upper</span><span class="p">()</span>
+            <span class="n">phase_score</span> <span class="o">=</span> <span class="n">pick</span><span class="p">[</span><span class="s2">&quot;phase_score&quot;</span><span class="p">]</span>
+            <span class="n">pick_time</span> <span class="o">=</span> <span class="p">(</span><span class="n">datetime</span><span class="o">.</span><span class="n">strptime</span><span class="p">(</span><span class="n">pick</span><span class="p">[</span><span class="s2">&quot;phase_time&quot;</span><span class="p">],</span> <span class="s2">&quot;%Y-%m-</span><span class="si">%d</span><span class="s2">T%H:%M:%S.</span><span class="si">%f</span><span class="s2">&quot;</span><span class="p">)</span> <span class="o">-</span> <span class="n">event_time</span><span class="p">)</span><span class="o">.</span><span class="n">total_seconds</span><span class="p">()</span>
+            <span class="n">tmp_code</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">station_code</span><span class="si">}{</span><span class="n">channel_code</span><span class="si">}</span><span class="s2">&quot;</span>
+            <span class="n">pick_line</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">tmp_code</span><span class="si">:</span><span class="s2">&lt;7s</span><span class="si">}</span><span class="s2">   </span><span class="si">{</span><span class="n">pick_time</span><span class="si">:</span><span class="s2">6.3f</span><span class="si">}</span><span class="s2">   </span><span class="si">{</span><span class="n">phase_score</span><span class="si">:</span><span class="s2">5.4f</span><span class="si">}</span><span class="s2">   </span><span class="si">{</span><span class="n">phase_type</span><span class="si">}</span><span class="se">\n</span><span class="s2">&quot;</span>
+            <span class="c1"># output_lines.append(pick_line)</span>
+            <span class="n">output_file</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">pick_line</span><span class="p">)</span>
+
+    <span class="c1"># with open(hypodd_phase, &quot;w&quot;) as fp:</span>
+    <span class="c1">#     fp.writelines(output_lines)</span>
+
+    <span class="c1">## upload to s3 bucket</span>
+    <span class="k">try</span><span class="p">:</span>
+        <span class="kn">from</span> <span class="nn">minio</span> <span class="kn">import</span> <span class="n">Minio</span>
+
+        <span class="n">minioClient</span> <span class="o">=</span> <span class="n">Minio</span><span class="p">(</span><span class="n">s3_url</span><span class="p">,</span> <span class="n">access_key</span><span class="o">=</span><span class="s2">&quot;minio&quot;</span><span class="p">,</span> <span class="n">secret_key</span><span class="o">=</span><span class="s2">&quot;minio123&quot;</span><span class="p">,</span> <span class="n">secure</span><span class="o">=</span><span class="n">secure</span><span class="p">)</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">minioClient</span><span class="o">.</span><span class="n">bucket_exists</span><span class="p">(</span><span class="n">bucket_name</span><span class="p">):</span>
+            <span class="n">minioClient</span><span class="o">.</span><span class="n">make_bucket</span><span class="p">(</span><span class="n">bucket_name</span><span class="p">)</span>
+
+        <span class="c1"># minioClient.fput_object(</span>
+        <span class="c1">#     bucket_name,</span>
+        <span class="c1">#     f&quot;{config[&#39;region&#39;]}/hypodd/phase_{node_i:03d}.pha&quot;,</span>
+        <span class="c1">#     hypodd_phase,</span>
+        <span class="c1"># )</span>
+
+    <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">err</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;ERROR: can not access minio service! </span><span class="se">\n</span><span class="si">{</span><span class="n">err</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+        <span class="k">pass</span>
+
+    <span class="k">return</span> <span class="n">hypodd_phase</span>
+</pre></div>
+<div id="cell-34" class="clipboard-copy-txt">def convert_phase(
+    node_i: int,
+    config_json: InputPath("json"),
+    picks_csv: InputPath(str),
+    catalog_csv: InputPath(str),
+    hypodd_phase: OutputPath(str),
+    data_path: str = "./",
+    bucket_name: str = "catalogs",
+    s3_url: str = "minio-service:9000",
+    secure: bool = True,
+):
+
+    import json
+    import os
+    from datetime import datetime
+
+    import pandas as pd
+    from tqdm import tqdm
+
+    with open(config_json, "r") as fp:
+        config = json.load(fp)
+    hypodd_path = os.path.join(data_path, "hypodd")
+    if not os.path.exists(hypodd_path):
+        os.mkdir(hypodd_path)
+
+    picks = pd.read_csv(picks_csv)
+    events = pd.read_csv(catalog_csv)
+
+    if "MAXEVENT" in config["hypodd"]:
+        MAXEVENT = config["hypodd"]["MAXEVENT"]
+    else:
+        MAXEVENT = 1e4  ## segment by time
+    MAXEVENT = len(events) // ((len(events) - 1) // MAXEVENT + 1) + 1
+    num_parallel = int((len(events) - 1) // MAXEVENT + 1)
+
+    events.sort_values("time", inplace=True)
+    events = events.iloc[node_i::num_parallel]
+    picks = picks.loc[picks["event_index"].isin(events["event_index"])]
+    # output_lines = []
+    output_file = open(hypodd_phase, "w")
+
+    picks_by_event = picks.groupby("event_index").groups
+    # for i in tqdm(range(node_i, len(events), num_parallel)):
+    #     event = events.iloc[i]
+    for i, event in events.iterrows():
+        event_time = datetime.strptime(event["time"], "%Y-%m-%dT%H:%M:%S.%f")
+        lat = event["latitude"]
+        lng = event["longitude"]
+        dep = event["depth(m)"] / 1e3
+        mag = event["magnitude"]
+        EH = 0
+        EZ = 0
+        RMS = event["sigma_time"]
+
+        year, month, day, hour, min, sec = (
+            event_time.year,
+            event_time.month,
+            event_time.day,
+            event_time.hour,
+            event_time.minute,
+            float(event_time.strftime("%S.%f")),
+        )
+        event_line = f"# {year:4d} {month:2d} {day:2d} {hour:2d} {min:2d} {sec:5.2f}  {lat:7.4f} {lng:9.4f}   {dep:5.2f} {mag:5.2f} {EH:5.2f} {EZ:5.2f} {RMS:5.2f} {event['event_index']:9d}\n"
+
+        # output_lines.append(event_line)
+        output_file.write(event_line)
+
+        picks_idx = picks_by_event[event["event_index"]]
+        for j in picks_idx:
+            # pick = picks.iloc[j]
+            pick = picks.loc[j]
+            network_code, station_code, comp_code, channel_code = pick["station_id"].split(".")
+            phase_type = pick["phase_type"].upper()
+            phase_score = pick["phase_score"]
+            pick_time = (datetime.strptime(pick["phase_time"], "%Y-%m-%dT%H:%M:%S.%f") - event_time).total_seconds()
+            tmp_code = f"{station_code}{channel_code}"
+            pick_line = f"{tmp_code:<7s}   {pick_time:6.3f}   {phase_score:5.4f}   {phase_type}\n"
+            # output_lines.append(pick_line)
+            output_file.write(pick_line)
+
+    # with open(hypodd_phase, "w") as fp:
+    #     fp.writelines(output_lines)
+
+    ## upload to s3 bucket
+    try:
+        from minio import Minio
+
+        minioClient = Minio(s3_url, access_key="minio", secret_key="minio123", secure=secure)
+        if not minioClient.bucket_exists(bucket_name):
+            minioClient.make_bucket(bucket_name)
+
+        # minioClient.fput_object(
+        #     bucket_name,
+        #     f"{config['region']}/hypodd/phase_{node_i:03d}.pha",
+        #     hypodd_phase,
+        # )
+
+    except Exception as err:
+        print(f"ERROR: can not access minio service! \n{err}")
+        pass
+
+    return hypodd_phase</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[57]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-35">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">if</span> <span class="n">run_local</span><span class="p">:</span>
+    <span class="k">for</span> <span class="n">node_i</span> <span class="ow">in</span> <span class="n">nodes</span><span class="p">:</span>
+        <span class="n">convert_phase</span><span class="p">(</span>
+            <span class="n">node_i</span><span class="p">,</span>
+            <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;config.json&quot;</span><span class="p">),</span>
+            <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;gamma_picks.csv&quot;</span><span class="p">),</span>
+            <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;gamma_catalog.csv&quot;</span><span class="p">),</span>
+            <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;hypodd/hypodd_phase.pha&quot;</span><span class="p">),</span>
+            <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;&quot;</span><span class="p">),</span>
+        <span class="p">)</span>
+</pre></div>
+<div id="cell-35" class="clipboard-copy-txt">if run_local:
+    for node_i in nodes:
+        convert_phase(
+            node_i,
+            root_dir("config.json"),
+            root_dir("gamma_picks.csv"),
+            root_dir("gamma_catalog.csv"),
+            root_dir("hypodd/hypodd_phase.pha"),
+            root_dir(""),
+        )</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
+<pre>ERROR: can not access minio service! 
+HTTPSConnectionPool(host=&#39;minio-service&#39;, port=9000): Max retries exceeded with url: /catalogs?location= (Caused by NewConnectionError(&#39;&lt;urllib3.connection.HTTPSConnection object at 0x2976262e0&gt;: Failed to establish a new connection: [Errno 8] nodename nor servname provided, or not known&#39;))
+</pre>
+</div>
+</div>
+
+</div>
+
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[58]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-36">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">convert_phase_op</span> <span class="o">=</span> <span class="n">comp</span><span class="o">.</span><span class="n">func_to_container_op</span><span class="p">(</span>
+    <span class="n">convert_phase</span><span class="p">,</span>
+    <span class="n">base_image</span><span class="o">=</span><span class="s2">&quot;python:3.8&quot;</span><span class="p">,</span>
+    <span class="n">packages_to_install</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;pandas&quot;</span><span class="p">,</span> <span class="s2">&quot;tqdm&quot;</span><span class="p">,</span> <span class="s2">&quot;minio&quot;</span><span class="p">],</span>
+<span class="p">)</span>
+</pre></div>
+<div id="cell-36" class="clipboard-copy-txt">convert_phase_op = comp.func_to_container_op(
+    convert_phase,
+    base_image="python:3.8",
+    packages_to_install=["pandas", "tqdm", "minio"],
+)</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h3 id="73-run-ph2dt-to-calculate-differential-time-between-phases">7.3. Run ph2dt to calculate differential time between phases<a class="anchor-link" href="#73-run-ph2dt-to-calculate-differential-time-between-phases">&#182;</a></h3>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[59]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-37">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">def</span> <span class="nf">ph2dt</span><span class="p">(</span>
+    <span class="n">node_i</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
+    <span class="n">config_json</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="s2">&quot;json&quot;</span><span class="p">),</span>
+    <span class="n">hypodd_phase</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span>
+    <span class="n">station_dat</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span>
+    <span class="n">ct_file</span><span class="p">:</span> <span class="n">OutputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span>
+    <span class="n">hypodd_event</span><span class="p">:</span> <span class="n">OutputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span>
+    <span class="n">data_path</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;./&quot;</span><span class="p">,</span>
+    <span class="n">bucket_name</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;catalogs&quot;</span><span class="p">,</span>
+    <span class="n">s3_url</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;minio-service:9000&quot;</span><span class="p">,</span>
+    <span class="n">secure</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+<span class="p">):</span>
+
+    <span class="kn">import</span> <span class="nn">json</span>
+    <span class="kn">import</span> <span class="nn">os</span>
+    <span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">datetime</span>
+
+    <span class="kn">from</span> <span class="nn">minio</span> <span class="kn">import</span> <span class="n">Minio</span>
+
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">config_json</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">config</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">fp</span><span class="p">)</span>
+
+    <span class="n">hypodd_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">data_path</span><span class="p">,</span> <span class="s2">&quot;hypodd&quot;</span><span class="p">)</span>
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">hypodd_path</span><span class="p">):</span>
+        <span class="n">os</span><span class="o">.</span><span class="n">mkdir</span><span class="p">(</span><span class="n">hypodd_path</span><span class="p">)</span>
+
+    <span class="c1"># try:</span>
+    <span class="c1">#     minioClient = Minio(s3_url, access_key=&#39;minio&#39;, secret_key=&#39;minio123&#39;, secure=secure)</span>
+    <span class="c1">#     minioClient.fget_object(bucket_name, f&quot;{config[&#39;region&#39;]}/hypodd_{node_i:03d}.pha&quot;, os.path.join(hypodd_path, f&quot;hypodd_{node_i:03d}.pha&quot;))</span>
+    <span class="c1"># except Exception as err:</span>
+    <span class="c1">#     print(f&quot;ERROR: can not access minio service! \n{err}&quot;)</span>
+    <span class="c1">#     pass</span>
+
+    <span class="n">ph2dt</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;* ph2dt.inp - input control file for program ph2dt</span>
+<span class="s2">* Input station file:</span>
+<span class="s2">stations.dat</span>
+<span class="s2">* Input phase file:</span>
+<span class="s2">hypodd.pha</span>
+<span class="s2">*MINWGHT: min. pick weight allowed [0]</span>
+<span class="s2">*MAXDIST: max. distance in km between event pair and stations [200]</span>
+<span class="s2">*MAXSEP: max. hypocentral separation in km [10]</span>
+<span class="s2">*MAXNGH: max. number of neighbors per event [10]</span>
+<span class="s2">*MINLNK: min. number of links required to define a neighbor [8]</span>
+<span class="s2">*MINOBS: min. number of links per pair saved [8]</span>
+<span class="s2">*MAXOBS: max. number of links per pair saved [20]</span>
+<span class="s2">*MINWGHT MAXDIST MAXSEP MAXNGH MINLNK MINOBS MAXOBS</span>
+<span class="s2">   0      120     10     50     8      8     100</span>
+<span class="s2">&quot;&quot;&quot;</span>
+
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">hypodd_path</span><span class="p">,</span> <span class="s2">&quot;ph2dt.inp&quot;</span><span class="p">),</span> <span class="s2">&quot;w&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">fp</span><span class="o">.</span><span class="n">writelines</span><span class="p">(</span><span class="n">ph2dt</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">copy_file</span><span class="p">(</span><span class="n">fp_from</span><span class="p">,</span> <span class="n">fp_to</span><span class="p">):</span>
+        <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">fp_from</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+            <span class="n">lines</span> <span class="o">=</span> <span class="n">fp</span><span class="o">.</span><span class="n">readlines</span><span class="p">()</span>
+        <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">fp_to</span><span class="p">,</span> <span class="s2">&quot;w&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+            <span class="n">fp</span><span class="o">.</span><span class="n">writelines</span><span class="p">(</span><span class="n">lines</span><span class="p">)</span>
+
+    <span class="n">copy_file</span><span class="p">(</span><span class="n">hypodd_phase</span><span class="p">,</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">hypodd_path</span><span class="p">,</span> <span class="s2">&quot;hypodd.pha&quot;</span><span class="p">))</span>
+    <span class="n">copy_file</span><span class="p">(</span><span class="n">station_dat</span><span class="p">,</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">hypodd_path</span><span class="p">,</span> <span class="s2">&quot;stations.dat&quot;</span><span class="p">))</span>
+
+    <span class="n">PH2DT_CMD</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;cd </span><span class="si">{</span><span class="n">hypodd_path</span><span class="si">}</span><span class="s2"> &amp;&amp; ./ph2dt ph2dt.inp&quot;</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">PH2DT_CMD</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="n">PH2DT_CMD</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="p">(</span><span class="s2">&quot;</span><span class="si">{PH2DT_CMD}</span><span class="s2">&quot;</span> <span class="o">+</span> <span class="s2">&quot; failed!&quot;</span><span class="p">)</span>
+
+    <span class="n">copy_file</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">hypodd_path</span><span class="p">,</span> <span class="s2">&quot;dt.ct&quot;</span><span class="p">),</span> <span class="n">ct_file</span><span class="p">)</span>
+    <span class="n">copy_file</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">hypodd_path</span><span class="p">,</span> <span class="s2">&quot;event.sel&quot;</span><span class="p">),</span> <span class="n">hypodd_event</span><span class="p">)</span>
+
+    <span class="c1">## upload to s3 bucket</span>
+    <span class="k">try</span><span class="p">:</span>
+        <span class="kn">from</span> <span class="nn">minio</span> <span class="kn">import</span> <span class="n">Minio</span>
+
+        <span class="n">minioClient</span> <span class="o">=</span> <span class="n">Minio</span><span class="p">(</span><span class="n">s3_url</span><span class="p">,</span> <span class="n">access_key</span><span class="o">=</span><span class="s2">&quot;minio&quot;</span><span class="p">,</span> <span class="n">secret_key</span><span class="o">=</span><span class="s2">&quot;minio123&quot;</span><span class="p">,</span> <span class="n">secure</span><span class="o">=</span><span class="n">secure</span><span class="p">)</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">minioClient</span><span class="o">.</span><span class="n">bucket_exists</span><span class="p">(</span><span class="n">bucket_name</span><span class="p">):</span>
+            <span class="n">minioClient</span><span class="o">.</span><span class="n">make_bucket</span><span class="p">(</span><span class="n">bucket_name</span><span class="p">)</span>
+
+        <span class="c1"># minioClient.fput_object(</span>
+        <span class="c1">#     bucket_name,</span>
+        <span class="c1">#     f&quot;{config[&#39;region&#39;]}/hypodd/dt_{node_i:03d}.ct&quot;,</span>
+        <span class="c1">#     ct_file,</span>
+        <span class="c1"># )</span>
+
+        <span class="n">minioClient</span><span class="o">.</span><span class="n">fput_object</span><span class="p">(</span>
+            <span class="n">bucket_name</span><span class="p">,</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;region&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">/hypodd/event_</span><span class="si">{</span><span class="n">node_i</span><span class="si">:</span><span class="s2">03d</span><span class="si">}</span><span class="s2">.sel&quot;</span><span class="p">,</span>
+            <span class="n">hypodd_event</span><span class="p">,</span>
+        <span class="p">)</span>
+
+    <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">err</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;ERROR: can not access minio service! </span><span class="se">\n</span><span class="si">{</span><span class="n">err</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+        <span class="k">pass</span>
+
+    <span class="c1"># try:</span>
+    <span class="c1">#     minioClient.fput_object(</span>
+    <span class="c1">#         bucket_name,</span>
+    <span class="c1">#         f&quot;{config[&#39;region&#39;]}/dt_{node_i:03d}.ct&quot;,</span>
+    <span class="c1">#         f&quot;{os.path.join(hypodd_path, &#39;dt.ct&#39;)}&quot;,</span>
+    <span class="c1">#     )</span>
+    <span class="c1">#     minioClient.fput_object(</span>
+    <span class="c1">#         bucket_name,</span>
+    <span class="c1">#         f&quot;{config[&#39;region&#39;]}/event_{node_i:03d}.dat&quot;,</span>
+    <span class="c1">#         f&quot;{os.path.join(hypodd_path, &#39;event.dat&#39;)}&quot;,</span>
+    <span class="c1">#     )</span>
+    <span class="c1">#     minioClient.fput_object(</span>
+    <span class="c1">#         bucket_name,</span>
+    <span class="c1">#         f&quot;{config[&#39;region&#39;]}/event_{node_i:03d}.sel&quot;,</span>
+    <span class="c1">#         f&quot;{os.path.join(hypodd_path, &#39;event.sel&#39;)}&quot;,</span>
+    <span class="c1">#     )</span>
+    <span class="c1"># except Exception as err:</span>
+    <span class="c1">#     print(f&quot;ERROR: can not access minio service! \n{err}&quot;)</span>
+    <span class="c1">#     pass</span>
+
+    <span class="k">return</span> <span class="mi">0</span>
+</pre></div>
+<div id="cell-37" class="clipboard-copy-txt">def ph2dt(
+    node_i: int,
+    config_json: InputPath("json"),
+    hypodd_phase: InputPath(str),
+    station_dat: InputPath(str),
+    ct_file: OutputPath(str),
+    hypodd_event: OutputPath(str),
+    data_path: str = "./",
+    bucket_name: str = "catalogs",
+    s3_url: str = "minio-service:9000",
+    secure: bool = True,
+):
+
+    import json
+    import os
+    from datetime import datetime
+
+    from minio import Minio
+
+    with open(config_json, "r") as fp:
+        config = json.load(fp)
+
+    hypodd_path = os.path.join(data_path, "hypodd")
+    if not os.path.exists(hypodd_path):
+        os.mkdir(hypodd_path)
+
+    # try:
+    #     minioClient = Minio(s3_url, access_key='minio', secret_key='minio123', secure=secure)
+    #     minioClient.fget_object(bucket_name, f"{config['region']}/hypodd_{node_i:03d}.pha", os.path.join(hypodd_path, f"hypodd_{node_i:03d}.pha"))
+    # except Exception as err:
+    #     print(f"ERROR: can not access minio service! \n{err}")
+    #     pass
+
+    ph2dt = """* ph2dt.inp - input control file for program ph2dt
+* Input station file:
+stations.dat
+* Input phase file:
+hypodd.pha
+*MINWGHT: min. pick weight allowed [0]
+*MAXDIST: max. distance in km between event pair and stations [200]
+*MAXSEP: max. hypocentral separation in km [10]
+*MAXNGH: max. number of neighbors per event [10]
+*MINLNK: min. number of links required to define a neighbor [8]
+*MINOBS: min. number of links per pair saved [8]
+*MAXOBS: max. number of links per pair saved [20]
+*MINWGHT MAXDIST MAXSEP MAXNGH MINLNK MINOBS MAXOBS
+   0      120     10     50     8      8     100
+"""
+
+    with open(os.path.join(hypodd_path, "ph2dt.inp"), "w") as fp:
+        fp.writelines(ph2dt)
+
+    def copy_file(fp_from, fp_to):
+        with open(fp_from, "r") as fp:
+            lines = fp.readlines()
+        with open(fp_to, "w") as fp:
+            fp.writelines(lines)
+
+    copy_file(hypodd_phase, os.path.join(hypodd_path, "hypodd.pha"))
+    copy_file(station_dat, os.path.join(hypodd_path, "stations.dat"))
+
+    PH2DT_CMD = f"cd {hypodd_path} && ./ph2dt ph2dt.inp"
+    print(PH2DT_CMD)
+    if os.system(PH2DT_CMD) != 0:
+        raise ("{PH2DT_CMD}" + " failed!")
+
+    copy_file(os.path.join(hypodd_path, "dt.ct"), ct_file)
+    copy_file(os.path.join(hypodd_path, "event.sel"), hypodd_event)
+
+    ## upload to s3 bucket
+    try:
+        from minio import Minio
+
+        minioClient = Minio(s3_url, access_key="minio", secret_key="minio123", secure=secure)
+        if not minioClient.bucket_exists(bucket_name):
+            minioClient.make_bucket(bucket_name)
+
+        # minioClient.fput_object(
+        #     bucket_name,
+        #     f"{config['region']}/hypodd/dt_{node_i:03d}.ct",
+        #     ct_file,
+        # )
+
+        minioClient.fput_object(
+            bucket_name,
+            f"{config['region']}/hypodd/event_{node_i:03d}.sel",
+            hypodd_event,
+        )
+
+    except Exception as err:
+        print(f"ERROR: can not access minio service! \n{err}")
+        pass
+
+    # try:
+    #     minioClient.fput_object(
+    #         bucket_name,
+    #         f"{config['region']}/dt_{node_i:03d}.ct",
+    #         f"{os.path.join(hypodd_path, 'dt.ct')}",
+    #     )
+    #     minioClient.fput_object(
+    #         bucket_name,
+    #         f"{config['region']}/event_{node_i:03d}.dat",
+    #         f"{os.path.join(hypodd_path, 'event.dat')}",
+    #     )
+    #     minioClient.fput_object(
+    #         bucket_name,
+    #         f"{config['region']}/event_{node_i:03d}.sel",
+    #         f"{os.path.join(hypodd_path, 'event.sel')}",
+    #     )
+    # except Exception as err:
+    #     print(f"ERROR: can not access minio service! \n{err}")
+    #     pass
+
+    return 0</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[60]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-38">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">if</span> <span class="n">run_local</span><span class="p">:</span>
+    <span class="n">ph2dt</span><span class="p">(</span>
+        <span class="mi">0</span><span class="p">,</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;config.json&quot;</span><span class="p">),</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;hypodd/hypodd_phase.pha&quot;</span><span class="p">),</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;hypodd/stations.dat&quot;</span><span class="p">),</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;hypodd/dt.ct&quot;</span><span class="p">),</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;hypodd/event.sel&quot;</span><span class="p">),</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;&quot;</span><span class="p">),</span>
+    <span class="p">)</span>
+</pre></div>
+<div id="cell-38" class="clipboard-copy-txt">if run_local:
+    ph2dt(
+        0,
+        root_dir("config.json"),
+        root_dir("hypodd/hypodd_phase.pha"),
+        root_dir("hypodd/stations.dat"),
+        root_dir("hypodd/dt.ct"),
+        root_dir("hypodd/event.sel"),
+        root_dir(""),
+    )</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
+<pre>cd Demo/hypodd &amp;&amp; ./ph2dt ph2dt.inp
+starting ph2dt (v1.3 - 08/2010)...                          
+
+reading data ...
+ &gt; stations =           46
+ &gt; events total =          208
+ &gt; events selected =          208
+ &gt; phases =        10907
+forming dtimes...
+ &gt; P-phase pairs total =       101464
+ &gt; S-phase pairs total =       111906
+ &gt; outliers =         1855  (           0 %)
+ &gt; phases at stations not in station list =            0
+ &gt; phases at distances larger than MAXDIST =            0
+ &gt; P-phase pairs selected =        99279  (          97 %)
+ &gt; S-phase pairs selected =       110831  (          99 %)
+ &gt; weakly linked events =           80  (          38 %)
+ &gt; linked event pairs =         4953
+ &gt; average links per pair =           42
+ &gt; average offset (km) betw. linked events =    5.32930326    
+ &gt; average offset (km) betw. strongly linked events =    5.32930326    
+ &gt; maximum offset (km) betw. strongly linked events =    9.99880028    
+
+Done.                      
+
+Output files: dt.ct; event.dat; event.sel; ph2dt.log
+ph2dt parameters were: 
+ (minwght,maxdist,maxsep,maxngh,minlnk,minobs,maxobs)
+   0.00000000       120.000000       10.0000000              50           8           8         100
+ERROR: can not access minio service! 
+HTTPSConnectionPool(host=&#39;minio-service&#39;, port=9000): Max retries exceeded with url: /catalogs?location= (Caused by NewConnectionError(&#39;&lt;urllib3.connection.HTTPSConnection object at 0x2976366a0&gt;: Failed to establish a new connection: [Errno 8] nodename nor servname provided, or not known&#39;))
+</pre>
+</div>
+</div>
+
+</div>
+
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[61]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-39">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">ph2dt_op</span> <span class="o">=</span> <span class="n">comp</span><span class="o">.</span><span class="n">func_to_container_op</span><span class="p">(</span><span class="n">ph2dt</span><span class="p">,</span> <span class="n">base_image</span><span class="o">=</span><span class="s2">&quot;zhuwq0/hypodd-api:1.0&quot;</span><span class="p">)</span>
+</pre></div>
+<div id="cell-39" class="clipboard-copy-txt">ph2dt_op = comp.func_to_container_op(ph2dt, base_image="zhuwq0/hypodd-api:1.0")</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h3 id="74-run-hypodd-re-location">7.4. Run HypoDD re-location<a class="anchor-link" href="#74-run-hypodd-re-location">&#182;</a></h3>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[62]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-40">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">def</span> <span class="nf">hypodd_ct</span><span class="p">(</span>
+    <span class="n">node_i</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
+    <span class="n">config_json</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="s2">&quot;json&quot;</span><span class="p">),</span>
+    <span class="n">ct_file</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span>
+    <span class="n">event</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span>
+    <span class="n">station</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span>
+    <span class="n">catalog_txt</span><span class="p">:</span> <span class="n">OutputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span>
+    <span class="n">data_path</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;./&quot;</span><span class="p">,</span>
+    <span class="n">bucket_name</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;catalogs&quot;</span><span class="p">,</span>
+    <span class="n">s3_url</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;minio-service:9000&quot;</span><span class="p">,</span>
+    <span class="n">secure</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+<span class="p">):</span>
+    <span class="kn">import</span> <span class="nn">json</span>
+    <span class="kn">import</span> <span class="nn">os</span>
+    <span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">datetime</span>
+
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">config_json</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">config</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">fp</span><span class="p">)</span>
+    <span class="n">hypodd_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">data_path</span><span class="p">,</span> <span class="s2">&quot;hypodd&quot;</span><span class="p">)</span>
+
+    <span class="n">ct_inp</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;* RELOC.INP:</span>
+<span class="s2">*--- input file selection</span>
+<span class="s2">* cross correlation diff times:</span>
+
+<span class="s2">*</span>
+<span class="s2">*catalog P diff times:</span>
+<span class="s2">dt.ct</span>
+<span class="s2">*</span>
+<span class="s2">* event file:</span>
+<span class="s2">event.sel</span>
+<span class="s2">*</span>
+<span class="s2">* station file:</span>
+<span class="s2">stations.dat</span>
+<span class="s2">*</span>
+<span class="s2">*--- output file selection</span>
+<span class="s2">* original locations:</span>
+<span class="s2">hypodd.loc</span>
+<span class="s2">* relocations:</span>
+<span class="s2">hypodd.reloc</span>
+<span class="s2">* station information:</span>
+<span class="s2">hypodd.sta</span>
+<span class="s2">* residual information:</span>
+<span class="s2">hypodd.res</span>
+<span class="s2">* source paramater information:</span>
+<span class="s2">hypodd.src</span>
+<span class="s2">*</span>
+<span class="s2">*--- data type selection: </span>
+<span class="s2">* IDAT:  0 = synthetics; 1= cross corr; 2= catalog; 3= cross &amp; cat </span>
+<span class="s2">* IPHA: 1= P; 2= S; 3= P&amp;S</span>
+<span class="s2">* DIST:max dist [km] between cluster centroid and station </span>
+<span class="s2">* IDAT   IPHA   DIST</span>
+<span class="s2">    2     3     120</span>
+<span class="s2">*</span>
+<span class="s2">*--- event clustering:</span>
+<span class="s2">* OBSCC:    min # of obs/pair for crosstime data (0= no clustering)</span>
+<span class="s2">* OBSCT:    min # of obs/pair for network data (0= no clustering)</span>
+<span class="s2">* OBSCC  OBSCT    </span>
+<span class="s2">     0     8        </span>
+<span class="s2">*</span>
+<span class="s2">*--- solution control:</span>
+<span class="s2">* ISTART:  	1 = from single source; 2 = from network sources</span>
+<span class="s2">* ISOLV:	1 = SVD, 2=lsqr</span>
+<span class="s2">* NSET:      	number of sets of iteration with specifications following</span>
+<span class="s2">*  ISTART  ISOLV  NSET</span>
+<span class="s2">    2        2      4</span>
+<span class="s2">*</span>
+<span class="s2">*--- data weighting and re-weighting: </span>
+<span class="s2">* NITER: 		last iteration to used the following weights</span>
+<span class="s2">* WTCCP, WTCCS:		weight cross P, S </span>
+<span class="s2">* WTCTP, WTCTS:		weight catalog P, S </span>
+<span class="s2">* WRCC, WRCT:		residual threshold in sec for cross, catalog data </span>
+<span class="s2">* WDCC, WDCT:  		max dist [km] between cross, catalog linked pairs</span>
+<span class="s2">* DAMP:    		damping (for lsqr only) </span>
+<span class="s2">*       ---  CROSS DATA ----- ----CATALOG DATA ----</span>
+<span class="s2">* NITER WTCCP WTCCS WRCC WDCC WTCTP WTCTS WRCT WDCT DAMP</span>
+<span class="s2">   4     -9     -9   -9    -9   1     1      8   -9  70 </span>
+<span class="s2">   4     -9     -9   -9    -9   1     1      6    4  70 </span>
+<span class="s2">   4     -9     -9   -9    -9   1    0.8     4    2  70 </span>
+<span class="s2">   4     -9     -9   -9    -9   1    0.8     3    2  70 </span>
+<span class="s2">*</span>
+<span class="s2">*--- 1D model:</span>
+<span class="s2">* NLAY:		number of model layers  </span>
+<span class="s2">* RATIO:	vp/vs ratio </span>
+<span class="s2">* TOP:		depths of top of layer (km) </span>
+<span class="s2">* VEL: 		layer velocities (km/s)</span>
+<span class="s2">* NLAY  RATIO </span>
+<span class="s2">   12     1.82</span>
+<span class="s2">* TOP </span>
+<span class="s2">0.0 1.0 3.0 5.0 7.0 9.0 11.0 13.0 17.0 21.0 31.00 31.10</span>
+<span class="s2">* VEL</span>
+<span class="s2">5.30 5.65 5.93 6.20 6.20 6.20 6.20 6.20 6.20 6.20 7.50 8.11</span>
+<span class="s2">*</span>
+<span class="s2">*--- event selection:</span>
+<span class="s2">* CID: 	cluster to be relocated (0 = all)</span>
+<span class="s2">* ID:	cuspids of event to be relocated (8 per line)</span>
+<span class="s2">* CID    </span>
+<span class="s2">    0      </span>
+<span class="s2">* ID</span>
+<span class="s2">&quot;&quot;&quot;</span>
+
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">hypodd_path</span><span class="p">,</span> <span class="s2">&quot;ct.inp&quot;</span><span class="p">),</span> <span class="s2">&quot;w&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">fp</span><span class="o">.</span><span class="n">writelines</span><span class="p">(</span><span class="n">ct_inp</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">copy_file</span><span class="p">(</span><span class="n">fp_from</span><span class="p">,</span> <span class="n">fp_to</span><span class="p">):</span>
+        <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">fp_from</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+            <span class="n">lines</span> <span class="o">=</span> <span class="n">fp</span><span class="o">.</span><span class="n">readlines</span><span class="p">()</span>
+        <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">fp_to</span><span class="p">,</span> <span class="s2">&quot;w&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+            <span class="n">fp</span><span class="o">.</span><span class="n">writelines</span><span class="p">(</span><span class="n">lines</span><span class="p">)</span>
+
+    <span class="n">copy_file</span><span class="p">(</span><span class="n">ct_file</span><span class="p">,</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">hypodd_path</span><span class="p">,</span> <span class="s2">&quot;dt.ct&quot;</span><span class="p">))</span>
+    <span class="n">copy_file</span><span class="p">(</span><span class="n">event</span><span class="p">,</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">hypodd_path</span><span class="p">,</span> <span class="s2">&quot;event.sel&quot;</span><span class="p">))</span>
+    <span class="n">copy_file</span><span class="p">(</span><span class="n">station</span><span class="p">,</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">hypodd_path</span><span class="p">,</span> <span class="s2">&quot;stations.dat&quot;</span><span class="p">))</span>
+
+    <span class="c1"># os.system(f&quot;cat {ct_file}&quot;)</span>
+    <span class="c1"># os.system(f&quot;cat {event}&quot;)</span>
+    <span class="c1"># os.system(f&quot;cat {station}&quot;)</span>
+
+    <span class="n">HYPODD_CMD</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;cd </span><span class="si">{</span><span class="n">hypodd_path</span><span class="si">}</span><span class="s2"> &amp;&amp; ./hypoDD ct.inp&quot;</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">HYPODD_CMD</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="n">HYPODD_CMD</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="p">(</span><span class="s2">&quot;</span><span class="si">{HYPODD_CMD}</span><span class="s2">&quot;</span> <span class="o">+</span> <span class="s2">&quot; failed!&quot;</span><span class="p">)</span>
+
+    <span class="n">copy_file</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">hypodd_path</span><span class="p">,</span> <span class="s2">&quot;hypodd.reloc&quot;</span><span class="p">),</span> <span class="n">catalog_txt</span><span class="p">)</span>
+
+    <span class="k">try</span><span class="p">:</span>
+        <span class="kn">from</span> <span class="nn">minio</span> <span class="kn">import</span> <span class="n">Minio</span>
+
+        <span class="n">minioClient</span> <span class="o">=</span> <span class="n">Minio</span><span class="p">(</span><span class="n">s3_url</span><span class="p">,</span> <span class="n">access_key</span><span class="o">=</span><span class="s2">&quot;minio&quot;</span><span class="p">,</span> <span class="n">secret_key</span><span class="o">=</span><span class="s2">&quot;minio123&quot;</span><span class="p">,</span> <span class="n">secure</span><span class="o">=</span><span class="n">secure</span><span class="p">)</span>
+        <span class="n">minioClient</span><span class="o">.</span><span class="n">fput_object</span><span class="p">(</span>
+            <span class="n">bucket_name</span><span class="p">,</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;region&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">/hypodd/hypodd_ct_</span><span class="si">{</span><span class="n">node_i</span><span class="si">:</span><span class="s2">03d</span><span class="si">}</span><span class="s2">.reloc&quot;</span><span class="p">,</span>
+            <span class="n">catalog_txt</span><span class="p">,</span>
+        <span class="p">)</span>
+    <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">err</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;ERROR: can not access minio service! </span><span class="se">\n</span><span class="si">{</span><span class="n">err</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+        <span class="k">pass</span>
+</pre></div>
+<div id="cell-40" class="clipboard-copy-txt">def hypodd_ct(
+    node_i: int,
+    config_json: InputPath("json"),
+    ct_file: InputPath(str),
+    event: InputPath(str),
+    station: InputPath(str),
+    catalog_txt: OutputPath(str),
+    data_path: str = "./",
+    bucket_name: str = "catalogs",
+    s3_url: str = "minio-service:9000",
+    secure: bool = True,
+):
+    import json
+    import os
+    from datetime import datetime
+
+    with open(config_json, "r") as fp:
+        config = json.load(fp)
+    hypodd_path = os.path.join(data_path, "hypodd")
+
+    ct_inp = """* RELOC.INP:
+*--- input file selection
+* cross correlation diff times:
+
+*
+*catalog P diff times:
+dt.ct
+*
+* event file:
+event.sel
+*
+* station file:
+stations.dat
+*
+*--- output file selection
+* original locations:
+hypodd.loc
+* relocations:
+hypodd.reloc
+* station information:
+hypodd.sta
+* residual information:
+hypodd.res
+* source paramater information:
+hypodd.src
+*
+*--- data type selection: 
+* IDAT:  0 = synthetics; 1= cross corr; 2= catalog; 3= cross & cat 
+* IPHA: 1= P; 2= S; 3= P&S
+* DIST:max dist [km] between cluster centroid and station 
+* IDAT   IPHA   DIST
+    2     3     120
+*
+*--- event clustering:
+* OBSCC:    min # of obs/pair for crosstime data (0= no clustering)
+* OBSCT:    min # of obs/pair for network data (0= no clustering)
+* OBSCC  OBSCT    
+     0     8        
+*
+*--- solution control:
+* ISTART:  	1 = from single source; 2 = from network sources
+* ISOLV:	1 = SVD, 2=lsqr
+* NSET:      	number of sets of iteration with specifications following
+*  ISTART  ISOLV  NSET
+    2        2      4
+*
+*--- data weighting and re-weighting: 
+* NITER: 		last iteration to used the following weights
+* WTCCP, WTCCS:		weight cross P, S 
+* WTCTP, WTCTS:		weight catalog P, S 
+* WRCC, WRCT:		residual threshold in sec for cross, catalog data 
+* WDCC, WDCT:  		max dist [km] between cross, catalog linked pairs
+* DAMP:    		damping (for lsqr only) 
+*       ---  CROSS DATA ----- ----CATALOG DATA ----
+* NITER WTCCP WTCCS WRCC WDCC WTCTP WTCTS WRCT WDCT DAMP
+   4     -9     -9   -9    -9   1     1      8   -9  70 
+   4     -9     -9   -9    -9   1     1      6    4  70 
+   4     -9     -9   -9    -9   1    0.8     4    2  70 
+   4     -9     -9   -9    -9   1    0.8     3    2  70 
+*
+*--- 1D model:
+* NLAY:		number of model layers  
+* RATIO:	vp/vs ratio 
+* TOP:		depths of top of layer (km) 
+* VEL: 		layer velocities (km/s)
+* NLAY  RATIO 
+   12     1.82
+* TOP 
+0.0 1.0 3.0 5.0 7.0 9.0 11.0 13.0 17.0 21.0 31.00 31.10
+* VEL
+5.30 5.65 5.93 6.20 6.20 6.20 6.20 6.20 6.20 6.20 7.50 8.11
+*
+*--- event selection:
+* CID: 	cluster to be relocated (0 = all)
+* ID:	cuspids of event to be relocated (8 per line)
+* CID    
+    0      
+* ID
+"""
+
+    with open(os.path.join(hypodd_path, "ct.inp"), "w") as fp:
+        fp.writelines(ct_inp)
+
+    def copy_file(fp_from, fp_to):
+        with open(fp_from, "r") as fp:
+            lines = fp.readlines()
+        with open(fp_to, "w") as fp:
+            fp.writelines(lines)
+
+    copy_file(ct_file, os.path.join(hypodd_path, "dt.ct"))
+    copy_file(event, os.path.join(hypodd_path, "event.sel"))
+    copy_file(station, os.path.join(hypodd_path, "stations.dat"))
+
+    # os.system(f"cat {ct_file}")
+    # os.system(f"cat {event}")
+    # os.system(f"cat {station}")
+
+    HYPODD_CMD = f"cd {hypodd_path} && ./hypoDD ct.inp"
+    print(HYPODD_CMD)
+    if os.system(HYPODD_CMD) != 0:
+        raise ("{HYPODD_CMD}" + " failed!")
+
+    copy_file(os.path.join(hypodd_path, "hypodd.reloc"), catalog_txt)
+
+    try:
+        from minio import Minio
+
+        minioClient = Minio(s3_url, access_key="minio", secret_key="minio123", secure=secure)
+        minioClient.fput_object(
+            bucket_name,
+            f"{config['region']}/hypodd/hypodd_ct_{node_i:03d}.reloc",
+            catalog_txt,
+        )
+    except Exception as err:
+        print(f"ERROR: can not access minio service! \n{err}")
+        pass</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[63]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-41">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">if</span> <span class="n">run_local</span><span class="p">:</span>
+    <span class="n">hypodd_ct</span><span class="p">(</span>
+        <span class="mi">0</span><span class="p">,</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;config.json&quot;</span><span class="p">),</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;hypodd/dt.ct&quot;</span><span class="p">),</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;hypodd/event.sel&quot;</span><span class="p">),</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;hypodd/stations.dat&quot;</span><span class="p">),</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;hypodd_ct_catalog.txt&quot;</span><span class="p">),</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;&quot;</span><span class="p">),</span>
+    <span class="p">)</span>
+</pre></div>
+<div id="cell-41" class="clipboard-copy-txt">if run_local:
+    hypodd_ct(
+        0,
+        root_dir("config.json"),
+        root_dir("hypodd/dt.ct"),
+        root_dir("hypodd/event.sel"),
+        root_dir("hypodd/stations.dat"),
+        root_dir("hypodd_ct_catalog.txt"),
+        root_dir(""),
+    )</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
+<pre>cd Demo/hypodd &amp;&amp; ./hypoDD ct.inp
+ERROR: can not access minio service! 
+HTTPSConnectionPool(host=&#39;minio-service&#39;, port=9000): Max retries exceeded with url: /catalogs?location= (Caused by NewConnectionError(&#39;&lt;urllib3.connection.HTTPSConnection object at 0x297636490&gt;: Failed to establish a new connection: [Errno 8] nodename nor servname provided, or not known&#39;))
+</pre>
+</div>
+</div>
+
+</div>
+
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[64]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-42">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">hypodd_ct_op</span> <span class="o">=</span> <span class="n">comp</span><span class="o">.</span><span class="n">func_to_container_op</span><span class="p">(</span><span class="n">hypodd_ct</span><span class="p">,</span> <span class="n">base_image</span><span class="o">=</span><span class="s2">&quot;zhuwq0/hypodd-api:1.0&quot;</span><span class="p">)</span>
+</pre></div>
+<div id="cell-42" class="clipboard-copy-txt">hypodd_ct_op = comp.func_to_container_op(hypodd_ct, base_image="zhuwq0/hypodd-api:1.0")</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[65]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-43">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">if</span> <span class="n">run_local</span><span class="p">:</span>
+    <span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">datetime</span>
+
+    <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+    <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+
+    <span class="n">catalog_hypodd</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;hypodd/hypodd.reloc&quot;</span><span class="p">),</span>
+        <span class="n">sep</span><span class="o">=</span><span class="s2">&quot;\s+&quot;</span><span class="p">,</span>
+        <span class="n">names</span><span class="o">=</span><span class="p">[</span>
+            <span class="s2">&quot;ID&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;LAT&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;LON&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;DEPTH&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;X&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;Y&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;Z&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;EX&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;EY&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;EZ&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;YR&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;MO&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;DY&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;HR&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;MI&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;SC&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;MAG&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;NCCP&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;NCCS&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;NCTP&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;NCTS&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;RCC&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;RCT&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;CID&quot;</span><span class="p">,</span>
+        <span class="p">],</span>
+    <span class="p">)</span>
+    <span class="n">catalog_hypodd</span><span class="p">[</span><span class="s2">&quot;time&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">catalog_hypodd</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span>
+        <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">x</span><span class="p">[</span><span class="s2">&quot;YR&quot;</span><span class="p">]</span><span class="si">:</span><span class="s1">04.0f</span><span class="si">}</span><span class="s1">-</span><span class="si">{</span><span class="n">x</span><span class="p">[</span><span class="s2">&quot;MO&quot;</span><span class="p">]</span><span class="si">:</span><span class="s1">02.0f</span><span class="si">}</span><span class="s1">-</span><span class="si">{</span><span class="n">x</span><span class="p">[</span><span class="s2">&quot;DY&quot;</span><span class="p">]</span><span class="si">:</span><span class="s1">02.0f</span><span class="si">}</span><span class="s1">T</span><span class="si">{</span><span class="n">x</span><span class="p">[</span><span class="s2">&quot;HR&quot;</span><span class="p">]</span><span class="si">:</span><span class="s1">02.0f</span><span class="si">}</span><span class="s1">:</span><span class="si">{</span><span class="n">x</span><span class="p">[</span><span class="s2">&quot;MI&quot;</span><span class="p">]</span><span class="si">:</span><span class="s1">02.0f</span><span class="si">}</span><span class="s1">:</span><span class="si">{</span><span class="nb">min</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="s2">&quot;SC&quot;</span><span class="p">],</span><span class="w"> </span><span class="mf">59.999</span><span class="p">)</span><span class="si">:</span><span class="s1">05.3f</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">,</span>
+        <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+    <span class="p">)</span>
+    <span class="n">catalog_hypodd</span><span class="p">[</span><span class="s2">&quot;time&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">catalog_hypodd</span><span class="p">[</span><span class="s2">&quot;time&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">datetime</span><span class="o">.</span><span class="n">strptime</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="s2">&quot;%Y-%m-</span><span class="si">%d</span><span class="s2">T%H:%M:%S.</span><span class="si">%f</span><span class="s2">&quot;</span><span class="p">))</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">catalog_hypodd</span><span class="p">[</span><span class="s2">&quot;LON&quot;</span><span class="p">],</span> <span class="n">catalog_hypodd</span><span class="p">[</span><span class="s2">&quot;LAT&quot;</span><span class="p">],</span> <span class="s2">&quot;.&quot;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+<div id="cell-43" class="clipboard-copy-txt">if run_local:
+    from datetime import datetime
+
+    import matplotlib.pyplot as plt
+    import pandas as pd
+
+    catalog_hypodd = pd.read_csv(
+        root_dir(f"hypodd/hypodd.reloc"),
+        sep="\s+",
+        names=[
+            "ID",
+            "LAT",
+            "LON",
+            "DEPTH",
+            "X",
+            "Y",
+            "Z",
+            "EX",
+            "EY",
+            "EZ",
+            "YR",
+            "MO",
+            "DY",
+            "HR",
+            "MI",
+            "SC",
+            "MAG",
+            "NCCP",
+            "NCCS",
+            "NCTP",
+            "NCTS",
+            "RCC",
+            "RCT",
+            "CID",
+        ],
+    )
+    catalog_hypodd["time"] = catalog_hypodd.apply(
+        lambda x: f'{x["YR"]:04.0f}-{x["MO"]:02.0f}-{x["DY"]:02.0f}T{x["HR"]:02.0f}:{x["MI"]:02.0f}:{min(x["SC"], 59.999):05.3f}',
+        axis=1,
+    )
+    catalog_hypodd["time"] = catalog_hypodd["time"].apply(lambda x: datetime.strptime(x, "%Y-%m-%dT%H:%M:%S.%f"))
+    plt.figure()
+    plt.plot(catalog_hypodd["LON"], catalog_hypodd["LAT"], ".")
+    plt.show()</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+
+
+<div class="jp-RenderedImage jp-OutputArea-output ">
+<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAYcAAAD4CAYAAAAHHSreAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAnxElEQVR4nO3de5QfZZ3n8fenOxeIq0sPxplgSGIEMmsyGpI+gLsrs1xkgMOI3GZA17sGzoIzc3DPKs5sxLDuigOLzpHDHMjqMiygMQFxsqLCDIwyx4TtjsgkQMYQCTREDKFRY5x0uvu7f1T9QuV3v1+6P69z+qR/VfXU76lKdX3rudTzKCIwMzPL6ut0BszMrPs4OJiZWQEHBzMzK+DgYGZmBRwczMyswIxOZ6AZXv/618eiRYs6nQ0zs54yPDz8UkTMLbZuSgSHRYsWMTQ01OlsmJn1FEm7Sq1ztZKZmRVwcDAzswIODmZmVsDBwczMCjg4mJlZAQcHMzMr4OBg1qDhXaPc/NAOhneNdjorZk0zJd5zMOuU4V2jvHftJsbGJ5k1o487P3oKKxcOdDpbZg1zycGsAZt27mVsfJLJgIPjk2zaubfTWTJrCgcHswacsvhoZs3oo18wc0Yfpyw+utNZMmsKVyuZNWDlwgHu/OgpbNq5l1MWH+0qJZsyKpYcJB0h6VFJP5a0TdJn0+XXSnpe0mPpz7lF0i7JrH9M0i8l/Vml9JKukbRD0nZJf9DE4zVrupULB7jytOMcGGxKqabkcAA4PSL2SZoJPCLp/nTdTRFxQ6mEEbEdWA4gqR94Hrg3s0lBeklvAS4FlgLHAA9KOiEiJqo8JjMza1DFkkMk9qUfZ6Y/Ucd3nQE8HRElRwFMnQ98LSIORMRPgR3ASXV8n1nXaVa3V3eftVarqs0hfeofBo4Dbo6IzZLOAa6S9H5gCPhERJS7Ui8F7s5bViz9G4FNmW1G0mVmPa1Ut9fhXaM1tVm4+6y1Q1W9lSJiIiKWA/OBkyQtA24B3kxSbbQbuLFUekmzgHcB38gsLpVexbJQZJ+rJA1JGtqzZ081h2HWdLU8wWe7vY6l3V5zN/obv7ed967dVNV+7tkywoGDxbvPukRhzVJTb6WIeEXSw8DZ2bYCSbcBG8skPQfYEhEvZvZ16Pe89CPAsZm084EXiuTlVuBWgMHBwXqqucwaUusT/MCcWUymV+pkJJ+LvSdRbh/Du0b5xtBzh56W+vtf7T7rEoU1UzW9leZKOir9/UjgTOApSfMym10AbC2zm8vIq1Iqk/5bwKWSZkt6E3A88GilfJq1W60vwI3uHztULO5LP9f6nsSmnXsZTyOMgItXzj8UAPxCnjVTNSWHecDtabtDH7AuIjZKukPScpIqn2eAywEkHQOsjYhz089zgHfm1md8oVj6iNgmaR3wBDAOXOmeStaNcjf2g+OTVd3YT1l8NLNnHr59re9J5H/nRSvm150fs3IU0fs1MoODg+E5pK0T6mlMbvSFuXL7aMb+bfqQNBwRg0XXOTiYTS0OEFatcsHBw2eYTSFulLZm8cB7ZlOIG6WtWRwczKYQjxJrzeJqJbMppFjvJ7dBWD0cHMymmJULBw4FAbdBWL1crWRWRi3DUXTj0BX5bRAbtox0XR6tO7nkYFZCLU/d3fqEnn0xrr9PrB8eYXyiu/Jo3cklB7MSaun50629hHJtEFeftYRLBo9lfCLJ44GDSSnCrBQHB7MSaun5k7/twJxZba2+KVellZup7sIV85nRl4zuFMD64RFXL1lJrlYyK6GWcY+y2w7MmcWajdvqqmKqp2dRtVVaKxcOcMngsdy1+VkCmJgoHAXWPZssx8HBrIxsz59i7tr8LPdv3c05y+bxnpMXsHLhADc/tKOmYbhz6m23qDTsd/aGf+GK+WzYMlJ0cL5ubTexznBwMKvTXZuf5dP3/hMAP/jJSwC85+QFdY+OWuvcDjm57xs7OIkkBubMOrSu2A2/VGmo3u+3qcltDmZ1un/r7qKfs43AtTx91/t288qFA6w+byl9fWIygjUbtx1qSyh1w7/ytOMK8lXv93djF15rnEsOZnU6Z9m8QyWG3OecStVRxeSCyj1bRgrnxa1gdP8YkxEFQaCWUkytc0uAq6KmMgcHsyKqaZh9z8kLAA5rc2iGDVtGGBuf5J4tI1XfbEsFgVpv+LUGNVdFTV0ODjbt5QeCWp6G33PygqYFBaj/ZlsuCNRTiqmWZ5+buhwcbForFghq6f3T7JtuIzfbaoPA8K7RQ1VXF62Y39Ax1FMVZb3BwcGmtWKBoNwNutV17K2+2Q7vGuWy25L8A6wfeo67V7294QDhoDD1VAwOko4Avg/MTrdfHxGfkXQt8DFgT7rppyPi23lplwBfzyxaDKyOiC9K+kvgD4Ex4GngQxHxiqRFwJPA9jTNpoi4os7jMysq9/Q/MGdWQSAod4NuRx17K2+2m3bu5WAaGAAOTkTDx+AX56amakoOB4DTI2KfpJnAI5LuT9fdFBE3lEoYEduB5QCS+oHngXvT1Q8A10TEuKTrgWuAT6brno6I5bUejFk18p/+V5+3lNH9Y4fd3IrdoId3jfL8K79hRn8fExO9Wcd+yuKjmTmj71DJYWa/GjoG91aauioGh4gIYF/6cWb6U2tPO4AzSG76u9L9fi+zbhNwcR37NKtZ/tP/6P4xrjztuLJpsjfBGX3i0pMWcGGD9fWdsHLhAHd/7JSibQ7VlADy2yvcW2nqqqrNIX3qHwaOA26OiM2SzgGukvR+YAj4RESUewvmUuDuEus+zOHVT2+S9CPgl8BfRMQPiuRpFbAKYMGC5vUWsamvnkbf7E1wYjI45qgje/YmWKpUVKkEUKy94tp3LXNvpSmqquAQERPAcklHAfdKWgbcAlxHUoq4DriR5CZfQNIs4F0kVUf56/4cGAfuTBftBhZExF5JK4FvSloaEb/My9OtwK0Ag4OD9ZRkbJqqp9F3qnfZrKYEUKy9YnT/mHsrTVE19VZKG4wfBs7OtjVIug3YWCbpOcCWiHgxu1DSB4DzgDPS6isi4gBJOwcRMSzpaeAEktKJWVPU2uhbTUDp5YbZaoJfqfYK91aamqrprTQXOJgGhiOBM4HrJc2LiNzgMhcAW8vs5jLyqpQknU3SAP37EbE/7/tejogJSYuB44GdtRyUWSuUuwl2qmG2WQGpmuBXrr3Cpp5qSg7zgNvTdoc+YF1EbJR0h6TlJNVKzwCXA0g6BlgbEeemn+cA78ytz/gySffYByTBq11WTwXWSBoHJoArIuLlho7SrMWKzdXc6lJEswNSpRJALhD1YkO81a6a3kqPAycWWf6+Etu/AJyb+bwfKCijRkTR7iERsQHYUClfZt2kE3M1t7OnkLusTj8estusCUrN1dzK+aTrHWK7Ht06R7a1jofPMGuSXLXM8K7RkrOtNfv72tVTaKr31rJCSjsJ9bTBwcEYGnJnJusevdxzqZSpeEzTnaThiBgsts4lB7MWqLZxt5dutO6yOr04OJi1mRt3m6cXg2wztOO4HRzM2szjERVX6w1vKgbZase3asdxOziYtZkbdwvVc8OrJsj2Usmi2nPQrocLBwezNvPsaYXqueFVCrLdVLKoJkhVew7a9XDh4GDWAW7cPVwtN7zsjTY/yGbXdUv1XbVBqtpz0K6HCwcHM+u4am94xW60ubk4suv6BIuOfs2hdJMBjz33CsO7RtseIGqp/lp93lK2vvALVGGf7Xi4cHAws65QzQ2v3I120869HDg4SZAEgx17fn0oXQAPPPEif//ki1z37t/jPSe3bw6YWqq/ZvQJJMYnkvG5OlkV5uBgZj2j3I12YM6silNUTgT8xTf/iSW/89q23XQrlYoOC3gTAQRB53uyOTiYWc8od6Md3T9Gn5JSQzmTAdff/yTrrvi3Lc7tq8qVivIHbUTqijnKHRzMrKeUutHmbrK5qqVy/t+u0Y60PxSzcuEAq89byv1bd3POsnks+Z3XdkVPNo/KamZdb3jXKDc/tIPhXaWnqc+VKi47eQEz+ys06QZtH1m21DEM7xplzcZt/OOOl1izcRsAV552XMcDl0sOZtbVanlfIVequGjFfDbt3MuvfnOQtY/8lMkIlKtyCpg9s71VNuWOoVu63OZzcDCzrlbPzTNb9fTOpb9zqJomt79K3WWbXa1T7hi69Y15Bwcz62qN3jzz2ygqjZbbireqyx1Dt74x7+BgZl2tnTfPVlXxVDqGbnxjvmJwkHQE8H1gdrr9+oj4jKRrgY8Be9JNPx0R385LuwT4embRYmB1RHxR0m+l6xYBzwB/FBGjabprgI8AE8CfRMR36z1AM+t92ZtnKwfTa2UVTzcGgHIqzgQnScBrImKfpJnAI8CfAmcD+yLihqq+SOoHngdOjohdkr4AvBwRn5f0KWAgIj4p6S3A3cBJwDHAg8AJETFRat+eCc5semjHYHq9NJJro8rNBFexK2sk9qUfZ6Y/9cwtegbwdETsSj+fD9ye/n478O7M8q9FxIGI+CmwgyRQmNk0NrxrlC8++M8cOHh4tU+zrVw4UHVX0mq62Paqqtoc0qf+YeA44OaI2CzpHOAqSe8HhoBP5KqFSriUpESQ89sRsRsgInZLekO6/I3Apsx2I+my/DytAlYBLFjQvnFSzKz9siWGIHmq7XTPnvxSzOrzljK6f2zKlDiqCg5plc5ySUcB90paBtwCXEdSirgOuBH4cLH0kmYB7wKuqeLrir29UlBSiYhbgVshqVaqYr9m1qOyDcV9gn933Ov5szNP6OhNOJunsfFJVt+3lcmIKRMoauqtFBGvSHoYODvb1iDpNmBjmaTnAFsi4sXMshclzUtLDfOAn6fLR4BjM9vNB16oJZ9mNrXkNxR3OjDk50kSE5PJgHljBw8PFL06fWnFNgdJc9MSA5KOBM4Enkpv6DkXAFvL7OYyDq9SAvgW8IH09w8A92WWXypptqQ3AccDj1bKp5lNXbmuoFeftaRrbrbZPK05fxmzZ/bRL+jrE5MRJdtFeqWdopqSwzzg9rTdoQ9YFxEbJd0haTlJlc8zwOUAko4B1kbEuennOcA7c+szPg+sk/QR4FngEoCI2CZpHfAEMA5cWa6nkplND93YFTSbp9yAeQNzZrFm47ai3WG7aerSSioGh4h4HDixyPL3ldj+BeDczOf9QEGrUUTsJenBVGwfnwM+VylvZmbdoligyG9z6NZxlIrxG9Jm1hLT6X2BfJWGFe+2cZSKcXAws6brpeqTdurWcZSKcXAws6brpeqTclpR+unGtpNiHBzMrOl6qfqklGpKP1O56szBwcyarpeqT0qpVPq5a/OzrL5vKxOTweyZU6/qzMHBzFqim6pP6nnCz5V+xg4mL7kNzJl12P5W37eV8clkcIaxg71bdVaK55A2s55VzQtlw7tGuey2Tdzw3e1cdtumql8+W7lwgNXnLT30UtuajdsOpd20cy+TmRGt+/rUk1Vn5bjkYGY9qdoeUfdsGWFsfBJIxkC6Z8tIxdngNmwZQSRv+Oa/7bxy4cCrpYrxSfok1py/bEqVGsDBwcx6VLU9ovJH5Sw3SufwrlEuu/WHjE0kW/X3CZEM9pdtWJ8KbSqVODiYWU+qtkfURSvms37oOQ5OBDP7xUUr5pfc56adezk48Wr4mEjbFGb0idXnLS2Yi3oqBoUcBwcz60nVPr2vXDjA3aveXtVT/imLj2Zmvw6VHHIigtH9Y03Nf7dzcDCznlXt03st29296u1s2DLCS786wMPbf87EZPTsuxqNcHAwM8vIBpKp/JJbJQ4OZtYz2n2znurtCuU4OJhZT/Bgfu3l4GBmPaFY19Xc8ulY7dNqDg5m1hStrPIZ3jXKC6/8hhl9OtRAPDBnlksSLeTgYGYNa2WVT274i4Pjk8zoF3980gIuWjG/p4YF78WGbQcHM2tYK2/U2eEvDk4EgkP77sZhwfMDQa+2lVQMDpKOAL4PzE63Xx8Rn5F0LfAxYE+66acj4ttF0h8FrAWWkby5/uGI+KGkrwNL0s2OAl6JiOWSFgFPAtvTdZsi4oq6js7MqtLok20r528oNfxFNw5hUSwQZAPngYOTbKgwtlO3qKbkcAA4PSL2SZoJPCLp/nTdTRFxQ4X0XwK+ExEXS5oFzAGIiD/ObSDpRuAXmTRPR8Tyag/CzOrXjCfbVt6oyw1/0W1dTYuVoE5ZfDQz+pK3rgNYPzzCRSvmV8x3p6uiKgaHiAhgX/pxZvpTbuyqQyS9DjgV+GC6rzFgLG8bAX8EnF5tps3ardN/qK3UrCqhZt6o8893tcNfdFqxEtTKhQNcMngsd21+lgAmJiqf42IBG9rbM6uqNgdJ/cAwcBxwc0RslnQOcJWk9wNDwCciIn+g9MUk1U5flfS2dB9/GhG/zmzzDuDFiPhJZtmbJP0I+CXwFxHxgyJ5WgWsAliwYEE1h2FWl1r/UHstkDRaJdTs480/36vPW8ro/rGeOJ+lSlAXrpjPhi0jVZ/j/IC9YcvIobaXdrVbVBUcImICWJ62H9wraRlwC3AdSSniOuBG4MNF9r8C+HgaUL4EfAr4r5ltLgPuznzeDSyIiL2SVgLflLQ0In6Zl6dbgVsBBgcHqyrJmNUj/w/1ni0jbCjxh9qLjY+NVAm14niz53tsfJLV921lMqKnzmd+Hms9x/kBW1C0dNfKB5GaeitFxCuSHgbOzrY1SLoN2FgkyQgwEhGb08/rSYJDLt0M4EJgZeY7DpC0cxARw5KeBk4gKZ2YtV3+H2pQ/A8VWttrp5XqrRJqxfFmz7eUvNcQ9Nb5LKaWc5wfTICCkkerH0Sq6a00FziYBoYjgTOB6yXNi4jd6WYXAFvz00bEzyQ9J2lJRGwHzgCeyGxyJvBURIzkfd/LETEhaTFwPLCz3gM0a1SxP9R7SlQRtLLXTjdqxfFmz/fAnFms2bit5P57rQqvFvnBJL/kcfNDO1r6IKKI8jUykt4K3A70k8w5vS4i1ki6A1hOUq30DHB5ROyWdAywNiLOTdMvJ+nKOovkJv+hXNuEpP9N0lX1rzPfdxGwBhgHJoDPRMTflsvj4OBgDA25YGGNqeVGU27bqXzDKqbVx1tq/71YhddMuePPBc56jl/ScEQMFl1XKTj0AgcHa9R0v9H0opsf2sGN39vOZEC/4OqzlnDlacd1Oltt1WhgLhcc/Ia0Gb3bVjCVVbrxTbcqvGJa+Z6Hg4MZvtFUo53VZdWU5LrxDempxMHBDN9oKqml2q0ZQaTakly3vSE9lTg4mKV8oymt2pt1s9puXJLrPAcHM6uo2pt1M4ficEmusxwczKyiam/WzXzid0mus9yV1WwaaHdjcvaFQT/9dy93ZTWbxtr9Dkfuib/djdjWXA4OZlNcp97haHcjtjVXX6czYGbJDfLmh3YwvCt/1PvG5doB+kVbe/5U+73Fgoh1nksOZh3W6JNzpSqZSo3JrarS6UQjtjWPg4NZhzVS7VNtYCnV8+euzc+WnC+hGUGjmh5H7rbanRwczDqskSfnRgPL6vu2Mj6Z9Fgcy5tEphON2NY9HBzMOqyRJ+dGA8vE5Ktd2fukw7qfdqIR272WuoeDg1kXqPfJuZHAMjBnFv19yUxr/X1izfnLDqXvRDuAey11FwcHsx5XT2AZ3jXKmo3bDgsM7zl5wWH7bHc7gIdN7y4ODmbTUO5GHEBEMLp/rGCbdrcDuNdSd3FwMJuGuvFG7F5L3aWaOaSPAL4PzCYJJusj4jOSrgU+BuxJN/10RHy7SPqjSOaQXkYy3/SHI+KH5dJLugb4CMkc0n8SEd8tl0ePrWRWWrk5mH0jnt4aHVvpAHB6ROyTNBN4RNL96bqbIuKGCum/BHwnIi6WNAuYk1lXkF7SW4BLgaXAMcCDkk6IiIkq8mpmGeUaed191MqpOHxGJPalH2emP1UN5SrpdcCpwP9K9zUWEa9USHY+8LWIOBARPwV2ACdV831mdjgPTWH1qmpsJUn9kh4Dfg48EBGb01VXSXpc0lckFXsEWUxSbfRVST+StFbSazLri6V/I/BcZpuRdFl+nlZJGpI0tGfPnvzVZkbnxlWy3ldVcIiIiYhYDswHTpK0DLgFeDOwHNgN3Fgk6QxgBXBLRJwI/Br4VLquVHoVy0KRPN0aEYMRMTh37txqDsNs2sk18l591hK/N2A1qWlU1rRK6GHg7Ih4MQ0ak8BtFK/6GQFGMiWN9STBgjLpR4BjM/uYD7xQSz6ni1aO5Gmv6vXzvHLhAFeedpwDg9WkYoO0pLnAwYh4RdKRwJnA9ZLmRcTudLMLgK35aSPiZ5Kek7QkIrYDZwBPpPstlf5bwF2S/idJg/TxwKP1H+LU5LdJ28Pn2aaranorzQNul9RPUtJYFxEbJd0haTlJlc8zwOUAko4B1kbEuWn6jwN3pj2VdgIfSpd/oVj6iNgmaR1JEBkHrnRPpUJ+m7Q9fJ5tuqoYHCLiceDEIsvfV2L7F4BzM58fAwr60ZZKn677HPC5SnmbzrrxJaapqBvPs99PsHbwG9I9KtfQuGHLSNEWfGuObntr19Vc1i4ODj3uni0jjI1PsmHLiG8ULdJNL4u5msvaxXNI97D8G8WGLSM93atmKivV46nWnlB+b8HaxSWHHpatD+/vE+uHRxifcHVDtylVFVRPFVG3VXPZ1OWSQw/LvuB0yeCxjE94mIRuVGoIi3qHtvB7C9YODg49LnejuHDFfFc3dKlSVUGuIrJuVnHI7l7gIbsT7uLYvTxstnWjckN2OziYmU1T5YKDq5XMzKyAg4OZmRVwcDAzswIODmZmVsDBwczMCjg4mJlZAQcHMzMr4OBgZmYFHBzMzKyAg4OZmRVwcLCWqnW+AjPrDhWDg6QjJD0q6ceStkn6bLr8WknPS3os/Tm3RPqjJK2X9JSkJyW9PV3+l+myxyXdK+modPkiSb/J7Pevm3i81ka5+Qpu/N523rt2kwOEWQ+ppuRwADg9It4GLAfOlnRKuu6miFie/ny7RPovAd+JiN8F3gY8mS5/AFgWEW8F/hm4JpPm6cx+r6jxmKzFqi0N1DtfgZl1XsWZ4CIZtnVf+nFm+lPVUK6SXgecCnww3dcYMJb+/r3MppuAi6vNtHVOLbOXZWeq83wFZr2lqjYHSf2SHgN+DjwQEZvTVVel1UJfkVTsDrEY2AN8VdKPJK2V9Joi230YuD/z+U3p9v8g6R0l8rRK0pCkoT179lRzGNYEtZQGsjPVedpSs95SVXCIiImIWA7MB06StAy4BXgzSVXTbuDGIklnACuAWyLiRODXwKeyG0j6c2AcuDNdtBtYkG5/NXBXWgLJz9OtETEYEYNz586t5jCsCWqdvcxTWpr1porVSlkR8Yqkh4GzI+KG3HJJtwEbiyQZAUYyJY31ZIKDpA8A5wFnpNVXRMQBknYOImJY0tPACYBn8+kCnuDebHqoGBwkzQUOpoHhSOBM4HpJ8yJid7rZBcDW/LQR8TNJz0laEhHbgTOAJ9L9ng18Evj9iNif930vR8SEpMXA8cDOxg7TalFp6sqVCwccFMymuGpKDvOA2yX1k1RDrYuIjZLukLScpHH6GeByAEnHAGsjIte19ePAnZJmkdzkP5Qu/zIwG3hAEsCmtGfSqcAaSePABHBFRLzc8JFaVWppcDazqaua3kqPAycWWf6+Etu/AJyb+fwYUDBHaUQcVyL9BmBDpXxZaxRrcHZwMJt+/Ia0HabWBmczm5pqapC2qc8NzmYGDg5WhBuczczVSmZmVsDBwczMCjg4mJlZAQcHMzMr4OBgZmYFHBysozxTnFl3cldW6xgP1WHWvVxysI7xTHFm3cvBwTqm1FAdrmoy6zxXK1nHFBuqw1VNZt3BwcE6Kn+oDo8Ka9YdXK1kXcWjwpp1B5ccrKt4VFiz7uDgYGVVmjK0FTwqrFnnOThYSW4cNpu+3OZgJWUbhw8cnOSeLSOdzpKZtUnF4CDpCEmPSvqxpG2SPpsuv1bS85IeS3/OLZH+KEnrJT0l6UlJb0+X/5akByT9JP13IJPmGkk7JG2X9AfNOlirzSmLj2ZGf3KJBPCNoecK3j1o9jsJfsfBrDtUU610ADg9IvZJmgk8Iun+dN1NEXFDhfRfAr4TERdLmgXMSZd/Cvi7iPi8pE+lnz8p6S3ApcBS4BjgQUknRMREjcdmZVTTlrBy4QAXr5zP3ZufJYCJyTj0FvOmnXv51W8OsvaRnzIZ0ZRqJ1djmXWPisEhIgLYl36cmf5ENTuX9DrgVOCD6b7GgLF09fnAf0h/vx14GPhkuvxrEXEA+KmkHcBJwA+r+U6rrJab8EUr5nPPlhEOjk8yc0YfA3NmHUo7mbkKxprwToLfcTDrHlW1OUjql/QY8HPggYjYnK66StLjkr6SrRbKWAzsAb4q6UeS1kp6TbrutyNiN0D67xvS5W8EnsvsYyRdlp+nVZKGJA3t2bOnmsOwVC1jGuW6ll591hLu/OgpjO4fKwgMAH1Sw+8k+B0Hs+5RVXCIiImIWA7MB06StAy4BXgzsBzYDdxYJOkMYAVwS0ScCPyapPqoHBXLQpE83RoRgxExOHfu3GoOw1K13oRXLhzgytOOY+XCgaQdou/w/6J+wZrzlzX8lJ8fiFxqMOucmrqyRsQrkh4Gzs62NUi6DdhYJMkIMJIpaazn1eDwoqR5EbFb0jySUkkuzbGZfcwHXqgln1ZeIy+arVw4wCWDx3JX2g4h4NKTFvCekxc0LW8OCmadV01vpbmSjkp/PxI4E3gqvaHnXABszU8bET8DnpO0JF10BvBE+vu3gA+kv38AuC+z/FJJsyW9CTgeeLSWg7LKsqWBWl24Yj6zZyYlj9kz+7hwxfwW5NDMOqmaksM84HZJ/STBZF1EbJR0h6TlJFU+zwCXA0g6BlgbEbmurR8H7kx7Ku0EPpQu/zywTtJHgGeBSwAiYpukdSRBZBy40j2VuouHuDCb+pR0Ruptg4ODMTQ01Ols9LxODJVhZp0jaTgiBout8/AZBvgdAzM7nIfPMMBTdprZ4RwcDPA7BmZ2OFcrGeBGZjM7nIODHeJ3DMwsx9VKZmZWwMHBzMwKODiYmVkBBwczMyvg4GBmZgUcHMzMrICDg5mZFXBwMDOzAg4OZmZWwMHBzMwKODi00fCuUW5+aAfDu0Y7nRUzs7I8tlKbeL4EM+sl077k0K6nec+XYGa9pGLJQdIRwPeB2en26yPiM5KuBT4G7Ek3/XREfLtI+meAXwETwHhuSjpJXweWpJsdBbwSEcslLQKeBLan6zZFxBX1HFwl7Xyaz82XcHB80vMlmFnXq6Za6QBwekTskzQTeETS/em6myLihir2cVpEvJRdEBF/nPtd0o3ALzKrn46I5VXstyHFnuZbFRw8X4KZ9ZKKwSEiAtiXfpyZ/kSzMiBJwB8Bpzdrn9Vq99O850sws15RVZuDpH5JjwE/Bx6IiM3pqqskPS7pK5JK3fUC+J6kYUmriqx/B/BiRPwks+xNkn4k6R8kvaNEnlZJGpI0tGfPnmKbVJR7mr/6rCVuIDYzy1BSMKhyY+ko4F7g4yRtDS+R3PyvA+ZFxIeLpDkmIl6Q9AbgAeDjEfH9zPpbgB0RcWP6eTbwryJir6SVwDeBpRHxy1L5GhwcjKGhoaqPw8zMQNJwrh04X029lSLiFeBh4OyIeDEiJiJiErgNOKlEmhfSf39OElgObSdpBnAh8PXM9gciYm/6+zDwNHBCLfk0M7PGVAwOkuamJQYkHQmcCTwlaV5mswuArUXSvkbSa3O/A2flbXcm8FREjOR9X3/6+2LgeGBnjcdlZmYNqKa30jzg9vSG3Qesi4iNku6QtJykWukZ4HJIqpGAtRFxLvDbwL1JmzMzgLsi4juZfV8K3J33facCaySNk3R/vSIiXq7z+MzMrA41tTl0K7c5mJnVrmltDmZmNj04OJiZWYEpUa0kaQ/wa5Kutb3g9fRGXnsln+C8tkKv5BN6J6/dls+FETG32IopERwAJA2VqjvrNr2S117JJzivrdAr+YTeyWuv5BNcrWRmZkU4OJiZWYGpFBxu7XQGatAree2VfILz2gq9kk/onbz2Sj6nTpuDmZk1z1QqOZiZWZM4OJiZWaGI6PgPcAmwDZgEBjPLjwYeIpls6MuZ5a8FHsv8vAR8scS+3wr8MN3/PwFHpMsfJpmKNLePN6TLZ5OMErsD2Aws6mRegTnA/wWeSpd/PrP9B0mGTs/t+6NdcF5Xpp93AH/Fq1WXJc9rq/IJLAJ+k9nuryul79Q5LZXXeq/VDpzTrrtOK5zTmq/TVv9NpdsvSPfxnxu9Vhu+LzdzZ3VnAv4NyXzSD+ed8NcA/x64InvCi6QfBk4tsnwG8Djwtsx/YH/mD26wSJr/lLngLwW+3sm8kvzRnZYumwX8ADgnc3GU+65OnNdHgbcDAu7P5LXkeW1hPhcBW6u4/g6l7+A5LZnXeq7VduezS6/Tcue05uu0lXnNrN8AfIM0ODRyrTb60xXVShHxZERsL7L81xHxCPAvpdJKOh54A8nFmO8s4PGI+HG6v70RMVEhO+cDt6e/rwfOSKcy7UheI2J/RDyULhsDtgDzKxxDR/KaDuP+uoj4YSRX798A707TlDyvLcxnRbWm72Rei+iac9ql12mpNHVdp63Oq6R3k0xPsK2e9M3WFcGhQZeRRPdi3a5OAELSdyVtkfRf8tZ/VdJjkv5r5gJ4I/AcQESMA78geTLudF5zM/H9IfB3mcUXpVO1rpd0bJPyWW9e3wiMZLYbSZfl1rXivJbLJ1SecrZY+k6c00p5bee12tA57aLrtFReO3Gdls1rOt/NJ4HP1pi+Vee1fcFB0oOSthb5Ob/BXRebEyJnBklR773pvxdIOiNd996I+D2SOazfAbwvl1Xg7lz+SJ5+ftDhvOZmzbsb+KuIyE1+9LfAEyT/j28HnuzweVWRbXMX8iLgwSLntZX53A0siIgTgauBuyS9rkL6Tp3Tcnktda0uosvOaZddp6XyWu467dTf/2eBmyJiXw3p/5akTeStwIO8WuJpimom+2mKiDiz2fuU9DZgRiTTiRYzAvxDRLyUbv9tYAXwdxHxfJqvX0m6i2T60r9J01wbET9ML/SfAW8p82TS8rym628FfhIRX8wliGQ61TPS7fuBlyNiWSP5bDCv/4fDqxLmAy+kv/8jTT6vlfIZEQeAA+nvw5JyU84OlUrfqXNaLq9lrtWuO6d00XVaJq8jlL5OO/X3fzJwsaQvAEcBk5L+JSK+XCp9el5zbgOubySP+Xq9WukySkdigO8Cb5U0J/2P/n3gCUkzJL0eQNJM4Dxenb70W8AH0t8vBv6+0QujkbymefxvwL8G/iybQIdP1fou4Mkm5LPuvEbEbuBXkk5Jqz7eD9yXpmnFeS2bT1WecrYgfafOaam8duBarfucdtt1WiqvHbhOK+Y1It4REYsiYhHwReC/5wJDqfQtPK+HMtXxH5I5qEdIovyLwHcz654BXibp3jVCEsVz63YCv5u3r3cBazKf/yNJA89W4Avxas+CYZIeN9uAL/Fqb5sjSHoL7CDp0bC4w3mdT1LkfZK8LmvA/0i3/zFJN7r8/bc1r+nywXTZ08CXebWLYMnz2qp8Ahdlzs8W4A/zti2WviPntFReqfNa7UA+u+46Lff/Tx3Xaav/pjLLryWvt1KJ9GXPa6M/Hj7DzMwK9Hq1kpmZtYCDg5mZFXBwMDOzAg4OZmZWwMHBzMwKODiYmVkBBwczMyvw/wH+61YpASZrkwAAAABJRU5ErkJggg=="
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+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-44">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
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+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">def</span> <span class="nf">cross_correlation</span><span class="p">(</span>
+    <span class="n">ct_file</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span>
+    <span class="n">catalog_file</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span>
+    <span class="n">picks_file</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span>
+    <span class="n">cc_file</span><span class="p">:</span> <span class="n">OutputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span>
+<span class="p">):</span>
+
+    <span class="kn">import</span> <span class="nn">time</span>
+    <span class="kn">from</span> <span class="nn">multiprocessing</span> <span class="kn">import</span> <span class="n">Manager</span><span class="p">,</span> <span class="n">Process</span>
+
+    <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+    <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+    <span class="kn">from</span> <span class="nn">pymongo</span> <span class="kn">import</span> <span class="n">MongoClient</span>
+    <span class="kn">from</span> <span class="nn">tqdm</span> <span class="kn">import</span> <span class="n">tqdm</span>
+
+    <span class="n">catalog</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span>
+        <span class="n">catalog_file</span><span class="p">,</span>
+        <span class="n">sep</span><span class="o">=</span><span class="s2">&quot;</span><span class="se">\t</span><span class="s2">&quot;</span><span class="p">,</span>
+        <span class="n">parse_dates</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;time&quot;</span><span class="p">],</span>
+        <span class="n">index_col</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;event_idx&quot;</span><span class="p">],</span>
+        <span class="n">dtype</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;event_idx&quot;</span><span class="p">:</span> <span class="nb">str</span><span class="p">},</span>
+    <span class="p">)</span>
+    <span class="n">picks</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">picks_file</span><span class="p">,</span> <span class="n">sep</span><span class="o">=</span><span class="s2">&quot;</span><span class="se">\t</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">parse_dates</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;timestamp&quot;</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;event_idx&quot;</span><span class="p">:</span> <span class="nb">str</span><span class="p">})</span>
+    <span class="n">picks</span><span class="p">[</span><span class="s2">&quot;station&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">picks</span><span class="p">[</span><span class="s2">&quot;id&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;.&quot;</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">x</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;.&quot;</span><span class="p">)[</span><span class="mi">3</span><span class="p">])</span>
+    <span class="n">picks</span> <span class="o">=</span> <span class="n">picks</span><span class="o">.</span><span class="n">set_index</span><span class="p">([</span><span class="s2">&quot;event_idx&quot;</span><span class="p">,</span> <span class="s2">&quot;station&quot;</span><span class="p">,</span> <span class="s2">&quot;type&quot;</span><span class="p">])</span>
+    <span class="n">picks</span> <span class="o">=</span> <span class="n">picks</span><span class="o">.</span><span class="n">sort_index</span><span class="p">()</span>
+
+    <span class="n">pick_index</span> <span class="o">=</span> <span class="mi">100</span>
+    <span class="n">lo</span> <span class="o">=</span> <span class="n">pick_index</span> <span class="o">-</span> <span class="mi">50</span>
+    <span class="n">hi</span> <span class="o">=</span> <span class="n">pick_index</span> <span class="o">+</span> <span class="mi">100</span>
+    <span class="n">dt</span> <span class="o">=</span> <span class="mf">0.01</span>
+
+    <span class="n">ct_dict</span> <span class="o">=</span> <span class="n">Manager</span><span class="p">()</span><span class="o">.</span><span class="n">dict</span><span class="p">()</span>
+    <span class="n">cc_dict</span> <span class="o">=</span> <span class="n">Manager</span><span class="p">()</span><span class="o">.</span><span class="n">dict</span><span class="p">()</span>
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">ct_file</span><span class="p">)</span> <span class="k">as</span> <span class="n">fct</span><span class="p">:</span>
+        <span class="n">meta</span> <span class="o">=</span> <span class="n">fct</span><span class="o">.</span><span class="n">readlines</span><span class="p">()</span>
+        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">line</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">meta</span><span class="p">):</span>
+            <span class="k">if</span> <span class="n">line</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;#&quot;</span><span class="p">:</span>
+                <span class="k">if</span> <span class="n">i</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+                    <span class="n">ct_dict</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
+                <span class="n">key</span> <span class="o">=</span> <span class="n">line</span>
+                <span class="n">value</span> <span class="o">=</span> <span class="p">[]</span>
+                <span class="k">continue</span>
+            <span class="n">value</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">line</span><span class="p">)</span>
+        <span class="n">ct_dict</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
+    <span class="n">keys</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">ct_dict</span><span class="o">.</span><span class="n">keys</span><span class="p">()))</span>
+
+    <span class="k">def</span> <span class="nf">calc_cross_correlation</span><span class="p">(</span><span class="n">keys</span><span class="p">,</span> <span class="n">ct_dict</span><span class="p">,</span> <span class="n">cc_dict</span><span class="p">):</span>
+        <span class="n">username</span> <span class="o">=</span> <span class="s2">&quot;root&quot;</span>
+        <span class="n">password</span> <span class="o">=</span> <span class="s2">&quot;quakeflow123&quot;</span>
+        <span class="c1"># client = MongoClient(f&quot;mongodb://{username}:{password}@127.0.0.1:27017&quot;)</span>
+        <span class="n">client</span> <span class="o">=</span> <span class="n">MongoClient</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;mongodb://</span><span class="si">{</span><span class="n">username</span><span class="si">}</span><span class="s2">:</span><span class="si">{</span><span class="n">password</span><span class="si">}</span><span class="s2">@quakeflow-mongodb.default.svc.cluster.local:27017&quot;</span><span class="p">)</span>
+        <span class="n">db</span> <span class="o">=</span> <span class="n">client</span><span class="p">[</span><span class="s2">&quot;quakeflow&quot;</span><span class="p">]</span>
+        <span class="n">collection</span> <span class="o">=</span> <span class="n">db</span><span class="p">[</span><span class="s2">&quot;waveform&quot;</span><span class="p">]</span>
+        <span class="c1"># normalize = lambda x: (x - np.mean(x, axis=0, keepdims=True)) / np.std(x, axis=0, keepdims=True)</span>
+
+        <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">keys</span><span class="p">:</span>
+            <span class="n">tmp</span> <span class="o">=</span> <span class="n">key</span><span class="o">.</span><span class="n">split</span><span class="p">()</span>
+            <span class="n">ID1</span><span class="p">,</span> <span class="n">ID2</span> <span class="o">=</span> <span class="n">tmp</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">tmp</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
+            <span class="n">key_cc</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;#    </span><span class="si">{</span><span class="n">ID1</span><span class="si">}</span><span class="s2">    </span><span class="si">{</span><span class="n">ID2</span><span class="si">}</span><span class="s2">    0.0</span><span class="se">\n</span><span class="s2">&quot;</span>
+            <span class="n">lines_cc</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">ct_dict</span><span class="p">[</span><span class="n">key</span><span class="p">]:</span>
+                <span class="n">tmp</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">split</span><span class="p">()</span>
+                <span class="n">STA</span><span class="p">,</span> <span class="n">TT1</span><span class="p">,</span> <span class="n">TT2</span><span class="p">,</span> <span class="n">WGT</span><span class="p">,</span> <span class="n">PHA</span> <span class="o">=</span> <span class="p">(</span>
+                    <span class="n">tmp</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
+                    <span class="n">tmp</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
+                    <span class="n">tmp</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span>
+                    <span class="n">tmp</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span>
+                    <span class="n">tmp</span><span class="p">[</span><span class="mi">4</span><span class="p">],</span>
+                <span class="p">)</span>  <span class="c1">##HypoDD format</span>
+
+                <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">row1</span> <span class="ow">in</span> <span class="n">picks</span><span class="o">.</span><span class="n">loc</span><span class="p">[(</span><span class="n">ID1</span><span class="p">,</span> <span class="n">STA</span><span class="p">,</span> <span class="n">PHA</span><span class="p">)]</span><span class="o">.</span><span class="n">iterrows</span><span class="p">():</span>
+
+                    <span class="n">data1</span> <span class="o">=</span> <span class="n">collection</span><span class="o">.</span><span class="n">find_one</span><span class="p">(</span>
+                        <span class="p">{</span><span class="s2">&quot;_id&quot;</span><span class="p">:</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">row1</span><span class="p">[</span><span class="s1">&#39;id&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">_</span><span class="si">{</span><span class="n">row1</span><span class="p">[</span><span class="s1">&#39;timestamp&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">isoformat</span><span class="p">(</span><span class="n">timespec</span><span class="o">=</span><span class="s1">&#39;milliseconds&#39;</span><span class="p">)</span><span class="si">}</span><span class="s2">_</span><span class="si">{</span><span class="n">PHA</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">}</span>
+                    <span class="p">)</span>
+
+                    <span class="k">for</span> <span class="n">j</span><span class="p">,</span> <span class="n">row2</span> <span class="ow">in</span> <span class="n">picks</span><span class="o">.</span><span class="n">loc</span><span class="p">[(</span><span class="n">ID2</span><span class="p">,</span> <span class="n">STA</span><span class="p">,</span> <span class="n">PHA</span><span class="p">)]</span><span class="o">.</span><span class="n">iterrows</span><span class="p">():</span>
+
+                        <span class="n">data2</span> <span class="o">=</span> <span class="n">collection</span><span class="o">.</span><span class="n">find_one</span><span class="p">(</span>
+                            <span class="p">{</span><span class="s2">&quot;_id&quot;</span><span class="p">:</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">row2</span><span class="p">[</span><span class="s1">&#39;id&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">_</span><span class="si">{</span><span class="n">row2</span><span class="p">[</span><span class="s1">&#39;timestamp&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">isoformat</span><span class="p">(</span><span class="n">timespec</span><span class="o">=</span><span class="s1">&#39;milliseconds&#39;</span><span class="p">)</span><span class="si">}</span><span class="s2">_</span><span class="si">{</span><span class="n">PHA</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">}</span>
+                        <span class="p">)</span>
+
+                        <span class="c1"># if PHA == &quot;P&quot;:  # Z</span>
+                        <span class="c1">#     waveform1 = np.array(data1[&quot;waveform&quot;])[lo:hi, -1:]</span>
+                        <span class="c1">#     waveform2 = np.array(data2[&quot;waveform&quot;])[lo:hi, -1:]</span>
+                        <span class="c1"># elif PHA == &quot;S&quot;:  # E, N</span>
+                        <span class="c1">#     waveform1 = np.array(data1[&quot;waveform&quot;])[lo:hi, :-1]</span>
+                        <span class="c1">#     waveform2 = np.array(data2[&quot;waveform&quot;])[lo:hi, :-1]</span>
+                        <span class="c1"># else:</span>
+                        <span class="c1">#     raise (Exception(&quot;PHA must be P or S&quot;))</span>
+                        <span class="n">waveform1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">data1</span><span class="p">[</span><span class="s2">&quot;waveform&quot;</span><span class="p">])[</span><span class="n">lo</span><span class="p">:</span><span class="n">hi</span><span class="p">,</span> <span class="p">:]</span>
+                        <span class="n">waveform2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">data2</span><span class="p">[</span><span class="s2">&quot;waveform&quot;</span><span class="p">])[</span><span class="n">lo</span><span class="p">:</span><span class="n">hi</span><span class="p">,</span> <span class="p">:]</span>
+
+                        <span class="n">cc</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">waveform1</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+                        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">waveform1</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]):</span>
+                            <span class="n">cc</span> <span class="o">+=</span> <span class="n">np</span><span class="o">.</span><span class="n">correlate</span><span class="p">(</span><span class="n">waveform1</span><span class="p">[:,</span> <span class="n">k</span><span class="p">],</span> <span class="n">waveform2</span><span class="p">[:,</span> <span class="n">k</span><span class="p">],</span> <span class="n">mode</span><span class="o">=</span><span class="s2">&quot;same&quot;</span><span class="p">)</span>
+                        <span class="n">norm</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">waveform1</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">waveform2</span><span class="o">**</span><span class="mi">2</span><span class="p">))</span>
+                        <span class="k">if</span> <span class="n">norm</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+                            <span class="k">continue</span>
+                        <span class="k">else</span><span class="p">:</span>
+                            <span class="n">cc</span> <span class="o">/=</span> <span class="n">norm</span>
+                        <span class="n">shift</span> <span class="o">=</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">cc</span><span class="p">))</span> <span class="o">-</span> <span class="n">waveform1</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">//</span> <span class="mi">2</span><span class="p">)</span> <span class="o">*</span> <span class="n">dt</span> <span class="o">+</span> <span class="nb">float</span><span class="p">(</span><span class="n">TT1</span><span class="p">)</span> <span class="o">-</span> <span class="nb">float</span><span class="p">(</span><span class="n">TT2</span><span class="p">)</span>
+                        <span class="n">coeff</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">cc</span><span class="p">))</span>
+
+                        <span class="k">if</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">coeff</span><span class="p">):</span>
+                            <span class="n">lines_cc</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">STA</span><span class="si">:</span><span class="s2">&lt;7s</span><span class="si">}</span><span class="s2">    </span><span class="si">{</span><span class="n">shift</span><span class="si">:</span><span class="s2">.5f</span><span class="si">}</span><span class="s2">    </span><span class="si">{</span><span class="n">coeff</span><span class="si">:</span><span class="s2">.3f</span><span class="si">}</span><span class="s2">    </span><span class="si">{</span><span class="n">PHA</span><span class="si">}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
+
+                <span class="n">cc_dict</span><span class="p">[</span><span class="n">key_cc</span><span class="p">]</span> <span class="o">=</span> <span class="n">lines_cc</span>
+
+        <span class="k">return</span> <span class="mi">0</span>
+
+    <span class="n">t0</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+    <span class="n">processes</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">num_process</span> <span class="o">=</span> <span class="mi">16</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_process</span><span class="p">):</span>
+        <span class="n">p</span> <span class="o">=</span> <span class="n">Process</span><span class="p">(</span><span class="n">target</span><span class="o">=</span><span class="n">calc_cross_correlation</span><span class="p">,</span> <span class="n">args</span><span class="o">=</span><span class="p">(</span><span class="n">keys</span><span class="p">[</span><span class="n">i</span><span class="p">::</span><span class="n">num_process</span><span class="p">],</span> <span class="n">ct_dict</span><span class="p">,</span> <span class="n">cc_dict</span><span class="p">))</span>
+        <span class="n">p</span><span class="o">.</span><span class="n">start</span><span class="p">()</span>
+        <span class="n">processes</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">processes</span><span class="p">:</span>
+        <span class="n">p</span><span class="o">.</span><span class="n">join</span><span class="p">()</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">num_process</span><span class="si">}</span><span class="s2"> process: time = </span><span class="si">{</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span><span class="o">-</span><span class="n">t0</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">cc_file</span><span class="p">,</span> <span class="s2">&quot;w&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fcc</span><span class="p">:</span>
+        <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">cc_dict</span><span class="p">:</span>
+            <span class="n">fcc</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
+            <span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">cc_dict</span><span class="p">[</span><span class="n">key</span><span class="p">]:</span>
+                <span class="n">fcc</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">line</span><span class="p">)</span>
+</pre></div>
+<div id="cell-44" class="clipboard-copy-txt">def cross_correlation(
+    ct_file: InputPath(str),
+    catalog_file: InputPath(str),
+    picks_file: InputPath(str),
+    cc_file: OutputPath(str),
+):
+
+    import time
+    from multiprocessing import Manager, Process
+
+    import numpy as np
+    import pandas as pd
+    from pymongo import MongoClient
+    from tqdm import tqdm
+
+    catalog = pd.read_csv(
+        catalog_file,
+        sep="\t",
+        parse_dates=["time"],
+        index_col=["event_idx"],
+        dtype={"event_idx": str},
+    )
+    picks = pd.read_csv(picks_file, sep="\t", parse_dates=["timestamp"], dtype={"event_idx": str})
+    picks["station"] = picks["id"].apply(lambda x: x.split(".")[1] + x.split(".")[3])
+    picks = picks.set_index(["event_idx", "station", "type"])
+    picks = picks.sort_index()
+
+    pick_index = 100
+    lo = pick_index - 50
+    hi = pick_index + 100
+    dt = 0.01
+
+    ct_dict = Manager().dict()
+    cc_dict = Manager().dict()
+    with open(ct_file) as fct:
+        meta = fct.readlines()
+        for i, line in enumerate(meta):
+            if line[0] == "#":
+                if i > 0:
+                    ct_dict[key] = value
+                key = line
+                value = []
+                continue
+            value.append(line)
+        ct_dict[key] = value
+    keys = sorted(list(ct_dict.keys()))
+
+    def calc_cross_correlation(keys, ct_dict, cc_dict):
+        username = "root"
+        password = "quakeflow123"
+        # client = MongoClient(f"mongodb://{username}:{password}@127.0.0.1:27017")
+        client = MongoClient(f"mongodb://{username}:{password}@quakeflow-mongodb.default.svc.cluster.local:27017")
+        db = client["quakeflow"]
+        collection = db["waveform"]
+        # normalize = lambda x: (x - np.mean(x, axis=0, keepdims=True)) / np.std(x, axis=0, keepdims=True)
+
+        for key in keys:
+            tmp = key.split()
+            ID1, ID2 = tmp[1], tmp[2]
+            key_cc = f"#    {ID1}    {ID2}    0.0\n"
+            lines_cc = []
+            for line in ct_dict[key]:
+                tmp = line.split()
+                STA, TT1, TT2, WGT, PHA = (
+                    tmp[0],
+                    tmp[1],
+                    tmp[2],
+                    tmp[3],
+                    tmp[4],
+                )  ##HypoDD format
+
+                for i, row1 in picks.loc[(ID1, STA, PHA)].iterrows():
+
+                    data1 = collection.find_one(
+                        {"_id": f"{row1['id']}_{row1['timestamp'].isoformat(timespec='milliseconds')}_{PHA}"}
+                    )
+
+                    for j, row2 in picks.loc[(ID2, STA, PHA)].iterrows():
+
+                        data2 = collection.find_one(
+                            {"_id": f"{row2['id']}_{row2['timestamp'].isoformat(timespec='milliseconds')}_{PHA}"}
+                        )
+
+                        # if PHA == "P":  # Z
+                        #     waveform1 = np.array(data1["waveform"])[lo:hi, -1:]
+                        #     waveform2 = np.array(data2["waveform"])[lo:hi, -1:]
+                        # elif PHA == "S":  # E, N
+                        #     waveform1 = np.array(data1["waveform"])[lo:hi, :-1]
+                        #     waveform2 = np.array(data2["waveform"])[lo:hi, :-1]
+                        # else:
+                        #     raise (Exception("PHA must be P or S"))
+                        waveform1 = np.array(data1["waveform"])[lo:hi, :]
+                        waveform2 = np.array(data2["waveform"])[lo:hi, :]
+
+                        cc = np.zeros(waveform1.shape[0])
+                        for k in range(waveform1.shape[1]):
+                            cc += np.correlate(waveform1[:, k], waveform2[:, k], mode="same")
+                        norm = np.sqrt(np.sum(waveform1**2) * np.sum(waveform2**2))
+                        if norm == 0:
+                            continue
+                        else:
+                            cc /= norm
+                        shift = (np.argmax(np.abs(cc)) - waveform1.shape[0] // 2) * dt + float(TT1) - float(TT2)
+                        coeff = np.max(np.abs(cc))
+
+                        if not np.isnan(coeff):
+                            lines_cc.append(f"{STA:<7s}    {shift:.5f}    {coeff:.3f}    {PHA}\n")
+
+                cc_dict[key_cc] = lines_cc
+
+        return 0
+
+    t0 = time.time()
+    processes = []
+    num_process = 16
+    for i in range(num_process):
+        p = Process(target=calc_cross_correlation, args=(keys[i::num_process], ct_dict, cc_dict))
+        p.start()
+        processes.append(p)
+    for p in processes:
+        p.join()
+    print(f"{num_process} process: time = {time.time()-t0:.1f}")
+
+    with open(cc_file, "w") as fcc:
+        for key in cc_dict:
+            fcc.write(key)
+            for line in cc_dict[key]:
+                fcc.write(line)</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[67]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-45">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">cc_op</span> <span class="o">=</span> <span class="n">comp</span><span class="o">.</span><span class="n">func_to_container_op</span><span class="p">(</span>
+    <span class="n">cross_correlation</span><span class="p">,</span>
+    <span class="n">base_image</span><span class="o">=</span><span class="s2">&quot;python:3.8&quot;</span><span class="p">,</span>
+    <span class="n">packages_to_install</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;pandas&quot;</span><span class="p">,</span> <span class="s2">&quot;tqdm&quot;</span><span class="p">,</span> <span class="s2">&quot;minio&quot;</span><span class="p">,</span> <span class="s2">&quot;pymongo&quot;</span><span class="p">],</span>
+<span class="p">)</span>
+</pre></div>
+<div id="cell-45" class="clipboard-copy-txt">cc_op = comp.func_to_container_op(
+    cross_correlation,
+    base_image="python:3.8",
+    packages_to_install=["pandas", "tqdm", "minio", "pymongo"],
+)</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[68]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-46">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
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+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">def</span> <span class="nf">hypodd_cc</span><span class="p">(</span>
+    <span class="n">i</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
+    <span class="n">config_json</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="s2">&quot;json&quot;</span><span class="p">),</span>
+    <span class="n">ct_file</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span>
+    <span class="n">cc_file</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span>
+    <span class="n">event</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span>
+    <span class="n">station</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span>
+    <span class="n">inp_file</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;hypodd_cc.inp&quot;</span><span class="p">,</span>
+    <span class="n">bucket_name</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;catalogs&quot;</span><span class="p">,</span>
+    <span class="n">s3_url</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;minio-service:9000&quot;</span><span class="p">,</span>
+    <span class="n">secure</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+<span class="p">):</span>
+    <span class="kn">import</span> <span class="nn">json</span>
+    <span class="kn">import</span> <span class="nn">os</span>
+
+    <span class="kn">from</span> <span class="nn">minio</span> <span class="kn">import</span> <span class="n">Minio</span>
+
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">config_json</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">config</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">fp</span><span class="p">)</span>
+
+    <span class="n">minioClient</span> <span class="o">=</span> <span class="n">Minio</span><span class="p">(</span><span class="n">s3_url</span><span class="p">,</span> <span class="n">access_key</span><span class="o">=</span><span class="s2">&quot;minio&quot;</span><span class="p">,</span> <span class="n">secret_key</span><span class="o">=</span><span class="s2">&quot;minio123&quot;</span><span class="p">,</span> <span class="n">secure</span><span class="o">=</span><span class="n">secure</span><span class="p">)</span>
+
+    <span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;cat </span><span class="si">{</span><span class="n">ct_file</span><span class="si">}</span><span class="s2"> &gt; dt.ct&quot;</span><span class="p">)</span>
+    <span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;cat </span><span class="si">{</span><span class="n">cc_file</span><span class="si">}</span><span class="s2"> &gt; dt.cc&quot;</span><span class="p">)</span>
+    <span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;cat </span><span class="si">{</span><span class="n">event</span><span class="si">}</span><span class="s2"> &gt; event.sel&quot;</span><span class="p">)</span>
+    <span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;cat </span><span class="si">{</span><span class="n">station</span><span class="si">}</span><span class="s2"> &gt; stations_hypodd.dat &quot;</span><span class="p">)</span>
+
+    <span class="n">HYPODD_CMD</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;HYPODD/src/hypodd/hypodd </span><span class="si">{</span><span class="n">inp_file</span><span class="si">}</span><span class="s2">&quot;</span>
+    <span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="n">HYPODD_CMD</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="p">(</span><span class="s2">&quot;</span><span class="si">{HYPODD_CMD}</span><span class="s2">&quot;</span> <span class="o">+</span> <span class="s2">&quot; failed!&quot;</span><span class="p">)</span>
+    <span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;mv hypodd.reloc hypodd_cc_</span><span class="si">{</span><span class="n">i</span><span class="si">:</span><span class="s2">03d</span><span class="si">}</span><span class="s2">.reloc&quot;</span><span class="p">)</span>
+
+    <span class="n">minioClient</span><span class="o">.</span><span class="n">fput_object</span><span class="p">(</span>
+        <span class="n">bucket_name</span><span class="p">,</span>
+        <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;region&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">/hypodd_cc_</span><span class="si">{</span><span class="n">i</span><span class="si">:</span><span class="s2">03d</span><span class="si">}</span><span class="s2">.reloc&quot;</span><span class="p">,</span>
+        <span class="sa">f</span><span class="s2">&quot;hypodd_cc_</span><span class="si">{</span><span class="n">i</span><span class="si">:</span><span class="s2">03d</span><span class="si">}</span><span class="s2">.reloc&quot;</span><span class="p">,</span>
+    <span class="p">)</span>
+</pre></div>
+<div id="cell-46" class="clipboard-copy-txt">def hypodd_cc(
+    i: int,
+    config_json: InputPath("json"),
+    ct_file: InputPath(str),
+    cc_file: InputPath(str),
+    event: InputPath(str),
+    station: InputPath(str),
+    inp_file: str = "hypodd_cc.inp",
+    bucket_name: str = "catalogs",
+    s3_url: str = "minio-service:9000",
+    secure: bool = True,
+):
+    import json
+    import os
+
+    from minio import Minio
+
+    with open(config_json, "r") as fp:
+        config = json.load(fp)
+
+    minioClient = Minio(s3_url, access_key="minio", secret_key="minio123", secure=secure)
+
+    os.system(f"cat {ct_file} > dt.ct")
+    os.system(f"cat {cc_file} > dt.cc")
+    os.system(f"cat {event} > event.sel")
+    os.system(f"cat {station} > stations_hypodd.dat ")
+
+    HYPODD_CMD = f"HYPODD/src/hypodd/hypodd {inp_file}"
+    if os.system(HYPODD_CMD) != 0:
+        raise ("{HYPODD_CMD}" + " failed!")
+    os.system(f"mv hypodd.reloc hypodd_cc_{i:03d}.reloc")
+
+    minioClient.fput_object(
+        bucket_name,
+        f"{config['region']}/hypodd_cc_{i:03d}.reloc",
+        f"hypodd_cc_{i:03d}.reloc",
+    )</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[69]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-47">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">hypodd_cc_op</span> <span class="o">=</span> <span class="n">comp</span><span class="o">.</span><span class="n">func_to_container_op</span><span class="p">(</span><span class="n">hypodd_cc</span><span class="p">,</span> <span class="n">base_image</span><span class="o">=</span><span class="s2">&quot;zhuwq0/hypodd-api:1.0&quot;</span><span class="p">)</span>
+</pre></div>
+<div id="cell-47" class="clipboard-copy-txt">hypodd_cc_op = comp.func_to_container_op(hypodd_cc, base_image="zhuwq0/hypodd-api:1.0")</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h3 id="merge-hypodd-results">Merge hypodd results<a class="anchor-link" href="#merge-hypodd-results">&#182;</a></h3>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[70]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-48">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">def</span> <span class="nf">merge_hypodd</span><span class="p">(</span>
+    <span class="n">index</span><span class="p">:</span> <span class="nb">list</span><span class="p">,</span>
+    <span class="n">config_json</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="s2">&quot;json&quot;</span><span class="p">),</span>
+    <span class="n">catalog_ct</span><span class="p">:</span> <span class="n">OutputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span>
+    <span class="n">catalog_cc</span><span class="p">:</span> <span class="n">OutputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span>
+    <span class="n">bucket_name</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;catalogs&quot;</span><span class="p">,</span>
+    <span class="n">s3_url</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;minio-service:9000&quot;</span><span class="p">,</span>
+    <span class="n">secure</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+<span class="p">):</span>
+    <span class="kn">import</span> <span class="nn">json</span>
+    <span class="kn">import</span> <span class="nn">os</span>
+    <span class="kn">from</span> <span class="nn">glob</span> <span class="kn">import</span> <span class="n">glob</span>
+
+    <span class="kn">from</span> <span class="nn">minio</span> <span class="kn">import</span> <span class="n">Minio</span>
+
+    <span class="n">minioClient</span> <span class="o">=</span> <span class="n">Minio</span><span class="p">(</span><span class="n">s3_url</span><span class="p">,</span> <span class="n">access_key</span><span class="o">=</span><span class="s2">&quot;minio&quot;</span><span class="p">,</span> <span class="n">secret_key</span><span class="o">=</span><span class="s2">&quot;minio123&quot;</span><span class="p">,</span> <span class="n">secure</span><span class="o">=</span><span class="n">secure</span><span class="p">)</span>
+
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">config_json</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">config</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">fp</span><span class="p">)</span>
+
+    <span class="n">objects</span> <span class="o">=</span> <span class="n">minioClient</span><span class="o">.</span><span class="n">list_objects</span><span class="p">(</span><span class="n">bucket_name</span><span class="p">,</span> <span class="n">prefix</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;region&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">/hypodd/hypodd_&quot;</span><span class="p">,</span> <span class="n">recursive</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+    <span class="n">tmp_path</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="s2">&quot;/tmp/&quot;</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">obj</span> <span class="ow">in</span> <span class="n">objects</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="n">obj</span><span class="o">.</span><span class="n">_object_name</span><span class="p">)</span>
+        <span class="n">minioClient</span><span class="o">.</span><span class="n">fget_object</span><span class="p">(</span><span class="n">bucket_name</span><span class="p">,</span> <span class="n">obj</span><span class="o">.</span><span class="n">_object_name</span><span class="p">,</span> <span class="n">tmp_path</span><span class="p">(</span><span class="n">obj</span><span class="o">.</span><span class="n">_object_name</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;/&quot;</span><span class="p">)[</span><span class="o">-</span><span class="mi">1</span><span class="p">]))</span>
+
+    <span class="c1"># tmp_ct_catalogs = sorted(glob(tmp_path(&quot;hypodd_ct_*.reloc&quot;)))</span>
+    <span class="n">hypodd_ct_catalogs</span> <span class="o">=</span> <span class="p">[</span><span class="n">tmp_path</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;hypodd_ct_</span><span class="si">{</span><span class="n">i</span><span class="si">:</span><span class="s2">03d</span><span class="si">}</span><span class="s2">.reloc&quot;</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">index</span><span class="p">]</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;cat </span><span class="si">{</span><span class="s1">&#39; &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">hypodd_ct_catalogs</span><span class="p">)</span><span class="si">}</span><span class="s2"> &gt; </span><span class="si">{</span><span class="n">tmp_path</span><span class="p">(</span><span class="s1">&#39;hypodd_ct_catalog.txt&#39;</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+    <span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;cat </span><span class="si">{</span><span class="s1">&#39; &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">hypodd_ct_catalogs</span><span class="p">)</span><span class="si">}</span><span class="s2"> &gt; </span><span class="si">{</span><span class="n">tmp_path</span><span class="p">(</span><span class="s1">&#39;hypodd_ct_catalog.txt&#39;</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+    <span class="n">minioClient</span><span class="o">.</span><span class="n">fput_object</span><span class="p">(</span>
+        <span class="n">bucket_name</span><span class="p">,</span>
+        <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;region&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">/hypodd_ct_catalog.txt&quot;</span><span class="p">,</span>
+        <span class="n">tmp_path</span><span class="p">(</span><span class="s2">&quot;hypodd_ct_catalog.txt&quot;</span><span class="p">),</span>
+    <span class="p">)</span>
+    <span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;cat </span><span class="si">{</span><span class="n">tmp_path</span><span class="p">(</span><span class="s1">&#39;hypodd_ct_catalog.txt&#39;</span><span class="p">)</span><span class="si">}</span><span class="s2"> &gt; </span><span class="si">{</span><span class="n">catalog_ct</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+
+    <span class="c1"># hypodd_cc_catalogs = sorted(glob(tmp_path(&quot;hypodd_cc_*.reloc&quot;)))</span>
+    <span class="n">hypodd_cc_catalogs</span> <span class="o">=</span> <span class="p">[</span><span class="n">tmp_path</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;hypodd_cc_</span><span class="si">{</span><span class="n">i</span><span class="si">:</span><span class="s2">03d</span><span class="si">}</span><span class="s2">.reloc&quot;</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">index</span><span class="p">]</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;cat </span><span class="si">{</span><span class="s1">&#39; &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">hypodd_cc_catalogs</span><span class="p">)</span><span class="si">}</span><span class="s2"> &gt; </span><span class="si">{</span><span class="n">tmp_path</span><span class="p">(</span><span class="s1">&#39;hypodd_cc_catalog.txt&#39;</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+    <span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;cat </span><span class="si">{</span><span class="s1">&#39; &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">hypodd_cc_catalogs</span><span class="p">)</span><span class="si">}</span><span class="s2"> &gt; </span><span class="si">{</span><span class="n">tmp_path</span><span class="p">(</span><span class="s1">&#39;hypodd_cc_catalog.txt&#39;</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+    <span class="n">minioClient</span><span class="o">.</span><span class="n">fput_object</span><span class="p">(</span>
+        <span class="n">bucket_name</span><span class="p">,</span>
+        <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;region&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">/hypodd_cc_catalog.txt&quot;</span><span class="p">,</span>
+        <span class="n">tmp_path</span><span class="p">(</span><span class="s2">&quot;hypodd_cc_catalog.txt&quot;</span><span class="p">),</span>
+    <span class="p">)</span>
+    <span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;cat </span><span class="si">{</span><span class="n">tmp_path</span><span class="p">(</span><span class="s1">&#39;hypodd_cc_catalog.txt&#39;</span><span class="p">)</span><span class="si">}</span><span class="s2"> &gt; </span><span class="si">{</span><span class="n">catalog_cc</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="mi">0</span>
+</pre></div>
+<div id="cell-48" class="clipboard-copy-txt">def merge_hypodd(
+    index: list,
+    config_json: InputPath("json"),
+    catalog_ct: OutputPath(str),
+    catalog_cc: OutputPath(str),
+    bucket_name: str = "catalogs",
+    s3_url: str = "minio-service:9000",
+    secure: bool = True,
+):
+    import json
+    import os
+    from glob import glob
+
+    from minio import Minio
+
+    minioClient = Minio(s3_url, access_key="minio", secret_key="minio123", secure=secure)
+
+    with open(config_json, "r") as fp:
+        config = json.load(fp)
+
+    objects = minioClient.list_objects(bucket_name, prefix=f"{config['region']}/hypodd/hypodd_", recursive=True)
+
+    tmp_path = lambda x: os.path.join("/tmp/", x)
+    for obj in objects:
+        print(obj._object_name)
+        minioClient.fget_object(bucket_name, obj._object_name, tmp_path(obj._object_name.split("/")[-1]))
+
+    # tmp_ct_catalogs = sorted(glob(tmp_path("hypodd_ct_*.reloc")))
+    hypodd_ct_catalogs = [tmp_path(f"hypodd_ct_{i:03d}.reloc") for i in index]
+    print(f"cat {' '.join(hypodd_ct_catalogs)} > {tmp_path('hypodd_ct_catalog.txt')}")
+    os.system(f"cat {' '.join(hypodd_ct_catalogs)} > {tmp_path('hypodd_ct_catalog.txt')}")
+    minioClient.fput_object(
+        bucket_name,
+        f"{config['region']}/hypodd_ct_catalog.txt",
+        tmp_path("hypodd_ct_catalog.txt"),
+    )
+    os.system(f"cat {tmp_path('hypodd_ct_catalog.txt')} > {catalog_ct}")
+
+    # hypodd_cc_catalogs = sorted(glob(tmp_path("hypodd_cc_*.reloc")))
+    hypodd_cc_catalogs = [tmp_path(f"hypodd_cc_{i:03d}.reloc") for i in index]
+    print(f"cat {' '.join(hypodd_cc_catalogs)} > {tmp_path('hypodd_cc_catalog.txt')}")
+    os.system(f"cat {' '.join(hypodd_cc_catalogs)} > {tmp_path('hypodd_cc_catalog.txt')}")
+    minioClient.fput_object(
+        bucket_name,
+        f"{config['region']}/hypodd_cc_catalog.txt",
+        tmp_path("hypodd_cc_catalog.txt"),
+    )
+    os.system(f"cat {tmp_path('hypodd_cc_catalog.txt')} > {catalog_cc}")
+
+    return 0</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[71]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-49">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">merge_hypodd_op</span> <span class="o">=</span> <span class="n">comp</span><span class="o">.</span><span class="n">func_to_container_op</span><span class="p">(</span>
+    <span class="n">merge_hypodd</span><span class="p">,</span>
+    <span class="n">base_image</span><span class="o">=</span><span class="s2">&quot;python:3.8&quot;</span><span class="p">,</span>
+    <span class="n">packages_to_install</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;pandas&quot;</span><span class="p">,</span> <span class="s2">&quot;tqdm&quot;</span><span class="p">,</span> <span class="s2">&quot;minio&quot;</span><span class="p">],</span>
+<span class="p">)</span>
+</pre></div>
+<div id="cell-49" class="clipboard-copy-txt">merge_hypodd_op = comp.func_to_container_op(
+    merge_hypodd,
+    base_image="python:3.8",
+    packages_to_install=["pandas", "tqdm", "minio"],
+)</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="8-visulization">8. Visulization<a class="anchor-link" href="#8-visulization">&#182;</a></h2>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[72]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-50">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">def</span> <span class="nf">visulization</span><span class="p">(</span>
+    <span class="n">config_json</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="s2">&quot;json&quot;</span><span class="p">),</span>
+    <span class="n">hypodd_catalog_ct</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span>
+    <span class="n">hypodd_catalog_cc</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span>
+    <span class="n">gamma_catalog</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span>
+    <span class="n">standard_catalog</span><span class="p">:</span> <span class="n">InputPath</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span>
+    <span class="n">data_path</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;./&quot;</span><span class="p">,</span>
+    <span class="n">bucket_name</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;catalogs&quot;</span><span class="p">,</span>
+    <span class="n">s3_url</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;minio-service:9000&quot;</span><span class="p">,</span>
+    <span class="n">secure</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+<span class="p">):</span>
+    <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+    <span class="kn">import</span> <span class="nn">plotly.graph_objects</span> <span class="k">as</span> <span class="nn">go</span>
+    <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+    <span class="kn">import</span> <span class="nn">json</span>
+    <span class="kn">import</span> <span class="nn">os</span>
+    <span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">datetime</span>
+    <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+    <span class="kn">import</span> <span class="nn">matplotlib.dates</span> <span class="k">as</span> <span class="nn">mdates</span>
+
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">config_json</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+        <span class="n">config</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">fp</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">plot3d</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">z</span><span class="p">,</span> <span class="n">fig_name</span><span class="p">):</span>
+        <span class="n">fig</span> <span class="o">=</span> <span class="n">go</span><span class="o">.</span><span class="n">Figure</span><span class="p">(</span>
+            <span class="n">data</span><span class="o">=</span><span class="p">[</span>
+                <span class="n">go</span><span class="o">.</span><span class="n">Scatter3d</span><span class="p">(</span>
+                    <span class="n">x</span><span class="o">=</span><span class="n">x</span><span class="p">,</span>
+                    <span class="n">y</span><span class="o">=</span><span class="n">y</span><span class="p">,</span>
+                    <span class="n">z</span><span class="o">=</span><span class="n">z</span><span class="p">,</span>
+                    <span class="n">mode</span><span class="o">=</span><span class="s2">&quot;markers&quot;</span><span class="p">,</span>
+                    <span class="n">marker</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="mf">3.0</span><span class="p">,</span> <span class="n">color</span><span class="o">=-</span><span class="n">z</span><span class="p">,</span> <span class="n">cmin</span><span class="o">=-</span><span class="mi">60</span><span class="p">,</span> <span class="n">cmax</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">colorscale</span><span class="o">=</span><span class="s2">&quot;Viridis&quot;</span><span class="p">,</span> <span class="n">opacity</span><span class="o">=</span><span class="mf">0.8</span><span class="p">),</span>
+                <span class="p">)</span>
+            <span class="p">],</span>
+        <span class="p">)</span>
+
+        <span class="n">fig</span><span class="o">.</span><span class="n">update_layout</span><span class="p">(</span>
+            <span class="n">scene</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span>
+                <span class="n">xaxis</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span>
+                    <span class="n">nticks</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span>
+                    <span class="nb">range</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;xlim_degree&quot;</span><span class="p">],</span>
+                <span class="p">),</span>
+                <span class="n">yaxis</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span>
+                    <span class="n">nticks</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span>
+                    <span class="nb">range</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;ylim_degree&quot;</span><span class="p">],</span>
+                <span class="p">),</span>
+                <span class="n">zaxis</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span>
+                    <span class="n">nticks</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span>
+                    <span class="c1"># range=[z.max(), z.min()],</span>
+                    <span class="nb">range</span><span class="o">=</span><span class="p">[</span><span class="mi">60</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">],</span>
+                <span class="p">),</span>
+                <span class="c1">#         aspectratio = dict(x=(xlim[1]-xlim[0])/2, y=(ylim[1]-ylim[0])/2, z=1),</span>
+                <span class="n">aspectratio</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">z</span><span class="o">=</span><span class="mf">0.5</span><span class="p">),</span>
+            <span class="p">),</span>
+            <span class="n">margin</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">r</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">l</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">b</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">t</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span>
+        <span class="p">)</span>
+        <span class="n">fig</span><span class="o">.</span><span class="n">write_html</span><span class="p">(</span><span class="n">fig_name</span><span class="p">)</span>
+
+    <span class="n">hypodd_ct_catalog</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span>
+        <span class="n">hypodd_catalog_ct</span><span class="p">,</span>
+        <span class="n">sep</span><span class="o">=</span><span class="s2">&quot;\s+&quot;</span><span class="p">,</span>
+        <span class="n">names</span><span class="o">=</span><span class="p">[</span>
+            <span class="s2">&quot;ID&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;LAT&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;LON&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;DEPTH&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;X&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;Y&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;Z&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;EX&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;EY&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;EZ&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;YR&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;MO&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;DY&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;HR&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;MI&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;SC&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;MAG&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;NCCP&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;NCCS&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;NCTP&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;NCTS&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;RCC&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;RCT&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;CID&quot;</span><span class="p">,</span>
+        <span class="p">],</span>
+    <span class="p">)</span>
+
+    <span class="n">hypodd_ct_catalog</span><span class="p">[</span><span class="s2">&quot;MO&quot;</span><span class="p">][</span><span class="n">hypodd_ct_catalog</span><span class="p">[</span><span class="s2">&quot;MO&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
+    <span class="n">hypodd_ct_catalog</span><span class="p">[</span><span class="s2">&quot;DY&quot;</span><span class="p">][</span><span class="n">hypodd_ct_catalog</span><span class="p">[</span><span class="s2">&quot;DY&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
+    <span class="n">hypodd_ct_catalog</span><span class="p">[</span><span class="s2">&quot;time&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">hypodd_ct_catalog</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span>
+        <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">x</span><span class="p">[</span><span class="s2">&quot;YR&quot;</span><span class="p">]</span><span class="si">:</span><span class="s1">04.0f</span><span class="si">}</span><span class="s1">-</span><span class="si">{</span><span class="n">x</span><span class="p">[</span><span class="s2">&quot;MO&quot;</span><span class="p">]</span><span class="si">:</span><span class="s1">02.0f</span><span class="si">}</span><span class="s1">-</span><span class="si">{</span><span class="n">x</span><span class="p">[</span><span class="s2">&quot;DY&quot;</span><span class="p">]</span><span class="si">:</span><span class="s1">02.0f</span><span class="si">}</span><span class="s1">T</span><span class="si">{</span><span class="n">x</span><span class="p">[</span><span class="s2">&quot;HR&quot;</span><span class="p">]</span><span class="si">:</span><span class="s1">02.0f</span><span class="si">}</span><span class="s1">:</span><span class="si">{</span><span class="n">x</span><span class="p">[</span><span class="s2">&quot;MI&quot;</span><span class="p">]</span><span class="si">:</span><span class="s1">02.0f</span><span class="si">}</span><span class="s1">:</span><span class="si">{</span><span class="nb">min</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="s2">&quot;SC&quot;</span><span class="p">],</span><span class="w"> </span><span class="mf">59.999</span><span class="p">)</span><span class="si">:</span><span class="s1">05.3f</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">,</span>
+        <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+    <span class="p">)</span>
+    <span class="n">hypodd_ct_catalog</span><span class="p">[</span><span class="s2">&quot;magnitude&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">hypodd_ct_catalog</span><span class="p">[</span><span class="s2">&quot;MAG&quot;</span><span class="p">]</span>
+    <span class="n">hypodd_ct_catalog</span><span class="p">[</span><span class="s2">&quot;time&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">hypodd_ct_catalog</span><span class="p">[</span><span class="s2">&quot;time&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">datetime</span><span class="o">.</span><span class="n">strptime</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="s2">&quot;%Y-%m-</span><span class="si">%d</span><span class="s2">T%H:%M:%S.</span><span class="si">%f</span><span class="s2">&quot;</span><span class="p">))</span>
+    <span class="n">plot3d</span><span class="p">(</span>
+        <span class="n">hypodd_ct_catalog</span><span class="p">[</span><span class="s2">&quot;LON&quot;</span><span class="p">],</span>
+        <span class="n">hypodd_ct_catalog</span><span class="p">[</span><span class="s2">&quot;LAT&quot;</span><span class="p">],</span>
+        <span class="n">hypodd_ct_catalog</span><span class="p">[</span><span class="s2">&quot;DEPTH&quot;</span><span class="p">],</span>
+        <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">data_path</span><span class="si">}</span><span class="s2">/hypodd_ct_catalog.html&quot;</span><span class="p">,</span>
+    <span class="p">)</span>
+
+    <span class="n">gamma_catalog</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">gamma_catalog</span><span class="p">,</span> <span class="n">parse_dates</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;time&quot;</span><span class="p">])</span>
+    <span class="n">gamma_catalog</span><span class="p">[</span><span class="s2">&quot;depth_km&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">gamma_catalog</span><span class="p">[</span><span class="s2">&quot;depth(m)&quot;</span><span class="p">]</span> <span class="o">/</span> <span class="mf">1e3</span>
+    <span class="n">plot3d</span><span class="p">(</span>
+        <span class="n">gamma_catalog</span><span class="p">[</span><span class="s2">&quot;longitude&quot;</span><span class="p">],</span>
+        <span class="n">gamma_catalog</span><span class="p">[</span><span class="s2">&quot;latitude&quot;</span><span class="p">],</span>
+        <span class="n">gamma_catalog</span><span class="p">[</span><span class="s2">&quot;depth_km&quot;</span><span class="p">],</span>
+        <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">data_path</span><span class="si">}</span><span class="s2">/gamma_catalog.html&quot;</span><span class="p">,</span>
+    <span class="p">)</span>
+
+    <span class="n">standard_catalog</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">standard_catalog</span><span class="p">,</span> <span class="n">parse_dates</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;time&quot;</span><span class="p">])</span>
+    <span class="n">standard_catalog</span><span class="p">[</span><span class="s2">&quot;depth_km&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">standard_catalog</span><span class="p">[</span><span class="s2">&quot;depth(m)&quot;</span><span class="p">]</span> <span class="o">/</span> <span class="mf">1e3</span>
+    <span class="n">plot3d</span><span class="p">(</span>
+        <span class="n">standard_catalog</span><span class="p">[</span><span class="s2">&quot;longitude&quot;</span><span class="p">],</span>
+        <span class="n">standard_catalog</span><span class="p">[</span><span class="s2">&quot;latitude&quot;</span><span class="p">],</span>
+        <span class="n">standard_catalog</span><span class="p">[</span><span class="s2">&quot;depth_km&quot;</span><span class="p">],</span>
+        <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">data_path</span><span class="si">}</span><span class="s2">/standard_catalog.html&quot;</span><span class="p">,</span>
+    <span class="p">)</span>
+
+    <span class="c1">## histogram</span>
+    <span class="n">bins</span> <span class="o">=</span> <span class="mi">30</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;starttime&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">datetime</span><span class="o">.</span><span class="n">fromisoformat</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;starttime&quot;</span><span class="p">])</span>
+    <span class="n">config</span><span class="p">[</span><span class="s2">&quot;endtime&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">datetime</span><span class="o">.</span><span class="n">fromisoformat</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;endtime&quot;</span><span class="p">])</span>
+    <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span>
+        <span class="n">gamma_catalog</span><span class="p">[</span><span class="s2">&quot;time&quot;</span><span class="p">],</span>
+        <span class="nb">range</span><span class="o">=</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;starttime&quot;</span><span class="p">],</span> <span class="n">config</span><span class="p">[</span><span class="s2">&quot;endtime&quot;</span><span class="p">]),</span>
+        <span class="n">bins</span><span class="o">=</span><span class="n">bins</span><span class="p">,</span>
+        <span class="n">edgecolor</span><span class="o">=</span><span class="s2">&quot;k&quot;</span><span class="p">,</span>
+        <span class="n">alpha</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span>
+        <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span>
+        <span class="n">label</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;GaMMA: </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">gamma_catalog</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span>
+    <span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span>
+        <span class="n">hypodd_ct_catalog</span><span class="p">[</span><span class="s2">&quot;time&quot;</span><span class="p">],</span>
+        <span class="nb">range</span><span class="o">=</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;starttime&quot;</span><span class="p">],</span> <span class="n">config</span><span class="p">[</span><span class="s2">&quot;endtime&quot;</span><span class="p">]),</span>
+        <span class="n">bins</span><span class="o">=</span><span class="n">bins</span><span class="p">,</span>
+        <span class="n">edgecolor</span><span class="o">=</span><span class="s2">&quot;k&quot;</span><span class="p">,</span>
+        <span class="n">alpha</span><span class="o">=</span><span class="mf">0.8</span><span class="p">,</span>
+        <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span>
+        <span class="n">label</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;HypoDD: </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">hypodd_ct_catalog</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span>
+    <span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span>
+        <span class="n">standard_catalog</span><span class="p">[</span><span class="s2">&quot;time&quot;</span><span class="p">],</span>
+        <span class="nb">range</span><span class="o">=</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;starttime&quot;</span><span class="p">],</span> <span class="n">config</span><span class="p">[</span><span class="s2">&quot;endtime&quot;</span><span class="p">]),</span>
+        <span class="n">bins</span><span class="o">=</span><span class="n">bins</span><span class="p">,</span>
+        <span class="n">edgecolor</span><span class="o">=</span><span class="s2">&quot;k&quot;</span><span class="p">,</span>
+        <span class="n">alpha</span><span class="o">=</span><span class="mf">0.6</span><span class="p">,</span>
+        <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span>
+        <span class="n">label</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;Standard: </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">standard_catalog</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span>
+    <span class="p">)</span>
+    <span class="c1"># ax.set_xlabel(&quot;Date&quot;)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;Frequency&quot;</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">autoscale</span><span class="p">(</span><span class="n">enable</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="s2">&quot;x&quot;</span><span class="p">,</span> <span class="n">tight</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="c1"># ax.xaxis.set_major_formatter(mdates.DateFormatter(&#39;%Y-%m-%d&#39;))</span>
+    <span class="n">fig</span><span class="o">.</span><span class="n">autofmt_xdate</span><span class="p">()</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+    <span class="n">fig</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">data_path</span><span class="si">}</span><span class="s2">/earthquake_frequency_time.png&quot;</span><span class="p">,</span> <span class="n">bbox_inches</span><span class="o">=</span><span class="s2">&quot;tight&quot;</span><span class="p">,</span> <span class="n">dpi</span><span class="o">=</span><span class="mi">300</span><span class="p">)</span>
+    <span class="c1"># fig.savefig(f&quot;{data_path}/earthquake_number.pdf&quot;, bbox_inches=&quot;tight&quot;)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+
+    <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
+    <span class="n">xrange</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mf">1.0</span><span class="p">,</span> <span class="nb">max</span><span class="p">(</span><span class="n">standard_catalog</span><span class="p">[</span><span class="s2">&quot;magnitude&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">(),</span> <span class="n">gamma_catalog</span><span class="p">[</span><span class="s2">&quot;magnitude&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">()))</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span>
+        <span class="n">gamma_catalog</span><span class="p">[</span><span class="s2">&quot;magnitude&quot;</span><span class="p">],</span>
+        <span class="nb">range</span><span class="o">=</span><span class="n">xrange</span><span class="p">,</span>
+        <span class="n">bins</span><span class="o">=</span><span class="n">bins</span><span class="p">,</span>
+        <span class="n">alpha</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span>
+        <span class="n">edgecolor</span><span class="o">=</span><span class="s2">&quot;k&quot;</span><span class="p">,</span>
+        <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span>
+        <span class="n">label</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;GaMMA: </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">gamma_catalog</span><span class="p">[</span><span class="s1">&#39;magnitude&#39;</span><span class="p">])</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span>
+    <span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span>
+        <span class="n">hypodd_ct_catalog</span><span class="p">[</span><span class="s2">&quot;magnitude&quot;</span><span class="p">],</span>
+        <span class="nb">range</span><span class="o">=</span><span class="n">xrange</span><span class="p">,</span>
+        <span class="n">bins</span><span class="o">=</span><span class="n">bins</span><span class="p">,</span>
+        <span class="n">alpha</span><span class="o">=</span><span class="mf">0.6</span><span class="p">,</span>
+        <span class="n">edgecolor</span><span class="o">=</span><span class="s2">&quot;k&quot;</span><span class="p">,</span>
+        <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span>
+        <span class="n">label</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;HypoDD: </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">hypodd_ct_catalog</span><span class="p">[</span><span class="s1">&#39;magnitude&#39;</span><span class="p">])</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span>
+    <span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span>
+        <span class="n">standard_catalog</span><span class="p">[</span><span class="s2">&quot;magnitude&quot;</span><span class="p">],</span>
+        <span class="nb">range</span><span class="o">=</span><span class="n">xrange</span><span class="p">,</span>
+        <span class="n">bins</span><span class="o">=</span><span class="n">bins</span><span class="p">,</span>
+        <span class="n">alpha</span><span class="o">=</span><span class="mf">0.6</span><span class="p">,</span>
+        <span class="n">edgecolor</span><span class="o">=</span><span class="s2">&quot;k&quot;</span><span class="p">,</span>
+        <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span>
+        <span class="n">label</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;Standard: </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">standard_catalog</span><span class="p">[</span><span class="s1">&#39;magnitude&#39;</span><span class="p">])</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span>
+    <span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">autoscale</span><span class="p">(</span><span class="n">enable</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="s2">&quot;x&quot;</span><span class="p">,</span> <span class="n">tight</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;Magnitude&quot;</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;Frequency&quot;</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_yscale</span><span class="p">(</span><span class="s2">&quot;log&quot;</span><span class="p">)</span>
+    <span class="n">fig</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">data_path</span><span class="si">}</span><span class="s2">/earthquake_magnitude_frequency.png&quot;</span><span class="p">,</span> <span class="n">bbox_inches</span><span class="o">=</span><span class="s2">&quot;tight&quot;</span><span class="p">,</span> <span class="n">dpi</span><span class="o">=</span><span class="mi">300</span><span class="p">)</span>
+    <span class="c1"># fig.savefig(f&quot;{data_path}/earthquake_magnitude_frequency.pdf&quot;, bbox_inches=&quot;tight&quot;)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+
+    <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span>
+        <span class="n">gamma_catalog</span><span class="p">[</span><span class="s2">&quot;time&quot;</span><span class="p">],</span>
+        <span class="n">gamma_catalog</span><span class="p">[</span><span class="s2">&quot;magnitude&quot;</span><span class="p">],</span>
+        <span class="s2">&quot;.&quot;</span><span class="p">,</span>
+        <span class="n">markersize</span><span class="o">=</span><span class="mf">5.0</span><span class="p">,</span>
+        <span class="n">alpha</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span>
+        <span class="n">rasterized</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+        <span class="n">label</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;GaMMA: </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">gamma_catalog</span><span class="p">[</span><span class="s1">&#39;magnitude&#39;</span><span class="p">])</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span>
+    <span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span>
+        <span class="n">hypodd_ct_catalog</span><span class="p">[</span><span class="s2">&quot;time&quot;</span><span class="p">],</span>
+        <span class="n">hypodd_ct_catalog</span><span class="p">[</span><span class="s2">&quot;magnitude&quot;</span><span class="p">],</span>
+        <span class="s2">&quot;.&quot;</span><span class="p">,</span>
+        <span class="n">markersize</span><span class="o">=</span><span class="mf">5.0</span><span class="p">,</span>
+        <span class="n">alpha</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span>
+        <span class="n">rasterized</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+        <span class="n">label</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;HypoDD: </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">hypodd_ct_catalog</span><span class="p">[</span><span class="s1">&#39;magnitude&#39;</span><span class="p">])</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span>
+    <span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span>
+        <span class="n">standard_catalog</span><span class="p">[</span><span class="s2">&quot;time&quot;</span><span class="p">],</span>
+        <span class="n">standard_catalog</span><span class="p">[</span><span class="s2">&quot;magnitude&quot;</span><span class="p">],</span>
+        <span class="s2">&quot;.&quot;</span><span class="p">,</span>
+        <span class="n">markersize</span><span class="o">=</span><span class="mf">5.0</span><span class="p">,</span>
+        <span class="n">alpha</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span>
+        <span class="n">rasterized</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+        <span class="n">label</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;Standard: </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">standard_catalog</span><span class="p">[</span><span class="s1">&#39;magnitude&#39;</span><span class="p">])</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span>
+    <span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;starttime&quot;</span><span class="p">],</span> <span class="n">config</span><span class="p">[</span><span class="s2">&quot;endtime&quot;</span><span class="p">])</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;Magnitude&quot;</span><span class="p">)</span>
+    <span class="c1"># ax.set_xlabel(&quot;Date&quot;)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="n">bottom</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">markerscale</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">grid</span><span class="p">()</span>
+    <span class="c1"># ax.xaxis.set_major_formatter(mdates.DateFormatter(&#39;%Y-%m-%d&#39;))</span>
+    <span class="n">fig</span><span class="o">.</span><span class="n">autofmt_xdate</span><span class="p">()</span>
+    <span class="n">fig</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">data_path</span><span class="si">}</span><span class="s2">/earthquake_magnitude_time.png&quot;</span><span class="p">,</span> <span class="n">bbox_inches</span><span class="o">=</span><span class="s2">&quot;tight&quot;</span><span class="p">,</span> <span class="n">dpi</span><span class="o">=</span><span class="mi">300</span><span class="p">)</span>
+    <span class="c1"># fig.savefig(f&quot;{data_path}/earthquake_magnitude_time.pdf&quot;, bbox_inches=&quot;tight&quot;, dpi=300)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+
+    <span class="k">try</span><span class="p">:</span>
+        <span class="n">hypodd_cc_catalog</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span>
+            <span class="n">hypodd_catalog_cc</span><span class="p">,</span>
+            <span class="n">sep</span><span class="o">=</span><span class="s2">&quot;\s+&quot;</span><span class="p">,</span>
+            <span class="n">names</span><span class="o">=</span><span class="p">[</span>
+                <span class="s2">&quot;ID&quot;</span><span class="p">,</span>
+                <span class="s2">&quot;LAT&quot;</span><span class="p">,</span>
+                <span class="s2">&quot;LON&quot;</span><span class="p">,</span>
+                <span class="s2">&quot;DEPTH&quot;</span><span class="p">,</span>
+                <span class="s2">&quot;X&quot;</span><span class="p">,</span>
+                <span class="s2">&quot;Y&quot;</span><span class="p">,</span>
+                <span class="s2">&quot;Z&quot;</span><span class="p">,</span>
+                <span class="s2">&quot;EX&quot;</span><span class="p">,</span>
+                <span class="s2">&quot;EY&quot;</span><span class="p">,</span>
+                <span class="s2">&quot;EZ&quot;</span><span class="p">,</span>
+                <span class="s2">&quot;YR&quot;</span><span class="p">,</span>
+                <span class="s2">&quot;MO&quot;</span><span class="p">,</span>
+                <span class="s2">&quot;DY&quot;</span><span class="p">,</span>
+                <span class="s2">&quot;HR&quot;</span><span class="p">,</span>
+                <span class="s2">&quot;MI&quot;</span><span class="p">,</span>
+                <span class="s2">&quot;SC&quot;</span><span class="p">,</span>
+                <span class="s2">&quot;MAG&quot;</span><span class="p">,</span>
+                <span class="s2">&quot;NCCP&quot;</span><span class="p">,</span>
+                <span class="s2">&quot;NCCS&quot;</span><span class="p">,</span>
+                <span class="s2">&quot;NCTP&quot;</span><span class="p">,</span>
+                <span class="s2">&quot;NCTS&quot;</span><span class="p">,</span>
+                <span class="s2">&quot;RCC&quot;</span><span class="p">,</span>
+                <span class="s2">&quot;RCT&quot;</span><span class="p">,</span>
+                <span class="s2">&quot;CID&quot;</span><span class="p">,</span>
+            <span class="p">],</span>
+        <span class="p">)</span>
+        <span class="n">hypodd_cc_catalog</span><span class="p">[</span><span class="s2">&quot;time&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">hypodd_cc_catalog</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span>
+            <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">x</span><span class="p">[</span><span class="s2">&quot;YR&quot;</span><span class="p">]</span><span class="si">:</span><span class="s1">04.0f</span><span class="si">}</span><span class="s1">-</span><span class="si">{</span><span class="n">x</span><span class="p">[</span><span class="s2">&quot;MO&quot;</span><span class="p">]</span><span class="si">:</span><span class="s1">02.0f</span><span class="si">}</span><span class="s1">-</span><span class="si">{</span><span class="n">x</span><span class="p">[</span><span class="s2">&quot;DY&quot;</span><span class="p">]</span><span class="si">:</span><span class="s1">02.0f</span><span class="si">}</span><span class="s1">T</span><span class="si">{</span><span class="n">x</span><span class="p">[</span><span class="s2">&quot;HR&quot;</span><span class="p">]</span><span class="si">:</span><span class="s1">02.0f</span><span class="si">}</span><span class="s1">:</span><span class="si">{</span><span class="n">x</span><span class="p">[</span><span class="s2">&quot;MI&quot;</span><span class="p">]</span><span class="si">:</span><span class="s1">02.0f</span><span class="si">}</span><span class="s1">:</span><span class="si">{</span><span class="nb">min</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="s2">&quot;SC&quot;</span><span class="p">],</span><span class="w"> </span><span class="mf">59.999</span><span class="p">)</span><span class="si">:</span><span class="s1">05.3f</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">,</span>
+            <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="n">hypodd_cc_catalog</span><span class="p">[</span><span class="s2">&quot;time&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">hypodd_cc_catalog</span><span class="p">[</span><span class="s2">&quot;time&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span>
+            <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">datetime</span><span class="o">.</span><span class="n">strptime</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="s2">&quot;%Y-%m-</span><span class="si">%d</span><span class="s2">T%H:%M:%S.</span><span class="si">%f</span><span class="s2">&quot;</span><span class="p">)</span>
+        <span class="p">)</span>
+        <span class="n">plot3d</span><span class="p">(</span>
+            <span class="n">hypodd_cc_catalog</span><span class="p">[</span><span class="s2">&quot;LON&quot;</span><span class="p">],</span>
+            <span class="n">hypodd_cc_catalog</span><span class="p">[</span><span class="s2">&quot;LAT&quot;</span><span class="p">],</span>
+            <span class="n">hypodd_cc_catalog</span><span class="p">[</span><span class="s2">&quot;DEPTH&quot;</span><span class="p">],</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">data_path</span><span class="si">}</span><span class="s2">/hypodd_cc_catalog.html&quot;</span><span class="p">,</span>
+        <span class="p">)</span>
+    <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">err</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">err</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+
+    <span class="c1">## upload to s3 bucket</span>
+    <span class="k">try</span><span class="p">:</span>
+        <span class="kn">from</span> <span class="nn">minio</span> <span class="kn">import</span> <span class="n">Minio</span>
+
+        <span class="n">minioClient</span> <span class="o">=</span> <span class="n">Minio</span><span class="p">(</span><span class="n">s3_url</span><span class="p">,</span> <span class="n">access_key</span><span class="o">=</span><span class="s2">&quot;minio&quot;</span><span class="p">,</span> <span class="n">secret_key</span><span class="o">=</span><span class="s2">&quot;minio123&quot;</span><span class="p">,</span> <span class="n">secure</span><span class="o">=</span><span class="n">secure</span><span class="p">)</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">minioClient</span><span class="o">.</span><span class="n">bucket_exists</span><span class="p">(</span><span class="n">bucket_name</span><span class="p">):</span>
+            <span class="n">minioClient</span><span class="o">.</span><span class="n">make_bucket</span><span class="p">(</span><span class="n">bucket_name</span><span class="p">)</span>
+
+        <span class="n">minioClient</span><span class="o">.</span><span class="n">fput_object</span><span class="p">(</span>
+            <span class="n">bucket_name</span><span class="p">,</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;region&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">/hypodd_ct_catalog.html&quot;</span><span class="p">,</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">data_path</span><span class="si">}</span><span class="s2">/hypodd_ct_catalog.html&quot;</span><span class="p">,</span>
+        <span class="p">)</span>
+
+        <span class="n">minioClient</span><span class="o">.</span><span class="n">fput_object</span><span class="p">(</span>
+            <span class="n">bucket_name</span><span class="p">,</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;region&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">/gamma_catalog.html&quot;</span><span class="p">,</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">data_path</span><span class="si">}</span><span class="s2">/gamma_catalog.html&quot;</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="n">minioClient</span><span class="o">.</span><span class="n">fput_object</span><span class="p">(</span>
+            <span class="n">bucket_name</span><span class="p">,</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;region&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">/standard_catalog.html&quot;</span><span class="p">,</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">data_path</span><span class="si">}</span><span class="s2">/standard_catalog.html&quot;</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="n">minioClient</span><span class="o">.</span><span class="n">fput_object</span><span class="p">(</span>
+            <span class="n">bucket_name</span><span class="p">,</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;region&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">/earthquake_frequency_time.png&quot;</span><span class="p">,</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">data_path</span><span class="si">}</span><span class="s2">/earthquake_frequency_time.png&quot;</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="n">minioClient</span><span class="o">.</span><span class="n">fput_object</span><span class="p">(</span>
+            <span class="n">bucket_name</span><span class="p">,</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;region&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">/earthquake_magnitude_frequency.png&quot;</span><span class="p">,</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">data_path</span><span class="si">}</span><span class="s2">/earthquake_magnitude_frequency.png&quot;</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="n">minioClient</span><span class="o">.</span><span class="n">fput_object</span><span class="p">(</span>
+            <span class="n">bucket_name</span><span class="p">,</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;region&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">/earthquake_magnitude_time.png&quot;</span><span class="p">,</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">data_path</span><span class="si">}</span><span class="s2">/earthquake_magnitude_time.png&quot;</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">data_path</span><span class="si">}</span><span class="s2">/hypodd_cc_catalog.html&quot;</span><span class="p">):</span>
+            <span class="n">minioClient</span><span class="o">.</span><span class="n">fput_object</span><span class="p">(</span>
+                <span class="n">bucket_name</span><span class="p">,</span>
+                <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;region&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">/hypodd_cc_catalog.html&quot;</span><span class="p">,</span>
+                <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">data_path</span><span class="si">}</span><span class="s2">/hypodd_cc_catalog.html&quot;</span><span class="p">,</span>
+            <span class="p">)</span>
+
+    <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">err</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;ERROR: can not access minio service! </span><span class="se">\n</span><span class="si">{</span><span class="n">err</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+        <span class="k">pass</span>
+</pre></div>
+<div id="cell-50" class="clipboard-copy-txt">def visulization(
+    config_json: InputPath("json"),
+    hypodd_catalog_ct: InputPath(str),
+    hypodd_catalog_cc: InputPath(str),
+    gamma_catalog: InputPath(str),
+    standard_catalog: InputPath(str),
+    data_path: str = "./",
+    bucket_name: str = "catalogs",
+    s3_url: str = "minio-service:9000",
+    secure: bool = True,
+):
+    import pandas as pd
+    import plotly.graph_objects as go
+    import numpy as np
+    import json
+    import os
+    from datetime import datetime
+    import matplotlib.pyplot as plt
+    import matplotlib.dates as mdates
+
+    with open(config_json, "r") as fp:
+        config = json.load(fp)
+
+    def plot3d(x, y, z, fig_name):
+        fig = go.Figure(
+            data=[
+                go.Scatter3d(
+                    x=x,
+                    y=y,
+                    z=z,
+                    mode="markers",
+                    marker=dict(size=3.0, color=-z, cmin=-60, cmax=2, colorscale="Viridis", opacity=0.8),
+                )
+            ],
+        )
+
+        fig.update_layout(
+            scene=dict(
+                xaxis=dict(
+                    nticks=4,
+                    range=config["xlim_degree"],
+                ),
+                yaxis=dict(
+                    nticks=4,
+                    range=config["ylim_degree"],
+                ),
+                zaxis=dict(
+                    nticks=4,
+                    # range=[z.max(), z.min()],
+                    range=[60, -2],
+                ),
+                #         aspectratio = dict(x=(xlim[1]-xlim[0])/2, y=(ylim[1]-ylim[0])/2, z=1),
+                aspectratio=dict(x=1, y=1, z=0.5),
+            ),
+            margin=dict(r=0, l=0, b=0, t=0),
+        )
+        fig.write_html(fig_name)
+
+    hypodd_ct_catalog = pd.read_csv(
+        hypodd_catalog_ct,
+        sep="\s+",
+        names=[
+            "ID",
+            "LAT",
+            "LON",
+            "DEPTH",
+            "X",
+            "Y",
+            "Z",
+            "EX",
+            "EY",
+            "EZ",
+            "YR",
+            "MO",
+            "DY",
+            "HR",
+            "MI",
+            "SC",
+            "MAG",
+            "NCCP",
+            "NCCS",
+            "NCTP",
+            "NCTS",
+            "RCC",
+            "RCT",
+            "CID",
+        ],
+    )
+
+    hypodd_ct_catalog["MO"][hypodd_ct_catalog["MO"] == 0] = 1
+    hypodd_ct_catalog["DY"][hypodd_ct_catalog["DY"] == 0] = 1
+    hypodd_ct_catalog["time"] = hypodd_ct_catalog.apply(
+        lambda x: f'{x["YR"]:04.0f}-{x["MO"]:02.0f}-{x["DY"]:02.0f}T{x["HR"]:02.0f}:{x["MI"]:02.0f}:{min(x["SC"], 59.999):05.3f}',
+        axis=1,
+    )
+    hypodd_ct_catalog["magnitude"] = hypodd_ct_catalog["MAG"]
+    hypodd_ct_catalog["time"] = hypodd_ct_catalog["time"].apply(lambda x: datetime.strptime(x, "%Y-%m-%dT%H:%M:%S.%f"))
+    plot3d(
+        hypodd_ct_catalog["LON"],
+        hypodd_ct_catalog["LAT"],
+        hypodd_ct_catalog["DEPTH"],
+        f"{data_path}/hypodd_ct_catalog.html",
+    )
+
+    gamma_catalog = pd.read_csv(gamma_catalog, parse_dates=["time"])
+    gamma_catalog["depth_km"] = gamma_catalog["depth(m)"] / 1e3
+    plot3d(
+        gamma_catalog["longitude"],
+        gamma_catalog["latitude"],
+        gamma_catalog["depth_km"],
+        f"{data_path}/gamma_catalog.html",
+    )
+
+    standard_catalog = pd.read_csv(standard_catalog, parse_dates=["time"])
+    standard_catalog["depth_km"] = standard_catalog["depth(m)"] / 1e3
+    plot3d(
+        standard_catalog["longitude"],
+        standard_catalog["latitude"],
+        standard_catalog["depth_km"],
+        f"{data_path}/standard_catalog.html",
+    )
+
+    ## histogram
+    bins = 30
+    config["starttime"] = datetime.fromisoformat(config["starttime"])
+    config["endtime"] = datetime.fromisoformat(config["endtime"])
+    fig, ax = plt.subplots()
+    ax.hist(
+        gamma_catalog["time"],
+        range=(config["starttime"], config["endtime"]),
+        bins=bins,
+        edgecolor="k",
+        alpha=1.0,
+        linewidth=0.5,
+        label=f"GaMMA: {len(gamma_catalog)}",
+    )
+    ax.hist(
+        hypodd_ct_catalog["time"],
+        range=(config["starttime"], config["endtime"]),
+        bins=bins,
+        edgecolor="k",
+        alpha=0.8,
+        linewidth=0.5,
+        label=f"HypoDD: {len(hypodd_ct_catalog)}",
+    )
+    ax.hist(
+        standard_catalog["time"],
+        range=(config["starttime"], config["endtime"]),
+        bins=bins,
+        edgecolor="k",
+        alpha=0.6,
+        linewidth=0.5,
+        label=f"Standard: {len(standard_catalog)}",
+    )
+    # ax.set_xlabel("Date")
+    ax.set_ylabel("Frequency")
+    ax.autoscale(enable=True, axis="x", tight=True)
+    # ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
+    fig.autofmt_xdate()
+    ax.legend()
+    fig.savefig(f"{data_path}/earthquake_frequency_time.png", bbox_inches="tight", dpi=300)
+    # fig.savefig(f"{data_path}/earthquake_number.pdf", bbox_inches="tight")
+    plt.show()
+
+    fig, ax = plt.subplots()
+    xrange = (-1.0, max(standard_catalog["magnitude"].max(), gamma_catalog["magnitude"].max()))
+    ax.hist(
+        gamma_catalog["magnitude"],
+        range=xrange,
+        bins=bins,
+        alpha=1.0,
+        edgecolor="k",
+        linewidth=0.5,
+        label=f"GaMMA: {len(gamma_catalog['magnitude'])}",
+    )
+    ax.hist(
+        hypodd_ct_catalog["magnitude"],
+        range=xrange,
+        bins=bins,
+        alpha=0.6,
+        edgecolor="k",
+        linewidth=0.5,
+        label=f"HypoDD: {len(hypodd_ct_catalog['magnitude'])}",
+    )
+    ax.hist(
+        standard_catalog["magnitude"],
+        range=xrange,
+        bins=bins,
+        alpha=0.6,
+        edgecolor="k",
+        linewidth=0.5,
+        label=f"Standard: {len(standard_catalog['magnitude'])}",
+    )
+    ax.legend()
+    ax.autoscale(enable=True, axis="x", tight=True)
+    ax.set_xlabel("Magnitude")
+    ax.set_ylabel("Frequency")
+    ax.set_yscale("log")
+    fig.savefig(f"{data_path}/earthquake_magnitude_frequency.png", bbox_inches="tight", dpi=300)
+    # fig.savefig(f"{data_path}/earthquake_magnitude_frequency.pdf", bbox_inches="tight")
+    plt.show()
+
+    fig, ax = plt.subplots()
+    ax.plot(
+        gamma_catalog["time"],
+        gamma_catalog["magnitude"],
+        ".",
+        markersize=5.0,
+        alpha=1.0,
+        rasterized=True,
+        label=f"GaMMA: {len(gamma_catalog['magnitude'])}",
+    )
+    ax.plot(
+        hypodd_ct_catalog["time"],
+        hypodd_ct_catalog["magnitude"],
+        ".",
+        markersize=5.0,
+        alpha=1.0,
+        rasterized=True,
+        label=f"HypoDD: {len(hypodd_ct_catalog['magnitude'])}",
+    )
+    ax.plot(
+        standard_catalog["time"],
+        standard_catalog["magnitude"],
+        ".",
+        markersize=5.0,
+        alpha=1.0,
+        rasterized=True,
+        label=f"Standard: {len(standard_catalog['magnitude'])}",
+    )
+    ax.set_xlim(config["starttime"], config["endtime"])
+    ax.set_ylabel("Magnitude")
+    # ax.set_xlabel("Date")
+    ax.set_ylim(bottom=-1)
+    ax.legend(markerscale=2)
+    ax.grid()
+    # ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
+    fig.autofmt_xdate()
+    fig.savefig(f"{data_path}/earthquake_magnitude_time.png", bbox_inches="tight", dpi=300)
+    # fig.savefig(f"{data_path}/earthquake_magnitude_time.pdf", bbox_inches="tight", dpi=300)
+    plt.show()
+
+    try:
+        hypodd_cc_catalog = pd.read_csv(
+            hypodd_catalog_cc,
+            sep="\s+",
+            names=[
+                "ID",
+                "LAT",
+                "LON",
+                "DEPTH",
+                "X",
+                "Y",
+                "Z",
+                "EX",
+                "EY",
+                "EZ",
+                "YR",
+                "MO",
+                "DY",
+                "HR",
+                "MI",
+                "SC",
+                "MAG",
+                "NCCP",
+                "NCCS",
+                "NCTP",
+                "NCTS",
+                "RCC",
+                "RCT",
+                "CID",
+            ],
+        )
+        hypodd_cc_catalog["time"] = hypodd_cc_catalog.apply(
+            lambda x: f'{x["YR"]:04.0f}-{x["MO"]:02.0f}-{x["DY"]:02.0f}T{x["HR"]:02.0f}:{x["MI"]:02.0f}:{min(x["SC"], 59.999):05.3f}',
+            axis=1,
+        )
+        hypodd_cc_catalog["time"] = hypodd_cc_catalog["time"].apply(
+            lambda x: datetime.strptime(x, "%Y-%m-%dT%H:%M:%S.%f")
+        )
+        plot3d(
+            hypodd_cc_catalog["LON"],
+            hypodd_cc_catalog["LAT"],
+            hypodd_cc_catalog["DEPTH"],
+            f"{data_path}/hypodd_cc_catalog.html",
+        )
+    except Exception as err:
+        print(f"{err}")
+
+    ## upload to s3 bucket
+    try:
+        from minio import Minio
+
+        minioClient = Minio(s3_url, access_key="minio", secret_key="minio123", secure=secure)
+        if not minioClient.bucket_exists(bucket_name):
+            minioClient.make_bucket(bucket_name)
+
+        minioClient.fput_object(
+            bucket_name,
+            f"{config['region']}/hypodd_ct_catalog.html",
+            f"{data_path}/hypodd_ct_catalog.html",
+        )
+
+        minioClient.fput_object(
+            bucket_name,
+            f"{config['region']}/gamma_catalog.html",
+            f"{data_path}/gamma_catalog.html",
+        )
+        minioClient.fput_object(
+            bucket_name,
+            f"{config['region']}/standard_catalog.html",
+            f"{data_path}/standard_catalog.html",
+        )
+        minioClient.fput_object(
+            bucket_name,
+            f"{config['region']}/earthquake_frequency_time.png",
+            f"{data_path}/earthquake_frequency_time.png",
+        )
+        minioClient.fput_object(
+            bucket_name,
+            f"{config['region']}/earthquake_magnitude_frequency.png",
+            f"{data_path}/earthquake_magnitude_frequency.png",
+        )
+        minioClient.fput_object(
+            bucket_name,
+            f"{config['region']}/earthquake_magnitude_time.png",
+            f"{data_path}/earthquake_magnitude_time.png",
+        )
+        if os.path.exists(f"{data_path}/hypodd_cc_catalog.html"):
+            minioClient.fput_object(
+                bucket_name,
+                f"{config['region']}/hypodd_cc_catalog.html",
+                f"{data_path}/hypodd_cc_catalog.html",
+            )
+
+    except Exception as err:
+        print(f"ERROR: can not access minio service! \n{err}")
+        pass</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[73]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-51">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="k">if</span> <span class="n">run_local</span><span class="p">:</span>
+    <span class="n">visulization</span><span class="p">(</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;config.json&quot;</span><span class="p">),</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;hypodd_ct_catalog.txt&quot;</span><span class="p">),</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;hypodd_cc_catalog.txt&quot;</span><span class="p">),</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;gamma_catalog.csv&quot;</span><span class="p">),</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;standard_catalog.csv&quot;</span><span class="p">),</span>
+        <span class="n">root_dir</span><span class="p">(</span><span class="s2">&quot;&quot;</span><span class="p">),</span>
+    <span class="p">)</span>
+</pre></div>
+<div id="cell-51" class="clipboard-copy-txt">if run_local:
+    visulization(
+        root_dir("config.json"),
+        root_dir("hypodd_ct_catalog.txt"),
+        root_dir("hypodd_cc_catalog.txt"),
+        root_dir("gamma_catalog.csv"),
+        root_dir("standard_catalog.csv"),
+        root_dir(""),
+    )</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>
+<span class="ansi-red-fg">---------------------------------------------------------------------------</span>
+<span class="ansi-red-fg">ModuleNotFoundError</span>                       Traceback (most recent call last)
+<span class="ansi-green-intense-fg ansi-bold">/Users/weiqiang/Research/QuakeFlow_cleanup/kubeflow/workflow.ipynb Cell 71</span> in <span class="ansi-cyan-fg">&lt;cell line: 1&gt;</span><span class="ansi-blue-fg">()</span>
+<span class="ansi-green-intense-fg ansi-bold">      &lt;a href=&#39;vscode-notebook-cell:/Users/weiqiang/Research/QuakeFlow_cleanup/kubeflow/workflow.ipynb#Y130sZmlsZQ%3D%3D?line=0&#39;&gt;1&lt;/a&gt;</span> if run_local:
+<span class="ansi-green-fg">----&gt; &lt;a href=&#39;vscode-notebook-cell:/Users/weiqiang/Research/QuakeFlow_cleanup/kubeflow/workflow.ipynb#Y130sZmlsZQ%3D%3D?line=1&#39;&gt;2&lt;/a&gt;</span>     visulization(
+<span class="ansi-green-intense-fg ansi-bold">      &lt;a href=&#39;vscode-notebook-cell:/Users/weiqiang/Research/QuakeFlow_cleanup/kubeflow/workflow.ipynb#Y130sZmlsZQ%3D%3D?line=2&#39;&gt;3&lt;/a&gt;</span>         root_dir(&#34;config.json&#34;),
+<span class="ansi-green-intense-fg ansi-bold">      &lt;a href=&#39;vscode-notebook-cell:/Users/weiqiang/Research/QuakeFlow_cleanup/kubeflow/workflow.ipynb#Y130sZmlsZQ%3D%3D?line=3&#39;&gt;4&lt;/a&gt;</span>         root_dir(&#34;hypodd_ct_catalog.txt&#34;),
+<span class="ansi-green-intense-fg ansi-bold">      &lt;a href=&#39;vscode-notebook-cell:/Users/weiqiang/Research/QuakeFlow_cleanup/kubeflow/workflow.ipynb#Y130sZmlsZQ%3D%3D?line=4&#39;&gt;5&lt;/a&gt;</span>         root_dir(&#34;hypodd_cc_catalog.txt&#34;),
+<span class="ansi-green-intense-fg ansi-bold">      &lt;a href=&#39;vscode-notebook-cell:/Users/weiqiang/Research/QuakeFlow_cleanup/kubeflow/workflow.ipynb#Y130sZmlsZQ%3D%3D?line=5&#39;&gt;6&lt;/a&gt;</span>         root_dir(&#34;gamma_catalog.csv&#34;),
+<span class="ansi-green-intense-fg ansi-bold">      &lt;a href=&#39;vscode-notebook-cell:/Users/weiqiang/Research/QuakeFlow_cleanup/kubeflow/workflow.ipynb#Y130sZmlsZQ%3D%3D?line=6&#39;&gt;7&lt;/a&gt;</span>         root_dir(&#34;standard_catalog.csv&#34;),
+<span class="ansi-green-intense-fg ansi-bold">      &lt;a href=&#39;vscode-notebook-cell:/Users/weiqiang/Research/QuakeFlow_cleanup/kubeflow/workflow.ipynb#Y130sZmlsZQ%3D%3D?line=7&#39;&gt;8&lt;/a&gt;</span>         root_dir(&#34;&#34;),
+<span class="ansi-green-intense-fg ansi-bold">      &lt;a href=&#39;vscode-notebook-cell:/Users/weiqiang/Research/QuakeFlow_cleanup/kubeflow/workflow.ipynb#Y130sZmlsZQ%3D%3D?line=8&#39;&gt;9&lt;/a&gt;</span>     )
+
+<span class="ansi-green-intense-fg ansi-bold">/Users/weiqiang/Research/QuakeFlow_cleanup/kubeflow/workflow.ipynb Cell 71</span> in <span class="ansi-cyan-fg">visulization</span><span class="ansi-blue-fg">(config_json, hypodd_catalog_ct, hypodd_catalog_cc, gamma_catalog, standard_catalog, data_path, bucket_name, s3_url, secure)</span>
+<span class="ansi-green-intense-fg ansi-bold">      &lt;a href=&#39;vscode-notebook-cell:/Users/weiqiang/Research/QuakeFlow_cleanup/kubeflow/workflow.ipynb#Y130sZmlsZQ%3D%3D?line=0&#39;&gt;1&lt;/a&gt;</span> def visulization(
+<span class="ansi-green-intense-fg ansi-bold">      &lt;a href=&#39;vscode-notebook-cell:/Users/weiqiang/Research/QuakeFlow_cleanup/kubeflow/workflow.ipynb#Y130sZmlsZQ%3D%3D?line=1&#39;&gt;2&lt;/a&gt;</span>     config_json: InputPath(&#34;json&#34;),
+<span class="ansi-green-intense-fg ansi-bold">      &lt;a href=&#39;vscode-notebook-cell:/Users/weiqiang/Research/QuakeFlow_cleanup/kubeflow/workflow.ipynb#Y130sZmlsZQ%3D%3D?line=2&#39;&gt;3&lt;/a&gt;</span>     hypodd_catalog_ct: InputPath(str),
+<span class="ansi-green-fg">   (...)</span>
+<span class="ansi-green-intense-fg ansi-bold">     &lt;a href=&#39;vscode-notebook-cell:/Users/weiqiang/Research/QuakeFlow_cleanup/kubeflow/workflow.ipynb#Y130sZmlsZQ%3D%3D?line=9&#39;&gt;10&lt;/a&gt;</span>     secure: bool = True,
+<span class="ansi-green-intense-fg ansi-bold">     &lt;a href=&#39;vscode-notebook-cell:/Users/weiqiang/Research/QuakeFlow_cleanup/kubeflow/workflow.ipynb#Y130sZmlsZQ%3D%3D?line=10&#39;&gt;11&lt;/a&gt;</span> ):
+<span class="ansi-green-intense-fg ansi-bold">     &lt;a href=&#39;vscode-notebook-cell:/Users/weiqiang/Research/QuakeFlow_cleanup/kubeflow/workflow.ipynb#Y130sZmlsZQ%3D%3D?line=11&#39;&gt;12&lt;/a&gt;</span>     import pandas as pd
+<span class="ansi-green-fg">---&gt; &lt;a href=&#39;vscode-notebook-cell:/Users/weiqiang/Research/QuakeFlow_cleanup/kubeflow/workflow.ipynb#Y130sZmlsZQ%3D%3D?line=12&#39;&gt;13&lt;/a&gt;</span>     import plotly.graph_objects as go
+<span class="ansi-green-intense-fg ansi-bold">     &lt;a href=&#39;vscode-notebook-cell:/Users/weiqiang/Research/QuakeFlow_cleanup/kubeflow/workflow.ipynb#Y130sZmlsZQ%3D%3D?line=13&#39;&gt;14&lt;/a&gt;</span>     import numpy as np
+<span class="ansi-green-intense-fg ansi-bold">     &lt;a href=&#39;vscode-notebook-cell:/Users/weiqiang/Research/QuakeFlow_cleanup/kubeflow/workflow.ipynb#Y130sZmlsZQ%3D%3D?line=14&#39;&gt;15&lt;/a&gt;</span>     import json
+
+<span class="ansi-red-fg">ModuleNotFoundError</span>: No module named &#39;plotly&#39;</pre>
+</div>
+</div>
+
+</div>
+
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-52">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">visulization_op</span> <span class="o">=</span> <span class="n">comp</span><span class="o">.</span><span class="n">func_to_container_op</span><span class="p">(</span>
+    <span class="n">visulization</span><span class="p">,</span>
+    <span class="n">base_image</span><span class="o">=</span><span class="s2">&quot;python:3.8&quot;</span><span class="p">,</span>
+    <span class="n">packages_to_install</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;matplotlib&quot;</span><span class="p">,</span> <span class="s2">&quot;pandas&quot;</span><span class="p">,</span> <span class="s2">&quot;plotly&quot;</span><span class="p">,</span> <span class="s2">&quot;minio&quot;</span><span class="p">],</span>
+<span class="p">)</span>
+</pre></div>
+<div id="cell-52" class="clipboard-copy-txt">visulization_op = comp.func_to_container_op(
+    visulization,
+    base_image="python:3.8",
+    packages_to_install=["matplotlib", "pandas", "plotly", "minio"],
+)</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="9-parallel-process-on-cloud">9. Parallel process on cloud<a class="anchor-link" href="#9-parallel-process-on-cloud">&#182;</a></h2>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-53">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="nd">@dsl</span><span class="o">.</span><span class="n">pipeline</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;QuakeFlow&quot;</span><span class="p">,</span> <span class="n">description</span><span class="o">=</span><span class="s2">&quot;&quot;</span><span class="p">)</span>
+<span class="k">def</span> <span class="nf">quakeflow_pipeline</span><span class="p">(</span>
+    <span class="n">data_path</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;/tmp/&quot;</span><span class="p">,</span>
+    <span class="n">num_parallel</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+    <span class="n">bucket_catalog</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;catalogs&quot;</span><span class="p">,</span>
+    <span class="n">s3_url</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;minio-service:9000&quot;</span><span class="p">,</span>
+    <span class="n">secure</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+<span class="p">):</span>
+
+    <span class="n">config</span> <span class="o">=</span> <span class="p">(</span>
+        <span class="n">config_op</span><span class="p">(</span><span class="n">region_name</span><span class="p">,</span> <span class="n">num_parallel</span><span class="p">,</span> <span class="n">bucket_name</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;catalogs&quot;</span><span class="p">,</span> <span class="n">s3_url</span><span class="o">=</span><span class="n">s3_url</span><span class="p">,</span> <span class="n">secure</span><span class="o">=</span><span class="n">secure</span><span class="p">)</span>
+        <span class="o">.</span><span class="n">set_retry</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
+        <span class="o">.</span><span class="n">add_node_selector_constraint</span><span class="p">(</span><span class="s2">&quot;cloud.google.com/gke-nodepool&quot;</span><span class="p">,</span> <span class="s2">&quot;spot-2g&quot;</span><span class="p">)</span>
+        <span class="o">.</span><span class="n">set_cpu_request</span><span class="p">(</span><span class="s2">&quot;700m&quot;</span><span class="p">)</span>
+    <span class="p">)</span>
+    <span class="n">config</span><span class="o">.</span><span class="n">execution_options</span><span class="o">.</span><span class="n">caching_strategy</span><span class="o">.</span><span class="n">max_cache_staleness</span> <span class="o">=</span> <span class="s2">&quot;P30D&quot;</span>
+
+    <span class="n">events</span> <span class="o">=</span> <span class="p">(</span>
+        <span class="n">download_events_op</span><span class="p">(</span><span class="n">config</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;config_json&quot;</span><span class="p">],</span> <span class="n">bucket_name</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;catalogs&quot;</span><span class="p">,</span> <span class="n">s3_url</span><span class="o">=</span><span class="n">s3_url</span><span class="p">,</span> <span class="n">secure</span><span class="o">=</span><span class="n">secure</span><span class="p">)</span>
+        <span class="o">.</span><span class="n">add_node_selector_constraint</span><span class="p">(</span><span class="s2">&quot;cloud.google.com/gke-nodepool&quot;</span><span class="p">,</span> <span class="s2">&quot;spot-16g&quot;</span><span class="p">)</span>
+        <span class="o">.</span><span class="n">set_retry</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
+        <span class="o">.</span><span class="n">set_memory_request</span><span class="p">(</span><span class="s2">&quot;4G&quot;</span><span class="p">)</span>
+        <span class="o">.</span><span class="n">set_cpu_request</span><span class="p">(</span><span class="s2">&quot;700m&quot;</span><span class="p">)</span>
+        <span class="o">.</span><span class="n">set_display_name</span><span class="p">(</span><span class="s2">&quot;Download Events&quot;</span><span class="p">)</span>
+    <span class="p">)</span>
+    <span class="n">events</span><span class="o">.</span><span class="n">execution_options</span><span class="o">.</span><span class="n">caching_strategy</span><span class="o">.</span><span class="n">max_cache_staleness</span> <span class="o">=</span> <span class="s2">&quot;P30D&quot;</span>
+
+    <span class="n">stations</span> <span class="o">=</span> <span class="p">(</span>
+        <span class="n">download_stations_op</span><span class="p">(</span><span class="n">config</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;config_json&quot;</span><span class="p">],</span> <span class="n">bucket_name</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;catalogs&quot;</span><span class="p">,</span> <span class="n">s3_url</span><span class="o">=</span><span class="n">s3_url</span><span class="p">,</span> <span class="n">secure</span><span class="o">=</span><span class="n">secure</span><span class="p">)</span>
+        <span class="o">.</span><span class="n">add_node_selector_constraint</span><span class="p">(</span><span class="s2">&quot;cloud.google.com/gke-nodepool&quot;</span><span class="p">,</span> <span class="s2">&quot;spot-2g&quot;</span><span class="p">)</span>
+        <span class="o">.</span><span class="n">set_retry</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
+        <span class="o">.</span><span class="n">set_cpu_request</span><span class="p">(</span><span class="s2">&quot;700m&quot;</span><span class="p">)</span>
+        <span class="o">.</span><span class="n">set_display_name</span><span class="p">(</span><span class="s2">&quot;Download Stations&quot;</span><span class="p">)</span>
+    <span class="p">)</span>
+    <span class="n">stations</span><span class="o">.</span><span class="n">execution_options</span><span class="o">.</span><span class="n">caching_strategy</span><span class="o">.</span><span class="n">max_cache_staleness</span> <span class="o">=</span> <span class="s2">&quot;P30D&quot;</span>
+
+    <span class="k">with</span> <span class="n">kfp</span><span class="o">.</span><span class="n">dsl</span><span class="o">.</span><span class="n">ParallelFor</span><span class="p">(</span><span class="n">config</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;output&quot;</span><span class="p">])</span> <span class="k">as</span> <span class="n">i</span><span class="p">:</span>
+
+        <span class="n">vop_</span> <span class="o">=</span> <span class="n">dsl</span><span class="o">.</span><span class="n">VolumeOp</span><span class="p">(</span>
+            <span class="n">name</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;Create volume </span><span class="si">{</span><span class="n">region_name</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span>
+            <span class="n">resource_name</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;data-volume-</span><span class="si">{</span><span class="nb">str</span><span class="p">(</span><span class="n">i</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span>
+            <span class="n">size</span><span class="o">=</span><span class="s2">&quot;120Gi&quot;</span><span class="p">,</span>
+            <span class="n">modes</span><span class="o">=</span><span class="n">dsl</span><span class="o">.</span><span class="n">VOLUME_MODE_RWO</span><span class="p">,</span>
+        <span class="p">)</span><span class="o">.</span><span class="n">set_retry</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
+
+        <span class="n">download_op_</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="n">download_waveform_op</span><span class="p">(</span>
+                <span class="n">i</span><span class="p">,</span>
+                <span class="n">config</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;index_json&quot;</span><span class="p">],</span>
+                <span class="n">config</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;config_json&quot;</span><span class="p">],</span>
+                <span class="n">config</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;datetime_json&quot;</span><span class="p">],</span>
+                <span class="n">stations</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;station_pkl&quot;</span><span class="p">],</span>
+                <span class="n">data_path</span><span class="o">=</span><span class="n">data_path</span><span class="p">,</span>
+                <span class="n">bucket_name</span><span class="o">=</span><span class="s2">&quot;waveforms&quot;</span><span class="p">,</span>
+                <span class="n">s3_url</span><span class="o">=</span><span class="n">s3_url</span><span class="p">,</span>
+                <span class="n">secure</span><span class="o">=</span><span class="n">secure</span><span class="p">,</span>
+            <span class="p">)</span>
+            <span class="o">.</span><span class="n">add_pvolumes</span><span class="p">({</span><span class="n">data_path</span><span class="p">:</span> <span class="n">vop_</span><span class="o">.</span><span class="n">volume</span><span class="p">})</span>
+            <span class="o">.</span><span class="n">set_cpu_request</span><span class="p">(</span><span class="s2">&quot;700m&quot;</span><span class="p">)</span>
+            <span class="c1"># .set_cpu_request(&quot;350m&quot;)</span>
+            <span class="o">.</span><span class="n">add_node_selector_constraint</span><span class="p">(</span><span class="s2">&quot;cloud.google.com/gke-nodepool&quot;</span><span class="p">,</span> <span class="s2">&quot;spot-2g&quot;</span><span class="p">)</span>
+            <span class="o">.</span><span class="n">set_retry</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
+            <span class="o">.</span><span class="n">set_display_name</span><span class="p">(</span><span class="s2">&quot;Download Waveforms&quot;</span><span class="p">)</span>
+        <span class="p">)</span>
+        <span class="n">download_op_</span><span class="o">.</span><span class="n">execution_options</span><span class="o">.</span><span class="n">caching_strategy</span><span class="o">.</span><span class="n">max_cache_staleness</span> <span class="o">=</span> <span class="s2">&quot;P30D&quot;</span>
+
+        <span class="n">phasenet_op_</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="n">phasenet_op</span><span class="p">(</span>
+                <span class="n">download_op_</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;Output&quot;</span><span class="p">],</span>
+                <span class="n">download_op_</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;fname_csv&quot;</span><span class="p">],</span>
+                <span class="n">stations</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;station_json&quot;</span><span class="p">],</span>
+            <span class="p">)</span>
+            <span class="o">.</span><span class="n">add_pvolumes</span><span class="p">({</span><span class="n">data_path</span><span class="p">:</span> <span class="n">download_op_</span><span class="o">.</span><span class="n">pvolume</span><span class="p">})</span>
+            <span class="o">.</span><span class="n">set_memory_request</span><span class="p">(</span><span class="s2">&quot;9G&quot;</span><span class="p">)</span>
+            <span class="o">.</span><span class="n">add_node_selector_constraint</span><span class="p">(</span><span class="s2">&quot;cloud.google.com/gke-nodepool&quot;</span><span class="p">,</span> <span class="s2">&quot;pool-16g&quot;</span><span class="p">)</span>
+            <span class="o">.</span><span class="n">set_retry</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
+            <span class="o">.</span><span class="n">set_display_name</span><span class="p">(</span><span class="s2">&quot;PhaseNet Picking&quot;</span><span class="p">)</span>
+        <span class="p">)</span>
+        <span class="n">phasenet_op_</span><span class="o">.</span><span class="n">execution_options</span><span class="o">.</span><span class="n">caching_strategy</span><span class="o">.</span><span class="n">max_cache_staleness</span> <span class="o">=</span> <span class="s2">&quot;P30D&quot;</span>
+        <span class="n">phasenet_op_</span><span class="o">.</span><span class="n">set_image_pull_policy</span><span class="p">(</span><span class="s2">&quot;Always&quot;</span><span class="p">)</span>
+
+        <span class="n">gamma_op_</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="n">gamma_op</span><span class="p">(</span>
+                <span class="n">i</span><span class="p">,</span>
+                <span class="n">config</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;index_json&quot;</span><span class="p">],</span>
+                <span class="n">config</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;config_json&quot;</span><span class="p">],</span>
+                <span class="n">phasenet_op_</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;picks&quot;</span><span class="p">],</span>
+                <span class="n">stations</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;station_json&quot;</span><span class="p">],</span>
+                <span class="n">bucket_name</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;catalogs&quot;</span><span class="p">,</span>
+                <span class="n">s3_url</span><span class="o">=</span><span class="n">s3_url</span><span class="p">,</span>
+                <span class="n">secure</span><span class="o">=</span><span class="n">secure</span><span class="p">,</span>
+            <span class="p">)</span>
+            <span class="o">.</span><span class="n">set_cpu_request</span><span class="p">(</span><span class="s2">&quot;800m&quot;</span><span class="p">)</span>
+            <span class="o">.</span><span class="n">add_node_selector_constraint</span><span class="p">(</span><span class="s2">&quot;cloud.google.com/gke-nodepool&quot;</span><span class="p">,</span> <span class="s2">&quot;pool-16g&quot;</span><span class="p">)</span>
+            <span class="o">.</span><span class="n">set_retry</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
+            <span class="o">.</span><span class="n">set_display_name</span><span class="p">(</span><span class="s2">&quot;GaMMA Association&quot;</span><span class="p">)</span>
+        <span class="p">)</span>
+        <span class="n">gamma_op_</span><span class="o">.</span><span class="n">execution_options</span><span class="o">.</span><span class="n">caching_strategy</span><span class="o">.</span><span class="n">max_cache_staleness</span> <span class="o">=</span> <span class="s2">&quot;P30D&quot;</span>
+
+        <span class="c1"># resume_op_ = (</span>
+        <span class="c1">#     resume_waveform_op(</span>
+        <span class="c1">#         i,</span>
+        <span class="c1">#         config.outputs[&quot;index_json&quot;],</span>
+        <span class="c1">#         config.outputs[&quot;config_json&quot;],</span>
+        <span class="c1">#         config.outputs[&quot;datetime_json&quot;],</span>
+        <span class="c1">#         data_path=data_path,</span>
+        <span class="c1">#         bucket_name=&quot;waveforms&quot;,</span>
+        <span class="c1">#         s3_url=s3_url,</span>
+        <span class="c1">#         secure=secure,</span>
+        <span class="c1">#     )</span>
+        <span class="c1">#     .add_pvolumes({data_path: vop_.volume})</span>
+        <span class="c1">#     .set_cpu_request(&quot;700m&quot;)</span>
+        <span class="c1">#     # .set_cpu_request(&quot;350m&quot;)</span>
+        <span class="c1">#     .set_retry(5)</span>
+        <span class="c1">#     .add_node_selector_constraint(&quot;cloud.google.com/gke-nodepool&quot;, &quot;spot-2g&quot;)</span>
+        <span class="c1">#     .set_display_name(&quot;Resume Waveforms&quot;)</span>
+        <span class="c1"># )</span>
+        <span class="c1"># resume_op_.execution_options.caching_strategy.max_cache_staleness = &quot;P0D&quot;</span>
+
+        <span class="c1"># save_op_ = (</span>
+        <span class="c1">#     save_waveform_op(</span>
+        <span class="c1">#         i,</span>
+        <span class="c1">#         config.outputs[&quot;index_json&quot;],</span>
+        <span class="c1">#         config.outputs[&quot;config_json&quot;],</span>
+        <span class="c1">#         config.outputs[&quot;datetime_json&quot;],</span>
+        <span class="c1">#         data_path=data_path,</span>
+        <span class="c1">#         bucket_name=&quot;waveforms&quot;,</span>
+        <span class="c1">#         s3_url=s3_url,</span>
+        <span class="c1">#         secure=secure,</span>
+        <span class="c1">#     )</span>
+        <span class="c1">#     .add_pvolumes({data_path: vop_.volume})</span>
+        <span class="c1">#     .set_cpu_request(&quot;700m&quot;)</span>
+        <span class="c1">#     # .set_cpu_request(&quot;350m&quot;)</span>
+        <span class="c1">#     .set_retry(5)</span>
+        <span class="c1">#     .add_node_selector_constraint(&quot;cloud.google.com/gke-nodepool&quot;, &quot;spot-2g&quot;)</span>
+        <span class="c1">#     .set_display_name(&quot;Save Waveforms&quot;)</span>
+        <span class="c1"># )</span>
+        <span class="c1"># save_op_.execution_options.caching_strategy.max_cache_staleness = &quot;P0D&quot;</span>
+
+    <span class="n">merge_op_</span> <span class="o">=</span> <span class="p">(</span>
+        <span class="n">merge_op</span><span class="p">(</span>
+            <span class="n">config</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;index_json&quot;</span><span class="p">],</span>
+            <span class="n">config</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;config_json&quot;</span><span class="p">],</span>
+            <span class="n">bucket_name</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;catalogs&quot;</span><span class="p">,</span>
+            <span class="n">s3_url</span><span class="o">=</span><span class="n">s3_url</span><span class="p">,</span>
+            <span class="n">secure</span><span class="o">=</span><span class="n">secure</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="o">.</span><span class="n">after</span><span class="p">(</span><span class="n">gamma_op_</span><span class="p">)</span>
+        <span class="o">.</span><span class="n">set_memory_request</span><span class="p">(</span><span class="s2">&quot;12G&quot;</span><span class="p">)</span>
+        <span class="o">.</span><span class="n">add_node_selector_constraint</span><span class="p">(</span><span class="s2">&quot;cloud.google.com/gke-nodepool&quot;</span><span class="p">,</span> <span class="s2">&quot;spot-64g&quot;</span><span class="p">)</span>
+        <span class="o">.</span><span class="n">set_display_name</span><span class="p">(</span><span class="s2">&quot;Merge Catalog&quot;</span><span class="p">)</span>
+    <span class="p">)</span>
+    <span class="n">merge_op_</span><span class="o">.</span><span class="n">execution_options</span><span class="o">.</span><span class="n">caching_strategy</span><span class="o">.</span><span class="n">max_cache_staleness</span> <span class="o">=</span> <span class="s2">&quot;P30D&quot;</span>
+
+    <span class="n">convert_station_op_</span> <span class="o">=</span> <span class="p">(</span>
+        <span class="n">convert_station_op</span><span class="p">(</span>
+            <span class="n">config_json</span><span class="o">=</span><span class="n">config</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;config_json&quot;</span><span class="p">],</span>
+            <span class="n">station_json</span><span class="o">=</span><span class="n">stations</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;station_json&quot;</span><span class="p">],</span>
+            <span class="n">bucket_name</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;catalogs&quot;</span><span class="p">,</span>
+            <span class="n">s3_url</span><span class="o">=</span><span class="n">s3_url</span><span class="p">,</span>
+            <span class="n">secure</span><span class="o">=</span><span class="n">secure</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="o">.</span><span class="n">add_node_selector_constraint</span><span class="p">(</span><span class="s2">&quot;cloud.google.com/gke-nodepool&quot;</span><span class="p">,</span> <span class="s2">&quot;spot-2g&quot;</span><span class="p">)</span>
+        <span class="o">.</span><span class="n">set_display_name</span><span class="p">(</span><span class="s2">&quot;Convert Station Format&quot;</span><span class="p">)</span>
+    <span class="p">)</span>
+    <span class="n">split_hypodd_op_</span> <span class="o">=</span> <span class="p">(</span>
+        <span class="n">split_hypodd_op</span><span class="p">(</span>
+            <span class="n">config</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;config_json&quot;</span><span class="p">],</span>
+            <span class="n">catalog_csv</span><span class="o">=</span><span class="n">merge_op_</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;catalog_csv&quot;</span><span class="p">],</span>
+        <span class="p">)</span>
+        <span class="o">.</span><span class="n">after</span><span class="p">(</span><span class="n">merge_op_</span><span class="p">)</span>
+        <span class="o">.</span><span class="n">add_node_selector_constraint</span><span class="p">(</span><span class="s2">&quot;cloud.google.com/gke-nodepool&quot;</span><span class="p">,</span> <span class="s2">&quot;spot-64g&quot;</span><span class="p">)</span>
+        <span class="o">.</span><span class="n">set_display_name</span><span class="p">(</span><span class="s2">&quot;Split Catalog&quot;</span><span class="p">)</span>
+    <span class="p">)</span>
+    <span class="n">split_hypodd_op_</span><span class="o">.</span><span class="n">execution_options</span><span class="o">.</span><span class="n">caching_strategy</span><span class="o">.</span><span class="n">max_cache_staleness</span> <span class="o">=</span> <span class="s2">&quot;P30D&quot;</span>
+    <span class="n">split_hypodd_op_</span><span class="o">.</span><span class="n">set_image_pull_policy</span><span class="p">(</span><span class="s2">&quot;Always&quot;</span><span class="p">)</span>
+
+    <span class="k">with</span> <span class="n">kfp</span><span class="o">.</span><span class="n">dsl</span><span class="o">.</span><span class="n">ParallelFor</span><span class="p">(</span><span class="n">split_hypodd_op_</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;output&quot;</span><span class="p">])</span> <span class="k">as</span> <span class="n">i</span><span class="p">:</span>
+
+        <span class="n">convert_phase_op_</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="n">convert_phase_op</span><span class="p">(</span>
+                <span class="n">i</span><span class="p">,</span>
+                <span class="n">config_json</span><span class="o">=</span><span class="n">config</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;config_json&quot;</span><span class="p">],</span>
+                <span class="n">picks_csv</span><span class="o">=</span><span class="n">merge_op_</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;picks_csv&quot;</span><span class="p">],</span>
+                <span class="n">catalog_csv</span><span class="o">=</span><span class="n">merge_op_</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;catalog_csv&quot;</span><span class="p">],</span>
+                <span class="n">bucket_name</span><span class="o">=</span><span class="s2">&quot;catalogs&quot;</span><span class="p">,</span>
+                <span class="n">s3_url</span><span class="o">=</span><span class="n">s3_url</span><span class="p">,</span>
+                <span class="n">secure</span><span class="o">=</span><span class="n">secure</span><span class="p">,</span>
+            <span class="p">)</span>
+            <span class="o">.</span><span class="n">set_retry</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
+            <span class="o">.</span><span class="n">set_display_name</span><span class="p">(</span><span class="s2">&quot;Convert Phase Format&quot;</span><span class="p">)</span>
+            <span class="c1"># .set_memory_request(&quot;48G&quot;)</span>
+            <span class="c1"># .add_node_selector_constraint(&quot;cloud.google.com/gke-nodepool&quot;, &quot;spot-64g&quot;)</span>
+            <span class="o">.</span><span class="n">set_memory_request</span><span class="p">(</span><span class="s2">&quot;12G&quot;</span><span class="p">)</span>
+            <span class="o">.</span><span class="n">add_node_selector_constraint</span><span class="p">(</span><span class="s2">&quot;cloud.google.com/gke-nodepool&quot;</span><span class="p">,</span> <span class="s2">&quot;pool-16g&quot;</span><span class="p">)</span>
+        <span class="p">)</span>
+        <span class="n">convert_phase_op_</span><span class="o">.</span><span class="n">execution_options</span><span class="o">.</span><span class="n">caching_strategy</span><span class="o">.</span><span class="n">max_cache_staleness</span> <span class="o">=</span> <span class="s2">&quot;P30D&quot;</span>
+        <span class="n">convert_phase_op_</span><span class="o">.</span><span class="n">set_image_pull_policy</span><span class="p">(</span><span class="s2">&quot;Always&quot;</span><span class="p">)</span>
+
+        <span class="n">ph2dt_op_</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="n">ph2dt_op</span><span class="p">(</span>
+                <span class="n">i</span><span class="p">,</span>
+                <span class="n">config_json</span><span class="o">=</span><span class="n">config</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;config_json&quot;</span><span class="p">],</span>
+                <span class="n">hypodd_phase</span><span class="o">=</span><span class="n">convert_phase_op_</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;hypodd_phase&quot;</span><span class="p">],</span>
+                <span class="n">station_dat</span><span class="o">=</span><span class="n">convert_station_op_</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;hypodd_station&quot;</span><span class="p">],</span>
+                <span class="n">bucket_name</span><span class="o">=</span><span class="s2">&quot;catalogs&quot;</span><span class="p">,</span>
+                <span class="n">s3_url</span><span class="o">=</span><span class="n">s3_url</span><span class="p">,</span>
+                <span class="n">secure</span><span class="o">=</span><span class="n">secure</span><span class="p">,</span>
+            <span class="p">)</span>
+            <span class="o">.</span><span class="n">set_display_name</span><span class="p">(</span><span class="s2">&quot;PH2DT&quot;</span><span class="p">)</span>
+            <span class="o">.</span><span class="n">set_memory_request</span><span class="p">(</span><span class="s2">&quot;12G&quot;</span><span class="p">)</span>
+            <span class="o">.</span><span class="n">add_node_selector_constraint</span><span class="p">(</span><span class="s2">&quot;cloud.google.com/gke-nodepool&quot;</span><span class="p">,</span> <span class="s2">&quot;spot-16g&quot;</span><span class="p">)</span>
+        <span class="p">)</span>
+        <span class="n">ph2dt_op_</span><span class="o">.</span><span class="n">execution_options</span><span class="o">.</span><span class="n">caching_strategy</span><span class="o">.</span><span class="n">max_cache_staleness</span> <span class="o">=</span> <span class="s2">&quot;P30D&quot;</span>
+        <span class="n">ph2dt_op_</span><span class="o">.</span><span class="n">set_image_pull_policy</span><span class="p">(</span><span class="s2">&quot;Always&quot;</span><span class="p">)</span>
+
+        <span class="n">hypodd_ct_op_</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="n">hypodd_ct_op</span><span class="p">(</span>
+                <span class="n">i</span><span class="p">,</span>
+                <span class="n">config_json</span><span class="o">=</span><span class="n">config</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;config_json&quot;</span><span class="p">],</span>
+                <span class="n">ct</span><span class="o">=</span><span class="n">ph2dt_op_</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;ct&quot;</span><span class="p">],</span>
+                <span class="n">event</span><span class="o">=</span><span class="n">ph2dt_op_</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;hypodd_event&quot;</span><span class="p">],</span>
+                <span class="n">station</span><span class="o">=</span><span class="n">convert_station_op_</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;hypodd_station&quot;</span><span class="p">],</span>
+                <span class="n">bucket_name</span><span class="o">=</span><span class="s2">&quot;catalogs&quot;</span><span class="p">,</span>
+                <span class="n">s3_url</span><span class="o">=</span><span class="n">s3_url</span><span class="p">,</span>
+                <span class="n">secure</span><span class="o">=</span><span class="n">secure</span><span class="p">,</span>
+            <span class="p">)</span>
+            <span class="o">.</span><span class="n">set_display_name</span><span class="p">(</span><span class="s2">&quot;HypoDD&quot;</span><span class="p">)</span>
+            <span class="o">.</span><span class="n">set_memory_request</span><span class="p">(</span><span class="s2">&quot;12G&quot;</span><span class="p">)</span>
+            <span class="o">.</span><span class="n">add_node_selector_constraint</span><span class="p">(</span><span class="s2">&quot;cloud.google.com/gke-nodepool&quot;</span><span class="p">,</span> <span class="s2">&quot;pool-16g&quot;</span><span class="p">)</span>
+        <span class="p">)</span>
+        <span class="n">hypodd_ct_op_</span><span class="o">.</span><span class="n">execution_options</span><span class="o">.</span><span class="n">caching_strategy</span><span class="o">.</span><span class="n">max_cache_staleness</span> <span class="o">=</span> <span class="s2">&quot;P30D&quot;</span>
+        <span class="n">hypodd_ct_op_</span><span class="o">.</span><span class="n">set_image_pull_policy</span><span class="p">(</span><span class="s2">&quot;Always&quot;</span><span class="p">)</span>
+
+        <span class="c1"># cc_op_ = cc_op(</span>
+        <span class="c1">#     ct=ph2dt_op_.outputs[&quot;ct&quot;],</span>
+        <span class="c1">#     picks=merge_op_.outputs[&quot;picks_csv&quot;],</span>
+        <span class="c1">#     catalog=merge_op_.outputs[&quot;catalog_csv&quot;],</span>
+        <span class="c1"># ).set_display_name(&#39;Cross Correlation&#39;)</span>
+        <span class="c1"># cc_op_.execution_options.caching_strategy.max_cache_staleness = &quot;P30D&quot;</span>
+        <span class="c1"># cc_op_.set_image_pull_policy(&quot;Always&quot;)</span>
+
+        <span class="c1"># hypodd_cc_op_ = hypodd_cc_op(</span>
+        <span class="c1">#     i,</span>
+        <span class="c1">#     config_json=config.outputs[&quot;config_json&quot;],</span>
+        <span class="c1">#     ct=ph2dt_op_.outputs[&quot;ct&quot;],</span>
+        <span class="c1">#     cc=cc_op_.outputs[&quot;cc&quot;],</span>
+        <span class="c1">#     event=ph2dt_op_.outputs[&quot;hypodd_event&quot;],</span>
+        <span class="c1">#     station=convert_station_op_.outputs[&quot;hypodd_station&quot;],</span>
+        <span class="c1">#     bucket_name=&quot;catalogs&quot;,</span>
+        <span class="c1">#     s3_url=s3_url,</span>
+        <span class="c1">#     secure=secure,</span>
+        <span class="c1"># ).set_display_name(&#39;HypoDD + CC&#39;)</span>
+        <span class="c1"># hypodd_cc_op_.execution_options.caching_strategy.max_cache_staleness = &quot;P30D&quot;</span>
+        <span class="c1"># hypodd_cc_op_.set_image_pull_policy(&quot;Always&quot;)</span>
+
+    <span class="n">merge_hypodd_op_</span> <span class="o">=</span> <span class="p">(</span>
+        <span class="n">merge_hypodd_op</span><span class="p">(</span>
+            <span class="n">split_hypodd_op_</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;output&quot;</span><span class="p">],</span>
+            <span class="n">config_json</span><span class="o">=</span><span class="n">config</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;config_json&quot;</span><span class="p">],</span>
+            <span class="n">bucket_name</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;catalogs&quot;</span><span class="p">,</span>
+            <span class="n">s3_url</span><span class="o">=</span><span class="n">s3_url</span><span class="p">,</span>
+            <span class="n">secure</span><span class="o">=</span><span class="n">secure</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="o">.</span><span class="n">after</span><span class="p">(</span><span class="n">hypodd_ct_op_</span><span class="p">)</span>
+        <span class="c1"># .after(hypodd_cc_op_)</span>
+        <span class="o">.</span><span class="n">set_display_name</span><span class="p">(</span><span class="s2">&quot;Merge Catalog&quot;</span><span class="p">)</span>
+        <span class="o">.</span><span class="n">add_node_selector_constraint</span><span class="p">(</span><span class="s2">&quot;cloud.google.com/gke-nodepool&quot;</span><span class="p">,</span> <span class="s2">&quot;spot-16g&quot;</span><span class="p">)</span>
+    <span class="p">)</span>
+    <span class="n">merge_hypodd_op_</span><span class="o">.</span><span class="n">execution_options</span><span class="o">.</span><span class="n">caching_strategy</span><span class="o">.</span><span class="n">max_cache_staleness</span> <span class="o">=</span> <span class="s2">&quot;P0D&quot;</span>
+    <span class="n">merge_hypodd_op_</span><span class="o">.</span><span class="n">set_image_pull_policy</span><span class="p">(</span><span class="s2">&quot;Always&quot;</span><span class="p">)</span>
+
+    <span class="n">visulization_op_</span> <span class="o">=</span> <span class="p">(</span>
+        <span class="n">visulization_op</span><span class="p">(</span>
+            <span class="n">config_json</span><span class="o">=</span><span class="n">config</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;config_json&quot;</span><span class="p">],</span>
+            <span class="n">hypodd_catalog_ct</span><span class="o">=</span><span class="n">merge_hypodd_op_</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;catalog_ct&quot;</span><span class="p">],</span>
+            <span class="n">hypodd_catalog_cc</span><span class="o">=</span><span class="n">merge_hypodd_op_</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;catalog_cc&quot;</span><span class="p">],</span>
+            <span class="n">gamma_catalog</span><span class="o">=</span><span class="n">merge_op_</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;catalog_csv&quot;</span><span class="p">],</span>
+            <span class="n">standard_catalog</span><span class="o">=</span><span class="n">events</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;standard_catalog&quot;</span><span class="p">],</span>
+            <span class="n">bucket_name</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;catalogs&quot;</span><span class="p">,</span>
+            <span class="n">s3_url</span><span class="o">=</span><span class="n">s3_url</span><span class="p">,</span>
+            <span class="n">secure</span><span class="o">=</span><span class="n">secure</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="o">.</span><span class="n">after</span><span class="p">(</span><span class="n">merge_hypodd_op_</span><span class="p">)</span>
+        <span class="o">.</span><span class="n">set_display_name</span><span class="p">(</span><span class="s2">&quot;Visulization&quot;</span><span class="p">)</span>
+    <span class="p">)</span>
+    <span class="n">visulization_op_</span><span class="o">.</span><span class="n">execution_options</span><span class="o">.</span><span class="n">caching_strategy</span><span class="o">.</span><span class="n">max_cache_staleness</span> <span class="o">=</span> <span class="s2">&quot;P0D&quot;</span>
+    <span class="n">visulization_op_</span><span class="o">.</span><span class="n">set_image_pull_policy</span><span class="p">(</span><span class="s2">&quot;Always&quot;</span><span class="p">)</span>
+
+    <span class="c1">#### vop_.delete().after(merge_hypodd_op_)</span>
+</pre></div>
+<div id="cell-53" class="clipboard-copy-txt">@dsl.pipeline(name="QuakeFlow", description="")
+def quakeflow_pipeline(
+    data_path: str = "/tmp/",
+    num_parallel=0,
+    bucket_catalog: str = "catalogs",
+    s3_url: str = "minio-service:9000",
+    secure: bool = False,
+):
+
+    config = (
+        config_op(region_name, num_parallel, bucket_name=f"catalogs", s3_url=s3_url, secure=secure)
+        .set_retry(3)
+        .add_node_selector_constraint("cloud.google.com/gke-nodepool", "spot-2g")
+        .set_cpu_request("700m")
+    )
+    config.execution_options.caching_strategy.max_cache_staleness = "P30D"
+
+    events = (
+        download_events_op(config.outputs["config_json"], bucket_name=f"catalogs", s3_url=s3_url, secure=secure)
+        .add_node_selector_constraint("cloud.google.com/gke-nodepool", "spot-16g")
+        .set_retry(3)
+        .set_memory_request("4G")
+        .set_cpu_request("700m")
+        .set_display_name("Download Events")
+    )
+    events.execution_options.caching_strategy.max_cache_staleness = "P30D"
+
+    stations = (
+        download_stations_op(config.outputs["config_json"], bucket_name=f"catalogs", s3_url=s3_url, secure=secure)
+        .add_node_selector_constraint("cloud.google.com/gke-nodepool", "spot-2g")
+        .set_retry(3)
+        .set_cpu_request("700m")
+        .set_display_name("Download Stations")
+    )
+    stations.execution_options.caching_strategy.max_cache_staleness = "P30D"
+
+    with kfp.dsl.ParallelFor(config.outputs["output"]) as i:
+
+        vop_ = dsl.VolumeOp(
+            name=f"Create volume {region_name}",
+            resource_name=f"data-volume-{str(i)}",
+            size="120Gi",
+            modes=dsl.VOLUME_MODE_RWO,
+        ).set_retry(3)
+
+        download_op_ = (
+            download_waveform_op(
+                i,
+                config.outputs["index_json"],
+                config.outputs["config_json"],
+                config.outputs["datetime_json"],
+                stations.outputs["station_pkl"],
+                data_path=data_path,
+                bucket_name="waveforms",
+                s3_url=s3_url,
+                secure=secure,
+            )
+            .add_pvolumes({data_path: vop_.volume})
+            .set_cpu_request("700m")
+            # .set_cpu_request("350m")
+            .add_node_selector_constraint("cloud.google.com/gke-nodepool", "spot-2g")
+            .set_retry(5)
+            .set_display_name("Download Waveforms")
+        )
+        download_op_.execution_options.caching_strategy.max_cache_staleness = "P30D"
+
+        phasenet_op_ = (
+            phasenet_op(
+                download_op_.outputs["Output"],
+                download_op_.outputs["fname_csv"],
+                stations.outputs["station_json"],
+            )
+            .add_pvolumes({data_path: download_op_.pvolume})
+            .set_memory_request("9G")
+            .add_node_selector_constraint("cloud.google.com/gke-nodepool", "pool-16g")
+            .set_retry(5)
+            .set_display_name("PhaseNet Picking")
+        )
+        phasenet_op_.execution_options.caching_strategy.max_cache_staleness = "P30D"
+        phasenet_op_.set_image_pull_policy("Always")
+
+        gamma_op_ = (
+            gamma_op(
+                i,
+                config.outputs["index_json"],
+                config.outputs["config_json"],
+                phasenet_op_.outputs["picks"],
+                stations.outputs["station_json"],
+                bucket_name=f"catalogs",
+                s3_url=s3_url,
+                secure=secure,
+            )
+            .set_cpu_request("800m")
+            .add_node_selector_constraint("cloud.google.com/gke-nodepool", "pool-16g")
+            .set_retry(3)
+            .set_display_name("GaMMA Association")
+        )
+        gamma_op_.execution_options.caching_strategy.max_cache_staleness = "P30D"
+
+        # resume_op_ = (
+        #     resume_waveform_op(
+        #         i,
+        #         config.outputs["index_json"],
+        #         config.outputs["config_json"],
+        #         config.outputs["datetime_json"],
+        #         data_path=data_path,
+        #         bucket_name="waveforms",
+        #         s3_url=s3_url,
+        #         secure=secure,
+        #     )
+        #     .add_pvolumes({data_path: vop_.volume})
+        #     .set_cpu_request("700m")
+        #     # .set_cpu_request("350m")
+        #     .set_retry(5)
+        #     .add_node_selector_constraint("cloud.google.com/gke-nodepool", "spot-2g")
+        #     .set_display_name("Resume Waveforms")
+        # )
+        # resume_op_.execution_options.caching_strategy.max_cache_staleness = "P0D"
+
+        # save_op_ = (
+        #     save_waveform_op(
+        #         i,
+        #         config.outputs["index_json"],
+        #         config.outputs["config_json"],
+        #         config.outputs["datetime_json"],
+        #         data_path=data_path,
+        #         bucket_name="waveforms",
+        #         s3_url=s3_url,
+        #         secure=secure,
+        #     )
+        #     .add_pvolumes({data_path: vop_.volume})
+        #     .set_cpu_request("700m")
+        #     # .set_cpu_request("350m")
+        #     .set_retry(5)
+        #     .add_node_selector_constraint("cloud.google.com/gke-nodepool", "spot-2g")
+        #     .set_display_name("Save Waveforms")
+        # )
+        # save_op_.execution_options.caching_strategy.max_cache_staleness = "P0D"
+
+    merge_op_ = (
+        merge_op(
+            config.outputs["index_json"],
+            config.outputs["config_json"],
+            bucket_name=f"catalogs",
+            s3_url=s3_url,
+            secure=secure,
+        )
+        .after(gamma_op_)
+        .set_memory_request("12G")
+        .add_node_selector_constraint("cloud.google.com/gke-nodepool", "spot-64g")
+        .set_display_name("Merge Catalog")
+    )
+    merge_op_.execution_options.caching_strategy.max_cache_staleness = "P30D"
+
+    convert_station_op_ = (
+        convert_station_op(
+            config_json=config.outputs["config_json"],
+            station_json=stations.outputs["station_json"],
+            bucket_name=f"catalogs",
+            s3_url=s3_url,
+            secure=secure,
+        )
+        .add_node_selector_constraint("cloud.google.com/gke-nodepool", "spot-2g")
+        .set_display_name("Convert Station Format")
+    )
+    split_hypodd_op_ = (
+        split_hypodd_op(
+            config.outputs["config_json"],
+            catalog_csv=merge_op_.outputs["catalog_csv"],
+        )
+        .after(merge_op_)
+        .add_node_selector_constraint("cloud.google.com/gke-nodepool", "spot-64g")
+        .set_display_name("Split Catalog")
+    )
+    split_hypodd_op_.execution_options.caching_strategy.max_cache_staleness = "P30D"
+    split_hypodd_op_.set_image_pull_policy("Always")
+
+    with kfp.dsl.ParallelFor(split_hypodd_op_.outputs["output"]) as i:
+
+        convert_phase_op_ = (
+            convert_phase_op(
+                i,
+                config_json=config.outputs["config_json"],
+                picks_csv=merge_op_.outputs["picks_csv"],
+                catalog_csv=merge_op_.outputs["catalog_csv"],
+                bucket_name="catalogs",
+                s3_url=s3_url,
+                secure=secure,
+            )
+            .set_retry(3)
+            .set_display_name("Convert Phase Format")
+            # .set_memory_request("48G")
+            # .add_node_selector_constraint("cloud.google.com/gke-nodepool", "spot-64g")
+            .set_memory_request("12G")
+            .add_node_selector_constraint("cloud.google.com/gke-nodepool", "pool-16g")
+        )
+        convert_phase_op_.execution_options.caching_strategy.max_cache_staleness = "P30D"
+        convert_phase_op_.set_image_pull_policy("Always")
+
+        ph2dt_op_ = (
+            ph2dt_op(
+                i,
+                config_json=config.outputs["config_json"],
+                hypodd_phase=convert_phase_op_.outputs["hypodd_phase"],
+                station_dat=convert_station_op_.outputs["hypodd_station"],
+                bucket_name="catalogs",
+                s3_url=s3_url,
+                secure=secure,
+            )
+            .set_display_name("PH2DT")
+            .set_memory_request("12G")
+            .add_node_selector_constraint("cloud.google.com/gke-nodepool", "spot-16g")
+        )
+        ph2dt_op_.execution_options.caching_strategy.max_cache_staleness = "P30D"
+        ph2dt_op_.set_image_pull_policy("Always")
+
+        hypodd_ct_op_ = (
+            hypodd_ct_op(
+                i,
+                config_json=config.outputs["config_json"],
+                ct=ph2dt_op_.outputs["ct"],
+                event=ph2dt_op_.outputs["hypodd_event"],
+                station=convert_station_op_.outputs["hypodd_station"],
+                bucket_name="catalogs",
+                s3_url=s3_url,
+                secure=secure,
+            )
+            .set_display_name("HypoDD")
+            .set_memory_request("12G")
+            .add_node_selector_constraint("cloud.google.com/gke-nodepool", "pool-16g")
+        )
+        hypodd_ct_op_.execution_options.caching_strategy.max_cache_staleness = "P30D"
+        hypodd_ct_op_.set_image_pull_policy("Always")
+
+        # cc_op_ = cc_op(
+        #     ct=ph2dt_op_.outputs["ct"],
+        #     picks=merge_op_.outputs["picks_csv"],
+        #     catalog=merge_op_.outputs["catalog_csv"],
+        # ).set_display_name('Cross Correlation')
+        # cc_op_.execution_options.caching_strategy.max_cache_staleness = "P30D"
+        # cc_op_.set_image_pull_policy("Always")
+
+        # hypodd_cc_op_ = hypodd_cc_op(
+        #     i,
+        #     config_json=config.outputs["config_json"],
+        #     ct=ph2dt_op_.outputs["ct"],
+        #     cc=cc_op_.outputs["cc"],
+        #     event=ph2dt_op_.outputs["hypodd_event"],
+        #     station=convert_station_op_.outputs["hypodd_station"],
+        #     bucket_name="catalogs",
+        #     s3_url=s3_url,
+        #     secure=secure,
+        # ).set_display_name('HypoDD + CC')
+        # hypodd_cc_op_.execution_options.caching_strategy.max_cache_staleness = "P30D"
+        # hypodd_cc_op_.set_image_pull_policy("Always")
+
+    merge_hypodd_op_ = (
+        merge_hypodd_op(
+            split_hypodd_op_.outputs["output"],
+            config_json=config.outputs["config_json"],
+            bucket_name=f"catalogs",
+            s3_url=s3_url,
+            secure=secure,
+        )
+        .after(hypodd_ct_op_)
+        # .after(hypodd_cc_op_)
+        .set_display_name("Merge Catalog")
+        .add_node_selector_constraint("cloud.google.com/gke-nodepool", "spot-16g")
+    )
+    merge_hypodd_op_.execution_options.caching_strategy.max_cache_staleness = "P0D"
+    merge_hypodd_op_.set_image_pull_policy("Always")
+
+    visulization_op_ = (
+        visulization_op(
+            config_json=config.outputs["config_json"],
+            hypodd_catalog_ct=merge_hypodd_op_.outputs["catalog_ct"],
+            hypodd_catalog_cc=merge_hypodd_op_.outputs["catalog_cc"],
+            gamma_catalog=merge_op_.outputs["catalog_csv"],
+            standard_catalog=events.outputs["standard_catalog"],
+            bucket_name=f"catalogs",
+            s3_url=s3_url,
+            secure=secure,
+        )
+        .after(merge_hypodd_op_)
+        .set_display_name("Visulization")
+    )
+    visulization_op_.execution_options.caching_strategy.max_cache_staleness = "P0D"
+    visulization_op_.set_image_pull_policy("Always")
+
+    #### vop_.delete().after(merge_hypodd_op_)</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-54">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
+
+<span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s2">&quot;GOOGLE_APPLICATION_CREDENTIALS&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;/Users/weiqiang/.dotbot/cloud/quakeflow_zhuwq.json&quot;</span>
+<span class="n">experiment_name</span> <span class="o">=</span> <span class="s2">&quot;QuakeFlow&quot;</span>
+<span class="n">pipeline_func</span> <span class="o">=</span> <span class="n">quakeflow_pipeline</span>
+<span class="n">run_name</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">pipeline_func</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s2">_</span><span class="si">{</span><span class="n">region_name</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span><span class="si">}</span><span class="s2">&quot;</span>
+
+<span class="n">arguments</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;data_path&quot;</span><span class="p">:</span> <span class="s2">&quot;/tmp&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;num_parallel&quot;</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span>
+    <span class="s2">&quot;bucket_catalog&quot;</span><span class="p">:</span> <span class="s2">&quot;catalogs&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;s3_url&quot;</span><span class="p">:</span> <span class="s2">&quot;minio-service:9000&quot;</span><span class="p">,</span>
+    <span class="c1"># &quot;s3_url&quot;: &quot;quakeflow-minio:9000&quot;,</span>
+    <span class="s2">&quot;secure&quot;</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span>
+<span class="p">}</span>
+
+<span class="k">if</span> <span class="ow">not</span> <span class="n">run_local</span><span class="p">:</span>
+    <span class="n">pipeline_conf</span> <span class="o">=</span> <span class="n">kfp</span><span class="o">.</span><span class="n">dsl</span><span class="o">.</span><span class="n">PipelineConf</span><span class="p">()</span>
+    <span class="n">pipeline_conf</span><span class="o">.</span><span class="n">set_image_pull_policy</span><span class="p">(</span><span class="s2">&quot;Always&quot;</span><span class="p">)</span>
+    <span class="n">pipeline_conf</span><span class="o">.</span><span class="n">ttl_seconds_after_finished</span> <span class="o">=</span> <span class="mi">60</span> <span class="o">*</span> <span class="mi">10</span>
+    <span class="n">client</span> <span class="o">=</span> <span class="n">kfp</span><span class="o">.</span><span class="n">Client</span><span class="p">(</span><span class="n">host</span><span class="o">=</span><span class="s2">&quot;56496b41855e5ce2-dot-us-west1.pipelines.googleusercontent.com&quot;</span><span class="p">)</span>
+    <span class="c1"># client = kfp.Client(host=&quot;http://localhost:8080&quot;)</span>
+    <span class="n">kfp</span><span class="o">.</span><span class="n">compiler</span><span class="o">.</span><span class="n">Compiler</span><span class="p">()</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">pipeline_func</span><span class="p">,</span> <span class="s2">&quot;</span><span class="si">{}</span><span class="s2">.zip&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">experiment_name</span><span class="p">),</span> <span class="n">pipeline_conf</span><span class="o">=</span><span class="n">pipeline_conf</span><span class="p">)</span>
+    <span class="n">results</span> <span class="o">=</span> <span class="n">client</span><span class="o">.</span><span class="n">create_run_from_pipeline_func</span><span class="p">(</span>
+        <span class="n">pipeline_func</span><span class="p">,</span>
+        <span class="n">experiment_name</span><span class="o">=</span><span class="n">experiment_name</span><span class="p">,</span>
+        <span class="n">run_name</span><span class="o">=</span><span class="n">run_name</span><span class="p">,</span>
+        <span class="n">arguments</span><span class="o">=</span><span class="n">arguments</span><span class="p">,</span>
+    <span class="p">)</span>
+</pre></div>
+<div id="cell-54" class="clipboard-copy-txt">import os
+
+os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "/Users/weiqiang/.dotbot/cloud/quakeflow_zhuwq.json"
+experiment_name = "QuakeFlow"
+pipeline_func = quakeflow_pipeline
+run_name = f"{pipeline_func.__name__}_{region_name.lower()}"
+
+arguments = {
+    "data_path": "/tmp",
+    "num_parallel": 0,
+    "bucket_catalog": "catalogs",
+    "s3_url": "minio-service:9000",
+    # "s3_url": "quakeflow-minio:9000",
+    "secure": False,
+}
+
+if not run_local:
+    pipeline_conf = kfp.dsl.PipelineConf()
+    pipeline_conf.set_image_pull_policy("Always")
+    pipeline_conf.ttl_seconds_after_finished = 60 * 10
+    client = kfp.Client(host="56496b41855e5ce2-dot-us-west1.pipelines.googleusercontent.com")
+    # client = kfp.Client(host="http://localhost:8080")
+    kfp.compiler.Compiler().compile(pipeline_func, "{}.zip".format(experiment_name), pipeline_conf=pipeline_conf)
+    results = client.create_run_from_pipeline_func(
+        pipeline_func,
+        experiment_name=experiment_name,
+        run_name=run_name,
+        arguments=arguments,
+    )</div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output " data-mime-type="text/html">
+<a href="http://56496b41855e5ce2-dot-us-west1.pipelines.googleusercontent.com/#/experiments/details/dffd451b-47f0-48ae-9895-9d7cf0fc372b" target="_blank" >Experiment details</a>.
+</div>
+
+</div>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output " data-mime-type="text/html">
+<a href="http://56496b41855e5ce2-dot-us-west1.pipelines.googleusercontent.com/#/runs/details/afa3baaa-75ae-49aa-b3d4-14e9f82a6719" target="_blank" >Run details</a>.
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-55">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span>
+</pre></div>
+<div id="cell-55" class="clipboard-copy-txt"></div>
+    
+        </div>
+    </div>
+
+</div>
+</div>
+
+</div>
+</div>
+</div>
+
+
+
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+            
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+        
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+</html>
\ No newline at end of file
diff --git a/phasenet/index.html b/phasenet/index.html
new file mode 100644
index 0000000..0926a1e
--- /dev/null
+++ b/phasenet/index.html
@@ -0,0 +1,302 @@
+
+<!doctype html>
+<html lang="en" class="no-js">
+  <head>
+    
+      <meta charset="utf-8">
+      <meta name="viewport" content="width=device-width,initial-scale=1">
+      
+        <meta name="description" content="QuakeFlow: a Scalable Deep-learning-based Earthquake Monitoring Workflow with Cloud Computing">
+      
+      
+        <meta name="author" content="Weiqiang Zhu">
+      
+      
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+      
+  
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+
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+            QuakeFlow
+          </span>
+        </div>
+        <div class="md-header__topic" data-md-component="header-topic">
+          <span class="md-ellipsis">
+            
+              Phasenet
+            
+          </span>
+        </div>
+      </div>
+    </div>
+    
+    
+    
+    
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+      <a href="../kubeflow/workflow/" class="md-nav__link">
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+  
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+        Standard Formats
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+    </div>
+    
+    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.ecf98df9.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}}</script>
+    
+    
+      <script src="../assets/javascripts/bundle.9c69f0bc.min.js"></script>
+      
+    
+  </body>
+</html>
\ No newline at end of file
diff --git a/sitemap.xml b/sitemap.xml
new file mode 100644
index 0000000..4f77470
--- /dev/null
+++ b/sitemap.xml
@@ -0,0 +1,118 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<urlset xmlns="http://www.sitemaps.org/schemas/sitemap/0.9">
+    <url>
+         <loc>None</loc>
+         <lastmod>2024-12-17</lastmod>
+         <changefreq>daily</changefreq>
+    </url>
+    <url>
+         <loc>None</loc>
+         <lastmod>2024-12-17</lastmod>
+         <changefreq>daily</changefreq>
+    </url>
+    <url>
+         <loc>None</loc>
+         <lastmod>2024-12-17</lastmod>
+         <changefreq>daily</changefreq>
+    </url>
+    <url>
+         <loc>None</loc>
+         <lastmod>2024-12-17</lastmod>
+         <changefreq>daily</changefreq>
+    </url>
+    <url>
+         <loc>None</loc>
+         <lastmod>2024-12-17</lastmod>
+         <changefreq>daily</changefreq>
+    </url>
+    <url>
+         <loc>None</loc>
+         <lastmod>2024-12-17</lastmod>
+         <changefreq>daily</changefreq>
+    </url>
+    <url>
+         <loc>None</loc>
+         <lastmod>2024-12-17</lastmod>
+         <changefreq>daily</changefreq>
+    </url>
+    <url>
+         <loc>None</loc>
+         <lastmod>2024-12-17</lastmod>
+         <changefreq>daily</changefreq>
+    </url>
+    <url>
+         <loc>None</loc>
+         <lastmod>2024-12-17</lastmod>
+         <changefreq>daily</changefreq>
+    </url>
+    <url>
+         <loc>None</loc>
+         <lastmod>2024-12-17</lastmod>
+         <changefreq>daily</changefreq>
+    </url>
+    <url>
+         <loc>None</loc>
+         <lastmod>2024-12-17</lastmod>
+         <changefreq>daily</changefreq>
+    </url>
+    <url>
+         <loc>None</loc>
+         <lastmod>2024-12-17</lastmod>
+         <changefreq>daily</changefreq>
+    </url>
+    <url>
+         <loc>None</loc>
+         <lastmod>2024-12-17</lastmod>
+         <changefreq>daily</changefreq>
+    </url>
+    <url>
+         <loc>None</loc>
+         <lastmod>2024-12-17</lastmod>
+         <changefreq>daily</changefreq>
+    </url>
+    <url>
+         <loc>None</loc>
+         <lastmod>2024-12-17</lastmod>
+         <changefreq>daily</changefreq>
+    </url>
+    <url>
+         <loc>None</loc>
+         <lastmod>2024-12-17</lastmod>
+         <changefreq>daily</changefreq>
+    </url>
+    <url>
+         <loc>None</loc>
+         <lastmod>2024-12-17</lastmod>
+         <changefreq>daily</changefreq>
+    </url>
+    <url>
+         <loc>None</loc>
+         <lastmod>2024-12-17</lastmod>
+         <changefreq>daily</changefreq>
+    </url>
+    <url>
+         <loc>None</loc>
+         <lastmod>2024-12-17</lastmod>
+         <changefreq>daily</changefreq>
+    </url>
+    <url>
+         <loc>None</loc>
+         <lastmod>2024-12-17</lastmod>
+         <changefreq>daily</changefreq>
+    </url>
+    <url>
+         <loc>None</loc>
+         <lastmod>2024-12-17</lastmod>
+         <changefreq>daily</changefreq>
+    </url>
+    <url>
+         <loc>None</loc>
+         <lastmod>2024-12-17</lastmod>
+         <changefreq>daily</changefreq>
+    </url>
+    <url>
+         <loc>None</loc>
+         <lastmod>2024-12-17</lastmod>
+         <changefreq>daily</changefreq>
+    </url>
+</urlset>
\ No newline at end of file
diff --git a/sitemap.xml.gz b/sitemap.xml.gz
new file mode 100644
index 0000000000000000000000000000000000000000..1ec22613beb2ad35becd3eb82ac73962e93ce305
GIT binary patch
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