04_Climate-Coding-Challenge-Part-4.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "5dedcedb-5dc1-4e4c-8b2a-c4b727a1b6ba",
      "metadata": {},
      "source": [
        "# Part 4: Plot your results\n",
        "\n",
        "First things first – make sure to load the climate `DataFrame` you\n",
        "stored in the previous notebooks using Jupyter cell magic:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "575469b7",
      "metadata": {},
      "outputs": [],
      "source": [
        "# Load all stored variables\n",
        "%store -r"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2bdb9714-d2a9-4128-a174-a0ef7409b7fb",
      "metadata": {},
      "source": [
        "You’ll also need some libraries later on. This is an extension to\n",
        "`pandas` that will allow you to easily make beautiful, interactive\n",
        "plots, and a related library that will let you save your plots:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "c09c8e24",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<script type=\"esms-options\">{\"shimMode\": true}</script><style>*[data-root-id],\n",
              "*[data-root-id] > * {\n",
              "  box-sizing: border-box;\n",
              "  font-family: var(--jp-ui-font-family);\n",
              "  font-size: var(--jp-ui-font-size1);\n",
              "  color: var(--vscode-editor-foreground, var(--jp-ui-font-color1));\n",
              "}\n",
              "\n",
              "/* Override VSCode background color */\n",
              ".cell-output-ipywidget-background:has(\n",
              "    > .cell-output-ipywidget-background > .lm-Widget > *[data-root-id]\n",
              "  ),\n",
              ".cell-output-ipywidget-background:has(> .lm-Widget > *[data-root-id]) {\n",
              "  background-color: transparent !important;\n",
              "}\n",
              "</style>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/javascript": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n  const py_version = '3.5.2'.replace('rc', '-rc.').replace('.dev', '-dev.');\n  const reloading = false;\n  const Bokeh = root.Bokeh;\n\n  // Set a timeout for this load but only if we are not already initializing\n  if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks;\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n    if (js_modules == null) js_modules = [];\n    if (js_exports == null) js_exports = {};\n\n    root._bokeh_onload_callbacks.push(callback);\n\n    if (root._bokeh_is_loading > 0) {\n      // Don't load bokeh if it is still initializing\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    } else if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n      // There is nothing to load\n      run_callbacks();\n      return null;\n    }\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n    window._bokeh_on_load = on_load\n\n    function on_error(e) {\n      const src_el = e.srcElement\n      console.error(\"failed to load \" + (src_el.href || src_el.src));\n    }\n\n    const skip = [];\n    if (window.requirejs) {\n      window.requirejs.config({'packages': {}, 'paths': {}, 'shim': {}});\n      root._bokeh_is_loading = css_urls.length + 0;\n    } else {\n      root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n    }\n\n    const existing_stylesheets = []\n    const links = document.getElementsByTagName('link')\n    for (let i = 0; i < links.length; i++) {\n      const link = links[i]\n      if (link.href != null) {\n        existing_stylesheets.push(link.href)\n      }\n    }\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const escaped = encodeURI(url)\n      if (existing_stylesheets.indexOf(escaped) !== -1) {\n        on_load()\n        continue;\n      }\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }    var existing_scripts = []\n    const scripts = document.getElementsByTagName('script')\n    for (let i = 0; i < scripts.length; i++) {\n      var script = scripts[i]\n      if (script.src != null) {\n        existing_scripts.push(script.src)\n      }\n    }\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const escaped = encodeURI(url)\n      if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n        if (!window.requirejs) {\n          on_load();\n        }\n        continue;\n      }\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    for (let i = 0; i < js_modules.length; i++) {\n      const url = js_modules[i];\n      const escaped = encodeURI(url)\n      if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n        if (!window.requirejs) {\n          on_load();\n        }\n        continue;\n      }\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      element.type = \"module\";\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    for (const name in js_exports) {\n      const url = js_exports[name];\n      const escaped = encodeURI(url)\n      if (skip.indexOf(escaped) >= 0 || root[name] != null) {\n        if (!window.requirejs) {\n          on_load();\n        }\n        continue;\n      }\n      var element = document.createElement('script');\n      element.onerror = on_error;\n      element.async = false;\n      element.type = \"module\";\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      element.textContent = `\n      import ${name} from \"${url}\"\n      window.${name} = ${name}\n      window._bokeh_on_load()\n      `\n      document.head.appendChild(element);\n    }\n    if (!js_urls.length && !js_modules.length) {\n      on_load()\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.holoviz.org/panel/1.5.1/dist/bundled/reactiveesm/es-module-shims@^1.10.0/dist/es-module-shims.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-3.5.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.5.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.5.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.5.2.min.js\", \"https://cdn.holoviz.org/panel/1.5.1/dist/panel.min.js\"];\n  const js_modules = [];\n  const js_exports = {};\n  const css_urls = [];\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n  ];\n\n  function run_inline_js() {\n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (let i = 0; i < inline_js.length; i++) {\n        try {\n          inline_js[i].call(root, root.Bokeh);\n        } catch(e) {\n          if (!reloading) {\n            throw e;\n          }\n        }\n      }\n      // Cache old bokeh versions\n      if (Bokeh != undefined && !reloading) {\n        var NewBokeh = root.Bokeh;\n        if (Bokeh.versions === undefined) {\n          Bokeh.versions = new Map();\n        }\n        if (NewBokeh.version !== Bokeh.version) {\n          Bokeh.versions.set(NewBokeh.version, NewBokeh)\n        }\n        root.Bokeh = Bokeh;\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    }\n    root._bokeh_is_initializing = false\n  }\n\n  function load_or_wait() {\n    // Implement a backoff loop that tries to ensure we do not load multiple\n    // versions of Bokeh and its dependencies at the same time.\n    // In recent versions we use the root._bokeh_is_initializing flag\n    // to determine whether there is an ongoing attempt to initialize\n    // bokeh, however for backward compatibility we also try to ensure\n    // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n    // before older versions are fully initialized.\n    if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n      // If the timeout and bokeh was not successfully loaded we reset\n      // everything and try loading again\n      root._bokeh_timeout = Date.now() + 5000;\n      root._bokeh_is_initializing = false;\n      root._bokeh_onload_callbacks = undefined;\n      root._bokeh_is_loading = 0\n      console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n      load_or_wait();\n    } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n      setTimeout(load_or_wait, 100);\n    } else {\n      root._bokeh_is_initializing = true\n      root._bokeh_onload_callbacks = []\n      const bokeh_loaded = root.Bokeh != null && (root.Bokeh.version === py_version || (root.Bokeh.versions !== undefined && root.Bokeh.versions.has(py_version)));\n      if (!reloading && !bokeh_loaded) {\n        if (root.Bokeh) {\n          root.Bokeh = undefined;\n        }\n        console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n      }\n      load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n        console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n        run_inline_js();\n      });\n    }\n  }\n  // Give older versions of the autoload script a head-start to ensure\n  // they initialize before we start loading newer version.\n  setTimeout(load_or_wait, 100)\n}(window));",
            "application/vnd.holoviews_load.v0+json": ""
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/javascript": "\nif ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n  window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n}\n\n\n    function JupyterCommManager() {\n    }\n\n    JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n      if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n        var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n        comm_manager.register_target(comm_id, function(comm) {\n          comm.on_msg(msg_handler);\n        });\n      } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n        window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n          comm.onMsg = msg_handler;\n        });\n      } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n        google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n          var messages = comm.messages[Symbol.asyncIterator]();\n          function processIteratorResult(result) {\n            var message = result.value;\n            console.log(message)\n            var content = {data: message.data, comm_id};\n            var buffers = []\n            for (var buffer of message.buffers || []) {\n              buffers.push(new DataView(buffer))\n            }\n            var metadata = message.metadata || {};\n            var msg = {content, buffers, metadata}\n            msg_handler(msg);\n            return messages.next().then(processIteratorResult);\n          }\n          return messages.next().then(processIteratorResult);\n        })\n      }\n    }\n\n    JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n      if (comm_id in window.PyViz.comms) {\n        return window.PyViz.comms[comm_id];\n      } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n        var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n        var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n        if (msg_handler) {\n          comm.on_msg(msg_handler);\n        }\n      } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n        var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n        comm.open();\n        if (msg_handler) {\n          comm.onMsg = msg_handler;\n        }\n      } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n        var comm_promise = google.colab.kernel.comms.open(comm_id)\n        comm_promise.then((comm) => {\n          window.PyViz.comms[comm_id] = comm;\n          if (msg_handler) {\n            var messages = comm.messages[Symbol.asyncIterator]();\n            function processIteratorResult(result) {\n              var message = result.value;\n              var content = {data: message.data};\n              var metadata = message.metadata || {comm_id};\n              var msg = {content, metadata}\n              msg_handler(msg);\n              return messages.next().then(processIteratorResult);\n            }\n            return messages.next().then(processIteratorResult);\n          }\n        }) \n        var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n          return comm_promise.then((comm) => {\n            comm.send(data, metadata, buffers, disposeOnDone);\n          });\n        };\n        var comm = {\n          send: sendClosure\n        };\n      }\n      window.PyViz.comms[comm_id] = comm;\n      return comm;\n    }\n    window.PyViz.comm_manager = new JupyterCommManager();\n    \n\n\nvar JS_MIME_TYPE = 'application/javascript';\nvar HTML_MIME_TYPE = 'text/html';\nvar EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\nvar CLASS_NAME = 'output';\n\n/**\n * Render data to the DOM node\n */\nfunction render(props, node) {\n  var div = document.createElement(\"div\");\n  var script = document.createElement(\"script\");\n  node.appendChild(div);\n  node.appendChild(script);\n}\n\n/**\n * Handle when a new output is added\n */\nfunction handle_add_output(event, handle) {\n  var output_area = handle.output_area;\n  var output = handle.output;\n  if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n    return\n  }\n  var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n  var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n  if (id !== undefined) {\n    var nchildren = toinsert.length;\n    var html_node = toinsert[nchildren-1].children[0];\n    html_node.innerHTML = output.data[HTML_MIME_TYPE];\n    var scripts = [];\n    var nodelist = html_node.querySelectorAll(\"script\");\n    for (var i in nodelist) {\n      if (nodelist.hasOwnProperty(i)) {\n        scripts.push(nodelist[i])\n      }\n    }\n\n    scripts.forEach( function (oldScript) {\n      var newScript = document.createElement(\"script\");\n      var attrs = [];\n      var nodemap = oldScript.attributes;\n      for (var j in nodemap) {\n        if (nodemap.hasOwnProperty(j)) {\n          attrs.push(nodemap[j])\n        }\n      }\n      attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n      newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n      oldScript.parentNode.replaceChild(newScript, oldScript);\n    });\n    if (JS_MIME_TYPE in output.data) {\n      toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n    }\n    output_area._hv_plot_id = id;\n    if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n      window.PyViz.plot_index[id] = Bokeh.index[id];\n    } else {\n      window.PyViz.plot_index[id] = null;\n    }\n  } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n    var bk_div = document.createElement(\"div\");\n    bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n    var script_attrs = bk_div.children[0].attributes;\n    for (var i = 0; i < script_attrs.length; i++) {\n      toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n    }\n    // store reference to server id on output_area\n    output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n  }\n}\n\n/**\n * Handle when an output is cleared or removed\n */\nfunction handle_clear_output(event, handle) {\n  var id = handle.cell.output_area._hv_plot_id;\n  var server_id = handle.cell.output_area._bokeh_server_id;\n  if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n  var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n  if (server_id !== null) {\n    comm.send({event_type: 'server_delete', 'id': server_id});\n    return;\n  } else if (comm !== null) {\n    comm.send({event_type: 'delete', 'id': id});\n  }\n  delete PyViz.plot_index[id];\n  if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n    var doc = window.Bokeh.index[id].model.document\n    doc.clear();\n    const i = window.Bokeh.documents.indexOf(doc);\n    if (i > -1) {\n      window.Bokeh.documents.splice(i, 1);\n    }\n  }\n}\n\n/**\n * Handle kernel restart event\n */\nfunction handle_kernel_cleanup(event, handle) {\n  delete PyViz.comms[\"hv-extension-comm\"];\n  window.PyViz.plot_index = {}\n}\n\n/**\n * Handle update_display_data messages\n */\nfunction handle_update_output(event, handle) {\n  handle_clear_output(event, {cell: {output_area: handle.output_area}})\n  handle_add_output(event, handle)\n}\n\nfunction register_renderer(events, OutputArea) {\n  function append_mime(data, metadata, element) {\n    // create a DOM node to render to\n    var toinsert = this.create_output_subarea(\n    metadata,\n    CLASS_NAME,\n    EXEC_MIME_TYPE\n    );\n    this.keyboard_manager.register_events(toinsert);\n    // Render to node\n    var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n    render(props, toinsert[0]);\n    element.append(toinsert);\n    return toinsert\n  }\n\n  events.on('output_added.OutputArea', handle_add_output);\n  events.on('output_updated.OutputArea', handle_update_output);\n  events.on('clear_output.CodeCell', handle_clear_output);\n  events.on('delete.Cell', handle_clear_output);\n  events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n\n  OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n    safe: true,\n    index: 0\n  });\n}\n\nif (window.Jupyter !== undefined) {\n  try {\n    var events = require('base/js/events');\n    var OutputArea = require('notebook/js/outputarea').OutputArea;\n    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n      register_renderer(events, OutputArea);\n    }\n  } catch(err) {\n  }\n}\n",
            "application/vnd.holoviews_load.v0+json": ""
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.holoviews_exec.v0+json": "",
            "text/html": [
              "<div id='p1002'>\n",
              "  <div id=\"c6b870bd-5799-46c7-90fe-3b2b729473a5\" data-root-id=\"p1002\" style=\"display: contents;\"></div>\n",
              "</div>\n",
              "<script type=\"application/javascript\">(function(root) {\n",
              "  var docs_json = {\"9234e455-04a7-4c87-a0ce-ff95cfb2b7ff\":{\"version\":\"3.5.2\",\"title\":\"Bokeh Application\",\"roots\":[{\"type\":\"object\",\"name\":\"panel.models.browser.BrowserInfo\",\"id\":\"p1002\"},{\"type\":\"object\",\"name\":\"panel.models.comm_manager.CommManager\",\"id\":\"p1003\",\"attributes\":{\"plot_id\":\"p1002\",\"comm_id\":\"e8b2373c36764a5dac7fc756051a5210\",\"client_comm_id\":\"b376bd51645d476183acff6ea4e7f2d5\"}}],\"defs\":[{\"type\":\"model\",\"name\":\"ReactiveHTML1\"},{\"type\":\"model\",\"name\":\"FlexBox1\",\"properties\":[{\"name\":\"align_content\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"align_items\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"flex_direction\",\"kind\":\"Any\",\"default\":\"row\"},{\"name\":\"flex_wrap\",\"kind\":\"Any\",\"default\":\"wrap\"},{\"name\":\"gap\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"justify_content\",\"kind\":\"Any\",\"default\":\"flex-start\"}]},{\"type\":\"model\",\"name\":\"FloatPanel1\",\"properties\":[{\"name\":\"config\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"contained\",\"kind\":\"Any\",\"default\":true},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"right-top\"},{\"name\":\"offsetx\",\"kind\":\"Any\",\"default\":null},{\"name\":\"offsety\",\"kind\":\"Any\",\"default\":null},{\"name\":\"theme\",\"kind\":\"Any\",\"default\":\"primary\"},{\"name\":\"status\",\"kind\":\"Any\",\"default\":\"normalized\"}]},{\"type\":\"model\",\"name\":\"GridStack1\",\"properties\":[{\"name\":\"mode\",\"kind\":\"Any\",\"default\":\"warn\"},{\"name\":\"ncols\",\"kind\":\"Any\",\"default\":null},{\"name\":\"nrows\",\"kind\":\"Any\",\"default\":null},{\"name\":\"allow_resize\",\"kind\":\"Any\",\"default\":true},{\"name\":\"allow_drag\",\"kind\":\"Any\",\"default\":true},{\"name\":\"state\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"drag1\",\"properties\":[{\"name\":\"slider_width\",\"kind\":\"Any\",\"default\":5},{\"name\":\"slider_color\",\"kind\":\"Any\",\"default\":\"black\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":50}]},{\"type\":\"model\",\"name\":\"click1\",\"properties\":[{\"name\":\"terminal_output\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"debug_name\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"clears\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"FastWrapper1\",\"properties\":[{\"name\":\"object\",\"kind\":\"Any\",\"default\":null},{\"name\":\"style\",\"kind\":\"Any\",\"default\":null}]},{\"type\":\"model\",\"name\":\"NotificationAreaBase1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"NotificationArea1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"notifications\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0},{\"name\":\"types\",\"kind\":\"Any\",\"default\":[{\"type\":\"map\",\"entries\":[[\"type\",\"warning\"],[\"background\",\"#ffc107\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-exclamation-triangle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]},{\"type\":\"map\",\"entries\":[[\"type\",\"info\"],[\"background\",\"#007bff\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-info-circle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]}]}]},{\"type\":\"model\",\"name\":\"Notification\",\"properties\":[{\"name\":\"background\",\"kind\":\"Any\",\"default\":null},{\"name\":\"duration\",\"kind\":\"Any\",\"default\":3000},{\"name\":\"icon\",\"kind\":\"Any\",\"default\":null},{\"name\":\"message\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"notification_type\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_destroyed\",\"kind\":\"Any\",\"default\":false}]},{\"type\":\"model\",\"name\":\"TemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"BootstrapTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"TemplateEditor1\",\"properties\":[{\"name\":\"layout\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"MaterialTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"ReactiveESM1\"},{\"type\":\"model\",\"name\":\"JSComponent1\"},{\"type\":\"model\",\"name\":\"ReactComponent1\"},{\"type\":\"model\",\"name\":\"AnyWidgetComponent1\"},{\"type\":\"model\",\"name\":\"request_value1\",\"properties\":[{\"name\":\"fill\",\"kind\":\"Any\",\"default\":\"none\"},{\"name\":\"_synced\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_request_sync\",\"kind\":\"Any\",\"default\":0}]}]}};\n",
              "  var render_items = [{\"docid\":\"9234e455-04a7-4c87-a0ce-ff95cfb2b7ff\",\"roots\":{\"p1002\":\"c6b870bd-5799-46c7-90fe-3b2b729473a5\"},\"root_ids\":[\"p1002\"]}];\n",
              "  var docs = Object.values(docs_json)\n",
              "  if (!docs) {\n",
              "    return\n",
              "  }\n",
              "  const py_version = docs[0].version.replace('rc', '-rc.').replace('.dev', '-dev.')\n",
              "  async function embed_document(root) {\n",
              "    var Bokeh = get_bokeh(root)\n",
              "    await Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
              "    for (const render_item of render_items) {\n",
              "      for (const root_id of render_item.root_ids) {\n",
              "\tconst id_el = document.getElementById(root_id)\n",
              "\tif (id_el.children.length && id_el.children[0].hasAttribute('data-root-id')) {\n",
              "\t  const root_el = id_el.children[0]\n",
              "\t  root_el.id = root_el.id + '-rendered'\n",
              "\t  for (const child of root_el.children) {\n",
              "            // Ensure JupyterLab does not capture keyboard shortcuts\n",
              "            // see: https://jupyterlab.readthedocs.io/en/4.1.x/extension/notebook.html#keyboard-interaction-model\n",
              "\t    child.setAttribute('data-lm-suppress-shortcuts', 'true')\n",
              "\t  }\n",
              "\t}\n",
              "      }\n",
              "    }\n",
              "  }\n",
              "  function get_bokeh(root) {\n",
              "    if (root.Bokeh === undefined) {\n",
              "      return null\n",
              "    } else if (root.Bokeh.version !== py_version) {\n",
              "      if (root.Bokeh.versions === undefined || !root.Bokeh.versions.has(py_version)) {\n",
              "\treturn null\n",
              "      }\n",
              "      return root.Bokeh.versions.get(py_version);\n",
              "    } else if (root.Bokeh.version === py_version) {\n",
              "      return root.Bokeh\n",
              "    }\n",
              "    return null\n",
              "  }\n",
              "  function is_loaded(root) {\n",
              "    var Bokeh = get_bokeh(root)\n",
              "    return (Bokeh != null && Bokeh.Panel !== undefined)\n",
              "  }\n",
              "  if (is_loaded(root)) {\n",
              "    embed_document(root);\n",
              "  } else {\n",
              "    var attempts = 0;\n",
              "    var timer = setInterval(function(root) {\n",
              "      if (is_loaded(root)) {\n",
              "        clearInterval(timer);\n",
              "        embed_document(root);\n",
              "      } else if (document.readyState == \"complete\") {\n",
              "        attempts++;\n",
              "        if (attempts > 200) {\n",
              "          clearInterval(timer);\n",
              "\t  var Bokeh = get_bokeh(root)\n",
              "\t  if (Bokeh == null || Bokeh.Panel == null) {\n",
              "            console.warn(\"Panel: ERROR: Unable to run Panel code because Bokeh or Panel library is missing\");\n",
              "\t  } else {\n",
              "\t    console.warn(\"Panel: WARNING: Attempting to render but not all required libraries could be resolved.\")\n",
              "\t    embed_document(root)\n",
              "\t  }\n",
              "        }\n",
              "      }\n",
              "    }, 25, root)\n",
              "  }\n",
              "})(window);</script>"
            ]
          },
          "metadata": {
            "application/vnd.holoviews_exec.v0+json": {
              "id": "p1002"
            }
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "import holoviews as hv\n",
        "import hvplot.pandas"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ef932c0f-a3a8-4481-94bf-21c2e609566a",
      "metadata": {},
      "source": [
        "## Plot the precpitation column (PRCP) vs time to explore the data\n",
        "\n",
        "Plotting in Python is easy, but not quite this easy:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "b4227691",
      "metadata": {
        "template": "student"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: xlabel='DATE'>"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "climate_df2.plot()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f1cc5ab6-60d6-4289-ba80-44eeca351a76",
      "metadata": {},
      "source": [
        "Looks like we have *both* precipitation and temperature on the same\n",
        "plot, and it’s hard to see what it is because it’s missing labels!\n",
        "\n",
        "> ****Label your plot****\n",
        ">\n",
        "> <figure>\n",
        "> <img src=\"https://imgs.xkcd.com/comics/convincing.png\"\n",
        "> alt=\"Source: https://xkcd.com/833\" />\n",
        "> <figcaption aria-hidden=\"true\">Source: https://xkcd.com/833</figcaption>\n",
        "> </figure>\n",
        ">\n",
        "> Make sure each plot has:\n",
        ">\n",
        "> -   A title that explains where and when the data are from\n",
        "> -   x- and y- axis labels with **units** where appropriate\n",
        "> -   A legend where appropriate\n",
        "\n",
        "When plotting in Python, you’ll always need to add some instructions on\n",
        "labels and how you want your plot to look.\n",
        "\n",
        "<link rel=\"stylesheet\" type=\"text/css\" href=\"./assets/styles.css\"><div class=\"callout callout-style-default callout-titled callout-task\"><div class=\"callout-header\"><div class=\"callout-icon-container\"><i class=\"callout-icon\"></i></div><div class=\"callout-title-container flex-fill\">Try It: Plot your data</div></div><div class=\"callout-body-container callout-body\"><ol type=\"1\">\n",
        "<li>Change <code>dataframe</code> to <strong>your</strong>\n",
        "<code>DataFrame</code> name.</li>\n",
        "<li>Change <code>y=</code> to the name of your <strong>observed\n",
        "temperature</strong> column name.</li>\n",
        "<li>Use the <code>title</code>, <code>ylabel</code>, and\n",
        "<code>xlabel</code> parameters to add key text to your plot.</li>\n",
        "<li>Adjust the size of your figure using <code>figsize=(x,y)</code>\n",
        "where <code>x</code> is figure width and <code>y</code> is figure\n",
        "height</li>\n",
        "</ol>\n",
        "<blockquote>\n",
        "<p><strong>HINT:</strong> labels have to be a <em>type</em> in Python\n",
        "called a <strong>string</strong>. You can make a string by putting\n",
        "quotes around your label, just like the column names in the sample code\n",
        "(eg <code>y='TOBS'</code>).</p>\n",
        "</blockquote></div></div>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "979205a0",
      "metadata": {},
      "outputs": [],
      "source": [
        "climate_df2['Year'] = climate_df2.index.year"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "a1378b6d",
      "metadata": {
        "template": "student"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'Daily Precipitation in Boulder, Colorado, 1893 - 2023'}, xlabel='Date', ylabel='Daily Precipitation (inches)'>"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Plot the data using .plot\n",
        "climate_df2.plot(\n",
        "    y='PRCP_IN',\n",
        "    title='Daily Precipitation in Boulder, Colorado, 1893 - 2023',\n",
        "    xlabel='Date',\n",
        "    ylabel='Daily Precipitation (inches)') "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "6784f09f",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'Daily Precipitation in Boulder, Colorado, 1893 - 2023'}, xlabel='Date', ylabel='Daily Precipitation (inches)'>"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Change plotting color and suppress the legend\n",
        "climate_df2.plot(\n",
        "    y='PRCP_IN', color='green',\n",
        "    title='Daily Precipitation in Boulder, Colorado, 1893 - 2023',\n",
        "    xlabel='Date',\n",
        "    ylabel='Daily Precipitation (inches)', legend=False) "
      ]
    },
    {
      "cell_type": "markdown",
      "id": "06764365-a1f5-40a7-8034-f04bc4ec77de",
      "metadata": {},
      "source": [
        "<link rel=\"stylesheet\" type=\"text/css\" href=\"./assets/styles.css\"><div class=\"callout callout-style-default callout-titled callout-extra\"><div class=\"callout-header\"><div class=\"callout-icon-container\"><i class=\"callout-icon\"></i></div><div class=\"callout-title-container flex-fill\">Looking for an Extra Challenge?</div></div><div class=\"callout-body-container callout-body\"><p>There are many other things you can do to customize your plot. Take a\n",
        "look at the <a\n",
        "href=\"https://pandas.pydata.org/docs/user_guide/visualization.html\">pandas\n",
        "plotting galleries</a> and the <a\n",
        "href=\"https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.html\">documentation\n",
        "of plot</a> to see if there’s other changes you want to make to your\n",
        "plot. Some possibilities include:</p>\n",
        "<ul>\n",
        "<li>Remove the legend since there’s only one data series</li>\n",
        "<li>Increase the figure size</li>\n",
        "<li>Increase the font size</li>\n",
        "<li>Change the colors</li>\n",
        "<li>Use a bar graph instead (usually we use lines for time series, but\n",
        "since this is annual it could go either way)</li>\n",
        "<li>Add a trend line</li>\n",
        "</ul>\n",
        "<p>Not sure how to do any of these? Try searching the internet, or\n",
        "asking an AI!</p></div></div>\n",
        "\n",
        "## Clean up time series plots by resampling\n",
        "\n",
        "You may notice that your plot looks a little “fuzzy”. This happens when\n",
        "Python is trying to plot a value for every date, but the resolution of\n",
        "the image is too low to actually do that. You can address this issue by\n",
        "**resampling** the data, or summarizing it over a time period of your\n",
        "choice. In this case, we will resample annually, giving us one data\n",
        "point per year.\n",
        "\n",
        "<link rel=\"stylesheet\" type=\"text/css\" href=\"./assets/styles.css\"><div class=\"callout callout-style-default callout-titled callout-task\"><div class=\"callout-header\"><div class=\"callout-icon-container\"><i class=\"callout-icon\"></i></div><div class=\"callout-title-container flex-fill\">Try It: Resample</div></div><div class=\"callout-body-container callout-body\"><ol type=\"1\">\n",
        "<li>Set the frequency of your final data by replacing\n",
        "<code>DT_OFFSET</code>with a <strong>Datetime Offset Code</strong>.\n",
        "Check out the table in the <a\n",
        "href=\"https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#dateoffset-objects\">pandas\n",
        "datetime documentation</a> to find the one you want (we recommend the\n",
        "start of the year).</li>\n",
        "<li>Choose how to summarize each year of data by replacing\n",
        "<code>agg_method_here</code> with a method that will calculate the\n",
        "<strong>average annual value</strong>. Check out the <a\n",
        "href=\"https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#basics\">pandas\n",
        "resampling documentation</a> for a list of common built-in options.</li>\n",
        "</ol></div></div>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "8f52fbc4",
      "metadata": {
        "template": "student"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PRCP_IN</th>\n",
              "      <th>TOBS_F</th>\n",
              "      <th>TOBS_C</th>\n",
              "      <th>Year</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DATE</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1893-01-01</th>\n",
              "      <td>0.025543</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1893.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1894-01-01</th>\n",
              "      <td>0.058841</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1894.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1895-01-01</th>\n",
              "      <td>0.117090</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1895.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1896-01-01</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1897-01-01</th>\n",
              "      <td>0.068922</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1897.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-01-01</th>\n",
              "      <td>0.057644</td>\n",
              "      <td>54.426997</td>\n",
              "      <td>12.459443</td>\n",
              "      <td>2019.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-01</th>\n",
              "      <td>0.046721</td>\n",
              "      <td>57.691460</td>\n",
              "      <td>14.273033</td>\n",
              "      <td>2020.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-01-01</th>\n",
              "      <td>0.056658</td>\n",
              "      <td>57.538462</td>\n",
              "      <td>14.188034</td>\n",
              "      <td>2021.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-01-01</th>\n",
              "      <td>0.051479</td>\n",
              "      <td>56.139726</td>\n",
              "      <td>13.410959</td>\n",
              "      <td>2022.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-01-01</th>\n",
              "      <td>0.076740</td>\n",
              "      <td>58.996337</td>\n",
              "      <td>14.997965</td>\n",
              "      <td>2023.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>131 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             PRCP_IN     TOBS_F     TOBS_C    Year\n",
              "DATE                                              \n",
              "1893-01-01  0.025543        NaN        NaN  1893.0\n",
              "1894-01-01  0.058841        NaN        NaN  1894.0\n",
              "1895-01-01  0.117090        NaN        NaN  1895.0\n",
              "1896-01-01       NaN        NaN        NaN     NaN\n",
              "1897-01-01  0.068922        NaN        NaN  1897.0\n",
              "...              ...        ...        ...     ...\n",
              "2019-01-01  0.057644  54.426997  12.459443  2019.0\n",
              "2020-01-01  0.046721  57.691460  14.273033  2020.0\n",
              "2021-01-01  0.056658  57.538462  14.188034  2021.0\n",
              "2022-01-01  0.051479  56.139726  13.410959  2022.0\n",
              "2023-01-01  0.076740  58.996337  14.997965  2023.0\n",
              "\n",
              "[131 rows x 4 columns]"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "ann_climate_df = climate_df2.resample('YS').mean()\n",
        "ann_climate_df"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "12bf0d69-8bfd-48f5-b76f-7827504ed784",
      "metadata": {},
      "source": [
        "<link rel=\"stylesheet\" type=\"text/css\" href=\"./assets/styles.css\"><div class=\"callout callout-style-default callout-titled callout-task\"><div class=\"callout-header\"><div class=\"callout-icon-container\"><i class=\"callout-icon\"></i></div><div class=\"callout-title-container flex-fill\">Try It: Plot Annual Data</div></div><div class=\"callout-body-container callout-body\"><ol type=\"1\">\n",
        "<li>Try plotting your new DataFrame in the cell below. Can you see what\n",
        "is going on more clearly now? Don’t forget to adjust your labels!</li>\n",
        "</ol></div></div>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "22a823d6",
      "metadata": {
        "template": "student"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'Average Annual Precipitation in Boulder, Colorado, 1893 - 2023'}, xlabel='Date', ylabel='Precipitation (inches)'>"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlAAAAHFCAYAAAA9occoAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAADH5ElEQVR4nOydd3wU1frGn23JphPSSCCEhI5UgygoFqSIBRUrKMoVbOAPAb0qYrnoVa4NsXNFELFy7Q2VKKgoqEizIQICoQXSE1K3nN8fy5mcmZ3dndme8H4/Hz7GzWT37O6Ud57nOe8xMMYYCIIgCIIgCM0YIz0AgiAIgiCI1gYVUARBEARBEDqhAoogCIIgCEInVEARBEEQBEHohAoogiAIgiAInVABRRAEQRAEoRMqoAiCIAiCIHRCBRRBEARBEIROqIAiCIIgCILQyXFTQD399NMwGAzo27dvpIcS1Zx44okwGAx4/PHHIz2UgJg8eTK6dOmiaTuDwSD9i42NRc+ePXH//fejsbEx9ANV0KVLF0yePNmvv/3Xv/4Fg8Ege+z555/HsmXLAhrTww8/jA8++MDt8a+//hoGgwFff/11QM/vD1q/X63s2bNHth8YDAYkJydjwIABWLhwIRwOR9Bey9vra/mu1L7nSHD48GHcdddd6NevHxITE2G1WtG9e3fceuut2LFjh+7nW7ZsGQwGA/bs2RP8wQZAuD7vhQsXYvz48cjPz4fBYMCZZ57pcds1a9Zg1KhRyMzMRGJiIvr374+nn37abT9tbm7Gfffdh/z8fMTExCAvLw9z5sxBQ0ODbLt9+/bh4osvRkFBARISEpCSkoJBgwbh2Wefhd1uD+r7PHToEO655x4MHToU6enpSE5ORmFhIV588UXV4+zo0aOYOXMmcnJyYLVaMXDgQLz11luybRwOBxYsWIBzzjkHnTp1Qnx8PHr37o277roLVVVVsm3r6upw5ZVXomfPnkhKSkJCQgJOOOEE/Pvf/0ZdXZ2+N8OOEwYMGMAAMADshx9+iPRwopLNmzdLn1GvXr0iPZyAuPbaa1leXp6m7eLi4tj69evZ+vXr2cqVK9mkSZMYAHb55ZeHfqAKNm3axHbu3OnX3+7bt4+tX79e9tgJJ5zAzjjjjIDGlJCQwK699lq3x6urq9n69etZdXV1QM/vDzt37mSbNm0K2vPt3r2bAWD/93//J+0Ln332Gbv55psZADZ79uygvZa313/55Zd9bnv//fezSJ+6f/zxR5aRkcHS09PZv/71L/bFF1+wNWvWsEWLFrHTTjuNtWvXTvdzvvzyywwA2717d/AHHADh+rx79uzJTjzxRHbdddexjIwMj8dtUVERMxqN7Mwzz2QffPABKyoqYv/3f//HALAZM2bIth0/fjyzWq3s4YcfZkVFReyBBx5gMTEx7IILLpBtt23bNnbNNdewpUuXsi+//JKtXLmS3XLLLQwAmzJlSlDf58cff8xyc3PZ3Llz2aeffspWrVrFZs2axYxGI/vHP/7htv2oUaNYu3bt2KJFi9jq1avZ1KlTGQD2+uuvS9vU1taypKQkdsMNN7C3336brVmzhj3xxBMsNTWV9enTh9XX10vbVlZWsssvv5wtWrSIffHFF6yoqIjde++9zGKxsLPPPlvXezkuCqgNGzYwAOy8885jANj1118f9jE4nU7ZlxiNTJ8+XfY5ff/995Eekt/oKaASEhLcHh8+fDgDwPbv3+/xb6P9+2QstAVUW4IXMI899pjb74YPH86ys7PD8vqRKKDq6up0bV9dXc06dOjAcnNz2b59+1S3efvtt3WPI9gFlN735YlwFVAOh0P62dtxe9VVV7HY2Fh29OhR2eOjR49mycnJ0v+vX7+eAWBPPPGEbLuHH36YAWCrVq3yOabLL7+cmc1m1tjYqOOdeKeiooI1Nze7Pc6vP8XFxdJjn376KQPA3njjDdm2o0aNYjk5OcxutzPGGLPb7aysrMztOd9++20GgL366qs+x3XHHXcwAGzXrl2a38txYeEtWbIEAPCf//wHw4YNw1tvvYX6+noAgM1mQ2ZmJiZNmuT2d1VVVYiLi8Ps2bOlx2pqanD77bdLkmjHjh0xc+ZMN+nPYDDglltuwaJFi9C7d2/ExsbilVdeAQDMmzcPJ598Mtq3b4/k5GSceOKJWLJkCZhiXeempibcdttt6NChA+Lj43H66adj48aNqjZPSUkJbrzxRnTq1AkxMTHIz8/HvHnzNMuvjY2NeOONN1BYWIgnn3wSALB06VK37bic/fvvv2PChAlISUlBVlYWrrvuOlRXV6t+Bq+++ip69+6N+Ph4DBgwAJ988olsO092jJp0/txzz+H0009HZmYmEhIS0K9fPzz66KOw2Wya3qdWTjnlFADA3r17AbistfPPPx/vvfceBg0aBKvVinnz5gHQ/tk3NTXhgQceQO/evWG1WpGWloazzjoL69atk7ZRfrfcJnvttdcwe/ZsdOjQAXFxcTjjjDOwefNm2fMrP68uXbrg999/xzfffCPZUvxzbmxsxG233YaBAwciJSUF7du3x9ChQ/Hhhx/KntNgMKCurg6vvPKK9BzcWvBk4X300UcYOnQo4uPjkZSUhFGjRmH9+vWqY9WyH6mhts9o3d/0kpKSAovFInvM6XTi0UcfRa9evRAbG4vMzExcc8012L9/v2w7T5bsmWee6dWi4Xz66acYOHAgYmNjkZ+f79FaZ4zh+eefx8CBAxEXF4fU1FRceuml+Pvvv91et2/fvvj2228xbNgwxMfH47rrrvM5DpHFixejpKQEjz76KDp16qS6zaWXXir7fy37hCeWLl2KAQMGwGq1on379rj44ouxbds22TaTJ09GYmIifv31V4wePRpJSUk4++yzAQBFRUW48MIL0alTJ1itVnTr1g033ngjysrK3F5L6+fd2NiIOXPmyK4D06dPd7OL9GA0arscWywWxMTEIC4uTvZ4u3btYLVapf///vvvAQDnnnuubLvzzz8fAPDuu+/6fK2MjAwYjUaYTCZNY9NCamqq2/EEAEOGDAEA2TH0/vvvIzExEZdddpls23/84x84ePAgfvzxRwCAyWRCWlqax+fct2+fz3FlZGQAAMxms8Z3grZv4dXX17OUlBR20kknMcYYe+mllxgAtmzZMmmbWbNmsbi4ODcr4vnnn2cA2C+//MIYc93RDBw4kKWnp7MFCxawL7/8kj311FMsJSWFjRgxgjmdTulvAbCOHTuy/v37szfeeIOtXr2a/fbbb4wxxiZPnsyWLFnCioqKWFFREXvwwQdZXFwcmzdvnuz1J0yYwIxGI7vrrrvYqlWr2MKFC1lubi5LSUmRKQKHDh1iubm5LC8vj/33v/9lX375JXvwwQdZbGwsmzx5sqbP6fXXX2cA2HPPPccYY+y0005jiYmJrLa2VrYdvxvr2bMnu++++1hRURFbsGABi42NdZNfAbAuXbqwIUOGsP/9739s5cqV7Mwzz2Rms1lW5XtSi9Tu/GbNmsVeeOEF9vnnn7PVq1ezJ598kqWnp7u9dqAK1MUXX8wAsL/++osxxlheXh7Lzs5mBQUFbOnSpWzNmjXsp59+0vzZ22w2dtZZZzGz2cxuv/12tnLlSvbRRx+xu+++m7355pvSdnl5ebLvds2aNQwAy83NZRdeeCH7+OOP2Wuvvca6devGkpOTZZ+j8vPatGkTKygoYIMGDZJsKW57VVVVscmTJ7NXX32VrV69mn3++efs9ttvZ0ajkb3yyivSc6xfv57FxcWxc889V3qO33//XTa2NWvWSNvz/Wj06NHsgw8+YCtWrGCFhYUsJiaGrV271m2sWvYjT9+b8vvVur+pwRWgRx55hNlsNmaz2VhZWRlbsmQJM5vNbO7cubLtb7jhBgaA3XLLLezzzz9nixYtYhkZGSw3N5eVlpZK2ym/T84ZZ5whUxjUFKgvv/ySmUwmdtppp7H33nuPvf322+ykk05inTt3djsurr/+emaxWNhtt93GPv/8c/bGG2+wXr16saysLFZSUiJ73fbt27Pc3Fz2zDPPsDVr1rBvvvnG62ejZPTo0cxkMrkpIJ7Quk+oKVBcLZkwYQL79NNP2fLly1lBQQFLSUmRjk3GXPuDxWJhXbp0YfPnz2dfffUV++KLLxhjjL3wwgts/vz57KOPPmLffPMNe+WVV9iAAQNYz549ZUqI1s/b6XSyMWPGMLPZzO699162atUq9vjjj7OEhAQ2aNCgoKg13hSoH374gcXGxrLp06ezAwcOsMrKSrZ8+XJmsVjY448/7vbZ/f3337K/3759OwPAhg4d6vbcTqeT2Ww2VlFRwd566y2WkJDA5syZE/D70cK1117LzGazTEk65ZRTpGu3yG+//cYAsP/+979en5PvUx9++KHb7/h7ra6uZp999hnr0KEDmzBhgq4xt/kCavny5QwAW7RoEWPM5ZUmJiay4cOHS9v88ssvDAB78cUXZX87ZMgQVlhYKP3//PnzmdFoZBs2bJBt98477zAAbOXKldJjAFhKSgqrqKjwOj6Hw8FsNht74IEHWFpamlSE/f777wwAu/POO2Xbv/nmmwyA7KR84403ssTERLZ3717Zto8//jgDIF3wvDFixAhmtVpZZWUlY6xlx1uyZIlsO37he/TRR2WPT5s2jVmtVrciMisri9XU1EiPlZSUMKPRyObPny89pqeAEuGf3fLly5nJZJJ91noLKH7RLC0tZU899RQzGAyyAzcvL4+ZTCa2fft22d9r/ez5frh48WKv4/FUQJ144omyz3bPnj3MYrGwqVOnSo+pfV5aLTy73c5sNhubMmUKGzRokOx3niw8ZQHlcDhYTk4O69evn8yOqK2tZZmZmWzYsGFuY9WyH6nhqYDSsr+pwQsYtX+TJ0+WrALGXHkRAGzatGmy5/jxxx8ZAHb33XdLjwVSQJ188sksJyeHNTQ0SI/V1NSw9u3by75nT1bNvn37WFxcHLvjjjtkrwuAffXVV14/D2/06tWLdejQQdO2evYJZQFVWVkpFe8ixcXFLDY2lk2cOFF67Nprr2UA2NKlS72Oh1809+7d63Zh1fp5f/7556r77ooVK1SvI/7g67j9/vvvWU5OjrSPmkwmt/F88MEHqvbVkiVLGADWo0cPt+edP3++9JwGg8HtxiFUfPHFF8xoNLJZs2bJHu/evTsbM2aM2/YHDx5kANjDDz/s8Tn379/PsrKy2ODBg2X7HodfS/m/f/zjH8xms+kad5u38JYsWYK4uDhceeWVACDJgWvXrpVmivTr1w+FhYV4+eWXpb/btm0bfvrpJ5m8/cknn6Bv374YOHAg7Ha79G/MmDGqVsaIESOQmprqNqbVq1dj5MiRSElJgclkgsViwX333Yfy8nIcOXIEAPDNN98AAC6//HLZ31566aVuEuMnn3yCs846Czk5ObJxjR07VvZcnti9ezfWrFmD8ePHo127dgCAyy67DElJSao2HgCMGzdO9v/9+/dHY2OjNH7OWWedhaSkJOn/s7KykJmZKVljetm8eTPGjRuHtLQ06bO75ppr4HA48Ndff/n1nHV1dbBYLLBYLMjIyMDMmTMxduxYvP/++7Lt+vfvjx49esge0/rZf/bZZ7BarbrtEs7EiRNl9lxeXh6GDRuGNWvW+PV8APD222/j1FNPRWJiIsxmMywWC5YsWeJmj2hl+/btOHjwICZNmiSzIxITE3HJJZfghx9+kKxzjtb9SCuB7m+33norNmzYgA0bNmDNmjV4+OGH8b///Q8TJkyQtuGfudKaGzJkCHr37o2vvvrKr7GL1NXVYcOGDRg/frzMlklKSsIFF1wg2/aTTz6BwWDA1VdfLdsHO3TogAEDBridl1JTUzFixIiAx6gFf/YJzvr169HQ0OD2Oefm5mLEiBGqn/Mll1zi9tiRI0dw0003ITc3V9rP8/LyAEDa1/V83qtXrwbg/v1fdtllSEhICMr3742NGzfi4osvRmFhIT7++GOsXr0ac+bMwT333IMHH3xQ2m7s2LHo1q0b7rzzThQVFaGqqgqff/457r77bphMJlXLcPLkydiwYQO++OIL3HHHHXjsscfwf//3fz7HJO53drvdLY7ijU2bNuHyyy/HKaecgvnz57v93tsMSE+/q6iowLnnngvGGFasWKH6XseMGYMNGzZg9erVeOihh/Duu+/ikksugdPp1Dx2HWZf62Pnzp349ttvcckll4AxJvnTl156KV5++WUsXbpU+sKuu+46TJ8+HX/++Sd69eqFl19+GbGxsbIT5+HDh7Fz505V/xaAm6eenZ3tts1PP/2E0aNH48wzz8TixYul3MwHH3yAhx56SJpeWl5eDsB1ARAxm81uXu/hw4fx8ccfax6XkqVLl4IxhksvvVTm4Y8bNw6vv/669JmIKMcQGxsLAG7TY9V86djYWLfttFBcXIzhw4ejZ8+eeOqpp9ClSxdYrVb89NNPmD59ul/PCQBxcXH49ttvpbHl5eUhOTnZbTu171PrZ19aWoqcnBzNOQclHTp0UH1s69atfj3fe++9h8svvxyXXXYZ/vnPf6JDhw4wm8144YUXPBbNvuD7rNrnlJOTA6fTicrKSsTHx0uPa92PtBLo/tapUycMHjxY+v8zzzwTBoMBc+bMwRdffIExY8b4fJ/+3hyIVFZWwul0evzeRQ4fPgzGmNu5glNQUCD7f7Vx66Fz587YsWMH6urqkJCQ4HVbf/YJrX9bVFQkeyw+Pt7tuHU6nRg9ejQOHjyIe++9F/369UNCQgKcTidOOeUUab/Q83mXl5fDbDZLmRmOwWBAhw4dpHGHiunTpyMrKwvvv/++lE0666yzYDQa8a9//QtXXXUVCgoKEBMTg88++wyTJk3C6NGjAQAJCQl4+OGH8eCDD6Jjx45uz92hQwfp/Y4ePRqpqam46667cN1112HQoEGq49mzZw/y8/Nlj61Zs0ZTxm/z5s0YNWoUunfvjpUrV0rHPyctLU3186yoqAAAtG/f3u13lZWVGDVqFA4cOIDVq1e77f+c1NRU6Vg/66yz0LVrV1x55ZX48MMPcfHFF/scO9DGCyheGLzzzjt455133H7/yiuv4N///jdMJhMmTJiA2bNnY9myZXjooYfw6quv4qKLLpIpSOnp6YiLi/N4gUlPT5f9v1p1/NZbb8FiseCTTz6R3eko++zwC8Hhw4dlO7rdbnfbodLT09G/f3889NBDquPKyclRfRxwnWB475nx48erbrN06VI8+uijHp8jUKxWK5qamtweVxZ+H3zwAerq6vDee+9Jd5AAsGXLloBe32g0yi6anlD7PrV+9hkZGfjuu+/gdDr9KqJKSkpUH1MrGLTw2muvIT8/HytWrJC9L7XvQSt8LIcOHXL73cGDB2E0GlUV2Winf//+AICtW7dizJgxsvepDFEfPHhQdh7wtm8rzxciqampMBgMHr93kfT0dBgMBqxdu9btAgTA7bFAexqNGTMGq1atwscffywp+54IZJ/w9bdazre//fYbtm7dimXLluHaa6+VHt+5c6dsOz2fd1paGux2O0pLS2VFFGMMJSUlOOmkk1TfT7DYsmULJkyY4BbsPumkk+B0OrFt2zapaOjWrRvWr1+PAwcOoKKiAl27dkV1dTVuvfVWnH766T5fi4ew//rrL48FVE5ODjZs2CB7rGfPnj6fe/PmzRg5ciTy8vKwatUqpKSkuG3Tr18/vPnmm7Db7TLn5ddffwUAt76OlZWVGDlyJHbv3o2vvvpKOna1IL5XrbRZC8/hcOCVV15B165dsWbNGrd/t912Gw4dOoTPPvsMgOsAuuiii7B8+XJ88sknKCkpcbNbzj//fOzatQtpaWkYPHiw2z8tjf0MBgPMZrNs529oaMCrr74q247v3CtWrJA9/s4777jN7jr//PPx22+/oWvXrqrj8lZAffHFF9i/fz+mT5+u+jmdcMIJWL58edCbqYl06dIFR44cweHDh6XHmpub8cUXX8i24ydI8YLAGMPixYtDNjZfaP3sx44di8bGRr+bWr755psyWXzv3r1Yt26dz7s8T+qLwWBATEyM7KJTUlLiNgvP23Mo6dmzJzp27Ig33nhDNta6ujq8++670iys1gYv0DMzMwFAsr9ee+012XYbNmzAtm3bpNlfgGvf/uWXX2Tb/fXXX9i+fbvX10xISMCQIUPw3nvvyRq61tbW4uOPP5Zte/7554MxhgMHDqjug/369dP3hn0wZcoUdOjQAXfccQcOHDigus17770HILB9YujQoYiLi3P7nPfv34/Vq1fLPmdPqJ0zAOC///2v7P/1fN78dZXjevfdd1FXV6dpXIGQk5ODn3/+2a3pJJ/VqDYzsmPHjujXrx/i4+Px2GOPISEhAVOmTPH5Wtyu7tatm8dtYmJi3PY50UZXY8uWLRg5ciQ6deqEoqIij0X0xRdfjKNHj7rNGHzllVeQk5ODk08+WXqMF09///03Vq1a5bHg84SW96qkzSpQn332GQ4ePIhHHnlE9SLTt29fPPvss1iyZIk0rfO6667DihUrcMstt6BTp04YOXKk7G9mzpyJd999F6effjpmzZqF/v37w+l0ori4GKtWrcJtt90m+0LVOO+887BgwQJMnDgRN9xwA8rLy/H444+7HeAnnHACJkyYgCeeeAImkwkjRozA77//jieeeAIpKSkyFeOBBx5AUVERhg0bhhkzZqBnz55obGzEnj17sHLlSixatMjjdOMlS5bAbDbj7rvvVi20brzxRsyYMQOffvopLrzwQq/vzV+uuOIK3Hfffbjyyivxz3/+E42NjapddUeNGoWYmBhMmDABd9xxBxobG/HCCy+gsrIyJOPSgtbPfsKECXj55Zdx0003Yfv27TjrrLPgdDrx448/onfv3j7v5I8cOYKLL74Y119/Paqrq3H//ffDarVizpw5Xv+uX79+eOutt7BixQoUFBTAarWiX79+UkuGadOm4dJLL8W+ffvw4IMPIjs7262LdL9+/fD111/j448/RnZ2NpKSklTvMI1GIx599FFcddVVOP/883HjjTeiqakJjz32GKqqqvCf//xH/wccZoqLi/HDDz8AcF3k169fj/nz5yMvL09SaHv27IkbbrgBzzzzDIxGI8aOHYs9e/bg3nvvRW5uLmbNmiU936RJk3D11Vdj2rRpuOSSS7B37148+uijbvaPGg8++CDOOeccjBo1CrfddhscDgceeeQRJCQkSBYGAJx66qm44YYb8I9//AM///wzTj/9dCQkJODQoUP47rvv0K9fP9x8880+X2/ZsmX4xz/+gZdfftlrN/yUlBR8+OGHOP/88zFo0CDccsstGDp0KGJiYrBjxw689tpr2Lp1K8aPHx/QPtGuXTvce++9uPvuu3HNNddgwoQJKC8vx7x582C1WnH//ff7fE+9evVC165dcdddd4Exhvbt2+Pjjz92s/8A7Z/3qFGjMGbMGNx5552oqanBqaeeil9++QX3338/Bg0aJGuJo/UzBYCff/5Z6sJeU1MjuSeAS13iqvusWbMwY8YMXHDBBbjxxhsRHx+Pr776Ck888QRGjhyJAQMGSM/56KOPokOHDujcuTMOHz6M//3vf/jggw/w6quvypyN+++/H4cPH8bpp5+Ojh07SnmpxYsX47LLLkNhYaHPz1or27dvl66tDz30EHbs2CE753Tt2lU6PsaOHYtRo0bh5ptvRk1NDbp164Y333wTn3/+OV577TVJiGhoaMCYMWOwefNmLFy4EHa7XTqOAZcD0LVrVwCu4nnt2rUYPXo0cnNzUVdXh7Vr1+KZZ57BsGHD9F3ndEXOWxEXXXQRi4mJYUeOHPG4zZVXXsnMZrM0zdfhcLDc3FwGwOPsg6NHj7J77rmH9ezZk8XExLCUlBTWr18/NmvWLNl0YQBs+vTpqs+xdOlS1rNnTxYbG8sKCgrY/PnzpZkR4hTexsZGNnv2bJaZmcmsVis75ZRT2Pr161lKSorbbIXS0lI2Y8YMlp+fzywWC2vfvj0rLCxkc+fO9TjduLS0lMXExLCLLrrI42fEZ8LwzrV89pQ4VZsx9SnInj4DtZlJK1euZAMHDmRxcXGsoKCAPfvss6qzyj7++GM2YMAAZrVaWceOHdk///lP9tlnn7lNpw+0jYHamM877zzV32n97BsaGth9993HunfvzmJiYlhaWhobMWIEW7duncfPhs90e/XVV9mMGTNYRkYGi42NZcOHD2c///yzbBxqn9eePXvY6NGjWVJSEgMg+0z+85//sC5durDY2FjWu3dvtnjxYtXn2LJlCzv11FNZfHw8AyDNDlJrY8CYa/bPySefzKxWK0tISGBnn322W1NWPfuRGp5m4Wnd35SozcKzWq2sR48ebObMmezQoUOy7R0OB3vkkUdYjx49mMViYenp6ezqq692ayzpdDrZo48+ygoKCpjVamWDBw9mq1ev1jQLjzHGPvroI9a/f38WExPDOnfuzP7zn/94nJ26dOlSdvLJJ7OEhAQWFxfHunbtyq655hrZfnLGGWewE044QfUzeOaZZxgA9vnnn3v9rDglJSXszjvvZCeccAKLj49nsbGxrFu3buzGG29kv/76q2xbLfuEp+/+pZdekj6DlJQUduGFF7rNLPZ2HP/xxx9s1KhRLCkpiaWmprLLLruMFRcXMwDs/vvvl22r9fNuaGhgd955J8vLy2MWi4VlZ2ezm2++WZrFzNHzmfKZhGr/lPvFu+++y0477TSWnp7OEhIS2AknnMAefPBBt3P9vHnzWNeuXVlsbCxr164dO+ecc9i3337r9tofffQRGzlyJMvKymJms5klJiayIUOGsKefflr3zDRf8O9Z63utra1lM2bMYB06dGAxMTGsf//+stYvjHmfRQvFrPXvv/+enX/++SwnJ4fFxMSw+Ph4NmDAAPbggw/qbr5qYExHXJ6IOOvWrcOpp56K119/HRMnToz0cIgQ8/XXX+Oss87C22+/7dackCCCyeWXX47du3e75VkI/6HPtG3TZi28tkBRURHWr1+PwsJCxMXFYevWrfjPf/6D7t27ewx8EwRB6IUxhq+//tot10P4D32mbR8qoKKY5ORkrFq1CgsXLkRtbS3S09MxduxYzJ8/XzaDjyAIIhAMBoPfvbcIdegzbfuQhUcQBEEQBKGTNtvGgCAIgiAIIlRQAUUQBEEQBKETKqAIgiAIgiB0QiFyFZxOJw4ePIikpKSAlz0gCIIgCCI8MMZQW1sb0NqjWqECSoWDBw8iNzc30sMgCIIgCMIP9u3b53EFjmBBBZQKfB2fffv2ua3uTRAEQRBEdFJTU4Pc3Fyf6/EFAyqgVOC2XXJyMhVQBEEQBNHKCEf8hkLkBEEQBEEQOqECiiAIgiAIQidUQBEEQRAEQeiECiiCIAiCIAidUAFFEARBEAShEyqgCIIgCIIgdEIFFEEQBEEQhE6ogCIIgiAIgtBJxAuo559/Hvn5+bBarSgsLMTatWs9bnvo0CFMnDgRPXv2hNFoxMyZM70+91tvvQWDwYCLLroouIMmCIIgCOK4JqIF1IoVKzBz5kzMnTsXmzdvxvDhwzF27FgUFxerbt/U1ISMjAzMnTsXAwYM8Prce/fuxe23347hw4eHYugEQRAEQRzHRLSAWrBgAaZMmYKpU6eid+/eWLhwIXJzc/HCCy+obt+lSxc89dRTuOaaa5CSkuLxeR0OB6666irMmzcPBQUFoRo+QRAEQRDHKREroJqbm7Fx40aMHj1a9vjo0aOxbt26gJ77gQceQEZGBqZMmaJp+6amJtTU1Mj+EQRBEARBeCJiBVRZWRkcDgeysrJkj2dlZaGkpMTv5/3++++xZMkSLF68WPPfzJ8/HykpKdK/3Nxcv1+fCC9O5kSjvTHSwyAIgiCOMyIeIleumMwY83sV5draWlx99dVYvHgx0tPTNf/dnDlzUF1dLf3bt2+fX69PhJ9zXjsH+U/lo665LtJDIQiCII4jzJF64fT0dJhMJje16ciRI26qlFZ27dqFPXv24IILLpAeczqdAACz2Yzt27eja9eubn8XGxuL2NhYv16TiCw/7P8Btc21KK4uRu+M3pEeDkEQBHGcEDEFKiYmBoWFhSgqKpI9XlRUhGHDhvn1nL169cKvv/6KLVu2SP/GjRuHs846C1u2bCFrrg1ic9oAAHanPcIjIQiCII4nIqZAAcDs2bMxadIkDB48GEOHDsWLL76I4uJi3HTTTQBc1tqBAwewfPly6W+2bNkCADh69ChKS0uxZcsWxMTEoE+fPrBarejbt6/sNdq1awcAbo8TbQNeOPFCiiAIgiDCQUQLqCuuuALl5eV44IEHcOjQIfTt2xcrV65EXl4eAFfjTGVPqEGDBkk/b9y4EW+88Qby8vKwZ8+ecA6diAIYY1IBRQoUQRAEEU4MjDEW6UFEGzU1NUhJSUF1dTWSk5MjPRzCA3anHZYHLQCA7/7xHU7tfGqER0QQBEFEknBevyM+C48g/EVUncjCIwiCIMIJFVBEq8XmaCmayMIjCIIgwgkVUESrRaZAOUiBIgiCIMIHFVBEq4UsPIIgCCJSUAFFtFrEooksPIIgCCKcUAFFtFrIwiMIgiAiBRVQRKtFLJrIwiMIgiDCCRVQRKtFVKDIwiMIgiDCCRVQRKuFLDyCIAgiUlABRbRaKEROEARBRAoqoIhWC7UxIAiCICIFFVBEq0UWIicLjyAIgggjVEARrRYKkRMEQRCRggoootVCFh5BEAQRKaiAIlotYtFEFh5BEAQRTqiAIlotZOERBEEQkYIKKKLVQp3ICYIgiEhBBRTRaqFGmgRBEESkoAKKaLWQhUcQBEFECiqgiFaLLEROFh5BEAQRRqiAIlotpEARBEEQkYIKKKLVQiFygiAIIlJQAUW0WihEThAEQUQKKqCIVgtZeARBEESkoAKKaLVQiJwgCIKIFFRAEa0WsvAIgiCISEEFFNFqEYsmsvAIgiCIcEIFFNFqkSlQZOERBEEQYYQKKKLVQhYeQRAEESmogCJaLaLqRBYeQRAEEU6ogCJaLWThEQRBEJGCCiii1UIhcoIgCCJSUAFFtFooA0UQBEFECiqgiFYLWXgEQRBEpKACimi1UIicIAiCiBRUQBGtFrLwCIIgiEhBBRTRaqG18AiCIIhIQQUU0WoRFSiy8AiCIIhwQgUU0WohC48gCIKIFFRAEa0WsWgiC48gCIIIJ1RAEa0WsvAIgiCISEEFFNFqkYXIycIjCIIgwggVUESrRVSdGBiczBnB0RAEQRDHE1RAEa0WpW1HKhRBEAQRLqiAIlotyoKJguQEQRBEuKACimi1KBUoCpITBEEQ4YIKKKLVolScyMIjCIIgwgUVUESrxS0DRRYeQRAEESYiXkA9//zzyM/Ph9VqRWFhIdauXetx20OHDmHixIno2bMnjEYjZs6c6bbN4sWLMXz4cKSmpiI1NRUjR47ETz/9FMJ3QEQKsvAIgiCISBHRAmrFihWYOXMm5s6di82bN2P48OEYO3YsiouLVbdvampCRkYG5s6diwEDBqhu8/XXX2PChAlYs2YN1q9fj86dO2P06NE4cOBAKN8KEQHcQuRk4REEQRBhwsAYY5F68ZNPPhknnngiXnjhBemx3r1746KLLsL8+fO9/u2ZZ56JgQMHYuHChV63czgcSE1NxbPPPotrrrlG07hqamqQkpKC6upqJCcna/obIvx0WtAJB2pbCuNt07ehV3qvCI6IIAiCiCThvH5HTIFqbm7Gxo0bMXr0aNnjo0ePxrp164L2OvX19bDZbGjfvr3HbZqamlBTUyP7R0Q/ZOERBEEQkSJiBVRZWRkcDgeysrJkj2dlZaGkpCRor3PXXXehY8eOGDlypMdt5s+fj5SUFOlfbm5u0F6fCB00C48gCIKIFBEPkRsMBtn/M8bcHvOXRx99FG+++Sbee+89WK1Wj9vNmTMH1dXV0r99+/YF5fWJ0EIKFEEQBBEpzJF64fT0dJhMJje16ciRI26qlD88/vjjePjhh/Hll1+if//+XreNjY1FbGxswK9JhBeuOJmNZtiddmpjQBAEQYSNiClQMTExKCwsRFFRkezxoqIiDBs2LKDnfuyxx/Dggw/i888/x+DBgwN6LiJ64YqT1exSF8nCIwiCIMJFxBQoAJg9ezYmTZqEwYMHY+jQoXjxxRdRXFyMm266CYDLWjtw4ACWL18u/c2WLVsAAEePHkVpaSm2bNmCmJgY9OnTB4DLtrv33nvxxhtvoEuXLpLClZiYiMTExPC+QSKk8AIqzhyHo81HycIjCIIgwkZEC6grrrgC5eXleOCBB3Do0CH07dsXK1euRF5eHgBX40xlT6hBgwZJP2/cuBFvvPEG8vLysGfPHgCuxpzNzc249NJLZX93//3341//+ldI3w8RPhxOBxhcHTjiLHEAqBM5QRAEET4iWkABwLRp0zBt2jTV3y1btsztMV9tq3ghRbRtRLUp3hIPgCw8giAIInxEvIBqbVQ3VuOZn55BnDkOtw27LdLDOW4R1aY4s0uBIguPIAiCCBdUQOmkvKEc9665F4kxiVRARRCxWJJC5GThEQRBEGEi4n2gWhvNjmYAgMVoifBIjm/EAkrKQJGFRxAEQYQJKqB0wguoGFNMhEdyfMOLJaPBiFiTq4cXWXgEQRBEuKACSif8wk0FVGThxZLZaIbZ6HKiycIjCIIgwgUVUDqRLDwTWXiRhBdLFqNF+i5IgSIIgiDCBRVQOiELLzoQFSieR6MMFEEQBBEuqIDSCVc+qICKLGThEQRBEJGECiid0Cy86ICrTRYTWXgEQRBE+KECSidk4UUHMgXKcEyBIguPIAiCCBNUQOmEZuFFB2ohcrLwCIIgiHBBBZROSIGKDtRC5GThEQRBEOGCCiidUBuD6EA1RE4WHkEQBBEmqIDSCc3Ciw7UQuRk4REEQRDhggoonZCFFx2QhUcQBEFEEiqgdEJtDKIDrjaRhUcQBEFEAiqgdEIKVHTA1SZayoUgCIKIBFRA6YTaGEQHqku5UAaKIAiCCBNUQOmELLzoQAyR01IuBEEQRLihAkonZOFFBzIFiiw8giAIIsxQAaUTamMQHVCInCAIgogkVEDphBppRgeyEDlloAiCIIgwQwWUTsjCiw7IwiMIgiAiCRVQOiELLzpQDZGThUcQBEGECSqgdEKz8KIDamNAEARBRBIqoHRCFl50IIXIDWThEQRBEOGHCiidUCPN6EAKkZOFRxAEQUQAKqB0QrPwogNaTJggCIKIJFRA6YQsvOhACpELa+FRBoogCIIIF1RA6YRm4UUHogJFFh5BEAQRbqiA0gnNwosOxE7kZOERBEEQ4YYKKJ2QhRcdqIbIycIjCIIgwgQVUDqhWXjRgVoncrLwCIIgiHBh1rPx9u3b8eabb2Lt2rXYs2cP6uvrkZGRgUGDBmHMmDG45JJLEBsbG6qxRgU0Cy86kIXIycIjCIIgwowmBWrz5s0YNWoUBgwYgG+//RYnnXQSZs6ciQcffBBXX301GGOYO3cucnJy8Mgjj6CpqSnU444YZOFFB3amEiInC48gCIIIE5oUqIsuugj//Oc/sWLFCrRv397jduvXr8eTTz6JJ554AnfffXfQBhlN0Cy86IArUGThEQRBEJFAUwG1Y8cOxMT4LhiGDh2KoUOHorm5OeCBRSukQEUHYoicLDyCIAgi3Giy8HwVT1VVVbq2b81QG4PoQLUPFFl4BEEQRJjQPQvvkUcewYoVK6T/v/zyy5GWloaOHTti69atQR1cNEKz8KIDXiyJncidzAknc0ZyWARBEMRxgu4C6r///S9yc3MBAEVFRSgqKsJnn32GsWPH4p///GfQBxhtkIUXHaithSc+ThAEQRChRFcbAwA4dOiQVEB98sknuPzyyzF69Gh06dIFJ598ctAHGE0wxlqUD2pjEFHEEDm38PjjVNwSBEEQoUa3ApWamop9+/YBAD7//HOMHDkSgKu4cDgcwR1dlCFmbOgiHVlkIXITKVAEQRBEeNGtQI0fPx4TJ05E9+7dUV5ejrFjxwIAtmzZgm7dugV9gNGEOE2eCqjIohYiByhIThAEQYQH3QXUk08+iS5dumDfvn149NFHkZiYCMBl7U2bNi3oA4wmeP4JoFl4kUYMkRsNRhgNRjiZk3pBEQRBEGFBdwFlsVhw++23uz0+c+bMYIwnqhELKFH1IMKPqEABrkKqydFEFh5BEAQRFvxaTPjVV1/FaaedhpycHOzduxcAsHDhQnz44YdBHVy0IXYhNxgMER7N8Y2ygKJeUARBEEQ40V1AvfDCC5g9ezbGjh2LqqoqKTjerl07LFy4MNjjiyqoiWb0IC0mfCxATsu5EARBEOFEdwH1zDPPYPHixZg7dy5MJpP0+ODBg/Hrr78GdXDRBvWAih7ULDzxcYIgCIIIJboLqN27d2PQoEFuj8fGxqKurk73AJ5//nnk5+fDarWisLAQa9eu9bjtoUOHMHHiRPTs2RNGo9Fj7urdd99Fnz59EBsbiz59+uD999/XPS41qAt59CCGyAGy8AiCIIjworuAys/Px5YtW9we/+yzz9CnTx9dz7VixQrMnDkTc+fOxebNmzF8+HCMHTsWxcXFqts3NTUhIyMDc+fOxYABA1S3Wb9+Pa644gpMmjQJW7duxaRJk3D55Zfjxx9/1DU2NSQLj5poRhw3BcpEChRBEAQRPnQXUP/85z8xffp0rFixAowx/PTTT3jooYdw9913617KZcGCBZgyZQqmTp2K3r17Y+HChcjNzcULL7ygun2XLl3w1FNP4ZprrkFKSorqNgsXLsSoUaMwZ84c9OrVC3PmzMHZZ58dlHwWWXjRgycLjzJQBEEQRDjQPRf/H//4B+x2O+644w7U19dj4sSJ6NixI5566ilceeWVmp+nubkZGzduxF133SV7fPTo0Vi3bp3eYUmsX78es2bNkj02ZswYrwVUU1MTmpqapP+vqalR3U6chUdEFmWInCw8giAIIpz41czo+uuvx/XXX4+ysjI4nU5kZmbqfo6ysjI4HA5kZWXJHs/KykJJSYk/wwIAlJSU6H7O+fPnY968eT6fm2bhRQ9k4REEQRCRxK8+UJz09HS/iicRZT8lxljAPZb0PuecOXNQXV0t/eNr/SkhCy964EqTWx8osvAIgiCIMKC7gDp8+DAmTZqEnJwcmM1mmEwm2T+tpKenw2QyuSlDR44ccVOQ9NChQwfdzxkbG4vk5GTZPzVoFl70IC0mfEwNlDJQZOERBEEQYUC3hTd58mQUFxfj3nvvRXZ2tt9qUUxMDAoLC1FUVISLL75YeryoqAgXXnihX88JAEOHDkVRUZEsB7Vq1SoMGzbM7+fk0Cy86IEsPIIgCCKS6C6gvvvuO6xduxYDBw4M+MVnz56NSZMmYfDgwRg6dChefPFFFBcX46abbgLgstYOHDiA5cuXS3/DWygcPXoUpaWl2LJlC2JiYqQWCrfeeitOP/10PPLII7jwwgvx4Ycf4ssvv8R3330X8HjJwosOnMwJJ3MCUAmRk4VHEARBhAHdBVRubi4YY0F58SuuuALl5eV44IEHcOjQIfTt2xcrV65EXl4eAFfjTGVPKLGJ58aNG/HGG28gLy8Pe/bsAQAMGzYMb731Fu655x7ce++96Nq1K1asWIGTTz454PHSLLzoQFSZ3NoYkIVHEARBhAHdBdTChQtx11134b///S+6dOkS8ACmTZuGadOmqf5u2bJlbo9pKd4uvfRSXHrppYEOzQ2ahRcdiCoTWXgEQRBEJNBUQKWmpsqyTnV1dejatSvi4+NhsciLiYqKiuCOMIogCy86EIskt6VcyMIjCIIgwoCmAioYXbzbAjQLLzrwZuGRAkUQBEGEA00F1LXXXhvqcbQKSIGKDnjOyQADTEZX6wxu4VEGiiAIgggHuvtArVy5El988YXb46tWrcJnn30WlEFFK5SBig6ULQzEn8nCIwiCIMKB7gLqrrvugsPhcHvc6XS6rWvX1iAFKjrgRZJYQJGFRxAEQYQT3QXUjh07pJ5LIr169cLOnTuDMqhohdoYRAdSF3KhoSktJkwQBEGEE90FVEpKCv7++2+3x3fu3ImEhISgDCpaoU7k0YGahSf1gSILjyAIgggDuguocePGYebMmdi1a5f02M6dO3Hbbbdh3LhxQR1ctEEWXnTAVSYxi0Z9oAiCIIhworuAeuyxx5CQkIBevXohPz8f+fn56N27N9LS0vD444+HYoxRA7UxiA68hsjJwiMIgiDCgO5O5CkpKVi3bh2KioqwdetWxMXFoX///jj99NNDMb6ootlJs/CiAW8hcrLwCIIgiHCgu4ACAIPBgNGjR2P06NHBHk9UQxZedKAWIicLjyAIgggnfhVQX331Fb766iscOXIETqdT9rulS5cGZWDRCFl40QFZeARBEJHlho9vwE8HfsIPU3+A1WyN9HAigu4Cat68eXjggQcwePBgZGdny9bIa+vQLLzoQDVETn2gCIIgwsbbf7yNqsYq/Fn2JwZ2GBjp4UQE3QXUokWLsGzZMkyaNCkU44lqyMKLDlTbGNBSLgRBEGGDXw/rmusiPJLIoXsWXnNzM4YNGxaKsUQ91EgzOlALkdNSLgRBEOGjyd4EAKizUQGlmalTp+KNN94IxViiHloLLzpQDZGThUcQBBEWHE4HHMy1pNvR5qMRHk3k0G3hNTY24sUXX8SXX36J/v37w2KRFxMLFiwI2uCiDbLwogMKkRMEQUQOfi0Ejm8LT3cB9csvv2DgwIEAgN9++032u7YeKKdZeNGBt07kZOERBEGEFlkBdRxbeLoLqDVr1oRiHK0CmoUXHXhbC48sPIIgiNDS5GiSfj6eLTzdGajjGbLwogOvIXKy8AiCIEIKWXguNClQ48ePx7Jly5CcnIzx48d73fa9994LysCiEZqFFx1460R+PFp4X/79JWJNsRieNzzSQyEI4jiAz8ADjm8FSlMBlZKSIuWbUlJSQjqgaIZm4UUHZOG1UNdch/PeOA+xplhU3VUFo4FEZYIgQgtloFxoKqBefvll1Z+PN8jCiw7UQuTHq4VX3VSNZkez9O94XVKBIIjwQRkoF3S7qgOahRcdeOtEfrwpUKKULt4VEgRBhApSoFxoKqDOOeccrFu3zud2tbW1eOSRR/Dcc88FPLBohGbhRQdqIXKuRh1vGSjxTpAKKIIgwoF440Yhch9cdtlluPzyy5GUlIRx48Zh8ODByMnJgdVqRWVlJf744w989913WLlyJc4//3w89thjoR53RCALLzqQQuRk4ZECRRBE2BHPNcezhaepgJoyZQomTZqEd955BytWrMDixYtRVVUFwNU8s0+fPhgzZgw2btyInj17hnK8EYMxRrPwogSy8FoQT2RUQBEEEQ5E5ft4tvA0N9KMiYnBxIkTMXHiRABAdXU1GhoakJaW5racS1tEvDBTARVZpBC5SUWBOo4tvOPtvRMEERlIgXKhuxM5JyUl5bhqaSDuMNTGILJ4a2NAFh5BEERooQyUC5qFpxHx4kQKVGRRDZEfpxYehcgJggg3NAvPBRVQGhGVDfHCTYQfryHy48zGIgWKIIhwo+wDxRiL4GgiBxVQGhG7kPOu7ERkIAuvBVKgCIIIN+K5xsmcsvPQ8QQVUBqhFgbRAy+SyMIjBYogiPAjnneA4zdI7rcX1dzcjCNHjsDpdMoe79y5c8CDikaoC3n0oLaYsJqF12hvxO2rbse4nuMwuuvo8A4yTJACRRBEuFEqTnXNdUiPT4/QaCKH7gJqx44duO6669w6kzPGYDAY4HA4gja4aIK6kEcPqgrUMQvPwRzSvrhq1yo8t+E5bCnZ0nYLKOFO8HizLwmCiAzKm7XjNUiuu4CaPHkyzGYzPvnkE2RnZx83eSCy8KIHtRC5WNjanXZYTBYcqTsCoG3Ly6RAEQQRbsjCc6G7gNqyZQs2btyIXr16hWI8UQt1IY8e1ELk4s82pw0WkwXl9eUA2nZhQRkogiDCjZsCdZz2gtIdIu/Tpw/KyspCMZaoRpyFR0QWb4sJAy0FVkVDBQB3v74tQQoUQRDhRnlOPV4VKN0F1COPPII77rgDX3/9NcrLy1FTUyP711YhCy968BYiB1oKLF5AteXCghQogiDCDWWgXOi28EaOHAkAOPvss2WPt/UQOc3Cix7ULDyT0QQDDGBoWfS5vMFl4Sn9+rYEKVAEQYQbtVl4xyO6C6g1a9aEYhxRD83Cix6kxYQVdqrFZEGzo9nNwmvLhYVsFt5x1oWdIIjIoDynHq8Wnu4C6owzzgjFOKIesvDkFFcXIyshC7Hm2LC/tpoCxf+/2dHsZuFRBoogCCJ4KFV9svB0UFVVhSVLlmDbtm0wGAzo06cPrrvuOqSkpAR7fFEDzcJr4a/yv9Dr2V64sNeFeP+K98P++mohcqBFkeIFlmjhcYu5rUEFFEEQ4YafaxIsCaiz1R23CpTuEPnPP/+Mrl274sknn0RFRQXKysqwYMECdO3aFZs2bQrFGKMCmoXXwo7yHWBg2Fa6LSKvrxYiF/+fF7tcgWJgcLC2mc2jEDlBEOGG37i1j2sPgDJQmpk1axbGjRuHxYsXw2x2/bndbsfUqVMxc+ZMfPvtt0EfZDRAFl4LjfZGAJHzvb1ZeIBLoWqwNUjjBFyFhjnG75WLohaxaKICiiCIcMDPNe3j2mNfzT6y8LTy888/y4onADCbzbjjjjswePDgoA4umqBZeC1EuoDyGCIXLDxu33GaHc1IQEJ4BhhGyMIjCCLccOWbK1Bk4WkkOTkZxcXFbo/v27cPSUlJQRlUNEKz8FrgF+06Wx0YY2F/fU8KlGjhcfuO01aD5GThEQQRbvi5JjUuFcDxGyLXXUBdccUVmDJlClasWIF9+/Zh//79eOuttzB16lRMmDAhFGOMClqbhddob0Thi4W4+ZObQ/LcgKuQicRF21OIXLTw3AqoNtoLSiwMaTFhgiDCgZSBspICpYvHH38c48ePxzXXXIMuXbogLy8PkydPxqWXXopHHnlE9wCef/555Ofnw2q1orCwEGvXrvW6/TfffIPCwkJYrVYUFBRg0aJFbtssXLgQPXv2RFxcHHJzczFr1iw0NjaqPJt2pFl4xtZRQG0r3YZNhzbhzd/eDPpzi8VIJA4cjyFy0cKrd7fw2iKkQBEEEW7EDBRw/IbIdRdQMTExeOqpp1BZWYktW7Zg8+bNqKiowJNPPonYWH09gVasWIGZM2di7ty52Lx5M4YPH46xY8eqWoQAsHv3bpx77rkYPnw4Nm/ejLvvvhszZszAu+++K23z+uuv46677sL999+Pbdu2YcmSJVixYgXmzJmj963KaG0WXlVjFQCgwd4Q9OcWw9mRLKA8KlDHk4VHGSiCCDk/7P8B579xPraXbY/0UKICZQaKLDydxMfHo1+/fujfvz/i4+P9eo4FCxZgypQpmDp1Knr37o2FCxciNzcXL7zwgur2ixYtQufOnbFw4UL07t0bU6dOxXXXXYfHH39c2mb9+vU49dRTMXHiRHTp0gWjR4/GhAkT8PPPP/s1Rk5rs/B4AdXsaIbDGdwp/GIBFYkDx1snckDdwmurxQUpUAQRel7e/DI+3fEp3vj1jUgPJSpQKlDHq4WnaRbe+PHjsWzZMiQnJ2P8+PFet33vvfc0vXBzczM2btyIu+66S/b46NGjsW7dOtW/Wb9+PUaPHi17bMyYMViyZAlsNhssFgtOO+00vPbaa/jpp58wZMgQ/P3331i5ciWuvfZaj2NpampCU1PLhUhtUeTWNguPF1CAS4VKjEkM2nOLqkc0KVDeZuEdDxkoKqAIIjQctbnOc+J59XiG+kC50FRApaSkSF2ck5OTg9LRuaysDA6HA1lZWbLHs7KyUFJSovo3JSUlqtvb7XaUlZUhOzsbV155JUpLS3HaaaeBMQa73Y6bb77ZrVATmT9/PubNm+d1vK1NgapuqpZ+rrfVB7WAirSF5zNErmLhtdXighQoggg99bZ6AEBVU1VkBxIlkALlQlMB9fLLL0s/L1u2LKgDUBZjvpbcUNtefPzrr7/GQw89hOeffx4nn3wydu7ciVtvvRXZ2dm49957VZ9zzpw5mD17tvT/NTU1yM3NlW3T2jqRi3dK/OAPFjILLwJ3Hr46kasqUMdBBopm4RFEaGiwubKk1Y3VPrY8PlBmoJocTbA77W43tW0d3RmoESNGoKqqyu3xmpoajBgxQvPzpKenw2QyualNR44ccVOZOB06dFDd3mw2Iy0tDQBw7733YtKkSZg6dSr69euHiy++GA8//DDmz58Pp9Op+ryxsbFITk6W/VPS7GxdCpTMwrMFN0ge7RbecdXGgBQoggg5kgJFFh4YY9LNGi+ggOPTxtNdQH399ddobnY/UTc2NvpsQSASExODwsJCFBUVyR4vKirCsGHDVP9m6NChbtuvWrUKgwcPhsXiunjW19fDaJS/LZPJBMZYQE0fW1sGSmnhBZNIWniMtaxrp1QDjzcLjzFGGSiCCAN8NrN4Xj1eEc8zSbFJMBlMAI7PmXia9bZffvlF+vmPP/6QKUEOhwOff/45OnbsqOvFZ8+ejUmTJmHw4MEYOnQoXnzxRRQXF+Omm24C4LLWDhw4gOXLlwMAbrrpJjz77LOYPXs2rr/+eqxfvx5LlizBm2+29Dq64IILsGDBAgwaNEiy8O69916MGzcOJpNJ1/hEWmsbAyDEFl6YDxquPgGeO5GLfaBSramobKxskxae0rKjAoogQgM/h3qy8BpsDWhyNKGdtV0YRxUZxPNMrCkWCTEJqGmqOS4VKM0F1MCBA2EwGGAwGFSturi4ODzzzDO6XvyKK65AeXk5HnjgARw6dAh9+/bFypUrkZeXBwA4dOiQrCdUfn4+Vq5ciVmzZuG5555DTk4Onn76aVxyySXSNvfccw8MBgPuueceHDhwABkZGbjgggvw0EMP6RqbktYWIlfOwgsmkWykKRYNWiy87KRsVDZWtsniQmlLtsX3SBDRAI9BeLLwhi4ZiuLqYhTPKg7qhJ1oRLwZjTHFIDEmETVNNcdlkFxzAbV7924wxlBQUICffvoJGRkZ0u9iYmKQmZnpl8Izbdo0TJs2TfV3aoH1M844A5s2bfL4fGazGffffz/uv/9+3WPxhtSJvBUWUG3JwhMVKKUayAuq6qZq6SDPTszGH6V/tMkMlFJVowKKIEKDpEA1VbtNdLI5bNh6eCsAYE/VHvTN7BuRMYYLfp4xGUwwGU1IsLgWaScLzwtcFfIUxG7rtLZZeKLU3JZm4Wmx8A4fPSz9Pi3eNbmgLRYXpEARRHjg51Anc+Jo81EkxSZJvxNvVsvqy8I9tLDDzztcTOCKGylQOvjjjz9QXFzsFigfN25cwIOKRlq1hRfKWXi2MFt4jhYLj4cXOWaDa3c+XOcqoNrHtUesybW8UFvMQCnfk/jZEAQRPMQYRHVTtayAEiesKNfgbIvwa2Gs2XVuTYg5pkBFSQZq7V7tk9kCRXcB9ffff+Piiy/Gr7/+CoPB4NaHyeEI7rIh0UJrmoXHGGuzs/DEFgbKnmCSAqVWQLVFC48UKIIIOTaHTaZ8VzVWoVNyJ+n/KxsrpZ+V/efaIvzGjV8Lo83Ce2htYHlnPehuY3DrrbciPz8fhw8fRnx8PH7//Xd8++23GDx4ML7++usQDDE6aE2z8I42H4WTtVitwS6goiFErtawjdur3MJLi0uTDvK2WFwo31NbfI8EEWmUk3CUM/FEBep4sPAkBerYzWm0WXjhHIduBWr9+vVYvXo1MjIyYDQaYTQacdppp2H+/PmYMWMGNm/eHIpxRpzWZOEpZ4oEexZeNGSg1LJovKiSKVDmtm/hGQ1GOJmTCiiCCAHKG1Dl+bWyQVCgjgMLT5mBijYLL5wFlG4FyuFwIDHRVXGmp6fj4MGDAFwh8+3btwd3dFFEa5qFpzzA26qFp4Srg/yOULTw2mJxwU9k/A6w2dEcULNYIroory/H+n3rIz2M4x5lhlTZTFOmQDUcRwrUsZvTREt0KVC1zbVhey3dBVTfvn2lpponn3wyHn30UXz//fd44IEHUFBQEPQBRgutaRZeqAuoSC7lwrNoalaq8rsRLbw2mYE69j0kxbgCrQwtXdpbG/W2erz2y2shuYPfU7UHd391N0qOqi9SHq1c+8G1GLZ0GDYd8ty2hQg9yvOnNwvvuFCglBmomOjKQNU2RXEBdc8990itDP79739j7969GD58OFauXImnnnoq6AOMFlqThae8Qwr2LLxo6ESupkApH2vzFp5CgQJa70y8ZVuWYdL7k/Dw2oeD/txP/fAU5n83Hy9teinozx1Kdlftdv23cneER3J8o4xAuFl4Qoj8eMxASSHyKLDw7E57WG+WdWegxowZI/1cUFCAP/74AxUVFUhNTXWbFdWWaE2z8NwUKHvwFCiH0yGbkRJVIXKFKtU+rr1U4LVJC48rUMKU6mZHM+IscZEakt8cqj0EADh49GDQn7u0vhQAcKTuSNCfO5Rw5SNa7uyPV9wUKC8W3nExC89TH6gwt7RRI5zqE+CHAnXdddehtlY+yPbt26O+vh7XXXdd0AYWbbSmWXihtPCUSs7R5qNhzd14C5G7WXjxaW27D5SKAtVaC0V+lx+KgpxnIlrbQrD8jj5asiXHKz5D5GFUoKobq3HKS6fg0e8fDenreCOa+0CFM/8E+FFAvfLKK2hocLeEGhoapEV/2yKtysI75tFbzVYAwbXwRPsOcHXmDWdxotfCa8ttDPjnbjVbpeKxtb5Pvo+G4iRc01Qj+29rgStP0XBhOp7REyKvaqySKfTBZm3xWvx44Ecs3bw0ZK/hC2UGSq2NAWMMr2x5BX+W/RnWsUWtAlVTU4Pqatc6QLW1taipqZH+VVZWYuXKlcjMzAzlWCNKa5yFl5OUAyC4ChQvoAxosWvDeYfMrVStFp6UgWqLIfJj7ynWFBsVheIXO7/AVe9d5XHBVW+EVIE6dlJVhn+jGcYYWXhRgp42BoC8oAo2fCJEuJUWEY8ZKGE/Xb17NSZ/OBk3fXJTWMcW7s9FcwaqXbt2MBgMMBgM6NGjh9vvDQYD5s2bF9TBRQuMsVY5Cy87MRt/V/4dXAvv2EU73hIPB3Og0d6Io81HkR6fHrTX8IZk4alYqcqiKi0urW23MTh2JxhrjnV9HrbIvs9Hvn8Ea/aswXndz8PEfhN1/S1ZeHLE4DIpUJGFfxcGGMDAvM7CA1wz8TITQiMm8CbBkbR1tfSB+u3IbwAQ9pmv4VagNBdQa9asAWMMI0aMwLvvvov27dtLv4uJiUFeXh5ycnJCMshII0qyrUKBaqoC0KJABbORJlegYs2xMBqMUgEVLrR0IueIFl5bzkBFiwLFLyR+KVC20BVQ3LprTQqUeNNDClRk4d9Fenw6SutLZfs3Y0za761mKxrtjSHNQfGChGdPIzFxy60PlIqFt7NiJ4DwK0JRq0CdccYZAIDdu3ejc+fObXrGnRJ+0QZaRwHFLxTZidkAQmPhWc1WxJhiUFZfFtY7ZK8hckGVMhvNSIxJbNsWnsO9gBL31XDDLyz+ZI14kR+KYkGy8FqRAiUeU1RARRZe3GcnZaO0vtRtnVF+zHVr3w2/HfktpDPxSupcBZSTOdFgb0C8JT5kr+UJKQNl9LwW3s5KVwEVbqUsKhWoX375BX379oXRaER1dTV+/fVXj9v2798/aIOLFsS7+tY0Cy87SXsB9clfn2DxpsVYfMFir/KzGFyOM7umy4fzINEaIk+LS4PBYIgKZSZUSAqUOToUqIAKqBApUA6nQzqxt6YQuXgxIgsvsvDzZ3ZiNn45/ItMyeTqk9loRl5KHn478ltYFCjAdaxEooDSo0CFWymLSgVq4MCBKCkpQWZmJgYOHAiDwaA6dd1gMMDhaJ2dkL0hK6BaUQZKsvA0zMJ7+senUfR3ES7tfSkmDZjkcTvJwjPFRmQRSa8hcuG7aR/nspjbdBsDFQVKbwH12PeP4Z1t7+Dzqz5Halyq32NxMqdUoPhzF8gVqGZHM2wOW9BuVMR9s9nRjEZ7ozQ7NZohCy964PsmP5/W2eqkfZS3MGgf117KgYayG7lYQNU21YYsa+UNbxkoxhjsTjv2VO0BEH6lLCoVqN27dyMjI0P6+XhDWj7EaGkV1iWXmEULz9ddAD8R+FKrRAuPF1DhPMF7C5GLj/ECKhqUmVARqALFGMNj6x5DaX0p1u1bh/N6nOf3WGqaasDguqmqadav9CgLhnamdn6PRUR5R1rdWA1rYvQXUDILjxSoiML3zQ6JHaTHappqkBafJlt3My0uDUBom2nyEDkQuSC5p1l4DAyN9kYcrD3o1mw5bAVUNCpQeXl5qj8fL7SmHlCMMTcLj4GhydHk9c6by9LKPk9K+EXbarZKdx7REiKXWXjxrpPZ8ZKB8qcP1N7qvVKX7kCLYDFYG4iFB7j2p3bWdgGNx9NYqpuqkZWYFZTnDiUyC48UqIjCC6jk2GQkWBJQZ6tDVWOVrIBKtaZKCpTSwmuwNeDeNfdifO/xGJY7zO9x1DXXyQqESLUyUPaBEoujo81HJfuOE06lLNw2ve5GmgCwfft23HLLLTj77LMxcuRI3HLLLdi+fXuwxxY1tKYu5I32Rmm8XHIGfNt4fMfzVUCJs/C0WnhPrn8SZyw7A/tr9nsfvAa0diJXWnhtUoFyBKZA/XTgJ+nnQIvggAsou7yAChZKSb+1zMQTFTnqRN7CS5tewkfbPwrra/J9M84chxRrCoAWlZ/3gGof1166aVMqUB9u/xBPrH8C9625L6BxHK47LPv/iCtQx25OTUaTlIets9W5FVDhHGfUdyJ/55130LdvX2zcuBEDBgxA//79sWnTJvTt2xdvv/12KMYYcVqTAsUvZEaDEanWVEmV8WXN8ROCTwVKCJEnWo5ZeD4shhd+fgHf7v0W17x/DZzM6fM9eMNbiFxm4VnlFl6bzEAF2MZALKACtYmCrUAFC+UJtbUEycnCc2df9T5c//H1mPS+54xmKODnznhLvKSM8v1dUqDiPCtQO8p3AJAv+eIPyp5K4c77cJQKFCAPku+o2CHbPpxFTdR2IufccccdmDNnDtavX48FCxZgwYIFWLduHe6++27ceeedoRhjxGlNXch5IZQSmwKDwSDdGXgroHi4FtChQJliNVt4/GSzZs8aPLHuCd9vwgveQuTHm4Un3glKbQwc2tsYbDi4Qfo5mApUICFyILgFg5qF1xqgELk7PJhc01QTVkWZF/dxljikxB5ToI4pmVKI3CpkoBQh8t1VrtxwoMeYsoCKuAJ1TN0H5EFyUqC8UFJSgmuuucbt8auvvholJeHtOhouWmMXcn6nxP1pb800xYuML6VGLUTu7QARM1kAMHf1XGw+tNnra3jDa4hcxcITlZlwLnocDgKZhWd32vHzwZ+l/49kBsrhdMjGTRYetTFQ40DtAenncCoNogLFLTw9ClSwCigxQA5EMANld1egxF5QvIAyGlzlRVgLqGhXoM4880ysXbvW7fHvvvsOw4cPD8qgoo3WaOHxA50XUN4UKPGioidErmUWXoO9QVLwRhWMgs1pw1XvXeV3c0+tIXJlBoqBhXSRz0gQyCy8baXbgpqzCaSAUu5zobTwWosCJRZNNqdNl7LYVjlQ01JAhdOKFTNQ/MZUykA1umegKhsr4XC2tPPZXdlGFShziwLFrwXVjdX4u/JvAEDPtJ4AwlvUROUsPJFx48bhzjvvxMaNG3HKKacAAH744Qe8/fbbmDdvHj766CPZtm0BfvLSUkAdqDmAPVV7kJmQie5p3UM9NDd4MaRUoLwVLOLJSJeFZ/Ft4fELq8lgwuvjX0f/Rf2xrWwb7ii6A8+e+6z3N6OC1k7kXE4Xv7NmR3OrmAigFTGLwN+X1gJKzD8Bwc1A1dnq4HA6YDKaNP2tUh0NpmXlZuG1EgVKebwGs7VDa0WmQIXxQilToBQWnlobAydzSrP0bA4b9tXsAxB4U8loy0CpWXh/lv0Jm9MGi9GCPhl9sK1sW5tWoHQXUNOmTQMAPP/883j++edVfwe0raaaembhPbfhOcz/bj5mDJmBp8Y+FeqhuaG08OIsrgyUt1l44l251gJKq4XHZ6m0s7ZDRkIGll24DOe8fg5e+PkFzD97PpJik7y/IQVeQ+Rqs/CEu6QmRxMSkKDr9aKZQELkvIDi07KP2oKnQAGuC5zWVgTKfTOUFl6rCZErisi65rqgtXaINDsrdqJLuy6qx7A3xAIqrArUsf3Ta4jcmgqLyYLk2GTUNNWgvKEcafFp2FezT5o442RONNobpXOyXvgyLhnxGSitL424AqUWIt9yeAsAoCC1QCo2wxoij/YMlNPp1PSvrRRPgD4LLynGVRBEyp/2lIEKlgIlm4WnoYBSjmdMtzGIMcXAyZx+zUrRGiLnBZTZaJa8+LYWJJe1MTDqLKAOugqo4Xku2z2YChSg705QuW8G88LA922+b0SbhefJVlZToNoCq3atQvdnuuPWz27V/beihReJDJQsRK7SxgCAWw6K23ecQPZtrkBxZyPSGSjx5pS7EVtLtgJwjZHfHIer0GOMRX8G6nhEzyw8vtNE6k5XykDFhiYDpdYHytvJXVlAiWPy56Kt2cI7lkcA2m43cjUFSstiwvW2evx62LWe5YguIwAENwMF6Nv/lRZeKDJQHZM6AoiuAmr9vvVo9592ePYndytbTYFqC/AeTr8e8byeqicipUBpbWMAwG0mHg+QcwLZt3mIvFv7bgE/VyB4U6D+Kv8LANAttZv0WLiKmkZ7IxwsvMKNJg316aefxg033ACr1Yqnn37a67YzZswIysCiCT2z8JJjkwFE7u6AXyAkC09DGwN/LTwtbQzUCqgESwKqGqv8CpJ7C5HzAzbeEi/dEQGuAqPR3tjmekH520hzS8kWOJgD2YnZ6JnuCnoGcxYeoLOAUlh4wSwW+HHYKbkT9lbvjaoM1Lp961Bnq8OXf3+JW4bcIvud8jNoKwrU2mLXBCReeGiFMYaDtQel/w/X+ZUx5rGRpsPpkM6dXIHiN27BVqAYY5IC1S3VVUBFuhO5LAMlLOcCuIo8Pr5A4wFaicTnoamAevLJJ3HVVVfBarXiySef9LidwWBo0wWULgsvQgG/QNsYhNrCA4SeIX5cFLy1MeiQ2AFPjnkS2YnZsqDm8aRAaXmPPP90UseTgrYgdLQqUHwcnZI7AYguBYq/b7UTfyhtzUhR1VglKZ9614srqy+T7dvhUqCaHc1ShkmpQIn7fKrVpUBJCwo3BFeBqm6qls690ahA8XM6p1v7blI7g3CNk19z4y3xqId/s7z1onkxYbWfjxf0zMLjFl6kM1Aht/BMgoXnRTHwpED5+jtPeAuRA8DMU2a6PdZWm2mKCpSeWXi8gBqSMySg70KEf8+xplg0OZoCUqCCecfKT6q8gIqmEDk/ltTG1BYtvHX71kkKRUVDha4ZaaJ9B4TvBlUs7pWNNLmKlhiTKB1/obLwuPqUEpsiFWkRm4WnkoHi1wJOt/bdpDGHa5z8OEqKTQpbAUUZKA3omYXHFahInaj9sfD8aWNgNVtlbQw8NakMtgLlLUTuCVKg5EgFVMchuhSoJnsTnv7xaSnnIMK/584pnQHou4Fwa2MQIgsPiK42BrxwVDtX8OOV7+eRtvDEvkb+snZvS//AZkezrvckBsiB8J1f+fdgMphgMVpkCpTYA4rjKUTOz8OBFlAdEjuEPZytRFWBEiITZqMZee3ywj5Ofqwri7lQoruAuvTSS/Gf//zH7fHHHnsMl112WVAGFW3osfCkDFS0WXhBamMg3n3wHZWBebQIvYXI/clA2ZnnELknuFdPGSjXnfGuyl0AgME5g3UVsx9t/wi3fn4r/ln0T9njTuaULmi8gApIgQqBhZebnAsgOi08VQXqWBHJV7GPpAL1zZ5vkPKfFLy48cWAnofnnzh6clBKBSpcBZS4jIvBYJBloMQWBhxJgWooR72tXloA+ITMEwD4v2/zAHmHxA4t4ewoykCJRQtvURHucfJrLhcxwoHuAuqbb77Beeed5/b4Oeecg2+//TYog4o2/JmFV9tcG5GlQ/zqRO5niJw/N+D5BF/V5BpPsCw8fxSotmjhMcZka1JpLaD4+nc90nogNS5VOsk12ht9qgx7q/cCaFmTjFPTVCNZM7kpudJjWuGFBC+KQ9EHqmOyaxZeva0+arp6ey2gjhW0GfEZsv+PBGuL16LOVocvdn3h93M02hulfY8fu8o147zBFSgDXJZfuC7K4gw8oOU8ZnfapTF5UqD4cZIcmywV8EFRoGKiUIESMlA8oxXucUoKVGwUK1BHjx5FTIx7IWGxWFBTEz35gmCiZxYe32nsTntEFA9PjTTr7cHvA2UymnxK0+EMkXuiLVp44nuRLSbso42BaN8Bcund1/dRWlcKwL0jMu+FE2eOQ3qc6wLijwKVkeAqFoJ1wnUyp5uFp3dsoYQfa/W2erd+UPzCHQ0KFH9t/v37w4YDG9DsaEZWQhZ6pPUA4J8C1aVdFwBhVKCEGXiA63gxGVwd9nm+SSyg+Cy88oZyyb7Lb5cfsJ3Fj7mshCzZTU+4l6dijHntAwW0zBIMdxsD/jqJliguoPr27YsVK1a4Pf7WW2+hT58+QRlUtKHHwhOlzHDbeDaHTTrx6rLw/AyRA/CZoQl3iFyNtmjhie9FjwK18dBGAMDg7MEAXIWw1kU/S+tdF9Cy+jKZWiV+x/5Y2Pwixe/eg6W2iPtX+7j20kUwWgoo8ZgUP3vGmLuFF0EFio+Nf//+wO274XnDJZvLnwKqd0ZvAJFToEQbjxdQooUnKlD89/mp+dJF3e8Cqs49AxXI8/mLgzkktVmtDxTQokAFa4avVvg+oXd1i0DQvZTLvffei0suuQS7du3CiBGuJnxfffUV3nzzTbz99ttBH2A0oGcWnslock2jtNWjpqlGuqsOB6IVxy9k/lh43mbHiBYe4DpISutLPZ7gg52B8tYHyhNtUYES7Ug9GahfDv8CABjYYSAA1wUhwZKA2uZanwUtv4A6mROl9aXokNgBgHoBVdOsvUjh+wG3q4J1wuUnVJPBJPXwaTjaEDU5KDE3WNNUIx0jjfZG6SIlWXiRVKBsgStQUgHVeThW714NQF8rA26X9UrrhZU7VoY9RC7GFVJiU1DRUCEtmitToIRZePz3+e3yA7anRQsvxhQDi9ECm9OGo81Hw7rEj+y8o7IWHiBYeMcKmQZ7g661Mf1FUqCiOUQ+btw4fPDBB9i5cyemTZuG2267Dfv378eXX36Jiy66KARDjDx6LDwgcs00+YUsMSZRKjD0zsJjYF5tIH4A8QLKVzNNrwpUIBaenhB5G8xAcQWKL1XDPw9vBVR1Y7WUy+if1V96XOudongBFW081QIqCiw8cVqzwWBwWwg20ogKlPh5iccq/0wiqUDx165oqPDLMnI4HVi3bx0AVwHFC46AFKhwtTEQQuQcSYE6ZtHxLuRAi4XnYA5sPexa1iS/XX5AsQVAHiIHhKxtmF0O8fyiVYECwqNCSRmoMFp4uhUoADjvvPNUg+RtFT0WHuDKQZWgJOw7N78wqKk9nmbJMcbcLnaN9kaP71VcygXwfvFljIUsA0UKlHwmjJb3yNWn3ORc2Ulfy5I8gNzC8VRA+bOUkWThHctP2Z12NDuaNR9vnlDOyhFnUEUDol0ufl78e4g1xUoFaUQLqGPqFwNDeX05shKzdP39r0d+RU1TDZJiktA/q79bryRfNNgapGKrV3ovAOFvYyAqUPxcxmfYiQoUb+9SZ6vDxoMuuzw/NR+2ctdNaTAUKMB1zFY0VITdwuM3bgYYZOdg/pmYjWYppxZrioXZaIbdacfR5qPS8RcqJAUqmkPkAFBVVYWXXnoJd999NyoqXDv2pk2bcODAAR9/2TrRMwsPiFwzTX/ssjpbndRpl+NNqVGz8AD1E4MYjhVzAsGYhacnRN4WM1DSDDyz9gKK3xEP6DBA9riWJXmA8ClQWsaiBX788TFFnQJl965AxVviNTWrDTVi8eZPDor3fxqWOwwmo6lFgWrUpkDxJVzizHF+9RkLBGWIHGjZjzhiAQW05KB4oZ7fLj+gPJDD6cCRuiMAIBWvkVq0XhQTxJhHh8QO+PdZ/8Zz5z4nnZMMBkNYWxlIGagwtjHQrUD98ssvGDlyJFJSUrBnzx5MnToV7du3x/vvv4+9e/di+fLloRhnRNHTSBOIXDNNZRdyQJiF56GA4hcTk8EEk9GEZkez1yC5OAsPgNcTPB+P2WiW3cH5q0A12huxuWQzgJbFYbXQli08pQLlbYo+V6AGZMkLKC0X6SZ7k+wk6KuA8idEnhSTJHUyP9p81O3CpBfRwgNaCqloUaBEC0/8vPj3kBCTIGtWGynE1/YnByXmnwBhpppGBYrbdx2TO0rfYbOjGU32JtlMsFDgTYHiiDeHgOv98ZYfgGvmIL958ed7LG8oh4M5YIBBysSFO6DNUZuBx5l7+ly3x5JiklDVWBVeCy+aM1CzZ8/G5MmTsWPHDlitVunxsWPHttk+UHotvEg10/SmQHmahccvJinWFOkuy1MBxRhzm4Xn7QQvjke8W/E3RP7pX5+iqrEKnZI74dTOp2r+uxhjG7bw/FCgxPwT4P075CiVh6AqUPaWnIlWNUwLbhbesRuLqJmF50GB4jcWCZaEgLMzwUAsrPUqUIwxqYA6rfNpAKA7A8UD5B2TOsrUhXCoGlIGSocCxS1KwDWLMiEmIaCChx9r6fHp0k18pDNQWq+F4Sz0WkWIfMOGDbjxxhvdHu/YsSNKSkpU/qL1ozsDFSELT7mMC+C7WOEn7pTYFElV8lRAiRdnLRaeWkEH+G/hvfrLqwCAiX0nSlPvtSApUG3IwuPvhe+Tvgooh9MhLeTqUYHycpFWKg+yAkpolioWUFobyfKLVLAtKzcLzxplFp6PEHm8JT5oaxUGgszC06lA7anag5KjJbAYLVLvMb1tDEQFis9yBsJTCGtSoOLkChS38ACXfQcEVkgoA+SBPl8gqHUh90Y4e0Epj/dwoLuAslqtqg0zt2/fjoyM8E3ZDye6M1Axkbk7ULXwfMzC4xeT5NhknwWUrPeQIkSudvH1WED5cVddXl+OlTtWAgAmDZik+e+A4yNE7msx4V2Vu9Bgb0CcOU6aJcPRovpoVaD4vm9z2jQXrGLOJJgXBqWFJ2WgosTC8xgiFy28Vq5AFVcXAwAKUgukOAFXbLS2MRAVKCC8Cr+ojnKUYWhvClR+auAFlNREUwjvR0MGSgvhXA+PH0NRrUBdeOGFeOCBB2CzuYoKg8GA4uJi3HXXXbjkkkuCPsBoQG8bg0jt3N4sPJvTpjoFWVKgrL4VKPFxvRaeiD931St+XwGb04aBHQaib2ZfzX8njrVNZqA0WnhbS1z2Xd/Mvm79WPi0X2/fB1ce+GfpqYAST15aFQJxqngwMz/RPAuPMeYzRJ5gSYg6BYqHmbXCF9UVVRmegapoqNCkUkoK1LECKhQZ0wZbA8557RwsWL9A9rgvBcpkMLmFloOtQCln4AX6fIHgLQOlRlhD5K3Bwnv88cdRWlqKzMxMNDQ04IwzzkC3bt2QlJSEhx56KBRjjDj+Vt3hzlp4s/AA9RwU/xstCpSYf+KZJn8sPH8yUNy+m9Rfn/oEtHxvbcrC09nGQJqBp7DvAG0KFL9w8j48ngook9GkW7bn+0GoFCiuWEgh8iiw8JT7oniB4QVLvCU+4gqUw+mQnQ/0KlBqBRRXbOxOu6YLq2jhAaHps7du3zp8sesLPPnDk7LHxX2TIyr8qXGpbk2HeYEIBLmASnAvoKI9AxXO9fAiESLXPQsvOTkZ3333HVavXo1NmzbB6XTixBNPxMiRI0MxvqhATydyIPKNNMWChRdFgOtkoGxzzy8mKbEpKDW7To4eLTyVuw9NFl5sO9njei8KO8p34If9P8BoMGJC3wma/kaEj7dNWXh6FSgPLQwAjRmoYxfOfpn9sKVkC6qbqtFga0CcJc5tv0uOTcbR5qPaFSjBJtHak0oLymnN0RQiV97MeLTwjilQzY5m2J12Xf3PgoHye9CbgeI2nWhrxZnjpNmWFQ0VPjMrSgsvFDeo/AahrL5MthID3zc9KVBqM0VlCpTCwmtyNMHmsOlqwyIu48KJ1ILCwcpA/XL4Fyzbsgz3nH5PwLNtAVehz4vdcLYx0KVA2e12mM1m/PbbbxgxYgRuv/123HHHHW26eAL8b2MQsQyU4NEbDAbp7kmtmaaeELmyBxTgXb3wZeHV2+rdelCp8fqvrwMARhWMQnZSts/tlbTFPlCeFChPXeR5CwPlDDxA4yy8YxfObu27Sd8/bySo/J71WiziTKdgKlAeQ+Q6LLyKhgqMeW0MXv/l9YDHI6I8xlRD5OZ42RIZkbDxlK8ZDAXKYDBobmXgZE6pD5SbAhXE8ysvoBrtjTJlXHUpF+H8qmxhACgyUAoFCtB/c+AtRB7qm3SlxcrPO4EqUA+vfRhP/vAklm1ZFvggFc8ftY00zWYz8vLy4HA4fG+skeeffx75+fmwWq0oLCzE2rVrvW7/zTffoLCwEFarFQUFBVi0aJHbNlVVVZg+fTqys7NhtVrRu3dvrFy50u8xtpZZeP5YZqKFx9UMXyFysYDyaxaecFHwtsgx4DqAX/vlNQD+2XdAGw2R61CgKhsqpTCvWgGlR4HKiM+QTuQlR0vgcDqki7+oQAE6Cih7aDJQHkPkOiy8ol1FWLVrFZ7d8GzA4xFR3syotjGISUCsKRYmg0n2eDgJVIFSK6AA7a0MyurLYHPaYIAB2Ymum6dQZKDEbBcfM6C+lItWBcpoMEqNP/n6dYD+fVs1RB6GcPZ7295Du0fa4ePtH0uPKRv4+sLT9YHbsn+W/RmMoUrXWrPRrFkdCwa6M1D33HMP5syZI3UgD4QVK1Zg5syZmDt3LjZv3ozhw4dj7NixKC4uVt1+9+7dOPfcczF8+HBs3rwZd999N2bMmIF3331X2qa5uRmjRo3Cnj178M4772D79u1YvHgxOnbU3nhRib+z8MKegVJZygXw3kzTnxC5uIN6baQpTG+XjUfIE/i6KKzfvx67KnchwZKAi3pd5HVbT7TJRprKWXjCWnjKu0auPuWl5KkuPKpnFl5mQqasgBL3cV6g6LWw1RSooLQxCEKInF/gg30sa7LwLAmuxZ5jIhck56/Ji7jyhnI4nNpvoCULT8gFAcKiuz5m4nH7LjMhU3IA/Ok15guupgLyAsrTYsIcZQsDAOiZ3hMFqQUY13OczLXwV131FiIP5U36y1teRk1TDYr+LpIeU7ZP8YWncXJVbUfFjmAMVXasKzNpoUS3of70009j586dyMnJQV5eHhISEmS/37Rpk+bnWrBgAaZMmYKpU6cCABYuXIgvvvgCL7zwAubPn++2/aJFi9C5c2csXLgQANC7d2/8/PPPePzxx6UZgEuXLkVFRQXWrVsHi8W18+bl5el9mzL8Xkw4CtoYAN6bafoTIpdZeH7MwjMZTbCarW5yuRrv/PEOAGB87/Ey5UoPx5MCBbjCueKJW+pArpJ/ArR1IufKQ0aCXIHi33GcOU4ai54LnJM5pfciZqBCuZRLbVMtnMypqZcYf3/BvtP3pkApL9oJlgTUNNVEpBs5v8HJScrBvpp9cDInKhoqZMvueCNQBUoZIAdCM8tZVKBEm1J1KRfBwmtvdVeg4i3x2PF/O9z2r8SYRFQ2Vur6HpsdzVKRGc4MlJM58X3x9wDk35GkQGlUeTwpZbxg3VEepAKK5x1jw5d/AvwooC688MKgVHjNzc3YuHEj7rrrLtnjo0ePxrp161T/Zv369Rg9erTssTFjxmDJkiWw2WywWCz46KOPMHToUEyfPh0ffvghMjIyMHHiRNx5550wmUyqz9vU1ISmphZ1QtnnqjVYeGX1ZVIxlJmQKfudVwtPCJH77ANlD46FB7guCo32Rp931T/s/wEAMLrraK/beeN4ykABrv1VLKCkDuSZ7vYdoK8TeUZ8hjQb6PDRw6rfsZ6Qr7ivxZmFTuS24Ft4vJBiYDjafFRTw73KxkoAwb8ZUh5jarPw+GcRyZl4fJ9IjUvF0eajqGysRGl9acAFlNZmmsoAORAaBcqThaemQMWYYhBnjkODvcFjAFqtOPfn5oBnxAwwyF4r1LPw/ij9Q9r3xe9IbwZKTYFqtDdK392B2gOoa67z++aYo1Sbw4XuAupf//pXUF64rKwMDocDWVnylb2zsrI8djQvKSlR3d5ut6OsrAzZ2dn4+++/sXr1alx11VVYuXIlduzYgenTp8Nut+O+++5Tfd758+dj3rx5HseqdxZeJELkfMHOPhl93GRlb800ZRaeSaOFpzILT3cBFZOA8oZyrxcFm8MmrX13Us5JHrfzhdTGoJVaePtr9mP17tWY0HeCVBh5WgsPcBVQCWg5IXmbgQf4zkDZHDbpu8xIyJCyGKICJX7HyTHaL3CiKhp0BUpxUrWarbAYLbA5bahurNZWQDVUBm08Ivx9p8WlobyhXOrcbjAYVBUoILIWXoIlARkJGa4Cqq4U0NgzmRcAYrAaEJpp+giRK3tAAaGZ5awnAwW4zpcNRxtULTxP+LNvi604xKIs1Bkofj0BPChQGjNQakoZt+84Oyt2ejw3aSVSCpTmDFR9fT2mT5+Ojh07IjMzExMnTkRZWZnvP/SBUs0Sp5Bq3V583Ol0IjMzEy+++CIKCwtx5ZVXYu7cuXjhhRc8PuecOXNQXV0t/du3b5/s97pn4R37EhvsDarNK0PBN3u/AQCckXeG2+8kC09lFl6gFh4/KajNqPOlQAHeLwq/l/6ORnsjkmOT0T2tu8ftfNHa2xjcvup2XPvBtfjgzw+kx5QtJcTp7eJMPLvTjt+O/AZAvQcU4DsDxS8oRoMR7ePat1h4dR4KKB0KAd8nLUYLzEZz0DJQjDE3C89gMOjOQfEcn81pC2oBzt83V4vtTrt0fIlr4QGRVaBENYwvZKt1Jp7NYZM+ZzcFijfTbNSoQIkWXgjbGAC+FSigZX/XMwXfnwJKXKtU7blC5XLw9QsBhQLFM1BGnQqUICYom7EGIwcVKQVKcwF1//33Y9myZTjvvPNw5ZVXoqioCDfffLPfL5yeng6TyeSmNh05csRNZeJ06NBBdXuz2Yy0NNcBmZ2djR49esjsut69e6OkpATNzeoX0NjYWCQnJ8v+ifjbPAwInwqlpYDyqkBpsfBUeoDwkzsDk6kJjDGvBZSWZpo/HfgJADA4Z7Cute+UBMPCq2ioQM9ne+L8N87Hvup9vv8giOyq3AUAshXeld+FwWBQzXrtrNiJRnsj4i3xKEgtUH1+X0ULv2CmxaXBaDCqZqDUCigtJ3epUeGxO/xgKVBiQS/eleqdiccVqGCMSYQfK2JhwY9FsQ8UEF0KFKB9Jh7P7hgNRrdzQFAUqCCdW+ua62TFqVoBJWagACA3ORcApFl2WvCrgBIiFiKisqN1zUmtiAtAA4EpUGrvWQzsA8HJQUViHTxARwH13nvvYcmSJXjxxRfx9NNP49NPP8UHH3zgd0uDmJgYFBYWoqioSPZ4UVERhg0bpvo3Q4cOddt+1apVGDx4sBQYP/XUU7Fz5044nS1qyF9//YXs7GzExGgrgJToDZHHmmOli1k4clCVDZXSUh1ndHEvoLzNwtOzFp6aAiXemYkHSb2tXlLfvM388nZXveHABgCB2XdAcELkn+/8HH+V/4VPd3yKfi/0w/Kty4N+4vIEl7zFE7taU1NxJh6H7xf9Mvu5LeHCES08tb5cYoAcgGoBJVoZuhQoxWr3wWpjwI87AwzScwL6Z+LxHIj4nMGAK1AJMQlus3bdLLxWqkDx4ijVmuq27wUjRK7cvw4fPYxpn07DzoqdmsbHUSoi/P2Jy+0oFahF5y/Cm5e8qXrD6gl/1FVfCpSTOVWdhUDYW70X+2v2S/9f2VgpnRd094FSsRqVFl4wFChl3jFcaC6g9u3bh+HDh0v/P2TIEJjNZhw8eNDvF589ezZeeuklLF26FNu2bcOsWbNQXFyMm266CYDLWrvmmmuk7W+66Sbs3bsXs2fPxrZt27B06VIsWbIEt99+u7TNzTffjPLyctx6663466+/8Omnn+Lhhx/G9OnT/Rqjt4PIG+HMQX1X/B0YGHqk9ZDN1OB4moVnc9ik9ya2MfCk1KgVUEaDseUOWTjB8wur2WhW/dy03FVvOBicAioYbQzW7XNNbLCarahuqsa1H1yL8f8br3lFeX9hjEl3bLICSkUNVCsUuWrVI62Hx9cQCwy1IlsMkAPyAooXGGK3eU8Wy6ZDm7C9bLvsMeVircHqRC6eUEXbX28Ame/HQHCPZX4sxZnj3BQ7pYUXzNYOeuEXvkRLYksBpVGB8hQgB/S3MdCSgVr08yK88PMLuPPLOzWNj6MsoPi4xRtJ5TmsILUAV/a9UteEqmAqUGLoOtg5KJ5/GtRhEABXkcaPF72z8ESrkd9w8vMZ/91f5X8FPOaot/AcDoebgmM2m2G3+5/xueKKK7Bw4UI88MADGDhwIL799lusXLlSajtw6NAhWU+o/Px8rFy5El9//TUGDhyIBx98EE8//bRsEePc3FysWrUKGzZsQP/+/TFjxgzceuutbrP9tNLsaJaqb10FVBhn4nH77vTOp6v+Pt6sbpeJFxEtCpRy5hdHLUMjWjtqJxlfd9X1tnopuzOk4xDVbbQSDAVq/f71AICl45bi4REPw2K04IM/P8Dcr+YGNDZfVDdVS+MWLzZqUrra++QnFuUJWCTOEgcDXN+R2kVaqUBlJbgs9kZ7o9Sg01cGqqKhAsOWDMOZr5wpU+6UClSwLDxPJ9Ros/DiLHFun5fHEHkkFCjBTuR5rSP12hYU9lZAaVGgaptqpQJdtMo8Fej8ZmH17tWqvaru+vIuDFk8xK0Q9lRAicqOMkTuD4FkoJTWlHjjGuybdG7fnZ1/tvQa/HvS2weKH392p106L3EFaminoQCClIFqjkwBpXkWHmMMkydPRmxsywm7sbERN910k6wX1HvvvadrANOmTcO0adNUf7ds2TK3x8444wyfvaaGDh2KH374Qdc4PCEWHf4oUOFopvnt3m8BqNt3gGcLjx+c8ZZ4mI1mvyw8wHViOFJ3xGMBpYavDNSWki1wMAeyErLQKbmT6jZaCTQDVddcJ1lhw/OGY0K/CchJysHkDydLM9xChSh3+6NAaZmdYjQYEW+JR52tDkebjyIL8gyiUoGKs8QhJTYF1U3VUidhXwXUb0d+Q5OjCSVHS9Bgb3Cb2MD3US1NPbXgKROhx8JzMqdcgQqBhWc1Wd0KgqjKQNkCz0Apm2iKj1U0VHjsycULolRrqmz/FTNQ4qQjbvdVNVbh54M/4+ROJ0t/U91YjSfWPwG7047vir/D2O5jpd/xAio3ORf7avZJxxk/N/EJDoESyCw8tRugpNgk6ZgNJryAGp43HCt+X4E6Wx0qGipQkFqgOwMlKmW1zbWINcdKBfhpnU9D0d9FOFJ3BDVNNS1tRhjDlI+mwMEcWHbhMk0qX9TPwrv22muRmZmJlJQU6d/VV1+NnJwc2WNtDX4QmY1mXQtASgpUiC282qZabDrkKig9+fGeZuEpD85ACihAfoL3VUD5uihI+aeOJwXcdyzQNgYbDm6AgznQKbmTVMz1Su8FALKsQCjwNDtILQOlVkCJ06C94c06kxSo+Ja569zG81ZAifu+KNOLqo43BSqQjJmnTIQeBaq2qRYMLWMIhwLFGGszs/AkBSrOswIl2kNK9la5Cqi8dvJGyPzm1Oa0yW6KuN0HQNY9GwA+2/mZlMncU7VH9jtuKfXJ6APAld1yMqfHFgb+Iu3bOnqcSRae1f3aGuhMPMYYHl77MP73+/+kx0rrSqVj+tTcU93C/noVKLPRLB3b/PjhN4Xd2neTVE0xSL6tbBte3vIylm9dLhXFvoj6PlAvv/xyKMcRtXiaxuqLUPQqUeP7fd/DwRzIb5eP3JRc1W08qT1igBzwXUApu19z1IK/mgsoDxeFYOWfgMDbGKzf57LvhuW2TG7ghdTB2oNwOB0eA9qBIs5YEWcseVOgeN8yQLu0nRCTANSpFwn8jlFsntghsQO2l2+XlBxZI00V9VUsoCoaKqRQsKcMFO9QrizWteLTwtOgQIkBcvE5g4FaBqqmqUY1MhAVFp4fCpQ3C89qtiLeEo96Wz0qGipUzxNcgVLOdBMX5q1tqpX2EfFi++XfX+Ke0++R/v+j7R+5PS+H36T0yeiDL3Z9AQdzoKqxyu9zvycCamOgpkAF2I186+GtmLvaFUGIMcXgol4X4bvi7wAAJ2ScgLT4NDerVW8GCnC97wZ7g3T88HNaVkIWeqT1wJG6I/ir/C8U5hQCAL76+yvpb4urizU5EFGvQB2v+HsQhStE/s2eY+0LPNh3gOdGmsoZHoEqULoKKB/rewW1gBIsPH9UjXX7XQFy7tkDrgLCZDDBwRxu03KDiWjhlTeUe50NwxVStQyUrxOLt6CyNwWK46mNAR+vTIFqdFeglMUCEJji48nC0xMiF5WyQMejRCwcRcVOLJKUs/AisZQLf01RgSqrL1OdranEm4UH+G5lwPN1eSlyBcpkNEn7iZgbE+3WdfvWSWO3OWxYuaNlMXmlAiVaePy8XVZfprqMSyD4M8PU0yw8IPC84K6KXdLPV793NX49/GuLfdfZNWFMWUDpnYWnNk5+TstKzEL39q7+fmIO6svdX0o/831AZHvZduQtzMOLG1+UHov6EPnxij8z8IDQrNekhrf+T5xgWXhc9dBVQAmzs0S83VVXNVZJF9yTOgZeQCnXidMDY0xSoMQCymQ0ITvJtTp8KPtCicWZkzkl1VBNDQzEwvN2cpcyUAn6Cijx+baXt8y+k1l4iosUXyfR01i04tHC05GBEi/IQJAzUIJ1KXZuF3M3vCCOhgxUYkyi9P1zhcYX3hQowPdyLlwpUhZQgLvCz+27BEsCurTrApvTJmVDv937rez79qRAZSZkSu+xrL4sOhQoD7PwxOfz9yZd/BzqbHUY99Y4fL7zcwCu/BPgXkDpzUAB8lYGNodNKqyzEtwLKLvTjq/3fN0yxir5dwUA7//5Poqri/HUj09Jj5ECFaX4rUCFoFuukrrmOkmp0VJABWrhSUu5eJiFp9bGwJ8Q+caDGwEAXdp18Xjy1YN4sOsNku+o2IHyhnLEmmIxKHuQ7He8mV4oc1DKnin8oqQ2I9JriNzHnZm/GSiO+D1bzVYpdFvTVAO70y6721VToMScSTCm7QdjFp7SwguFAmU1y0PkygC5+HOkLbwYU4z0+Slnrqnhq4CSFCgPrQw8ZaAAdyVR7Bc1qmAUAKBolysH9eH2DwEAAzsMBOBZgcpMyJTGKhZQQc9A+bmUi5JAl3Phn++UQVPQNbUr9lTtwbaybQC8KFA6M1CAPKslrmqQFp8mtVfhGaifD/4su2aqKVC7K3cDcK3Xx787UqCilIAzUCG08NbvXw+7047c5Fx0adfF43aeZuEpFSheaOi28CwBWHgqF4Vg2neA+zpxeuDq0+CcwW4nDe7Nh7SAqvNQQGlUoLRaeJ5sIofTIZ08xUWqvRVQBoNBZmHvrdorW15GVKDUOj0Ho5WBpwuPHgVKaeGFJAMlhsiba9wC5OLPegvKw0cPB9zsVbmwsZ4cFLfmfBVQ/ihQyhtUsV+UVED9XQTGmJR/mjFkBgBX/zLxHMePMWUBpbSXAyWYS7kALeddf5VR/vkO6jAIH034SDpm81LypDytpBI2+p+BErNa/LPOiM+A0WCUlujijgPPP5kMrkxpcY1KAVW1W/qZR1hIgYpSAs5AhdDCE9sXeJup5qmRprLHiOY+UAr5VtXCO7aGmD+z8PgSLsEqoMxGszRNWu9MPN5AUwyQc3gBta8mdBae8k6f361rVaB0z8JTfB/lDeXSTDQxy6IsoJQneFEhEO07QKFAqeRMgtGN3JPypkeBUtpUIclAKULkaucbf5qLvrz5ZXR4ogOWbVkW0DhFBQqArpl4vNhXLiTMkZppqmSgmh3NOFR7CIB3BYoXtaICNSJ/BAww4PfS3/HFri+wt3ov4sxxuKLvFdL74MqGw+mQxpmVmCUVUKV1pR6XcfGXYFt4AStQ1S0KX5+MPnjr0reQFJOEyQMnS9sEMwNV21Qryz8Brpl4gOucUF5fjq92uwqo83qcB0Bdgfq78m/pZ2738f0gapdyOV4J1MILSwHlYzkBXxZeoCFytUA4v3sPRIEKtIGmiNTKQKeFxxtoivknTlgsvGN3bGK4FfCuQHG1R1xQ1+csPA9FC1ca2se1l/XCEQuoeEu82wlVLAqUnYZV2xioWHhBKaA8ZKA0hciPFXq8+A5JBkrRxsCrhadDgXrt19cAyBeF9QcxRA5oV6C8LSTM8aZA7aveBwaGOHOczDrmKGd6igpUWnwaTsw+EQAw+4vZAIBRXUch3hIvKfXcvuJ9qPg4xaC8v/lXTwRdgQo0A1UlV/jO7X4uKu6swL/O/Je0TbAzUOIMPMD12fIu878c/gXf7/seAPCPgf+QjZHjcDpkRdXXe79Gk71JOueRhRdl+HsXEupGmk7mlPo/ndzxZK/bepqFV9McnD5Q/KTDF70F/M9AlRwtwf6a/TDAIJ0EgwFXavRYeDVNNVI39KG57gVUWCy8o/IeNXoyUE2OJik0r3kWnqKgVTbR5IgFlNp3rFZA8e/clwIVjOVcPClv/P+rm6p92lu80OMn+JA00jRbVWfhqVp4Gj+PRnsjvi92XYgCnSEqhsgB7QoUv+CqLSTMkZppNroXUGILAzV13S1Erlh0mNt4PNMzrsc4AC1qFs9BcYU3LS4NZqO5xcJrCF2IvK5Zfc1JJc2OZumc67WNgY6+Uhyxy7uo8CkbhgYlAyVEPJQKFNCyzNTLW15Gs6MZucm5GFkwEoDrOBXV4gO1B2Bz2iSL74/SP2TXHbHFRTigAsoHAStQIcpA7ana4+rsaoqVmjp6wtMsPL/7QCn877PyzwLgutvld9b+NtL8+eDPAIDeGb2D6mf7sx7ej/t/BANDfrt81TUGQ23hiavEi03+AHUFSrmYsFi8+zqx+FKgxBl4QEuGAfBeQNU210oW3uCcwQA8FFDBVqB8hMjtTrvPRVi5Dc3zIMG08MQ+UOLNltr5Rq8CtX7femn/KDla4vcYxeU3+P7Bc3C+FChe6KstJMzx1saAKw/KHlAcNwVKsejwqK6jpG0NMOD8HucDALqkdHE9/7ECTcw/AVDNQAXbwmNgbnEKNcTCQe1cGIgCJS7B5M32ClYfKMB1LlAqUACkmXi8oefZBWcjMSZRem3x/Mrtu/zUfPTP6g8A+Hj7xwBcx0yo+vF5ggooH0RrI83NhzYDAPpm9vXZId2T2iOFyBUWnoM5VKf7e1Kgeqf3RqfkTmi0N0q2or8hcj7Dond6b6/vSS/+rIfH7Tu1/BPQcmHlzTSDDT/ZWM1W5LfLB6BPgeIn1sSYRNWlMkT0KlAmo0l6TO07FkO+XIE6peMpALx3IgeC0/fIUx8o8bPwlYPi4+RWbTBvhnRZeMeKlyZHk6b9bPXu1dLPgRRQYsEmWXjHvnNf6+H5moEHeG9j4C1ADqhkoBSLDg/LHSbtU6d0OkVSPDwpUPz3arPwgqVAiWtOatm3+fk5wZKgupRMIBkoX58vRyygGGN+ZaDEcYozHjk8SM6L/rPzzwbQUjyLlh2/PuS3y8eZeWcCAD7+y1VAhdu+A6iA8km0NtLcUrIFQMvUXG+Is/BE28JTiBxQV6E8FVAGgwFjuo4BAHyx6wswxvxWoPiJVzzAgoE/6+HxALla/glw2VhGgxF2p13TtG69SCf3hJZwK2+myT1/b7Pw9Cyw6aloUWthwOEXHVUF6lhvo0O1hySLk69N5lOBsgTexsBTHyiDwSCz8bzBx8kLKH8uVFWNVaqFmp4QuVhMabHxVu9pKaCO1B3RZBepwV/LaDBKx4/WDBSf7OCtgPLWxkBqoqkSIAfkBbqTOXHoqCtwzhUoq9mKM7ucCQC4qNdF0t/xgoEXEMoLuhgiV9s3A8FoMOq6OfCWfwICW8rFW4sIEf4d2Z12HG0+6lcGypcCxS08jrKAEnNQfAZeQWqB9P3yG91wz8ADqIDySbSGyLcc3gJAWwEljl0sIJQzPEQ1Q62A8jQLD4CsgKqz1cHBXHfKvjJQyrtqT4pHoGhdD8/utGPN7jWY+flMqUmpWv4JcOUFshOPNdMMgY0n5gXEO2NRRdOiQGk5sXiahafWRJPDbU1vFt6mEldOLy0uDV1TuwKQKw5qU8VDaeEBLfu7r3wivwngJ3K9x7LNYUPv53qj/6L+bsoRf99iBqrOVie9hpiBijXFSqqZr8+ktqlWmsUKuPZnT20CfCEFyC0JUg5JawZKmoHnoQu5+LuAFKjmWhypOwK70w4DDDKr/dlzn8Vjox7DrSffKj3GQ+RKBSozPlP2/kKhQAH69m1vM/CAwJZy0apAxVnipBvmioYKvzJQsjYGKhkobuEBrqgCb1DMxyZToKpaFKjT804HAOkGgRSoKCQYClSgvVjU0KVACfaIaOMpLTyT0STlaNQKDU8KFACMLBgJo8GIP0r/wK+HfwXgKjA8fW7iXbU4Jn5iDkYDTRFf6+HZHDbM/WouMh/LxIjlI/DUj0+h0d6Ibu27SV67GqEMkot3a2IBJX433hQorS0MAC8ZKC8FrVRAqXSb56/JM2090nogNS4VgMsa48eEahuDAC08cfah2nvnjyn7PCmRLDw/M1Cl9aUoOVqC4upit2JNbSkXoMVyEwsog8GguRfUd8Xfwe60I79dvmSR+Wvj8dcS83NaFShvCwlzuLpR2VDpVmD6UkjEDBS377ISs2RWV0FqAW4fdrvsGOHPd7D2IJodzR4VqOqmakkBClYGCtBZQGlVoASX45O/PsGg/w7C70d+9/rcntYZVENUCgPKQDWpK1AFqQXSDcLI/JHS45KFJ/SCEjNQafFpsnMzKVBRSKAZKAYW9A7CZfVl0gV7QNYAn9tbTBapMOLvhzGmeoH1FiT3VkClxqVKbQdW/L4CgEuZ8NSfKs7ckgcQPx9+4lVTPALBWxuD4upinL7sdDz83cOobKxEenw6Jg+cjPeveB9bb9qqmj/g8ItrSAqooy0BV363Xt5QLnsP4p2gcjFhPRaexwyUhxA5APTN6AugJcMgwk9mXF3omd4TqVZXAWVz2qT9UK3bsz+r1os02hu9zj7MT3Xlyf7vs/9z60rNYYy5WXj1tnpdWTfRuhPtQsaYLEQea46Vjk9e7CjPN1pnJvL804j8EdJdvrKbvVaUTTQBuULj7cbQVxNNoOXCzMBkn4+TOSVFV4sCpZyB542shCxYzVY4mRP7a/a7hcjbWdtJF3N+TEetAqWSgXps3WPYUrIF/934X6/PrWxh4A0xB+XNhfCEaLfy84moQMWaYyV1ms++A3xnoABIOSiAFKioxN8CKt4S39I/Jsg5qK0lWwG4mpBprbqV3cjrbfWSzSYeoN4KKE+z8Djcxnv7j7cBeLbvANddNf9Mxbtqb5mbQPDUxuDj7R9j4KKB+GH/D2hnbYcVl65AyW0lePnCl3FRr4t8fu+dko7NxAvBenhqClR5fbn03ViMFlk4XDkLzx8LT3li53foat/Hrafciu/+8R2mnzTd7XdK5adH+x5IjEmUph/z4kQtRB7oUi6i1aY2+/DxUY8jLyUPOyp2YNiSYZJiKtJob5Q+R14kA/paK4hFgahA2Zw2yXbgxyX/vHiWRyxaxP/39Znw/NOI/BGSQhioAiWqYbyQtjltXjNkZQ2+LbwYU4z0/Ygz8UqOlqDZ0QyTwSRlmpSIuTEpQO5hWxGDwSDL1ihD5CajSSoY+IU7WBkoQF+LDmVG1dNz8f29wdaAH/b/AKAlv+kJsYmmL/jnUVpXKl0z/GmkWVxdLP298nzy3/P/i/+c/R+pgSbgnoFqsDVIx0dBagEAVxNpDilQUYi/BZTBYAgo5OcNPfYdR9mNnB+cJoNJ9t48FVAOZ8vMPDUFCmgpoA7WHgQASXHwhNpMvFBbeKL99fovr2PcW+NQ2ViJk3JOwqYbNuHyEy7XNRVWsvBqQ2jhJWZJdoyDOaSTvvIuMCALz8MF2lsGKsYUg1M7n6o6C9StgErrAYPBILPxgNC0MeDv29Psw+5p3fH9dd+jb2ZfHDp6CKcvOx3fFX8n20ZsopkRnyEVfnpuhmQKlPCzOIWdH0v88/KkQGnpBVXRUCHNzj2ry1lSAeVvLyg1Bcpqtkp3+t5sPC2z8AD1Zpr8gtkxuaNH9VdsE6NHgQJaclB7q/eqzgrjF3demEVKgVIutaWEfw9ccf1h/w/Ssb+lZIvHYlvW5V2HAiUW4v5YePyYah/X3u2ccVb+WbjztDtlxysf24HaA7A77ZJanBSTJI2J56D44+GGCigfBNKNNlTNNDeXuE6SA7MGav4bZSsDsQeUaLN5KqBE28iTfHtSx5NkqpM3BUptTE7mlO5EQ2XhiQrUG7+9AQC4uv/V+O667yRrRw+htPDEWXix5ljpRMQLVOVJLJBZeGondtn3oVMRVBZQPdN7Amgpqr0pUIEu5aJlYdGOyR3x7eRvcWruqahqrMK5r58rK3LETvoGg8GvKeOeFCh+TjHAIH2HkgJ17MImqj6ANgXqmz3fgIGhd3pvZCdlSzmTYCpQQMux6W3mqRYLD1BvZaAlnyNToHQWUPzCvKdqj2oBxcfM1ZKIZaAatWWg+PPxZU0A19j5ig5K9tfsBwOD1WzVNNu5vdW9gPInRM4R80/eyErMgsVogZM5cbD2YEuAPDVfumalx6ejX2Y/1dcJB1RA+SCQmRihaqbpjwKl7EauDJBzPBVQ4v97UqDMRrPMw/ZVQCmDsVWNVdJJK+gKlEobA34HfVmfy3SdEERCGiJXzFjhn4lUQHlSoJwKC09LGwNB4eDZlsqGSr+/D7GAMsAgZRzCoUB5C5CLpMalYtWkVchOzEZtcy1+L20J3vIZeLzg82dtS08ZKDFLyC8EfKx8/3Sz8BQKVKO9ESNeGYHL375cUkrE/BOAgC08/vkrbVAtM/F8rYPHUWtloCWfI34feiw8oEWB2la2TXqPagUUJ2IZqCbvGSgxO3e0+Si+3vu16/Fj5zpPNp7YpNTbGqoc/h1x+wzwz8LjiPknbxgNRukGtbi6WMo/cfuOc273cwG0fK/hhAooHwRSQIWimWaDrQF/lv0JwE8Lzy638JQXGV8FlMlg8hqq5jYeoKGAUlh4vKBJjk32u6DxhFobAymwHkDeihdQB2oO+N1vxxPKGSv8xM4vGL4UKE+9kNTgJzknc0rfNb9AJscm6wqNAvKirXNKZ6lAEhUoZZhaORZ/J1/oed/xlnhJHRMD5Vwh4wWfP12fPSpQKuv/KcfqZuEpFKhv9nyDNXvW4O0/3kaf5/tgyaYl0kKsvIDi+00wLTxA20w8rRaeWisDLVPs+XnL7rRLS3noVaA2HHApNLGmWNn+GtICyuJHAeVBgQJa9svSulIp/zT1xKkAvBRQGlsYcJQWnsVo0VR4cZT7tlYFChD6dlXtlbUwELnvjPvw+VWf48bBN2p+3mBBBZQPAlKgAmym+Xfl3xi6ZCje2/ae9Njvpb/DwRzIiM9ATlKO5ufyZOEp7248WngaZ1/oKqAUClSoekAB6m0MgpG3yk7MhtFghM1pC2ozzSZ7k6SC8LtjfjfPLQtPCpRyFp6WDJS4f/ML547yHQD8u7MTX5MXKIB86rq4j4nFRKBtDPQob0DL++N3uHx8QMs+7JeF5ykDpdK6QfkduVl4CgWKZ7YsRgtqmmow9eOp2Fa2DQYYpMXFQxEiB1p6Jnna37UsJMzh+7TYLNFXE01AXtTx702vAsUvyFmJWbKCQDnmUITIgzELT3y+L//+Es2OZuQk5eCaAdcAcDWYVLup0zMDD3BXoPTeTMWZ42TZJj0FlDgTT2phoCig4i3xGNNtTNBvurVABZQPgmLh+alA/e/3/+GH/T9g6kdTpRO6aN/puQtQzsLzFDDmB4cnBcqTfcfJTcmVlmHRm4HyNmU+UGKM8jYGjfZG6SQWyOtZTBbpQhVMG49fnMxGs6SCKC085QmDBzP9yUCZjCbpgs4/lz9K/wAAnJBxgu7xi/tVj/YtnYZFBUpci05NgfK3gOJqhrc7dxF+QuYXVMDdwvNnQogvBUo8lnjndo7HEPmxooavWr/wnIV4bNRj0nMN7DBQUnW8FVBVjVUeWzhw1BY2Fp/Xk7LFP38DDD7PAWd1ca2jufyX5dLnokUhMRqM0n7N4LKcNStQisJMmQNS3sBF2sLzdgPErzF8OZMz8s7AoA6DEGeOQ0VDhbSMkoieHlCAuwKlt1ARJ1QB+laZEAsoMQMVLVAB5YNgKFD+hsi5VVPZWImH1z4MwL/8E+B5Fp7eDJSvAgoApgyaghhTjMclUDhKCy8YlponlAoUfy2z0ez1Dk8L0qLCQWxlIPan4XdvbgqUrxC5jjYGgLtN9EeZq4DiCxnrQXxNcakGMQPF90Wz0SyblSO2MfCnCe2OCpdyxnNXvlB2pwYEC0+RgfI3RK6WgRKVDTcFylMbA1sdbA4bfjzwIwDXBfP2Ybdj601bccOJN2DBmAXS3/CsSVl9mVv/qpHLR6LHMz2k0Loaauvyic/rSdnix1b7uPY+Z7Re3Pti5KXkoay+DMu3LgdjTPMyI+I+lmBJ0KS0Ai7VmGeHAPcLupsCFeIQ+UubXkKf5/pIii/HU05V7fl4QX1mlzNhMVlwUseTAKjbeHpaGAAtBRT/XvXMwFOOE9CegQKEVgbVeyUFSpmBiiRUQHmBMdbS6M+Pg0i54KVeDh49KP389E9PY3fl7pYZeH4WUH5beD56QIncNuw21M6pxVn5Z3ndzpOFF+wAOSCEyI9ZkWJGQ4+SpwZvtBgKBUqUu90yUEFsYwC4n9x5N2N/FCiz0SwdM6KFp6ZAKY8t2ar1gkqlle3l212vm9bTx5Yu1BQopYXnVwaq0YMC5YeFJ343Ww9vRb2tHu2s7dA7w6X29kjrgf9e8F9pfTDAdSNiNBjhZE5Z4LvB1oBNhzbB5rRh6+GtHsfPG5m6hYB9ZKu0rIPHMRvNmHnKTADAgh8WoKKhQlL5fCkk4mfWMbmj5uPYZDTJenv5KqBCrUA9t+E5bCvbhs92fibbVouFxwt7btXx739YJ9cC6KoFlE4LT9nLyx+rTFZA+ZGB2np4q3QMRSIs7gkqoLwgFhEBZaD8tPC4VZMUk4RmRzPmfDVHaqKpt4ASZ+HZHDa8+dubANyr+WAoUIC2g0yZ6whVE01xPLy44K8VjGItFDPx1NaM4mPl6kgw2xgA8u/DyZzYVrYNgH8KFAD0z+qPBEsCTsw+UXqMK1AVDRWqXcgB+bHmj423vexYAZWusYA6ZgkUVxdLSk1VU5VsvMFUoNRC5MoCylsfKJ5/GpY7TLXPFcdkNEnHkqgW7azYKdle/K5eDU8ZKF/ZKi3r4IlMGTQFKbEp+Kv8L7zw8wsAXOcAX+dccb/Wat9xxOJBeUEPZwaq0d6I3478BsD9/KEnRA64lDW+rtywXPUCStblXacCxdGbgQLk35U/ChS/FmYlZAW1oA0UKqC8IDa88+cgCrSNAVcaHh31KAwwYMXvK1Bnq4PVbHVbwdoX4iy8Fze+iB0VO5ARn4EbCm+QbWc1BaeA0oLSMuLdi0ORgZIaaTrkClQwijXJwgvigsJqa0YpL0i+FCi9Fp54ct9btRf1tnrEmGLQtb02K0zJ6mtXY/etu2UXJJkCpdIDCnDlW/j+qreAarQ3SlacVgWKWzp2p12yR7kCFVAGyocCJR5Lyu/IWydybtecmnuqzzGoLefCFTpAHpxX4mkWnq8lYrTOwOMkxSZJ56H5380HoC2fo1Sg9CCqGG4ZKMX5J5QW3i+Hf5EaFIsFlMPpkLbxqkAJ+82ZXc6UVDi+APq2sm2yGY68y7vRYNRcdCoLqHAqUKJSCESXfQdQAeUVfoccY4rxOnXfE1IGqll/BsrJnNKsh/O6n4dJAyZJv+uX2U/3ePgFqeRoCeZ9Mw8A8K8z/6W5jYE/ayBpHZMyRB4KC0/ZxiCYdmEoFShv/Wm0tjHQauGJF2keIO+Z1tOvfR9wfb/Ki5EsA6XSA4rj73IuXF1JiU3RHFY1GU3S3TgvKLjKF9AsPF9tDLxYeN4UqO+LtRdQamqRGCwWbUslvhSo6qZq1SWfpCaaXhYSVjLj5BkwG83SuUCLOiIroAJQoLxZeDGmGF0rE/hCOcN048GN0u/E84e4v3g7fnlbBADS7EvA9R74TTZvbwC0zHDsmNRRdQUB1TFbEmSZMX8yUGKhpydEHm+Jl93kRlOAHKACyiuBdCEHAlOgyurLYHfaYYABHRI74N9n/VsqbgZ1GKT7+fjJ+rVfXkNpfSl6pvXE9Sde77ZdsCw8LbhZeKFsY8DXwnPKQ+TBeK1QZKDUFCi3AkpRzPKTnM1pg81hk9Q2rRaeeHfMCyh/7TtPaFGgAP+7kYv2nZ5smzIHJc3CU/aB8reRpvCzrxC52Wh2u8vnF97fjvyGQ0cPwWw0S0Fhb6gVUKIC5dXC86BApcSmSONTU6H0WniA6ybkihOukP5fSz5HvCjrLaC8KVAJlgTpfBFsu0ipQP188Gfpd2oFVKwp1utNq1KBElGz8bQG9EUMBoNMhQpEgUqKSdLt5ohqpLKFQaShAsoLgczAAwJrpMk938yETFhMFuSm5GLemfNggAEX9bpI9/Px92BzunoEPTLyEdU7EF8FlD93H57w1EgzJG0MlApUiDJQwWqmKa6Dx1F2dfamQIn7nOZZeEJBy7ty+xMg94ZeBUp3AXWsONBrcStn4iktPL0ZKMaYz6VcPClQSsVHfIwrCIXZhZrOS2qBb15kAt4VKP5eleMxGAxeWxlwK17vsXXb0Nukn7UUUGLrB70WnlhAKAsog8EgjT3UBdTGQy0K1IHaA9KsUy35J/H5OiR2cNvn1YLkeptocsQCKpAMlJ78E4cKqFYKv0P2W4EKoJEmzz+JzTLvOPUONMxtwNjuY3U/n/gehncejnE9x6lu52sWXkgUKJ6BCmcbgyDmrXKScmCAATanzWt3Zj2ozcJzy0B5KaD4Bdtqtmq24MKpQNmcNsnqUVOg/O1Gzu0prfknjlKB8mThaT2W6231UrYFUA+Ry/pACQWU2vlGqQJpse8AdwWKMSZToKoaq6RiUQk/LpWz8AB4XWdP6zp4SgZlD8J53c8DAJzc6WSf2wdLgVLL5PCxBzP/BMiPsQZbgxQgB1zHLT8HapmBB7S879FdR7sprlyB+vHAj9K+KC7joodgKVB68k8ccayUgWpFBKpABdJIkytQym7j/maQxLv8x0c/7tHe4Cd1cc04IDQWnpiBqmuuk+7MQ9rGwBF8BSoUzTTVZuFZzVbZxcxbiJxf6LXmn4CWgra2qTZkBVRiTKJU0PF9PBQKlN4CSuxGbnfapdf118ITCybA9Z1w9VNNgRJtVmWxBLirQKd29q+AKqsvQ1VjlazJpScVypOFB3gPkmtdB0+N/132P/x28284pdMpPrcNJETeKbkTClILUJBaoJrJ4TdWoVKgbE4bfj74s7SqBC8s+PlDqwJ1Vf+r8NrFr+HxUY+7/a53Rm+kxKag3laPWZ/PQkVDRXAUqAD6QPmjQIljpQxUKyLgDFQAjTQ9FVD+MqTjEFiMFtw8+GYM6TjE43Y+LbwghshFC4/nn2JNsap3vIGitPCCrXYFcyae3WmXxqe8YxMvSlosPD0rlPPPfVvZNtTZ6mAxWtCtfTf9b8ALBoNBUqH4Pu5NceFZJC0wxnS3MODwE/Oeqj2y15QUKJ0WHlcQxIs8vyj6ykCpWXjKY0KrAqW08LhC1zmls1RkepqJ5ylEDgAdEjy3MtA7C08k3hKPEzK12cb8OzEajFKhqBWz0Yzfbv4Nv938m2pIXFKggtjCAJB/lt/s/QYAUJhTKM02kwoojQqU1WzFVf2vUlXSjQYjpp00DQDw7IZnUfBUgWTn6clAAYErUP2z+gMABmcP1v23XIEyGUzSeTZaoALKC4EWUFoaaf5Z9qdqu30+nVqvNO2JPhl9UDOnBs+d+5zX7XzNwuNtDoKBaOFJBU1CRsCNLdVQWnjBbtoZzJl4ZfVlYGAwwOBm24nj1aJAac0/AS0X6Z8O/ATAlSPSOlNHD1zV4fu4mk3SN6MvAODdbe9qft6y+jJUNlbCAIPUD0cr3MLbX7Nfsk9FtcxTI81P/voEo18d7bGHT/u49tLf8hsptfC8WCD5svC6pnbVfCevVKDEjBi3Q9QUqGZHs5SX9KpAqWSg9DTSDAR+fs1KyPJrpmicJc5jgcRnEAZbgbKYLNKNz9d7vgbgKiqU5w8ty7ho4aERD+Gzqz5D/6z+qG6qlqzpcGegLu1zKYpnFuOu0+7S/bdcBe+T0cfvGcGhggooLwScgTp28bI5bVIBIlLXXIchi4fglJdOcft9sBUowFUc+SpOwjoLT1SgQtjCABAUKEcTnMwp5TSCFVjnFtDOip0BPxe3RdLj091OGLICSqFA8WLH5rDpbmEAtHwf/KKoVQnQi1KBUiugbii8ASaDCV/v+Rq/HP5F0/Py4qBzSmfdykFmQibizHFgYFKzWj5OwHMbg6d/fBpFfxfhgz8/kD0uKgj8O+CPqYXnTUaTVET5svC02ndASwFV0VCBZkdzi0KX1lMqGtVm4ontI1QVKA8hcnER7FAXUPz59aopep472BkooKVY5mpQYU4hOiXJCygty7howWAw4Jxu52DTDZuw7MJl6JzSGT3Seuju7RaoAgW4ejr5c3PcO6M3Pr/qc7x7ufabqXBBBZQXAs1AiXeVatmJP0r/QG1zLSobK7Grcpfsd6EooLTgcymXEPSBqmuuC2kLA0BoY+BoRnVjNRzM1XHan5yGGn0zXYrJr0d+Dfi51GbgcURFypMCZXO2FFD+WHicPunBzT9xuALlLQOVm5KL8b3HAwCe+fEZTc/rr30HuC40vAjmyyXxcQLyDJS4Ph+3bLlqxREzLNyGkRQolUaaQEux60uBOi33NM3vKzUuVSrCj9QdwV8VLrW7R1oPybZUU6B4/kmtpQLgOUTOZzEmxiS6NWAMNmcXnI17ht+jmv8JFG6phaII5PsS3w8KswtbFKhafRaeVkxGE64deC323LoHf0z7Q3cRFGgGKlDGdBuD7mn6VOVwQAWUFwK18MT1wNRyUDyoC8inFgMtFxe94chAiUQfqHpbfUhbGADyDBQv1pJikoJWEPbL7AcA+PVw4AWU2gw8jtic0FMGCoDUfViPhadUGoIdIOdwZYe/T093+beefCsA4LVfX5MsXm9I9lR7fS0MOLyg4AUUzz8B8jXH+LHAGJMWkHYroNQUKGUGSvG++WuoKT6xpljp+9ajQBkNRlmxIxaZkoWnkoES809qqoGnELm44GsorHiRGFMMHhzxoK7PQytXnHAFnhj9BO4/4/6gP7d4o5KZkIlOyZ08WnjBKqA4BoPBr8agwVCg2iJUQHlBsvDM/vvg3qY/iwXUn2V/Sj/bHDbphBwtClQoLTyb0yZ1XdfTvVgPYgZKzFsFixMyT4ABBpTWl3pc4kIrajPwOFoyUEBLkFfsleMLpQIVaguPr8fmyW4bljsMJ2afiEZ7I17a9JLP55VaGPihQAFAl5QuAIBNhzbJxgnIFSCuJlc1VklKjVcFyqpQoFTWwgNaFCi1AspgMOCZsc9g/tnzdRe23G47WHtQsph7pPWQtW5Q9i/zNgNPfE6lAsWV9K6p/i3/Ey0kxCRg9tDZfi9j5A3xOCvMLoTBYPBcQAVo4QWLSCtQ0QoVUF7wtNipHrw10+TNCgHgz/KWAqrkaAkYGCxGS8hzBEp8WnjBbKQpXCj49NpQKVBiG4NQ5K3iLfHSyTZQG++3UldvGN7hXERm4XlRoHiQV5cCJVwszUZz0GfgcURrDPCsQBkMBkmFem7Dc7A5bF6f198WBhyuQHH1Thyn0WB0644uzrjkqiZHUwbKrF5AeVK8ry+83q8QLi/Ef9j/A2xOG2JNseic0hm5KbkwGUxodjTjUO0h2d94m4EHtKijtc21sjVDRQWKUEcsoAbnuGaliQUUYyzoFl6gkAKlDhVQXgjUwgO8N9P0ZOFx+y47KdvrauuhgKsa4VCgYkwxMBlccjLPToQqAyVaeKFq2BkMG8/hdODTvz4F4GqOp8SbAmUymGCAyzaRCig/M1A90nqE7EQpKjuA9xuUK064ApkJmdhfs98tqC1id9qxq8KlfvirQCm7HLeLbSf7f+VMPG7fAV4UqFiVDJRKI01AUKA8qD7+wlsOfLv3WwBA97TuMBqMMBvN0hRxZQ7KlwKVHJssjV8MkrcVBSqUKBUooCWqUW+rR1VjVdBm4QWLQGfhtVWogPJCoCFyoEUBUGag6prrpKIBcFl4PJzKp3eH274DwtsHymAwSJ8t75AbMgVKsPCC3cKAIxVQAShQPx74EaX1pWhnbYfhnYe7/V4MvSsLHIPBID3GZxn6m4EKVf4J0K5AAa7v7abCmwAAT//0tMftdlfuhs1pQ5w5zu9eMWJ3arVxKmfiiQqUNwvPYwZKUTgqe04FC263bTi4AYBcoeOqm3ImHn+PnnqyGQwG1SA5KVC+UVOgrGardD7aX7M/aLPwggUpUOpQAeWFQNsYAC0SLFcEOH+W/QkGhnbWdjAajKhuqpbu5CI1Aw/Q0AcqiAoU0HKHG6qihiO2MQiZApUVeAH10faPAADndj9XtQeTtzYGQEsrA76/6bmDFU/soZqBB+hToADgpsE3wWw047vi77CtdJvqNmJ/I39VW2WXY+U4JTW52V2BqmqsknqMAXILz9MsPGXhePPgm3FZn8twZd8r/Rq/J7iFx8cnrplW0E49SO7LwgOEVgbHMnuMMamACkV2qK0gLmsinuNFGy/aLLyU2BTJLaAMVAtUQHkhGAoUVyV4c0IOt+8GZA2QrANu40kz8ILURFMP4QyRA+4n6HC0MQi1AvX7kd/9XlT4w+0fAgDG9VBfq9CbhQfAXYHSoWaIdk2oAuQA3Ka3+zq+spOycWL2iQCg2nQWaDl29C4iLJJqTZV9XuIsPEDFwlN0nRfXQfSmQHkKkZ/U8ST877L/BX25CmWXblUFqkquQPmy8ICWwowrUCVHS1Bvq4fRYNS91trxBN+PCnMKZTMVZQVUlIXIDQaDpMiSAtUCFVBeCEYG6rTOrp4t3xV/J3ucB8j7ZPSRMht8Jl40WHg2pw0Op0N6XLLwgnz3oTxBh7qNAdBSoAb7tbq17war2YoGe4Nqc0Jf/FX+F/4s+xMWowXndDtHdRtvIXJAKKD8CZFHoYXHyU7MBqC+dAgQeIAccF0kxOJFj4UHyG08mQKlnIXnoQ9UqFAWUGKRKc3E80OBUi4Tw/f5zimd6SLrhZM7ngwDDLi418Wyx3kzzX01+1osvChRoICWGx/KQLVABZQXgqFADcsdBgMM2FW5SzbThStQJ2ScgF5pvQC0FFDRYOEB8gWF+c/BPumLn60BBjfbJFiIB/2BGleBGmwFymQ0SYWHP0Hyj7d/DAA4s8uZHu88rWardFHzpkDx1df1WHgWkwWXn3A5RuSPQK/0XrrGrge9Fh7QUgTwdhdKAm1hwBGD5MpxKhcUVi7fIiugVBQob0u5hBJlPzHxM/K0nIukQGmw8HhRywPklH/yzmUnXIaaOTWYeuJU2eM8SP5n2Z+Sgh0tIXKgpYCi4rgFKqC8EJQMlDUFAzoMAACsLV4rPS6uds8vVvwuOhosPEBu44XDwkuLT/OryZsWRLWGK3yhsAsDCZJ/9Jcr/zSup7p9x+mV3gsGGFT3D+XJTW8gecWlK/DVNV+FdM0pfxQoT32HOMFQoAB5kNxNgRIWFGaMSQUUb/fgUYE6piJUN1bD7rRLXfCDvVCtJ0QFKj0+XWahcsXtQM0B2XJSvkLkgGcFimbg+Ubtc+UWHr82mAymoK/FFwindz4dsaZYDOwwMNJDiRqogPJCMBQooGXpBW7j1dvqpZONWEBFgwJlNpqlsKBaARVs+Va08EKVfwJc6hAPF3MlIBSBdX8LqPL6cmn/uKDHBV63/XjCx9h4w0ZpuQkRtwJKh4UXLhIsCbICTY8CpVZA1TTVSI8HU4HyloEqqy9Do70RBhikCwovoBhjHhUosWdSuBSo5Nhk6QZCmRHLiM9AgiUBDEzqxQYIFp6XDJQyRE4KVGDwAoqrqSnWlJB3c9fDI6MeQcWdFeif1T/SQ4kaqIDyQqPDVTQEWkANz3NNR+cK1Pay7WBgSItLQ2ZCpnTS31O1B+X15dJinJEooAD1IHnIZuEJClSo8k8cZWYoFK8nzcTTaeF9uuNTOJkTA7IG+FwcNTspG4OyB6n+zmKUz9yLJguAYzDIrdpAFSh+wclKyAr4/coyUB5m4R1tPirln7ISs6TsCi+gGuwNkoUqZqCqm6ql/BMQviyJwWCQPj+lQifmvsQclBYLTxkiJwUqMHgBZXO6GsZGU/6JE02KWDRABZQXgmHhAS1B8q0lW1HdWC2z7wwGAzLiM5BqTQUDwzd7v5FeM1IXP14kiZJ+OCy8UHddF9UZs9EckhMUV6B2VOyQqQ1K9lbtlfX+4u0LLux5YUCvH6iFFy5EeyxQBYq3E1D2cfIHrkBZzVa3cYkZKP6aucm5yEzIBAAcqXcVUNy+MxqMSIxJlClQXNWONcWGtUku//zUZiny9yxOfNA0C09h4fFGpqRA+YfSko+WGXiEZyJeQD3//PPIz8+H1WpFYWEh1q5d63X7b775BoWFhbBarSgoKMCiRYs8bvvWW2/BYDDgoosu8mtswbLwcpJyUJBaAAaG9fvXy2bgAa67QG7jrdm9BoDrYIqUfKuqQIVgKRdA/tmG0sID5Hf86fHpIfl8OyR2QFpcGpzMiW1l6j2LSutKccLzJ6D3c73R5akuuPHjG/HFri8A+M4/+UIsoCxGS9TOmAlEgeJFJ4dnkfxtoClyQuYJuG7gdbjv9PvcfifOwuMKVG6KUEAdU6DELtIGg0Eq1J3MKbWXCFf+iTO883AYDUaMyB/h9ju1ILmePlBHm4/iSN0RqZCiHlD+kRSbJLupi0YFipAT0QJqxYoVmDlzJubOnYvNmzdj+PDhGDt2LIqLi1W33717N84991wMHz4cmzdvxt13340ZM2bg3Xffddt27969uP322zF8uHs3Z61wBSYYsiXvKr1271rZDDwOL6C+2v0VgMjZd4B7AcUYC50CFaYMFCAvLkKldhkMBp823paSLdIdfnF1MV7c9CKONh9FTlKO1O/IX8T3GI35J45eBYqrHU2OJqlA4QSzgDIajFhy4RLMGT7H7XdiI02uQHVK6iQVULwPlLIJYrwlXsoV8iIjXPknzqOjHkXlnZUY0nGI2+/ERYU5WkLkiTGJ0vv4Yf8PAFyFsTI7RmhH3Iej0X4n5ES0gFqwYAGmTJmCqVOnonfv3li4cCFyc3PxwgsvqG6/aNEidO7cGQsXLkTv3r0xdepUXHfddXj88cdl2zkcDlx11VWYN28eCgoCl5ODWkAVr5VZeByeTeCqRTQVUGKH5aCHyMNo4YnqWSiLNV9Bcp7ZOafbOVg5cSVmDJmBIR2H4KERDwWsiokFVDSfgLkCZTKY3HJbasRZ4qSCRGnj7a8NXgHlDV5MaFWguAVjMBik74IHrsOtQIljUKK2nIsWC0/MVq3btw4AqU+BIu7DZOFFPxEroJqbm7Fx40aMHi1fMHX06NFYt26d6t+sX7/ebfsxY8bg559/hs3WslL7Aw88gIyMDEyZMkXTWJqamlBTUyP7JxIM1YXnoH468JM0W0UsoJR9dyLRwoCjLKDEflAhVaBCHSJXWHihom9mXwDAb0d+U/09L6D6ZfbD2O5j8dTYp/Dj1B8xeeDkgF9bpkBFaf4JaOkpE2eJ01w0ZiepN9PkClRusvusxGDCFb3aplrZa4oFFGNMdRkOXrzwsYeriaYWuIX3d+Xfkj2qxcIDWoLkvICi/FNgyAoosvCinogVUGVlZXA4HMjKkjd5y8rKQkmJeq+XkpIS1e3tdjvKylzrm33//fdYsmQJFi9erHks8+fPR0pKivQvN7flRGw1W4MS9uyR1gOZCZlocjTByZxItabK+rMoC6hoUqB4FgwIfhO1cGagwmHhAb4VqB0VOwAA3dt3D/pri+vnRbWFd0yB0mNleQqSB9PC84YsRK6iQDXYG1Bnq1NdhoP/HCkLzxvd2ndDYkwiqhqrsHr3agDaFCjAfaFimoEXGFRAtS4iHiJX3n0yxrzekaptzx+vra3F1VdfjcWLFyM9XfsFcs6cOaiurpb+7dvXskRDsKZtGgwGSYUCWmbgcQpSC2S9caKpgNpb5eoP0zGpY9BnDrVFC48rUAdrD6KiocLt91yBCmTdNk+0GgvvWAZKj5UldSMXOvqLDS1DXUBxRa+mqUbqZp+bnIuEmATpPHGk7ohXBUoqoMJs4XnDarbimv7XAACe2/AcAB0K1LFsGj9XkAIVGGThtS4iVkClp6fDZDK5qU1HjhxxU5k4HTp0UN3ebDYjLS0Nu3btwp49e3DBBRfAbDbDbDZj+fLl+Oijj2A2m7Fr1y7V542NjUVycrLsHyeYfS94Q03Afa0xi8kiu3vjbf0jAbe6+ElxZ8VOAKHJN4TTwguXApUUmyRNqVcGyZsdzdhTtQcA0D0t+ApUa7Hw/FKgEtwVqLL6MjQ7mmGAQbL4QgVXoCoaKmBz2mA0GKXXFG08SYFSmVElZaCiSIECgGknTQPgWsx6X/U+TSFywH2ZGFKgAoMUqNZFxAqomJgYFBYWoqioSPZ4UVERhg0bpvo3Q4cOddt+1apVGDx4MCwWC3r16oVff/0VW7Zskf6NGzcOZ511FrZs2SKz5rQSzAKKN9QE5DPwOKKNF00KFM9sheLkKGukGcY2BqEu1gZkuZbv2VKyRfb47srdcDAHEiwJ0gK5wSTG2DoKKJ6B0nN8SRZeXUsBJTa0DPUaXUpLNCcpR1KNZQVUo7uFF80ZKMDVvuHMLmfCyZx45qdnpOVmtFp4HFKgAoNm4bUuImrhzZ49Gy+99BKWLl2Kbdu2YdasWSguLsZNN90EwGWtXXPNNdL2N910E/bu3YvZs2dj27ZtWLp0KZYsWYLbb78dAGC1WtG3b1/Zv3bt2iEpKQl9+/ZFTIz+E2wwC6iBHQZKd3Rqq92LBVQoLq5a8VRA8TW/ggn/fJNikkLesyhcChQADOrg6hS+qWST7HEp/5TWPSR9qFqLhTcifwTO7X4ubj35Vs1/o5aBCpd9B7irMWJoXbMCFYUWHmf6SdMBAIt+bumtpzVEDrj6joXje2jLkIXXugjdiqEauOKKK1BeXo4HHngAhw4dQt++fbFy5Urk5bmWsjh06JCsJ1R+fj5WrlyJWbNm4bnnnkNOTg6efvppXHLJJSEbYzALKLPRjCfHPIkf9/+Is/LPcvs9b2XQPq59RE+wVpO8gJIsvBAoUN3TusNqtqIwpzDoz60kXBkoAFI/p82HNsseD2X+CWg9faCSYpPw6cRPdf1NpAuoGFMMYkwxUlsP8TX5/iQroFQUKJ6JizYLD3B1wc9JypHW4rQYLbJJCWqIClSXdl1Cthj48UJKbAoSLAmos9WRhdcKiGgBBQDTpk3DtGnTVH+3bNkyt8fOOOMMbNq0yX1jD6g9hx6CvfbP1BOnYuqJU1V/x5vcqdl74cRNgTq2REMoMlCZCZkonlkcFrUkXG0MAEhr1f1R+gca7Y3SZyoVUO1DU0DJZuFFsYXnD14LqKTwKB+JMYlSEeRRgVIJkSvVhGgsoCwmC24svBH3f30/AN/2HSDPQFEPqMAxGAwY020Mvt37rapLQUQXEZ+FF+2Ec/HEEzJPwI9Tf8Q7l78TttdUQyygjjYfbVmiIUQB0YyEjLAsORJOC69jUkekx6fDwRyyILlo4YWC1qJA+QMvoErrSqXFeqV+TCmh7QHFEYtS8TVVLTwVBYoTbRkozvUnXi/lunzZd4DcwitoR/mnYPDOZe/gwOwDsm79RHRCBZQPwr369JCOQ6STcaQQCyiuPrWPa9/qD2hu4YUjb2UwGKQc1OaSFhsvnBZeNGeg/CE9Ph1GgxEMTFo2JZwWHiAvSnUpUAo7JhozUICrWen43uMB+J6Bx7fhhRYpUMHBYDCEfEIEERyogPJBvDm8BVQ0ICugQjgDL9zwk1KoZ+BxlDmoelu9dMEPRRNNoPW0MfAHk9EkWUbcxgt3ASUWFWoKVGl9KaoaqwB4V6Ci0cL7//buPSjK6+4D+Hd32QW5raLIgiDQt4nVBFQ0ITpS0pQgXmvS6cU6Em0yU2LUBJp4qzPk0oihaqy16kwq2ukliTZeyB+SkgLaKGpEiKQaL3it2ZXgZbkpsHDeP+g+Yd1ll5W9PMt+PzM7I+c5e/ac49mHH+ec53nM8p7Ig0alQXJUcp/ym2cGeQUe+Ruv74GSO0/PQMmBFEB1fjsD5Y4r8DzNPAPl7uU7s/tnoMyb8SMGRWBo8FC3fOZAXsIDun9Z65v1MDQbPHoTTTOLJTwbM1A3mm/Yvgrv/j1QMp2BAoDU2FRcWHKhz9+TpalLse+rffhh4g/dXDMieWEA5YA/B1Btpja3XoHnadIMlJuvwDMzbyT/4sYXMHWZcP6m+x7hYjaQl/CAHncjb9bj9r3buGu6C8Bz900zz0CplWqL/T9SAPW//YKAb+6BMnNmT9nS1KVYmrrUjbUhkicGUA74cwB1z3QPDa3dzxgcCDNQ5nZ5agbK/Iyx5vZmnG046/b9T0D3L3azgbaEB1heiWeefYoMjvRYQGKe1RsRbvlYo/vHlAIKi+U+X1rCI6K+4R4oB/w9gJL2QA2ADaLPjn4WaSPTsHDcQo98nlKhlO5IXm2oxrlb3QGUp2agBuoSHmAZQHny5o2h6lCbn6lRaaTH0wDdAVPPAMtXNpETUd8xgHLAH0905gCqsa0RV43dNzIdCEt4SVFJOLTwENIT0j32meaN5Cf1J6UlPHfOQPnLEp6h2YBrxu7HuHgygDIHpT33P5n1vHr2/j1PnIEiGngYQDngzzNQXzV8hS7RhWB1sNUzr6hvem4k98QSnjmAUiqUA/KXtK0ZKFvBjLukjUxDUEAQsr6bZXXMIoC6b8YpMCDQ4k74/viHGdFAwz1QDvhzAHX73m0A3bNP7nhumz8wbyQ/fv04WjtaAbh3P5k5gArThA3I/zOLAKrJ80t4Mx6egcYVjTYfcWJvBgronoX6prX7/lVy30RORI5xBsoBfw6gzAbC/idvGRM5BmqlWgqeokOj3bo3SQqgBuD+J8D7e6AA9Pp8uJ5Xd9p6jlnPoGogzg4S+RsGUA4wgAK+O8T3r8DzFo1Kg6SoJOlndz3Cxcx85VfEoAi3fo63RIdGAwCa2ptwtuEsAM8HUL3pywyUGZfwiHwfAygH/DGAuv8xJ5yB6h/zPijAfQ8RNpsUNwmvTX4NhRmFbv0cbwnVhErfyWuNnt9Ebo+9PVD3p3EGisj3cQ+UA/4YQFnNQA2Ae0B5U88Ayt0zUAHKABQ+PTCDJ6D7OWG6UB0u3r4opY0IH+HFGn3LUQDVcwaKe6CIfB9noBxgADUwbmHgTeaN5IB7r8DzFz2vCI0YFCGb76ijJTyLPVBcwiPyeQygHJDLydmTegZQAcoApx7rQNbGRo2VbqrIAKr/egZQclm+A/owA6XpsQeKS3hEPo9LeA74ewCVODgRAUoOk/4I0YTg7afexlXjVYweNtrb1fF5upBvAyhP3gPKEWdmoLiER+T7+JvRAX8PoLiB3DVWTFnh7SoMGHKdgRoyaAhUChU6RafdPVBqpRoqpcrT1SMiF+MSngM97x7sL9RKNRTovgkj9z+R3Mg1gFIqlIgM6b4X1OCgwVbHzUEV9z8RDQycgbIjWB08IO/m7IhCoUBQQBDumu7yCjySHbkGUACwbPIylF8ux8SYiVbHzDNQ3P9ENDBwBsoOf/5L0byMxxkokhs5B1C5k3JRPLfY6l5qwLd7oPz5vEI0kDCAssOfT3Txg+MRoAzAON04b1eFyEJ0WLT0b7kFUPaY/xiJ18Z7uSZE5ApcwrPDn6faD8w7gPqWet7CgGQnKiQK2kAtTF0mjNSO9HZ1+mzUsFE49sIxJAxO8HZViMgFGEDZ4Y9X4JnpQnUWSyVEcqFWqXFo4SGYukw+9x19fMTj3q4CEbkIAyg7/HkJj0jOkqOSvV0FIvJz3ANlh6/9dUtERESewQDKDn/eA0VERES9YwBlB2egiIiIyBYGUHZwBoqIiIhsYQBlBzeRExERkS0MoOwIDuASHhEREVljAGUHZ6CIiIjIFgZQdnATOREREdnCAMoObiInIiIiWxhA2cEZKCIiIrKFAZQd3ANFREREtjCAsoMBFBEREdnCAMqOqf831dtVICIiIhliAGWHQqHwdhWIiIhIhhhAERERETmJARQRERGRkxhAERERETmJARQRERGRkxhAERERETmJARQRERGRkxhAERERETmJARQRERGRkxhAERERETnJ6wHUli1bkJiYiKCgIEyYMAH//ve/7eY/ePAgJkyYgKCgIHznO9/Btm3bLI6/9957SEtLw5AhQzBkyBBkZGTg+PHj7mwCERER+RmvBlAffvghXnnlFfzmN79BdXU10tLSMG3aNFy9etVm/kuXLmH69OlIS0tDdXU1Vq1ahaVLl+Kjjz6S8lRUVGDu3LkoLy9HZWUlRo4ciczMTFy/ft1TzSIiIqIBTiGEEN768NTUVKSkpGDr1q1S2ujRozFnzhwUFBRY5V++fDmKi4tx5swZKS0nJwdffPEFKisrbX5GZ2cnhgwZgs2bNyM7O7tP9WpsbIRWq4XRaER4eLiTrSIiIiJv8OTvb6/NQLW3t6OqqgqZmZkW6ZmZmThy5IjN91RWVlrlnzp1Kk6cOIGOjg6b72ltbUVHRwciIiJ6rUtbWxsaGxstXkRERES9CfDWBzc0NKCzsxNRUVEW6VFRUTAYDDbfYzAYbOY3mUxoaGhAdHS01XtWrFiBESNGICMjo9e6FBQU4I033rBKZyBFRETkO8y/tz2xuOa1AMpMoVBY/CyEsEpzlN9WOgAUFhbi/fffR0VFBYKCgnotc+XKlcjLy5N+vn79OsaMGYO4uLg+tYGIiIjko6mpCVqt1q2f4bUAatiwYVCpVFazTfX19VazTGY6nc5m/oCAAAwdOtQifd26dVizZg0+/fRTJCcn261LYGAgAgMDpZ9DQ0Nx7do1hIWF2QzMGhsbERcXh2vXrrlkjfWxxx7D559/3u9y/KEsf+l7V5fH/vf9stj33ivL1X0PyLOdci3Lmf4XQqCpqQkxMTH9/lxHvBZAaTQaTJgwAaWlpXjmmWek9NLSUvzoRz+y+Z5Jkybh448/tkj75z//iYkTJ0KtVktpv/vd7/Db3/4Wn3zyCSZOnOh03ZRKJWJjYx3mCw8Pd8mXSaVSuexL6Q9lAQO/711dHvt/YJQFsO+9VRbgur4H5NtOuZYF9L3/3T3zZObV2xjk5eXhT3/6E4qKinDmzBnk5ubi6tWryMnJAdC9tNbzyrmcnBxcuXIFeXl5OHPmDIqKirB9+3a8+uqrUp7CwkKsXr0aRUVFSEhIgMFggMFgQHNzs8fb11cvvfQSy/ISObdRznVzFbm2Ua5luZJc2yjXslxNru2Ua1myJLzsj3/8o4iPjxcajUakpKSIgwcPSseee+45kZ6ebpG/oqJCjB8/Xmg0GpGQkCC2bt1qcTw+Pl4AsHrl5+e7rM5Go1EAEEaj0WVlUt+w772L/e897HvvYd97l1z73+ubyBctWoRFixbZPLZz506rtPT0dJw8ebLX8i5fvuyimvUuMDAQ+fn5FvumyDPY997F/vce9r33sO+9S67979UbaRIRERH5Iq8/C4+IiIjI1zCAIiIiInISAygiIiIiJzGAIiIiInKS3wZQhw4dwqxZsxATEwOFQoF9+/ZZHG9ubsbixYsRGxuLQYMGYfTo0di6datFnrq6OjzzzDOIjIxEeHg4fvrTn+LGjRsWeRISEqBQKCxeK1ascHfzZM1R39+4cQMLFixATEwMgoODkZWVhfPnz0vHb926hSVLlmDUqFEIDg7GyJEjsXTpUhiNRotybt++jfnz50Or1UKr1WL+/Pm4c+eOB1ooX/3t+56EEJg2bZrNcjjubetv/1++fNmqX82v3bt3S/k49q25YuzznP9gCgoK8NhjjyEsLAzDhw/HnDlzcPbsWYs8Qgi8/vrriImJwaBBg/Dkk0/iP//5j3Rcjud9vw2gWlpaMHbsWGzevNnm8dzcXJSUlOCvf/2rdJPPJUuWYP/+/dL7MzMzoVAoUFZWhsOHD6O9vR2zZs1CV1eXRVlvvvkm9Hq99Fq9erXb2ydn9vpeCIE5c+bg4sWL2L9/P6qrqxEfH4+MjAy0tLQAAL7++mt8/fXXWLduHWpra7Fz506UlJTg+eeftyjrF7/4BWpqalBSUoKSkhLU1NRg/vz5HmmjXPW373vauHGj3edWctxb62//x8XFWfSpXq/HG2+8gZCQEEybNk0qi2PfWn/7nuf8B3fw4EG89NJLOHr0KEpLS2EymZCZmWlxXiksLMSGDRuwefNmfP7559DpdHj66afR1NQEQKbnfe/dgko+AIi9e/dapD3yyCPizTfftEhLSUkRq1evFkII8cknnwilUmlxY69bt24JAKK0tFRKi4+PF++++67b6u7r7u/7s2fPCgDiyy+/lNJMJpOIiIgQ7733Xq/l7Nq1S2g0GtHR0SGEEOL06dMCgDh69KiUp7KyUgAQX331lesb4oP60/c1NTUiNjZW6PV6m98fjnvHXDX2x40bJ375y19KP3PsO/Ygfc9zvuvU19cLANKNs7u6uoROpxNr166V8ty7d09otVqxbdu2Xsvx9nnfb2egHJkyZQqKi4tx/fp1CCFQXl6Oc+fOYerUqQCAtrY2KBQKixt7BQUFQalU4rPPPrMo65133sHQoUMxbtw4vP3222hvb/doW3xJW1sbgO6+NFOpVNBoNFb92pPRaER4eDgCArrvDVtZWQmtVovU1FQpzxNPPAGtVosjR464qfa+ra9939rairlz52Lz5s3Q6XS9lsdx75wHGftVVVWoqamx+CucY995fel7nvNdx7zsFhERAQC4dOkSDAYDMjMzpTyBgYFIT0+3O2a9fd5nANWLTZs2YcyYMYiNjYVGo0FWVha2bNmCKVOmAOj+TwkJCcHy5cvR2tqKlpYWvPbaa+jq6oJer5fKefnll/HBBx+gvLwcixcvxsaNG3u98zoB3/ve9xAfH4+VK1fi9u3baG9vx9q1a2EwGCz6taebN2/irbfewq9+9SspzWAwYPjw4VZ5hw8fDoPB4Lb6+7K+9n1ubi4mT57c60O/AY77B/EgY3/79u0YPXo0Jk+eLKVx7DuvL33Pc75rCCGQl5eHKVOm4NFHHwUAaVxGRUVZ5I2Kiup1zMrhvM8AqhebNm3C0aNHUVxcjKqqKqxfvx6LFi3Cp59+CgCIjIzE7t278fHHHyM0NBRarRZGoxEpKSlQqVRSObm5uUhPT0dycjJeeOEFbNu2Ddu3b8fNmze91TRZU6vV+Oijj3Du3DlEREQgODgYFRUVmDZtmkW/mjU2NmLGjBkYM2YM8vPzLY7Z2p8jhLC7b8ef9aXvi4uLUVZWho0bN9oti+Peec6O/bt37+Lvf/+71R4QgGPfWX3pe57zXWPx4sU4deoU3n//fatj94/P3sasXM77Xn8WnhzdvXsXq1atwt69ezFjxgwAQHJyMmpqarBu3TpkZGQAADIzM1FXV4eGhgYEBARg8ODB0Ol0SExM7LXsJ554AgBw4cIFDB061P2N8UETJkxATU0NjEYj2tvbERkZidTUVEycONEiX1NTE7KyshAaGoq9e/dCrVZLx3Q6ndXVMQDwzTffWP2VQ99y1PdlZWWoq6vD4MGDLd734x//GGlpaaioqLBZLsd93/R17APAP/7xD7S2tiI7O9sinWP/wfSl73nO758lS5aguLgYhw4dQmxsrJRu3gpgMBgQHR0tpdfX11uNWTmd9zkDZUNHRwc6OjqgVFp2j0qlsrraAgCGDRuGwYMHo6ysDPX19Zg9e3avZVdXVwOAxSAh27RaLSIjI3H+/HmcOHHCYsmosbERmZmZ0Gg0KC4utti7AACTJk2C0WjE8ePHpbRjx47BaDRaLHeQbb31/YoVK3Dq1CnU1NRILwB49913sWPHjl7L47h3jr2xb7Z9+3bMnj0bkZGRFukc+/3Tl77nOd85QggsXrwYe/bsQVlZmVXAmZiYCJ1Oh9LSUimtvb0dBw8etBizsjvvu3xbuo9oamoS1dXVorq6WgAQGzZsENXV1eLKlStCCCHS09PFI488IsrLy8XFixfFjh07RFBQkNiyZYtURlFRkaisrBQXLlwQf/nLX0RERITIy8uTjh85ckQq9+LFi+LDDz8UMTExYvbs2R5vr5w46vtdu3aJ8vJyUVdXJ/bt2yfi4+PFs88+K72/sbFRpKamiqSkJHHhwgWh1+ull8lkkvJlZWWJ5ORkUVlZKSorK0VSUpKYOXOmx9srJ/3te1tw3xVNHPe9c1X/nz9/XigUCnHgwAGbn8Oxb80Vfc9z/oN58cUXhVarFRUVFRbn69bWVinP2rVrhVarFXv27BG1tbVi7ty5Ijo6WjQ2Ngoh5Hne99sAqry8XACwej333HNCCCH0er1YsGCBiImJEUFBQWLUqFFi/fr1oqurSypj+fLlIioqSqjVavHQQw9ZHa+qqhKpqalCq9VKZeTn54uWlhZPN1dWHPX973//exEbGyvUarUYOXKkWL16tWhra3P4fgDi0qVLUr6bN2+KefPmibCwMBEWFibmzZsnbt++7dnGykx/+96W+wMojvveuar/V65cKWJjY0VnZ6fNz+HYt+aKvuc5/8H0dr7esWOHlKerq0vk5+cLnU4nAgMDxfe//31RW1srHZfjeV/xv8YRERERUR9xDxQRERGRkxhAERERETmJARQRERGRkxhAERERETmJARQRERGRkxhAERERETmJARQRERGRkxhAERERETmJARQR+aQFCxZAoVBAoVBArVYjKioKTz/9NIqKimw+s7I3O3futHo4MhGRIwygiMhnZWVlQa/X4/Llyzhw4AB+8IMf4OWXX8bMmTNhMpm8XT0iGsAYQBGRzwoMDIROp8OIESOQkpKCVatWYf/+/Thw4AB27twJANiwYQOSkpIQEhKCuLg4LFq0CM3NzQCAiooKLFy4EEajUZrNev311wF0Pw1+2bJlGDFiBEJCQpCamoqKigrvNJSIZIcBFBENKE899RTGjh2LPXv2AACUSiU2bdqEL7/8En/+859RVlaGZcuWAQAmT56MjRs3Ijw8HHq9Hnq9Hq+++ioAYOHChTh8+DA++OADnDp1Cj/5yU+QlZWF8+fPe61tRCQffJgwEfmkBQsW4M6dO9i3b5/VsZ///Oc4deoUTp8+bXVs9+7dePHFF9HQ0ACgew/UK6+8gjt37kh56urq8NBDD+G///0vYmJipPSMjAw8/vjjWLNmjcvbQ0S+JcDbFSAicjUhBBQKBQCgvLwca9aswenTp9HY2AiTyYR79+6hpaUFISEhNt9/8uRJCCHw8MMPW6S3tbVh6NChbq8/EckfAygiGnDOnDmDxMREXLlyBdOnT0dOTg7eeustRERE4LPPPsPzzz+Pjo6OXt/f1dUFlUqFqqoqqFQqi2OhoaHurj4R+QAGUEQ0oJSVlaG2tha5ubk4ceIETCYT1q9fD6Wye8vnrl27LPJrNBp0dnZapI0fPx6dnZ2or69HWlqax+pORL6DARQR+ay2tjYYDAZ0dnbixo0bKCkpQUFBAWbOnIns7GzU1tbCZDLhD3/4A2bNmoXDhw9j27ZtFmUkJCSgubkZ//rXvzB27FgEBwfj4Ycfxrx585CdnY3169dj/PjxaGhoQFlZGZKSkjB9+nQvtZiI5IJX4RGRzyopKUF0dDQSEhKQlZWF8vJybNq0Cfv374dKpcK4ceOwYcMGvPPOO3j00Ufxt7/9DQUFBRZlTJ48GTk5OfjZz36GyMhIFBYWAgB27NiB7Oxs/PrXv8aoUaMwe/ZsHDt2DHFxcd5oKhHJDK/CIyIiInISZ6CIiIiInMQAioiIiMhJDKCIiIiInMQAioiIiMhJDKCIiIiInMQAioiIiMhJDKCIiIiInMQAioiIiMhJDKCIiIiInMQAioiIiMhJDKCIiIiInMQAioiIiMhJ/w+u2YLNJ7XXbgAAAABJRU5ErkJggg==",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Plot the annual data\n",
        "ann_climate_df.plot(\n",
        "    y='PRCP_IN', color='green',\n",
        "    title='Average Annual Precipitation in Boulder, Colorado, 1893 - 2023',\n",
        "    xlabel='Date',\n",
        "    ylabel='Precipitation (inches)', legend=False) "
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9da60a12-6ece-4043-8870-5a3d0388c494",
      "metadata": {},
      "source": [
        "<link rel=\"stylesheet\" type=\"text/css\" href=\"./assets/styles.css\"><div class=\"callout callout-style-default callout-titled callout-respond\"><div class=\"callout-header\"><div class=\"callout-icon-container\"><i class=\"callout-icon\"></i></div><div class=\"callout-title-container flex-fill\">Reflect and Respond: Interpret your plot</div></div><div class=\"callout-body-container callout-body\"><ol type=\"1\">\n",
        "<li><p>Create a new Markdown cell below this one.</p></li>\n",
        "<li><p>In the new cell, answer the following questions using a\n",
        "<strong>bulleted list</strong> in Markdown – what are 2 things you\n",
        "notice about this data? What physical phenomena or data anomaly could be\n",
        "causing each one?</p></li>\n",
        "</ol>\n",
        "<div data-__quarto_custom=\"true\"\n",
        "data-__quarto_custom_type=\"ConditionalBlock\"\n",
        "data-__quarto_custom_context=\"Block\" data-__quarto_custom_id=\"6\">\n",
        "<div data-__quarto_custom_scaffold=\"true\">\n",
        "<div>\n",
        "\n",
        "</div>\n",
        "</div>\n",
        "</div></div></div>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "504cab72",
      "metadata": {},
      "source": [
        "Reflection/response\n",
        "* The first thing that I notice is that there is substantial interannual variability in average daily precipitation. \n",
        "* The second thing that stands out is the very high precipitation total in 1920. There was possibly flooding at some point in that year, unless the precipitation was very spaced out across seasons. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "f7ca6466",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'Average Annual Temperature in Boulder, Colorado, 1893 - 2023'}, xlabel='Date', ylabel='Temperature (°F)'>"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Plot the temperature data\n",
        "ann_climate_df.plot(\n",
        "    y='TOBS_F', color='red',\n",
        "    title='Average Annual Temperature in Boulder, Colorado, 1893 - 2023',\n",
        "    xlabel='Date',\n",
        "    ylabel='Temperature (°F)', legend=False) "
      ]
    },
    {
      "cell_type": "markdown",
      "id": "fa38ae19",
      "metadata": {},
      "source": [
        "Here we have a gap in the data in the late 1930s to early 1940s. The trend shows a real break around this gap, as well, with a big spike before the data gap starts, and the series picks up at a very low total. It is possible that there are additional missing observations in the gap start and end years. "
      ]
    },
    {
      "cell_type": "markdown",
      "id": "de00a236-e176-4362-88c0-a2e8de9f8517",
      "metadata": {},
      "source": [
        "## Check specific values with an interactive plot\n",
        "\n",
        "You can use the `.hvplot()` method with similar arguments to create an\n",
        "interactive plot.\n",
        "\n",
        "<link rel=\"stylesheet\" type=\"text/css\" href=\"./assets/styles.css\"><div class=\"callout callout-style-default callout-titled callout-task\"><div class=\"callout-header\"><div class=\"callout-icon-container\"><i class=\"callout-icon\"></i></div><div class=\"callout-title-container flex-fill\">Try It: Interactive Plot</div></div><div class=\"callout-body-container callout-body\"><ol type=\"1\">\n",
        "<li>Copy your plotting code into the cell below.</li>\n",
        "<li>Replace <code>.plot</code> in your code with\n",
        "<code>.hvplot</code></li>\n",
        "</ol>\n",
        "<p>Now, you should be able to hover over data points and see their\n",
        "values!</p></div></div>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "4bff67b5",
      "metadata": {
        "template": "student"
      },
      "outputs": [
        {
          "data": {},
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.holoviews_exec.v0+json": "",
            "text/html": [
              "<div id='p1312'>\n",
              "  <div id=\"e9a37220-f923-446c-9f0b-46bc4a25fd12\" data-root-id=\"p1312\" style=\"display: contents;\"></div>\n",
              "</div>\n",
              "<script type=\"application/javascript\">(function(root) {\n",
              "  var docs_json = {\"3a7ff180-cd4d-42e8-a9ce-015811f4a265\":{\"version\":\"3.5.2\",\"title\":\"Bokeh Application\",\"roots\":[{\"type\":\"object\",\"name\":\"Row\",\"id\":\"p1312\",\"attributes\":{\"name\":\"Row01274\",\"tags\":[\"embedded\"],\"stylesheets\":[\"\\n:host(.pn-loading):before, .pn-loading:before {\\n  background-color: #c3c3c3;\\n  mask-size: auto calc(min(50%, 400px));\\n  -webkit-mask-size: auto calc(min(50%, 400px));\\n}\",{\"type\":\"object\",\"name\":\"ImportedStyleSheet\",\"id\":\"p1315\",\"attributes\":{\"url\":\"https://cdn.holoviz.org/panel/1.5.1/dist/css/loading.css\"}},{\"type\":\"object\",\"name\":\"ImportedStyleSheet\",\"id\":\"p1385\",\"attributes\":{\"url\":\"https://cdn.holoviz.org/panel/1.5.1/dist/css/listpanel.css\"}},{\"type\":\"object\",\"name\":\"ImportedStyleSheet\",\"id\":\"p1313\",\"attributes\":{\"url\":\"https://cdn.holoviz.org/panel/1.5.1/dist/bundled/theme/default.css\"}},{\"type\":\"object\",\"name\":\"ImportedStyleSheet\",\"id\":\"p1314\",\"attributes\":{\"url\":\"https://cdn.holoviz.org/panel/1.5.1/dist/bundled/theme/native.css\"}}],\"min_width\":700,\"margin\":0,\"sizing_mode\":\"stretch_width\",\"align\":\"start\",\"children\":[{\"type\":\"object\",\"name\":\"Spacer\",\"id\":\"p1316\",\"attributes\":{\"name\":\"HSpacer01281\",\"stylesheets\":[\"\\n:host(.pn-loading):before, .pn-loading:before {\\n  background-color: #c3c3c3;\\n  mask-size: auto calc(min(50%, 400px));\\n  -webkit-mask-size: auto calc(min(50%, 400px));\\n}\",{\"id\":\"p1315\"},{\"id\":\"p1313\"},{\"id\":\"p1314\"}],\"margin\":0,\"sizing_mode\":\"stretch_width\",\"align\":\"start\"}},{\"type\":\"object\",\"name\":\"Figure\",\"id\":\"p1324\",\"attributes\":{\"width\":700,\"height\":300,\"margin\":[5,10],\"sizing_mode\":\"fixed\",\"align\":\"start\",\"x_range\":{\"type\":\"object\",\"name\":\"Range1d\",\"id\":\"p1317\",\"attributes\":{\"tags\":[[[\"DATE\",null]],[]],\"start\":-2429827200000.0,\"end\":1672531200000.0,\"reset_start\":-2429827200000.0,\"reset_end\":1672531200000.0}},\"y_range\":{\"type\":\"object\",\"name\":\"Range1d\",\"id\":\"p1318\",\"attributes\":{\"tags\":[[[\"TOBS_F\",null]],{\"type\":\"map\",\"entries\":[[\"invert_yaxis\",false],[\"autorange\",false]]}],\"start\":35.64736842105263,\"end\":72.46842105263158,\"reset_start\":35.64736842105263,\"reset_end\":72.46842105263158}},\"x_scale\":{\"type\":\"object\",\"name\":\"LinearScale\",\"id\":\"p1334\"},\"y_scale\":{\"type\":\"object\",\"name\":\"LinearScale\",\"id\":\"p1335\"},\"title\":{\"type\":\"object\",\"name\":\"Title\",\"id\":\"p1327\",\"attributes\":{\"text\":\"Annual Average Temperature in Boulder, Colorado, 1893 - 2023\",\"text_color\":\"black\",\"text_font_size\":\"12pt\"}},\"renderers\":[{\"type\":\"object\",\"name\":\"GlyphRenderer\",\"id\":\"p1378\",\"attributes\":{\"data_source\":{\"type\":\"object\",\"name\":\"ColumnDataSource\",\"id\":\"p1369\",\"attributes\":{\"selected\":{\"type\":\"object\",\"name\":\"Selection\",\"id\":\"p1370\",\"attributes\":{\"indices\":[],\"line_indices\":[]}},\"selection_policy\":{\"type\":\"object\",\"name\":\"UnionRenderers\",\"id\":\"p1371\"},\"data\":{\"type\":\"map\",\"entries\":[[\"DATE\",{\"type\":\"ndarray\",\"array\":{\"type\":\"bytes\",\"data\":\"AACg5eetgcIAAEBcKnOBwgAA4NJsOIHCAACASa/9gMIAAECNyMKAwgAA4AMLiIDCAACAek1NgMIAACDxjxKAwgAAgM+kr3/CAADAvCk6f8IAAACqrsR+wgAAQJczT37CAADAHmbZfcIAAAAM62N9wgAAQPlv7nzCAACA5vR4fMIAAABuJwN8wgAAQFusjXvCAACASDEYe8IAAMA1tqJ6wgAAQL3oLHrCAACAqm23ecIAAMCX8kF5wgAAAIV3zHjCAACADKpWeMIAAMD5LuF3wgAAAOeza3fCAABA1Dj2dsIAAMBba4B2wgAAAEnwCnbCAABANnWVdcIAAIAj+h91wgAAAKssqnTCAABAmLE0dMIAAICFNr9zwgAAwHK7SXPCAABA+u3TcsIAAIDncl5ywgAAwNT36HHCAAAAwnxzccIAAIBJr/1wwgAAwDY0iHDCAAAAJLkScMIAAIAifDpvwgAAgDHhTm7CAAAADOtjbcIAAIDm9HhswgAAAMH+jWvCAAAA0GOiasIAAICqbbdpwgAAAIV3zGjCAACAX4HhZ8IAAIBu5vVmwgAAAEnwCmbCAACAI/ofZcIAAAD+AzVkwgAAAA1pSWPCAACA53JeYsIAAADCfHNhwgAAgJyGiGDCAAAAV9c5X8IAAAAM62NdwgAAAMH+jVvCAAAAdhK4WcIAAACU3OBXwgAAAEnwClbCAAAA/gM1VMIAAACzF19SwgAAANHhh1DCAAAADOtjTcIAAAB2ErhJwgAAAOA5DEbCAAAAHM5dQsIAAAAM62M9wgAAAOA5DDbCAAAAaBFpLcIAAACwxF4dwgAAAAAAAAAAAAAAsMReHUIAAACwxF4tQgAAAOA5DDZCAAAADOtjPUIAAAAczl1CQgAAALKmCUZCAAAAdhK4SUIAAAAM62NNQgAAANHhh1BCAAAAHM5dUkIAAAD+AzVUQgAAAEnwClZCAAAAlNzgV0IAAADfyLZZQgAAAMH+jVtCAAAADOtjXUIAAABX1zlfQgAAANHhh2BCAAAAwnxzYUIAAIDncl5iQgAAAA1pSWNCAACAMl80ZEIAAIAj+h9lQgAAAEnwCmZCAACAbub1ZkIAAACU3OBnQgAAAIV3zGhCAACAqm23aUIAAADQY6JqQgAAgPVZjWtCAACA5vR4bEIAAAAM62NtQgAAgDHhTm5CAAAAV9c5b0IAAAAkuRJwQgAAwDY0iHBCAACASa/9cEIAAEBcKnNxQgAAwNT36HFCAACA53JeckIAAED67dNyQgAAAA1pSXNCAACAhTa/c0IAAECYsTR0QgAAAKssqnRCAADAvacfdUIAAEA2dZV1QgAAAEnwCnZCAADAW2uAdkIAAIBu5vV2QgAAAOeza3dCAADA+S7hd0IAAIAMqlZ4Qg==\"},\"shape\":[131],\"dtype\":\"float64\",\"order\":\"little\"}],[\"TOBS_F\",{\"type\":\"ndarray\",\"array\":{\"type\":\"bytes\",\"data\":\"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\"},\"shape\":[131],\"dtype\":\"float64\",\"order\":\"little\"}]]}}},\"view\":{\"type\":\"object\",\"name\":\"CDSView\",\"id\":\"p1379\",\"attributes\":{\"filter\":{\"type\":\"object\",\"name\":\"AllIndices\",\"id\":\"p1380\"}}},\"glyph\":{\"type\":\"object\",\"name\":\"Line\",\"id\":\"p1375\",\"attributes\":{\"tags\":[\"apply_ranges\"],\"x\":{\"type\":\"field\",\"field\":\"DATE\"},\"y\":{\"type\":\"field\",\"field\":\"TOBS_F\"},\"line_color\":\"red\",\"line_width\":2}},\"selection_glyph\":{\"type\":\"object\",\"name\":\"Line\",\"id\":\"p1381\",\"attributes\":{\"tags\":[\"apply_ranges\"],\"x\":{\"type\":\"field\",\"field\":\"DATE\"},\"y\":{\"type\":\"field\",\"field\":\"TOBS_F\"},\"line_color\":\"red\",\"line_width\":2}},\"nonselection_glyph\":{\"type\":\"object\",\"name\":\"Line\",\"id\":\"p1376\",\"attributes\":{\"tags\":[\"apply_ranges\"],\"x\":{\"type\":\"field\",\"field\":\"DATE\"},\"y\":{\"type\":\"field\",\"field\":\"TOBS_F\"},\"line_color\":\"red\",\"line_alpha\":0.1,\"line_width\":2}},\"muted_glyph\":{\"type\":\"object\",\"name\":\"Line\",\"id\":\"p1377\",\"attributes\":{\"tags\":[\"apply_ranges\"],\"x\":{\"type\":\"field\",\"field\":\"DATE\"},\"y\":{\"type\":\"field\",\"field\":\"TOBS_F\"},\"line_color\":\"red\",\"line_alpha\":0.2,\"line_width\":2}}}}],\"toolbar\":{\"type\":\"object\",\"name\":\"Toolbar\",\"id\":\"p1333\",\"attributes\":{\"tools\":[{\"type\":\"object\",\"name\":\"WheelZoomTool\",\"id\":\"p1322\",\"attributes\":{\"tags\":[\"hv_created\"],\"renderers\":\"auto\",\"zoom_together\":\"none\"}},{\"type\":\"object\",\"name\":\"HoverTool\",\"id\":\"p1323\",\"attributes\":{\"tags\":[\"hv_created\"],\"renderers\":[{\"id\":\"p1378\"}],\"tooltips\":[[\"DATE\",\"@{DATE}{%F %T}\"],[\"TOBS_F\",\"@{TOBS_F}\"]],\"formatters\":{\"type\":\"map\",\"entries\":[[\"@{DATE}\",\"datetime\"]]}}},{\"type\":\"object\",\"name\":\"SaveTool\",\"id\":\"p1358\"},{\"type\":\"object\",\"name\":\"PanTool\",\"id\":\"p1359\"},{\"type\":\"object\",\"name\":\"BoxZoomTool\",\"id\":\"p1360\",\"attributes\":{\"overlay\":{\"type\":\"object\",\"name\":\"BoxAnnotation\",\"id\":\"p1361\",\"attributes\":{\"syncable\":false,\"line_color\":\"black\",\"line_alpha\":1.0,\"line_width\":2,\"line_dash\":[4,4],\"fill_color\":\"lightgrey\",\"fill_alpha\":0.5,\"level\":\"overlay\",\"visible\":false,\"left\":{\"type\":\"number\",\"value\":\"nan\"},\"right\":{\"type\":\"number\",\"value\":\"nan\"},\"top\":{\"type\":\"number\",\"value\":\"nan\"},\"bottom\":{\"type\":\"number\",\"value\":\"nan\"},\"left_units\":\"canvas\",\"right_units\":\"canvas\",\"top_units\":\"canvas\",\"bottom_units\":\"canvas\",\"handles\":{\"type\":\"object\",\"name\":\"BoxInteractionHandles\",\"id\":\"p1367\",\"attributes\":{\"all\":{\"type\":\"object\",\"name\":\"AreaVisuals\",\"id\":\"p1366\",\"attributes\":{\"fill_color\":\"white\",\"hover_fill_color\":\"lightgray\"}}}}}}}},{\"type\":\"object\",\"name\":\"ResetTool\",\"id\":\"p1368\"}],\"active_drag\":{\"id\":\"p1359\"},\"active_scroll\":{\"id\":\"p1322\"}}},\"left\":[{\"type\":\"object\",\"name\":\"LinearAxis\",\"id\":\"p1353\",\"attributes\":{\"ticker\":{\"type\":\"object\",\"name\":\"BasicTicker\",\"id\":\"p1354\",\"attributes\":{\"mantissas\":[1,2,5]}},\"formatter\":{\"type\":\"object\",\"name\":\"BasicTickFormatter\",\"id\":\"p1355\"},\"axis_label\":\"Temperature (\\u00b0F)\",\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p1356\"}}}],\"below\":[{\"type\":\"object\",\"name\":\"DatetimeAxis\",\"id\":\"p1336\",\"attributes\":{\"ticker\":{\"type\":\"object\",\"name\":\"DatetimeTicker\",\"id\":\"p1337\",\"attributes\":{\"num_minor_ticks\":5,\"tickers\":[{\"type\":\"object\",\"name\":\"AdaptiveTicker\",\"id\":\"p1338\",\"attributes\":{\"num_minor_ticks\":0,\"mantissas\":[1,2,5],\"max_interval\":500.0}},{\"type\":\"object\",\"name\":\"AdaptiveTicker\",\"id\":\"p1339\",\"attributes\":{\"num_minor_ticks\":0,\"base\":60,\"mantissas\":[1,2,5,10,15,20,30],\"min_interval\":1000.0,\"max_interval\":1800000.0}},{\"type\":\"object\",\"name\":\"AdaptiveTicker\",\"id\":\"p1340\",\"attributes\":{\"num_minor_ticks\":0,\"base\":24,\"mantissas\":[1,2,4,6,8,12],\"min_interval\":3600000.0,\"max_interval\":43200000.0}},{\"type\":\"object\",\"name\":\"DaysTicker\",\"id\":\"p1341\",\"attributes\":{\"days\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31]}},{\"type\":\"object\",\"name\":\"DaysTicker\",\"id\":\"p1342\",\"attributes\":{\"days\":[1,4,7,10,13,16,19,22,25,28]}},{\"type\":\"object\",\"name\":\"DaysTicker\",\"id\":\"p1343\",\"attributes\":{\"days\":[1,8,15,22]}},{\"type\":\"object\",\"name\":\"DaysTicker\",\"id\":\"p1344\",\"attributes\":{\"days\":[1,15]}},{\"type\":\"object\",\"name\":\"MonthsTicker\",\"id\":\"p1345\",\"attributes\":{\"months\":[0,1,2,3,4,5,6,7,8,9,10,11]}},{\"type\":\"object\",\"name\":\"MonthsTicker\",\"id\":\"p1346\",\"attributes\":{\"months\":[0,2,4,6,8,10]}},{\"type\":\"object\",\"name\":\"MonthsTicker\",\"id\":\"p1347\",\"attributes\":{\"months\":[0,4,8]}},{\"type\":\"object\",\"name\":\"MonthsTicker\",\"id\":\"p1348\",\"attributes\":{\"months\":[0,6]}},{\"type\":\"object\",\"name\":\"YearsTicker\",\"id\":\"p1349\"}]}},\"formatter\":{\"type\":\"object\",\"name\":\"DatetimeTickFormatter\",\"id\":\"p1350\"},\"axis_label\":\"Date\",\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p1351\"}}}],\"center\":[{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p1352\",\"attributes\":{\"axis\":{\"id\":\"p1336\"},\"grid_line_color\":null}},{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p1357\",\"attributes\":{\"dimension\":1,\"axis\":{\"id\":\"p1353\"},\"grid_line_color\":null}}],\"min_border_top\":10,\"min_border_bottom\":10,\"min_border_left\":10,\"min_border_right\":10,\"output_backend\":\"webgl\"}},{\"type\":\"object\",\"name\":\"Spacer\",\"id\":\"p1383\",\"attributes\":{\"name\":\"HSpacer01282\",\"stylesheets\":[\"\\n:host(.pn-loading):before, .pn-loading:before {\\n  background-color: #c3c3c3;\\n  mask-size: auto calc(min(50%, 400px));\\n  -webkit-mask-size: auto calc(min(50%, 400px));\\n}\",{\"id\":\"p1315\"},{\"id\":\"p1313\"},{\"id\":\"p1314\"}],\"margin\":0,\"sizing_mode\":\"stretch_width\",\"align\":\"start\"}}]}}],\"defs\":[{\"type\":\"model\",\"name\":\"ReactiveHTML1\"},{\"type\":\"model\",\"name\":\"FlexBox1\",\"properties\":[{\"name\":\"align_content\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"align_items\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"flex_direction\",\"kind\":\"Any\",\"default\":\"row\"},{\"name\":\"flex_wrap\",\"kind\":\"Any\",\"default\":\"wrap\"},{\"name\":\"gap\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"justify_content\",\"kind\":\"Any\",\"default\":\"flex-start\"}]},{\"type\":\"model\",\"name\":\"FloatPanel1\",\"properties\":[{\"name\":\"config\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"contained\",\"kind\":\"Any\",\"default\":true},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"right-top\"},{\"name\":\"offsetx\",\"kind\":\"Any\",\"default\":null},{\"name\":\"offsety\",\"kind\":\"Any\",\"default\":null},{\"name\":\"theme\",\"kind\":\"Any\",\"default\":\"primary\"},{\"name\":\"status\",\"kind\":\"Any\",\"default\":\"normalized\"}]},{\"type\":\"model\",\"name\":\"GridStack1\",\"properties\":[{\"name\":\"mode\",\"kind\":\"Any\",\"default\":\"warn\"},{\"name\":\"ncols\",\"kind\":\"Any\",\"default\":null},{\"name\":\"nrows\",\"kind\":\"Any\",\"default\":null},{\"name\":\"allow_resize\",\"kind\":\"Any\",\"default\":true},{\"name\":\"allow_drag\",\"kind\":\"Any\",\"default\":true},{\"name\":\"state\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"drag1\",\"properties\":[{\"name\":\"slider_width\",\"kind\":\"Any\",\"default\":5},{\"name\":\"slider_color\",\"kind\":\"Any\",\"default\":\"black\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":50}]},{\"type\":\"model\",\"name\":\"click1\",\"properties\":[{\"name\":\"terminal_output\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"debug_name\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"clears\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"FastWrapper1\",\"properties\":[{\"name\":\"object\",\"kind\":\"Any\",\"default\":null},{\"name\":\"style\",\"kind\":\"Any\",\"default\":null}]},{\"type\":\"model\",\"name\":\"NotificationAreaBase1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"NotificationArea1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"notifications\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0},{\"name\":\"types\",\"kind\":\"Any\",\"default\":[{\"type\":\"map\",\"entries\":[[\"type\",\"warning\"],[\"background\",\"#ffc107\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-exclamation-triangle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]},{\"type\":\"map\",\"entries\":[[\"type\",\"info\"],[\"background\",\"#007bff\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-info-circle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]}]}]},{\"type\":\"model\",\"name\":\"Notification\",\"properties\":[{\"name\":\"background\",\"kind\":\"Any\",\"default\":null},{\"name\":\"duration\",\"kind\":\"Any\",\"default\":3000},{\"name\":\"icon\",\"kind\":\"Any\",\"default\":null},{\"name\":\"message\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"notification_type\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_destroyed\",\"kind\":\"Any\",\"default\":false}]},{\"type\":\"model\",\"name\":\"TemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"BootstrapTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"TemplateEditor1\",\"properties\":[{\"name\":\"layout\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"MaterialTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"ReactiveESM1\"},{\"type\":\"model\",\"name\":\"JSComponent1\"},{\"type\":\"model\",\"name\":\"ReactComponent1\"},{\"type\":\"model\",\"name\":\"AnyWidgetComponent1\"},{\"type\":\"model\",\"name\":\"request_value1\",\"properties\":[{\"name\":\"fill\",\"kind\":\"Any\",\"default\":\"none\"},{\"name\":\"_synced\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_request_sync\",\"kind\":\"Any\",\"default\":0}]}]}};\n",
              "  var render_items = [{\"docid\":\"3a7ff180-cd4d-42e8-a9ce-015811f4a265\",\"roots\":{\"p1312\":\"e9a37220-f923-446c-9f0b-46bc4a25fd12\"},\"root_ids\":[\"p1312\"]}];\n",
              "  var docs = Object.values(docs_json)\n",
              "  if (!docs) {\n",
              "    return\n",
              "  }\n",
              "  const py_version = docs[0].version.replace('rc', '-rc.').replace('.dev', '-dev.')\n",
              "  async function embed_document(root) {\n",
              "    var Bokeh = get_bokeh(root)\n",
              "    await Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
              "    for (const render_item of render_items) {\n",
              "      for (const root_id of render_item.root_ids) {\n",
              "\tconst id_el = document.getElementById(root_id)\n",
              "\tif (id_el.children.length && id_el.children[0].hasAttribute('data-root-id')) {\n",
              "\t  const root_el = id_el.children[0]\n",
              "\t  root_el.id = root_el.id + '-rendered'\n",
              "\t  for (const child of root_el.children) {\n",
              "            // Ensure JupyterLab does not capture keyboard shortcuts\n",
              "            // see: https://jupyterlab.readthedocs.io/en/4.1.x/extension/notebook.html#keyboard-interaction-model\n",
              "\t    child.setAttribute('data-lm-suppress-shortcuts', 'true')\n",
              "\t  }\n",
              "\t}\n",
              "      }\n",
              "    }\n",
              "  }\n",
              "  function get_bokeh(root) {\n",
              "    if (root.Bokeh === undefined) {\n",
              "      return null\n",
              "    } else if (root.Bokeh.version !== py_version) {\n",
              "      if (root.Bokeh.versions === undefined || !root.Bokeh.versions.has(py_version)) {\n",
              "\treturn null\n",
              "      }\n",
              "      return root.Bokeh.versions.get(py_version);\n",
              "    } else if (root.Bokeh.version === py_version) {\n",
              "      return root.Bokeh\n",
              "    }\n",
              "    return null\n",
              "  }\n",
              "  function is_loaded(root) {\n",
              "    var Bokeh = get_bokeh(root)\n",
              "    return (Bokeh != null && Bokeh.Panel !== undefined)\n",
              "  }\n",
              "  if (is_loaded(root)) {\n",
              "    embed_document(root);\n",
              "  } else {\n",
              "    var attempts = 0;\n",
              "    var timer = setInterval(function(root) {\n",
              "      if (is_loaded(root)) {\n",
              "        clearInterval(timer);\n",
              "        embed_document(root);\n",
              "      } else if (document.readyState == \"complete\") {\n",
              "        attempts++;\n",
              "        if (attempts > 200) {\n",
              "          clearInterval(timer);\n",
              "\t  var Bokeh = get_bokeh(root)\n",
              "\t  if (Bokeh == null || Bokeh.Panel == null) {\n",
              "            console.warn(\"Panel: ERROR: Unable to run Panel code because Bokeh or Panel library is missing\");\n",
              "\t  } else {\n",
              "\t    console.warn(\"Panel: WARNING: Attempting to render but not all required libraries could be resolved.\")\n",
              "\t    embed_document(root)\n",
              "\t  }\n",
              "        }\n",
              "      }\n",
              "    }, 25, root)\n",
              "  }\n",
              "})(window);</script>"
            ],
            "text/plain": [
              ":Curve   [DATE]   (TOBS_F)"
            ]
          },
          "execution_count": 19,
          "metadata": {
            "application/vnd.holoviews_exec.v0+json": {
              "id": "p1312"
            }
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Plot the annual data interactively with hvplot\n",
        "ann_temp_plot = ann_climate_df.hvplot(\n",
        "    y='TOBS_F', color='red',\n",
        "    title='Annual Average Temperature in Boulder, Colorado, 1893 - 2023',\n",
        "    xlabel='Date',\n",
        "    ylabel='Temperature (°F)', legend=False) \n",
        "ann_temp_plot"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "fdc24928-65f1-4176-b8f9-eacca630730f",
      "metadata": {},
      "source": [
        "<link rel=\"stylesheet\" type=\"text/css\" href=\"./assets/styles.css\"><div class=\"callout callout-style-default callout-titled callout-task\"><div class=\"callout-header\"><div class=\"callout-icon-container\"><i class=\"callout-icon\"></i></div><div class=\"callout-title-container flex-fill\">Try It: Explore the data</div></div><div class=\"callout-body-container callout-body\"><ol type=\"1\">\n",
        "<li><p>Create a new Markdown cell below this one.</p></li>\n",
        "<li><p>Hover over the lowest point on your plot. What is the overall\n",
        "minimum annual average temperature?</p></li>\n",
        "</ol></div></div>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b7fb23d9",
      "metadata": {},
      "source": [
        "The year with the lowest average temperature was 1994, with a mean daily temperature of 38.7 °F."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "42513d90-2d8a-4c42-a1bc-a66194d8951e",
      "metadata": {},
      "source": [
        "## BONUS: Save your work\n",
        "\n",
        "You will need to save your analyses and plots to tell others about what\n",
        "you find.\n",
        "\n",
        "<link rel=\"stylesheet\" type=\"text/css\" href=\"./assets/styles.css\"><div class=\"callout callout-style-default callout-titled callout-task\"><div class=\"callout-header\"><div class=\"callout-icon-container\"><i class=\"callout-icon\"></i></div><div class=\"callout-title-container flex-fill\">Try It: Save Your Plot</div></div><div class=\"callout-body-container callout-body\"><p>Just like with any other type of object in Python, if you want to\n",
        "reuse your work, you need to give it a name.</p>\n",
        "<ol type=\"1\">\n",
        "<li>Go back to your <code>hvplot</code> code, and give your plot a name\n",
        "by assigning it to a variable. HINT: if you still want your plot to\n",
        "display in your notebook, make sure to <strong>call</strong> its name at\n",
        "the end of the cell.</li>\n",
        "<li>Replace <code>my_plot</code> with the name you gave to your\n",
        "plot.</li>\n",
        "<li>Replace <code>'my_plot.html'</code> with the name you want for your\n",
        "plot. If you change the file extension, <code>.html</code>, to\n",
        "<code>.png</code>, you will get an image instead of an interactive\n",
        "webpage, provided you have the necessary libraries installed.</li>\n",
        "</ol>\n",
        "<p>Once you run the code, you should see your saved plot in your files –\n",
        "go ahead and open it up.</p>\n",
        "<div data-__quarto_custom=\"true\" data-__quarto_custom_type=\"Callout\"\n",
        "data-__quarto_custom_context=\"Block\" data-__quarto_custom_id=\"8\">\n",
        "<div data-__quarto_custom_scaffold=\"true\">\n",
        "\n",
        "</div>\n",
        "<div data-__quarto_custom_scaffold=\"true\">\n",
        "<p>You may need to right-click on your file and download it to be able\n",
        "to view it.</p>\n",
        "</div>\n",
        "</div></div></div>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "id": "3ca1b32d",
      "metadata": {
        "template": "student"
      },
      "outputs": [],
      "source": [
        "hv.save(ann_temp_plot, 'boulder_ann_temp_plot.html')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "id": "12175da4",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Stored 'ann_climate_df' (DataFrame)\n",
            "Stored 'ann_temp_plot' (Curve)\n"
          ]
        }
      ],
      "source": [
        "%store ann_climate_df ann_temp_plot"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "base",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.6"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}