From be4b363d08b4bb0180c80fbf0da34f2da095402c Mon Sep 17 00:00:00 2001
From: Serge Rey <sjsrey@gmail.com>
Date: Wed, 18 Jan 2023 20:32:21 -0800
Subject: [PATCH 01/28] add skater_reg to api

---
 docs/api.rst | 1 +
 1 file changed, 1 insertion(+)

diff --git a/docs/api.rst b/docs/api.rst
index e70fc232..7ca22b87 100644
--- a/docs/api.rst
+++ b/docs/api.rst
@@ -59,6 +59,7 @@ Regimes models are variants of spatial regression models which allow for structu
     spreg.GM_Endog_Error_Regimes
     spreg.GM_Endog_Error_Hom_Regimes
     spreg.GM_Endog_Error_Het_Regimes
+    spreg.Skater_reg
 
 Seemingly-Unrelated Regressions
 --------------------------------

From 8882826fd4f54f0f0eb57b4847271abe3b6f1334 Mon Sep 17 00:00:00 2001
From: Pedro Amaral <pedro@Pedros-MacBook-Pro.local>
Date: Fri, 24 Mar 2023 13:31:31 -0500
Subject: [PATCH 02/28] Revision of Skater_reg's example notebook and its
 inclusion in the docs.

---
 docs/tutorials.rst         |     6 +
 notebooks/skater_reg.ipynb | 43367 +----------------------------------
 2 files changed, 395 insertions(+), 42978 deletions(-)

diff --git a/docs/tutorials.rst b/docs/tutorials.rst
index a2a450a0..b3f6e1b1 100644
--- a/docs/tutorials.rst
+++ b/docs/tutorials.rst
@@ -15,3 +15,9 @@ Tutorials
     :caption: Spatial Panel Models with Fixed Effects
     
     notebooks/Panel_FE_example.ipynb
+
+.. toctree::
+    :maxdepth: 1
+    :caption: Skater_reg: Endogenous Spatial Regimes
+    
+    notebooks/skater_reg.ipynb
diff --git a/notebooks/skater_reg.ipynb b/notebooks/skater_reg.ipynb
index 4427a740..38f05ac6 100644
--- a/notebooks/skater_reg.ipynb
+++ b/notebooks/skater_reg.ipynb
@@ -2,6 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
+   "id": "0a500c14",
    "metadata": {},
    "source": [
     "# Skater Regression"
@@ -9,6 +10,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "1847d3c6",
    "metadata": {},
    "source": [
     "### This notebook shows the use of the Skater Regression funcion (Skater_reg), introduced by Anselin & Amaral (2021). For more information on the method, check:\n",
@@ -16,1076 +18,109 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 1,
+   "cell_type": "markdown",
+   "id": "e4afc8e0",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/opt/anaconda3/envs/analysis/lib/python3.9/site-packages/geopandas/_compat.py:106: UserWarning: The Shapely GEOS version (3.9.1dev-CAPI-1.14.1) is incompatible with the GEOS version PyGEOS was compiled with (3.9.1-CAPI-1.14.2). Conversions between both will be slow.\n",
-      "  warnings.warn(\n"
-     ]
-    }
-   ],
    "source": [
-    "# Required imports\n",
-    "import libpysal as ps\n",
-    "import numpy as np\n",
-    "import spreg\n",
-    "from spreg.skater_reg import Skater_reg"
+    "In this example, in addition to the required packages, we will use geopandas to load the data and matplotlib to plot the results. Alternatively, PySAL's own IO could also be used to load the data."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
+   "id": "a7a0de53",
    "metadata": {},
    "outputs": [],
    "source": [
+    "# Required imports\n",
+    "import libpysal as ps\n",
+    "import numpy as np\n",
+    "import spreg\n",
+    "from spreg.skater_reg import Skater_reg\n",
+    "\n",
     "# Optional imports\n",
-    "import geopandas as gpd\n",
-    "import pandas as pd\n",
-    "import plotly.express as px\n",
-    "import plotly.graph_objects as go"
+    "import matplotlib.pyplot as plt\n",
+    "import geopandas as gpd"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "c01ad58d",
    "metadata": {},
    "source": [
-    "### To load the data, we can use pure PySAL or (Geo)Pandas\n",
-    "### Data can be any discrete spatial objects such as points or polygons. Here we use Columbus for simplicity"
+    "We use Messner et al. (2000) data on homicides and selected socio-economic characteristics for continental U.S. counties to exemplify the use of Skater_reg. It can be downloaded from PySAL's examples repository."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
+   "id": "52f82039",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Pure PySAL alternative\n",
-    "data = ps.io.open(ps.examples.get_path('columbus.dbf'))\n",
-    "y = np.array(data.by_col('HOVAL')).reshape((-1,1))\n",
-    "x = np.array([data.by_col(name) for name in ['INC','CRIME']]).T\n",
-    "w = ps.weights.Queen.from_shapefile(ps.examples.get_path(\"columbus.shp\"))"
+    "# Load the example from PySAL\n",
+    "ps.examples.load_example(\"NCOVR\")\n",
+    "data = gpd.read_file(ps.examples.get_path('NAT.shp')).set_index('FIPS')\n",
+    "\n",
+    "# Set depedent and independent variables and the W matrix.\n",
+    "y = data['HR90'].to_numpy()\n",
+    "x = data[['RD90','PS90','UE90']].to_numpy()\n",
+    "w = ps.weights.Queen.from_dataframe(data, use_index=True)"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 4,
+   "cell_type": "markdown",
+   "id": "25be6fcf",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "# GeoPandas approach\n",
-    "data = gpd.read_file(ps.examples.get_path('columbus.shp'))\n",
-    "y = data['HOVAL'].to_numpy()\n",
-    "x = data[['INC','CRIME']].to_numpy()\n",
-    "w = ps.weights.Queen.from_dataframe(data)"
+    "Skater_reg by default uses Euclidean distance to compute the Minimum Spanning Tree (MST). Therefore, we standardize the variables that will be used to compute the MST before calling the main Skater_reg function. Here, we use the X variables to compute the MST. Alternative specifications can be used.\n",
+    "\n",
+    "We set the number of clusters to 20 and minimum quorum to 100."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
+   "id": "ae2bd006",
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "array([0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 2, 2, 1, 2, 0,\n",
-       "       1, 2, 0, 0, 3, 3, 0, 0, 2, 1, 3, 2, 3, 2, 3, 0, 2, 1, 1, 2, 3, 3,\n",
-       "       3, 2, 1, 3, 3], dtype=int32)"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 1min 33s, sys: 31.3 s, total: 2min 4s\n",
+      "Wall time: 34.7 s\n"
+     ]
     }
    ],
    "source": [
+    "%%time\n",
     "# Standardize the variables to be used to compute the minimum spanning tree (could add/remove any variable)\n",
     "x_std = (x - np.mean(x,axis=0)) / np.std(x,axis=0)\n",
     "\n",
     "# Call the Skater_reg method based on OLS\n",
-    "results = Skater_reg().fit(4, w, x_std, {'reg':spreg.OLS,'y':y,'x':x}, quorum=10)\n",
-    "\n",
-    "# The cluster allocations from the final step are stored in the attribute current_labels_\n",
-    "results.current_labels_"
+    "results = Skater_reg().fit(20, w, x_std, {'reg':spreg.OLS,'y':y,'x':x}, quorum=100)"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "53218349",
    "metadata": {},
    "source": [
-    "### Optionally, to see the decrease in the Sum of Squared Residuals (SSR), we can use the trace from Skater_reg to plot the SSR from each step"
+    "The intermediate steps are stored in the attibute \\_trace. We can use this information to plot the decrease in the total sum of squared residuals by number of clusters. This information can be helpful to select the number of desired clusters."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
+   "id": "4e862e49",
    "metadata": {},
    "outputs": [
     {
      "data": {
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-       "        define('plotly', function(require, exports, module) {\n",
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-       "* plotly.js v2.2.0\n",
-       "* Copyright 2012-2021, Plotly, Inc.\n",
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this._expand(r+1),this.words[r]=e?this.words[r]|1<<i:this.words[r]&~(1<<i),this.strip()},a.prototype.iadd=function(t){var e,r,n;if(0!==this.negative&&0===t.negative)return this.negative=0,e=this.isub(t),this.negative^=1,this._normSign();if(0===this.negative&&0!==t.negative)return t.negative=0,e=this.isub(t),t.negative=1,e._normSign();this.length>t.length?(r=this,n=t):(r=t,n=this);for(var i=0,a=0;a<n.length;a++)e=(0|r.words[a])+(0|n.words[a])+i,this.words[a]=67108863&e,i=e>>>26;for(;0!==i&&a<r.length;a++)e=(0|r.words[a])+i,this.words[a]=67108863&e,i=e>>>26;if(this.length=r.length,0!==i)this.words[this.length]=i,this.length++;else if(r!==this)for(;a<r.length;a++)this.words[a]=r.words[a];return this},a.prototype.add=function(t){var e;return 0!==t.negative&&0===this.negative?(t.negative=0,e=this.sub(t),t.negative^=1,e):0===t.negative&&0!==this.negative?(this.negative=0,e=t.sub(this),this.negative=1,e):this.length>t.length?this.clone().iadd(t):t.clone().iadd(this)},a.prototype.isub=function(t){if(0!==t.negative){t.negative=0;var e=this.iadd(t);return t.negative=1,e._normSign()}if(0!==this.negative)return this.negative=0,this.iadd(t),this.negative=1,this._normSign();var r,n,i=this.cmp(t);if(0===i)return this.negative=0,this.length=1,this.words[0]=0,this;i>0?(r=this,n=t):(r=t,n=this);for(var a=0,o=0;o<n.length;o++)a=(e=(0|r.words[o])-(0|n.words[o])+a)>>26,this.words[o]=67108863&e;for(;0!==a&&o<r.length;o++)a=(e=(0|r.words[o])+a)>>26,this.words[o]=67108863&e;if(0===a&&o<r.length&&r!==this)for(;o<r.length;o++)this.words[o]=r.words[o];return this.length=Math.max(this.length,o),r!==this&&(this.negative=1),this.strip()},a.prototype.sub=function(t){return this.clone().isub(t)};var p=function(t,e,r){var n,i,a,o=t.words,s=e.words,l=r.words,c=0,u=0|o[0],f=8191&u,h=u>>>13,p=0|o[1],d=8191&p,m=p>>>13,g=0|o[2],v=8191&g,y=g>>>13,x=0|o[3],b=8191&x,_=x>>>13,w=0|o[4],T=8191&w,k=w>>>13,M=0|o[5],A=8191&M,S=M>>>13,E=0|o[6],L=8191&E,C=E>>>13,P=0|o[7],I=8191&P,O=P>>>13,z=0|o[8],D=8191&z,R=z>>>13,F=0|o[9],B=8191&F,N=F>>>13,j=0|s[0],U=8191&j,V=j>>>13,q=0|s[1],H=8191&q,G=q>>>13,Y=0|s[2],W=8191&Y,X=Y>>>13,Z=0|s[3],J=8191&Z,K=Z>>>13,Q=0|s[4],$=8191&Q,tt=Q>>>13,et=0|s[5],rt=8191&et,nt=et>>>13,it=0|s[6],at=8191&it,ot=it>>>13,st=0|s[7],lt=8191&st,ct=st>>>13,ut=0|s[8],ft=8191&ut,ht=ut>>>13,pt=0|s[9],dt=8191&pt,mt=pt>>>13;r.negative=t.negative^e.negative,r.length=19;var gt=(c+(n=Math.imul(f,U))|0)+((8191&(i=(i=Math.imul(f,V))+Math.imul(h,U)|0))<<13)|0;c=((a=Math.imul(h,V))+(i>>>13)|0)+(gt>>>26)|0,gt&=67108863,n=Math.imul(d,U),i=(i=Math.imul(d,V))+Math.imul(m,U)|0,a=Math.imul(m,V);var vt=(c+(n=n+Math.imul(f,H)|0)|0)+((8191&(i=(i=i+Math.imul(f,G)|0)+Math.imul(h,H)|0))<<13)|0;c=((a=a+Math.imul(h,G)|0)+(i>>>13)|0)+(vt>>>26)|0,vt&=67108863,n=Math.imul(v,U),i=(i=Math.imul(v,V))+Math.imul(y,U)|0,a=Math.imul(y,V),n=n+Math.imul(d,H)|0,i=(i=i+Math.imul(d,G)|0)+Math.imul(m,H)|0,a=a+Math.imul(m,G)|0;var yt=(c+(n=n+Math.imul(f,W)|0)|0)+((8191&(i=(i=i+Math.imul(f,X)|0)+Math.imul(h,W)|0))<<13)|0;c=((a=a+Math.imul(h,X)|0)+(i>>>13)|0)+(yt>>>26)|0,yt&=67108863,n=Math.imul(b,U),i=(i=Math.imul(b,V))+Math.imul(_,U)|0,a=Math.imul(_,V),n=n+Math.imul(v,H)|0,i=(i=i+Math.imul(v,G)|0)+Math.imul(y,H)|0,a=a+Math.imul(y,G)|0,n=n+Math.imul(d,W)|0,i=(i=i+Math.imul(d,X)|0)+Math.imul(m,W)|0,a=a+Math.imul(m,X)|0;var xt=(c+(n=n+Math.imul(f,J)|0)|0)+((8191&(i=(i=i+Math.imul(f,K)|0)+Math.imul(h,J)|0))<<13)|0;c=((a=a+Math.imul(h,K)|0)+(i>>>13)|0)+(xt>>>26)|0,xt&=67108863,n=Math.imul(T,U),i=(i=Math.imul(T,V))+Math.imul(k,U)|0,a=Math.imul(k,V),n=n+Math.imul(b,H)|0,i=(i=i+Math.imul(b,G)|0)+Math.imul(_,H)|0,a=a+Math.imul(_,G)|0,n=n+Math.imul(v,W)|0,i=(i=i+Math.imul(v,X)|0)+Math.imul(y,W)|0,a=a+Math.imul(y,X)|0,n=n+Math.imul(d,J)|0,i=(i=i+Math.imul(d,K)|0)+Math.imul(m,J)|0,a=a+Math.imul(m,K)|0;var bt=(c+(n=n+Math.imul(f,$)|0)|0)+((8191&(i=(i=i+Math.imul(f,tt)|0)+Math.imul(h,$)|0))<<13)|0;c=((a=a+Math.imul(h,tt)|0)+(i>>>13)|0)+(bt>>>26)|0,bt&=67108863,n=Math.imul(A,U),i=(i=Math.imul(A,V))+Math.imul(S,U)|0,a=Math.imul(S,V),n=n+Math.imul(T,H)|0,i=(i=i+Math.imul(T,G)|0)+Math.imul(k,H)|0,a=a+Math.imul(k,G)|0,n=n+Math.imul(b,W)|0,i=(i=i+Math.imul(b,X)|0)+Math.imul(_,W)|0,a=a+Math.imul(_,X)|0,n=n+Math.imul(v,J)|0,i=(i=i+Math.imul(v,K)|0)+Math.imul(y,J)|0,a=a+Math.imul(y,K)|0,n=n+Math.imul(d,$)|0,i=(i=i+Math.imul(d,tt)|0)+Math.imul(m,$)|0,a=a+Math.imul(m,tt)|0;var _t=(c+(n=n+Math.imul(f,rt)|0)|0)+((8191&(i=(i=i+Math.imul(f,nt)|0)+Math.imul(h,rt)|0))<<13)|0;c=((a=a+Math.imul(h,nt)|0)+(i>>>13)|0)+(_t>>>26)|0,_t&=67108863,n=Math.imul(L,U),i=(i=Math.imul(L,V))+Math.imul(C,U)|0,a=Math.imul(C,V),n=n+Math.imul(A,H)|0,i=(i=i+Math.imul(A,G)|0)+Math.imul(S,H)|0,a=a+Math.imul(S,G)|0,n=n+Math.imul(T,W)|0,i=(i=i+Math.imul(T,X)|0)+Math.imul(k,W)|0,a=a+Math.imul(k,X)|0,n=n+Math.imul(b,J)|0,i=(i=i+Math.imul(b,K)|0)+Math.imul(_,J)|0,a=a+Math.imul(_,K)|0,n=n+Math.imul(v,$)|0,i=(i=i+Math.imul(v,tt)|0)+Math.imul(y,$)|0,a=a+Math.imul(y,tt)|0,n=n+Math.imul(d,rt)|0,i=(i=i+Math.imul(d,nt)|0)+Math.imul(m,rt)|0,a=a+Math.imul(m,nt)|0;var wt=(c+(n=n+Math.imul(f,at)|0)|0)+((8191&(i=(i=i+Math.imul(f,ot)|0)+Math.imul(h,at)|0))<<13)|0;c=((a=a+Math.imul(h,ot)|0)+(i>>>13)|0)+(wt>>>26)|0,wt&=67108863,n=Math.imul(I,U),i=(i=Math.imul(I,V))+Math.imul(O,U)|0,a=Math.imul(O,V),n=n+Math.imul(L,H)|0,i=(i=i+Math.imul(L,G)|0)+Math.imul(C,H)|0,a=a+Math.imul(C,G)|0,n=n+Math.imul(A,W)|0,i=(i=i+Math.imul(A,X)|0)+Math.imul(S,W)|0,a=a+Math.imul(S,X)|0,n=n+Math.imul(T,J)|0,i=(i=i+Math.imul(T,K)|0)+Math.imul(k,J)|0,a=a+Math.imul(k,K)|0,n=n+Math.imul(b,$)|0,i=(i=i+Math.imul(b,tt)|0)+Math.imul(_,$)|0,a=a+Math.imul(_,tt)|0,n=n+Math.imul(v,rt)|0,i=(i=i+Math.imul(v,nt)|0)+Math.imul(y,rt)|0,a=a+Math.imul(y,nt)|0,n=n+Math.imul(d,at)|0,i=(i=i+Math.imul(d,ot)|0)+Math.imul(m,at)|0,a=a+Math.imul(m,ot)|0;var Tt=(c+(n=n+Math.imul(f,lt)|0)|0)+((8191&(i=(i=i+Math.imul(f,ct)|0)+Math.imul(h,lt)|0))<<13)|0;c=((a=a+Math.imul(h,ct)|0)+(i>>>13)|0)+(Tt>>>26)|0,Tt&=67108863,n=Math.imul(D,U),i=(i=Math.imul(D,V))+Math.imul(R,U)|0,a=Math.imul(R,V),n=n+Math.imul(I,H)|0,i=(i=i+Math.imul(I,G)|0)+Math.imul(O,H)|0,a=a+Math.imul(O,G)|0,n=n+Math.imul(L,W)|0,i=(i=i+Math.imul(L,X)|0)+Math.imul(C,W)|0,a=a+Math.imul(C,X)|0,n=n+Math.imul(A,J)|0,i=(i=i+Math.imul(A,K)|0)+Math.imul(S,J)|0,a=a+Math.imul(S,K)|0,n=n+Math.imul(T,$)|0,i=(i=i+Math.imul(T,tt)|0)+Math.imul(k,$)|0,a=a+Math.imul(k,tt)|0,n=n+Math.imul(b,rt)|0,i=(i=i+Math.imul(b,nt)|0)+Math.imul(_,rt)|0,a=a+Math.imul(_,nt)|0,n=n+Math.imul(v,at)|0,i=(i=i+Math.imul(v,ot)|0)+Math.imul(y,at)|0,a=a+Math.imul(y,ot)|0,n=n+Math.imul(d,lt)|0,i=(i=i+Math.imul(d,ct)|0)+Math.imul(m,lt)|0,a=a+Math.imul(m,ct)|0;var kt=(c+(n=n+Math.imul(f,ft)|0)|0)+((8191&(i=(i=i+Math.imul(f,ht)|0)+Math.imul(h,ft)|0))<<13)|0;c=((a=a+Math.imul(h,ht)|0)+(i>>>13)|0)+(kt>>>26)|0,kt&=67108863,n=Math.imul(B,U),i=(i=Math.imul(B,V))+Math.imul(N,U)|0,a=Math.imul(N,V),n=n+Math.imul(D,H)|0,i=(i=i+Math.imul(D,G)|0)+Math.imul(R,H)|0,a=a+Math.imul(R,G)|0,n=n+Math.imul(I,W)|0,i=(i=i+Math.imul(I,X)|0)+Math.imul(O,W)|0,a=a+Math.imul(O,X)|0,n=n+Math.imul(L,J)|0,i=(i=i+Math.imul(L,K)|0)+Math.imul(C,J)|0,a=a+Math.imul(C,K)|0,n=n+Math.imul(A,$)|0,i=(i=i+Math.imul(A,tt)|0)+Math.imul(S,$)|0,a=a+Math.imul(S,tt)|0,n=n+Math.imul(T,rt)|0,i=(i=i+Math.imul(T,nt)|0)+Math.imul(k,rt)|0,a=a+Math.imul(k,nt)|0,n=n+Math.imul(b,at)|0,i=(i=i+Math.imul(b,ot)|0)+Math.imul(_,at)|0,a=a+Math.imul(_,ot)|0,n=n+Math.imul(v,lt)|0,i=(i=i+Math.imul(v,ct)|0)+Math.imul(y,lt)|0,a=a+Math.imul(y,ct)|0,n=n+Math.imul(d,ft)|0,i=(i=i+Math.imul(d,ht)|0)+Math.imul(m,ft)|0,a=a+Math.imul(m,ht)|0;var Mt=(c+(n=n+Math.imul(f,dt)|0)|0)+((8191&(i=(i=i+Math.imul(f,mt)|0)+Math.imul(h,dt)|0))<<13)|0;c=((a=a+Math.imul(h,mt)|0)+(i>>>13)|0)+(Mt>>>26)|0,Mt&=67108863,n=Math.imul(B,H),i=(i=Math.imul(B,G))+Math.imul(N,H)|0,a=Math.imul(N,G),n=n+Math.imul(D,W)|0,i=(i=i+Math.imul(D,X)|0)+Math.imul(R,W)|0,a=a+Math.imul(R,X)|0,n=n+Math.imul(I,J)|0,i=(i=i+Math.imul(I,K)|0)+Math.imul(O,J)|0,a=a+Math.imul(O,K)|0,n=n+Math.imul(L,$)|0,i=(i=i+Math.imul(L,tt)|0)+Math.imul(C,$)|0,a=a+Math.imul(C,tt)|0,n=n+Math.imul(A,rt)|0,i=(i=i+Math.imul(A,nt)|0)+Math.imul(S,rt)|0,a=a+Math.imul(S,nt)|0,n=n+Math.imul(T,at)|0,i=(i=i+Math.imul(T,ot)|0)+Math.imul(k,at)|0,a=a+Math.imul(k,ot)|0,n=n+Math.imul(b,lt)|0,i=(i=i+Math.imul(b,ct)|0)+Math.imul(_,lt)|0,a=a+Math.imul(_,ct)|0,n=n+Math.imul(v,ft)|0,i=(i=i+Math.imul(v,ht)|0)+Math.imul(y,ft)|0,a=a+Math.imul(y,ht)|0;var At=(c+(n=n+Math.imul(d,dt)|0)|0)+((8191&(i=(i=i+Math.imul(d,mt)|0)+Math.imul(m,dt)|0))<<13)|0;c=((a=a+Math.imul(m,mt)|0)+(i>>>13)|0)+(At>>>26)|0,At&=67108863,n=Math.imul(B,W),i=(i=Math.imul(B,X))+Math.imul(N,W)|0,a=Math.imul(N,X),n=n+Math.imul(D,J)|0,i=(i=i+Math.imul(D,K)|0)+Math.imul(R,J)|0,a=a+Math.imul(R,K)|0,n=n+Math.imul(I,$)|0,i=(i=i+Math.imul(I,tt)|0)+Math.imul(O,$)|0,a=a+Math.imul(O,tt)|0,n=n+Math.imul(L,rt)|0,i=(i=i+Math.imul(L,nt)|0)+Math.imul(C,rt)|0,a=a+Math.imul(C,nt)|0,n=n+Math.imul(A,at)|0,i=(i=i+Math.imul(A,ot)|0)+Math.imul(S,at)|0,a=a+Math.imul(S,ot)|0,n=n+Math.imul(T,lt)|0,i=(i=i+Math.imul(T,ct)|0)+Math.imul(k,lt)|0,a=a+Math.imul(k,ct)|0,n=n+Math.imul(b,ft)|0,i=(i=i+Math.imul(b,ht)|0)+Math.imul(_,ft)|0,a=a+Math.imul(_,ht)|0;var St=(c+(n=n+Math.imul(v,dt)|0)|0)+((8191&(i=(i=i+Math.imul(v,mt)|0)+Math.imul(y,dt)|0))<<13)|0;c=((a=a+Math.imul(y,mt)|0)+(i>>>13)|0)+(St>>>26)|0,St&=67108863,n=Math.imul(B,J),i=(i=Math.imul(B,K))+Math.imul(N,J)|0,a=Math.imul(N,K),n=n+Math.imul(D,$)|0,i=(i=i+Math.imul(D,tt)|0)+Math.imul(R,$)|0,a=a+Math.imul(R,tt)|0,n=n+Math.imul(I,rt)|0,i=(i=i+Math.imul(I,nt)|0)+Math.imul(O,rt)|0,a=a+Math.imul(O,nt)|0,n=n+Math.imul(L,at)|0,i=(i=i+Math.imul(L,ot)|0)+Math.imul(C,at)|0,a=a+Math.imul(C,ot)|0,n=n+Math.imul(A,lt)|0,i=(i=i+Math.imul(A,ct)|0)+Math.imul(S,lt)|0,a=a+Math.imul(S,ct)|0,n=n+Math.imul(T,ft)|0,i=(i=i+Math.imul(T,ht)|0)+Math.imul(k,ft)|0,a=a+Math.imul(k,ht)|0;var Et=(c+(n=n+Math.imul(b,dt)|0)|0)+((8191&(i=(i=i+Math.imul(b,mt)|0)+Math.imul(_,dt)|0))<<13)|0;c=((a=a+Math.imul(_,mt)|0)+(i>>>13)|0)+(Et>>>26)|0,Et&=67108863,n=Math.imul(B,$),i=(i=Math.imul(B,tt))+Math.imul(N,$)|0,a=Math.imul(N,tt),n=n+Math.imul(D,rt)|0,i=(i=i+Math.imul(D,nt)|0)+Math.imul(R,rt)|0,a=a+Math.imul(R,nt)|0,n=n+Math.imul(I,at)|0,i=(i=i+Math.imul(I,ot)|0)+Math.imul(O,at)|0,a=a+Math.imul(O,ot)|0,n=n+Math.imul(L,lt)|0,i=(i=i+Math.imul(L,ct)|0)+Math.imul(C,lt)|0,a=a+Math.imul(C,ct)|0,n=n+Math.imul(A,ft)|0,i=(i=i+Math.imul(A,ht)|0)+Math.imul(S,ft)|0,a=a+Math.imul(S,ht)|0;var Lt=(c+(n=n+Math.imul(T,dt)|0)|0)+((8191&(i=(i=i+Math.imul(T,mt)|0)+Math.imul(k,dt)|0))<<13)|0;c=((a=a+Math.imul(k,mt)|0)+(i>>>13)|0)+(Lt>>>26)|0,Lt&=67108863,n=Math.imul(B,rt),i=(i=Math.imul(B,nt))+Math.imul(N,rt)|0,a=Math.imul(N,nt),n=n+Math.imul(D,at)|0,i=(i=i+Math.imul(D,ot)|0)+Math.imul(R,at)|0,a=a+Math.imul(R,ot)|0,n=n+Math.imul(I,lt)|0,i=(i=i+Math.imul(I,ct)|0)+Math.imul(O,lt)|0,a=a+Math.imul(O,ct)|0,n=n+Math.imul(L,ft)|0,i=(i=i+Math.imul(L,ht)|0)+Math.imul(C,ft)|0,a=a+Math.imul(C,ht)|0;var Ct=(c+(n=n+Math.imul(A,dt)|0)|0)+((8191&(i=(i=i+Math.imul(A,mt)|0)+Math.imul(S,dt)|0))<<13)|0;c=((a=a+Math.imul(S,mt)|0)+(i>>>13)|0)+(Ct>>>26)|0,Ct&=67108863,n=Math.imul(B,at),i=(i=Math.imul(B,ot))+Math.imul(N,at)|0,a=Math.imul(N,ot),n=n+Math.imul(D,lt)|0,i=(i=i+Math.imul(D,ct)|0)+Math.imul(R,lt)|0,a=a+Math.imul(R,ct)|0,n=n+Math.imul(I,ft)|0,i=(i=i+Math.imul(I,ht)|0)+Math.imul(O,ft)|0,a=a+Math.imul(O,ht)|0;var Pt=(c+(n=n+Math.imul(L,dt)|0)|0)+((8191&(i=(i=i+Math.imul(L,mt)|0)+Math.imul(C,dt)|0))<<13)|0;c=((a=a+Math.imul(C,mt)|0)+(i>>>13)|0)+(Pt>>>26)|0,Pt&=67108863,n=Math.imul(B,lt),i=(i=Math.imul(B,ct))+Math.imul(N,lt)|0,a=Math.imul(N,ct),n=n+Math.imul(D,ft)|0,i=(i=i+Math.imul(D,ht)|0)+Math.imul(R,ft)|0,a=a+Math.imul(R,ht)|0;var It=(c+(n=n+Math.imul(I,dt)|0)|0)+((8191&(i=(i=i+Math.imul(I,mt)|0)+Math.imul(O,dt)|0))<<13)|0;c=((a=a+Math.imul(O,mt)|0)+(i>>>13)|0)+(It>>>26)|0,It&=67108863,n=Math.imul(B,ft),i=(i=Math.imul(B,ht))+Math.imul(N,ft)|0,a=Math.imul(N,ht);var Ot=(c+(n=n+Math.imul(D,dt)|0)|0)+((8191&(i=(i=i+Math.imul(D,mt)|0)+Math.imul(R,dt)|0))<<13)|0;c=((a=a+Math.imul(R,mt)|0)+(i>>>13)|0)+(Ot>>>26)|0,Ot&=67108863;var zt=(c+(n=Math.imul(B,dt))|0)+((8191&(i=(i=Math.imul(B,mt))+Math.imul(N,dt)|0))<<13)|0;return c=((a=Math.imul(N,mt))+(i>>>13)|0)+(zt>>>26)|0,zt&=67108863,l[0]=gt,l[1]=vt,l[2]=yt,l[3]=xt,l[4]=bt,l[5]=_t,l[6]=wt,l[7]=Tt,l[8]=kt,l[9]=Mt,l[10]=At,l[11]=St,l[12]=Et,l[13]=Lt,l[14]=Ct,l[15]=Pt,l[16]=It,l[17]=Ot,l[18]=zt,0!==c&&(l[19]=c,r.length++),r};function d(t,e,r){return(new m).mulp(t,e,r)}function m(t,e){this.x=t,this.y=e}Math.imul||(p=h),a.prototype.mulTo=function(t,e){var r=this.length+t.length;return 10===this.length&&10===t.length?p(this,t,e):r<63?h(this,t,e):r<1024?function(t,e,r){r.negative=e.negative^t.negative,r.length=t.length+e.length;for(var n=0,i=0,a=0;a<r.length-1;a++){var o=i;i=0;for(var s=67108863&n,l=Math.min(a,e.length-1),c=Math.max(0,a-t.length+1);c<=l;c++){var u=a-c,f=(0|t.words[u])*(0|e.words[c]),h=67108863&f;s=67108863&(h=h+s|0),i+=(o=(o=o+(f/67108864|0)|0)+(h>>>26)|0)>>>26,o&=67108863}r.words[a]=s,n=o,o=i}return 0!==n?r.words[a]=n:r.length--,r.strip()}(this,t,e):d(this,t,e)},m.prototype.makeRBT=function(t){for(var e=new Array(t),r=a.prototype._countBits(t)-1,n=0;n<t;n++)e[n]=this.revBin(n,r,t);return e},m.prototype.revBin=function(t,e,r){if(0===t||t===r-1)return t;for(var n=0,i=0;i<e;i++)n|=(1&t)<<e-i-1,t>>=1;return n},m.prototype.permute=function(t,e,r,n,i,a){for(var o=0;o<a;o++)n[o]=e[t[o]],i[o]=r[t[o]]},m.prototype.transform=function(t,e,r,n,i,a){this.permute(a,t,e,r,n,i);for(var o=1;o<i;o<<=1)for(var s=o<<1,l=Math.cos(2*Math.PI/s),c=Math.sin(2*Math.PI/s),u=0;u<i;u+=s)for(var f=l,h=c,p=0;p<o;p++){var d=r[u+p],m=n[u+p],g=r[u+p+o],v=n[u+p+o],y=f*g-h*v;v=f*v+h*g,g=y,r[u+p]=d+g,n[u+p]=m+v,r[u+p+o]=d-g,n[u+p+o]=m-v,p!==s&&(y=l*f-c*h,h=l*h+c*f,f=y)}},m.prototype.guessLen13b=function(t,e){var r=1|Math.max(e,t),n=1&r,i=0;for(r=r/2|0;r;r>>>=1)i++;return 1<<i+1+n},m.prototype.conjugate=function(t,e,r){if(!(r<=1))for(var n=0;n<r/2;n++){var i=t[n];t[n]=t[r-n-1],t[r-n-1]=i,i=e[n],e[n]=-e[r-n-1],e[r-n-1]=-i}},m.prototype.normalize13b=function(t,e){for(var r=0,n=0;n<e/2;n++){var i=8192*Math.round(t[2*n+1]/e)+Math.round(t[2*n]/e)+r;t[n]=67108863&i,r=i<67108864?0:i/67108864|0}return t},m.prototype.convert13b=function(t,e,r,i){for(var a=0,o=0;o<e;o++)a+=0|t[o],r[2*o]=8191&a,a>>>=13,r[2*o+1]=8191&a,a>>>=13;for(o=2*e;o<i;++o)r[o]=0;n(0===a),n(0==(-8192&a))},m.prototype.stub=function(t){for(var e=new Array(t),r=0;r<t;r++)e[r]=0;return e},m.prototype.mulp=function(t,e,r){var n=2*this.guessLen13b(t.length,e.length),i=this.makeRBT(n),a=this.stub(n),o=new Array(n),s=new Array(n),l=new Array(n),c=new Array(n),u=new Array(n),f=new Array(n),h=r.words;h.length=n,this.convert13b(t.words,t.length,o,n),this.convert13b(e.words,e.length,c,n),this.transform(o,a,s,l,n,i),this.transform(c,a,u,f,n,i);for(var p=0;p<n;p++){var d=s[p]*u[p]-l[p]*f[p];l[p]=s[p]*f[p]+l[p]*u[p],s[p]=d}return this.conjugate(s,l,n),this.transform(s,l,h,a,n,i),this.conjugate(h,a,n),this.normalize13b(h,n),r.negative=t.negative^e.negative,r.length=t.length+e.length,r.strip()},a.prototype.mul=function(t){var e=new a(null);return e.words=new Array(this.length+t.length),this.mulTo(t,e)},a.prototype.mulf=function(t){var e=new a(null);return e.words=new Array(this.length+t.length),d(this,t,e)},a.prototype.imul=function(t){return this.clone().mulTo(t,this)},a.prototype.imuln=function(t){n(\"number\"==typeof t),n(t<67108864);for(var e=0,r=0;r<this.length;r++){var i=(0|this.words[r])*t,a=(67108863&i)+(67108863&e);e>>=26,e+=i/67108864|0,e+=a>>>26,this.words[r]=67108863&a}return 0!==e&&(this.words[r]=e,this.length++),this},a.prototype.muln=function(t){return this.clone().imuln(t)},a.prototype.sqr=function(){return this.mul(this)},a.prototype.isqr=function(){return this.imul(this.clone())},a.prototype.pow=function(t){var e=function(t){for(var e=new Array(t.bitLength()),r=0;r<e.length;r++){var n=r/26|0,i=r%26;e[r]=(t.words[n]&1<<i)>>>i}return e}(t);if(0===e.length)return new a(1);for(var r=this,n=0;n<e.length&&0===e[n];n++,r=r.sqr());if(++n<e.length)for(var i=r.sqr();n<e.length;n++,i=i.sqr())0!==e[n]&&(r=r.mul(i));return r},a.prototype.iushln=function(t){n(\"number\"==typeof t&&t>=0);var e,r=t%26,i=(t-r)/26,a=67108863>>>26-r<<26-r;if(0!==r){var o=0;for(e=0;e<this.length;e++){var s=this.words[e]&a,l=(0|this.words[e])-s<<r;this.words[e]=l|o,o=s>>>26-r}o&&(this.words[e]=o,this.length++)}if(0!==i){for(e=this.length-1;e>=0;e--)this.words[e+i]=this.words[e];for(e=0;e<i;e++)this.words[e]=0;this.length+=i}return this.strip()},a.prototype.ishln=function(t){return n(0===this.negative),this.iushln(t)},a.prototype.iushrn=function(t,e,r){var i;n(\"number\"==typeof t&&t>=0),i=e?(e-e%26)/26:0;var a=t%26,o=Math.min((t-a)/26,this.length),s=67108863^67108863>>>a<<a,l=r;if(i-=o,i=Math.max(0,i),l){for(var c=0;c<o;c++)l.words[c]=this.words[c];l.length=o}if(0===o);else if(this.length>o)for(this.length-=o,c=0;c<this.length;c++)this.words[c]=this.words[c+o];else this.words[0]=0,this.length=1;var u=0;for(c=this.length-1;c>=0&&(0!==u||c>=i);c--){var f=0|this.words[c];this.words[c]=u<<26-a|f>>>a,u=f&s}return l&&0!==u&&(l.words[l.length++]=u),0===this.length&&(this.words[0]=0,this.length=1),this.strip()},a.prototype.ishrn=function(t,e,r){return n(0===this.negative),this.iushrn(t,e,r)},a.prototype.shln=function(t){return this.clone().ishln(t)},a.prototype.ushln=function(t){return this.clone().iushln(t)},a.prototype.shrn=function(t){return this.clone().ishrn(t)},a.prototype.ushrn=function(t){return this.clone().iushrn(t)},a.prototype.testn=function(t){n(\"number\"==typeof t&&t>=0);var e=t%26,r=(t-e)/26,i=1<<e;return!(this.length<=r)&&!!(this.words[r]&i)},a.prototype.imaskn=function(t){n(\"number\"==typeof t&&t>=0);var e=t%26,r=(t-e)/26;if(n(0===this.negative,\"imaskn works only with positive numbers\"),this.length<=r)return this;if(0!==e&&r++,this.length=Math.min(r,this.length),0!==e){var i=67108863^67108863>>>e<<e;this.words[this.length-1]&=i}return this.strip()},a.prototype.maskn=function(t){return this.clone().imaskn(t)},a.prototype.iaddn=function(t){return n(\"number\"==typeof t),n(t<67108864),t<0?this.isubn(-t):0!==this.negative?1===this.length&&(0|this.words[0])<t?(this.words[0]=t-(0|this.words[0]),this.negative=0,this):(this.negative=0,this.isubn(t),this.negative=1,this):this._iaddn(t)},a.prototype._iaddn=function(t){this.words[0]+=t;for(var e=0;e<this.length&&this.words[e]>=67108864;e++)this.words[e]-=67108864,e===this.length-1?this.words[e+1]=1:this.words[e+1]++;return this.length=Math.max(this.length,e+1),this},a.prototype.isubn=function(t){if(n(\"number\"==typeof t),n(t<67108864),t<0)return this.iaddn(-t);if(0!==this.negative)return this.negative=0,this.iaddn(t),this.negative=1,this;if(this.words[0]-=t,1===this.length&&this.words[0]<0)this.words[0]=-this.words[0],this.negative=1;else for(var e=0;e<this.length&&this.words[e]<0;e++)this.words[e]+=67108864,this.words[e+1]-=1;return this.strip()},a.prototype.addn=function(t){return this.clone().iaddn(t)},a.prototype.subn=function(t){return this.clone().isubn(t)},a.prototype.iabs=function(){return this.negative=0,this},a.prototype.abs=function(){return this.clone().iabs()},a.prototype._ishlnsubmul=function(t,e,r){var i,a,o=t.length+r;this._expand(o);var s=0;for(i=0;i<t.length;i++){a=(0|this.words[i+r])+s;var l=(0|t.words[i])*e;s=((a-=67108863&l)>>26)-(l/67108864|0),this.words[i+r]=67108863&a}for(;i<this.length-r;i++)s=(a=(0|this.words[i+r])+s)>>26,this.words[i+r]=67108863&a;if(0===s)return this.strip();for(n(-1===s),s=0,i=0;i<this.length;i++)s=(a=-(0|this.words[i])+s)>>26,this.words[i]=67108863&a;return this.negative=1,this.strip()},a.prototype._wordDiv=function(t,e){var r=(this.length,t.length),n=this.clone(),i=t,o=0|i.words[i.length-1];0!==(r=26-this._countBits(o))&&(i=i.ushln(r),n.iushln(r),o=0|i.words[i.length-1]);var s,l=n.length-i.length;if(\"mod\"!==e){(s=new a(null)).length=l+1,s.words=new Array(s.length);for(var c=0;c<s.length;c++)s.words[c]=0}var u=n.clone()._ishlnsubmul(i,1,l);0===u.negative&&(n=u,s&&(s.words[l]=1));for(var f=l-1;f>=0;f--){var h=67108864*(0|n.words[i.length+f])+(0|n.words[i.length+f-1]);for(h=Math.min(h/o|0,67108863),n._ishlnsubmul(i,h,f);0!==n.negative;)h--,n.negative=0,n._ishlnsubmul(i,1,f),n.isZero()||(n.negative^=1);s&&(s.words[f]=h)}return s&&s.strip(),n.strip(),\"div\"!==e&&0!==r&&n.iushrn(r),{div:s||null,mod:n}},a.prototype.divmod=function(t,e,r){return n(!t.isZero()),this.isZero()?{div:new a(0),mod:new a(0)}:0!==this.negative&&0===t.negative?(s=this.neg().divmod(t,e),\"mod\"!==e&&(i=s.div.neg()),\"div\"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.iadd(t)),{div:i,mod:o}):0===this.negative&&0!==t.negative?(s=this.divmod(t.neg(),e),\"mod\"!==e&&(i=s.div.neg()),{div:i,mod:s.mod}):0!=(this.negative&t.negative)?(s=this.neg().divmod(t.neg(),e),\"div\"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.isub(t)),{div:s.div,mod:o}):t.length>this.length||this.cmp(t)<0?{div:new a(0),mod:this}:1===t.length?\"div\"===e?{div:this.divn(t.words[0]),mod:null}:\"mod\"===e?{div:null,mod:new a(this.modn(t.words[0]))}:{div:this.divn(t.words[0]),mod:new a(this.modn(t.words[0]))}:this._wordDiv(t,e);var i,o,s},a.prototype.div=function(t){return this.divmod(t,\"div\",!1).div},a.prototype.mod=function(t){return this.divmod(t,\"mod\",!1).mod},a.prototype.umod=function(t){return this.divmod(t,\"mod\",!0).mod},a.prototype.divRound=function(t){var e=this.divmod(t);if(e.mod.isZero())return e.div;var r=0!==e.div.negative?e.mod.isub(t):e.mod,n=t.ushrn(1),i=t.andln(1),a=r.cmp(n);return a<0||1===i&&0===a?e.div:0!==e.div.negative?e.div.isubn(1):e.div.iaddn(1)},a.prototype.modn=function(t){n(t<=67108863);for(var e=(1<<26)%t,r=0,i=this.length-1;i>=0;i--)r=(e*r+(0|this.words[i]))%t;return r},a.prototype.idivn=function(t){n(t<=67108863);for(var e=0,r=this.length-1;r>=0;r--){var i=(0|this.words[r])+67108864*e;this.words[r]=i/t|0,e=i%t}return this.strip()},a.prototype.divn=function(t){return this.clone().idivn(t)},a.prototype.egcd=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i=new a(1),o=new a(0),s=new a(0),l=new a(1),c=0;e.isEven()&&r.isEven();)e.iushrn(1),r.iushrn(1),++c;for(var u=r.clone(),f=e.clone();!e.isZero();){for(var h=0,p=1;0==(e.words[0]&p)&&h<26;++h,p<<=1);if(h>0)for(e.iushrn(h);h-- >0;)(i.isOdd()||o.isOdd())&&(i.iadd(u),o.isub(f)),i.iushrn(1),o.iushrn(1);for(var d=0,m=1;0==(r.words[0]&m)&&d<26;++d,m<<=1);if(d>0)for(r.iushrn(d);d-- >0;)(s.isOdd()||l.isOdd())&&(s.iadd(u),l.isub(f)),s.iushrn(1),l.iushrn(1);e.cmp(r)>=0?(e.isub(r),i.isub(s),o.isub(l)):(r.isub(e),s.isub(i),l.isub(o))}return{a:s,b:l,gcd:r.iushln(c)}},a.prototype._invmp=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i,o=new a(1),s=new a(0),l=r.clone();e.cmpn(1)>0&&r.cmpn(1)>0;){for(var c=0,u=1;0==(e.words[0]&u)&&c<26;++c,u<<=1);if(c>0)for(e.iushrn(c);c-- >0;)o.isOdd()&&o.iadd(l),o.iushrn(1);for(var f=0,h=1;0==(r.words[0]&h)&&f<26;++f,h<<=1);if(f>0)for(r.iushrn(f);f-- >0;)s.isOdd()&&s.iadd(l),s.iushrn(1);e.cmp(r)>=0?(e.isub(r),o.isub(s)):(r.isub(e),s.isub(o))}return(i=0===e.cmpn(1)?o:s).cmpn(0)<0&&i.iadd(t),i},a.prototype.gcd=function(t){if(this.isZero())return t.abs();if(t.isZero())return this.abs();var e=this.clone(),r=t.clone();e.negative=0,r.negative=0;for(var n=0;e.isEven()&&r.isEven();n++)e.iushrn(1),r.iushrn(1);for(;;){for(;e.isEven();)e.iushrn(1);for(;r.isEven();)r.iushrn(1);var i=e.cmp(r);if(i<0){var a=e;e=r,r=a}else if(0===i||0===r.cmpn(1))break;e.isub(r)}return r.iushln(n)},a.prototype.invm=function(t){return this.egcd(t).a.umod(t)},a.prototype.isEven=function(){return 0==(1&this.words[0])},a.prototype.isOdd=function(){return 1==(1&this.words[0])},a.prototype.andln=function(t){return this.words[0]&t},a.prototype.bincn=function(t){n(\"number\"==typeof t);var e=t%26,r=(t-e)/26,i=1<<e;if(this.length<=r)return this._expand(r+1),this.words[r]|=i,this;for(var a=i,o=r;0!==a&&o<this.length;o++){var s=0|this.words[o];a=(s+=a)>>>26,s&=67108863,this.words[o]=s}return 0!==a&&(this.words[o]=a,this.length++),this},a.prototype.isZero=function(){return 1===this.length&&0===this.words[0]},a.prototype.cmpn=function(t){var e,r=t<0;if(0!==this.negative&&!r)return-1;if(0===this.negative&&r)return 1;if(this.strip(),this.length>1)e=1;else{r&&(t=-t),n(t<=67108863,\"Number is too big\");var i=0|this.words[0];e=i===t?0:i<t?-1:1}return 0!==this.negative?0|-e:e},a.prototype.cmp=function(t){if(0!==this.negative&&0===t.negative)return-1;if(0===this.negative&&0!==t.negative)return 1;var e=this.ucmp(t);return 0!==this.negative?0|-e:e},a.prototype.ucmp=function(t){if(this.length>t.length)return 1;if(this.length<t.length)return-1;for(var e=0,r=this.length-1;r>=0;r--){var n=0|this.words[r],i=0|t.words[r];if(n!==i){n<i?e=-1:n>i&&(e=1);break}}return e},a.prototype.gtn=function(t){return 1===this.cmpn(t)},a.prototype.gt=function(t){return 1===this.cmp(t)},a.prototype.gten=function(t){return this.cmpn(t)>=0},a.prototype.gte=function(t){return this.cmp(t)>=0},a.prototype.ltn=function(t){return-1===this.cmpn(t)},a.prototype.lt=function(t){return-1===this.cmp(t)},a.prototype.lten=function(t){return this.cmpn(t)<=0},a.prototype.lte=function(t){return this.cmp(t)<=0},a.prototype.eqn=function(t){return 0===this.cmpn(t)},a.prototype.eq=function(t){return 0===this.cmp(t)},a.red=function(t){return new w(t)},a.prototype.toRed=function(t){return n(!this.red,\"Already a number in reduction context\"),n(0===this.negative,\"red works only with positives\"),t.convertTo(this)._forceRed(t)},a.prototype.fromRed=function(){return n(this.red,\"fromRed works only with numbers in reduction context\"),this.red.convertFrom(this)},a.prototype._forceRed=function(t){return this.red=t,this},a.prototype.forceRed=function(t){return n(!this.red,\"Already a number 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numbers\"),this.red._verify1(this),this.red.sqr(this)},a.prototype.redISqr=function(){return n(this.red,\"redISqr works only with red numbers\"),this.red._verify1(this),this.red.isqr(this)},a.prototype.redSqrt=function(){return n(this.red,\"redSqrt works only with red numbers\"),this.red._verify1(this),this.red.sqrt(this)},a.prototype.redInvm=function(){return n(this.red,\"redInvm works only with red numbers\"),this.red._verify1(this),this.red.invm(this)},a.prototype.redNeg=function(){return n(this.red,\"redNeg works only with red numbers\"),this.red._verify1(this),this.red.neg(this)},a.prototype.redPow=function(t){return n(this.red&&!t.red,\"redPow(normalNum)\"),this.red._verify1(this),this.red.pow(this,t)};var g={k256:null,p224:null,p192:null,p25519:null};function v(t,e){this.name=t,this.p=new a(e,16),this.n=this.p.bitLength(),this.k=new a(1).iushln(this.n).isub(this.p),this.tmp=this._tmp()}function y(){v.call(this,\"k256\",\"ffffffff ffffffff ffffffff ffffffff ffffffff ffffffff 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0===t.words[t.length-1]&&(t.length--,0===t.words[t.length-1]&&t.length--),t},i(x,v),i(b,v),i(_,v),_.prototype.imulK=function(t){for(var e=0,r=0;r<t.length;r++){var n=19*(0|t.words[r])+e,i=67108863&n;n>>>=26,t.words[r]=i,e=n}return 0!==e&&(t.words[t.length++]=e),t},a._prime=function(t){if(g[t])return g[t];var e;if(\"k256\"===t)e=new y;else if(\"p224\"===t)e=new x;else if(\"p192\"===t)e=new b;else{if(\"p25519\"!==t)throw new Error(\"Unknown prime \"+t);e=new _}return g[t]=e,e},w.prototype._verify1=function(t){n(0===t.negative,\"red works only with positives\"),n(t.red,\"red works only with red numbers\")},w.prototype._verify2=function(t,e){n(0==(t.negative|e.negative),\"red works only with positives\"),n(t.red&&t.red===e.red,\"red works only with red numbers\")},w.prototype.imod=function(t){return this.prime?this.prime.ireduce(t)._forceRed(this):t.umod(this.m)._forceRed(this)},w.prototype.neg=function(t){return 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r=t.mul(e),n=r.maskn(this.shift).mul(this.minv).imaskn(this.shift).mul(this.m),i=r.isub(n).iushrn(this.shift),o=i;return i.cmp(this.m)>=0?o=i.isub(this.m):i.cmpn(0)<0&&(o=i.iadd(this.m)),o._forceRed(this)},T.prototype.invm=function(t){return this.imod(t._invmp(this.m).mul(this.r2))._forceRed(this)}}(void 0===e||e,this)},{buffer:115}],107:[function(t,e,r){\"use strict\";e.exports=function(t){var e,r,n,i=t.length,a=0;for(e=0;e<i;++e)a+=t[e].length;var o=new Array(a),s=0;for(e=0;e<i;++e){var l=t[e],c=l.length;for(r=0;r<c;++r){var u=o[s++]=new Array(c-1),f=0;for(n=0;n<c;++n)n!==r&&(u[f++]=l[n]);if(1&r){var h=u[1];u[1]=u[0],u[0]=h}}}return o}},{}],108:[function(t,e,r){\"use strict\";e.exports=function(t,e,r){switch(arguments.length){case 1:return f(t);case 2:return\"function\"==typeof e?c(t,t,e,!0):h(t,e);case 3:return c(t,e,r,!1);default:throw new Error(\"box-intersect: Invalid arguments\")}};var n,i=t(\"typedarray-pool\"),a=t(\"./lib/sweep\"),o=t(\"./lib/intersect\");function 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-       "/*!\n",
-       " * The buffer module from node.js, for the browser.\n",
-       " *\n",
-       " * @author   Feross Aboukhadijeh <https://feross.org>\n",
-       " * @license  MIT\n",
-       " */\n",
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t<e?-1:t>e?1:0}Object.defineProperty(p,\"valid\",{get:function(){return this._stack.length>0}}),Object.defineProperty(p,\"node\",{get:function(){return this._stack.length>0?this._stack[this._stack.length-1]:null},enumerable:!0}),p.clone=function(){return new h(this.tree,this._stack.slice())},p.remove=function(){var t=this._stack;if(0===t.length)return this.tree;var e=new Array(t.length),r=t[t.length-1];e[e.length-1]=new n(r._color,r.key,r.value,r.left,r.right,r._count);for(var l=t.length-2;l>=0;--l){(r=t[l]).left===t[l+1]?e[l]=new n(r._color,r.key,r.value,e[l+1],r.right,r._count):e[l]=new n(r._color,r.key,r.value,r.left,e[l+1],r._count)}if((r=e[e.length-1]).left&&r.right){var c=e.length;for(r=r.left;r.right;)e.push(r),r=r.right;var u=e[c-1];e.push(new n(r._color,u.key,u.value,r.left,r.right,r._count)),e[c-1].key=r.key,e[c-1].value=r.value;for(l=e.length-2;l>=c;--l)r=e[l],e[l]=new n(r._color,r.key,r.value,r.left,e[l+1],r._count);e[c-1].left=e[c]}if(0===(r=e[e.length-1])._color){var f=e[e.length-2];f.left===r?f.left=null:f.right===r&&(f.right=null),e.pop();for(l=0;l<e.length;++l)e[l]._count--;return new s(this.tree._compare,e[0])}if(r.left||r.right){r.left?d(r,r.left):r.right&&d(r,r.right),r._color=1;for(l=0;l<e.length-1;++l)e[l]._count--;return new s(this.tree._compare,e[0])}if(1===e.length)return new s(this.tree._compare,null);for(l=0;l<e.length;++l)e[l]._count--;var h=e[e.length-2];return function(t){for(var e,r,n,s,l=t.length-1;l>=0;--l){if(e=t[l],0===l)return void(e._color=1);if((r=t[l-1]).left===e){if((n=r.right).right&&0===n.right._color){if(s=(n=r.right=i(n)).right=i(n.right),r.right=n.left,n.left=r,n.right=s,n._color=r._color,e._color=1,r._color=1,s._color=1,o(r),o(n),l>1)(c=t[l-2]).left===r?c.left=n:c.right=n;return void(t[l-1]=n)}if(n.left&&0===n.left._color){if(s=(n=r.right=i(n)).left=i(n.left),r.right=s.left,n.left=s.right,s.left=r,s.right=n,s._color=r._color,r._color=1,n._color=1,e._color=1,o(r),o(n),o(s),l>1)(c=t[l-2]).left===r?c.left=s:c.right=s;return void(t[l-1]=s)}if(1===n._color){if(0===r._color)return r._color=1,void(r.right=a(0,n));r.right=a(0,n);continue}n=i(n),r.right=n.left,n.left=r,n._color=r._color,r._color=0,o(r),o(n),l>1&&((c=t[l-2]).left===r?c.left=n:c.right=n),t[l-1]=n,t[l]=r,l+1<t.length?t[l+1]=e:t.push(e),l+=2}else{if((n=r.left).left&&0===n.left._color){if(s=(n=r.left=i(n)).left=i(n.left),r.left=n.right,n.right=r,n.left=s,n._color=r._color,e._color=1,r._color=1,s._color=1,o(r),o(n),l>1)(c=t[l-2]).right===r?c.right=n:c.left=n;return void(t[l-1]=n)}if(n.right&&0===n.right._color){if(s=(n=r.left=i(n)).right=i(n.right),r.left=s.right,n.right=s.left,s.right=r,s.left=n,s._color=r._color,r._color=1,n._color=1,e._color=1,o(r),o(n),o(s),l>1)(c=t[l-2]).right===r?c.right=s:c.left=s;return void(t[l-1]=s)}if(1===n._color){if(0===r._color)return r._color=1,void(r.left=a(0,n));r.left=a(0,n);continue}var c;n=i(n),r.left=n.right,n.right=r,n._color=r._color,r._color=0,o(r),o(n),l>1&&((c=t[l-2]).right===r?c.right=n:c.left=n),t[l-1]=n,t[l]=r,l+1<t.length?t[l+1]=e:t.push(e),l+=2}}}(e),h.left===r?h.left=null:h.right=null,new s(this.tree._compare,e[0])},Object.defineProperty(p,\"key\",{get:function(){if(this._stack.length>0)return this._stack[this._stack.length-1].key},enumerable:!0}),Object.defineProperty(p,\"value\",{get:function(){if(this._stack.length>0)return this._stack[this._stack.length-1].value},enumerable:!0}),Object.defineProperty(p,\"index\",{get:function(){var t=0,e=this._stack;if(0===e.length){var r=this.tree.root;return r?r._count:0}e[e.length-1].left&&(t=e[e.length-1].left._count);for(var n=e.length-2;n>=0;--n)e[n+1]===e[n].right&&(++t,e[n].left&&(t+=e[n].left._count));return t},enumerable:!0}),p.next=function(){var t=this._stack;if(0!==t.length){var e=t[t.length-1];if(e.right)for(e=e.right;e;)t.push(e),e=e.left;else for(t.pop();t.length>0&&t[t.length-1].right===e;)e=t[t.length-1],t.pop()}},Object.defineProperty(p,\"hasNext\",{get:function(){var t=this._stack;if(0===t.length)return!1;if(t[t.length-1].right)return!0;for(var e=t.length-1;e>0;--e)if(t[e-1].left===t[e])return!0;return!1}}),p.update=function(t){var e=this._stack;if(0===e.length)throw new Error(\"Can't update empty node!\");var r=new Array(e.length),i=e[e.length-1];r[r.length-1]=new n(i._color,i.key,t,i.left,i.right,i._count);for(var a=e.length-2;a>=0;--a)(i=e[a]).left===e[a+1]?r[a]=new n(i._color,i.key,i.value,r[a+1],i.right,i._count):r[a]=new n(i._color,i.key,i.value,i.left,r[a+1],i._count);return new s(this.tree._compare,r[0])},p.prev=function(){var t=this._stack;if(0!==t.length){var e=t[t.length-1];if(e.left)for(e=e.left;e;)t.push(e),e=e.right;else for(t.pop();t.length>0&&t[t.length-1].left===e;)e=t[t.length-1],t.pop()}},Object.defineProperty(p,\"hasPrev\",{get:function(){var t=this._stack;if(0===t.length)return!1;if(t[t.length-1].left)return!0;for(var e=t.length-1;e>0;--e)if(t[e-1].right===t[e])return!0;return!1}})},{}],252:[function(t,e,r){var n=[.9999999999998099,676.5203681218851,-1259.1392167224028,771.3234287776531,-176.6150291621406,12.507343278686905,-.13857109526572012,9984369578019572e-21,1.5056327351493116e-7],i=[.9999999999999971,57.15623566586292,-59.59796035547549,14.136097974741746,-.4919138160976202,3399464998481189e-20,4652362892704858e-20,-9837447530487956e-20,.0001580887032249125,-.00021026444172410488,.00021743961811521265,-.0001643181065367639,8441822398385275e-20,-26190838401581408e-21,36899182659531625e-22];function a(t){if(t<0)return Number(\"0/0\");for(var e=i[0],r=i.length-1;r>0;--r)e+=i[r]/(t+r);var n=t+607/128+.5;return.5*Math.log(2*Math.PI)+(t+.5)*Math.log(n)-n+Math.log(e)-Math.log(t)}e.exports=function t(e){if(e<.5)return Math.PI/(Math.sin(Math.PI*e)*t(1-e));if(e>100)return Math.exp(a(e));e-=1;for(var r=n[0],i=1;i<9;i++)r+=n[i]/(e+i);var o=e+7+.5;return Math.sqrt(2*Math.PI)*Math.pow(o,e+.5)*Math.exp(-o)*r},e.exports.log=a},{}],253:[function(t,e,r){e.exports=function(t,e){if(\"string\"!=typeof t)throw new TypeError(\"must specify type string\");if(e=e||{},\"undefined\"==typeof document&&!e.canvas)return null;var r=e.canvas||document.createElement(\"canvas\");\"number\"==typeof e.width&&(r.width=e.width);\"number\"==typeof e.height&&(r.height=e.height);var n,i=e;try{var a=[t];0===t.indexOf(\"webgl\")&&a.push(\"experimental-\"+t);for(var o=0;o<a.length;o++)if(n=r.getContext(a[o],i))return n}catch(t){n=null}return n||null}},{}],254:[function(t,e,r){\"use strict\";e.exports=function(t,e){var r=new u(t);return r.update(e),r};var n=t(\"./lib/text.js\"),i=t(\"./lib/lines.js\"),a=t(\"./lib/background.js\"),o=t(\"./lib/cube.js\"),s=t(\"./lib/ticks.js\"),l=new Float32Array([1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]);function c(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t}function u(t){this.gl=t,this.pixelRatio=1,this.bounds=[[-10,-10,-10],[10,10,10]],this.ticks=[[],[],[]],this.autoTicks=!0,this.tickSpacing=[1,1,1],this.tickEnable=[!0,!0,!0],this.tickFont=[\"sans-serif\",\"sans-serif\",\"sans-serif\"],this.tickSize=[12,12,12],this.tickAngle=[0,0,0],this.tickAlign=[\"auto\",\"auto\",\"auto\"],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[10,10,10],this.lastCubeProps={cubeEdges:[0,0,0],axis:[0,0,0]},this.labels=[\"x\",\"y\",\"z\"],this.labelEnable=[!0,!0,!0],this.labelFont=\"sans-serif\",this.labelSize=[20,20,20],this.labelAngle=[0,0,0],this.labelAlign=[\"auto\",\"auto\",\"auto\"],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[10,10,10],this.lineEnable=[!0,!0,!0],this.lineMirror=[!1,!1,!1],this.lineWidth=[1,1,1],this.lineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.lineTickEnable=[!0,!0,!0],this.lineTickMirror=[!1,!1,!1],this.lineTickLength=[0,0,0],this.lineTickWidth=[1,1,1],this.lineTickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.gridEnable=[!0,!0,!0],this.gridWidth=[1,1,1],this.gridColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroEnable=[!0,!0,!0],this.zeroLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroLineWidth=[2,2,2],this.backgroundEnable=[!1,!1,!1],this.backgroundColor=[[.8,.8,.8,.5],[.8,.8,.8,.5],[.8,.8,.8,.5]],this._firstInit=!0,this._text=null,this._lines=null,this._background=a(t)}var f=u.prototype;function h(){this.primalOffset=[0,0,0],this.primalMinor=[0,0,0],this.mirrorOffset=[0,0,0],this.mirrorMinor=[0,0,0]}f.update=function(t){function e(e,r,n){if(n in t){var i,a=t[n],o=this[n];(e?Array.isArray(a)&&Array.isArray(a[0]):Array.isArray(a))?this[n]=i=[r(a[0]),r(a[1]),r(a[2])]:this[n]=i=[r(a),r(a),r(a)];for(var s=0;s<3;++s)if(i[s]!==o[s])return!0}return!1}t=t||{};var r,a=e.bind(this,!1,Number),o=e.bind(this,!1,Boolean),l=e.bind(this,!1,String),c=e.bind(this,!0,(function(t){if(Array.isArray(t)){if(3===t.length)return[+t[0],+t[1],+t[2],1];if(4===t.length)return[+t[0],+t[1],+t[2],+t[3]]}return[0,0,0,1]})),u=!1,f=!1;if(\"bounds\"in t)for(var h=t.bounds,p=0;p<2;++p)for(var d=0;d<3;++d)h[p][d]!==this.bounds[p][d]&&(f=!0),this.bounds[p][d]=h[p][d];if(\"ticks\"in t){r=t.ticks,u=!0,this.autoTicks=!1;for(p=0;p<3;++p)this.tickSpacing[p]=0}else a(\"tickSpacing\")&&(this.autoTicks=!0,f=!0);if(this._firstInit&&(\"ticks\"in t||\"tickSpacing\"in t||(this.autoTicks=!0),f=!0,u=!0,this._firstInit=!1),f&&this.autoTicks&&(r=s.create(this.bounds,this.tickSpacing),u=!0),u){for(p=0;p<3;++p)r[p].sort((function(t,e){return t.x-e.x}));s.equal(r,this.ticks)?u=!1:this.ticks=r}o(\"tickEnable\"),l(\"tickFont\")&&(u=!0),a(\"tickSize\"),a(\"tickAngle\"),a(\"tickPad\"),c(\"tickColor\");var m=l(\"labels\");l(\"labelFont\")&&(m=!0),o(\"labelEnable\"),a(\"labelSize\"),a(\"labelPad\"),c(\"labelColor\"),o(\"lineEnable\"),o(\"lineMirror\"),a(\"lineWidth\"),c(\"lineColor\"),o(\"lineTickEnable\"),o(\"lineTickMirror\"),a(\"lineTickLength\"),a(\"lineTickWidth\"),c(\"lineTickColor\"),o(\"gridEnable\"),a(\"gridWidth\"),c(\"gridColor\"),o(\"zeroEnable\"),c(\"zeroLineColor\"),a(\"zeroLineWidth\"),o(\"backgroundEnable\"),c(\"backgroundColor\"),this._text?this._text&&(m||u)&&this._text.update(this.bounds,this.labels,this.labelFont,this.ticks,this.tickFont):this._text=n(this.gl,this.bounds,this.labels,this.labelFont,this.ticks,this.tickFont),this._lines&&u&&(this._lines.dispose(),this._lines=null),this._lines||(this._lines=i(this.gl,this.bounds,this.ticks))};var p=[new h,new h,new h];function d(t,e,r,n,i){for(var a=t.primalOffset,o=t.primalMinor,s=t.mirrorOffset,l=t.mirrorMinor,c=n[e],u=0;u<3;++u)if(e!==u){var f=a,h=s,p=o,d=l;c&1<<u&&(f=s,h=a,p=l,d=o),f[u]=r[0][u],h[u]=r[1][u],i[u]>0?(p[u]=-1,d[u]=0):(p[u]=0,d[u]=1)}}var m=[0,0,0],g={model:l,view:l,projection:l,_ortho:!1};f.isOpaque=function(){return!0},f.isTransparent=function(){return!1},f.drawTransparent=function(t){};var v=[0,0,0],y=[0,0,0],x=[0,0,0];f.draw=function(t){t=t||g;for(var e=this.gl,r=t.model||l,n=t.view||l,i=t.projection||l,a=this.bounds,s=t._ortho||!1,u=o(r,n,i,a,s),f=u.cubeEdges,h=u.axis,b=n[12],_=n[13],w=n[14],T=n[15],k=(s?2:1)*this.pixelRatio*(i[3]*b+i[7]*_+i[11]*w+i[15]*T)/e.drawingBufferHeight,M=0;M<3;++M)this.lastCubeProps.cubeEdges[M]=f[M],this.lastCubeProps.axis[M]=h[M];var A=p;for(M=0;M<3;++M)d(p[M],M,this.bounds,f,h);e=this.gl;var S,E=m;for(M=0;M<3;++M)this.backgroundEnable[M]?E[M]=h[M]:E[M]=0;this._background.draw(r,n,i,a,E,this.backgroundColor),this._lines.bind(r,n,i,this);for(M=0;M<3;++M){var L=[0,0,0];h[M]>0?L[M]=a[1][M]:L[M]=a[0][M];for(var C=0;C<2;++C){var P=(M+1+C)%3,I=(M+1+(1^C))%3;this.gridEnable[P]&&this._lines.drawGrid(P,I,this.bounds,L,this.gridColor[P],this.gridWidth[P]*this.pixelRatio)}for(C=0;C<2;++C){P=(M+1+C)%3,I=(M+1+(1^C))%3;this.zeroEnable[I]&&Math.min(a[0][I],a[1][I])<=0&&Math.max(a[0][I],a[1][I])>=0&&this._lines.drawZero(P,I,this.bounds,L,this.zeroLineColor[I],this.zeroLineWidth[I]*this.pixelRatio)}}for(M=0;M<3;++M){this.lineEnable[M]&&this._lines.drawAxisLine(M,this.bounds,A[M].primalOffset,this.lineColor[M],this.lineWidth[M]*this.pixelRatio),this.lineMirror[M]&&this._lines.drawAxisLine(M,this.bounds,A[M].mirrorOffset,this.lineColor[M],this.lineWidth[M]*this.pixelRatio);var O=c(v,A[M].primalMinor),z=c(y,A[M].mirrorMinor),D=this.lineTickLength;for(C=0;C<3;++C){var R=k/r[5*C];O[C]*=D[C]*R,z[C]*=D[C]*R}this.lineTickEnable[M]&&this._lines.drawAxisTicks(M,A[M].primalOffset,O,this.lineTickColor[M],this.lineTickWidth[M]*this.pixelRatio),this.lineTickMirror[M]&&this._lines.drawAxisTicks(M,A[M].mirrorOffset,z,this.lineTickColor[M],this.lineTickWidth[M]*this.pixelRatio)}this._lines.unbind(),this._text.bind(r,n,i,this.pixelRatio);var F,B;function N(t){(B=[0,0,0])[t]=1}function j(t,e,r){var n=(t+1)%3,i=(t+2)%3,a=e[n],o=e[i],s=r[n],l=r[i];a>0&&l>0||a>0&&l<0||a<0&&l>0||a<0&&l<0?N(n):(o>0&&s>0||o>0&&s<0||o<0&&s>0||o<0&&s<0)&&N(i)}for(M=0;M<3;++M){var U=A[M].primalMinor,V=A[M].mirrorMinor,q=c(x,A[M].primalOffset);for(C=0;C<3;++C)this.lineTickEnable[M]&&(q[C]+=k*U[C]*Math.max(this.lineTickLength[C],0)/r[5*C]);var H=[0,0,0];if(H[M]=1,this.tickEnable[M]){-3600===this.tickAngle[M]?(this.tickAngle[M]=0,this.tickAlign[M]=\"auto\"):this.tickAlign[M]=-1,F=1,\"auto\"===(S=[this.tickAlign[M],.5,F])[0]?S[0]=0:S[0]=parseInt(\"\"+S[0]),B=[0,0,0],j(M,U,V);for(C=0;C<3;++C)q[C]+=k*U[C]*this.tickPad[C]/r[5*C];this._text.drawTicks(M,this.tickSize[M],this.tickAngle[M],q,this.tickColor[M],H,B,S)}if(this.labelEnable[M]){F=0,B=[0,0,0],this.labels[M].length>4&&(N(M),F=1),\"auto\"===(S=[this.labelAlign[M],.5,F])[0]?S[0]=0:S[0]=parseInt(\"\"+S[0]);for(C=0;C<3;++C)q[C]+=k*U[C]*this.labelPad[C]/r[5*C];q[M]+=.5*(a[0][M]+a[1][M]),this._text.drawLabel(M,this.labelSize[M],this.labelAngle[M],q,this.labelColor[M],[0,0,0],B,S)}}this._text.unbind()},f.dispose=function(){this._text.dispose(),this._lines.dispose(),this._background.dispose(),this._lines=null,this._text=null,this._background=null,this.gl=null}},{\"./lib/background.js\":255,\"./lib/cube.js\":256,\"./lib/lines.js\":257,\"./lib/text.js\":259,\"./lib/ticks.js\":260}],255:[function(t,e,r){\"use strict\";e.exports=function(t){for(var e=[],r=[],s=0,l=0;l<3;++l)for(var c=(l+1)%3,u=(l+2)%3,f=[0,0,0],h=[0,0,0],p=-1;p<=1;p+=2){r.push(s,s+2,s+1,s+1,s+2,s+3),f[l]=p,h[l]=p;for(var d=-1;d<=1;d+=2){f[c]=d;for(var m=-1;m<=1;m+=2)f[u]=m,e.push(f[0],f[1],f[2],h[0],h[1],h[2]),s+=1}var g=c;c=u,u=g}var v=n(t,new Float32Array(e)),y=n(t,new Uint16Array(r),t.ELEMENT_ARRAY_BUFFER),x=i(t,[{buffer:v,type:t.FLOAT,size:3,offset:0,stride:24},{buffer:v,type:t.FLOAT,size:3,offset:12,stride:24}],y),b=a(t);return b.attributes.position.location=0,b.attributes.normal.location=1,new o(t,v,x,b)};var n=t(\"gl-buffer\"),i=t(\"gl-vao\"),a=t(\"./shaders\").bg;function o(t,e,r,n){this.gl=t,this.buffer=e,this.vao=r,this.shader=n}var s=o.prototype;s.draw=function(t,e,r,n,i,a){for(var o=!1,s=0;s<3;++s)o=o||i[s];if(o){var l=this.gl;l.enable(l.POLYGON_OFFSET_FILL),l.polygonOffset(1,2),this.shader.bind(),this.shader.uniforms={model:t,view:e,projection:r,bounds:n,enable:i,colors:a},this.vao.bind(),this.vao.draw(this.gl.TRIANGLES,36),this.vao.unbind(),l.disable(l.POLYGON_OFFSET_FILL)}},s.dispose=function(){this.vao.dispose(),this.buffer.dispose(),this.shader.dispose()}},{\"./shaders\":258,\"gl-buffer\":263,\"gl-vao\":357}],256:[function(t,e,r){\"use strict\";e.exports=function(t,e,r,a,p){i(s,e,t),i(s,r,s);for(var y=0,x=0;x<2;++x){u[2]=a[x][2];for(var b=0;b<2;++b){u[1]=a[b][1];for(var _=0;_<2;++_)u[0]=a[_][0],h(l[y],u,s),y+=1}}var w=-1;for(x=0;x<8;++x){for(var T=l[x][3],k=0;k<3;++k)c[x][k]=l[x][k]/T;p&&(c[x][2]*=-1),T<0&&(w<0||c[x][2]<c[w][2])&&(w=x)}if(w<0){w=0;for(var M=0;M<3;++M){for(var A=(M+2)%3,S=(M+1)%3,E=-1,L=-1,C=0;C<2;++C){var P=(O=C<<M)+(C<<A)+(1-C<<S),I=O+(1-C<<A)+(C<<S);o(c[O],c[P],c[I],f)<0||(C?E=1:L=1)}if(E<0||L<0)L>E&&(w|=1<<M);else{for(C=0;C<2;++C){P=(O=C<<M)+(C<<A)+(1-C<<S),I=O+(1-C<<A)+(C<<S);var O,z=d([l[O],l[P],l[I],l[O+(1<<A)+(1<<S)]]);C?E=z:L=z}L>E&&(w|=1<<M)}}}var D=7^w,R=-1;for(x=0;x<8;++x)x!==w&&x!==D&&(R<0||c[R][1]>c[x][1])&&(R=x);var F=-1;for(x=0;x<3;++x){if((N=R^1<<x)!==w&&N!==D)F<0&&(F=N),(S=c[N])[0]<c[F][0]&&(F=N)}var B=-1;for(x=0;x<3;++x){var N;if((N=R^1<<x)!==w&&N!==D&&N!==F)B<0&&(B=N),(S=c[N])[0]>c[B][0]&&(B=N)}var j=m;j[0]=j[1]=j[2]=0,j[n.log2(F^R)]=R&F,j[n.log2(R^B)]=R&B;var U=7^B;U===w||U===D?(U=7^F,j[n.log2(B^U)]=U&B):j[n.log2(F^U)]=U&F;var V=g,q=w;for(M=0;M<3;++M)V[M]=q&1<<M?-1:1;return v};var n=t(\"bit-twiddle\"),i=t(\"gl-mat4/multiply\"),a=t(\"split-polygon\"),o=t(\"robust-orientation\"),s=new Array(16),l=new Array(8),c=new Array(8),u=new Array(3),f=[0,0,0];function h(t,e,r){for(var n=0;n<4;++n){t[n]=r[12+n];for(var i=0;i<3;++i)t[n]+=e[i]*r[4*i+n]}}!function(){for(var t=0;t<8;++t)l[t]=[1,1,1,1],c[t]=[1,1,1]}();var p=[[0,0,1,0,0],[0,0,-1,1,0],[0,-1,0,1,0],[0,1,0,1,0],[-1,0,0,1,0],[1,0,0,1,0]];function d(t){for(var e=0;e<p.length;++e)if((t=a.positive(t,p[e])).length<3)return 0;var r=t[0],n=r[0]/r[3],i=r[1]/r[3],o=0;for(e=1;e+1<t.length;++e){var s=t[e],l=t[e+1],c=s[0]/s[3]-n,u=s[1]/s[3]-i,f=l[0]/l[3]-n,h=l[1]/l[3]-i;o+=Math.abs(c*h-u*f)}return o}var m=[1,1,1],g=[0,0,0],v={cubeEdges:m,axis:g}},{\"bit-twiddle\":104,\"gl-mat4/multiply\":297,\"robust-orientation\":544,\"split-polygon\":563}],257:[function(t,e,r){\"use strict\";e.exports=function(t,e,r){var o=[],s=[0,0,0],l=[0,0,0],c=[0,0,0],u=[0,0,0];o.push(0,0,1,0,1,1,0,0,-1,0,0,-1,0,1,1,0,1,-1);for(var f=0;f<3;++f){for(var h=o.length/3|0,d=0;d<r[f].length;++d){var m=+r[f][d].x;o.push(m,0,1,m,1,1,m,0,-1,m,0,-1,m,1,1,m,1,-1)}var g=o.length/3|0;s[f]=h,l[f]=g-h;h=o.length/3|0;for(var v=0;v<r[f].length;++v){m=+r[f][v].x;o.push(m,0,1,m,1,1,m,0,-1,m,0,-1,m,1,1,m,1,-1)}g=o.length/3|0;c[f]=h,u[f]=g-h}var y=n(t,new Float32Array(o)),x=i(t,[{buffer:y,type:t.FLOAT,size:3,stride:0,offset:0}]),b=a(t);return b.attributes.position.location=0,new p(t,y,x,b,l,s,u,c)};var n=t(\"gl-buffer\"),i=t(\"gl-vao\"),a=t(\"./shaders\").line,o=[0,0,0],s=[0,0,0],l=[0,0,0],c=[0,0,0],u=[1,1];function f(t){return t[0]=t[1]=t[2]=0,t}function h(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t}function p(t,e,r,n,i,a,o,s){this.gl=t,this.vertBuffer=e,this.vao=r,this.shader=n,this.tickCount=i,this.tickOffset=a,this.gridCount=o,this.gridOffset=s}var d=p.prototype;d.bind=function(t,e,r){this.shader.bind(),this.shader.uniforms.model=t,this.shader.uniforms.view=e,this.shader.uniforms.projection=r,u[0]=this.gl.drawingBufferWidth,u[1]=this.gl.drawingBufferHeight,this.shader.uniforms.screenShape=u,this.vao.bind()},d.unbind=function(){this.vao.unbind()},d.drawAxisLine=function(t,e,r,n,i){var a=f(s);this.shader.uniforms.majorAxis=s,a[t]=e[1][t]-e[0][t],this.shader.uniforms.minorAxis=a;var o,u=h(c,r);u[t]+=e[0][t],this.shader.uniforms.offset=u,this.shader.uniforms.lineWidth=i,this.shader.uniforms.color=n,(o=f(l))[(t+2)%3]=1,this.shader.uniforms.screenAxis=o,this.vao.draw(this.gl.TRIANGLES,6),(o=f(l))[(t+1)%3]=1,this.shader.uniforms.screenAxis=o,this.vao.draw(this.gl.TRIANGLES,6)},d.drawAxisTicks=function(t,e,r,n,i){if(this.tickCount[t]){var a=f(o);a[t]=1,this.shader.uniforms.majorAxis=a,this.shader.uniforms.offset=e,this.shader.uniforms.minorAxis=r,this.shader.uniforms.color=n,this.shader.uniforms.lineWidth=i;var s=f(l);s[t]=1,this.shader.uniforms.screenAxis=s,this.vao.draw(this.gl.TRIANGLES,this.tickCount[t],this.tickOffset[t])}},d.drawGrid=function(t,e,r,n,i,a){if(this.gridCount[t]){var u=f(s);u[e]=r[1][e]-r[0][e],this.shader.uniforms.minorAxis=u;var p=h(c,n);p[e]+=r[0][e],this.shader.uniforms.offset=p;var d=f(o);d[t]=1,this.shader.uniforms.majorAxis=d;var m=f(l);m[t]=1,this.shader.uniforms.screenAxis=m,this.shader.uniforms.lineWidth=a,this.shader.uniforms.color=i,this.vao.draw(this.gl.TRIANGLES,this.gridCount[t],this.gridOffset[t])}},d.drawZero=function(t,e,r,n,i,a){var o=f(s);this.shader.uniforms.majorAxis=o,o[t]=r[1][t]-r[0][t],this.shader.uniforms.minorAxis=o;var u=h(c,n);u[t]+=r[0][t],this.shader.uniforms.offset=u;var p=f(l);p[e]=1,this.shader.uniforms.screenAxis=p,this.shader.uniforms.lineWidth=a,this.shader.uniforms.color=i,this.vao.draw(this.gl.TRIANGLES,6)},d.dispose=function(){this.vao.dispose(),this.vertBuffer.dispose(),this.shader.dispose()}},{\"./shaders\":258,\"gl-buffer\":263,\"gl-vao\":357}],258:[function(t,e,r){\"use strict\";var n=t(\"glslify\"),i=t(\"gl-shader\"),a=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 offset, majorAxis, minorAxis, screenAxis;\\nuniform float lineWidth;\\nuniform vec2 screenShape;\\n\\nvec3 project(vec3 p) {\\n  vec4 pp = projection * view * model * vec4(p, 1.0);\\n  return pp.xyz / max(pp.w, 0.0001);\\n}\\n\\nvoid main() {\\n  vec3 major = position.x * majorAxis;\\n  vec3 minor = position.y * minorAxis;\\n\\n  vec3 vPosition = major + minor + offset;\\n  vec3 pPosition = project(vPosition);\\n  vec3 offset = project(vPosition + screenAxis * position.z);\\n\\n  vec2 screen = normalize((offset - pPosition).xy * screenShape) / screenShape;\\n\\n  gl_Position = vec4(pPosition + vec3(0.5 * screen * lineWidth, 0), 1.0);\\n}\\n\"]),o=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color;\\nvoid main() {\\n  gl_FragColor = color;\\n}\"]);r.line=function(t){return i(t,a,o,null,[{name:\"position\",type:\"vec3\"}])};var s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 offset, axis, alignDir, alignOpt;\\nuniform float scale, angle, pixelScale;\\nuniform vec2 resolution;\\n\\nvec3 project(vec3 p) {\\n  vec4 pp = projection * view * model * vec4(p, 1.0);\\n  return pp.xyz / max(pp.w, 0.0001);\\n}\\n\\nfloat computeViewAngle(vec3 a, vec3 b) {\\n  vec3 A = project(a);\\n  vec3 B = project(b);\\n\\n  return atan(\\n    (B.y - A.y) * resolution.y,\\n    (B.x - A.x) * resolution.x\\n  );\\n}\\n\\nconst float PI = 3.141592;\\nconst float TWO_PI = 2.0 * PI;\\nconst float HALF_PI = 0.5 * PI;\\nconst float ONE_AND_HALF_PI = 1.5 * PI;\\n\\nint option = int(floor(alignOpt.x + 0.001));\\nfloat hv_ratio =       alignOpt.y;\\nbool enableAlign =    (alignOpt.z != 0.0);\\n\\nfloat mod_angle(float a) {\\n  return mod(a, PI);\\n}\\n\\nfloat positive_angle(float a) {\\n  return mod_angle((a < 0.0) ?\\n    a + TWO_PI :\\n    a\\n  );\\n}\\n\\nfloat look_upwards(float a) {\\n  float b = positive_angle(a);\\n  return ((b > HALF_PI) && (b <= ONE_AND_HALF_PI)) ?\\n    b - PI :\\n    b;\\n}\\n\\nfloat look_horizontal_or_vertical(float a, float ratio) {\\n  // ratio controls the ratio between being horizontal to (vertical + horizontal)\\n  // if ratio is set to 0.5 then it is 50%, 50%.\\n  // when using a higher ratio e.g. 0.75 the result would\\n  // likely be more horizontal than vertical.\\n\\n  float b = positive_angle(a);\\n\\n  return\\n    (b < (      ratio) * HALF_PI) ? 0.0 :\\n    (b < (2.0 - ratio) * HALF_PI) ? -HALF_PI :\\n    (b < (2.0 + ratio) * HALF_PI) ? 0.0 :\\n    (b < (4.0 - ratio) * HALF_PI) ? HALF_PI :\\n                                    0.0;\\n}\\n\\nfloat roundTo(float a, float b) {\\n  return float(b * floor((a + 0.5 * b) / b));\\n}\\n\\nfloat look_round_n_directions(float a, int n) {\\n  float b = positive_angle(a);\\n  float div = TWO_PI / float(n);\\n  float c = roundTo(b, div);\\n  return look_upwards(c);\\n}\\n\\nfloat applyAlignOption(float rawAngle, float delta) {\\n  return\\n    (option >  2) ? look_round_n_directions(rawAngle + delta, option) :       // option 3-n: round to n directions\\n    (option == 2) ? look_horizontal_or_vertical(rawAngle + delta, hv_ratio) : // horizontal or vertical\\n    (option == 1) ? rawAngle + delta :       // use free angle, and flip to align with one direction of the axis\\n    (option == 0) ? look_upwards(rawAngle) : // use free angle, and stay upwards\\n    (option ==-1) ? 0.0 :                    // useful for backward compatibility, all texts remains horizontal\\n                    rawAngle;                // otherwise return back raw input angle\\n}\\n\\nbool isAxisTitle = (axis.x == 0.0) &&\\n                   (axis.y == 0.0) &&\\n                   (axis.z == 0.0);\\n\\nvoid main() {\\n  //Compute world offset\\n  float axisDistance = position.z;\\n  vec3 dataPosition = axisDistance * axis + offset;\\n\\n  float beta = angle; // i.e. user defined attributes for each tick\\n\\n  float axisAngle;\\n  float clipAngle;\\n  float flip;\\n\\n  if (enableAlign) {\\n    axisAngle = (isAxisTitle) ? HALF_PI :\\n                      computeViewAngle(dataPosition, dataPosition + axis);\\n    clipAngle = computeViewAngle(dataPosition, dataPosition + alignDir);\\n\\n    axisAngle += (sin(axisAngle) < 0.0) ? PI : 0.0;\\n    clipAngle += (sin(clipAngle) < 0.0) ? PI : 0.0;\\n\\n    flip = (dot(vec2(cos(axisAngle), sin(axisAngle)),\\n                vec2(sin(clipAngle),-cos(clipAngle))) > 0.0) ? 1.0 : 0.0;\\n\\n    beta += applyAlignOption(clipAngle, flip * PI);\\n  }\\n\\n  //Compute plane offset\\n  vec2 planeCoord = position.xy * pixelScale;\\n\\n  mat2 planeXform = scale * mat2(\\n     cos(beta), sin(beta),\\n    -sin(beta), cos(beta)\\n  );\\n\\n  vec2 viewOffset = 2.0 * planeXform * planeCoord / resolution;\\n\\n  //Compute clip position\\n  vec3 clipPosition = project(dataPosition);\\n\\n  //Apply text offset in clip coordinates\\n  clipPosition += vec3(viewOffset, 0.0);\\n\\n  //Done\\n  gl_Position = vec4(clipPosition, 1.0);\\n}\"]),l=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color;\\nvoid main() {\\n  gl_FragColor = color;\\n}\"]);r.text=function(t){return i(t,s,l,null,[{name:\"position\",type:\"vec3\"}])};var c=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec3 normal;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 enable;\\nuniform vec3 bounds[2];\\n\\nvarying vec3 colorChannel;\\n\\nvoid main() {\\n\\n  vec3 signAxis = sign(bounds[1] - bounds[0]);\\n\\n  vec3 realNormal = signAxis * normal;\\n\\n  if(dot(realNormal, enable) > 0.0) {\\n    vec3 minRange = min(bounds[0], bounds[1]);\\n    vec3 maxRange = max(bounds[0], bounds[1]);\\n    vec3 nPosition = mix(minRange, maxRange, 0.5 * (position + 1.0));\\n    gl_Position = projection * view * model * vec4(nPosition, 1.0);\\n  } else {\\n    gl_Position = vec4(0,0,0,0);\\n  }\\n\\n  colorChannel = abs(realNormal);\\n}\"]),u=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec4 colors[3];\\n\\nvarying vec3 colorChannel;\\n\\nvoid main() {\\n  gl_FragColor = colorChannel.x * colors[0] +\\n                 colorChannel.y * colors[1] +\\n                 colorChannel.z * colors[2];\\n}\"]);r.bg=function(t){return i(t,c,u,null,[{name:\"position\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}])}},{\"gl-shader\":335,glslify:261}],259:[function(t,e,r){(function(r){(function(){\"use strict\";e.exports=function(t,e,r,a,s,l){var u=n(t),f=i(t,[{buffer:u,size:3}]),h=o(t);h.attributes.position.location=0;var p=new c(t,h,u,f);return p.update(e,r,a,s,l),p};var n=t(\"gl-buffer\"),i=t(\"gl-vao\"),a=t(\"vectorize-text\"),o=t(\"./shaders\").text,s=window||r.global||{},l=s.__TEXT_CACHE||{};s.__TEXT_CACHE={};function c(t,e,r,n){this.gl=t,this.shader=e,this.buffer=r,this.vao=n,this.tickOffset=this.tickCount=this.labelOffset=this.labelCount=null}var u=c.prototype,f=[0,0];u.bind=function(t,e,r,n){this.vao.bind(),this.shader.bind();var i=this.shader.uniforms;i.model=t,i.view=e,i.projection=r,i.pixelScale=n,f[0]=this.gl.drawingBufferWidth,f[1]=this.gl.drawingBufferHeight,this.shader.uniforms.resolution=f},u.unbind=function(){this.vao.unbind()},u.update=function(t,e,r,n,i){var o=[];function s(t,e,r,n,i,s){var 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m=0;m<n[d].length;++m)n[d][m].text&&s(n[d][m].x,n[d][m].text,n[d][m].font||i,n[d][m].fontSize||12,1.25,p);u[d]=(o.length/3|0)-c[d]}this.buffer.update(o),this.tickOffset=c,this.tickCount=u,this.labelOffset=f,this.labelCount=h},u.drawTicks=function(t,e,r,n,i,a,o,s){this.tickCount[t]&&(this.shader.uniforms.axis=a,this.shader.uniforms.color=i,this.shader.uniforms.angle=r,this.shader.uniforms.scale=e,this.shader.uniforms.offset=n,this.shader.uniforms.alignDir=o,this.shader.uniforms.alignOpt=s,this.vao.draw(this.gl.TRIANGLES,this.tickCount[t],this.tickOffset[t]))},u.drawLabel=function(t,e,r,n,i,a,o,s){this.labelCount[t]&&(this.shader.uniforms.axis=a,this.shader.uniforms.color=i,this.shader.uniforms.angle=r,this.shader.uniforms.scale=e,this.shader.uniforms.offset=n,this.shader.uniforms.alignDir=o,this.shader.uniforms.alignOpt=s,this.vao.draw(this.gl.TRIANGLES,this.labelCount[t],this.labelOffset[t]))},u.dispose=function(){this.shader.dispose(),this.vao.dispose(),this.buffer.dispose()}}).call(this)}).call(this,t(\"_process\"))},{\"./shaders\":258,_process:524,\"gl-buffer\":263,\"gl-vao\":357,\"vectorize-text\":619}],260:[function(t,e,r){\"use 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n=t(\"extract-frustum-planes\"),i=t(\"split-polygon\"),a=t(\"./lib/cube.js\"),o=t(\"gl-mat4/multiply\"),s=t(\"gl-mat4/transpose\"),l=t(\"gl-vec4/transformMat4\"),c=new Float32Array([1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]),u=new Float32Array(16);function f(t,e,r){this.lo=t,this.hi=e,this.pixelsPerDataUnit=r}var h=[0,0,0,1],p=[0,0,0,1];function d(t,e,r,n,i){for(var a=0;a<3;++a){for(var o=h,s=p,c=0;c<3;++c)s[c]=o[c]=r[c];s[3]=o[3]=1,s[a]+=1,l(s,s,e),s[3]<0&&(t[a]=1/0),o[a]-=1,l(o,o,e),o[3]<0&&(t[a]=1/0);var u=(o[0]/o[3]-s[0]/s[3])*n,f=(o[1]/o[3]-s[1]/s[3])*i;t[a]=.25*Math.sqrt(u*u+f*f)}return t}var m=[new f(1/0,-1/0,1/0),new f(1/0,-1/0,1/0),new f(1/0,-1/0,1/0)],g=[0,0,0]},{\"./lib/cube.js\":256,\"extract-frustum-planes\":244,\"gl-mat4/multiply\":297,\"gl-mat4/transpose\":308,\"gl-vec4/transformMat4\":428,\"split-polygon\":563}],263:[function(t,e,r){\"use strict\";var n=t(\"typedarray-pool\"),i=t(\"ndarray-ops\"),a=t(\"ndarray\"),o=[\"uint8\",\"uint8_clamped\",\"uint16\",\"uint32\",\"int8\",\"int16\",\"int32\",\"float32\"];function s(t,e,r,n,i){this.gl=t,this.type=e,this.handle=r,this.length=n,this.usage=i}var l=s.prototype;function c(t,e,r,n,i,a){var o=i.length*i.BYTES_PER_ELEMENT;if(a<0)return t.bufferData(e,i,n),o;if(o+a>r)throw new Error(\"gl-buffer: If resizing buffer, must not specify offset\");return t.bufferSubData(e,a,i),r}function u(t,e){for(var r=n.malloc(t.length,e),i=t.length,a=0;a<i;++a)r[a]=t[a];return r}l.bind=function(){this.gl.bindBuffer(this.type,this.handle)},l.unbind=function(){this.gl.bindBuffer(this.type,null)},l.dispose=function(){this.gl.deleteBuffer(this.handle)},l.update=function(t,e){if(\"number\"!=typeof e&&(e=-1),this.bind(),\"object\"==typeof t&&void 0!==t.shape){var r=t.dtype;if(o.indexOf(r)<0&&(r=\"float32\"),this.type===this.gl.ELEMENT_ARRAY_BUFFER)r=gl.getExtension(\"OES_element_index_uint\")&&\"uint16\"!==r?\"uint32\":\"uint16\";if(r===t.dtype&&function(t,e){for(var r=1,n=e.length-1;n>=0;--n){if(e[n]!==r)return!1;r*=t[n]}return!0}(t.shape,t.stride))0===t.offset&&t.data.length===t.shape[0]?this.length=c(this.gl,this.type,this.length,this.usage,t.data,e):this.length=c(this.gl,this.type,this.length,this.usage,t.data.subarray(t.offset,t.shape[0]),e);else{var s=n.malloc(t.size,r),l=a(s,t.shape);i.assign(l,t),this.length=c(this.gl,this.type,this.length,this.usage,e<0?s:s.subarray(0,t.size),e),n.free(s)}}else if(Array.isArray(t)){var f;f=this.type===this.gl.ELEMENT_ARRAY_BUFFER?u(t,\"uint16\"):u(t,\"float32\"),this.length=c(this.gl,this.type,this.length,this.usage,e<0?f:f.subarray(0,t.length),e),n.free(f)}else if(\"object\"==typeof t&&\"number\"==typeof t.length)this.length=c(this.gl,this.type,this.length,this.usage,t,e);else{if(\"number\"!=typeof t&&void 0!==t)throw new Error(\"gl-buffer: Invalid data type\");if(e>=0)throw new Error(\"gl-buffer: Cannot specify offset when resizing buffer\");(t|=0)<=0&&(t=1),this.gl.bufferData(this.type,0|t,this.usage),this.length=t}},e.exports=function(t,e,r,n){if(r=r||t.ARRAY_BUFFER,n=n||t.DYNAMIC_DRAW,r!==t.ARRAY_BUFFER&&r!==t.ELEMENT_ARRAY_BUFFER)throw new Error(\"gl-buffer: Invalid type for webgl buffer, must be either gl.ARRAY_BUFFER or gl.ELEMENT_ARRAY_BUFFER\");if(n!==t.DYNAMIC_DRAW&&n!==t.STATIC_DRAW&&n!==t.STREAM_DRAW)throw new Error(\"gl-buffer: Invalid usage for buffer, must be either gl.DYNAMIC_DRAW, gl.STATIC_DRAW or gl.STREAM_DRAW\");var i=t.createBuffer(),a=new s(t,r,i,0,n);return a.update(e),a}},{ndarray:479,\"ndarray-ops\":474,\"typedarray-pool\":613}],264:[function(t,e,r){\"use strict\";var n=t(\"gl-vec3\");e.exports=function(t,e){var r=t.positions,i=t.vectors,a={positions:[],vertexIntensity:[],vertexIntensityBounds:t.vertexIntensityBounds,vectors:[],cells:[],coneOffset:t.coneOffset,colormap:t.colormap};if(0===t.positions.length)return e&&(e[0]=[0,0,0],e[1]=[0,0,0]),a;for(var o=0,s=1/0,l=-1/0,c=1/0,u=-1/0,f=1/0,h=-1/0,p=null,d=null,m=[],g=1/0,v=!1,y=0;y<r.length;y++){var x=r[y];s=Math.min(x[0],s),l=Math.max(x[0],l),c=Math.min(x[1],c),u=Math.max(x[1],u),f=Math.min(x[2],f),h=Math.max(x[2],h);var b=i[y];if(n.length(b)>o&&(o=n.length(b)),y){var _=2*n.distance(p,x)/(n.length(d)+n.length(b));_?(g=Math.min(g,_),v=!1):v=!0}v||(p=x,d=b),m.push(b)}var w=[s,c,f],T=[l,u,h];e&&(e[0]=w,e[1]=T),0===o&&(o=1);var k=1/o;isFinite(g)||(g=1),a.vectorScale=g;var M=t.coneSize||.5;t.absoluteConeSize&&(M=t.absoluteConeSize*k),a.coneScale=M;y=0;for(var A=0;y<r.length;y++)for(var S=(x=r[y])[0],E=x[1],L=x[2],C=m[y],P=n.length(C)*k,I=0;I<8;I++){a.positions.push([S,E,L,A++]),a.positions.push([S,E,L,A++]),a.positions.push([S,E,L,A++]),a.positions.push([S,E,L,A++]),a.positions.push([S,E,L,A++]),a.positions.push([S,E,L,A++]),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vertexIntensity.push(P,P,P),a.vertexIntensity.push(P,P,P);var O=a.positions.length;a.cells.push([O-6,O-5,O-4],[O-3,O-2,O-1])}return a};var i=t(\"./lib/shaders\");e.exports.createMesh=t(\"./create_mesh\"),e.exports.createConeMesh=function(t,r){return e.exports.createMesh(t,r,{shaders:i,traceType:\"cone\"})}},{\"./create_mesh\":265,\"./lib/shaders\":266,\"gl-vec3\":376}],265:[function(t,e,r){\"use strict\";var n=t(\"gl-shader\"),i=t(\"gl-buffer\"),a=t(\"gl-vao\"),o=t(\"gl-texture2d\"),s=t(\"gl-mat4/multiply\"),l=t(\"gl-mat4/invert\"),c=t(\"ndarray\"),u=t(\"colormap\"),f=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function h(t,e,r,n,i,a,o,s,l,c,u){this.gl=t,this.pixelRatio=1,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.pickShader=n,this.trianglePositions=i,this.triangleVectors=a,this.triangleColors=s,this.triangleUVs=l,this.triangleIds=o,this.triangleVAO=c,this.triangleCount=0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this.traceType=u,this.tubeScale=1,this.coneScale=2,this.vectorScale=1,this.coneOffset=.25,this._model=f,this._view=f,this._projection=f,this._resolution=[1,1]}var p=h.prototype;function d(t,e){var r=n(t,e.meshShader.vertex,e.meshShader.fragment,null,e.meshShader.attributes);return r.attributes.position.location=0,r.attributes.color.location=2,r.attributes.uv.location=3,r.attributes.vector.location=4,r}function m(t,e){var r=n(t,e.pickShader.vertex,e.pickShader.fragment,null,e.pickShader.attributes);return r.attributes.position.location=0,r.attributes.id.location=1,r.attributes.vector.location=4,r}p.isOpaque=function(){return this.opacity>=1},p.isTransparent=function(){return this.opacity<1},p.pickSlots=1,p.setPickBase=function(t){this.pickId=t},p.update=function(t){t=t||{};var e=this.gl;this.dirty=!0,\"lightPosition\"in t&&(this.lightPosition=t.lightPosition),\"opacity\"in t&&(this.opacity=t.opacity),\"ambient\"in t&&(this.ambientLight=t.ambient),\"diffuse\"in t&&(this.diffuseLight=t.diffuse),\"specular\"in t&&(this.specularLight=t.specular),\"roughness\"in t&&(this.roughness=t.roughness),\"fresnel\"in t&&(this.fresnel=t.fresnel),void 0!==t.tubeScale&&(this.tubeScale=t.tubeScale),void 0!==t.vectorScale&&(this.vectorScale=t.vectorScale),void 0!==t.coneScale&&(this.coneScale=t.coneScale),void 0!==t.coneOffset&&(this.coneOffset=t.coneOffset),t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(function(t){for(var e=u({colormap:t,nshades:256,format:\"rgba\"}),r=new Uint8Array(1024),n=0;n<256;++n){for(var i=e[n],a=0;a<3;++a)r[4*n+a]=i[a];r[4*n+3]=255*i[3]}return c(r,[256,256,4],[4,0,1])}(t.colormap)),this.texture.generateMipmap());var r=t.cells,n=t.positions,i=t.vectors;if(n&&r&&i){var a=[],o=[],s=[],l=[],f=[];this.cells=r,this.positions=n,this.vectors=i;var h=t.meshColor||[1,1,1,1],p=t.vertexIntensity,d=1/0,m=-1/0;if(p)if(t.vertexIntensityBounds)d=+t.vertexIntensityBounds[0],m=+t.vertexIntensityBounds[1];else for(var g=0;g<p.length;++g){var v=p[g];d=Math.min(d,v),m=Math.max(m,v)}else for(g=0;g<n.length;++g){v=n[g][2];d=Math.min(d,v),m=Math.max(m,v)}this.intensity=p||function(t){for(var e=t.length,r=new Array(e),n=0;n<e;++n)r[n]=t[n][2];return r}(n),this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(g=0;g<n.length;++g)for(var y=n[g],x=0;x<3;++x)!isNaN(y[x])&&isFinite(y[x])&&(this.bounds[0][x]=Math.min(this.bounds[0][x],y[x]),this.bounds[1][x]=Math.max(this.bounds[1][x],y[x]));var b=0;t:for(g=0;g<r.length;++g){var _=r[g];switch(_.length){case 3:for(x=0;x<3;++x){y=n[T=_[x]];for(var w=0;w<3;++w)if(isNaN(y[w])||!isFinite(y[w]))continue t}for(x=0;x<3;++x){var T;y=n[T=_[2-x]];a.push(y[0],y[1],y[2],y[3]);var k=i[T];o.push(k[0],k[1],k[2],k[3]||0);var M,A=h;3===A.length?s.push(A[0],A[1],A[2],1):s.push(A[0],A[1],A[2],A[3]),M=p?[(p[T]-d)/(m-d),0]:[(y[2]-d)/(m-d),0],l.push(M[0],M[1]),f.push(g)}b+=1}}this.triangleCount=b,this.trianglePositions.update(a),this.triangleVectors.update(o),this.triangleColors.update(s),this.triangleUVs.update(l),this.triangleIds.update(new Uint32Array(f))}},p.drawTransparent=p.draw=function(t){t=t||{};for(var e=this.gl,r=t.model||f,n=t.view||f,i=t.projection||f,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);var c={model:r,view:n,projection:i,inverseModel:f.slice(),clipBounds:a,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],opacity:this.opacity,tubeScale:this.tubeScale,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,texture:0};c.inverseModel=l(c.inverseModel,c.model),e.disable(e.CULL_FACE),this.texture.bind(0);var u=new Array(16);s(u,c.view,c.model),s(u,c.projection,u),l(u,u);for(o=0;o<3;++o)c.eyePosition[o]=u[12+o]/u[15];var h=u[15];for(o=0;o<3;++o)h+=this.lightPosition[o]*u[4*o+3];for(o=0;o<3;++o){for(var p=u[12+o],d=0;d<3;++d)p+=u[4*d+o]*this.lightPosition[d];c.lightPosition[o]=p/h}if(this.triangleCount>0){var m=this.triShader;m.bind(),m.uniforms=c,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()}},p.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||f,n=t.view||f,i=t.projection||f,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s={model:r,view:n,projection:i,clipBounds:a,tubeScale:this.tubeScale,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,pickId:this.pickId/255},l=this.pickShader;l.bind(),l.uniforms=s,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind())},p.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions[r[1]].slice(0,3),i={position:n,dataCoordinate:n,index:Math.floor(r[1]/48)};return\"cone\"===this.traceType?i.index=Math.floor(r[1]/48):\"streamtube\"===this.traceType&&(i.intensity=this.intensity[r[1]],i.velocity=this.vectors[r[1]].slice(0,3),i.divergence=this.vectors[r[1]][3],i.index=e),i},p.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.pickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleVectors.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleIds.dispose()},e.exports=function(t,e,r){var n=r.shaders;1===arguments.length&&(t=(e=t).gl);var s=d(t,n),l=m(t,n),u=o(t,c(new Uint8Array([255,255,255,255]),[1,1,4]));u.generateMipmap(),u.minFilter=t.LINEAR_MIPMAP_LINEAR,u.magFilter=t.LINEAR;var f=i(t),p=i(t),g=i(t),v=i(t),y=i(t),x=a(t,[{buffer:f,type:t.FLOAT,size:4},{buffer:y,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:g,type:t.FLOAT,size:4},{buffer:v,type:t.FLOAT,size:2},{buffer:p,type:t.FLOAT,size:4}]),b=new h(t,u,s,l,f,p,y,g,v,x,r.traceType||\"cone\");return b.update(e),b}},{colormap:137,\"gl-buffer\":263,\"gl-mat4/invert\":295,\"gl-mat4/multiply\":297,\"gl-shader\":335,\"gl-texture2d\":352,\"gl-vao\":357,ndarray:479}],266:[function(t,e,r){var n=t(\"glslify\"),i=n([\"precision highp float;\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nvec3 getOrthogonalVector(vec3 v) {\\n  // Return up-vector for only-z vector.\\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\\n  // Assign z = 0, x = -b, y = a:\\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\\n    return normalize(vec3(-v.y, v.x, 0.0));\\n  } else {\\n    return normalize(vec3(0.0, v.z, -v.y));\\n  }\\n}\\n\\n// Calculate the cone vertex and normal at the given index.\\n//\\n// The returned vertex is for a cone with its top at origin and height of 1.0,\\n// pointing in the direction of the vector attribute.\\n//\\n// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\\n// These vertices are used to make up the triangles of the cone by the following:\\n//   segment + 0 top vertex\\n//   segment + 1 perimeter vertex a+1\\n//   segment + 2 perimeter vertex a\\n//   segment + 3 center base vertex\\n//   segment + 4 perimeter vertex a\\n//   segment + 5 perimeter vertex a+1\\n// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.\\n// To go from index to segment, floor(index / 6)\\n// To go from segment to angle, 2*pi * (segment/segmentCount)\\n// To go from index to segment index, index - (segment*6)\\n//\\nvec3 getConePosition(vec3 d, float rawIndex, float coneOffset, out vec3 normal) {\\n\\n  const float segmentCount = 8.0;\\n\\n  float index = rawIndex - floor(rawIndex /\\n    (segmentCount * 6.0)) *\\n    (segmentCount * 6.0);\\n\\n  float segment = floor(0.001 + index/6.0);\\n  float segmentIndex = index - (segment*6.0);\\n\\n  normal = -normalize(d);\\n\\n  if (segmentIndex > 2.99 && segmentIndex < 3.01) {\\n    return mix(vec3(0.0), -d, coneOffset);\\n  }\\n\\n  float nextAngle = (\\n    (segmentIndex > 0.99 &&  segmentIndex < 1.01) ||\\n    (segmentIndex > 4.99 &&  segmentIndex < 5.01)\\n  ) ? 1.0 : 0.0;\\n  float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\\n\\n  vec3 v1 = mix(d, vec3(0.0), coneOffset);\\n  vec3 v2 = v1 - d;\\n\\n  vec3 u = getOrthogonalVector(d);\\n  vec3 v = normalize(cross(u, d));\\n\\n  vec3 x = u * cos(angle) * length(d)*0.25;\\n  vec3 y = v * sin(angle) * length(d)*0.25;\\n  vec3 v3 = v2 + x + y;\\n  if (segmentIndex < 3.0) {\\n    vec3 tx = u * sin(angle);\\n    vec3 ty = v * -cos(angle);\\n    vec3 tangent = tx + ty;\\n    normal = normalize(cross(v3 - v1, tangent));\\n  }\\n\\n  if (segmentIndex == 0.0) {\\n    return mix(d, vec3(0.0), coneOffset);\\n  }\\n  return v3;\\n}\\n\\nattribute vec3 vector;\\nattribute vec4 color, position;\\nattribute vec2 uv;\\n\\nuniform float vectorScale, coneScale, coneOffset;\\nuniform mat4 model, view, projection, inverseModel;\\nuniform vec3 eyePosition, lightPosition;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  // Scale the vector magnitude to stay constant with\\n  // model & view changes.\\n  vec3 normal;\\n  vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector), position.w, coneOffset, normal);\\n  vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n\\n  //Lighting geometry parameters\\n  vec4 cameraCoordinate = view * conePosition;\\n  cameraCoordinate.xyz /= cameraCoordinate.w;\\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\\n  f_normal = normalize((vec4(normal, 0.0) * inverseModel).xyz);\\n\\n  // vec4 m_position  = model * vec4(conePosition, 1.0);\\n  vec4 t_position  = view * conePosition;\\n  gl_Position      = projection * t_position;\\n\\n  f_color          = color;\\n  f_data           = conePosition.xyz;\\n  f_position       = position.xyz;\\n  f_uv             = uv;\\n}\\n\"]),a=n([\"#extension GL_OES_standard_derivatives : enable\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n  float NdotH = max(x, 0.0001);\\n  float cos2Alpha = NdotH * NdotH;\\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n  float roughness2 = roughness * roughness;\\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n  return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat cookTorranceSpecular(\\n  vec3 lightDirection,\\n  vec3 viewDirection,\\n  vec3 surfaceNormal,\\n  float roughness,\\n  float fresnel) {\\n\\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\\n\\n  //Half angle vector\\n  vec3 H = normalize(lightDirection + viewDirection);\\n\\n  //Geometric term\\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\\n  float VdotH = max(dot(viewDirection, H), 0.000001);\\n  float LdotH = max(dot(lightDirection, H), 0.000001);\\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\\n  float G = min(1.0, min(G1, G2));\\n  \\n  //Distribution term\\n  float D = beckmannDistribution(NdotH, roughness);\\n\\n  //Fresnel term\\n  float F = pow(1.0 - VdotN, fresnel);\\n\\n  //Multiply terms and done\\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\\nuniform sampler2D texture;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n  vec3 N = normalize(f_normal);\\n  vec3 L = normalize(f_lightDirection);\\n  vec3 V = normalize(f_eyeDirection);\\n\\n  if(gl_FrontFacing) {\\n    N = -N;\\n  }\\n\\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n  vec4 surfaceColor = f_color * texture2D(texture, f_uv);\\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\\n\\n  gl_FragColor = litColor * opacity;\\n}\\n\"]),o=n([\"precision highp float;\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nvec3 getOrthogonalVector(vec3 v) {\\n  // Return up-vector for only-z vector.\\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\\n  // Assign z = 0, x = -b, y = a:\\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\\n    return normalize(vec3(-v.y, v.x, 0.0));\\n  } else {\\n    return normalize(vec3(0.0, v.z, -v.y));\\n  }\\n}\\n\\n// Calculate the cone vertex and normal at the given index.\\n//\\n// The returned vertex is for a cone with its top at origin and height of 1.0,\\n// pointing in the direction of the vector attribute.\\n//\\n// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\\n// These vertices are used to make up the triangles of the cone by the following:\\n//   segment + 0 top vertex\\n//   segment + 1 perimeter vertex a+1\\n//   segment + 2 perimeter vertex a\\n//   segment + 3 center base vertex\\n//   segment + 4 perimeter vertex a\\n//   segment + 5 perimeter vertex a+1\\n// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.\\n// To go from index to segment, floor(index / 6)\\n// To go from segment to angle, 2*pi * (segment/segmentCount)\\n// To go from index to segment index, index - (segment*6)\\n//\\nvec3 getConePosition(vec3 d, float rawIndex, float coneOffset, out vec3 normal) {\\n\\n  const float segmentCount = 8.0;\\n\\n  float index = rawIndex - floor(rawIndex /\\n    (segmentCount * 6.0)) *\\n    (segmentCount * 6.0);\\n\\n  float segment = floor(0.001 + index/6.0);\\n  float segmentIndex = index - (segment*6.0);\\n\\n  normal = -normalize(d);\\n\\n  if (segmentIndex > 2.99 && segmentIndex < 3.01) {\\n    return mix(vec3(0.0), -d, coneOffset);\\n  }\\n\\n  float nextAngle = (\\n    (segmentIndex > 0.99 &&  segmentIndex < 1.01) ||\\n    (segmentIndex > 4.99 &&  segmentIndex < 5.01)\\n  ) ? 1.0 : 0.0;\\n  float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\\n\\n  vec3 v1 = mix(d, vec3(0.0), coneOffset);\\n  vec3 v2 = v1 - d;\\n\\n  vec3 u = getOrthogonalVector(d);\\n  vec3 v = normalize(cross(u, d));\\n\\n  vec3 x = u * cos(angle) * length(d)*0.25;\\n  vec3 y = v * sin(angle) * length(d)*0.25;\\n  vec3 v3 = v2 + x + y;\\n  if (segmentIndex < 3.0) {\\n    vec3 tx = u * sin(angle);\\n    vec3 ty = v * -cos(angle);\\n    vec3 tangent = tx + ty;\\n    normal = normalize(cross(v3 - v1, tangent));\\n  }\\n\\n  if (segmentIndex == 0.0) {\\n    return mix(d, vec3(0.0), coneOffset);\\n  }\\n  return v3;\\n}\\n\\nattribute vec4 vector;\\nattribute vec4 position;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform float vectorScale, coneScale, coneOffset;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  vec3 normal;\\n  vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector.xyz), position.w, coneOffset, normal);\\n  vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n  gl_Position = projection * view * conePosition;\\n  f_id        = id;\\n  f_position  = position.xyz;\\n}\\n\"]),s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3  clipBounds[2];\\nuniform float pickId;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n\\n  gl_FragColor = vec4(pickId, f_id.xyz);\\n}\"]);r.meshShader={vertex:i,fragment:a,attributes:[{name:\"position\",type:\"vec4\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"},{name:\"vector\",type:\"vec3\"}]},r.pickShader={vertex:o,fragment:s,attributes:[{name:\"position\",type:\"vec4\"},{name:\"id\",type:\"vec4\"},{name:\"vector\",type:\"vec3\"}]}},{glslify:438}],267:[function(t,e,r){e.exports={0:\"NONE\",1:\"ONE\",2:\"LINE_LOOP\",3:\"LINE_STRIP\",4:\"TRIANGLES\",5:\"TRIANGLE_STRIP\",6:\"TRIANGLE_FAN\",256:\"DEPTH_BUFFER_BIT\",512:\"NEVER\",513:\"LESS\",514:\"EQUAL\",515:\"LEQUAL\",516:\"GREATER\",517:\"NOTEQUAL\",518:\"GEQUAL\",519:\"ALWAYS\",768:\"SRC_COLOR\",769:\"ONE_MINUS_SRC_COLOR\",770:\"SRC_ALPHA\",771:\"ONE_MINUS_SRC_ALPHA\",772:\"DST_ALPHA\",773:\"ONE_MINUS_DST_ALPHA\",774:\"DST_COLOR\",775:\"ONE_MINUS_DST_COLOR\",776:\"SRC_ALPHA_SATURATE\",1024:\"STENCIL_BUFFER_BIT\",1028:\"FRONT\",1029:\"BACK\",1032:\"FRONT_AND_BACK\",1280:\"INVALID_ENUM\",1281:\"INVALID_VALUE\",1282:\"INVALID_OPERATION\",1285:\"OUT_OF_MEMORY\",1286:\"INVALID_FRAMEBUFFER_OPERATION\",2304:\"CW\",2305:\"CCW\",2849:\"LINE_WIDTH\",2884:\"CULL_FACE\",2885:\"CULL_FACE_MODE\",2886:\"FRONT_FACE\",2928:\"DEPTH_RANGE\",2929:\"DEPTH_TEST\",2930:\"DEPTH_WRITEMASK\",2931:\"DEPTH_CLEAR_VALUE\",2932:\"DEPTH_FUNC\",2960:\"STENCIL_TEST\",2961:\"STENCIL_CLEAR_VALUE\",2962:\"STENCIL_FUNC\",2963:\"STENCIL_VALUE_MASK\",2964:\"STENCIL_FAIL\",2965:\"STENCIL_PASS_DEPTH_FAIL\",2966:\"STENCIL_PASS_DEPTH_PASS\",2967:\"STENCIL_REF\",2968:\"STENCIL_WRITEMASK\",2978:\"VIEWPORT\",3024:\"DITHER\",3042:\"BLEND\",3088:\"SCISSOR_BOX\",3089:\"SCISSOR_TEST\",3106:\"COLOR_CLEAR_VALUE\",3107:\"COLOR_WRITEMASK\",3317:\"UNPACK_ALIGNMENT\",3333:\"PACK_ALIGNMENT\",3379:\"MAX_TEXTURE_SIZE\",3386:\"MAX_VIEWPORT_DIMS\",3408:\"SUBPIXEL_BITS\",3410:\"RED_BITS\",3411:\"GREEN_BITS\",3412:\"BLUE_BITS\",3413:\"ALPHA_BITS\",3414:\"DEPTH_BITS\",3415:\"STENCIL_BITS\",3553:\"TEXTURE_2D\",4352:\"DONT_CARE\",4353:\"FASTEST\",4354:\"NICEST\",5120:\"BYTE\",5121:\"UNSIGNED_BYTE\",5122:\"SHORT\",5123:\"UNSIGNED_SHORT\",5124:\"INT\",5125:\"UNSIGNED_INT\",5126:\"FLOAT\",5386:\"INVERT\",5890:\"TEXTURE\",6401:\"STENCIL_INDEX\",6402:\"DEPTH_COMPONENT\",6406:\"ALPHA\",6407:\"RGB\",6408:\"RGBA\",6409:\"LUMINANCE\",6410:\"LUMINANCE_ALPHA\",7680:\"KEEP\",7681:\"REPLACE\",7682:\"INCR\",7683:\"DECR\",7936:\"VENDOR\",7937:\"RENDERER\",7938:\"VERSION\",9728:\"NEAREST\",9729:\"LINEAR\",9984:\"NEAREST_MIPMAP_NEAREST\",9985:\"LINEAR_MIPMAP_NEAREST\",9986:\"NEAREST_MIPMAP_LINEAR\",9987:\"LINEAR_MIPMAP_LINEAR\",10240:\"TEXTURE_MAG_FILTER\",10241:\"TEXTURE_MIN_FILTER\",10242:\"TEXTURE_WRAP_S\",10243:\"TEXTURE_WRAP_T\",10497:\"REPEAT\",10752:\"POLYGON_OFFSET_UNITS\",16384:\"COLOR_BUFFER_BIT\",32769:\"CONSTANT_COLOR\",32770:\"ONE_MINUS_CONSTANT_COLOR\",32771:\"CONSTANT_ALPHA\",32772:\"ONE_MINUS_CONSTANT_ALPHA\",32773:\"BLEND_COLOR\",32774:\"FUNC_ADD\",32777:\"BLEND_EQUATION_RGB\",32778:\"FUNC_SUBTRACT\",32779:\"FUNC_REVERSE_SUBTRACT\",32819:\"UNSIGNED_SHORT_4_4_4_4\",32820:\"UNSIGNED_SHORT_5_5_5_1\",32823:\"POLYGON_OFFSET_FILL\",32824:\"POLYGON_OFFSET_FACTOR\",32854:\"RGBA4\",32855:\"RGB5_A1\",32873:\"TEXTURE_BINDING_2D\",32926:\"SAMPLE_ALPHA_TO_COVERAGE\",32928:\"SAMPLE_COVERAGE\",32936:\"SAMPLE_BUFFERS\",32937:\"SAMPLES\",32938:\"SAMPLE_COVERAGE_VALUE\",32939:\"SAMPLE_COVERAGE_INVERT\",32968:\"BLEND_DST_RGB\",32969:\"BLEND_SRC_RGB\",32970:\"BLEND_DST_ALPHA\",32971:\"BLEND_SRC_ALPHA\",33071:\"CLAMP_TO_EDGE\",33170:\"GENERATE_MIPMAP_HINT\",33189:\"DEPTH_COMPONENT16\",33306:\"DEPTH_STENCIL_ATTACHMENT\",33635:\"UNSIGNED_SHORT_5_6_5\",33648:\"MIRRORED_REPEAT\",33901:\"ALIASED_POINT_SIZE_RANGE\",33902:\"ALIASED_LINE_WIDTH_RANGE\",33984:\"TEXTURE0\",33985:\"TEXTURE1\",33986:\"TEXTURE2\",33987:\"TEXTURE3\",33988:\"TEXTURE4\",33989:\"TEXTURE5\",33990:\"TEXTURE6\",33991:\"TEXTURE7\",33992:\"TEXTURE8\",33993:\"TEXTURE9\",33994:\"TEXTURE10\",33995:\"TEXTURE11\",33996:\"TEXTURE12\",33997:\"TEXTURE13\",33998:\"TEXTURE14\",33999:\"TEXTURE15\",34e3:\"TEXTURE16\",34001:\"TEXTURE17\",34002:\"TEXTURE18\",34003:\"TEXTURE19\",34004:\"TEXTURE20\",34005:\"TEXTURE21\",34006:\"TEXTURE22\",34007:\"TEXTURE23\",34008:\"TEXTURE24\",34009:\"TEXTURE25\",34010:\"TEXTURE26\",34011:\"TEXTURE27\",34012:\"TEXTURE28\",34013:\"TEXTURE29\",34014:\"TEXTURE30\",34015:\"TEXTURE31\",34016:\"ACTIVE_TEXTURE\",34024:\"MAX_RENDERBUFFER_SIZE\",34041:\"DEPTH_STENCIL\",34055:\"INCR_WRAP\",34056:\"DECR_WRAP\",34067:\"TEXTURE_CUBE_MAP\",34068:\"TEXTURE_BINDING_CUBE_MAP\",34069:\"TEXTURE_CUBE_MAP_POSITIVE_X\",34070:\"TEXTURE_CUBE_MAP_NEGATIVE_X\",34071:\"TEXTURE_CUBE_MAP_POSITIVE_Y\",34072:\"TEXTURE_CUBE_MAP_NEGATIVE_Y\",34073:\"TEXTURE_CUBE_MAP_POSITIVE_Z\",34074:\"TEXTURE_CUBE_MAP_NEGATIVE_Z\",34076:\"MAX_CUBE_MAP_TEXTURE_SIZE\",34338:\"VERTEX_ATTRIB_ARRAY_ENABLED\",34339:\"VERTEX_ATTRIB_ARRAY_SIZE\",34340:\"VERTEX_ATTRIB_ARRAY_STRIDE\",34341:\"VERTEX_ATTRIB_ARRAY_TYPE\",34342:\"CURRENT_VERTEX_ATTRIB\",34373:\"VERTEX_ATTRIB_ARRAY_POINTER\",34466:\"NUM_COMPRESSED_TEXTURE_FORMATS\",34467:\"COMPRESSED_TEXTURE_FORMATS\",34660:\"BUFFER_SIZE\",34661:\"BUFFER_USAGE\",34816:\"STENCIL_BACK_FUNC\",34817:\"STENCIL_BACK_FAIL\",34818:\"STENCIL_BACK_PASS_DEPTH_FAIL\",34819:\"STENCIL_BACK_PASS_DEPTH_PASS\",34877:\"BLEND_EQUATION_ALPHA\",34921:\"MAX_VERTEX_ATTRIBS\",34922:\"VERTEX_ATTRIB_ARRAY_NORMALIZED\",34930:\"MAX_TEXTURE_IMAGE_UNITS\",34962:\"ARRAY_BUFFER\",34963:\"ELEMENT_ARRAY_BUFFER\",34964:\"ARRAY_BUFFER_BINDING\",34965:\"ELEMENT_ARRAY_BUFFER_BINDING\",34975:\"VERTEX_ATTRIB_ARRAY_BUFFER_BINDING\",35040:\"STREAM_DRAW\",35044:\"STATIC_DRAW\",35048:\"DYNAMIC_DRAW\",35632:\"FRAGMENT_SHADER\",35633:\"VERTEX_SHADER\",35660:\"MAX_VERTEX_TEXTURE_IMAGE_UNITS\",35661:\"MAX_COMBINED_TEXTURE_IMAGE_UNITS\",35663:\"SHADER_TYPE\",35664:\"FLOAT_VEC2\",35665:\"FLOAT_VEC3\",35666:\"FLOAT_VEC4\",35667:\"INT_VEC2\",35668:\"INT_VEC3\",35669:\"INT_VEC4\",35670:\"BOOL\",35671:\"BOOL_VEC2\",35672:\"BOOL_VEC3\",35673:\"BOOL_VEC4\",35674:\"FLOAT_MAT2\",35675:\"FLOAT_MAT3\",35676:\"FLOAT_MAT4\",35678:\"SAMPLER_2D\",35680:\"SAMPLER_CUBE\",35712:\"DELETE_STATUS\",35713:\"COMPILE_STATUS\",35714:\"LINK_STATUS\",35715:\"VALIDATE_STATUS\",35716:\"INFO_LOG_LENGTH\",35717:\"ATTACHED_SHADERS\",35718:\"ACTIVE_UNIFORMS\",35719:\"ACTIVE_UNIFORM_MAX_LENGTH\",35720:\"SHADER_SOURCE_LENGTH\",35721:\"ACTIVE_ATTRIBUTES\",35722:\"ACTIVE_ATTRIBUTE_MAX_LENGTH\",35724:\"SHADING_LANGUAGE_VERSION\",35725:\"CURRENT_PROGRAM\",36003:\"STENCIL_BACK_REF\",36004:\"STENCIL_BACK_VALUE_MASK\",36005:\"STENCIL_BACK_WRITEMASK\",36006:\"FRAMEBUFFER_BINDING\",36007:\"RENDERBUFFER_BINDING\",36048:\"FRAMEBUFFER_ATTACHMENT_OBJECT_TYPE\",36049:\"FRAMEBUFFER_ATTACHMENT_OBJECT_NAME\",36050:\"FRAMEBUFFER_ATTACHMENT_TEXTURE_LEVEL\",36051:\"FRAMEBUFFER_ATTACHMENT_TEXTURE_CUBE_MAP_FACE\",36053:\"FRAMEBUFFER_COMPLETE\",36054:\"FRAMEBUFFER_INCOMPLETE_ATTACHMENT\",36055:\"FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT\",36057:\"FRAMEBUFFER_INCOMPLETE_DIMENSIONS\",36061:\"FRAMEBUFFER_UNSUPPORTED\",36064:\"COLOR_ATTACHMENT0\",36096:\"DEPTH_ATTACHMENT\",36128:\"STENCIL_ATTACHMENT\",36160:\"FRAMEBUFFER\",36161:\"RENDERBUFFER\",36162:\"RENDERBUFFER_WIDTH\",36163:\"RENDERBUFFER_HEIGHT\",36164:\"RENDERBUFFER_INTERNAL_FORMAT\",36168:\"STENCIL_INDEX8\",36176:\"RENDERBUFFER_RED_SIZE\",36177:\"RENDERBUFFER_GREEN_SIZE\",36178:\"RENDERBUFFER_BLUE_SIZE\",36179:\"RENDERBUFFER_ALPHA_SIZE\",36180:\"RENDERBUFFER_DEPTH_SIZE\",36181:\"RENDERBUFFER_STENCIL_SIZE\",36194:\"RGB565\",36336:\"LOW_FLOAT\",36337:\"MEDIUM_FLOAT\",36338:\"HIGH_FLOAT\",36339:\"LOW_INT\",36340:\"MEDIUM_INT\",36341:\"HIGH_INT\",36346:\"SHADER_COMPILER\",36347:\"MAX_VERTEX_UNIFORM_VECTORS\",36348:\"MAX_VARYING_VECTORS\",36349:\"MAX_FRAGMENT_UNIFORM_VECTORS\",37440:\"UNPACK_FLIP_Y_WEBGL\",37441:\"UNPACK_PREMULTIPLY_ALPHA_WEBGL\",37442:\"CONTEXT_LOST_WEBGL\",37443:\"UNPACK_COLORSPACE_CONVERSION_WEBGL\",37444:\"BROWSER_DEFAULT_WEBGL\"}},{}],268:[function(t,e,r){var 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v=0;v<o;++v)t.color[v]=h(r,i,a,g,r.RGBA,r.COLOR_ATTACHMENT0+v);0===o?(t._color_rb=p(r,i,a,r.RGBA4,r.COLOR_ATTACHMENT0),s&&s.drawBuffersWEBGL(l[0])):o>1&&s.drawBuffersWEBGL(l[o]);var y=r.getExtension(\"WEBGL_depth_texture\");y?d?t.depth=h(r,i,a,y.UNSIGNED_INT_24_8_WEBGL,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):m&&(t.depth=h(r,i,a,r.UNSIGNED_SHORT,r.DEPTH_COMPONENT,r.DEPTH_ATTACHMENT)):m&&d?t._depth_rb=p(r,i,a,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):m?t._depth_rb=p(r,i,a,r.DEPTH_COMPONENT16,r.DEPTH_ATTACHMENT):d&&(t._depth_rb=p(r,i,a,r.STENCIL_INDEX,r.STENCIL_ATTACHMENT));var x=r.checkFramebufferStatus(r.FRAMEBUFFER);if(x!==r.FRAMEBUFFER_COMPLETE){t._destroyed=!0,r.bindFramebuffer(r.FRAMEBUFFER,null),r.deleteFramebuffer(t.handle),t.handle=null,t.depth&&(t.depth.dispose(),t.depth=null),t._depth_rb&&(r.deleteRenderbuffer(t._depth_rb),t._depth_rb=null);for(v=0;v<t.color.length;++v)t.color[v].dispose(),t.color[v]=null;t._color_rb&&(r.deleteRenderbuffer(t._color_rb),t._color_rb=null),u(r,e),f(x)}u(r,e)}(this)}var m=d.prototype;function g(t,e,r){if(t._destroyed)throw new Error(\"gl-fbo: Can't resize destroyed FBO\");if(t._shape[0]!==e||t._shape[1]!==r){var n=t.gl,i=n.getParameter(n.MAX_RENDERBUFFER_SIZE);if(e<0||e>i||r<0||r>i)throw new Error(\"gl-fbo: Can't resize FBO, invalid dimensions\");t._shape[0]=e,t._shape[1]=r;for(var a=c(n),o=0;o<t.color.length;++o)t.color[o].shape=t._shape;t._color_rb&&(n.bindRenderbuffer(n.RENDERBUFFER,t._color_rb),n.renderbufferStorage(n.RENDERBUFFER,n.RGBA4,t._shape[0],t._shape[1])),t.depth&&(t.depth.shape=t._shape),t._depth_rb&&(n.bindRenderbuffer(n.RENDERBUFFER,t._depth_rb),t._useDepth&&t._useStencil?n.renderbufferStorage(n.RENDERBUFFER,n.DEPTH_STENCIL,t._shape[0],t._shape[1]):t._useDepth?n.renderbufferStorage(n.RENDERBUFFER,n.DEPTH_COMPONENT16,t._shape[0],t._shape[1]):t._useStencil&&n.renderbufferStorage(n.RENDERBUFFER,n.STENCIL_INDEX,t._shape[0],t._shape[1])),n.bindFramebuffer(n.FRAMEBUFFER,t.handle);var s=n.checkFramebufferStatus(n.FRAMEBUFFER);s!==n.FRAMEBUFFER_COMPLETE&&(t.dispose(),u(n,a),f(s)),u(n,a)}}Object.defineProperties(m,{shape:{get:function(){return this._destroyed?[0,0]:this._shapeVector},set:function(t){if(Array.isArray(t)||(t=[0|t,0|t]),2!==t.length)throw new Error(\"gl-fbo: Shape vector must be length 2\");var e=0|t[0],r=0|t[1];return g(this,e,r),[e,r]},enumerable:!1},width:{get:function(){return this._destroyed?0:this._shape[0]},set:function(t){return g(this,t|=0,this._shape[1]),t},enumerable:!1},height:{get:function(){return this._destroyed?0:this._shape[1]},set:function(t){return t|=0,g(this,this._shape[0],t),t},enumerable:!1}}),m.bind=function(){if(!this._destroyed){var t=this.gl;t.bindFramebuffer(t.FRAMEBUFFER,this.handle),t.viewport(0,0,this._shape[0],this._shape[1])}},m.dispose=function(){if(!this._destroyed){this._destroyed=!0;var t=this.gl;t.deleteFramebuffer(this.handle),this.handle=null,this.depth&&(this.depth.dispose(),this.depth=null),this._depth_rb&&(t.deleteRenderbuffer(this._depth_rb),this._depth_rb=null);for(var e=0;e<this.color.length;++e)this.color[e].dispose(),this.color[e]=null;this._color_rb&&(t.deleteRenderbuffer(this._color_rb),this._color_rb=null)}}},{\"gl-texture2d\":352}],273:[function(t,e,r){var n=t(\"sprintf-js\").sprintf,i=t(\"gl-constants/lookup\"),a=t(\"glsl-shader-name\"),o=t(\"add-line-numbers\");e.exports=function(t,e,r){\"use strict\";var s=a(e)||\"of unknown name (see npm glsl-shader-name)\",l=\"unknown type\";void 0!==r&&(l=r===i.FRAGMENT_SHADER?\"fragment\":\"vertex\");for(var c=n(\"Error compiling %s shader %s:\\n\",l,s),u=n(\"%s%s\",c,t),f=t.split(\"\\n\"),h={},p=0;p<f.length;p++){var d=f[p];if(\"\"!==d&&\"\\0\"!==d){var m=parseInt(d.split(\":\")[2]);if(isNaN(m))throw new Error(n(\"Could not parse error: %s\",d));h[m]=d}}var g=o(e).split(\"\\n\");for(p=0;p<g.length;p++)if(h[p+3]||h[p+2]||h[p+1]){var v=g[p];if(c+=v+\"\\n\",h[p+1]){var y=h[p+1];y=y.substr(y.split(\":\",3).join(\":\").length+1).trim(),c+=n(\"^^^ %s\\n\\n\",y)}}return{long:c.trim(),short:u.trim()}}},{\"add-line-numbers\":72,\"gl-constants/lookup\":268,\"glsl-shader-name\":430,\"sprintf-js\":564}],274:[function(t,e,r){\"use strict\";e.exports=function(t,e){var r=t.gl,n=o(r,l.vertex,l.fragment),i=o(r,l.pickVertex,l.pickFragment),a=s(r),u=s(r),f=s(r),h=s(r),p=new c(t,n,i,a,u,f,h);return p.update(e),t.addObject(p),p};var n=t(\"binary-search-bounds\"),i=t(\"iota-array\"),a=t(\"typedarray-pool\"),o=t(\"gl-shader\"),s=t(\"gl-buffer\"),l=t(\"./lib/shaders\");function c(t,e,r,n,i,a,o){this.plot=t,this.shader=e,this.pickShader=r,this.positionBuffer=n,this.weightBuffer=i,this.colorBuffer=a,this.idBuffer=o,this.xData=[],this.yData=[],this.shape=[0,0],this.bounds=[1/0,1/0,-1/0,-1/0],this.pickOffset=0}var u,f=c.prototype,h=[0,0,1,0,0,1,1,0,1,1,0,1];f.draw=(u=[1,0,0,0,1,0,0,0,1],function(){var t=this.plot,e=this.shader,r=this.bounds,n=this.numVertices;if(!(n<=0)){var i=t.gl,a=t.dataBox,o=r[2]-r[0],s=r[3]-r[1],l=a[2]-a[0],c=a[3]-a[1];u[0]=2*o/l,u[4]=2*s/c,u[6]=2*(r[0]-a[0])/l-1,u[7]=2*(r[1]-a[1])/c-1,e.bind();var f=e.uniforms;f.viewTransform=u,f.shape=this.shape;var h=e.attributes;this.positionBuffer.bind(),h.position.pointer(),this.weightBuffer.bind(),h.weight.pointer(i.UNSIGNED_BYTE,!1),this.colorBuffer.bind(),h.color.pointer(i.UNSIGNED_BYTE,!0),i.drawArrays(i.TRIANGLES,0,n)}}),f.drawPick=function(){var t=[1,0,0,0,1,0,0,0,1],e=[0,0,0,0];return function(r){var n=this.plot,i=this.pickShader,a=this.bounds,o=this.numVertices;if(!(o<=0)){var s=n.gl,l=n.dataBox,c=a[2]-a[0],u=a[3]-a[1],f=l[2]-l[0],h=l[3]-l[1];t[0]=2*c/f,t[4]=2*u/h,t[6]=2*(a[0]-l[0])/f-1,t[7]=2*(a[1]-l[1])/h-1;for(var p=0;p<4;++p)e[p]=r>>8*p&255;this.pickOffset=r,i.bind();var d=i.uniforms;d.viewTransform=t,d.pickOffset=e,d.shape=this.shape;var m=i.attributes;return this.positionBuffer.bind(),m.position.pointer(),this.weightBuffer.bind(),m.weight.pointer(s.UNSIGNED_BYTE,!1),this.idBuffer.bind(),m.pickId.pointer(s.UNSIGNED_BYTE,!1),s.drawArrays(s.TRIANGLES,0,o),r+this.shape[0]*this.shape[1]}}}(),f.pick=function(t,e,r){var n=this.pickOffset,i=this.shape[0]*this.shape[1];if(r<n||r>=n+i)return null;var a=r-n,o=this.xData,s=this.yData;return{object:this,pointId:a,dataCoord:[o[a%this.shape[0]],s[a/this.shape[0]|0]]}},f.update=function(t){var e=(t=t||{}).shape||[0,0],r=t.x||i(e[0]),o=t.y||i(e[1]),s=t.z||new Float32Array(e[0]*e[1]),l=!1!==t.zsmooth;this.xData=r,this.yData=o;var c,u,f,p,d=t.colorLevels||[0],m=t.colorValues||[0,0,0,1],g=d.length,v=this.bounds;l?(c=v[0]=r[0],u=v[1]=o[0],f=v[2]=r[r.length-1],p=v[3]=o[o.length-1]):(c=v[0]=r[0]+(r[1]-r[0])/2,u=v[1]=o[0]+(o[1]-o[0])/2,f=v[2]=r[r.length-1]+(r[r.length-1]-r[r.length-2])/2,p=v[3]=o[o.length-1]+(o[o.length-1]-o[o.length-2])/2);var y=1/(f-c),x=1/(p-u),b=e[0],_=e[1];this.shape=[b,_];var w=(l?(b-1)*(_-1):b*_)*(h.length>>>1);this.numVertices=w;for(var T=a.mallocUint8(4*w),k=a.mallocFloat32(2*w),M=a.mallocUint8(2*w),A=a.mallocUint32(w),S=0,E=l?b-1:b,L=l?_-1:_,C=0;C<L;++C){var P,I;l?(P=x*(o[C]-u),I=x*(o[C+1]-u)):(P=C<_-1?x*(o[C]-(o[C+1]-o[C])/2-u):x*(o[C]-(o[C]-o[C-1])/2-u),I=C<_-1?x*(o[C]+(o[C+1]-o[C])/2-u):x*(o[C]+(o[C]-o[C-1])/2-u));for(var O=0;O<E;++O){var z,D;l?(z=y*(r[O]-c),D=y*(r[O+1]-c)):(z=O<b-1?y*(r[O]-(r[O+1]-r[O])/2-c):y*(r[O]-(r[O]-r[O-1])/2-c),D=O<b-1?y*(r[O]+(r[O+1]-r[O])/2-c):y*(r[O]+(r[O]-r[O-1])/2-c));for(var R=0;R<h.length;R+=2){var F,B,N,j,U=h[R],V=h[R+1],q=s[l?(C+V)*b+(O+U):C*b+O],H=n.le(d,q);if(H<0)F=m[0],B=m[1],N=m[2],j=m[3];else if(H===g-1)F=m[4*g-4],B=m[4*g-3],N=m[4*g-2],j=m[4*g-1];else{var G=(q-d[H])/(d[H+1]-d[H]),Y=1-G,W=4*H,X=4*(H+1);F=Y*m[W]+G*m[X],B=Y*m[W+1]+G*m[X+1],N=Y*m[W+2]+G*m[X+2],j=Y*m[W+3]+G*m[X+3]}T[4*S]=255*F,T[4*S+1]=255*B,T[4*S+2]=255*N,T[4*S+3]=255*j,k[2*S]=.5*z+.5*D,k[2*S+1]=.5*P+.5*I,M[2*S]=U,M[2*S+1]=V,A[S]=C*b+O,S+=1}}}this.positionBuffer.update(k),this.weightBuffer.update(M),this.colorBuffer.update(T),this.idBuffer.update(A),a.free(k),a.free(T),a.free(M),a.free(A)},f.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.positionBuffer.dispose(),this.weightBuffer.dispose(),this.colorBuffer.dispose(),this.idBuffer.dispose(),this.plot.removeObject(this)}},{\"./lib/shaders\":275,\"binary-search-bounds\":103,\"gl-buffer\":263,\"gl-shader\":335,\"iota-array\":447,\"typedarray-pool\":613}],275:[function(t,e,r){\"use strict\";var n=t(\"glslify\");e.exports={fragment:n([\"precision lowp float;\\n#define GLSLIFY 1\\nvarying vec4 fragColor;\\nvoid main() {\\n  gl_FragColor = vec4(fragColor.rgb * fragColor.a, fragColor.a);\\n}\\n\"]),vertex:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\nattribute vec4 color;\\nattribute vec2 weight;\\n\\nuniform vec2 shape;\\nuniform mat3 viewTransform;\\n\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n  vec3 vPosition = viewTransform * vec3( position + (weight-.5)/(shape-1.) , 1.0);\\n  fragColor = color;\\n  gl_Position = vec4(vPosition.xy, 0, vPosition.z);\\n}\\n\"]),pickFragment:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragId;\\nvarying vec2 vWeight;\\n\\nuniform vec2 shape;\\nuniform vec4 pickOffset;\\n\\nvoid main() {\\n  vec2 d = step(.5, vWeight);\\n  vec4 id = fragId + pickOffset;\\n  id.x += d.x + d.y*shape.x;\\n\\n  id.y += floor(id.x / 256.0);\\n  id.x -= floor(id.x / 256.0) * 256.0;\\n\\n  id.z += floor(id.y / 256.0);\\n  id.y -= floor(id.y / 256.0) * 256.0;\\n\\n  id.w += floor(id.z / 256.0);\\n  id.z -= floor(id.z / 256.0) * 256.0;\\n\\n  gl_FragColor = id/255.;\\n}\\n\"]),pickVertex:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\nattribute vec4 pickId;\\nattribute vec2 weight;\\n\\nuniform vec2 shape;\\nuniform mat3 viewTransform;\\n\\nvarying vec4 fragId;\\nvarying vec2 vWeight;\\n\\nvoid main() {\\n  vWeight = weight;\\n\\n  fragId = pickId;\\n\\n  vec3 vPosition = viewTransform * vec3( position + (weight-.5)/(shape-1.) , 1.0);\\n  gl_Position = vec4(vPosition.xy, 0, vPosition.z);\\n}\\n\"])}},{glslify:438}],276:[function(t,e,r){var n=t(\"glslify\"),i=t(\"gl-shader\"),a=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position, nextPosition;\\nattribute float arcLength, lineWidth;\\nattribute vec4 color;\\n\\nuniform vec2 screenShape;\\nuniform float pixelRatio;\\nuniform mat4 model, view, projection;\\n\\nvarying vec4 fragColor;\\nvarying vec3 worldPosition;\\nvarying float pixelArcLength;\\n\\nvec4 project(vec3 p) {\\n  return projection * view * model * vec4(p, 1.0);\\n}\\n\\nvoid main() {\\n  vec4 startPoint = project(position);\\n  vec4 endPoint   = project(nextPosition);\\n\\n  vec2 A = startPoint.xy / startPoint.w;\\n  vec2 B =   endPoint.xy /   endPoint.w;\\n\\n  float clipAngle = atan(\\n    (B.y - A.y) * screenShape.y,\\n    (B.x - A.x) * screenShape.x\\n  );\\n\\n  vec2 offset = 0.5 * pixelRatio * lineWidth * vec2(\\n    sin(clipAngle),\\n    -cos(clipAngle)\\n  ) / screenShape;\\n\\n  gl_Position = vec4(startPoint.xy + startPoint.w * offset, startPoint.zw);\\n\\n  worldPosition = position;\\n  pixelArcLength = arcLength;\\n  fragColor = color;\\n}\\n\"]),o=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3      clipBounds[2];\\nuniform sampler2D dashTexture;\\nuniform float     dashScale;\\nuniform float     opacity;\\n\\nvarying vec3    worldPosition;\\nvarying float   pixelArcLength;\\nvarying vec4    fragColor;\\n\\nvoid main() {\\n  if (\\n    outOfRange(clipBounds[0], clipBounds[1], worldPosition) ||\\n    fragColor.a * opacity == 0.\\n  ) discard;\\n\\n  float dashWeight = texture2D(dashTexture, vec2(dashScale * pixelArcLength, 0)).r;\\n  if(dashWeight < 0.5) {\\n    discard;\\n  }\\n  gl_FragColor = fragColor * opacity;\\n}\\n\"]),s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\n#define FLOAT_MAX  1.70141184e38\\n#define FLOAT_MIN  1.17549435e-38\\n\\n// https://github.com/mikolalysenko/glsl-read-float/blob/master/index.glsl\\nvec4 packFloat(float v) {\\n  float av = abs(v);\\n\\n  //Handle special cases\\n  if(av < FLOAT_MIN) {\\n    return vec4(0.0, 0.0, 0.0, 0.0);\\n  } else if(v > FLOAT_MAX) {\\n    return vec4(127.0, 128.0, 0.0, 0.0) / 255.0;\\n  } else if(v < -FLOAT_MAX) {\\n    return vec4(255.0, 128.0, 0.0, 0.0) / 255.0;\\n  }\\n\\n  vec4 c = vec4(0,0,0,0);\\n\\n  //Compute exponent and mantissa\\n  float e = floor(log2(av));\\n  float m = av * pow(2.0, -e) - 1.0;\\n\\n  //Unpack mantissa\\n  c[1] = floor(128.0 * m);\\n  m -= c[1] / 128.0;\\n  c[2] = floor(32768.0 * m);\\n  m -= c[2] / 32768.0;\\n  c[3] = floor(8388608.0 * m);\\n\\n  //Unpack exponent\\n  float ebias = e + 127.0;\\n  c[0] = floor(ebias / 2.0);\\n  ebias -= c[0] * 2.0;\\n  c[1] += floor(ebias) * 128.0;\\n\\n  //Unpack sign bit\\n  c[0] += 128.0 * step(0.0, -v);\\n\\n  //Scale back to range\\n  return c / 255.0;\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform float pickId;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec3 worldPosition;\\nvarying float pixelArcLength;\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], worldPosition)) discard;\\n\\n  gl_FragColor = vec4(pickId/255.0, packFloat(pixelArcLength).xyz);\\n}\"]),l=[{name:\"position\",type:\"vec3\"},{name:\"nextPosition\",type:\"vec3\"},{name:\"arcLength\",type:\"float\"},{name:\"lineWidth\",type:\"float\"},{name:\"color\",type:\"vec4\"}];r.createShader=function(t){return i(t,a,o,null,l)},r.createPickShader=function(t){return i(t,a,s,null,l)}},{\"gl-shader\":335,glslify:278}],277:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl||t.scene&&t.scene.gl,r=f(e);r.attributes.position.location=0,r.attributes.nextPosition.location=1,r.attributes.arcLength.location=2,r.attributes.lineWidth.location=3,r.attributes.color.location=4;var o=h(e);o.attributes.position.location=0,o.attributes.nextPosition.location=1,o.attributes.arcLength.location=2,o.attributes.lineWidth.location=3,o.attributes.color.location=4;for(var s=n(e),l=i(e,[{buffer:s,size:3,offset:0,stride:48},{buffer:s,size:3,offset:12,stride:48},{buffer:s,size:1,offset:24,stride:48},{buffer:s,size:1,offset:28,stride:48},{buffer:s,size:4,offset:32,stride:48}]),u=c(new Array(1024),[256,1,4]),p=0;p<1024;++p)u.data[p]=255;var d=a(e,u);d.wrap=e.REPEAT;var m=new v(e,r,o,s,l,d);return m.update(t),m};var n=t(\"gl-buffer\"),i=t(\"gl-vao\"),a=t(\"gl-texture2d\"),o=new Uint8Array(4),s=new Float32Array(o.buffer);var l=t(\"binary-search-bounds\"),c=t(\"ndarray\"),u=t(\"./lib/shaders\"),f=u.createShader,h=u.createPickShader,p=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function d(t,e){for(var r=0,n=0;n<3;++n){var i=t[n]-e[n];r+=i*i}return Math.sqrt(r)}function m(t){for(var e=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],r=0;r<3;++r)e[0][r]=Math.max(t[0][r],e[0][r]),e[1][r]=Math.min(t[1][r],e[1][r]);return e}function g(t,e,r,n){this.arcLength=t,this.position=e,this.index=r,this.dataCoordinate=n}function v(t,e,r,n,i,a){this.gl=t,this.shader=e,this.pickShader=r,this.buffer=n,this.vao=i,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.points=[],this.arcLength=[],this.vertexCount=0,this.bounds=[[0,0,0],[0,0,0]],this.pickId=0,this.lineWidth=1,this.texture=a,this.dashScale=1,this.opacity=1,this.hasAlpha=!1,this.dirty=!0,this.pixelRatio=1}var y=v.prototype;y.isTransparent=function(){return this.hasAlpha},y.isOpaque=function(){return!this.hasAlpha},y.pickSlots=1,y.setPickBase=function(t){this.pickId=t},y.drawTransparent=y.draw=function(t){if(this.vertexCount){var e=this.gl,r=this.shader,n=this.vao;r.bind(),r.uniforms={model:t.model||p,view:t.view||p,projection:t.projection||p,clipBounds:m(this.clipBounds),dashTexture:this.texture.bind(),dashScale:this.dashScale/this.arcLength[this.arcLength.length-1],opacity:this.opacity,screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount),n.unbind()}},y.drawPick=function(t){if(this.vertexCount){var e=this.gl,r=this.pickShader,n=this.vao;r.bind(),r.uniforms={model:t.model||p,view:t.view||p,projection:t.projection||p,pickId:this.pickId,clipBounds:m(this.clipBounds),screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount),n.unbind()}},y.update=function(t){var e,r;this.dirty=!0;var n=!!t.connectGaps;\"dashScale\"in t&&(this.dashScale=t.dashScale),this.hasAlpha=!1,\"opacity\"in t&&(this.opacity=+t.opacity,this.opacity<1&&(this.hasAlpha=!0));var i=[],a=[],o=[],s=0,u=0,f=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],h=t.position||t.positions;if(h){var p=t.color||t.colors||[0,0,0,1],m=t.lineWidth||1,g=!1;t:for(e=1;e<h.length;++e){var v,y,x,b=h[e-1],_=h[e];for(a.push(s),o.push(b.slice()),r=0;r<3;++r){if(isNaN(b[r])||isNaN(_[r])||!isFinite(b[r])||!isFinite(_[r])){if(!n&&i.length>0){for(var w=0;w<24;++w)i.push(i[i.length-12]);u+=2,g=!0}continue t}f[0][r]=Math.min(f[0][r],b[r],_[r]),f[1][r]=Math.max(f[1][r],b[r],_[r])}Array.isArray(p[0])?(v=p.length>e-1?p[e-1]:p.length>0?p[p.length-1]:[0,0,0,1],y=p.length>e?p[e]:p.length>0?p[p.length-1]:[0,0,0,1]):v=y=p,3===v.length&&(v=[v[0],v[1],v[2],1]),3===y.length&&(y=[y[0],y[1],y[2],1]),!this.hasAlpha&&v[3]<1&&(this.hasAlpha=!0),x=Array.isArray(m)?m.length>e-1?m[e-1]:m.length>0?m[m.length-1]:[0,0,0,1]:m;var T=s;if(s+=d(b,_),g){for(r=0;r<2;++r)i.push(b[0],b[1],b[2],_[0],_[1],_[2],T,x,v[0],v[1],v[2],v[3]);u+=2,g=!1}i.push(b[0],b[1],b[2],_[0],_[1],_[2],T,x,v[0],v[1],v[2],v[3],b[0],b[1],b[2],_[0],_[1],_[2],T,-x,v[0],v[1],v[2],v[3],_[0],_[1],_[2],b[0],b[1],b[2],s,-x,y[0],y[1],y[2],y[3],_[0],_[1],_[2],b[0],b[1],b[2],s,x,y[0],y[1],y[2],y[3]),u+=4}}if(this.buffer.update(i),a.push(s),o.push(h[h.length-1].slice()),this.bounds=f,this.vertexCount=u,this.points=o,this.arcLength=a,\"dashes\"in t){var k=t.dashes.slice();for(k.unshift(0),e=1;e<k.length;++e)k[e]=k[e-1]+k[e];var M=c(new Array(1024),[256,1,4]);for(e=0;e<256;++e){for(r=0;r<4;++r)M.set(e,0,r,0);1&l.le(k,k[k.length-1]*e/255)?M.set(e,0,0,0):M.set(e,0,0,255)}this.texture.setPixels(M)}},y.dispose=function(){this.shader.dispose(),this.vao.dispose(),this.buffer.dispose()},y.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=function(t,e,r,n){return o[0]=n,o[1]=r,o[2]=e,o[3]=t,s[0]}(t.value[0],t.value[1],t.value[2],0),r=l.le(this.arcLength,e);if(r<0)return null;if(r===this.arcLength.length-1)return new g(this.arcLength[this.arcLength.length-1],this.points[this.points.length-1].slice(),r);for(var n=this.points[r],i=this.points[Math.min(r+1,this.points.length-1)],a=(e-this.arcLength[r])/(this.arcLength[r+1]-this.arcLength[r]),c=1-a,u=[0,0,0],f=0;f<3;++f)u[f]=c*n[f]+a*i[f];var h=Math.min(a<.5?r:r+1,this.points.length-1);return new g(e,u,h,this.points[h])}},{\"./lib/shaders\":276,\"binary-search-bounds\":103,\"gl-buffer\":263,\"gl-texture2d\":352,\"gl-vao\":357,ndarray:479}],278:[function(t,e,r){arguments[4][261][0].apply(r,arguments)},{dup:261}],279:[function(t,e,r){e.exports=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=e[4],s=e[5],l=e[6],c=e[7],u=e[8],f=e[9],h=e[10],p=e[11],d=e[12],m=e[13],g=e[14],v=e[15];return t[0]=s*(h*v-p*g)-f*(l*v-c*g)+m*(l*p-c*h),t[1]=-(n*(h*v-p*g)-f*(i*v-a*g)+m*(i*p-a*h)),t[2]=n*(l*v-c*g)-s*(i*v-a*g)+m*(i*c-a*l),t[3]=-(n*(l*p-c*h)-s*(i*p-a*h)+f*(i*c-a*l)),t[4]=-(o*(h*v-p*g)-u*(l*v-c*g)+d*(l*p-c*h)),t[5]=r*(h*v-p*g)-u*(i*v-a*g)+d*(i*p-a*h),t[6]=-(r*(l*v-c*g)-o*(i*v-a*g)+d*(i*c-a*l)),t[7]=r*(l*p-c*h)-o*(i*p-a*h)+u*(i*c-a*l),t[8]=o*(f*v-p*m)-u*(s*v-c*m)+d*(s*p-c*f),t[9]=-(r*(f*v-p*m)-u*(n*v-a*m)+d*(n*p-a*f)),t[10]=r*(s*v-c*m)-o*(n*v-a*m)+d*(n*c-a*s),t[11]=-(r*(s*p-c*f)-o*(n*p-a*f)+u*(n*c-a*s)),t[12]=-(o*(f*g-h*m)-u*(s*g-l*m)+d*(s*h-l*f)),t[13]=r*(f*g-h*m)-u*(n*g-i*m)+d*(n*h-i*f),t[14]=-(r*(s*g-l*m)-o*(n*g-i*m)+d*(n*l-i*s)),t[15]=r*(s*h-l*f)-o*(n*h-i*f)+u*(n*l-i*s),t}},{}],280:[function(t,e,r){e.exports=function(t){var e=new Float32Array(16);return 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t[0]=1-(p+m),t[1]=f+y,t[2]=h-v,t[3]=0,t[4]=f-y,t[5]=1-(u+m),t[6]=d+g,t[7]=0,t[8]=h+v,t[9]=d-g,t[10]=1-(u+p),t[11]=0,t[12]=r[0],t[13]=r[1],t[14]=r[2],t[15]=1,t}},{}],287:[function(t,e,r){e.exports=function(t,e){return t[0]=e[0],t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=e[1],t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=e[2],t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],288:[function(t,e,r){e.exports=function(t,e){return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=e[0],t[13]=e[1],t[14]=e[2],t[15]=1,t}},{}],289:[function(t,e,r){e.exports=function(t,e){var r=Math.sin(e),n=Math.cos(e);return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=n,t[6]=r,t[7]=0,t[8]=0,t[9]=-r,t[10]=n,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],290:[function(t,e,r){e.exports=function(t,e){var r=Math.sin(e),n=Math.cos(e);return t[0]=n,t[1]=0,t[2]=-r,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=r,t[9]=0,t[10]=n,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],291:[function(t,e,r){e.exports=function(t,e){var r=Math.sin(e),n=Math.cos(e);return t[0]=n,t[1]=r,t[2]=0,t[3]=0,t[4]=-r,t[5]=n,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],292:[function(t,e,r){e.exports=function(t,e,r,n,i,a,o){var s=1/(r-e),l=1/(i-n),c=1/(a-o);return t[0]=2*a*s,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=2*a*l,t[6]=0,t[7]=0,t[8]=(r+e)*s,t[9]=(i+n)*l,t[10]=(o+a)*c,t[11]=-1,t[12]=0,t[13]=0,t[14]=o*a*2*c,t[15]=0,t}},{}],293:[function(t,e,r){e.exports=function(t){return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],294:[function(t,e,r){e.exports={create:t(\"./create\"),clone:t(\"./clone\"),copy:t(\"./copy\"),identity:t(\"./identity\"),transpose:t(\"./transpose\"),invert:t(\"./invert\"),adjoint:t(\"./adjoint\"),determinant:t(\"./determinant\"),multiply:t(\"./multiply\"),translate:t(\"./translate\"),scale:t(\"./scale\"),rotate:t(\"./rotate\"),rotateX:t(\"./rotateX\"),rotateY:t(\"./rotateY\"),rotateZ:t(\"./rotateZ\"),fromRotation:t(\"./fromRotation\"),fromRotationTranslation:t(\"./fromRotationTranslation\"),fromScaling:t(\"./fromScaling\"),fromTranslation:t(\"./fromTranslation\"),fromXRotation:t(\"./fromXRotation\"),fromYRotation:t(\"./fromYRotation\"),fromZRotation:t(\"./fromZRotation\"),fromQuat:t(\"./fromQuat\"),frustum:t(\"./frustum\"),perspective:t(\"./perspective\"),perspectiveFromFieldOfView:t(\"./perspectiveFromFieldOfView\"),ortho:t(\"./ortho\"),lookAt:t(\"./lookAt\"),str:t(\"./str\")}},{\"./adjoint\":279,\"./clone\":280,\"./copy\":281,\"./create\":282,\"./determinant\":283,\"./fromQuat\":284,\"./fromRotation\":285,\"./fromRotationTranslation\":286,\"./fromScaling\":287,\"./fromTranslation\":288,\"./fromXRotation\":289,\"./fromYRotation\":290,\"./fromZRotation\":291,\"./frustum\":292,\"./identity\":293,\"./invert\":295,\"./lookAt\":296,\"./multiply\":297,\"./ortho\":298,\"./perspective\":299,\"./perspectiveFromFieldOfView\":300,\"./rotate\":301,\"./rotateX\":302,\"./rotateY\":303,\"./rotateZ\":304,\"./scale\":305,\"./str\":306,\"./translate\":307,\"./transpose\":308}],295:[function(t,e,r){e.exports=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=e[4],s=e[5],l=e[6],c=e[7],u=e[8],f=e[9],h=e[10],p=e[11],d=e[12],m=e[13],g=e[14],v=e[15],y=r*s-n*o,x=r*l-i*o,b=r*c-a*o,_=n*l-i*s,w=n*c-a*s,T=i*c-a*l,k=u*m-f*d,M=u*g-h*d,A=u*v-p*d,S=f*g-h*m,E=f*v-p*m,L=h*v-p*g,C=y*L-x*E+b*S+_*A-w*M+T*k;if(!C)return null;return C=1/C,t[0]=(s*L-l*E+c*S)*C,t[1]=(i*E-n*L-a*S)*C,t[2]=(m*T-g*w+v*_)*C,t[3]=(h*w-f*T-p*_)*C,t[4]=(l*A-o*L-c*M)*C,t[5]=(r*L-i*A+a*M)*C,t[6]=(g*b-d*T-v*x)*C,t[7]=(u*T-h*b+p*x)*C,t[8]=(o*E-s*A+c*k)*C,t[9]=(n*A-r*E-a*k)*C,t[10]=(d*w-m*b+v*y)*C,t[11]=(f*b-u*w-p*y)*C,t[12]=(s*M-o*S-l*k)*C,t[13]=(r*S-n*M+i*k)*C,t[14]=(m*x-d*_-g*y)*C,t[15]=(u*_-f*x+h*y)*C,t}},{}],296:[function(t,e,r){var n=t(\"./identity\");e.exports=function(t,e,r,i){var a,o,s,l,c,u,f,h,p,d,m=e[0],g=e[1],v=e[2],y=i[0],x=i[1],b=i[2],_=r[0],w=r[1],T=r[2];if(Math.abs(m-_)<1e-6&&Math.abs(g-w)<1e-6&&Math.abs(v-T)<1e-6)return n(t);f=m-_,h=g-w,p=v-T,d=1/Math.sqrt(f*f+h*h+p*p),a=x*(p*=d)-b*(h*=d),o=b*(f*=d)-y*p,s=y*h-x*f,(d=Math.sqrt(a*a+o*o+s*s))?(a*=d=1/d,o*=d,s*=d):(a=0,o=0,s=0);l=h*s-p*o,c=p*a-f*s,u=f*o-h*a,(d=Math.sqrt(l*l+c*c+u*u))?(l*=d=1/d,c*=d,u*=d):(l=0,c=0,u=0);return t[0]=a,t[1]=l,t[2]=f,t[3]=0,t[4]=o,t[5]=c,t[6]=h,t[7]=0,t[8]=s,t[9]=u,t[10]=p,t[11]=0,t[12]=-(a*m+o*g+s*v),t[13]=-(l*m+c*g+u*v),t[14]=-(f*m+h*g+p*v),t[15]=1,t}},{\"./identity\":293}],297:[function(t,e,r){e.exports=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],c=e[6],u=e[7],f=e[8],h=e[9],p=e[10],d=e[11],m=e[12],g=e[13],v=e[14],y=e[15],x=r[0],b=r[1],_=r[2],w=r[3];return t[0]=x*n+b*s+_*f+w*m,t[1]=x*i+b*l+_*h+w*g,t[2]=x*a+b*c+_*p+w*v,t[3]=x*o+b*u+_*d+w*y,x=r[4],b=r[5],_=r[6],w=r[7],t[4]=x*n+b*s+_*f+w*m,t[5]=x*i+b*l+_*h+w*g,t[6]=x*a+b*c+_*p+w*v,t[7]=x*o+b*u+_*d+w*y,x=r[8],b=r[9],_=r[10],w=r[11],t[8]=x*n+b*s+_*f+w*m,t[9]=x*i+b*l+_*h+w*g,t[10]=x*a+b*c+_*p+w*v,t[11]=x*o+b*u+_*d+w*y,x=r[12],b=r[13],_=r[14],w=r[15],t[12]=x*n+b*s+_*f+w*m,t[13]=x*i+b*l+_*h+w*g,t[14]=x*a+b*c+_*p+w*v,t[15]=x*o+b*u+_*d+w*y,t}},{}],298:[function(t,e,r){e.exports=function(t,e,r,n,i,a,o){var s=1/(e-r),l=1/(n-i),c=1/(a-o);return t[0]=-2*s,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=-2*l,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=2*c,t[11]=0,t[12]=(e+r)*s,t[13]=(i+n)*l,t[14]=(o+a)*c,t[15]=1,t}},{}],299:[function(t,e,r){e.exports=function(t,e,r,n,i){var a=1/Math.tan(e/2),o=1/(n-i);return t[0]=a/r,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=a,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=(i+n)*o,t[11]=-1,t[12]=0,t[13]=0,t[14]=2*i*n*o,t[15]=0,t}},{}],300:[function(t,e,r){e.exports=function(t,e,r,n){var i=Math.tan(e.upDegrees*Math.PI/180),a=Math.tan(e.downDegrees*Math.PI/180),o=Math.tan(e.leftDegrees*Math.PI/180),s=Math.tan(e.rightDegrees*Math.PI/180),l=2/(o+s),c=2/(i+a);return t[0]=l,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=c,t[6]=0,t[7]=0,t[8]=-(o-s)*l*.5,t[9]=(i-a)*c*.5,t[10]=n/(r-n),t[11]=-1,t[12]=0,t[13]=0,t[14]=n*r/(r-n),t[15]=0,t}},{}],301:[function(t,e,r){e.exports=function(t,e,r,n){var i,a,o,s,l,c,u,f,h,p,d,m,g,v,y,x,b,_,w,T,k,M,A,S,E=n[0],L=n[1],C=n[2],P=Math.sqrt(E*E+L*L+C*C);if(Math.abs(P)<1e-6)return null;E*=P=1/P,L*=P,C*=P,i=Math.sin(r),a=Math.cos(r),o=1-a,s=e[0],l=e[1],c=e[2],u=e[3],f=e[4],h=e[5],p=e[6],d=e[7],m=e[8],g=e[9],v=e[10],y=e[11],x=E*E*o+a,b=L*E*o+C*i,_=C*E*o-L*i,w=E*L*o-C*i,T=L*L*o+a,k=C*L*o+E*i,M=E*C*o+L*i,A=L*C*o-E*i,S=C*C*o+a,t[0]=s*x+f*b+m*_,t[1]=l*x+h*b+g*_,t[2]=c*x+p*b+v*_,t[3]=u*x+d*b+y*_,t[4]=s*w+f*T+m*k,t[5]=l*w+h*T+g*k,t[6]=c*w+p*T+v*k,t[7]=u*w+d*T+y*k,t[8]=s*M+f*A+m*S,t[9]=l*M+h*A+g*S,t[10]=c*M+p*A+v*S,t[11]=u*M+d*A+y*S,e!==t&&(t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t}},{}],302:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[4],o=e[5],s=e[6],l=e[7],c=e[8],u=e[9],f=e[10],h=e[11];e!==t&&(t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=e[3],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[4]=a*i+c*n,t[5]=o*i+u*n,t[6]=s*i+f*n,t[7]=l*i+h*n,t[8]=c*i-a*n,t[9]=u*i-o*n,t[10]=f*i-s*n,t[11]=h*i-l*n,t}},{}],303:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],c=e[8],u=e[9],f=e[10],h=e[11];e!==t&&(t[4]=e[4],t[5]=e[5],t[6]=e[6],t[7]=e[7],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[0]=a*i-c*n,t[1]=o*i-u*n,t[2]=s*i-f*n,t[3]=l*i-h*n,t[8]=a*n+c*i,t[9]=o*n+u*i,t[10]=s*n+f*i,t[11]=l*n+h*i,t}},{}],304:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],c=e[4],u=e[5],f=e[6],h=e[7];e!==t&&(t[8]=e[8],t[9]=e[9],t[10]=e[10],t[11]=e[11],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[0]=a*i+c*n,t[1]=o*i+u*n,t[2]=s*i+f*n,t[3]=l*i+h*n,t[4]=c*i-a*n,t[5]=u*i-o*n,t[6]=f*i-s*n,t[7]=h*i-l*n,t}},{}],305:[function(t,e,r){e.exports=function(t,e,r){var n=r[0],i=r[1],a=r[2];return t[0]=e[0]*n,t[1]=e[1]*n,t[2]=e[2]*n,t[3]=e[3]*n,t[4]=e[4]*i,t[5]=e[5]*i,t[6]=e[6]*i,t[7]=e[7]*i,t[8]=e[8]*a,t[9]=e[9]*a,t[10]=e[10]*a,t[11]=e[11]*a,t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15],t}},{}],306:[function(t,e,r){e.exports=function(t){return\"mat4(\"+t[0]+\", \"+t[1]+\", \"+t[2]+\", \"+t[3]+\", \"+t[4]+\", \"+t[5]+\", \"+t[6]+\", \"+t[7]+\", \"+t[8]+\", \"+t[9]+\", \"+t[10]+\", \"+t[11]+\", \"+t[12]+\", \"+t[13]+\", \"+t[14]+\", \"+t[15]+\")\"}},{}],307:[function(t,e,r){e.exports=function(t,e,r){var n,i,a,o,s,l,c,u,f,h,p,d,m=r[0],g=r[1],v=r[2];e===t?(t[12]=e[0]*m+e[4]*g+e[8]*v+e[12],t[13]=e[1]*m+e[5]*g+e[9]*v+e[13],t[14]=e[2]*m+e[6]*g+e[10]*v+e[14],t[15]=e[3]*m+e[7]*g+e[11]*v+e[15]):(n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],c=e[6],u=e[7],f=e[8],h=e[9],p=e[10],d=e[11],t[0]=n,t[1]=i,t[2]=a,t[3]=o,t[4]=s,t[5]=l,t[6]=c,t[7]=u,t[8]=f,t[9]=h,t[10]=p,t[11]=d,t[12]=n*m+s*g+f*v+e[12],t[13]=i*m+l*g+h*v+e[13],t[14]=a*m+c*g+p*v+e[14],t[15]=o*m+u*g+d*v+e[15]);return t}},{}],308:[function(t,e,r){e.exports=function(t,e){if(t===e){var r=e[1],n=e[2],i=e[3],a=e[6],o=e[7],s=e[11];t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=r,t[6]=e[9],t[7]=e[13],t[8]=n,t[9]=a,t[11]=e[14],t[12]=i,t[13]=o,t[14]=s}else t[0]=e[0],t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=e[1],t[5]=e[5],t[6]=e[9],t[7]=e[13],t[8]=e[2],t[9]=e[6],t[10]=e[10],t[11]=e[14],t[12]=e[3],t[13]=e[7],t[14]=e[11],t[15]=e[15];return t}},{}],309:[function(t,e,r){\"use strict\";var n=t(\"barycentric\"),i=t(\"polytope-closest-point/lib/closest_point_2d.js\");function a(t,e){for(var r=[0,0,0,0],n=0;n<4;++n)for(var i=0;i<4;++i)r[i]+=t[4*n+i]*e[n];return r}function o(t,e,r,n,i){for(var o=a(n,a(r,a(e,[t[0],t[1],t[2],1]))),s=0;s<3;++s)o[s]/=o[3];return[.5*i[0]*(1+o[0]),.5*i[1]*(1-o[1])]}function s(t,e){for(var r=[0,0,0],n=0;n<t.length;++n)for(var i=t[n],a=e[n],o=0;o<3;++o)r[o]+=a*i[o];return r}e.exports=function(t,e,r,a,l,c){if(1===t.length)return[0,t[0].slice()];for(var u=new Array(t.length),f=0;f<t.length;++f)u[f]=o(t[f],r,a,l,c);var h=0,p=1/0;for(f=0;f<u.length;++f){for(var d=0,m=0;m<2;++m)d+=Math.pow(u[f][m]-e[m],2);d<p&&(p=d,h=f)}var g=function(t,e){if(2===t.length){for(var r=0,a=0,o=0;o<2;++o)r+=Math.pow(e[o]-t[0][o],2),a+=Math.pow(e[o]-t[1][o],2);return r=Math.sqrt(r),a=Math.sqrt(a),r+a<1e-6?[1,0]:[a/(r+a),r/(a+r)]}if(3===t.length){var s=[0,0];return i(t[0],t[1],t[2],e,s),n(t,s)}return[]}(u,e),v=0;for(f=0;f<3;++f){if(g[f]<-.001||g[f]>1.0001)return null;v+=g[f]}if(Math.abs(v-1)>.001)return null;return[h,s(t,g),g]}},{barycentric:85,\"polytope-closest-point/lib/closest_point_2d.js\":508}],310:[function(t,e,r){var n=t(\"glslify\"),i=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position, normal;\\nattribute vec4 color;\\nattribute vec2 uv;\\n\\nuniform mat4 model\\n           , view\\n           , projection\\n           , inverseModel;\\nuniform vec3 eyePosition\\n           , lightPosition;\\n\\nvarying vec3 f_normal\\n           , f_lightDirection\\n           , f_eyeDirection\\n           , f_data;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvec4 project(vec3 p) {\\n  return projection * view * model * vec4(p, 1.0);\\n}\\n\\nvoid main() {\\n  gl_Position      = project(position);\\n\\n  //Lighting geometry parameters\\n  vec4 cameraCoordinate = view * vec4(position , 1.0);\\n  cameraCoordinate.xyz /= cameraCoordinate.w;\\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\\n  f_normal  = normalize((vec4(normal, 0.0) * inverseModel).xyz);\\n\\n  f_color          = color;\\n  f_data           = position;\\n  f_uv             = uv;\\n}\\n\"]),a=n([\"#extension GL_OES_standard_derivatives : enable\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n  float NdotH = max(x, 0.0001);\\n  float cos2Alpha = NdotH * NdotH;\\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n  float roughness2 = roughness * roughness;\\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n  return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat cookTorranceSpecular(\\n  vec3 lightDirection,\\n  vec3 viewDirection,\\n  vec3 surfaceNormal,\\n  float roughness,\\n  float fresnel) {\\n\\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\\n\\n  //Half angle vector\\n  vec3 H = normalize(lightDirection + viewDirection);\\n\\n  //Geometric term\\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\\n  float VdotH = max(dot(viewDirection, H), 0.000001);\\n  float LdotH = max(dot(lightDirection, H), 0.000001);\\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\\n  float G = min(1.0, min(G1, G2));\\n  \\n  //Distribution term\\n  float D = beckmannDistribution(NdotH, roughness);\\n\\n  //Fresnel term\\n  float F = pow(1.0 - VdotN, fresnel);\\n\\n  //Multiply terms and done\\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\\n}\\n\\n//#pragma glslify: beckmann = require(glsl-specular-beckmann) // used in gl-surface3d\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float roughness\\n            , fresnel\\n            , kambient\\n            , kdiffuse\\n            , kspecular;\\nuniform sampler2D texture;\\n\\nvarying vec3 f_normal\\n           , f_lightDirection\\n           , f_eyeDirection\\n           , f_data;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if (f_color.a == 0.0 ||\\n    outOfRange(clipBounds[0], clipBounds[1], f_data)\\n  ) discard;\\n\\n  vec3 N = normalize(f_normal);\\n  vec3 L = normalize(f_lightDirection);\\n  vec3 V = normalize(f_eyeDirection);\\n\\n  if(gl_FrontFacing) {\\n    N = -N;\\n  }\\n\\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\\n  //float specular = max(0.0, beckmann(L, V, N, roughness)); // used in gl-surface3d\\n\\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n  vec4 surfaceColor = vec4(f_color.rgb, 1.0) * texture2D(texture, f_uv);\\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\\n\\n  gl_FragColor = litColor * f_color.a;\\n}\\n\"]),o=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 uv;\\n\\nuniform mat4 model, view, projection;\\n\\nvarying vec4 f_color;\\nvarying vec3 f_data;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  gl_Position = projection * view * model * vec4(position, 1.0);\\n  f_color = color;\\n  f_data  = position;\\n  f_uv    = uv;\\n}\"]),s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform sampler2D texture;\\nuniform float opacity;\\n\\nvarying vec4 f_color;\\nvarying vec3 f_data;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_data)) discard;\\n\\n  gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\\n}\"]),l=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 uv;\\nattribute float pointSize;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0.0, 0.0 ,0.0 ,0.0);\\n  } else {\\n    gl_Position = projection * view * model * vec4(position, 1.0);\\n  }\\n  gl_PointSize = pointSize;\\n  f_color = color;\\n  f_uv = uv;\\n}\"]),c=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform sampler2D texture;\\nuniform float opacity;\\n\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  vec2 pointR = gl_PointCoord.xy - vec2(0.5, 0.5);\\n  if(dot(pointR, pointR) > 0.25) {\\n    discard;\\n  }\\n  gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\\n}\"]),u=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  gl_Position = projection * view * model * vec4(position, 1.0);\\n  f_id        = id;\\n  f_position  = position;\\n}\"]),f=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3  clipBounds[2];\\nuniform float pickId;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n\\n  gl_FragColor = vec4(pickId, f_id.xyz);\\n}\"]),h=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3  position;\\nattribute float pointSize;\\nattribute vec4  id;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0.0, 0.0, 0.0, 0.0);\\n  } else {\\n    gl_Position  = projection * view * model * vec4(position, 1.0);\\n    gl_PointSize = pointSize;\\n  }\\n  f_id         = id;\\n  f_position   = position;\\n}\"]),p=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\n\\nuniform mat4 model, view, projection;\\n\\nvoid main() {\\n  gl_Position = projection * view * model * vec4(position, 1.0);\\n}\"]),d=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec3 contourColor;\\n\\nvoid main() {\\n  gl_FragColor = vec4(contourColor, 1.0);\\n}\\n\"]);r.meshShader={vertex:i,fragment:a,attributes:[{name:\"position\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"}]},r.wireShader={vertex:o,fragment:s,attributes:[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"}]},r.pointShader={vertex:l,fragment:c,attributes:[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"},{name:\"pointSize\",type:\"float\"}]},r.pickShader={vertex:u,fragment:f,attributes:[{name:\"position\",type:\"vec3\"},{name:\"id\",type:\"vec4\"}]},r.pointPickShader={vertex:h,fragment:f,attributes:[{name:\"position\",type:\"vec3\"},{name:\"pointSize\",type:\"float\"},{name:\"id\",type:\"vec4\"}]},r.contourShader={vertex:p,fragment:d,attributes:[{name:\"position\",type:\"vec3\"}]}},{glslify:438}],311:[function(t,e,r){\"use strict\";var n=t(\"gl-shader\"),i=t(\"gl-buffer\"),a=t(\"gl-vao\"),o=t(\"gl-texture2d\"),s=t(\"normals\"),l=t(\"gl-mat4/multiply\"),c=t(\"gl-mat4/invert\"),u=t(\"ndarray\"),f=t(\"colormap\"),h=t(\"simplicial-complex-contour\"),p=t(\"typedarray-pool\"),d=t(\"./lib/shaders\"),m=t(\"./lib/closest-point\"),g=d.meshShader,v=d.wireShader,y=d.pointShader,x=d.pickShader,b=d.pointPickShader,_=d.contourShader,w=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function T(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g,v,y,x,b,_,T,k,M,A,S){this.gl=t,this.pixelRatio=1,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.lineShader=n,this.pointShader=i,this.pickShader=a,this.pointPickShader=o,this.contourShader=s,this.trianglePositions=l,this.triangleColors=u,this.triangleNormals=h,this.triangleUVs=f,this.triangleIds=c,this.triangleVAO=p,this.triangleCount=0,this.lineWidth=1,this.edgePositions=d,this.edgeColors=g,this.edgeUVs=v,this.edgeIds=m,this.edgeVAO=y,this.edgeCount=0,this.pointPositions=x,this.pointColors=_,this.pointUVs=T,this.pointSizes=k,this.pointIds=b,this.pointVAO=M,this.pointCount=0,this.contourLineWidth=1,this.contourPositions=A,this.contourVAO=S,this.contourCount=0,this.contourColor=[0,0,0],this.contourEnable=!0,this.pickVertex=!0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this.hasAlpha=!1,this.opacityscale=!1,this._model=w,this._view=w,this._projection=w,this._resolution=[1,1]}var k=T.prototype;function M(t,e){if(!e)return 1;if(!e.length)return 1;for(var r=0;r<e.length;++r){if(e.length<2)return 1;if(e[r][0]===t)return e[r][1];if(e[r][0]>t&&r>0){var n=(e[r][0]-t)/(e[r][0]-e[r-1][0]);return e[r][1]*(1-n)+n*e[r-1][1]}}return 1}function A(t){var e=n(t,g.vertex,g.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.normal.location=4,e}function S(t){var e=n(t,v.vertex,v.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e}function E(t){var e=n(t,y.vertex,y.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.pointSize.location=4,e}function L(t){var e=n(t,x.vertex,x.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e}function C(t){var e=n(t,b.vertex,b.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e.attributes.pointSize.location=4,e}function P(t){var e=n(t,_.vertex,_.fragment);return e.attributes.position.location=0,e}k.isOpaque=function(){return!this.hasAlpha},k.isTransparent=function(){return this.hasAlpha},k.pickSlots=1,k.setPickBase=function(t){this.pickId=t},k.highlight=function(t){if(t&&this.contourEnable){for(var e=h(this.cells,this.intensity,t.intensity),r=e.cells,n=e.vertexIds,i=e.vertexWeights,a=r.length,o=p.mallocFloat32(6*a),s=0,l=0;l<a;++l)for(var c=r[l],u=0;u<2;++u){var f=c[0];2===c.length&&(f=c[u]);for(var d=n[f][0],m=n[f][1],g=i[f],v=1-g,y=this.positions[d],x=this.positions[m],b=0;b<3;++b)o[s++]=g*y[b]+v*x[b]}this.contourCount=s/3|0,this.contourPositions.update(o.subarray(0,s)),p.free(o)}else this.contourCount=0},k.update=function(t){t=t||{};var e=this.gl;this.dirty=!0,\"contourEnable\"in t&&(this.contourEnable=t.contourEnable),\"contourColor\"in t&&(this.contourColor=t.contourColor),\"lineWidth\"in t&&(this.lineWidth=t.lineWidth),\"lightPosition\"in t&&(this.lightPosition=t.lightPosition),this.hasAlpha=!1,\"opacity\"in t&&(this.opacity=t.opacity,this.opacity<1&&(this.hasAlpha=!0)),\"opacityscale\"in t&&(this.opacityscale=t.opacityscale,this.hasAlpha=!0),\"ambient\"in t&&(this.ambientLight=t.ambient),\"diffuse\"in t&&(this.diffuseLight=t.diffuse),\"specular\"in t&&(this.specularLight=t.specular),\"roughness\"in t&&(this.roughness=t.roughness),\"fresnel\"in t&&(this.fresnel=t.fresnel),t.texture?(this.texture.dispose(),this.texture=o(e,t.texture)):t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(function(t,e){for(var r=f({colormap:t,nshades:256,format:\"rgba\"}),n=new Uint8Array(1024),i=0;i<256;++i){for(var a=r[i],o=0;o<3;++o)n[4*i+o]=a[o];n[4*i+3]=e?255*M(i/255,e):255*a[3]}return u(n,[256,256,4],[4,0,1])}(t.colormap,this.opacityscale)),this.texture.generateMipmap());var r=t.cells,n=t.positions;if(n&&r){var i=[],a=[],l=[],c=[],h=[],p=[],d=[],m=[],g=[],v=[],y=[],x=[],b=[],_=[];this.cells=r,this.positions=n;var w=t.vertexNormals,T=t.cellNormals,k=void 0===t.vertexNormalsEpsilon?1e-6:t.vertexNormalsEpsilon,A=void 0===t.faceNormalsEpsilon?1e-6:t.faceNormalsEpsilon;t.useFacetNormals&&!T&&(T=s.faceNormals(r,n,A)),T||w||(w=s.vertexNormals(r,n,k));var S=t.vertexColors,E=t.cellColors,L=t.meshColor||[1,1,1,1],C=t.vertexUVs,P=t.vertexIntensity,I=t.cellUVs,O=t.cellIntensity,z=1/0,D=-1/0;if(!C&&!I)if(P)if(t.vertexIntensityBounds)z=+t.vertexIntensityBounds[0],D=+t.vertexIntensityBounds[1];else for(var R=0;R<P.length;++R){var F=P[R];z=Math.min(z,F),D=Math.max(D,F)}else if(O)if(t.cellIntensityBounds)z=+t.cellIntensityBounds[0],D=+t.cellIntensityBounds[1];else for(R=0;R<O.length;++R){F=O[R];z=Math.min(z,F),D=Math.max(D,F)}else for(R=0;R<n.length;++R){F=n[R][2];z=Math.min(z,F),D=Math.max(D,F)}this.intensity=P||(O||function(t){for(var e=t.length,r=new Array(e),n=0;n<e;++n)r[n]=t[n][2];return r}(n)),this.pickVertex=!(O||E);var B=t.pointSizes,N=t.pointSize||1;this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(R=0;R<n.length;++R)for(var j=n[R],U=0;U<3;++U)!isNaN(j[U])&&isFinite(j[U])&&(this.bounds[0][U]=Math.min(this.bounds[0][U],j[U]),this.bounds[1][U]=Math.max(this.bounds[1][U],j[U]));var V=0,q=0,H=0;t:for(R=0;R<r.length;++R){var G=r[R];switch(G.length){case 1:for(j=n[W=G[0]],U=0;U<3;++U)if(isNaN(j[U])||!isFinite(j[U]))continue t;v.push(j[0],j[1],j[2]),X=S?S[W]:E?E[R]:L,this.opacityscale&&P?a.push(X[0],X[1],X[2],this.opacity*M((P[W]-z)/(D-z),this.opacityscale)):3===X.length?y.push(X[0],X[1],X[2],this.opacity):(y.push(X[0],X[1],X[2],X[3]*this.opacity),X[3]<1&&(this.hasAlpha=!0)),Z=C?C[W]:P?[(P[W]-z)/(D-z),0]:I?I[R]:O?[(O[R]-z)/(D-z),0]:[(j[2]-z)/(D-z),0],x.push(Z[0],Z[1]),B?b.push(B[W]):b.push(N),_.push(R),H+=1;break;case 2:for(U=0;U<2;++U){j=n[W=G[U]];for(var Y=0;Y<3;++Y)if(isNaN(j[Y])||!isFinite(j[Y]))continue t}for(U=0;U<2;++U){j=n[W=G[U]];p.push(j[0],j[1],j[2]),X=S?S[W]:E?E[R]:L,this.opacityscale&&P?a.push(X[0],X[1],X[2],this.opacity*M((P[W]-z)/(D-z),this.opacityscale)):3===X.length?d.push(X[0],X[1],X[2],this.opacity):(d.push(X[0],X[1],X[2],X[3]*this.opacity),X[3]<1&&(this.hasAlpha=!0)),Z=C?C[W]:P?[(P[W]-z)/(D-z),0]:I?I[R]:O?[(O[R]-z)/(D-z),0]:[(j[2]-z)/(D-z),0],m.push(Z[0],Z[1]),g.push(R)}q+=1;break;case 3:for(U=0;U<3;++U)for(j=n[W=G[U]],Y=0;Y<3;++Y)if(isNaN(j[Y])||!isFinite(j[Y]))continue t;for(U=0;U<3;++U){var W,X,Z,J;j=n[W=G[2-U]];i.push(j[0],j[1],j[2]),(X=S?S[W]:E?E[R]:L)?this.opacityscale&&P?a.push(X[0],X[1],X[2],this.opacity*M((P[W]-z)/(D-z),this.opacityscale)):3===X.length?a.push(X[0],X[1],X[2],this.opacity):(a.push(X[0],X[1],X[2],X[3]*this.opacity),X[3]<1&&(this.hasAlpha=!0)):a.push(.5,.5,.5,1),Z=C?C[W]:P?[(P[W]-z)/(D-z),0]:I?I[R]:O?[(O[R]-z)/(D-z),0]:[(j[2]-z)/(D-z),0],c.push(Z[0],Z[1]),J=w?w[W]:T[R],l.push(J[0],J[1],J[2]),h.push(R)}V+=1}}this.pointCount=H,this.edgeCount=q,this.triangleCount=V,this.pointPositions.update(v),this.pointColors.update(y),this.pointUVs.update(x),this.pointSizes.update(b),this.pointIds.update(new Uint32Array(_)),this.edgePositions.update(p),this.edgeColors.update(d),this.edgeUVs.update(m),this.edgeIds.update(new Uint32Array(g)),this.trianglePositions.update(i),this.triangleColors.update(a),this.triangleUVs.update(c),this.triangleNormals.update(l),this.triangleIds.update(new Uint32Array(h))}},k.drawTransparent=k.draw=function(t){t=t||{};for(var e=this.gl,r=t.model||w,n=t.view||w,i=t.projection||w,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);var s={model:r,view:n,projection:i,inverseModel:w.slice(),clipBounds:a,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],contourColor:this.contourColor,texture:0};s.inverseModel=c(s.inverseModel,s.model),e.disable(e.CULL_FACE),this.texture.bind(0);var u=new Array(16);l(u,s.view,s.model),l(u,s.projection,u),c(u,u);for(o=0;o<3;++o)s.eyePosition[o]=u[12+o]/u[15];var f,h=u[15];for(o=0;o<3;++o)h+=this.lightPosition[o]*u[4*o+3];for(o=0;o<3;++o){for(var p=u[12+o],d=0;d<3;++d)p+=u[4*d+o]*this.lightPosition[d];s.lightPosition[o]=p/h}this.triangleCount>0&&((f=this.triShader).bind(),f.uniforms=s,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind());this.edgeCount>0&&this.lineWidth>0&&((f=this.lineShader).bind(),f.uniforms=s,this.edgeVAO.bind(),e.lineWidth(this.lineWidth*this.pixelRatio),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind());this.pointCount>0&&((f=this.pointShader).bind(),f.uniforms=s,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind());this.contourEnable&&this.contourCount>0&&this.contourLineWidth>0&&((f=this.contourShader).bind(),f.uniforms=s,this.contourVAO.bind(),e.drawArrays(e.LINES,0,this.contourCount),this.contourVAO.unbind())},k.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||w,n=t.view||w,i=t.projection||w,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s,l={model:r,view:n,projection:i,clipBounds:a,pickId:this.pickId/255};((s=this.pickShader).bind(),s.uniforms=l,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()),this.edgeCount>0&&(this.edgeVAO.bind(),e.lineWidth(this.lineWidth*this.pixelRatio),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind()),this.pointCount>0)&&((s=this.pointPickShader).bind(),s.uniforms=l,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind())},k.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;for(var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions,i=new Array(r.length),a=0;a<r.length;++a)i[a]=n[r[a]];var o=t.coord[0],s=t.coord[1];if(!this.pickVertex){var l=this.positions[r[0]],c=this.positions[r[1]],u=this.positions[r[2]],f=[(l[0]+c[0]+u[0])/3,(l[1]+c[1]+u[1])/3,(l[2]+c[2]+u[2])/3];return{_cellCenter:!0,position:[o,s],index:e,cell:r,cellId:e,intensity:this.intensity[e],dataCoordinate:f}}var h=m(i,[o*this.pixelRatio,this._resolution[1]-s*this.pixelRatio],this._model,this._view,this._projection,this._resolution);if(!h)return null;var p=h[2],d=0;for(a=0;a<r.length;++a)d+=p[a]*this.intensity[r[a]];return{position:h[1],index:r[h[0]],cell:r,cellId:e,intensity:d,dataCoordinate:this.positions[r[h[0]]]}},k.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.lineShader.dispose(),this.pointShader.dispose(),this.pickShader.dispose(),this.pointPickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleNormals.dispose(),this.triangleIds.dispose(),this.edgeVAO.dispose(),this.edgePositions.dispose(),this.edgeColors.dispose(),this.edgeUVs.dispose(),this.edgeIds.dispose(),this.pointVAO.dispose(),this.pointPositions.dispose(),this.pointColors.dispose(),this.pointUVs.dispose(),this.pointSizes.dispose(),this.pointIds.dispose(),this.contourVAO.dispose(),this.contourPositions.dispose(),this.contourShader.dispose()},e.exports=function(t,e){1===arguments.length&&(t=(e=t).gl);var r=t.getExtension(\"OES_standard_derivatives\")||t.getExtension(\"MOZ_OES_standard_derivatives\")||t.getExtension(\"WEBKIT_OES_standard_derivatives\");if(!r)throw new Error(\"derivatives not supported\");var n=A(t),s=S(t),l=E(t),c=L(t),f=C(t),h=P(t),p=o(t,u(new Uint8Array([255,255,255,255]),[1,1,4]));p.generateMipmap(),p.minFilter=t.LINEAR_MIPMAP_LINEAR,p.magFilter=t.LINEAR;var d=i(t),m=i(t),g=i(t),v=i(t),y=i(t),x=a(t,[{buffer:d,type:t.FLOAT,size:3},{buffer:y,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:m,type:t.FLOAT,size:4},{buffer:g,type:t.FLOAT,size:2},{buffer:v,type:t.FLOAT,size:3}]),b=i(t),_=i(t),w=i(t),k=i(t),M=a(t,[{buffer:b,type:t.FLOAT,size:3},{buffer:k,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:_,type:t.FLOAT,size:4},{buffer:w,type:t.FLOAT,size:2}]),I=i(t),O=i(t),z=i(t),D=i(t),R=i(t),F=a(t,[{buffer:I,type:t.FLOAT,size:3},{buffer:R,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:O,type:t.FLOAT,size:4},{buffer:z,type:t.FLOAT,size:2},{buffer:D,type:t.FLOAT,size:1}]),B=i(t),N=a(t,[{buffer:B,type:t.FLOAT,size:3}]),j=new T(t,p,n,s,l,c,f,h,d,y,m,g,v,x,b,k,_,w,M,I,R,O,z,D,F,B,N);return j.update(e),j}},{\"./lib/closest-point\":309,\"./lib/shaders\":310,colormap:137,\"gl-buffer\":263,\"gl-mat4/invert\":295,\"gl-mat4/multiply\":297,\"gl-shader\":335,\"gl-texture2d\":352,\"gl-vao\":357,ndarray:479,normals:482,\"simplicial-complex-contour\":553,\"typedarray-pool\":613}],312:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e,[0,0,0,1,1,0,1,1]),s=i(e,a.boxVert,a.lineFrag);return new o(t,r,s)};var n=t(\"gl-buffer\"),i=t(\"gl-shader\"),a=t(\"./shaders\");function o(t,e,r){this.plot=t,this.vbo=e,this.shader=r}var s,l,c=o.prototype;c.bind=function(){var t=this.shader;this.vbo.bind(),this.shader.bind(),t.attributes.coord.pointer(),t.uniforms.screenBox=this.plot.screenBox},c.drawBox=(s=[0,0],l=[0,0],function(t,e,r,n,i){var a=this.plot,o=this.shader,c=a.gl;s[0]=t,s[1]=e,l[0]=r,l[1]=n,o.uniforms.lo=s,o.uniforms.hi=l,o.uniforms.color=i,c.drawArrays(c.TRIANGLE_STRIP,0,4)}),c.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{\"./shaders\":315,\"gl-buffer\":263,\"gl-shader\":335}],313:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e),a=i(e,o.gridVert,o.gridFrag),l=i(e,o.tickVert,o.gridFrag);return new s(t,r,a,l)};var n=t(\"gl-buffer\"),i=t(\"gl-shader\"),a=t(\"binary-search-bounds\"),o=t(\"./shaders\");function s(t,e,r,n){this.plot=t,this.vbo=e,this.shader=r,this.tickShader=n,this.ticks=[[],[]]}function l(t,e){return t-e}var c,u,f,h,p,d=s.prototype;d.draw=(c=[0,0],u=[0,0],f=[0,0],function(){for(var t=this.plot,e=this.vbo,r=this.shader,n=this.ticks,i=t.gl,a=t._tickBounds,o=t.dataBox,s=t.viewBox,l=t.gridLineWidth,h=t.gridLineColor,p=t.gridLineEnable,d=t.pixelRatio,m=0;m<2;++m){var g=a[m],v=a[m+2]-g,y=.5*(o[m+2]+o[m]),x=o[m+2]-o[m];u[m]=2*v/x,c[m]=2*(g-y)/x}r.bind(),e.bind(),r.attributes.dataCoord.pointer(),r.uniforms.dataShift=c,r.uniforms.dataScale=u;var b=0;for(m=0;m<2;++m){f[0]=f[1]=0,f[m]=1,r.uniforms.dataAxis=f,r.uniforms.lineWidth=l[m]/(s[m+2]-s[m])*d,r.uniforms.color=h[m];var _=6*n[m].length;p[m]&&_&&i.drawArrays(i.TRIANGLES,b,_),b+=_}}),d.drawTickMarks=function(){var t=[0,0],e=[0,0],r=[1,0],n=[0,1],i=[0,0],o=[0,0];return function(){for(var s=this.plot,c=this.vbo,u=this.tickShader,f=this.ticks,h=s.gl,p=s._tickBounds,d=s.dataBox,m=s.viewBox,g=s.pixelRatio,v=s.screenBox,y=v[2]-v[0],x=v[3]-v[1],b=m[2]-m[0],_=m[3]-m[1],w=0;w<2;++w){var T=p[w],k=p[w+2]-T,M=.5*(d[w+2]+d[w]),A=d[w+2]-d[w];e[w]=2*k/A,t[w]=2*(T-M)/A}e[0]*=b/y,t[0]*=b/y,e[1]*=_/x,t[1]*=_/x,u.bind(),c.bind(),u.attributes.dataCoord.pointer();var S=u.uniforms;S.dataShift=t,S.dataScale=e;var E=s.tickMarkLength,L=s.tickMarkWidth,C=s.tickMarkColor,P=6*f[0].length,I=Math.min(a.ge(f[0],(d[0]-p[0])/(p[2]-p[0]),l),f[0].length),O=Math.min(a.gt(f[0],(d[2]-p[0])/(p[2]-p[0]),l),f[0].length),z=0+6*I,D=6*Math.max(0,O-I),R=Math.min(a.ge(f[1],(d[1]-p[1])/(p[3]-p[1]),l),f[1].length),F=Math.min(a.gt(f[1],(d[3]-p[1])/(p[3]-p[1]),l),f[1].length),B=P+6*R,N=6*Math.max(0,F-R);i[0]=2*(m[0]-E[1])/y-1,i[1]=(m[3]+m[1])/x-1,o[0]=E[1]*g/y,o[1]=L[1]*g/x,N&&(S.color=C[1],S.tickScale=o,S.dataAxis=n,S.screenOffset=i,h.drawArrays(h.TRIANGLES,B,N)),i[0]=(m[2]+m[0])/y-1,i[1]=2*(m[1]-E[0])/x-1,o[0]=L[0]*g/y,o[1]=E[0]*g/x,D&&(S.color=C[0],S.tickScale=o,S.dataAxis=r,S.screenOffset=i,h.drawArrays(h.TRIANGLES,z,D)),i[0]=2*(m[2]+E[3])/y-1,i[1]=(m[3]+m[1])/x-1,o[0]=E[3]*g/y,o[1]=L[3]*g/x,N&&(S.color=C[3],S.tickScale=o,S.dataAxis=n,S.screenOffset=i,h.drawArrays(h.TRIANGLES,B,N)),i[0]=(m[2]+m[0])/y-1,i[1]=2*(m[3]+E[2])/x-1,o[0]=L[2]*g/y,o[1]=E[2]*g/x,D&&(S.color=C[2],S.tickScale=o,S.dataAxis=r,S.screenOffset=i,h.drawArrays(h.TRIANGLES,z,D))}}(),d.update=(h=[1,1,-1,-1,1,-1],p=[1,-1,1,1,-1,-1],function(t){for(var e=t.ticks,r=t.bounds,n=new Float32Array(18*(e[0].length+e[1].length)),i=(this.plot.zeroLineEnable,0),a=[[],[]],o=0;o<2;++o)for(var s=a[o],l=e[o],c=r[o],u=r[o+2],f=0;f<l.length;++f){var d=(l[f].x-c)/(u-c);s.push(d);for(var m=0;m<6;++m)n[i++]=d,n[i++]=h[m],n[i++]=p[m]}this.ticks=a,this.vbo.update(n)}),d.dispose=function(){this.vbo.dispose(),this.shader.dispose(),this.tickShader.dispose()}},{\"./shaders\":315,\"binary-search-bounds\":103,\"gl-buffer\":263,\"gl-shader\":335}],314:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e,[-1,-1,-1,1,1,-1,1,1]),s=i(e,a.lineVert,a.lineFrag);return new o(t,r,s)};var n=t(\"gl-buffer\"),i=t(\"gl-shader\"),a=t(\"./shaders\");function o(t,e,r){this.plot=t,this.vbo=e,this.shader=r}var s,l,c=o.prototype;c.bind=function(){var t=this.shader;this.vbo.bind(),this.shader.bind(),t.attributes.coord.pointer(),t.uniforms.screenBox=this.plot.screenBox},c.drawLine=(s=[0,0],l=[0,0],function(t,e,r,n,i,a){var o=this.plot,c=this.shader,u=o.gl;s[0]=t,s[1]=e,l[0]=r,l[1]=n,c.uniforms.start=s,c.uniforms.end=l,c.uniforms.width=i*o.pixelRatio,c.uniforms.color=a,u.drawArrays(u.TRIANGLE_STRIP,0,4)}),c.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{\"./shaders\":315,\"gl-buffer\":263,\"gl-shader\":335}],315:[function(t,e,r){\"use strict\";var n=t(\"glslify\"),i=n([\"precision lowp float;\\n#define GLSLIFY 1\\nuniform vec4 color;\\nvoid main() {\\n  gl_FragColor = vec4(color.xyz * color.w, color.w);\\n}\\n\"]);e.exports={lineVert:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 coord;\\n\\nuniform vec4 screenBox;\\nuniform vec2 start, end;\\nuniform float width;\\n\\nvec2 perp(vec2 v) {\\n  return vec2(v.y, -v.x);\\n}\\n\\nvec2 screen(vec2 v) {\\n  return 2.0 * (v - screenBox.xy) / (screenBox.zw - screenBox.xy) - 1.0;\\n}\\n\\nvoid main() {\\n  vec2 delta = normalize(perp(start - end));\\n  vec2 offset = mix(start, end, 0.5 * (coord.y+1.0));\\n  gl_Position = vec4(screen(offset + 0.5 * width * delta * coord.x), 0, 1);\\n}\\n\"]),lineFrag:i,textVert:n([\"#define GLSLIFY 1\\nattribute vec3 textCoordinate;\\n\\nuniform vec2 dataScale, dataShift, dataAxis, screenOffset, textScale;\\nuniform float angle;\\n\\nvoid main() {\\n  float dataOffset  = textCoordinate.z;\\n  vec2 glyphOffset  = textCoordinate.xy;\\n  mat2 glyphMatrix = mat2(cos(angle), sin(angle), -sin(angle), cos(angle));\\n  vec2 screenCoordinate = dataAxis * (dataScale * dataOffset + dataShift) +\\n    glyphMatrix * glyphOffset * textScale + screenOffset;\\n  gl_Position = vec4(screenCoordinate, 0, 1);\\n}\\n\"]),textFrag:i,gridVert:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 dataCoord;\\n\\nuniform vec2 dataAxis, dataShift, dataScale;\\nuniform float lineWidth;\\n\\nvoid main() {\\n  vec2 pos = dataAxis * (dataScale * dataCoord.x + dataShift);\\n  pos += 10.0 * dataCoord.y * vec2(dataAxis.y, -dataAxis.x) + dataCoord.z * lineWidth;\\n  gl_Position = vec4(pos, 0, 1);\\n}\\n\"]),gridFrag:i,boxVert:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 coord;\\n\\nuniform vec4 screenBox;\\nuniform vec2 lo, hi;\\n\\nvec2 screen(vec2 v) {\\n  return 2.0 * (v - screenBox.xy) / (screenBox.zw - screenBox.xy) - 1.0;\\n}\\n\\nvoid main() {\\n  gl_Position = vec4(screen(mix(lo, hi, coord)), 0, 1);\\n}\\n\"]),tickVert:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 dataCoord;\\n\\nuniform vec2 dataAxis, dataShift, dataScale, screenOffset, tickScale;\\n\\nvoid main() {\\n  vec2 pos = dataAxis * (dataScale * dataCoord.x + dataShift);\\n  gl_Position = vec4(pos + tickScale*dataCoord.yz + screenOffset, 0, 1);\\n}\\n\"])}},{glslify:317}],316:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e),a=i(e,s.textVert,s.textFrag);return new l(t,r,a)};var n=t(\"gl-buffer\"),i=t(\"gl-shader\"),a=t(\"text-cache\"),o=t(\"binary-search-bounds\"),s=t(\"./shaders\");function l(t,e,r){this.plot=t,this.vbo=e,this.shader=r,this.tickOffset=[[],[]],this.tickX=[[],[]],this.labelOffset=[0,0],this.labelCount=[0,0]}var c,u,f,h,p,d,m=l.prototype;m.drawTicks=(c=[0,0],u=[0,0],f=[0,0],function(t){var e=this.plot,r=this.shader,n=this.tickX[t],i=this.tickOffset[t],a=e.gl,s=e.viewBox,l=e.dataBox,h=e.screenBox,p=e.pixelRatio,d=e.tickEnable,m=e.tickPad,g=e.tickColor,v=e.tickAngle,y=e.labelEnable,x=e.labelPad,b=e.labelColor,_=e.labelAngle,w=this.labelOffset[t],T=this.labelCount[t],k=o.lt(n,l[t]),M=o.le(n,l[t+2]);c[0]=c[1]=0,c[t]=1,u[t]=(s[2+t]+s[t])/(h[2+t]-h[t])-1;var A=2/h[2+(1^t)]-h[1^t];u[1^t]=A*s[1^t]-1,d[t]&&(u[1^t]-=A*p*m[t],k<M&&i[M]>i[k]&&(r.uniforms.dataAxis=c,r.uniforms.screenOffset=u,r.uniforms.color=g[t],r.uniforms.angle=v[t],a.drawArrays(a.TRIANGLES,i[k],i[M]-i[k]))),y[t]&&T&&(u[1^t]-=A*p*x[t],r.uniforms.dataAxis=f,r.uniforms.screenOffset=u,r.uniforms.color=b[t],r.uniforms.angle=_[t],a.drawArrays(a.TRIANGLES,w,T)),u[1^t]=A*s[2+(1^t)]-1,d[t+2]&&(u[1^t]+=A*p*m[t+2],k<M&&i[M]>i[k]&&(r.uniforms.dataAxis=c,r.uniforms.screenOffset=u,r.uniforms.color=g[t+2],r.uniforms.angle=v[t+2],a.drawArrays(a.TRIANGLES,i[k],i[M]-i[k]))),y[t+2]&&T&&(u[1^t]+=A*p*x[t+2],r.uniforms.dataAxis=f,r.uniforms.screenOffset=u,r.uniforms.color=b[t+2],r.uniforms.angle=_[t+2],a.drawArrays(a.TRIANGLES,w,T))}),m.drawTitle=function(){var t=[0,0],e=[0,0];return function(){var r=this.plot,n=this.shader,i=r.gl,a=r.screenBox,o=r.titleCenter,s=r.titleAngle,l=r.titleColor,c=r.pixelRatio;if(this.titleCount){for(var u=0;u<2;++u)e[u]=2*(o[u]*c-a[u])/(a[2+u]-a[u])-1;n.bind(),n.uniforms.dataAxis=t,n.uniforms.screenOffset=e,n.uniforms.angle=s,n.uniforms.color=l,i.drawArrays(i.TRIANGLES,this.titleOffset,this.titleCount)}}}(),m.bind=(h=[0,0],p=[0,0],d=[0,0],function(){var t=this.plot,e=this.shader,r=t._tickBounds,n=t.dataBox,i=t.screenBox,a=t.viewBox;e.bind();for(var o=0;o<2;++o){var s=r[o],l=r[o+2]-s,c=.5*(n[o+2]+n[o]),u=n[o+2]-n[o],f=a[o],m=a[o+2]-f,g=i[o],v=i[o+2]-g;p[o]=2*l/u*m/v,h[o]=2*(s-c)/u*m/v}d[1]=2*t.pixelRatio/(i[3]-i[1]),d[0]=d[1]*(i[3]-i[1])/(i[2]-i[0]),e.uniforms.dataScale=p,e.uniforms.dataShift=h,e.uniforms.textScale=d,this.vbo.bind(),e.attributes.textCoordinate.pointer()}),m.update=function(t){var e,r,n,i,o,s=[],l=t.ticks,c=t.bounds;for(o=0;o<2;++o){var u=[Math.floor(s.length/3)],f=[-1/0],h=l[o];for(e=0;e<h.length;++e){var p=h[e],d=p.x,m=p.text,g=p.font||\"sans-serif\";i=p.fontSize||12;for(var v=1/(c[o+2]-c[o]),y=c[o],x=m.split(\"\\n\"),b=0;b<x.length;b++)for(n=a(g,x[b]).data,r=0;r<n.length;r+=2)s.push(n[r]*i,-n[r+1]*i-b*i*1.2,(d-y)*v);u.push(Math.floor(s.length/3)),f.push(d)}this.tickOffset[o]=u,this.tickX[o]=f}for(o=0;o<2;++o){for(this.labelOffset[o]=Math.floor(s.length/3),n=a(t.labelFont[o],t.labels[o],{textAlign:\"center\"}).data,i=t.labelSize[o],e=0;e<n.length;e+=2)s.push(n[e]*i,-n[e+1]*i,0);this.labelCount[o]=Math.floor(s.length/3)-this.labelOffset[o]}for(this.titleOffset=Math.floor(s.length/3),n=a(t.titleFont,t.title).data,i=t.titleSize,e=0;e<n.length;e+=2)s.push(n[e]*i,-n[e+1]*i,0);this.titleCount=Math.floor(s.length/3)-this.titleOffset,this.vbo.update(s)},m.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{\"./shaders\":315,\"binary-search-bounds\":103,\"gl-buffer\":263,\"gl-shader\":335,\"text-cache\":592}],317:[function(t,e,r){arguments[4][261][0].apply(r,arguments)},{dup:261}],318:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e,[e.drawingBufferWidth,e.drawingBufferHeight]),c=new l(e,r);return c.grid=i(c),c.text=a(c),c.line=o(c),c.box=s(c),c.update(t),c};var n=t(\"gl-select-static\"),i=t(\"./lib/grid\"),a=t(\"./lib/text\"),o=t(\"./lib/line\"),s=t(\"./lib/box\");function l(t,e){this.gl=t,this.pickBuffer=e,this.screenBox=[0,0,t.drawingBufferWidth,t.drawingBufferHeight],this.viewBox=[0,0,0,0],this.dataBox=[-10,-10,10,10],this.gridLineEnable=[!0,!0],this.gridLineWidth=[1,1],this.gridLineColor=[[0,0,0,1],[0,0,0,1]],this.pixelRatio=1,this.tickMarkLength=[0,0,0,0],this.tickMarkWidth=[0,0,0,0],this.tickMarkColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[15,15,15,15],this.tickAngle=[0,0,0,0],this.tickEnable=[!0,!0,!0,!0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[15,15,15,15],this.labelAngle=[0,Math.PI/2,0,3*Math.PI/2],this.labelEnable=[!0,!0,!0,!0],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.titleCenter=[0,0],this.titleEnable=!0,this.titleAngle=0,this.titleColor=[0,0,0,1],this.borderColor=[0,0,0,0],this.backgroundColor=[0,0,0,0],this.zeroLineEnable=[!0,!0],this.zeroLineWidth=[4,4],this.zeroLineColor=[[0,0,0,1],[0,0,0,1]],this.borderLineEnable=[!0,!0,!0,!0],this.borderLineWidth=[2,2,2,2],this.borderLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.grid=null,this.text=null,this.line=null,this.box=null,this.objects=[],this.overlays=[],this._tickBounds=[1/0,1/0,-1/0,-1/0],this.static=!1,this.dirty=!1,this.pickDirty=!1,this.pickDelay=120,this.pickRadius=10,this._pickTimeout=null,this._drawPick=this.drawPick.bind(this),this._depthCounter=0}var c=l.prototype;function u(t){for(var e=t.slice(),r=0;r<e.length;++r)e[r]=e[r].slice();return e}function f(t,e){return t.x-e.x}c.setDirty=function(){this.dirty=this.pickDirty=!0},c.setOverlayDirty=function(){this.dirty=!0},c.nextDepthValue=function(){return this._depthCounter++/65536},c.draw=function(){var t=this.gl,e=this.screenBox,r=this.viewBox,n=this.dataBox,i=this.pixelRatio,a=this.grid,o=this.line,s=this.text,l=this.objects;if(this._depthCounter=0,this.pickDirty&&(this._pickTimeout&&clearTimeout(this._pickTimeout),this.pickDirty=!1,this._pickTimeout=setTimeout(this._drawPick,this.pickDelay)),this.dirty){if(this.dirty=!1,t.bindFramebuffer(t.FRAMEBUFFER,null),t.enable(t.SCISSOR_TEST),t.disable(t.DEPTH_TEST),t.depthFunc(t.LESS),t.depthMask(!1),t.enable(t.BLEND),t.blendEquation(t.FUNC_ADD,t.FUNC_ADD),t.blendFunc(t.ONE,t.ONE_MINUS_SRC_ALPHA),this.borderColor){t.scissor(e[0],e[1],e[2]-e[0],e[3]-e[1]);var c=this.borderColor;t.clearColor(c[0]*c[3],c[1]*c[3],c[2]*c[3],c[3]),t.clear(t.COLOR_BUFFER_BIT|t.DEPTH_BUFFER_BIT)}t.scissor(r[0],r[1],r[2]-r[0],r[3]-r[1]),t.viewport(r[0],r[1],r[2]-r[0],r[3]-r[1]);var u=this.backgroundColor;t.clearColor(u[0]*u[3],u[1]*u[3],u[2]*u[3],u[3]),t.clear(t.COLOR_BUFFER_BIT),a.draw();var f=this.zeroLineEnable,h=this.zeroLineColor,p=this.zeroLineWidth;if(f[0]||f[1]){o.bind();for(var d=0;d<2;++d)if(f[d]&&n[d]<=0&&n[d+2]>=0){var m=e[d]-n[d]*(e[d+2]-e[d])/(n[d+2]-n[d]);0===d?o.drawLine(m,e[1],m,e[3],p[d],h[d]):o.drawLine(e[0],m,e[2],m,p[d],h[d])}}for(d=0;d<l.length;++d)l[d].draw();t.viewport(e[0],e[1],e[2]-e[0],e[3]-e[1]),t.scissor(e[0],e[1],e[2]-e[0],e[3]-e[1]),this.grid.drawTickMarks(),o.bind();var g=this.borderLineEnable,v=this.borderLineWidth,y=this.borderLineColor;for(g[1]&&o.drawLine(r[0],r[1]-.5*v[1]*i,r[0],r[3]+.5*v[3]*i,v[1],y[1]),g[0]&&o.drawLine(r[0]-.5*v[0]*i,r[1],r[2]+.5*v[2]*i,r[1],v[0],y[0]),g[3]&&o.drawLine(r[2],r[1]-.5*v[1]*i,r[2],r[3]+.5*v[3]*i,v[3],y[3]),g[2]&&o.drawLine(r[0]-.5*v[0]*i,r[3],r[2]+.5*v[2]*i,r[3],v[2],y[2]),s.bind(),d=0;d<2;++d)s.drawTicks(d);this.titleEnable&&s.drawTitle();var x=this.overlays;for(d=0;d<x.length;++d)x[d].draw();t.disable(t.SCISSOR_TEST),t.disable(t.BLEND),t.depthMask(!0)}},c.drawPick=function(){if(!this.static){var t=this.pickBuffer;this.gl,this._pickTimeout=null,t.begin();for(var e=1,r=this.objects,n=0;n<r.length;++n)e=r[n].drawPick(e);t.end()}},c.pick=function(t,e){if(!this.static){var r=this.pixelRatio,n=this.pickPixelRatio,i=this.viewBox,a=0|Math.round((t-i[0]/r)*n),o=0|Math.round((e-i[1]/r)*n),s=this.pickBuffer.query(a,o,this.pickRadius);if(!s)return null;for(var l=s.id+(s.value[0]<<8)+(s.value[1]<<16)+(s.value[2]<<24),c=this.objects,u=0;u<c.length;++u){var f=c[u].pick(a,o,l);if(f)return f}return null}},c.setScreenBox=function(t){var e=this.screenBox,r=this.pixelRatio;e[0]=0|Math.round(t[0]*r),e[1]=0|Math.round(t[1]*r),e[2]=0|Math.round(t[2]*r),e[3]=0|Math.round(t[3]*r),this.setDirty()},c.setDataBox=function(t){var e=this.dataBox;(e[0]!==t[0]||e[1]!==t[1]||e[2]!==t[2]||e[3]!==t[3])&&(e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],this.setDirty())},c.setViewBox=function(t){var e=this.pixelRatio,r=this.viewBox;r[0]=0|Math.round(t[0]*e),r[1]=0|Math.round(t[1]*e),r[2]=0|Math.round(t[2]*e),r[3]=0|Math.round(t[3]*e);var n=this.pickPixelRatio;this.pickBuffer.shape=[0|Math.round((t[2]-t[0])*n),0|Math.round((t[3]-t[1])*n)],this.setDirty()},c.update=function(t){t=t||{};var e=this.gl;this.pixelRatio=t.pixelRatio||1;var r=this.pixelRatio;this.pickPixelRatio=Math.max(r,1),this.setScreenBox(t.screenBox||[0,0,e.drawingBufferWidth/r,e.drawingBufferHeight/r]);this.screenBox;this.setViewBox(t.viewBox||[.125*(this.screenBox[2]-this.screenBox[0])/r,.125*(this.screenBox[3]-this.screenBox[1])/r,.875*(this.screenBox[2]-this.screenBox[0])/r,.875*(this.screenBox[3]-this.screenBox[1])/r]);var n=this.viewBox,i=(n[2]-n[0])/(n[3]-n[1]);this.setDataBox(t.dataBox||[-10,-10/i,10,10/i]),this.borderColor=!1!==t.borderColor&&(t.borderColor||[0,0,0,0]).slice(),this.backgroundColor=(t.backgroundColor||[0,0,0,0]).slice(),this.gridLineEnable=(t.gridLineEnable||[!0,!0]).slice(),this.gridLineWidth=(t.gridLineWidth||[1,1]).slice(),this.gridLineColor=u(t.gridLineColor||[[.5,.5,.5,1],[.5,.5,.5,1]]),this.zeroLineEnable=(t.zeroLineEnable||[!0,!0]).slice(),this.zeroLineWidth=(t.zeroLineWidth||[4,4]).slice(),this.zeroLineColor=u(t.zeroLineColor||[[0,0,0,1],[0,0,0,1]]),this.tickMarkLength=(t.tickMarkLength||[0,0,0,0]).slice(),this.tickMarkWidth=(t.tickMarkWidth||[0,0,0,0]).slice(),this.tickMarkColor=u(t.tickMarkColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.titleCenter=(t.titleCenter||[.5*(n[0]+n[2])/r,(n[3]+120)/r]).slice(),this.titleEnable=!(\"titleEnable\"in t)||!!t.titleEnable,this.titleAngle=t.titleAngle||0,this.titleColor=(t.titleColor||[0,0,0,1]).slice(),this.labelPad=(t.labelPad||[15,15,15,15]).slice(),this.labelAngle=(t.labelAngle||[0,Math.PI/2,0,3*Math.PI/2]).slice(),this.labelEnable=(t.labelEnable||[!0,!0,!0,!0]).slice(),this.labelColor=u(t.labelColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.tickPad=(t.tickPad||[15,15,15,15]).slice(),this.tickAngle=(t.tickAngle||[0,0,0,0]).slice(),this.tickEnable=(t.tickEnable||[!0,!0,!0,!0]).slice(),this.tickColor=u(t.tickColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.borderLineEnable=(t.borderLineEnable||[!0,!0,!0,!0]).slice(),this.borderLineWidth=(t.borderLineWidth||[2,2,2,2]).slice(),this.borderLineColor=u(t.borderLineColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]);var a=t.ticks||[[],[]],o=this._tickBounds;o[0]=o[1]=1/0,o[2]=o[3]=-1/0;for(var s=0;s<2;++s){var l=a[s].slice(0);0!==l.length&&(l.sort(f),o[s]=Math.min(o[s],l[0].x),o[s+2]=Math.max(o[s+2],l[l.length-1].x))}this.grid.update({bounds:o,ticks:a}),this.text.update({bounds:o,ticks:a,labels:t.labels||[\"x\",\"y\"],labelSize:t.labelSize||[12,12],labelFont:t.labelFont||[\"sans-serif\",\"sans-serif\"],title:t.title||\"\",titleSize:t.titleSize||18,titleFont:t.titleFont||\"sans-serif\"}),this.static=!!t.static,this.setDirty()},c.dispose=function(){this.box.dispose(),this.grid.dispose(),this.text.dispose(),this.line.dispose();for(var t=this.objects.length-1;t>=0;--t)this.objects[t].dispose();this.objects.length=0;for(t=this.overlays.length-1;t>=0;--t)this.overlays[t].dispose();this.overlays.length=0,this.gl=null},c.addObject=function(t){this.objects.indexOf(t)<0&&(this.objects.push(t),this.setDirty())},c.removeObject=function(t){for(var e=this.objects,r=0;r<e.length;++r)if(e[r]===t){e.splice(r,1),this.setDirty();break}},c.addOverlay=function(t){this.overlays.indexOf(t)<0&&(this.overlays.push(t),this.setOverlayDirty())},c.removeOverlay=function(t){for(var e=this.overlays,r=0;r<e.length;++r)if(e[r]===t){e.splice(r,1),this.setOverlayDirty();break}}},{\"./lib/box\":312,\"./lib/grid\":313,\"./lib/line\":314,\"./lib/text\":316,\"gl-select-static\":334}],319:[function(t,e,r){\"use strict\";e.exports=function(t,e){t=t||document.body,e=e||{};var r=[.01,1/0];\"distanceLimits\"in e&&(r[0]=e.distanceLimits[0],r[1]=e.distanceLimits[1]);\"zoomMin\"in e&&(r[0]=e.zoomMin);\"zoomMax\"in e&&(r[1]=e.zoomMax);var c=i({center:e.center||[0,0,0],up:e.up||[0,1,0],eye:e.eye||[0,0,10],mode:e.mode||\"orbit\",distanceLimits:r}),u=[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],f=0,h=t.clientWidth,p=t.clientHeight,d={keyBindingMode:\"rotate\",enableWheel:!0,view:c,element:t,delay:e.delay||16,rotateSpeed:e.rotateSpeed||1,zoomSpeed:e.zoomSpeed||1,translateSpeed:e.translateSpeed||1,flipX:!!e.flipX,flipY:!!e.flipY,modes:c.modes,_ortho:e._ortho||e.projection&&\"orthographic\"===e.projection.type||!1,tick:function(){var e=n(),r=this.delay,i=e-2*r;c.idle(e-r),c.recalcMatrix(i),c.flush(e-(100+2*r));for(var a=!0,o=c.computedMatrix,s=0;s<16;++s)a=a&&u[s]===o[s],u[s]=o[s];var l=t.clientWidth===h&&t.clientHeight===p;return h=t.clientWidth,p=t.clientHeight,a?!l:(f=Math.exp(c.computedRadius[0]),!0)},lookAt:function(t,e,r){c.lookAt(c.lastT(),t,e,r)},rotate:function(t,e,r){c.rotate(c.lastT(),t,e,r)},pan:function(t,e,r){c.pan(c.lastT(),t,e,r)},translate:function(t,e,r){c.translate(c.lastT(),t,e,r)}};return Object.defineProperties(d,{matrix:{get:function(){return c.computedMatrix},set:function(t){return c.setMatrix(c.lastT(),t),c.computedMatrix},enumerable:!0},mode:{get:function(){return c.getMode()},set:function(t){var e=c.computedUp.slice(),r=c.computedEye.slice(),i=c.computedCenter.slice();if(c.setMode(t),\"turntable\"===t){var a=n();c._active.lookAt(a,r,i,e),c._active.lookAt(a+500,r,i,[0,0,1]),c._active.flush(a)}return c.getMode()},enumerable:!0},center:{get:function(){return c.computedCenter},set:function(t){return c.lookAt(c.lastT(),null,t),c.computedCenter},enumerable:!0},eye:{get:function(){return c.computedEye},set:function(t){return c.lookAt(c.lastT(),t),c.computedEye},enumerable:!0},up:{get:function(){return c.computedUp},set:function(t){return c.lookAt(c.lastT(),null,null,t),c.computedUp},enumerable:!0},distance:{get:function(){return f},set:function(t){return c.setDistance(c.lastT(),t),t},enumerable:!0},distanceLimits:{get:function(){return c.getDistanceLimits(r)},set:function(t){return c.setDistanceLimits(t),t},enumerable:!0}}),t.addEventListener(\"contextmenu\",(function(t){return t.preventDefault(),!1})),d._lastX=-1,d._lastY=-1,d._lastMods={shift:!1,control:!1,alt:!1,meta:!1},d.enableMouseListeners=function(){function e(e,r,i,a){var o=d.keyBindingMode;if(!1!==o){var s=\"rotate\"===o,l=\"pan\"===o,u=\"zoom\"===o,h=!!a.control,p=!!a.alt,m=!!a.shift,g=!!(1&e),v=!!(2&e),y=!!(4&e),x=1/t.clientHeight,b=x*(r-d._lastX),_=x*(i-d._lastY),w=d.flipX?1:-1,T=d.flipY?1:-1,k=Math.PI*d.rotateSpeed,M=n();if(-1!==d._lastX&&-1!==d._lastY&&((s&&g&&!h&&!p&&!m||g&&!h&&!p&&m)&&c.rotate(M,w*k*b,-T*k*_,0),(l&&g&&!h&&!p&&!m||v||g&&h&&!p&&!m)&&c.pan(M,-d.translateSpeed*b*f,d.translateSpeed*_*f,0),u&&g&&!h&&!p&&!m||y||g&&!h&&p&&!m)){var A=-d.zoomSpeed*_/window.innerHeight*(M-c.lastT())*100;c.pan(M,0,0,f*(Math.exp(A)-1))}return d._lastX=r,d._lastY=i,d._lastMods=a,!0}}d.mouseListener=a(t,e),t.addEventListener(\"touchstart\",(function(r){var n=s(r.changedTouches[0],t);e(0,n[0],n[1],d._lastMods),e(1,n[0],n[1],d._lastMods)}),!!l&&{passive:!0}),t.addEventListener(\"touchmove\",(function(r){var n=s(r.changedTouches[0],t);e(1,n[0],n[1],d._lastMods),r.preventDefault()}),!!l&&{passive:!1}),t.addEventListener(\"touchend\",(function(t){e(0,d._lastX,d._lastY,d._lastMods)}),!!l&&{passive:!0}),d.wheelListener=o(t,(function(t,e){if(!1!==d.keyBindingMode&&d.enableWheel){var r=d.flipX?1:-1,i=d.flipY?1:-1,a=n();if(Math.abs(t)>Math.abs(e))c.rotate(a,0,0,-t*r*Math.PI*d.rotateSpeed/window.innerWidth);else if(!d._ortho){var o=-d.zoomSpeed*i*e/window.innerHeight*(a-c.lastT())/20;c.pan(a,0,0,f*(Math.exp(o)-1))}}}),!0)},d.enableMouseListeners(),d};var n=t(\"right-now\"),i=t(\"3d-view\"),a=t(\"mouse-change\"),o=t(\"mouse-wheel\"),s=t(\"mouse-event-offset\"),l=t(\"has-passive-events\")},{\"3d-view\":55,\"has-passive-events\":440,\"mouse-change\":466,\"mouse-event-offset\":467,\"mouse-wheel\":469,\"right-now\":538}],320:[function(t,e,r){var n=t(\"glslify\"),i=t(\"gl-shader\"),a=n([\"precision mediump float;\\n#define GLSLIFY 1\\nattribute vec2 position;\\nvarying vec2 uv;\\nvoid main() {\\n  uv = position;\\n  gl_Position = vec4(position, 0, 1);\\n}\"]),o=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform sampler2D accumBuffer;\\nvarying vec2 uv;\\n\\nvoid main() {\\n  vec4 accum = texture2D(accumBuffer, 0.5 * (uv + 1.0));\\n  gl_FragColor = min(vec4(1,1,1,1), accum);\\n}\"]);e.exports=function(t){return i(t,a,o,null,[{name:\"position\",type:\"vec2\"}])}},{\"gl-shader\":335,glslify:321}],321:[function(t,e,r){arguments[4][261][0].apply(r,arguments)},{dup:261}],322:[function(t,e,r){\"use strict\";var n=t(\"./camera.js\"),i=t(\"gl-axes3d\"),a=t(\"gl-axes3d/properties\"),o=t(\"gl-spikes3d\"),s=t(\"gl-select-static\"),l=t(\"gl-fbo\"),c=t(\"a-big-triangle\"),u=t(\"mouse-change\"),f=t(\"gl-mat4/perspective\"),h=t(\"gl-mat4/ortho\"),p=t(\"./lib/shader\"),d=t(\"is-mobile\")({tablet:!0,featureDetect:!0});function m(){this.mouse=[-1,-1],this.screen=null,this.distance=1/0,this.index=null,this.dataCoordinate=null,this.dataPosition=null,this.object=null,this.data=null}function g(t){var e=Math.round(Math.log(Math.abs(t))/Math.log(10));if(e<0){var r=Math.round(Math.pow(10,-e));return Math.ceil(t*r)/r}if(e>0){r=Math.round(Math.pow(10,e));return Math.ceil(t/r)*r}return Math.ceil(t)}function v(t){return\"boolean\"!=typeof t||t}e.exports={createScene:function(t){(t=t||{}).camera=t.camera||{};var e=t.canvas;if(!e){if(e=document.createElement(\"canvas\"),t.container)t.container.appendChild(e);else document.body.appendChild(e)}var r=t.gl;r||(t.glOptions&&(d=!!t.glOptions.preserveDrawingBuffer),r=function(t,e){var r=null;try{(r=t.getContext(\"webgl\",e))||(r=t.getContext(\"experimental-webgl\",e))}catch(t){return null}return r}(e,t.glOptions||{premultipliedAlpha:!0,antialias:!0,preserveDrawingBuffer:d}));if(!r)throw new Error(\"webgl not supported\");var y=t.bounds||[[-10,-10,-10],[10,10,10]],x=new m,b=l(r,r.drawingBufferWidth,r.drawingBufferHeight,{preferFloat:!d}),_=p(r),w=t.cameraObject&&!0===t.cameraObject._ortho||t.camera.projection&&\"orthographic\"===t.camera.projection.type||!1,T={eye:t.camera.eye||[2,0,0],center:t.camera.center||[0,0,0],up:t.camera.up||[0,1,0],zoomMin:t.camera.zoomMax||.1,zoomMax:t.camera.zoomMin||100,mode:t.camera.mode||\"turntable\",_ortho:w},k=t.axes||{},M=i(r,k);M.enable=!k.disable;var A=t.spikes||{},S=o(r,A),E=[],L=[],C=[],P=[],I=!0,O=!0,z=new Array(16),D=new Array(16),R={view:null,projection:z,model:D,_ortho:!1},F=(O=!0,[r.drawingBufferWidth,r.drawingBufferHeight]),B=t.cameraObject||n(e,T),N={gl:r,contextLost:!1,pixelRatio:t.pixelRatio||1,canvas:e,selection:x,camera:B,axes:M,axesPixels:null,spikes:S,bounds:y,objects:E,shape:F,aspect:t.aspectRatio||[1,1,1],pickRadius:t.pickRadius||10,zNear:t.zNear||.01,zFar:t.zFar||1e3,fovy:t.fovy||Math.PI/4,clearColor:t.clearColor||[0,0,0,0],autoResize:v(t.autoResize),autoBounds:v(t.autoBounds),autoScale:!!t.autoScale,autoCenter:v(t.autoCenter),clipToBounds:v(t.clipToBounds),snapToData:!!t.snapToData,onselect:t.onselect||null,onrender:t.onrender||null,onclick:t.onclick||null,cameraParams:R,oncontextloss:null,mouseListener:null,_stopped:!1,getAspectratio:function(){return{x:this.aspect[0],y:this.aspect[1],z:this.aspect[2]}},setAspectratio:function(t){this.aspect[0]=t.x,this.aspect[1]=t.y,this.aspect[2]=t.z,O=!0},setBounds:function(t,e){this.bounds[0][t]=e.min,this.bounds[1][t]=e.max},setClearColor:function(t){this.clearColor=t},clearRGBA:function(){this.gl.clearColor(this.clearColor[0],this.clearColor[1],this.clearColor[2],this.clearColor[3]),this.gl.clear(this.gl.COLOR_BUFFER_BIT|this.gl.DEPTH_BUFFER_BIT)}},j=[r.drawingBufferWidth/N.pixelRatio|0,r.drawingBufferHeight/N.pixelRatio|0];function U(){if(!N._stopped&&N.autoResize){var t=e.parentNode,r=1,n=1;t&&t!==document.body?(r=t.clientWidth,n=t.clientHeight):(r=window.innerWidth,n=window.innerHeight);var i=0|Math.ceil(r*N.pixelRatio),a=0|Math.ceil(n*N.pixelRatio);if(i!==e.width||a!==e.height){e.width=i,e.height=a;var o=e.style;o.position=o.position||\"absolute\",o.left=\"0px\",o.top=\"0px\",o.width=r+\"px\",o.height=n+\"px\",I=!0}}}N.autoResize&&U();function V(){for(var t=E.length,e=P.length,n=0;n<e;++n)C[n]=0;t:for(n=0;n<t;++n){var i=E[n],a=i.pickSlots;if(a){for(var o=0;o<e;++o)if(C[o]+a<255){L[n]=o,i.setPickBase(C[o]+1),C[o]+=a;continue t}var l=s(r,F);L[n]=e,P.push(l),C.push(a),i.setPickBase(1),e+=1}else L[n]=-1}for(;e>0&&0===C[e-1];)C.pop(),P.pop().dispose()}function q(){if(N.contextLost)return!0;r.isContextLost()&&(N.contextLost=!0,N.mouseListener.enabled=!1,N.selection.object=null,N.oncontextloss&&N.oncontextloss())}window.addEventListener(\"resize\",U),N.update=function(t){N._stopped||(t=t||{},I=!0,O=!0)},N.add=function(t){N._stopped||(t.axes=M,E.push(t),L.push(-1),I=!0,O=!0,V())},N.remove=function(t){if(!N._stopped){var e=E.indexOf(t);e<0||(E.splice(e,1),L.pop(),I=!0,O=!0,V())}},N.dispose=function(){if(!N._stopped&&(N._stopped=!0,window.removeEventListener(\"resize\",U),e.removeEventListener(\"webglcontextlost\",q),N.mouseListener.enabled=!1,!N.contextLost)){M.dispose(),S.dispose();for(var t=0;t<E.length;++t)E[t].dispose();b.dispose();for(t=0;t<P.length;++t)P[t].dispose();_.dispose(),r=null,M=null,S=null,E=[]}},N._mouseRotating=!1,N._prevButtons=0,N.enableMouseListeners=function(){N.mouseListener=u(e,(function(t,e,r){if(!N._stopped){var n=P.length,i=E.length,a=x.object;x.distance=1/0,x.mouse[0]=e,x.mouse[1]=r,x.object=null,x.screen=null,x.dataCoordinate=x.dataPosition=null;var o=!1;if(t&&N._prevButtons)N._mouseRotating=!0;else{N._mouseRotating&&(O=!0),N._mouseRotating=!1;for(var s=0;s<n;++s){var l=P[s].query(e,j[1]-r-1,N.pickRadius);if(l){if(l.distance>x.distance)continue;for(var c=0;c<i;++c){var u=E[c];if(L[c]===s){var f=u.pick(l);f&&(x.buttons=t,x.screen=l.coord,x.distance=l.distance,x.object=u,x.index=f.distance,x.dataPosition=f.position,x.dataCoordinate=f.dataCoordinate,x.data=f,o=!0)}}}}}a&&a!==x.object&&(a.highlight&&a.highlight(null),I=!0),x.object&&(x.object.highlight&&x.object.highlight(x.data),I=!0),(o=o||x.object!==a)&&N.onselect&&N.onselect(x),1&t&&!(1&N._prevButtons)&&N.onclick&&N.onclick(x),N._prevButtons=t}}))},e.addEventListener(\"webglcontextlost\",q);var H=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],G=[H[0].slice(),H[1].slice()];function Y(){if(!q()){U();var t=N.camera.tick();R.view=N.camera.matrix,I=I||t,O=O||t,M.pixelRatio=N.pixelRatio,S.pixelRatio=N.pixelRatio;var e=E.length,n=H[0],i=H[1];n[0]=n[1]=n[2]=1/0,i[0]=i[1]=i[2]=-1/0;for(var o=0;o<e;++o){(C=E[o]).pixelRatio=N.pixelRatio,C.axes=N.axes,I=I||!!C.dirty,O=O||!!C.dirty;var s=C.bounds;if(s)for(var l=s[0],u=s[1],p=0;p<3;++p)n[p]=Math.min(n[p],l[p]),i[p]=Math.max(i[p],u[p])}var d=N.bounds;if(N.autoBounds)for(p=0;p<3;++p){if(i[p]<n[p])n[p]=-1,i[p]=1;else{n[p]===i[p]&&(n[p]-=1,i[p]+=1);var m=.05*(i[p]-n[p]);n[p]=n[p]-m,i[p]=i[p]+m}d[0][p]=n[p],d[1][p]=i[p]}var v=!1;for(p=0;p<3;++p)v=v||G[0][p]!==d[0][p]||G[1][p]!==d[1][p],G[0][p]=d[0][p],G[1][p]=d[1][p];if(O=O||v,I=I||v){if(v){var y=[0,0,0];for(o=0;o<3;++o)y[o]=g((d[1][o]-d[0][o])/10);M.autoTicks?M.update({bounds:d,tickSpacing:y}):M.update({bounds:d})}var T=r.drawingBufferWidth,k=r.drawingBufferHeight;F[0]=T,F[1]=k,j[0]=0|Math.max(T/N.pixelRatio,1),j[1]=0|Math.max(k/N.pixelRatio,1),function(t,e){var r=t.bounds,n=t.cameraParams,i=n.projection,a=n.model,o=t.gl.drawingBufferWidth,s=t.gl.drawingBufferHeight,l=t.zNear,c=t.zFar,u=t.fovy,p=o/s;e?(h(i,-p,p,-1,1,l,c),n._ortho=!0):(f(i,u,p,l,c),n._ortho=!1);for(var d=0;d<16;++d)a[d]=0;a[15]=1;var m=0;for(d=0;d<3;++d)m=Math.max(m,r[1][d]-r[0][d]);for(d=0;d<3;++d)t.autoScale?a[5*d]=t.aspect[d]/(r[1][d]-r[0][d]):a[5*d]=1/m,t.autoCenter&&(a[12+d]=.5*-a[5*d]*(r[0][d]+r[1][d]))}(N,w);for(o=0;o<e;++o){(C=E[o]).axesBounds=d,N.clipToBounds&&(C.clipBounds=d)}x.object&&(N.snapToData?S.position=x.dataCoordinate:S.position=x.dataPosition,S.bounds=d),O&&(O=!1,function(){if(!q()){r.colorMask(!0,!0,!0,!0),r.depthMask(!0),r.disable(r.BLEND),r.enable(r.DEPTH_TEST),r.depthFunc(r.LEQUAL);for(var t=E.length,e=P.length,n=0;n<e;++n){var i=P[n];i.shape=j,i.begin();for(var a=0;a<t;++a)if(L[a]===n){var o=E[a];o.drawPick&&(o.pixelRatio=1,o.drawPick(R))}i.end()}}}()),N.axesPixels=a(N.axes,R,T,k),N.onrender&&N.onrender(),r.bindFramebuffer(r.FRAMEBUFFER,null),r.viewport(0,0,T,k),N.clearRGBA(),r.depthMask(!0),r.colorMask(!0,!0,!0,!0),r.enable(r.DEPTH_TEST),r.depthFunc(r.LEQUAL),r.disable(r.BLEND),r.disable(r.CULL_FACE);var A=!1;M.enable&&(A=A||M.isTransparent(),M.draw(R)),S.axes=M,x.object&&S.draw(R),r.disable(r.CULL_FACE);for(o=0;o<e;++o){(C=E[o]).axes=M,C.pixelRatio=N.pixelRatio,C.isOpaque&&C.isOpaque()&&C.draw(R),C.isTransparent&&C.isTransparent()&&(A=!0)}if(A){b.shape=F,b.bind(),r.clear(r.DEPTH_BUFFER_BIT),r.colorMask(!1,!1,!1,!1),r.depthMask(!0),r.depthFunc(r.LESS),M.enable&&M.isTransparent()&&M.drawTransparent(R);for(o=0;o<e;++o){(C=E[o]).isOpaque&&C.isOpaque()&&C.draw(R)}r.enable(r.BLEND),r.blendEquation(r.FUNC_ADD),r.blendFunc(r.ONE,r.ONE_MINUS_SRC_ALPHA),r.colorMask(!0,!0,!0,!0),r.depthMask(!1),r.clearColor(0,0,0,0),r.clear(r.COLOR_BUFFER_BIT),M.isTransparent()&&M.drawTransparent(R);for(o=0;o<e;++o){var C;(C=E[o]).isTransparent&&C.isTransparent()&&C.drawTransparent(R)}r.bindFramebuffer(r.FRAMEBUFFER,null),r.blendFunc(r.ONE,r.ONE_MINUS_SRC_ALPHA),r.disable(r.DEPTH_TEST),_.bind(),b.color[0].bind(0),_.uniforms.accumBuffer=0,c(r),r.disable(r.BLEND)}I=!1;for(o=0;o<e;++o)E[o].dirty=!1}}}return N.enableMouseListeners(),function t(){if(N._stopped||N.contextLost)return;Y(),requestAnimationFrame(t)}(),N.redraw=function(){N._stopped||(I=!0,Y())},N},createCamera:n}},{\"./camera.js\":319,\"./lib/shader\":320,\"a-big-triangle\":70,\"gl-axes3d\":254,\"gl-axes3d/properties\":262,\"gl-fbo\":272,\"gl-mat4/ortho\":298,\"gl-mat4/perspective\":299,\"gl-select-static\":334,\"gl-spikes3d\":345,\"is-mobile\":451,\"mouse-change\":466}],323:[function(t,e,r){var n=t(\"glslify\");r.pointVertex=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\n\\nuniform mat3 matrix;\\nuniform float pointSize;\\nuniform float pointCloud;\\n\\nhighp float rand(vec2 co) {\\n  highp float a = 12.9898;\\n  highp float b = 78.233;\\n  highp float c = 43758.5453;\\n  highp float d = dot(co.xy, vec2(a, b));\\n  highp float e = mod(d, 3.14);\\n  return fract(sin(e) * c);\\n}\\n\\nvoid main() {\\n  vec3 hgPosition = matrix * vec3(position, 1);\\n  gl_Position  = vec4(hgPosition.xy, 0, hgPosition.z);\\n    // if we don't jitter the point size a bit, overall point cloud\\n    // saturation 'jumps' on zooming, which is disturbing and confusing\\n  gl_PointSize = pointSize * ((19.5 + rand(position)) / 20.0);\\n  if(pointCloud != 0.0) { // pointCloud is truthy\\n    // get the same square surface as circle would be\\n    gl_PointSize *= 0.886;\\n  }\\n}\"]),r.pointFragment=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color, borderColor;\\nuniform float centerFraction;\\nuniform float pointCloud;\\n\\nvoid main() {\\n  float radius;\\n  vec4 baseColor;\\n  if(pointCloud != 0.0) { // pointCloud is truthy\\n    if(centerFraction == 1.0) {\\n      gl_FragColor = color;\\n    } else {\\n      gl_FragColor = mix(borderColor, color, centerFraction);\\n    }\\n  } else {\\n    radius = length(2.0 * gl_PointCoord.xy - 1.0);\\n    if(radius > 1.0) {\\n      discard;\\n    }\\n    baseColor = mix(borderColor, color, step(radius, centerFraction));\\n    gl_FragColor = vec4(baseColor.rgb * baseColor.a, baseColor.a);\\n  }\\n}\\n\"]),r.pickVertex=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\nattribute vec4 pickId;\\n\\nuniform mat3 matrix;\\nuniform float pointSize;\\nuniform vec4 pickOffset;\\n\\nvarying vec4 fragId;\\n\\nvoid main() {\\n  vec3 hgPosition = matrix * vec3(position, 1);\\n  gl_Position  = vec4(hgPosition.xy, 0, hgPosition.z);\\n  gl_PointSize = pointSize;\\n\\n  vec4 id = pickId + pickOffset;\\n  id.y += floor(id.x / 256.0);\\n  id.x -= floor(id.x / 256.0) * 256.0;\\n\\n  id.z += floor(id.y / 256.0);\\n  id.y -= floor(id.y / 256.0) * 256.0;\\n\\n  id.w += floor(id.z / 256.0);\\n  id.z -= floor(id.z / 256.0) * 256.0;\\n\\n  fragId = id;\\n}\\n\"]),r.pickFragment=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragId;\\n\\nvoid main() {\\n  float radius = length(2.0 * gl_PointCoord.xy - 1.0);\\n  if(radius > 1.0) {\\n    discard;\\n  }\\n  gl_FragColor = fragId / 255.0;\\n}\\n\"])},{glslify:438}],324:[function(t,e,r){\"use strict\";var n=t(\"gl-shader\"),i=t(\"gl-buffer\"),a=t(\"typedarray-pool\"),o=t(\"./lib/shader\");function s(t,e,r,n,i){this.plot=t,this.offsetBuffer=e,this.pickBuffer=r,this.shader=n,this.pickShader=i,this.sizeMin=.5,this.sizeMinCap=2,this.sizeMax=20,this.areaRatio=1,this.pointCount=0,this.color=[1,0,0,1],this.borderColor=[0,0,0,1],this.blend=!1,this.pickOffset=0,this.points=null}e.exports=function(t,e){var r=t.gl,a=i(r),l=i(r),c=n(r,o.pointVertex,o.pointFragment),u=n(r,o.pickVertex,o.pickFragment),f=new s(t,a,l,c,u);return f.update(e),t.addObject(f),f};var l,c,u=s.prototype;u.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.offsetBuffer.dispose(),this.pickBuffer.dispose(),this.plot.removeObject(this)},u.update=function(t){var e;function r(e,r){return e in t?t[e]:r}t=t||{},this.sizeMin=r(\"sizeMin\",.5),this.sizeMax=r(\"sizeMax\",20),this.color=r(\"color\",[1,0,0,1]).slice(),this.areaRatio=r(\"areaRatio\",1),this.borderColor=r(\"borderColor\",[0,0,0,1]).slice(),this.blend=r(\"blend\",!1);var n=t.positions.length>>>1,i=t.positions instanceof Float32Array,o=t.idToIndex instanceof Int32Array&&t.idToIndex.length>=n,s=t.positions,l=i?s:a.mallocFloat32(s.length),c=o?t.idToIndex:a.mallocInt32(n);if(i||l.set(s),!o)for(l.set(s),e=0;e<n;e++)c[e]=e;this.points=s,this.offsetBuffer.update(l),this.pickBuffer.update(c),i||a.free(l),o||a.free(c),this.pointCount=n,this.pickOffset=0},u.unifiedDraw=(l=[1,0,0,0,1,0,0,0,1],c=[0,0,0,0],function(t){var e=void 0!==t,r=e?this.pickShader:this.shader,n=this.plot.gl,i=this.plot.dataBox;if(0===this.pointCount)return t;var a=i[2]-i[0],o=i[3]-i[1],s=function(t,e){var r,n=0,i=t.length>>>1;for(r=0;r<i;r++){var a=t[2*r],o=t[2*r+1];a>=e[0]&&a<=e[2]&&o>=e[1]&&o<=e[3]&&n++}return n}(this.points,i),u=this.plot.pickPixelRatio*Math.max(Math.min(this.sizeMinCap,this.sizeMin),Math.min(this.sizeMax,this.sizeMax/Math.pow(s,.33333)));l[0]=2/a,l[4]=2/o,l[6]=-2*i[0]/a-1,l[7]=-2*i[1]/o-1,this.offsetBuffer.bind(),r.bind(),r.attributes.position.pointer(),r.uniforms.matrix=l,r.uniforms.color=this.color,r.uniforms.borderColor=this.borderColor,r.uniforms.pointCloud=u<5,r.uniforms.pointSize=u,r.uniforms.centerFraction=Math.min(1,Math.max(0,Math.sqrt(1-this.areaRatio))),e&&(c[0]=255&t,c[1]=t>>8&255,c[2]=t>>16&255,c[3]=t>>24&255,this.pickBuffer.bind(),r.attributes.pickId.pointer(n.UNSIGNED_BYTE),r.uniforms.pickOffset=c,this.pickOffset=t);var f=n.getParameter(n.BLEND),h=n.getParameter(n.DITHER);return f&&!this.blend&&n.disable(n.BLEND),h&&n.disable(n.DITHER),n.drawArrays(n.POINTS,0,this.pointCount),f&&!this.blend&&n.enable(n.BLEND),h&&n.enable(n.DITHER),t+this.pointCount}),u.draw=u.unifiedDraw,u.drawPick=u.unifiedDraw,u.pick=function(t,e,r){var n=this.pickOffset,i=this.pointCount;if(r<n||r>=n+i)return null;var a=r-n,o=this.points;return{object:this,pointId:a,dataCoord:[o[2*a],o[2*a+1]]}}},{\"./lib/shader\":323,\"gl-buffer\":263,\"gl-shader\":335,\"typedarray-pool\":613}],325:[function(t,e,r){e.exports=function(t,e,r,n){var i,a,o,s,l,c=e[0],u=e[1],f=e[2],h=e[3],p=r[0],d=r[1],m=r[2],g=r[3];(a=c*p+u*d+f*m+h*g)<0&&(a=-a,p=-p,d=-d,m=-m,g=-g);1-a>1e-6?(i=Math.acos(a),o=Math.sin(i),s=Math.sin((1-n)*i)/o,l=Math.sin(n*i)/o):(s=1-n,l=n);return t[0]=s*c+l*p,t[1]=s*u+l*d,t[2]=s*f+l*m,t[3]=s*h+l*g,t}},{}],326:[function(t,e,r){\"use strict\";e.exports=function(t){return t||0===t?t.toString():\"\"}},{}],327:[function(t,e,r){\"use strict\";var n=t(\"vectorize-text\");e.exports=function(t,e,r){var a=i[e];a||(a=i[e]={});if(t in a)return a[t];var o={textAlign:\"center\",textBaseline:\"middle\",lineHeight:1,font:e,lineSpacing:1.25,styletags:{breaklines:!0,bolds:!0,italics:!0,subscripts:!0,superscripts:!0},triangles:!0},s=n(t,o);o.triangles=!1;var l,c,u=n(t,o);if(r&&1!==r){for(l=0;l<s.positions.length;++l)for(c=0;c<s.positions[l].length;++c)s.positions[l][c]/=r;for(l=0;l<u.positions.length;++l)for(c=0;c<u.positions[l].length;++c)u.positions[l][c]/=r}var f=[[1/0,1/0],[-1/0,-1/0]],h=u.positions.length;for(l=0;l<h;++l){var p=u.positions[l];for(c=0;c<2;++c)f[0][c]=Math.min(f[0][c],p[c]),f[1][c]=Math.max(f[1][c],p[c])}return a[t]=[s,u,f]};var i={}},{\"vectorize-text\":619}],328:[function(t,e,r){var n=t(\"gl-shader\"),i=t(\"glslify\"),a=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform vec4 highlightId;\\nuniform float highlightScale;\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0,0,0,0);\\n  } else {\\n    float scale = 1.0;\\n    if(distance(highlightId, id) < 0.0001) {\\n      scale = highlightScale;\\n    }\\n\\n    vec4 worldPosition = model * vec4(position, 1);\\n    vec4 viewPosition = view * worldPosition;\\n    viewPosition = viewPosition / viewPosition.w;\\n    vec4 clipPosition = projection * (viewPosition + scale * vec4(glyph.x, -glyph.y, 0, 0));\\n\\n    gl_Position = clipPosition;\\n    interpColor = color;\\n    pickId = id;\\n    dataCoordinate = position;\\n  }\\n}\"]),o=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform vec2 screenSize;\\nuniform vec3 clipBounds[2];\\nuniform float highlightScale, pixelRatio;\\nuniform vec4 highlightId;\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0,0,0,0);\\n  } else {\\n    float scale = pixelRatio;\\n    if(distance(highlightId.bgr, id.bgr) < 0.001) {\\n      scale *= highlightScale;\\n    }\\n\\n    vec4 worldPosition = model * vec4(position, 1.0);\\n    vec4 viewPosition = view * worldPosition;\\n    vec4 clipPosition = projection * viewPosition;\\n    clipPosition /= clipPosition.w;\\n\\n    gl_Position = clipPosition + vec4(screenSize * scale * vec2(glyph.x, -glyph.y), 0.0, 0.0);\\n    interpColor = color;\\n    pickId = id;\\n    dataCoordinate = position;\\n  }\\n}\"]),s=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform float highlightScale;\\nuniform vec4 highlightId;\\nuniform vec3 axes[2];\\nuniform mat4 model, view, projection;\\nuniform vec2 screenSize;\\nuniform vec3 clipBounds[2];\\nuniform float scale, pixelRatio;\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0,0,0,0);\\n  } else {\\n    float lscale = pixelRatio * scale;\\n    if(distance(highlightId, id) < 0.0001) {\\n      lscale *= highlightScale;\\n    }\\n\\n    vec4 clipCenter   = projection * view * model * vec4(position, 1);\\n    vec3 dataPosition = position + 0.5*lscale*(axes[0] * glyph.x + axes[1] * glyph.y) * clipCenter.w * screenSize.y;\\n    vec4 clipPosition = projection * view * model * vec4(dataPosition, 1);\\n\\n    gl_Position = clipPosition;\\n    interpColor = color;\\n    pickId = id;\\n    dataCoordinate = dataPosition;\\n  }\\n}\\n\"]),l=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 fragClipBounds[2];\\nuniform float opacity;\\n\\nvarying vec4 interpColor;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (\\n    outOfRange(fragClipBounds[0], fragClipBounds[1], dataCoordinate) ||\\n    interpColor.a * opacity == 0.\\n  ) discard;\\n  gl_FragColor = interpColor * opacity;\\n}\\n\"]),c=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 fragClipBounds[2];\\nuniform float pickGroup;\\n\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (outOfRange(fragClipBounds[0], fragClipBounds[1], dataCoordinate)) discard;\\n\\n  gl_FragColor = vec4(pickGroup, pickId.bgr);\\n}\"]),u=[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"glyph\",type:\"vec2\"},{name:\"id\",type:\"vec4\"}],f={vertex:a,fragment:l,attributes:u},h={vertex:o,fragment:l,attributes:u},p={vertex:s,fragment:l,attributes:u},d={vertex:a,fragment:c,attributes:u},m={vertex:o,fragment:c,attributes:u},g={vertex:s,fragment:c,attributes:u};function v(t,e){var r=n(t,e),i=r.attributes;return i.position.location=0,i.color.location=1,i.glyph.location=2,i.id.location=3,r}r.createPerspective=function(t){return v(t,f)},r.createOrtho=function(t){return v(t,h)},r.createProject=function(t){return v(t,p)},r.createPickPerspective=function(t){return v(t,d)},r.createPickOrtho=function(t){return v(t,m)},r.createPickProject=function(t){return v(t,g)}},{\"gl-shader\":335,glslify:329}],329:[function(t,e,r){arguments[4][261][0].apply(r,arguments)},{dup:261}],330:[function(t,e,r){\"use strict\";var n=t(\"is-string-blank\"),i=t(\"gl-buffer\"),a=t(\"gl-vao\"),o=t(\"typedarray-pool\"),s=t(\"gl-mat4/multiply\"),l=t(\"./lib/shaders\"),c=t(\"./lib/glyphs\"),u=t(\"./lib/get-simple-string\"),f=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function h(t,e){var r=t[0],n=t[1],i=t[2],a=t[3];return t[0]=e[0]*r+e[4]*n+e[8]*i+e[12]*a,t[1]=e[1]*r+e[5]*n+e[9]*i+e[13]*a,t[2]=e[2]*r+e[6]*n+e[10]*i+e[14]*a,t[3]=e[3]*r+e[7]*n+e[11]*i+e[15]*a,t}function p(t,e,r,n){return h(n,n),h(n,n),h(n,n)}function d(t,e){this.index=t,this.dataCoordinate=this.position=e}function m(t){return!0===t||t>1?1:t}function g(t,e,r,n,i,a,o,s,l,c,u,f){this.gl=t,this.pixelRatio=1,this.shader=e,this.orthoShader=r,this.projectShader=n,this.pointBuffer=i,this.colorBuffer=a,this.glyphBuffer=o,this.idBuffer=s,this.vao=l,this.vertexCount=0,this.lineVertexCount=0,this.opacity=1,this.hasAlpha=!1,this.lineWidth=0,this.projectScale=[2/3,2/3,2/3],this.projectOpacity=[1,1,1],this.projectHasAlpha=!1,this.pickId=0,this.pickPerspectiveShader=c,this.pickOrthoShader=u,this.pickProjectShader=f,this.points=[],this._selectResult=new d(0,[0,0,0]),this.useOrtho=!0,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.axesProject=[!0,!0,!0],this.axesBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.highlightId=[1,1,1,1],this.highlightScale=2,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.dirty=!0}e.exports=function(t){var e=t.gl,r=l.createPerspective(e),n=l.createOrtho(e),o=l.createProject(e),s=l.createPickPerspective(e),c=l.createPickOrtho(e),u=l.createPickProject(e),f=i(e),h=i(e),p=i(e),d=i(e),m=a(e,[{buffer:f,size:3,type:e.FLOAT},{buffer:h,size:4,type:e.FLOAT},{buffer:p,size:2,type:e.FLOAT},{buffer:d,size:4,type:e.UNSIGNED_BYTE,normalized:!0}]),v=new g(e,r,n,o,f,h,p,d,m,s,c,u);return v.update(t),v};var v=g.prototype;v.pickSlots=1,v.setPickBase=function(t){this.pickId=t},v.isTransparent=function(){if(this.hasAlpha)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&this.projectHasAlpha)return!0;return!1},v.isOpaque=function(){if(!this.hasAlpha)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&!this.projectHasAlpha)return!0;return!1};var y=[0,0],x=[0,0,0],b=[0,0,0],_=[0,0,0,1],w=[0,0,0,1],T=f.slice(),k=[0,0,0],M=[[0,0,0],[0,0,0]];function A(t){return t[0]=t[1]=t[2]=0,t}function S(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=1,t}function E(t,e,r,n){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[r]=n,t}function L(t,e,r,n){var i,a=e.axesProject,o=e.gl,l=t.uniforms,c=r.model||f,u=r.view||f,h=r.projection||f,d=e.axesBounds,m=function(t){for(var e=M,r=0;r<2;++r)for(var n=0;n<3;++n)e[r][n]=Math.max(Math.min(t[r][n],1e8),-1e8);return e}(e.clipBounds);i=e.axes&&e.axes.lastCubeProps?e.axes.lastCubeProps.axis:[1,1,1],y[0]=2/o.drawingBufferWidth,y[1]=2/o.drawingBufferHeight,t.bind(),l.view=u,l.projection=h,l.screenSize=y,l.highlightId=e.highlightId,l.highlightScale=e.highlightScale,l.clipBounds=m,l.pickGroup=e.pickId/255,l.pixelRatio=n;for(var g=0;g<3;++g)if(a[g]){l.scale=e.projectScale[g],l.opacity=e.projectOpacity[g];for(var v=T,L=0;L<16;++L)v[L]=0;for(L=0;L<4;++L)v[5*L]=1;v[5*g]=0,i[g]<0?v[12+g]=d[0][g]:v[12+g]=d[1][g],s(v,c,v),l.model=v;var C=(g+1)%3,P=(g+2)%3,I=A(x),O=A(b);I[C]=1,O[P]=1;var z=p(0,0,0,S(_,I)),D=p(0,0,0,S(w,O));if(Math.abs(z[1])>Math.abs(D[1])){var R=z;z=D,D=R,R=I,I=O,O=R;var F=C;C=P,P=F}z[0]<0&&(I[C]=-1),D[1]>0&&(O[P]=-1);var B=0,N=0;for(L=0;L<4;++L)B+=Math.pow(c[4*C+L],2),N+=Math.pow(c[4*P+L],2);I[C]/=Math.sqrt(B),O[P]/=Math.sqrt(N),l.axes[0]=I,l.axes[1]=O,l.fragClipBounds[0]=E(k,m[0],g,-1e8),l.fragClipBounds[1]=E(k,m[1],g,1e8),e.vao.bind(),e.vao.draw(o.TRIANGLES,e.vertexCount),e.lineWidth>0&&(o.lineWidth(e.lineWidth*n),e.vao.draw(o.LINES,e.lineVertexCount,e.vertexCount)),e.vao.unbind()}}var C=[[-1e8,-1e8,-1e8],[1e8,1e8,1e8]];function P(t,e,r,n,i,a,o){var s=r.gl;if((a===r.projectHasAlpha||o)&&L(e,r,n,i),a===r.hasAlpha||o){t.bind();var l=t.uniforms;l.model=n.model||f,l.view=n.view||f,l.projection=n.projection||f,y[0]=2/s.drawingBufferWidth,y[1]=2/s.drawingBufferHeight,l.screenSize=y,l.highlightId=r.highlightId,l.highlightScale=r.highlightScale,l.fragClipBounds=C,l.clipBounds=r.axes.bounds,l.opacity=r.opacity,l.pickGroup=r.pickId/255,l.pixelRatio=i,r.vao.bind(),r.vao.draw(s.TRIANGLES,r.vertexCount),r.lineWidth>0&&(s.lineWidth(r.lineWidth*i),r.vao.draw(s.LINES,r.lineVertexCount,r.vertexCount)),r.vao.unbind()}}function I(t,e,r,i){var a;a=Array.isArray(t)?e<t.length?t[e]:void 0:t,a=u(a);var o=!0;n(a)&&(a=\"\\u25bc\",o=!1);var s=c(a,r,i);return{mesh:s[0],lines:s[1],bounds:s[2],visible:o}}v.draw=function(t){P(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,t,this.pixelRatio,!1,!1)},v.drawTransparent=function(t){P(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,t,this.pixelRatio,!0,!1)},v.drawPick=function(t){P(this.useOrtho?this.pickOrthoShader:this.pickPerspectiveShader,this.pickProjectShader,this,t,1,!0,!0)},v.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[2]+(t.value[1]<<8)+(t.value[0]<<16);if(e>=this.pointCount||e<0)return null;var r=this.points[e],n=this._selectResult;n.index=e;for(var i=0;i<3;++i)n.position[i]=n.dataCoordinate[i]=r[i];return n},v.highlight=function(t){if(t){var e=t.index,r=255&e,n=e>>8&255,i=e>>16&255;this.highlightId=[r/255,n/255,i/255,0]}else this.highlightId=[1,1,1,1]},v.update=function(t){if(\"perspective\"in(t=t||{})&&(this.useOrtho=!t.perspective),\"orthographic\"in t&&(this.useOrtho=!!t.orthographic),\"lineWidth\"in t&&(this.lineWidth=t.lineWidth),\"project\"in t)if(Array.isArray(t.project))this.axesProject=t.project;else{var e=!!t.project;this.axesProject=[e,e,e]}if(\"projectScale\"in t)if(Array.isArray(t.projectScale))this.projectScale=t.projectScale.slice();else{var r=+t.projectScale;this.projectScale=[r,r,r]}if(this.projectHasAlpha=!1,\"projectOpacity\"in t){if(Array.isArray(t.projectOpacity))this.projectOpacity=t.projectOpacity.slice();else{r=+t.projectOpacity;this.projectOpacity=[r,r,r]}for(var n=0;n<3;++n)this.projectOpacity[n]=m(this.projectOpacity[n]),this.projectOpacity[n]<1&&(this.projectHasAlpha=!0)}this.hasAlpha=!1,\"opacity\"in t&&(this.opacity=m(t.opacity),this.opacity<1&&(this.hasAlpha=!0)),this.dirty=!0;var i,a,s=t.position,l=t.font||\"normal\",c=t.alignment||[0,0];if(2===c.length)i=c[0],a=c[1];else{i=[],a=[];for(n=0;n<c.length;++n)i[n]=c[n][0],a[n]=c[n][1]}var u=[1/0,1/0,1/0],f=[-1/0,-1/0,-1/0],h=t.glyph,p=t.color,d=t.size,g=t.angle,v=t.lineColor,y=-1,x=0,b=0,_=0;if(s.length){_=s.length;t:for(n=0;n<_;++n){for(var w=s[n],T=0;T<3;++T)if(isNaN(w[T])||!isFinite(w[T]))continue t;var k=(N=I(h,n,l,this.pixelRatio)).mesh,M=N.lines,A=N.bounds;x+=3*k.cells.length,b+=2*M.edges.length}}var S=x+b,E=o.mallocFloat(3*S),L=o.mallocFloat(4*S),C=o.mallocFloat(2*S),P=o.mallocUint32(S);if(S>0){var O=0,z=x,D=[0,0,0,1],R=[0,0,0,1],F=Array.isArray(p)&&Array.isArray(p[0]),B=Array.isArray(v)&&Array.isArray(v[0]);t:for(n=0;n<_;++n){y+=1;for(w=s[n],T=0;T<3;++T){if(isNaN(w[T])||!isFinite(w[T]))continue t;f[T]=Math.max(f[T],w[T]),u[T]=Math.min(u[T],w[T])}k=(N=I(h,n,l,this.pixelRatio)).mesh,M=N.lines,A=N.bounds;var N,j=N.visible;if(j)if(Array.isArray(p)){if(3===(U=F?n<p.length?p[n]:[0,0,0,0]:p).length){for(T=0;T<3;++T)D[T]=U[T];D[3]=1}else if(4===U.length){for(T=0;T<4;++T)D[T]=U[T];!this.hasAlpha&&U[3]<1&&(this.hasAlpha=!0)}}else D[0]=D[1]=D[2]=0,D[3]=1;else D=[1,1,1,0];if(j)if(Array.isArray(v)){var U;if(3===(U=B?n<v.length?v[n]:[0,0,0,0]:v).length){for(T=0;T<3;++T)R[T]=U[T];R[T]=1}else if(4===U.length){for(T=0;T<4;++T)R[T]=U[T];!this.hasAlpha&&U[3]<1&&(this.hasAlpha=!0)}}else R[0]=R[1]=R[2]=0,R[3]=1;else R=[1,1,1,0];var V=.5;j?Array.isArray(d)?V=n<d.length?+d[n]:12:d?V=+d:this.useOrtho&&(V=12):V=0;var q=0;Array.isArray(g)?q=n<g.length?+g[n]:0:g&&(q=+g);var H=Math.cos(q),G=Math.sin(q);for(w=s[n],T=0;T<3;++T)f[T]=Math.max(f[T],w[T]),u[T]=Math.min(u[T],w[T]);var Y=i,W=a;Y=0;Array.isArray(i)?Y=n<i.length?i[n]:0:i&&(Y=i);W=0;Array.isArray(a)?W=n<a.length?a[n]:0:a&&(W=a);var X=[Y*=Y>0?1-A[0][0]:Y<0?1+A[1][0]:1,W*=W>0?1-A[0][1]:W<0?1+A[1][1]:1],Z=k.cells||[],J=k.positions||[];for(T=0;T<Z.length;++T)for(var K=Z[T],Q=0;Q<3;++Q){for(var $=0;$<3;++$)E[3*O+$]=w[$];for($=0;$<4;++$)L[4*O+$]=D[$];P[O]=y;var tt=J[K[Q]];C[2*O]=V*(H*tt[0]-G*tt[1]+X[0]),C[2*O+1]=V*(G*tt[0]+H*tt[1]+X[1]),O+=1}for(Z=M.edges,J=M.positions,T=0;T<Z.length;++T)for(K=Z[T],Q=0;Q<2;++Q){for($=0;$<3;++$)E[3*z+$]=w[$];for($=0;$<4;++$)L[4*z+$]=R[$];P[z]=y;tt=J[K[Q]];C[2*z]=V*(H*tt[0]-G*tt[1]+X[0]),C[2*z+1]=V*(G*tt[0]+H*tt[1]+X[1]),z+=1}}}this.bounds=[u,f],this.points=s,this.pointCount=s.length,this.vertexCount=x,this.lineVertexCount=b,this.pointBuffer.update(E),this.colorBuffer.update(L),this.glyphBuffer.update(C),this.idBuffer.update(P),o.free(E),o.free(L),o.free(C),o.free(P)},v.dispose=function(){this.shader.dispose(),this.orthoShader.dispose(),this.pickPerspectiveShader.dispose(),this.pickOrthoShader.dispose(),this.vao.dispose(),this.pointBuffer.dispose(),this.colorBuffer.dispose(),this.glyphBuffer.dispose(),this.idBuffer.dispose()}},{\"./lib/get-simple-string\":326,\"./lib/glyphs\":327,\"./lib/shaders\":328,\"gl-buffer\":263,\"gl-mat4/multiply\":297,\"gl-vao\":357,\"is-string-blank\":454,\"typedarray-pool\":613}],331:[function(t,e,r){\"use strict\";var n=t(\"glslify\");r.boxVertex=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 vertex;\\n\\nuniform vec2 cornerA, cornerB;\\n\\nvoid main() {\\n  gl_Position = vec4(mix(cornerA, cornerB, vertex), 0, 1);\\n}\\n\"]),r.boxFragment=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color;\\n\\nvoid main() {\\n  gl_FragColor = color;\\n}\\n\"])},{glslify:332}],332:[function(t,e,r){arguments[4][261][0].apply(r,arguments)},{dup:261}],333:[function(t,e,r){\"use strict\";var n=t(\"gl-shader\"),i=t(\"gl-buffer\"),a=t(\"./lib/shaders\");function o(t,e,r){this.plot=t,this.boxBuffer=e,this.boxShader=r,this.enabled=!0,this.selectBox=[1/0,1/0,-1/0,-1/0],this.borderColor=[0,0,0,1],this.innerFill=!1,this.innerColor=[0,0,0,.25],this.outerFill=!0,this.outerColor=[0,0,0,.5],this.borderWidth=10}e.exports=function(t,e){var r=t.gl,s=i(r,[0,0,0,1,1,0,1,1]),l=n(r,a.boxVertex,a.boxFragment),c=new o(t,s,l);return c.update(e),t.addOverlay(c),c};var s=o.prototype;s.draw=function(){if(this.enabled){var t=this.plot,e=this.selectBox,r=this.borderWidth,n=(this.innerFill,this.innerColor),i=(this.outerFill,this.outerColor),a=this.borderColor,o=t.box,s=t.screenBox,l=t.dataBox,c=t.viewBox,u=t.pixelRatio,f=(e[0]-l[0])*(c[2]-c[0])/(l[2]-l[0])+c[0],h=(e[1]-l[1])*(c[3]-c[1])/(l[3]-l[1])+c[1],p=(e[2]-l[0])*(c[2]-c[0])/(l[2]-l[0])+c[0],d=(e[3]-l[1])*(c[3]-c[1])/(l[3]-l[1])+c[1];if(f=Math.max(f,c[0]),h=Math.max(h,c[1]),p=Math.min(p,c[2]),d=Math.min(d,c[3]),!(p<f||d<h)){o.bind();var m=s[2]-s[0],g=s[3]-s[1];if(this.outerFill&&(o.drawBox(0,0,m,h,i),o.drawBox(0,h,f,d,i),o.drawBox(0,d,m,g,i),o.drawBox(p,h,m,d,i)),this.innerFill&&o.drawBox(f,h,p,d,n),r>0){var v=r*u;o.drawBox(f-v,h-v,p+v,h+v,a),o.drawBox(f-v,d-v,p+v,d+v,a),o.drawBox(f-v,h-v,f+v,d+v,a),o.drawBox(p-v,h-v,p+v,d+v,a)}}}},s.update=function(t){t=t||{},this.innerFill=!!t.innerFill,this.outerFill=!!t.outerFill,this.innerColor=(t.innerColor||[0,0,0,.5]).slice(),this.outerColor=(t.outerColor||[0,0,0,.5]).slice(),this.borderColor=(t.borderColor||[0,0,0,1]).slice(),this.borderWidth=t.borderWidth||0,this.selectBox=(t.selectBox||this.selectBox).slice()},s.dispose=function(){this.boxBuffer.dispose(),this.boxShader.dispose(),this.plot.removeOverlay(this)}},{\"./lib/shaders\":331,\"gl-buffer\":263,\"gl-shader\":335}],334:[function(t,e,r){\"use strict\";e.exports=function(t,e){var r=e[0],a=e[1],o=n(t,r,a,{}),s=i.mallocUint8(r*a*4);return new l(t,o,s)};var n=t(\"gl-fbo\"),i=t(\"typedarray-pool\"),a=t(\"ndarray\"),o=t(\"bit-twiddle\").nextPow2;function s(t,e,r,n,i){this.coord=[t,e],this.id=r,this.value=n,this.distance=i}function 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a=[t.id,e.id,r.join(\":\"),i.join(\":\")].join(\"@\"),o=this.programs[a];return o&&this.gl.isProgram(o)||(this.programs[a]=o=function(t,e,r,i,a){var o=t.createProgram();t.attachShader(o,e),t.attachShader(o,r);for(var s=0;s<i.length;++s)t.bindAttribLocation(o,a[s],i[s]);if(t.linkProgram(o),!t.getProgramParameter(o,t.LINK_STATUS)){var l=t.getProgramInfoLog(o);throw new n(l,\"Error linking program: \"+l)}return o}(this.gl,t.shader,e.shader,r,i),t.programs.push(a),e.programs.push(a)),o}},{\"./GLError\":336,\"gl-format-compiler-error\":273,\"weakmap-shim\":624}],342:[function(t,e,r){\"use strict\";function n(t){this.plot=t,this.enable=[!0,!0,!1,!1],this.width=[1,1,1,1],this.color=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.center=[1/0,1/0]}e.exports=function(t,e){var r=new n(t);return r.update(e),t.addOverlay(r),r};var 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1\\n\\nattribute vec3 position, color;\\nattribute float weight;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 coordinates[3];\\nuniform vec4 colors[3];\\nuniform vec2 screenShape;\\nuniform float lineWidth;\\n\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n  vec3 vertexPosition = mix(coordinates[0],\\n    mix(coordinates[2], coordinates[1], 0.5 * (position + 1.0)), abs(position));\\n\\n  vec4 clipPos = projection * view * model * vec4(vertexPosition, 1.0);\\n  vec2 clipOffset = (projection * view * model * vec4(color, 0.0)).xy;\\n  vec2 delta = weight * clipOffset * screenShape;\\n  vec2 lineOffset = normalize(vec2(delta.y, -delta.x)) / screenShape;\\n\\n  gl_Position   = vec4(clipPos.xy + clipPos.w * 0.5 * lineWidth * lineOffset, clipPos.z, clipPos.w);\\n  fragColor     = color.x * colors[0] + color.y * colors[1] + color.z * colors[2];\\n}\\n\"]),o=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n  gl_FragColor = fragColor;\\n}\"]);e.exports=function(t){return i(t,a,o,null,[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec3\"},{name:\"weight\",type:\"float\"}])}},{\"gl-shader\":335,glslify:343}],345:[function(t,e,r){\"use strict\";var n=t(\"gl-buffer\"),i=t(\"gl-vao\"),a=t(\"./shaders/index\");e.exports=function(t,e){var r=[];function o(t,e,n,i,a,o){var s=[t,e,n,0,0,0,1];s[i+3]=1,s[i]=a,r.push.apply(r,s),s[6]=-1,r.push.apply(r,s),s[i]=o,r.push.apply(r,s),r.push.apply(r,s),s[6]=1,r.push.apply(r,s),s[i]=a,r.push.apply(r,s)}o(0,0,0,0,0,1),o(0,0,0,1,0,1),o(0,0,0,2,0,1),o(1,0,0,1,-1,1),o(1,0,0,2,-1,1),o(0,1,0,0,-1,1),o(0,1,0,2,-1,1),o(0,0,1,0,-1,1),o(0,0,1,1,-1,1);var l=n(t,r),c=i(t,[{type:t.FLOAT,buffer:l,size:3,offset:0,stride:28},{type:t.FLOAT,buffer:l,size:3,offset:12,stride:28},{type:t.FLOAT,buffer:l,size:1,offset:24,stride:28}]),u=a(t);u.attributes.position.location=0,u.attributes.color.location=1,u.attributes.weight.location=2;var f=new s(t,l,c,u);return f.update(e),f};var o=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function s(t,e,r,n){this.gl=t,this.buffer=e,this.vao=r,this.shader=n,this.pixelRatio=1,this.bounds=[[-1e3,-1e3,-1e3],[1e3,1e3,1e3]],this.position=[0,0,0],this.lineWidth=[2,2,2],this.colors=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.enabled=[!0,!0,!0],this.drawSides=[!0,!0,!0],this.axes=null}var l=s.prototype,c=[0,0,0],u=[0,0,0],f=[0,0];l.isTransparent=function(){return!1},l.drawTransparent=function(t){},l.draw=function(t){var e=this.gl,r=this.vao,n=this.shader;r.bind(),n.bind();var i,a=t.model||o,s=t.view||o,l=t.projection||o;this.axes&&(i=this.axes.lastCubeProps.axis);for(var h=c,p=u,d=0;d<3;++d)i&&i[d]<0?(h[d]=this.bounds[0][d],p[d]=this.bounds[1][d]):(h[d]=this.bounds[1][d],p[d]=this.bounds[0][d]);f[0]=e.drawingBufferWidth,f[1]=e.drawingBufferHeight,n.uniforms.model=a,n.uniforms.view=s,n.uniforms.projection=l,n.uniforms.coordinates=[this.position,h,p],n.uniforms.colors=this.colors,n.uniforms.screenShape=f;for(d=0;d<3;++d)n.uniforms.lineWidth=this.lineWidth[d]*this.pixelRatio,this.enabled[d]&&(r.draw(e.TRIANGLES,6,6*d),this.drawSides[d]&&r.draw(e.TRIANGLES,12,18+12*d));r.unbind()},l.update=function(t){t&&(\"bounds\"in t&&(this.bounds=t.bounds),\"position\"in t&&(this.position=t.position),\"lineWidth\"in t&&(this.lineWidth=t.lineWidth),\"colors\"in t&&(this.colors=t.colors),\"enabled\"in t&&(this.enabled=t.enabled),\"drawSides\"in t&&(this.drawSides=t.drawSides))},l.dispose=function(){this.vao.dispose(),this.buffer.dispose(),this.shader.dispose()}},{\"./shaders/index\":344,\"gl-buffer\":263,\"gl-vao\":357}],346:[function(t,e,r){var n=t(\"glslify\"),i=n([\"precision highp float;\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nvec3 getOrthogonalVector(vec3 v) {\\n  // Return up-vector for only-z vector.\\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\\n  // Assign z = 0, x = -b, y = a:\\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\\n    return normalize(vec3(-v.y, v.x, 0.0));\\n  } else {\\n    return normalize(vec3(0.0, v.z, -v.y));\\n  }\\n}\\n\\n// Calculate the tube vertex and normal at the given index.\\n//\\n// The returned vertex is for a tube ring with its center at origin, radius of length(d), pointing in the direction of d.\\n//\\n// Each tube segment is made up of a ring of vertices.\\n// These vertices are used to make up the triangles of the tube by connecting them together in the vertex array.\\n// The indexes of tube segments run from 0 to 8.\\n//\\nvec3 getTubePosition(vec3 d, float index, out vec3 normal) {\\n  float segmentCount = 8.0;\\n\\n  float angle = 2.0 * 3.14159 * (index / segmentCount);\\n\\n  vec3 u = getOrthogonalVector(d);\\n  vec3 v = normalize(cross(u, d));\\n\\n  vec3 x = u * cos(angle) * length(d);\\n  vec3 y = v * sin(angle) * length(d);\\n  vec3 v3 = x + y;\\n\\n  normal = normalize(v3);\\n\\n  return v3;\\n}\\n\\nattribute vec4 vector;\\nattribute vec4 color, position;\\nattribute vec2 uv;\\n\\nuniform float vectorScale, tubeScale;\\nuniform mat4 model, view, projection, inverseModel;\\nuniform vec3 eyePosition, lightPosition;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  // Scale the vector magnitude to stay constant with\\n  // model & view changes.\\n  vec3 normal;\\n  vec3 XYZ = getTubePosition(mat3(model) * (tubeScale * vector.w * normalize(vector.xyz)), position.w, normal);\\n  vec4 tubePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n\\n  //Lighting geometry parameters\\n  vec4 cameraCoordinate = view * tubePosition;\\n  cameraCoordinate.xyz /= cameraCoordinate.w;\\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\\n  f_normal = normalize((vec4(normal, 0.0) * inverseModel).xyz);\\n\\n  // vec4 m_position  = model * vec4(tubePosition, 1.0);\\n  vec4 t_position  = view * tubePosition;\\n  gl_Position      = projection * t_position;\\n\\n  f_color          = color;\\n  f_data           = tubePosition.xyz;\\n  f_position       = position.xyz;\\n  f_uv             = uv;\\n}\\n\"]),a=n([\"#extension GL_OES_standard_derivatives : enable\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n  float NdotH = max(x, 0.0001);\\n  float cos2Alpha = NdotH * NdotH;\\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n  float roughness2 = roughness * roughness;\\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n  return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat cookTorranceSpecular(\\n  vec3 lightDirection,\\n  vec3 viewDirection,\\n  vec3 surfaceNormal,\\n  float roughness,\\n  float fresnel) {\\n\\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\\n\\n  //Half angle vector\\n  vec3 H = normalize(lightDirection + viewDirection);\\n\\n  //Geometric term\\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\\n  float VdotH = max(dot(viewDirection, H), 0.000001);\\n  float LdotH = max(dot(lightDirection, H), 0.000001);\\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\\n  float G = min(1.0, min(G1, G2));\\n  \\n  //Distribution term\\n  float D = beckmannDistribution(NdotH, roughness);\\n\\n  //Fresnel term\\n  float F = pow(1.0 - VdotN, fresnel);\\n\\n  //Multiply terms and done\\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\\nuniform sampler2D texture;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n  vec3 N = normalize(f_normal);\\n  vec3 L = normalize(f_lightDirection);\\n  vec3 V = normalize(f_eyeDirection);\\n\\n  if(gl_FrontFacing) {\\n    N = -N;\\n  }\\n\\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n  vec4 surfaceColor = f_color * texture2D(texture, f_uv);\\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\\n\\n  gl_FragColor = litColor * opacity;\\n}\\n\"]),o=n([\"precision highp float;\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nvec3 getOrthogonalVector(vec3 v) {\\n  // Return up-vector for only-z vector.\\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\\n  // Assign z = 0, x = -b, y = a:\\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\\n    return normalize(vec3(-v.y, v.x, 0.0));\\n  } else {\\n    return normalize(vec3(0.0, v.z, -v.y));\\n  }\\n}\\n\\n// Calculate the tube vertex and normal at the given index.\\n//\\n// The returned vertex is for a tube ring with its center at origin, radius of length(d), pointing in the direction of d.\\n//\\n// Each tube segment is made up of a ring of vertices.\\n// These vertices are used to make up the triangles of the tube by connecting them together in the vertex array.\\n// The indexes of tube segments run from 0 to 8.\\n//\\nvec3 getTubePosition(vec3 d, float index, out vec3 normal) {\\n  float segmentCount = 8.0;\\n\\n  float angle = 2.0 * 3.14159 * (index / segmentCount);\\n\\n  vec3 u = getOrthogonalVector(d);\\n  vec3 v = normalize(cross(u, d));\\n\\n  vec3 x = u * cos(angle) * length(d);\\n  vec3 y = v * sin(angle) * length(d);\\n  vec3 v3 = x + y;\\n\\n  normal = normalize(v3);\\n\\n  return v3;\\n}\\n\\nattribute vec4 vector;\\nattribute vec4 position;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform float tubeScale;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  vec3 normal;\\n  vec3 XYZ = getTubePosition(mat3(model) * (tubeScale * vector.w * normalize(vector.xyz)), position.w, normal);\\n  vec4 tubePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n\\n  gl_Position = projection * view * tubePosition;\\n  f_id        = id;\\n  f_position  = position.xyz;\\n}\\n\"]),s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3  clipBounds[2];\\nuniform float pickId;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n\\n  gl_FragColor = vec4(pickId, f_id.xyz);\\n}\"]);r.meshShader={vertex:i,fragment:a,attributes:[{name:\"position\",type:\"vec4\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"},{name:\"vector\",type:\"vec4\"}]},r.pickShader={vertex:o,fragment:s,attributes:[{name:\"position\",type:\"vec4\"},{name:\"id\",type:\"vec4\"},{name:\"vector\",type:\"vec4\"}]}},{glslify:347}],347:[function(t,e,r){arguments[4][261][0].apply(r,arguments)},{dup:261}],348:[function(t,e,r){\"use strict\";var n=t(\"gl-vec3\"),i=t(\"gl-vec4\"),a=[\"xyz\",\"xzy\",\"yxz\",\"yzx\",\"zxy\",\"zyx\"],o=function(t,e,r,a){for(var o=0,s=0;s<t.length;s++)for(var l=t[s].velocities,c=0;c<l.length;c++)o=Math.max(o,n.length(l[c]));var u=t.map((function(t){return function(t,e,r,a){for(var o=t.points,s=t.velocities,l=t.divergences,c=[],u=[],f=[],h=[],p=[],d=[],m=0,g=0,v=i.create(),y=i.create(),x=0;x<o.length;x++){var b=o[x],_=s[x],w=l[x];0===e&&(w=.05*r),g=n.length(_)/a,v=i.create(),n.copy(v,_),v[3]=w;for(var T=0;T<8;T++)p[T]=[b[0],b[1],b[2],T];if(h.length>0)for(T=0;T<8;T++){var k=(T+1)%8;c.push(h[T],p[T],p[k],p[k],h[k],h[T]),f.push(y,v,v,v,y,y),d.push(m,g,g,g,m,m);var M=c.length;u.push([M-6,M-5,M-4],[M-3,M-2,M-1])}var A=h;h=p,p=A;var S=y;y=v,v=S;var E=m;m=g,g=E}return{positions:c,cells:u,vectors:f,vertexIntensity:d}}(t,r,a,o)})),f=[],h=[],p=[],d=[];for(s=0;s<u.length;s++){var m=u[s],g=f.length;f=f.concat(m.positions),p=p.concat(m.vectors),d=d.concat(m.vertexIntensity);for(c=0;c<m.cells.length;c++){var v=m.cells[c],y=[];h.push(y);for(var x=0;x<v.length;x++)y.push(v[x]+g)}}return{positions:f,cells:h,vectors:p,vertexIntensity:d,colormap:e}},s=function(t,e){var r,n=t.length;for(r=0;r<n;r++){var i=t[r];if(i===e)return r;if(i>e)return r-1}return r},l=function(t,e,r){return t<e?e:t>r?r:t},c=function(t){var e=1/0;t.sort((function(t,e){return t-e}));for(var r=t.length,n=1;n<r;n++){var i=Math.abs(t[n]-t[n-1]);i<e&&(e=i)}return e};e.exports=function(t,e){var r=t.startingPositions,i=t.maxLength||1e3,u=t.tubeSize||1,f=t.absoluteTubeSize,h=t.gridFill||\"+x+y+z\",p={};-1!==h.indexOf(\"-x\")&&(p.reversedX=!0),-1!==h.indexOf(\"-y\")&&(p.reversedY=!0),-1!==h.indexOf(\"-z\")&&(p.reversedZ=!0),p.filled=a.indexOf(h.replace(/-/g,\"\").replace(/\\+/g,\"\"));var d=t.getVelocity||function(e){return function(t,e,r){var i=e.vectors,a=e.meshgrid,o=t[0],c=t[1],u=t[2],f=a[0].length,h=a[1].length,p=a[2].length,d=s(a[0],o),m=s(a[1],c),g=s(a[2],u),v=d+1,y=m+1,x=g+1;if(d=l(d,0,f-1),v=l(v,0,f-1),m=l(m,0,h-1),y=l(y,0,h-1),g=l(g,0,p-1),x=l(x,0,p-1),d<0||m<0||g<0||v>f-1||y>h-1||x>p-1)return n.create();var b,_,w,T,k,M,A=a[0][d],S=a[0][v],E=a[1][m],L=a[1][y],C=a[2][g],P=(o-A)/(S-A),I=(c-E)/(L-E),O=(u-C)/(a[2][x]-C);switch(isFinite(P)||(P=.5),isFinite(I)||(I=.5),isFinite(O)||(O=.5),r.reversedX&&(d=f-1-d,v=f-1-v),r.reversedY&&(m=h-1-m,y=h-1-y),r.reversedZ&&(g=p-1-g,x=p-1-x),r.filled){case 5:k=g,M=x,w=m*p,T=y*p,b=d*p*h,_=v*p*h;break;case 4:k=g,M=x,b=d*p,_=v*p,w=m*p*f,T=y*p*f;break;case 3:w=m,T=y,k=g*h,M=x*h,b=d*h*p,_=v*h*p;break;case 2:w=m,T=y,b=d*h,_=v*h,k=g*h*f,M=x*h*f;break;case 1:b=d,_=v,k=g*f,M=x*f,w=m*f*p,T=y*f*p;break;default:b=d,_=v,w=m*f,T=y*f,k=g*f*h,M=x*f*h}var z=i[b+w+k],D=i[b+w+M],R=i[b+T+k],F=i[b+T+M],B=i[_+w+k],N=i[_+w+M],j=i[_+T+k],U=i[_+T+M],V=n.create(),q=n.create(),H=n.create(),G=n.create();n.lerp(V,z,B,P),n.lerp(q,D,N,P),n.lerp(H,R,j,P),n.lerp(G,F,U,P);var Y=n.create(),W=n.create();n.lerp(Y,V,H,I),n.lerp(W,q,G,I);var X=n.create();return n.lerp(X,Y,W,O),X}(e,t,p)},m=t.getDivergence||function(t,e){var r=n.create(),i=1e-4;n.add(r,t,[i,0,0]);var a=d(r);n.subtract(a,a,e),n.scale(a,a,1/i),n.add(r,t,[0,i,0]);var o=d(r);n.subtract(o,o,e),n.scale(o,o,1/i),n.add(r,t,[0,0,i]);var s=d(r);return n.subtract(s,s,e),n.scale(s,s,1/i),n.add(r,a,o),n.add(r,r,s),r},g=[],v=e[0][0],y=e[0][1],x=e[0][2],b=e[1][0],_=e[1][1],w=e[1][2],T=function(t){var e=t[0],r=t[1],n=t[2];return!(e<v||e>b||r<y||r>_||n<x||n>w)},k=10*n.distance(e[0],e[1])/i,M=k*k,A=1,S=0,E=r.length;E>1&&(A=function(t){for(var e=[],r=[],n=[],i={},a={},o={},s=t.length,l=0;l<s;l++){var u=t[l],f=u[0],h=u[1],p=u[2];i[f]||(e.push(f),i[f]=!0),a[h]||(r.push(h),a[h]=!0),o[p]||(n.push(p),o[p]=!0)}var d=c(e),m=c(r),g=c(n),v=Math.min(d,m,g);return isFinite(v)?v:1}(r));for(var L=0;L<E;L++){var C=n.create();n.copy(C,r[L]);var P=[C],I=[],O=d(C),z=C;I.push(O);var D=[],R=m(C,O),F=n.length(R);isFinite(F)&&F>S&&(S=F),D.push(F),g.push({points:P,velocities:I,divergences:D});for(var B=0;B<100*i&&P.length<i&&T(C);){B++;var N=n.clone(O),j=n.squaredLength(N);if(0===j)break;if(j>M&&n.scale(N,N,k/Math.sqrt(j)),n.add(N,N,C),O=d(N),n.squaredDistance(z,N)-M>-1e-4*M){P.push(N),z=N,I.push(O);R=m(N,O),F=n.length(R);isFinite(F)&&F>S&&(S=F),D.push(F)}C=N}}var U=o(g,t.colormap,S,A);return f?U.tubeScale=f:(0===S&&(S=1),U.tubeScale=.5*u*A/S),U};var u=t(\"./lib/shaders\"),f=t(\"gl-cone3d\").createMesh;e.exports.createTubeMesh=function(t,e){return f(t,e,{shaders:u,traceType:\"streamtube\"})}},{\"./lib/shaders\":346,\"gl-cone3d\":264,\"gl-vec3\":376,\"gl-vec4\":412}],349:[function(t,e,r){var n=t(\"gl-shader\"),i=t(\"glslify\"),a=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec4 uv;\\nattribute vec3 f;\\nattribute vec3 normal;\\n\\nuniform vec3 objectOffset;\\nuniform mat4 model, view, projection, inverseModel;\\nuniform vec3 lightPosition, eyePosition;\\nuniform sampler2D colormap;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec2 planeCoordinate;\\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\\nvarying vec4 vColor;\\n\\nvoid main() {\\n  vec3 localCoordinate = vec3(uv.zw, f.x);\\n  worldCoordinate = objectOffset + localCoordinate;\\n  vec4 worldPosition = model * vec4(worldCoordinate, 1.0);\\n  vec4 clipPosition = projection * view * worldPosition;\\n  gl_Position = clipPosition;\\n  kill = f.y;\\n  value = f.z;\\n  planeCoordinate = uv.xy;\\n\\n  vColor = texture2D(colormap, vec2(value, value));\\n\\n  //Lighting geometry parameters\\n  vec4 cameraCoordinate = view * worldPosition;\\n  cameraCoordinate.xyz /= cameraCoordinate.w;\\n  lightDirection = lightPosition - cameraCoordinate.xyz;\\n  eyeDirection   = eyePosition - cameraCoordinate.xyz;\\n  surfaceNormal  = normalize((vec4(normal,0) * inverseModel).xyz);\\n}\\n\"]),o=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n  float NdotH = max(x, 0.0001);\\n  float cos2Alpha = NdotH * NdotH;\\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n  float roughness2 = roughness * roughness;\\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n  return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat beckmannSpecular(\\n  vec3 lightDirection,\\n  vec3 viewDirection,\\n  vec3 surfaceNormal,\\n  float roughness) {\\n  return beckmannDistribution(dot(surfaceNormal, normalize(lightDirection + viewDirection)), roughness);\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 lowerBound, upperBound;\\nuniform float contourTint;\\nuniform vec4 contourColor;\\nuniform sampler2D colormap;\\nuniform vec3 clipBounds[2];\\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\\nuniform float vertexColor;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\\nvarying vec4 vColor;\\n\\nvoid main() {\\n  if (\\n    kill > 0.0 ||\\n    vColor.a == 0.0 ||\\n    outOfRange(clipBounds[0], clipBounds[1], worldCoordinate)\\n  ) discard;\\n\\n  vec3 N = normalize(surfaceNormal);\\n  vec3 V = normalize(eyeDirection);\\n  vec3 L = normalize(lightDirection);\\n\\n  if(gl_FrontFacing) {\\n    N = -N;\\n  }\\n\\n  float specular = max(beckmannSpecular(L, V, N, roughness), 0.);\\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n  //decide how to interpolate color \\u2014 in vertex or in fragment\\n  vec4 surfaceColor =\\n    step(vertexColor, .5) * texture2D(colormap, vec2(value, value)) +\\n    step(.5, vertexColor) * vColor;\\n\\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\\n\\n  gl_FragColor = mix(litColor, contourColor, contourTint) * opacity;\\n}\\n\"]),s=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec4 uv;\\nattribute float f;\\n\\nuniform vec3 objectOffset;\\nuniform mat3 permutation;\\nuniform mat4 model, view, projection;\\nuniform float height, zOffset;\\nuniform sampler2D colormap;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec2 planeCoordinate;\\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\\nvarying vec4 vColor;\\n\\nvoid main() {\\n  vec3 dataCoordinate = permutation * vec3(uv.xy, height);\\n  worldCoordinate = objectOffset + dataCoordinate;\\n  vec4 worldPosition = model * vec4(worldCoordinate, 1.0);\\n\\n  vec4 clipPosition = projection * view * worldPosition;\\n  clipPosition.z += zOffset;\\n\\n  gl_Position = clipPosition;\\n  value = f + objectOffset.z;\\n  kill = -1.0;\\n  planeCoordinate = uv.zw;\\n\\n  vColor = texture2D(colormap, vec2(value, value));\\n\\n  //Don't do lighting for contours\\n  surfaceNormal   = vec3(1,0,0);\\n  eyeDirection    = vec3(0,1,0);\\n  lightDirection  = vec3(0,0,1);\\n}\\n\"]),l=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec2 shape;\\nuniform vec3 clipBounds[2];\\nuniform float pickId;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec2 planeCoordinate;\\nvarying vec3 surfaceNormal;\\n\\nvec2 splitFloat(float v) {\\n  float vh = 255.0 * v;\\n  float upper = floor(vh);\\n  float lower = fract(vh);\\n  return vec2(upper / 255.0, floor(lower * 16.0) / 16.0);\\n}\\n\\nvoid main() {\\n  if ((kill > 0.0) ||\\n      (outOfRange(clipBounds[0], clipBounds[1], worldCoordinate))) discard;\\n\\n  vec2 ux = splitFloat(planeCoordinate.x / shape.x);\\n  vec2 uy = splitFloat(planeCoordinate.y / shape.y);\\n  gl_FragColor = vec4(pickId, ux.x, uy.x, ux.y + (uy.y/16.0));\\n}\\n\"]);r.createShader=function(t){var e=n(t,a,o,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},r.createPickShader=function(t){var e=n(t,a,l,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},r.createContourShader=function(t){var e=n(t,s,o,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"float\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e},r.createPickContourShader=function(t){var e=n(t,s,l,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"float\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e}},{\"gl-shader\":335,glslify:438}],350:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=y(e),n=b(e),s=x(e),l=_(e),c=i(e),u=a(e,[{buffer:c,size:4,stride:40,offset:0},{buffer:c,size:3,stride:40,offset:16},{buffer:c,size:3,stride:40,offset:28}]),f=i(e),h=a(e,[{buffer:f,size:4,stride:20,offset:0},{buffer:f,size:1,stride:20,offset:16}]),p=i(e),d=a(e,[{buffer:p,size:2,type:e.FLOAT}]),m=o(e,1,256,e.RGBA,e.UNSIGNED_BYTE);m.minFilter=e.LINEAR,m.magFilter=e.LINEAR;var g=new A(e,[0,0],[[0,0,0],[0,0,0]],r,n,c,u,m,s,l,f,h,p,d,[0,0,0]),v={levels:[[],[],[]]};for(var w in t)v[w]=t[w];return v.colormap=v.colormap||\"jet\",g.update(v),g};var n=t(\"bit-twiddle\"),i=t(\"gl-buffer\"),a=t(\"gl-vao\"),o=t(\"gl-texture2d\"),s=t(\"typedarray-pool\"),l=t(\"colormap\"),c=t(\"ndarray-ops\"),u=t(\"ndarray-pack\"),f=t(\"ndarray\"),h=t(\"surface-nets\"),p=t(\"gl-mat4/multiply\"),d=t(\"gl-mat4/invert\"),m=t(\"binary-search-bounds\"),g=t(\"ndarray-gradient\"),v=t(\"./lib/shaders\"),y=v.createShader,x=v.createContourShader,b=v.createPickShader,_=v.createPickContourShader,w=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],T=[[0,0],[0,1],[1,0],[1,1],[1,0],[0,1]],k=[[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0]];function M(t,e,r,n,i){this.position=t,this.index=e,this.uv=r,this.level=n,this.dataCoordinate=i}!function(){for(var t=0;t<3;++t){var e=k[t],r=(t+2)%3;e[(t+1)%3+0]=1,e[r+3]=1,e[t+6]=1}}();function A(t,e,r,n,i,a,o,l,c,u,h,p,d,m,g){this.gl=t,this.shape=e,this.bounds=r,this.objectOffset=g,this.intensityBounds=[],this._shader=n,this._pickShader=i,this._coordinateBuffer=a,this._vao=o,this._colorMap=l,this._contourShader=c,this._contourPickShader=u,this._contourBuffer=h,this._contourVAO=p,this._contourOffsets=[[],[],[]],this._contourCounts=[[],[],[]],this._vertexCount=0,this._pickResult=new M([0,0,0],[0,0],[0,0],[0,0,0],[0,0,0]),this._dynamicBuffer=d,this._dynamicVAO=m,this._dynamicOffsets=[0,0,0],this._dynamicCounts=[0,0,0],this.contourWidth=[1,1,1],this.contourLevels=[[1],[1],[1]],this.contourTint=[0,0,0],this.contourColor=[[.5,.5,.5,1],[.5,.5,.5,1],[.5,.5,.5,1]],this.showContour=!0,this.showSurface=!0,this.enableHighlight=[!0,!0,!0],this.highlightColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.highlightTint=[1,1,1],this.highlightLevel=[-1,-1,-1],this.enableDynamic=[!0,!0,!0],this.dynamicLevel=[NaN,NaN,NaN],this.dynamicColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.dynamicTint=[1,1,1],this.dynamicWidth=[1,1,1],this.axesBounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.surfaceProject=[!1,!1,!1],this.contourProject=[[!1,!1,!1],[!1,!1,!1],[!1,!1,!1]],this.colorBounds=[!1,!1],this._field=[f(s.mallocFloat(1024),[0,0]),f(s.mallocFloat(1024),[0,0]),f(s.mallocFloat(1024),[0,0])],this.pickId=1,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.snapToData=!1,this.pixelRatio=1,this.opacity=1,this.lightPosition=[10,1e4,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.vertexColor=0,this.dirty=!0}var S=A.prototype;S.genColormap=function(t,e){var r=!1,n=u([l({colormap:t,nshades:256,format:\"rgba\"}).map((function(t,n){var i=e?function(t,e){if(!e)return 1;if(!e.length)return 1;for(var r=0;r<e.length;++r){if(e.length<2)return 1;if(e[r][0]===t)return e[r][1];if(e[r][0]>t&&r>0){var n=(e[r][0]-t)/(e[r][0]-e[r-1][0]);return e[r][1]*(1-n)+n*e[r-1][1]}}return 1}(n/255,e):t[3];return i<1&&(r=!0),[t[0],t[1],t[2],255*i]}))]);return c.divseq(n,255),this.hasAlphaScale=r,n},S.isTransparent=function(){return this.opacity<1||this.hasAlphaScale},S.isOpaque=function(){return!this.isTransparent()},S.pickSlots=1,S.setPickBase=function(t){this.pickId=t};var E=[0,0,0],L={showSurface:!1,showContour:!1,projections:[w.slice(),w.slice(),w.slice()],clipBounds:[[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]]]};function C(t,e){var r,n,i,a=e.axes&&e.axes.lastCubeProps.axis||E,o=e.showSurface,s=e.showContour;for(r=0;r<3;++r)for(o=o||e.surfaceProject[r],n=0;n<3;++n)s=s||e.contourProject[r][n];for(r=0;r<3;++r){var l=L.projections[r];for(n=0;n<16;++n)l[n]=0;for(n=0;n<4;++n)l[5*n]=1;l[5*r]=0,l[12+r]=e.axesBounds[+(a[r]>0)][r],p(l,t.model,l);var c=L.clipBounds[r];for(i=0;i<2;++i)for(n=0;n<3;++n)c[i][n]=t.clipBounds[i][n];c[0][r]=-1e8,c[1][r]=1e8}return L.showSurface=o,L.showContour=s,L}var P={model:w,view:w,projection:w,inverseModel:w.slice(),lowerBound:[0,0,0],upperBound:[0,0,0],colorMap:0,clipBounds:[[0,0,0],[0,0,0]],height:0,contourTint:0,contourColor:[0,0,0,1],permutation:[1,0,0,0,1,0,0,0,1],zOffset:-1e-4,objectOffset:[0,0,0],kambient:1,kdiffuse:1,kspecular:1,lightPosition:[1e3,1e3,1e3],eyePosition:[0,0,0],roughness:1,fresnel:1,opacity:1,vertexColor:0},I=w.slice(),O=[1,0,0,0,1,0,0,0,1];function z(t,e){t=t||{};var r=this.gl;r.disable(r.CULL_FACE),this._colorMap.bind(0);var n=P;n.model=t.model||w,n.view=t.view||w,n.projection=t.projection||w,n.lowerBound=[this.bounds[0][0],this.bounds[0][1],this.colorBounds[0]||this.bounds[0][2]],n.upperBound=[this.bounds[1][0],this.bounds[1][1],this.colorBounds[1]||this.bounds[1][2]],n.objectOffset=this.objectOffset,n.contourColor=this.contourColor[0],n.inverseModel=d(n.inverseModel,n.model);for(var i=0;i<2;++i)for(var a=n.clipBounds[i],o=0;o<3;++o)a[o]=Math.min(Math.max(this.clipBounds[i][o],-1e8),1e8);n.kambient=this.ambientLight,n.kdiffuse=this.diffuseLight,n.kspecular=this.specularLight,n.roughness=this.roughness,n.fresnel=this.fresnel,n.opacity=this.opacity,n.height=0,n.permutation=O,n.vertexColor=this.vertexColor;var s=I;for(p(s,n.view,n.model),p(s,n.projection,s),d(s,s),i=0;i<3;++i)n.eyePosition[i]=s[12+i]/s[15];var l=s[15];for(i=0;i<3;++i)l+=this.lightPosition[i]*s[4*i+3];for(i=0;i<3;++i){var c=s[12+i];for(o=0;o<3;++o)c+=s[4*o+i]*this.lightPosition[o];n.lightPosition[i]=c/l}var u=C(n,this);if(u.showSurface){for(this._shader.bind(),this._shader.uniforms=n,this._vao.bind(),this.showSurface&&this._vertexCount&&this._vao.draw(r.TRIANGLES,this._vertexCount),i=0;i<3;++i)this.surfaceProject[i]&&this.vertexCount&&(this._shader.uniforms.model=u.projections[i],this._shader.uniforms.clipBounds=u.clipBounds[i],this._vao.draw(r.TRIANGLES,this._vertexCount));this._vao.unbind()}if(u.showContour){var f=this._contourShader;n.kambient=1,n.kdiffuse=0,n.kspecular=0,n.opacity=1,f.bind(),f.uniforms=n;var h=this._contourVAO;for(h.bind(),i=0;i<3;++i)for(f.uniforms.permutation=k[i],r.lineWidth(this.contourWidth[i]*this.pixelRatio),o=0;o<this.contourLevels[i].length;++o)o===this.highlightLevel[i]?(f.uniforms.contourColor=this.highlightColor[i],f.uniforms.contourTint=this.highlightTint[i]):0!==o&&o-1!==this.highlightLevel[i]||(f.uniforms.contourColor=this.contourColor[i],f.uniforms.contourTint=this.contourTint[i]),this._contourCounts[i][o]&&(f.uniforms.height=this.contourLevels[i][o],h.draw(r.LINES,this._contourCounts[i][o],this._contourOffsets[i][o]));for(i=0;i<3;++i)for(f.uniforms.model=u.projections[i],f.uniforms.clipBounds=u.clipBounds[i],o=0;o<3;++o)if(this.contourProject[i][o]){f.uniforms.permutation=k[o],r.lineWidth(this.contourWidth[o]*this.pixelRatio);for(var m=0;m<this.contourLevels[o].length;++m)m===this.highlightLevel[o]?(f.uniforms.contourColor=this.highlightColor[o],f.uniforms.contourTint=this.highlightTint[o]):0!==m&&m-1!==this.highlightLevel[o]||(f.uniforms.contourColor=this.contourColor[o],f.uniforms.contourTint=this.contourTint[o]),this._contourCounts[o][m]&&(f.uniforms.height=this.contourLevels[o][m],h.draw(r.LINES,this._contourCounts[o][m],this._contourOffsets[o][m]))}for(h.unbind(),(h=this._dynamicVAO).bind(),i=0;i<3;++i)if(0!==this._dynamicCounts[i])for(f.uniforms.model=n.model,f.uniforms.clipBounds=n.clipBounds,f.uniforms.permutation=k[i],r.lineWidth(this.dynamicWidth[i]*this.pixelRatio),f.uniforms.contourColor=this.dynamicColor[i],f.uniforms.contourTint=this.dynamicTint[i],f.uniforms.height=this.dynamicLevel[i],h.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]),o=0;o<3;++o)this.contourProject[o][i]&&(f.uniforms.model=u.projections[o],f.uniforms.clipBounds=u.clipBounds[o],h.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]));h.unbind()}}S.draw=function(t){return z.call(this,t,!1)},S.drawTransparent=function(t){return z.call(this,t,!0)};var D={model:w,view:w,projection:w,inverseModel:w,clipBounds:[[0,0,0],[0,0,0]],height:0,shape:[0,0],pickId:0,lowerBound:[0,0,0],upperBound:[0,0,0],zOffset:0,objectOffset:[0,0,0],permutation:[1,0,0,0,1,0,0,0,1],lightPosition:[0,0,0],eyePosition:[0,0,0]};function R(t,e){return Array.isArray(t)?[e(t[0]),e(t[1]),e(t[2])]:[e(t),e(t),e(t)]}function F(t){return Array.isArray(t)?3===t.length?[t[0],t[1],t[2],1]:[t[0],t[1],t[2],t[3]]:[0,0,0,1]}function B(t){if(Array.isArray(t)){if(Array.isArray(t))return[F(t[0]),F(t[1]),F(t[2])];var e=F(t);return[e.slice(),e.slice(),e.slice()]}}S.drawPick=function(t){t=t||{};var e=this.gl;e.disable(e.CULL_FACE);var r=D;r.model=t.model||w,r.view=t.view||w,r.projection=t.projection||w,r.shape=this._field[2].shape,r.pickId=this.pickId/255,r.lowerBound=this.bounds[0],r.upperBound=this.bounds[1],r.objectOffset=this.objectOffset,r.permutation=O;for(var n=0;n<2;++n)for(var i=r.clipBounds[n],a=0;a<3;++a)i[a]=Math.min(Math.max(this.clipBounds[n][a],-1e8),1e8);var o=C(r,this);if(o.showSurface){for(this._pickShader.bind(),this._pickShader.uniforms=r,this._vao.bind(),this._vao.draw(e.TRIANGLES,this._vertexCount),n=0;n<3;++n)this.surfaceProject[n]&&(this._pickShader.uniforms.model=o.projections[n],this._pickShader.uniforms.clipBounds=o.clipBounds[n],this._vao.draw(e.TRIANGLES,this._vertexCount));this._vao.unbind()}if(o.showContour){var s=this._contourPickShader;s.bind(),s.uniforms=r;var l=this._contourVAO;for(l.bind(),a=0;a<3;++a)for(e.lineWidth(this.contourWidth[a]*this.pixelRatio),s.uniforms.permutation=k[a],n=0;n<this.contourLevels[a].length;++n)this._contourCounts[a][n]&&(s.uniforms.height=this.contourLevels[a][n],l.draw(e.LINES,this._contourCounts[a][n],this._contourOffsets[a][n]));for(n=0;n<3;++n)for(s.uniforms.model=o.projections[n],s.uniforms.clipBounds=o.clipBounds[n],a=0;a<3;++a)if(this.contourProject[n][a]){s.uniforms.permutation=k[a],e.lineWidth(this.contourWidth[a]*this.pixelRatio);for(var c=0;c<this.contourLevels[a].length;++c)this._contourCounts[a][c]&&(s.uniforms.height=this.contourLevels[a][c],l.draw(e.LINES,this._contourCounts[a][c],this._contourOffsets[a][c]))}l.unbind()}},S.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=this._field[2].shape,r=this._pickResult,n=e[0]*(t.value[0]+(t.value[2]>>4)/16)/255,i=Math.floor(n),a=n-i,o=e[1]*(t.value[1]+(15&t.value[2])/16)/255,s=Math.floor(o),l=o-s;i+=1,s+=1;var c=r.position;c[0]=c[1]=c[2]=0;for(var u=0;u<2;++u)for(var f=u?a:1-a,h=0;h<2;++h)for(var p=i+u,d=s+h,g=f*(h?l:1-l),v=0;v<3;++v)c[v]+=this._field[v].get(p,d)*g;for(var y=this._pickResult.level,x=0;x<3;++x)if(y[x]=m.le(this.contourLevels[x],c[x]),y[x]<0)this.contourLevels[x].length>0&&(y[x]=0);else if(y[x]<this.contourLevels[x].length-1){var b=this.contourLevels[x][y[x]],_=this.contourLevels[x][y[x]+1];Math.abs(b-c[x])>Math.abs(_-c[x])&&(y[x]+=1)}for(r.index[0]=a<.5?i:i+1,r.index[1]=l<.5?s:s+1,r.uv[0]=n/e[0],r.uv[1]=o/e[1],v=0;v<3;++v)r.dataCoordinate[v]=this._field[v].get(r.index[0],r.index[1]);return r},S.padField=function(t,e){var r=e.shape.slice(),n=t.shape.slice();c.assign(t.lo(1,1).hi(r[0],r[1]),e),c.assign(t.lo(1).hi(r[0],1),e.hi(r[0],1)),c.assign(t.lo(1,n[1]-1).hi(r[0],1),e.lo(0,r[1]-1).hi(r[0],1)),c.assign(t.lo(0,1).hi(1,r[1]),e.hi(1)),c.assign(t.lo(n[0]-1,1).hi(1,r[1]),e.lo(r[0]-1)),t.set(0,0,e.get(0,0)),t.set(0,n[1]-1,e.get(0,r[1]-1)),t.set(n[0]-1,0,e.get(r[0]-1,0)),t.set(n[0]-1,n[1]-1,e.get(r[0]-1,r[1]-1))},S.update=function(t){t=t||{},this.objectOffset=t.objectOffset||this.objectOffset,this.dirty=!0,\"contourWidth\"in t&&(this.contourWidth=R(t.contourWidth,Number)),\"showContour\"in t&&(this.showContour=R(t.showContour,Boolean)),\"showSurface\"in t&&(this.showSurface=!!t.showSurface),\"contourTint\"in t&&(this.contourTint=R(t.contourTint,Boolean)),\"contourColor\"in t&&(this.contourColor=B(t.contourColor)),\"contourProject\"in t&&(this.contourProject=R(t.contourProject,(function(t){return R(t,Boolean)}))),\"surfaceProject\"in t&&(this.surfaceProject=t.surfaceProject),\"dynamicColor\"in t&&(this.dynamicColor=B(t.dynamicColor)),\"dynamicTint\"in t&&(this.dynamicTint=R(t.dynamicTint,Number)),\"dynamicWidth\"in t&&(this.dynamicWidth=R(t.dynamicWidth,Number)),\"opacity\"in t&&(this.opacity=t.opacity),\"opacityscale\"in t&&(this.opacityscale=t.opacityscale),\"colorBounds\"in t&&(this.colorBounds=t.colorBounds),\"vertexColor\"in t&&(this.vertexColor=t.vertexColor?1:0),\"colormap\"in t&&this._colorMap.setPixels(this.genColormap(t.colormap,this.opacityscale));var e=t.field||t.coords&&t.coords[2]||null,r=!1;if(e||(e=this._field[2].shape[0]||this._field[2].shape[2]?this._field[2].lo(1,1).hi(this._field[2].shape[0]-2,this._field[2].shape[1]-2):this._field[2].hi(0,0)),\"field\"in t||\"coords\"in t){var i=(e.shape[0]+2)*(e.shape[1]+2);i>this._field[2].data.length&&(s.freeFloat(this._field[2].data),this._field[2].data=s.mallocFloat(n.nextPow2(i))),this._field[2]=f(this._field[2].data,[e.shape[0]+2,e.shape[1]+2]),this.padField(this._field[2],e),this.shape=e.shape.slice();for(var a=this.shape,o=0;o<2;++o)this._field[2].size>this._field[o].data.length&&(s.freeFloat(this._field[o].data),this._field[o].data=s.mallocFloat(this._field[2].size)),this._field[o]=f(this._field[o].data,[a[0]+2,a[1]+2]);if(t.coords){var l=t.coords;if(!Array.isArray(l)||3!==l.length)throw new Error(\"gl-surface: invalid coordinates for x/y\");for(o=0;o<2;++o){var c=l[o];for(v=0;v<2;++v)if(c.shape[v]!==a[v])throw new Error(\"gl-surface: coords have incorrect shape\");this.padField(this._field[o],c)}}else if(t.ticks){var u=t.ticks;if(!Array.isArray(u)||2!==u.length)throw new Error(\"gl-surface: invalid ticks\");for(o=0;o<2;++o){var p=u[o];if((Array.isArray(p)||p.length)&&(p=f(p)),p.shape[0]!==a[o])throw new Error(\"gl-surface: invalid tick length\");var d=f(p.data,a);d.stride[o]=p.stride[0],d.stride[1^o]=0,this.padField(this._field[o],d)}}else{for(o=0;o<2;++o){var m=[0,0];m[o]=1,this._field[o]=f(this._field[o].data,[a[0]+2,a[1]+2],m,0)}this._field[0].set(0,0,0);for(var v=0;v<a[0];++v)this._field[0].set(v+1,0,v);for(this._field[0].set(a[0]+1,0,a[0]-1),this._field[1].set(0,0,0),v=0;v<a[1];++v)this._field[1].set(0,v+1,v);this._field[1].set(0,a[1]+1,a[1]-1)}var y=this._field,x=f(s.mallocFloat(3*y[2].size*2),[3,a[0]+2,a[1]+2,2]);for(o=0;o<3;++o)g(x.pick(o),y[o],\"mirror\");var b=f(s.mallocFloat(3*y[2].size),[a[0]+2,a[1]+2,3]);for(o=0;o<a[0]+2;++o)for(v=0;v<a[1]+2;++v){var _=x.get(0,o,v,0),w=x.get(0,o,v,1),k=x.get(1,o,v,0),M=x.get(1,o,v,1),A=x.get(2,o,v,0),S=x.get(2,o,v,1),E=k*S-M*A,L=A*w-S*_,C=_*M-w*k,P=Math.sqrt(E*E+L*L+C*C);P<1e-8?(P=Math.max(Math.abs(E),Math.abs(L),Math.abs(C)))<1e-8?(C=1,L=E=0,P=1):P=1/P:P=1/Math.sqrt(P),b.set(o,v,0,E*P),b.set(o,v,1,L*P),b.set(o,v,2,C*P)}s.free(x.data);var I=[1/0,1/0,1/0],O=[-1/0,-1/0,-1/0],z=1/0,D=-1/0,F=(a[0]-1)*(a[1]-1)*6,N=s.mallocFloat(n.nextPow2(10*F)),j=0,U=0;for(o=0;o<a[0]-1;++o)t:for(v=0;v<a[1]-1;++v){for(var V=0;V<2;++V)for(var q=0;q<2;++q)for(var H=0;H<3;++H){var G=this._field[H].get(1+o+V,1+v+q);if(isNaN(G)||!isFinite(G))continue t}for(H=0;H<6;++H){var Y=o+T[H][0],W=v+T[H][1],X=this._field[0].get(Y+1,W+1),Z=this._field[1].get(Y+1,W+1);G=this._field[2].get(Y+1,W+1),E=b.get(Y+1,W+1,0),L=b.get(Y+1,W+1,1),C=b.get(Y+1,W+1,2),t.intensity&&(J=t.intensity.get(Y,W));var J=t.intensity?t.intensity.get(Y,W):G+this.objectOffset[2];N[j++]=Y,N[j++]=W,N[j++]=X,N[j++]=Z,N[j++]=G,N[j++]=0,N[j++]=J,N[j++]=E,N[j++]=L,N[j++]=C,I[0]=Math.min(I[0],X+this.objectOffset[0]),I[1]=Math.min(I[1],Z+this.objectOffset[1]),I[2]=Math.min(I[2],G+this.objectOffset[2]),z=Math.min(z,J),O[0]=Math.max(O[0],X+this.objectOffset[0]),O[1]=Math.max(O[1],Z+this.objectOffset[1]),O[2]=Math.max(O[2],G+this.objectOffset[2]),D=Math.max(D,J),U+=1}}for(t.intensityBounds&&(z=+t.intensityBounds[0],D=+t.intensityBounds[1]),o=6;o<j;o+=10)N[o]=(N[o]-z)/(D-z);this._vertexCount=U,this._coordinateBuffer.update(N.subarray(0,j)),s.freeFloat(N),s.free(b.data),this.bounds=[I,O],this.intensity=t.intensity||this._field[2],this.intensityBounds[0]===z&&this.intensityBounds[1]===D||(r=!0),this.intensityBounds=[z,D]}if(\"levels\"in t){var K=t.levels;for(K=Array.isArray(K[0])?K.slice():[[],[],K],o=0;o<3;++o)K[o]=K[o].slice(),K[o].sort((function(t,e){return t-e}));for(o=0;o<3;++o)for(v=0;v<K[o].length;++v)K[o][v]-=this.objectOffset[o];t:for(o=0;o<3;++o){if(K[o].length!==this.contourLevels[o].length){r=!0;break}for(v=0;v<K[o].length;++v)if(K[o][v]!==this.contourLevels[o][v]){r=!0;break t}}this.contourLevels=K}if(r){y=this._field,a=this.shape;for(var Q=[],$=0;$<3;++$){var tt=this.contourLevels[$],et=[],rt=[],nt=[0,0,0];for(o=0;o<tt.length;++o){var it=h(this._field[$],tt[o]);et.push(Q.length/5|0),U=0;t:for(v=0;v<it.cells.length;++v){var at=it.cells[v];for(H=0;H<2;++H){var ot=it.positions[at[H]],st=ot[0],lt=0|Math.floor(st),ct=st-lt,ut=ot[1],ft=0|Math.floor(ut),ht=ut-ft,pt=!1;e:for(var dt=0;dt<3;++dt){nt[dt]=0;var mt=($+dt+1)%3;for(V=0;V<2;++V){var gt=V?ct:1-ct;for(Y=0|Math.min(Math.max(lt+V,0),a[0]),q=0;q<2;++q){var vt=q?ht:1-ht;if(W=0|Math.min(Math.max(ft+q,0),a[1]),G=dt<2?this._field[mt].get(Y,W):(this.intensity.get(Y,W)-this.intensityBounds[0])/(this.intensityBounds[1]-this.intensityBounds[0]),!isFinite(G)||isNaN(G)){pt=!0;break e}var yt=gt*vt;nt[dt]+=yt*G}}}if(pt){if(H>0){for(var xt=0;xt<5;++xt)Q.pop();U-=1}continue t}Q.push(nt[0],nt[1],ot[0],ot[1],nt[2]),U+=1}}rt.push(U)}this._contourOffsets[$]=et,this._contourCounts[$]=rt}var bt=s.mallocFloat(Q.length);for(o=0;o<Q.length;++o)bt[o]=Q[o];this._contourBuffer.update(bt),s.freeFloat(bt)}},S.dispose=function(){this._shader.dispose(),this._vao.dispose(),this._coordinateBuffer.dispose(),this._colorMap.dispose(),this._contourBuffer.dispose(),this._contourVAO.dispose(),this._contourShader.dispose(),this._contourPickShader.dispose(),this._dynamicBuffer.dispose(),this._dynamicVAO.dispose();for(var t=0;t<3;++t)s.freeFloat(this._field[t].data)},S.highlight=function(t){var e,r;if(!t)return this._dynamicCounts=[0,0,0],this.dyanamicLevel=[NaN,NaN,NaN],void(this.highlightLevel=[-1,-1,-1]);for(e=0;e<3;++e)this.enableHighlight[e]?this.highlightLevel[e]=t.level[e]:this.highlightLevel[e]=-1;for(r=this.snapToData?t.dataCoordinate:t.position,e=0;e<3;++e)r[e]-=this.objectOffset[e];if(this.enableDynamic[0]&&r[0]!==this.dynamicLevel[0]||this.enableDynamic[1]&&r[1]!==this.dynamicLevel[1]||this.enableDynamic[2]&&r[2]!==this.dynamicLevel[2]){for(var n=0,i=this.shape,a=s.mallocFloat(12*i[0]*i[1]),o=0;o<3;++o)if(this.enableDynamic[o]){this.dynamicLevel[o]=r[o];var l=(o+1)%3,c=(o+2)%3,u=this._field[o],f=this._field[l],p=this._field[c],d=h(u,r[o]),m=d.cells,g=d.positions;for(this._dynamicOffsets[o]=n,e=0;e<m.length;++e)for(var v=m[e],y=0;y<2;++y){var x=g[v[y]],b=+x[0],_=0|b,w=0|Math.min(_+1,i[0]),T=b-_,k=1-T,M=+x[1],A=0|M,S=0|Math.min(A+1,i[1]),E=M-A,L=1-E,C=k*L,P=k*E,I=T*L,O=T*E,z=C*f.get(_,A)+P*f.get(_,S)+I*f.get(w,A)+O*f.get(w,S),D=C*p.get(_,A)+P*p.get(_,S)+I*p.get(w,A)+O*p.get(w,S);if(isNaN(z)||isNaN(D)){y&&(n-=1);break}a[2*n+0]=z,a[2*n+1]=D,n+=1}this._dynamicCounts[o]=n-this._dynamicOffsets[o]}else this.dynamicLevel[o]=NaN,this._dynamicCounts[o]=0;this._dynamicBuffer.update(a.subarray(0,2*n)),s.freeFloat(a)}}},{\"./lib/shaders\":349,\"binary-search-bounds\":103,\"bit-twiddle\":104,colormap:137,\"gl-buffer\":263,\"gl-mat4/invert\":295,\"gl-mat4/multiply\":297,\"gl-texture2d\":352,\"gl-vao\":357,ndarray:479,\"ndarray-gradient\":472,\"ndarray-ops\":474,\"ndarray-pack\":475,\"surface-nets\":587,\"typedarray-pool\":613}],351:[function(t,e,r){\"use strict\";var n=t(\"css-font\"),i=t(\"pick-by-alias\"),a=t(\"regl\"),o=t(\"gl-util/context\"),s=t(\"es6-weak-map\"),l=t(\"color-normalize\"),c=t(\"font-atlas\"),u=t(\"typedarray-pool\"),f=t(\"parse-rect\"),h=t(\"is-plain-obj\"),p=t(\"parse-unit\"),d=t(\"to-px\"),m=t(\"detect-kerning\"),g=t(\"object-assign\"),v=t(\"font-measure\"),y=t(\"flatten-vertex-data\"),x=t(\"bit-twiddle\").nextPow2,b=new s,_=!1;if(document.body){var w=document.body.appendChild(document.createElement(\"div\"));w.style.font=\"italic small-caps bold condensed 16px/2 cursive\",getComputedStyle(w).fontStretch&&(_=!0),document.body.removeChild(w)}var T=function(t){!function(t){return\"function\"==typeof t&&t._gl&&t.prop&&t.texture&&t.buffer}(t)?this.gl=o(t):(t={regl:t},this.gl=t.regl._gl),this.shader=b.get(this.gl),this.shader?this.regl=this.shader.regl:this.regl=t.regl||a({gl:this.gl}),this.charBuffer=this.regl.buffer({type:\"uint8\",usage:\"stream\"}),this.sizeBuffer=this.regl.buffer({type:\"float\",usage:\"stream\"}),this.shader||(this.shader=this.createShader(),b.set(this.gl,this.shader)),this.batch=[],this.fontSize=[],this.font=[],this.fontAtlas=[],this.draw=this.shader.draw.bind(this),this.render=function(){this.regl._refresh(),this.draw(this.batch)},this.canvas=this.gl.canvas,this.update(h(t)?t:{})};T.prototype.createShader=function(){var t=this.regl,e=t({blend:{enable:!0,color:[0,0,0,1],func:{srcRGB:\"src alpha\",dstRGB:\"one minus src alpha\",srcAlpha:\"one minus dst alpha\",dstAlpha:\"one\"}},stencil:{enable:!1},depth:{enable:!1},count:t.prop(\"count\"),offset:t.prop(\"offset\"),attributes:{charOffset:{offset:4,stride:8,buffer:t.this(\"sizeBuffer\")},width:{offset:0,stride:8,buffer:t.this(\"sizeBuffer\")},char:t.this(\"charBuffer\"),position:t.this(\"position\")},uniforms:{atlasSize:function(t,e){return[e.atlas.width,e.atlas.height]},atlasDim:function(t,e){return[e.atlas.cols,e.atlas.rows]},atlas:function(t,e){return e.atlas.texture},charStep:function(t,e){return e.atlas.step},em:function(t,e){return e.atlas.em},color:t.prop(\"color\"),opacity:t.prop(\"opacity\"),viewport:t.this(\"viewportArray\"),scale:t.this(\"scale\"),align:t.prop(\"align\"),baseline:t.prop(\"baseline\"),translate:t.this(\"translate\"),positionOffset:t.prop(\"positionOffset\")},primitive:\"points\",viewport:t.this(\"viewport\"),vert:\"\\n\\t\\t\\tprecision highp float;\\n\\t\\t\\tattribute float width, charOffset, char;\\n\\t\\t\\tattribute vec2 position;\\n\\t\\t\\tuniform float fontSize, charStep, em, align, baseline;\\n\\t\\t\\tuniform vec4 viewport;\\n\\t\\t\\tuniform vec4 color;\\n\\t\\t\\tuniform vec2 atlasSize, atlasDim, scale, translate, positionOffset;\\n\\t\\t\\tvarying vec2 charCoord, charId;\\n\\t\\t\\tvarying float charWidth;\\n\\t\\t\\tvarying vec4 fontColor;\\n\\t\\t\\tvoid main () {\\n\\t\\t\\t\\t\"+(T.normalViewport?\"\":\"vec2 positionOffset = vec2(positionOffset.x,- positionOffset.y);\")+\"\\n\\n\\t\\t\\t\\tvec2 offset = floor(em * (vec2(align + charOffset, baseline)\\n\\t\\t\\t\\t\\t+ positionOffset))\\n\\t\\t\\t\\t\\t/ (viewport.zw * scale.xy);\\n\\n\\t\\t\\t\\tvec2 position = (position + translate) * scale;\\n\\t\\t\\t\\tposition += offset * scale;\\n\\n\\t\\t\\t\\t\"+(T.normalViewport?\"position.y = 1. - position.y;\":\"\")+\"\\n\\n\\t\\t\\t\\tcharCoord = position * viewport.zw + viewport.xy;\\n\\n\\t\\t\\t\\tgl_Position = vec4(position * 2. - 1., 0, 1);\\n\\n\\t\\t\\t\\tgl_PointSize = charStep;\\n\\n\\t\\t\\t\\tcharId.x = mod(char, atlasDim.x);\\n\\t\\t\\t\\tcharId.y = floor(char / atlasDim.x);\\n\\n\\t\\t\\t\\tcharWidth = width * em;\\n\\n\\t\\t\\t\\tfontColor = color / 255.;\\n\\t\\t\\t}\",frag:\"\\n\\t\\t\\tprecision highp float;\\n\\t\\t\\tuniform sampler2D atlas;\\n\\t\\t\\tuniform float fontSize, charStep, opacity;\\n\\t\\t\\tuniform vec2 atlasSize;\\n\\t\\t\\tuniform vec4 viewport;\\n\\t\\t\\tvarying vec4 fontColor;\\n\\t\\t\\tvarying vec2 charCoord, charId;\\n\\t\\t\\tvarying float charWidth;\\n\\n\\t\\t\\tfloat lightness(vec4 color) {\\n\\t\\t\\t\\treturn color.r * 0.299 + color.g * 0.587 + color.b * 0.114;\\n\\t\\t\\t}\\n\\n\\t\\t\\tvoid main () {\\n\\t\\t\\t\\tvec2 uv = gl_FragCoord.xy - charCoord + charStep * .5;\\n\\t\\t\\t\\tfloat halfCharStep = floor(charStep * .5 + .5);\\n\\n\\t\\t\\t\\t// invert y and shift by 1px (FF expecially needs that)\\n\\t\\t\\t\\tuv.y = charStep - uv.y;\\n\\n\\t\\t\\t\\t// ignore points outside of character bounding box\\n\\t\\t\\t\\tfloat halfCharWidth = ceil(charWidth * .5);\\n\\t\\t\\t\\tif (floor(uv.x) > halfCharStep + halfCharWidth ||\\n\\t\\t\\t\\t\\tfloor(uv.x) < halfCharStep - halfCharWidth) return;\\n\\n\\t\\t\\t\\tuv += charId * charStep;\\n\\t\\t\\t\\tuv = uv / atlasSize;\\n\\n\\t\\t\\t\\tvec4 color = fontColor;\\n\\t\\t\\t\\tvec4 mask = texture2D(atlas, uv);\\n\\n\\t\\t\\t\\tfloat maskY = lightness(mask);\\n\\t\\t\\t\\t// float colorY = lightness(color);\\n\\t\\t\\t\\tcolor.a *= maskY;\\n\\t\\t\\t\\tcolor.a *= opacity;\\n\\n\\t\\t\\t\\t// color.a += .1;\\n\\n\\t\\t\\t\\t// antialiasing, see yiq color space y-channel formula\\n\\t\\t\\t\\t// color.rgb += (1. - color.rgb) * (1. - mask.rgb);\\n\\n\\t\\t\\t\\tgl_FragColor = color;\\n\\t\\t\\t}\"});return{regl:t,draw:e,atlas:{}}},T.prototype.update=function(t){var e=this;if(\"string\"==typeof t)t={text:t};else if(!t)return;null!=(t=i(t,{position:\"position positions coord coords coordinates\",font:\"font fontFace fontface typeface cssFont css-font family fontFamily\",fontSize:\"fontSize fontsize size font-size\",text:\"text texts chars characters value values symbols\",align:\"align alignment textAlign textbaseline\",baseline:\"baseline textBaseline textbaseline\",direction:\"dir direction textDirection\",color:\"color colour fill fill-color fillColor textColor textcolor\",kerning:\"kerning kern\",range:\"range dataBox\",viewport:\"vp viewport viewBox viewbox viewPort\",opacity:\"opacity alpha transparency visible visibility opaque\",offset:\"offset positionOffset padding shift indent indentation\"},!0)).opacity&&(Array.isArray(t.opacity)?this.opacity=t.opacity.map((function(t){return parseFloat(t)})):this.opacity=parseFloat(t.opacity)),null!=t.viewport&&(this.viewport=f(t.viewport),T.normalViewport&&(this.viewport.y=this.canvas.height-this.viewport.y-this.viewport.height),this.viewportArray=[this.viewport.x,this.viewport.y,this.viewport.width,this.viewport.height]),null==this.viewport&&(this.viewport={x:0,y:0,width:this.gl.drawingBufferWidth,height:this.gl.drawingBufferHeight},this.viewportArray=[this.viewport.x,this.viewport.y,this.viewport.width,this.viewport.height]),null!=t.kerning&&(this.kerning=t.kerning),null!=t.offset&&(\"number\"==typeof t.offset&&(t.offset=[t.offset,0]),this.positionOffset=y(t.offset)),t.direction&&(this.direction=t.direction),t.range&&(this.range=t.range,this.scale=[1/(t.range[2]-t.range[0]),1/(t.range[3]-t.range[1])],this.translate=[-t.range[0],-t.range[1]]),t.scale&&(this.scale=t.scale),t.translate&&(this.translate=t.translate),this.scale||(this.scale=[1/this.viewport.width,1/this.viewport.height]),this.translate||(this.translate=[0,0]),this.font.length||t.font||(t.font=T.baseFontSize+\"px sans-serif\");var r,a=!1,o=!1;if(t.font&&(Array.isArray(t.font)?t.font:[t.font]).forEach((function(t,r){if(\"string\"==typeof t)try{t=n.parse(t)}catch(e){t=n.parse(T.baseFontSize+\"px \"+t)}else t=n.parse(n.stringify(t));var i=n.stringify({size:T.baseFontSize,family:t.family,stretch:_?t.stretch:void 0,variant:t.variant,weight:t.weight,style:t.style}),s=p(t.size),l=Math.round(s[0]*d(s[1]));if(l!==e.fontSize[r]&&(o=!0,e.fontSize[r]=l),!(e.font[r]&&i==e.font[r].baseString||(a=!0,e.font[r]=T.fonts[i],e.font[r]))){var c=t.family.join(\", \"),u=[t.style];t.style!=t.variant&&u.push(t.variant),t.variant!=t.weight&&u.push(t.weight),_&&t.weight!=t.stretch&&u.push(t.stretch),e.font[r]={baseString:i,family:c,weight:t.weight,stretch:t.stretch,style:t.style,variant:t.variant,width:{},kerning:{},metrics:v(c,{origin:\"top\",fontSize:T.baseFontSize,fontStyle:u.join(\" \")})},T.fonts[i]=e.font[r]}})),(a||o)&&this.font.forEach((function(r,i){var a=n.stringify({size:e.fontSize[i],family:r.family,stretch:_?r.stretch:void 0,variant:r.variant,weight:r.weight,style:r.style});if(e.fontAtlas[i]=e.shader.atlas[a],!e.fontAtlas[i]){var o=r.metrics;e.shader.atlas[a]=e.fontAtlas[i]={fontString:a,step:2*Math.ceil(e.fontSize[i]*o.bottom*.5),em:e.fontSize[i],cols:0,rows:0,height:0,width:0,chars:[],ids:{},texture:e.regl.texture()}}null==t.text&&(t.text=e.text)})),\"string\"==typeof t.text&&t.position&&t.position.length>2){for(var s=Array(.5*t.position.length),h=0;h<s.length;h++)s[h]=t.text;t.text=s}if(null!=t.text||a){if(this.textOffsets=[0],Array.isArray(t.text)){this.count=t.text[0].length,this.counts=[this.count];for(var b=1;b<t.text.length;b++)this.textOffsets[b]=this.textOffsets[b-1]+t.text[b-1].length,this.count+=t.text[b].length,this.counts.push(t.text[b].length);this.text=t.text.join(\"\")}else this.text=t.text,this.count=this.text.length,this.counts=[this.count];r=[],this.font.forEach((function(t,n){T.atlasContext.font=t.baseString;for(var i=e.fontAtlas[n],a=0;a<e.text.length;a++){var o=e.text.charAt(a);if(null==i.ids[o]&&(i.ids[o]=i.chars.length,i.chars.push(o),r.push(o)),null==t.width[o]&&(t.width[o]=T.atlasContext.measureText(o).width/T.baseFontSize,e.kerning)){var s=[];for(var l in t.width)s.push(l+o,o+l);g(t.kerning,m(t.family,{pairs:s}))}}}))}if(t.position)if(t.position.length>2){for(var w=!t.position[0].length,k=u.mallocFloat(2*this.count),M=0,A=0;M<this.counts.length;M++){var S=this.counts[M];if(w)for(var E=0;E<S;E++)k[A++]=t.position[2*M],k[A++]=t.position[2*M+1];else for(var L=0;L<S;L++)k[A++]=t.position[M][0],k[A++]=t.position[M][1]}this.position.call?this.position({type:\"float\",data:k}):this.position=this.regl.buffer({type:\"float\",data:k}),u.freeFloat(k)}else this.position.destroy&&this.position.destroy(),this.position={constant:t.position};if(t.text||a){var C=u.mallocUint8(this.count),P=u.mallocFloat(2*this.count);this.textWidth=[];for(var I=0,O=0;I<this.counts.length;I++){for(var z=this.counts[I],D=this.font[I]||this.font[0],R=this.fontAtlas[I]||this.fontAtlas[0],F=0;F<z;F++){var B=this.text.charAt(O),N=this.text.charAt(O-1);if(C[O]=R.ids[B],P[2*O]=D.width[B],F){var j=P[2*O-2],U=P[2*O],V=P[2*O-1]+.5*j+.5*U;if(this.kerning){var q=D.kerning[N+B];q&&(V+=.001*q)}P[2*O+1]=V}else P[2*O+1]=.5*P[2*O];O++}this.textWidth.push(P.length?.5*P[2*O-2]+P[2*O-1]:0)}t.align||(t.align=this.align),this.charBuffer({data:C,type:\"uint8\",usage:\"stream\"}),this.sizeBuffer({data:P,type:\"float\",usage:\"stream\"}),u.freeUint8(C),u.freeFloat(P),r.length&&this.font.forEach((function(t,r){var n=e.fontAtlas[r],i=n.step,a=Math.floor(T.maxAtlasSize/i),o=Math.min(a,n.chars.length),s=Math.ceil(n.chars.length/o),l=x(o*i),u=x(s*i);n.width=l,n.height=u,n.rows=s,n.cols=o,n.em&&n.texture({data:c({canvas:T.atlasCanvas,font:n.fontString,chars:n.chars,shape:[l,u],step:[i,i]})})}))}if(t.align&&(this.align=t.align,this.alignOffset=this.textWidth.map((function(t,r){var n=Array.isArray(e.align)?e.align.length>1?e.align[r]:e.align[0]:e.align;if(\"number\"==typeof n)return n;switch(n){case\"right\":case\"end\":return-t;case\"center\":case\"centre\":case\"middle\":return.5*-t}return 0}))),null==this.baseline&&null==t.baseline&&(t.baseline=0),null!=t.baseline&&(this.baseline=t.baseline,Array.isArray(this.baseline)||(this.baseline=[this.baseline]),this.baselineOffset=this.baseline.map((function(t,r){var n=(e.font[r]||e.font[0]).metrics,i=0;return i+=.5*n.bottom,i+=\"number\"==typeof t?t-n.baseline:-n[t],T.normalViewport||(i*=-1),i}))),null!=t.color)if(t.color||(t.color=\"transparent\"),\"string\"!=typeof t.color&&isNaN(t.color)){var H;if(\"number\"==typeof t.color[0]&&t.color.length>this.counts.length){var G=t.color.length;H=u.mallocUint8(G);for(var Y=(t.color.subarray||t.color.slice).bind(t.color),W=0;W<G;W+=4)H.set(l(Y(W,W+4),\"uint8\"),W)}else{var X=t.color.length;H=u.mallocUint8(4*X);for(var Z=0;Z<X;Z++)H.set(l(t.color[Z]||0,\"uint8\"),4*Z)}this.color=H}else this.color=l(t.color,\"uint8\");if(t.position||t.text||t.color||t.baseline||t.align||t.font||t.offset||t.opacity)if(this.color.length>4||this.baselineOffset.length>1||this.align&&this.align.length>1||this.fontAtlas.length>1||this.positionOffset.length>2){var J=Math.max(.5*this.position.length||0,.25*this.color.length||0,this.baselineOffset.length||0,this.alignOffset.length||0,this.font.length||0,this.opacity.length||0,.5*this.positionOffset.length||0);this.batch=Array(J);for(var K=0;K<this.batch.length;K++)this.batch[K]={count:this.counts.length>1?this.counts[K]:this.counts[0],offset:this.textOffsets.length>1?this.textOffsets[K]:this.textOffsets[0],color:this.color?this.color.length<=4?this.color:this.color.subarray(4*K,4*K+4):[0,0,0,255],opacity:Array.isArray(this.opacity)?this.opacity[K]:this.opacity,baseline:null!=this.baselineOffset[K]?this.baselineOffset[K]:this.baselineOffset[0],align:this.align?null!=this.alignOffset[K]?this.alignOffset[K]:this.alignOffset[0]:0,atlas:this.fontAtlas[K]||this.fontAtlas[0],positionOffset:this.positionOffset.length>2?this.positionOffset.subarray(2*K,2*K+2):this.positionOffset}}else this.count?this.batch=[{count:this.count,offset:0,color:this.color||[0,0,0,255],opacity:Array.isArray(this.opacity)?this.opacity[0]:this.opacity,baseline:this.baselineOffset[0],align:this.alignOffset?this.alignOffset[0]:0,atlas:this.fontAtlas[0],positionOffset:this.positionOffset}]:this.batch=[]},T.prototype.destroy=function(){},T.prototype.kerning=!0,T.prototype.position={constant:new Float32Array(2)},T.prototype.translate=null,T.prototype.scale=null,T.prototype.font=null,T.prototype.text=\"\",T.prototype.positionOffset=[0,0],T.prototype.opacity=1,T.prototype.color=new Uint8Array([0,0,0,255]),T.prototype.alignOffset=[0,0],T.normalViewport=!1,T.maxAtlasSize=1024,T.atlasCanvas=document.createElement(\"canvas\"),T.atlasContext=T.atlasCanvas.getContext(\"2d\",{alpha:!1}),T.baseFontSize=64,T.fonts={},e.exports=T},{\"bit-twiddle\":104,\"color-normalize\":131,\"css-font\":150,\"detect-kerning\":177,\"es6-weak-map\":237,\"flatten-vertex-data\":248,\"font-atlas\":249,\"font-measure\":250,\"gl-util/context\":353,\"is-plain-obj\":453,\"object-assign\":483,\"parse-rect\":488,\"parse-unit\":490,\"pick-by-alias\":494,regl:536,\"to-px\":596,\"typedarray-pool\":613}],352:[function(t,e,r){\"use strict\";var n=t(\"ndarray\"),i=t(\"ndarray-ops\"),a=t(\"typedarray-pool\");e.exports=function(t){if(arguments.length<=1)throw new Error(\"gl-texture2d: Missing arguments for texture2d constructor\");o||c(t);if(\"number\"==typeof arguments[1])return v(t,arguments[1],arguments[2],arguments[3]||t.RGBA,arguments[4]||t.UNSIGNED_BYTE);if(Array.isArray(arguments[1]))return v(t,0|arguments[1][0],0|arguments[1][1],arguments[2]||t.RGBA,arguments[3]||t.UNSIGNED_BYTE);if(\"object\"==typeof arguments[1]){var e=arguments[1],r=u(e)?e:e.raw;if(r)return y(t,r,0|e.width,0|e.height,arguments[2]||t.RGBA,arguments[3]||t.UNSIGNED_BYTE);if(e.shape&&e.data&&e.stride)return x(t,e)}throw new Error(\"gl-texture2d: Invalid arguments for texture2d constructor\")};var o=null,s=null,l=null;function c(t){o=[t.LINEAR,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_NEAREST],s=[t.NEAREST,t.LINEAR,t.NEAREST_MIPMAP_NEAREST,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_LINEAR],l=[t.REPEAT,t.CLAMP_TO_EDGE,t.MIRRORED_REPEAT]}function u(t){return\"undefined\"!=typeof HTMLCanvasElement&&t instanceof HTMLCanvasElement||\"undefined\"!=typeof HTMLImageElement&&t instanceof HTMLImageElement||\"undefined\"!=typeof HTMLVideoElement&&t instanceof HTMLVideoElement||\"undefined\"!=typeof ImageData&&t instanceof ImageData}var f=function(t,e){i.muls(t,e,255)};function h(t,e,r){var n=t.gl,i=n.getParameter(n.MAX_TEXTURE_SIZE);if(e<0||e>i||r<0||r>i)throw new Error(\"gl-texture2d: Invalid texture size\");return t._shape=[e,r],t.bind(),n.texImage2D(n.TEXTURE_2D,0,t.format,e,r,0,t.format,t.type,null),t._mipLevels=[0],t}function p(t,e,r,n,i,a){this.gl=t,this.handle=e,this.format=i,this.type=a,this._shape=[r,n],this._mipLevels=[0],this._magFilter=t.NEAREST,this._minFilter=t.NEAREST,this._wrapS=t.CLAMP_TO_EDGE,this._wrapT=t.CLAMP_TO_EDGE,this._anisoSamples=1;var o=this,s=[this._wrapS,this._wrapT];Object.defineProperties(s,[{get:function(){return o._wrapS},set:function(t){return o.wrapS=t}},{get:function(){return o._wrapT},set:function(t){return o.wrapT=t}}]),this._wrapVector=s;var l=[this._shape[0],this._shape[1]];Object.defineProperties(l,[{get:function(){return o._shape[0]},set:function(t){return o.width=t}},{get:function(){return o._shape[1]},set:function(t){return o.height=t}}]),this._shapeVector=l}var d=p.prototype;function m(t,e){return 3===t.length?1===e[2]&&e[1]===t[0]*t[2]&&e[0]===t[2]:1===e[0]&&e[1]===t[0]}function g(t){var e=t.createTexture();return t.bindTexture(t.TEXTURE_2D,e),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MIN_FILTER,t.NEAREST),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MAG_FILTER,t.NEAREST),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_S,t.CLAMP_TO_EDGE),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_T,t.CLAMP_TO_EDGE),e}function v(t,e,r,n,i){var a=t.getParameter(t.MAX_TEXTURE_SIZE);if(e<0||e>a||r<0||r>a)throw new Error(\"gl-texture2d: Invalid texture shape\");if(i===t.FLOAT&&!t.getExtension(\"OES_texture_float\"))throw new Error(\"gl-texture2d: Floating point textures not supported on this platform\");var o=g(t);return t.texImage2D(t.TEXTURE_2D,0,n,e,r,0,n,i,null),new p(t,o,e,r,n,i)}function y(t,e,r,n,i,a){var o=g(t);return t.texImage2D(t.TEXTURE_2D,0,i,i,a,e),new p(t,o,r,n,i,a)}function x(t,e){var r=e.dtype,o=e.shape.slice(),s=t.getParameter(t.MAX_TEXTURE_SIZE);if(o[0]<0||o[0]>s||o[1]<0||o[1]>s)throw new Error(\"gl-texture2d: Invalid texture size\");var l=m(o,e.stride.slice()),c=0;\"float32\"===r?c=t.FLOAT:\"float64\"===r?(c=t.FLOAT,l=!1,r=\"float32\"):\"uint8\"===r?c=t.UNSIGNED_BYTE:(c=t.UNSIGNED_BYTE,l=!1,r=\"uint8\");var u,h,d=0;if(2===o.length)d=t.LUMINANCE,o=[o[0],o[1],1],e=n(e.data,o,[e.stride[0],e.stride[1],1],e.offset);else{if(3!==o.length)throw new Error(\"gl-texture2d: Invalid shape for texture\");if(1===o[2])d=t.ALPHA;else if(2===o[2])d=t.LUMINANCE_ALPHA;else if(3===o[2])d=t.RGB;else{if(4!==o[2])throw new Error(\"gl-texture2d: Invalid shape for pixel coords\");d=t.RGBA}}c!==t.FLOAT||t.getExtension(\"OES_texture_float\")||(c=t.UNSIGNED_BYTE,l=!1);var v=e.size;if(l)u=0===e.offset&&e.data.length===v?e.data:e.data.subarray(e.offset,e.offset+v);else{var y=[o[2],o[2]*o[0],1];h=a.malloc(v,r);var 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f(this.rightPoints,t,e)}return h(this.leftPoints,e)},a.queryInterval=function(t,e,r){var n;if(t<this.mid&&this.left&&(n=this.left.queryInterval(t,e,r)))return n;if(e>this.mid&&this.right&&(n=this.right.queryInterval(t,e,r)))return n;return e<this.mid?u(this.leftPoints,e,r):t>this.mid?f(this.rightPoints,t,r):h(this.leftPoints,r)};var y=v.prototype;y.insert=function(t){this.root?this.root.insert(t):this.root=new i(t[0],null,null,[t],[t])},y.remove=function(t){if(this.root){var e=this.root.remove(t);return 2===e&&(this.root=null),0!==e}return!1},y.queryPoint=function(t,e){if(this.root)return this.root.queryPoint(t,e)},y.queryInterval=function(t,e,r){if(t<=e&&this.root)return this.root.queryInterval(t,e,r)},Object.defineProperty(y,\"count\",{get:function(){return this.root?this.root.count:0}}),Object.defineProperty(y,\"intervals\",{get:function(){return this.root?this.root.intervals([]):[]}})},{\"binary-search-bounds\":445}],445:[function(t,e,r){arguments[4][247][0].apply(r,arguments)},{dup:247}],446:[function(t,e,r){\"use strict\";e.exports=function(t,e){e=e||new Array(t.length);for(var r=0;r<t.length;++r)e[t[r]]=r;return e}},{}],447:[function(t,e,r){\"use strict\";e.exports=function(t){for(var e=new Array(t),r=0;r<t;++r)e[r]=r;return e}},{}],448:[function(t,e,r){e.exports=!0},{}],449:[function(t,e,r){function n(t){return!!t.constructor&&\"function\"==typeof t.constructor.isBuffer&&t.constructor.isBuffer(t)}\n",
-       "/*!\n",
-       " * Determine if an object is a Buffer\n",
-       " *\n",
-       " * @author   Feross Aboukhadijeh <https://feross.org>\n",
-       " * @license  MIT\n",
-       " */\n",
-       "e.exports=function(t){return null!=t&&(n(t)||function(t){return\"function\"==typeof t.readFloatLE&&\"function\"==typeof t.slice&&n(t.slice(0,0))}(t)||!!t._isBuffer)}},{}],450:[function(t,e,r){\"use strict\";e.exports=\"undefined\"!=typeof navigator&&(/MSIE/.test(navigator.userAgent)||/Trident\\//.test(navigator.appVersion))},{}],451:[function(t,e,r){\"use strict\";e.exports=a,e.exports.isMobile=a,e.exports.default=a;var n=/(android|bb\\d+|meego).+mobile|avantgo|bada\\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\\/|plucker|pocket|psp|series[46]0|symbian|treo|up\\.(browser|link)|vodafone|wap|windows (ce|phone)|xda|xiino/i,i=/(android|bb\\d+|meego).+mobile|avantgo|bada\\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\\/|plucker|pocket|psp|series[46]0|symbian|treo|up\\.(browser|link)|vodafone|wap|windows (ce|phone)|xda|xiino|android|ipad|playbook|silk/i;function a(t){t||(t={});var e=t.ua;if(e||\"undefined\"==typeof navigator||(e=navigator.userAgent),e&&e.headers&&\"string\"==typeof e.headers[\"user-agent\"]&&(e=e.headers[\"user-agent\"]),\"string\"!=typeof e)return!1;var r=t.tablet?i.test(e):n.test(e);return!r&&t.tablet&&t.featureDetect&&navigator&&navigator.maxTouchPoints>1&&-1!==e.indexOf(\"Macintosh\")&&-1!==e.indexOf(\"Safari\")&&(r=!0),r}},{}],452:[function(t,e,r){\"use strict\";e.exports=function(t){var e=typeof t;return null!==t&&(\"object\"===e||\"function\"===e)}},{}],453:[function(t,e,r){\"use strict\";var n=Object.prototype.toString;e.exports=function(t){var e;return\"[object Object]\"===n.call(t)&&(null===(e=Object.getPrototypeOf(t))||e===Object.getPrototypeOf({}))}},{}],454:[function(t,e,r){\"use strict\";e.exports=function(t){for(var e,r=t.length,n=0;n<r;n++)if(((e=t.charCodeAt(n))<9||e>13)&&32!==e&&133!==e&&160!==e&&5760!==e&&6158!==e&&(e<8192||e>8205)&&8232!==e&&8233!==e&&8239!==e&&8287!==e&&8288!==e&&12288!==e&&65279!==e)return!1;return!0}},{}],455:[function(t,e,r){\"use strict\";e.exports=function(t){return\"string\"==typeof t&&(t=t.trim(),!!(/^[mzlhvcsqta]\\s*[-+.0-9][^mlhvzcsqta]+/i.test(t)&&/[\\dz]$/i.test(t)&&t.length>4))}},{}],456:[function(t,e,r){e.exports=function(t,e,r){return t*(1-r)+e*r}},{}],457:[function(t,e,r){!function(t,n){\"object\"==typeof r&&void 0!==e?e.exports=n():(t=t||self).mapboxgl=n()}(this,(function(){\"use strict\";var t,e,r;function n(n,i){if(t)if(e){var a=\"var sharedChunk = {}; (\"+t+\")(sharedChunk); (\"+e+\")(sharedChunk);\",o={};t(o),(r=i(o)).workerUrl=window.URL.createObjectURL(new Blob([a],{type:\"text/javascript\"}))}else e=i;else t=i}return n(0,(function(t){function e(t,e){return t(e={exports:{}},e.exports),e.exports}var r=n;function n(t,e,r,n){this.cx=3*t,this.bx=3*(r-t)-this.cx,this.ax=1-this.cx-this.bx,this.cy=3*e,this.by=3*(n-e)-this.cy,this.ay=1-this.cy-this.by,this.p1x=t,this.p1y=n,this.p2x=r,this.p2y=n}n.prototype.sampleCurveX=function(t){return((this.ax*t+this.bx)*t+this.cx)*t},n.prototype.sampleCurveY=function(t){return((this.ay*t+this.by)*t+this.cy)*t},n.prototype.sampleCurveDerivativeX=function(t){return(3*this.ax*t+2*this.bx)*t+this.cx},n.prototype.solveCurveX=function(t,e){var r,n,i,a,o;for(void 0===e&&(e=1e-6),i=t,o=0;o<8;o++){if(a=this.sampleCurveX(i)-t,Math.abs(a)<e)return i;var s=this.sampleCurveDerivativeX(i);if(Math.abs(s)<1e-6)break;i-=a/s}if((i=t)<(r=0))return r;if(i>(n=1))return n;for(;r<n;){if(a=this.sampleCurveX(i),Math.abs(a-t)<e)return i;t>a?r=i:n=i,i=.5*(n-r)+r}return i},n.prototype.solve=function(t,e){return this.sampleCurveY(this.solveCurveX(t,e))};var i=a;function a(t,e){this.x=t,this.y=e}function o(t,e,n,i){var a=new r(t,e,n,i);return function(t){return a.solve(t)}}a.prototype={clone:function(){return new a(this.x,this.y)},add:function(t){return this.clone()._add(t)},sub:function(t){return this.clone()._sub(t)},multByPoint:function(t){return this.clone()._multByPoint(t)},divByPoint:function(t){return this.clone()._divByPoint(t)},mult:function(t){return this.clone()._mult(t)},div:function(t){return this.clone()._div(t)},rotate:function(t){return this.clone()._rotate(t)},rotateAround:function(t,e){return this.clone()._rotateAround(t,e)},matMult:function(t){return this.clone()._matMult(t)},unit:function(){return this.clone()._unit()},perp:function(){return this.clone()._perp()},round:function(){return this.clone()._round()},mag:function(){return Math.sqrt(this.x*this.x+this.y*this.y)},equals:function(t){return this.x===t.x&&this.y===t.y},dist:function(t){return Math.sqrt(this.distSqr(t))},distSqr:function(t){var e=t.x-this.x,r=t.y-this.y;return e*e+r*r},angle:function(){return Math.atan2(this.y,this.x)},angleTo:function(t){return Math.atan2(this.y-t.y,this.x-t.x)},angleWith:function(t){return this.angleWithSep(t.x,t.y)},angleWithSep:function(t,e){return Math.atan2(this.x*e-this.y*t,this.x*t+this.y*e)},_matMult:function(t){var e=t[0]*this.x+t[1]*this.y,r=t[2]*this.x+t[3]*this.y;return this.x=e,this.y=r,this},_add:function(t){return this.x+=t.x,this.y+=t.y,this},_sub:function(t){return this.x-=t.x,this.y-=t.y,this},_mult:function(t){return this.x*=t,this.y*=t,this},_div:function(t){return this.x/=t,this.y/=t,this},_multByPoint:function(t){return this.x*=t.x,this.y*=t.y,this},_divByPoint:function(t){return this.x/=t.x,this.y/=t.y,this},_unit:function(){return this._div(this.mag()),this},_perp:function(){var t=this.y;return this.y=this.x,this.x=-t,this},_rotate:function(t){var e=Math.cos(t),r=Math.sin(t),n=e*this.x-r*this.y,i=r*this.x+e*this.y;return this.x=n,this.y=i,this},_rotateAround:function(t,e){var r=Math.cos(t),n=Math.sin(t),i=e.x+r*(this.x-e.x)-n*(this.y-e.y),a=e.y+n*(this.x-e.x)+r*(this.y-e.y);return this.x=i,this.y=a,this},_round:function(){return this.x=Math.round(this.x),this.y=Math.round(this.y),this}},a.convert=function(t){return t instanceof a?t:Array.isArray(t)?new a(t[0],t[1]):t};var s=o(.25,.1,.25,1);function l(t,e,r){return Math.min(r,Math.max(e,t))}function c(t,e,r){var n=r-e,i=((t-e)%n+n)%n+e;return i===e?r:i}function u(t){for(var e=[],r=arguments.length-1;r-- >0;)e[r]=arguments[r+1];for(var n=0,i=e;n<i.length;n+=1){var a=i[n];for(var o in a)t[o]=a[o]}return t}var f=1;function h(){return f++}function p(){return function t(e){return e?(e^16*Math.random()>>e/4).toString(16):([1e7]+-[1e3]+-4e3+-8e3+-1e11).replace(/[018]/g,t)}()}function d(t){return!!t&&/^[0-9a-f]{8}-[0-9a-f]{4}-[4][0-9a-f]{3}-[89ab][0-9a-f]{3}-[0-9a-f]{12}$/i.test(t)}function m(t,e){t.forEach((function(t){e[t]&&(e[t]=e[t].bind(e))}))}function g(t,e){return-1!==t.indexOf(e,t.length-e.length)}function v(t,e,r){var n={};for(var i in t)n[i]=e.call(r||this,t[i],i,t);return n}function y(t,e,r){var n={};for(var i 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r=X(t),n=R.devicePixelRatio>=2||512===e?\"@2x\":\"\",i=B.supported?\".webp\":\"$1\";r.path=r.path.replace(/(\\.(png|jpg)\\d*)(?=$)/,\"\"+n+i),r.path=r.path.replace(/^.+\\/v4\\//,\"/\"),r.path=\"/v4\"+r.path;var a=this._customAccessToken||function(t){for(var e=0,r=t;e<r.length;e+=1){var n=r[e].match(/^access_token=(.*)$/);if(n)return n[1]}return null}(r.params)||F.ACCESS_TOKEN;return F.REQUIRE_ACCESS_TOKEN&&a&&this._skuToken&&r.params.push(\"sku=\"+this._skuToken),this._makeAPIURL(r,a)},q.prototype.canonicalizeTileURL=function(t,e){var r=X(t);if(!r.path.match(/(^\\/v4\\/)/)||!r.path.match(/\\.[\\w]+$/))return t;var n=\"mapbox://tiles/\";n+=r.path.replace(\"/v4/\",\"\");var i=r.params;return e&&(i=i.filter((function(t){return!t.match(/^access_token=/)}))),i.length&&(n+=\"?\"+i.join(\"&\")),n},q.prototype.canonicalizeTileset=function(t,e){for(var r=!!e&&H(e),n=[],i=0,a=t.tiles||[];i<a.length;i+=1){var o=a[i];Y(o)?n.push(this.canonicalizeTileURL(o,r)):n.push(o)}return 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r=e.parse(t[1],1,Bt);if(!r)return null;var n=[],i=null;e.expectedType&&\"value\"!==e.expectedType.kind&&(i=e.expectedType);for(var a=1;a<t.length;a+=2){var o=1===a?-1/0:t[a],s=t[a+1],l=a,c=a+1;if(\"number\"!=typeof o)return e.error('Input/output pairs for \"step\" expressions must be defined using literal numeric values (not computed expressions) for the input values.',l);if(n.length&&n[n.length-1][0]>=o)return e.error('Input/output pairs for \"step\" expressions must be arranged with input values in strictly ascending order.',l);var u=e.parse(s,c,i);if(!u)return null;i=i||u.type,n.push([o,u])}return new qe(i,r,n)},qe.prototype.evaluate=function(t){var e=this.labels,r=this.outputs;if(1===e.length)return r[0].evaluate(t);var n=this.input.evaluate(t);if(n<=e[0])return r[0].evaluate(t);var i=e.length;return n>=e[i-1]?r[i-1].evaluate(t):r[Ve(e,n)].evaluate(t)},qe.prototype.eachChild=function(t){t(this.input);for(var e=0,r=this.outputs;e<r.length;e+=1){t(r[e])}},qe.prototype.outputDefined=function(){return this.outputs.every((function(t){return t.outputDefined()}))},qe.prototype.serialize=function(){for(var t=[\"step\",this.input.serialize()],e=0;e<this.labels.length;e++)e>0&&t.push(this.labels[e]),t.push(this.outputs[e].serialize());return t};var Ge=Object.freeze({__proto__:null,number:He,color:function(t,e,r){return new te(He(t.r,e.r,r),He(t.g,e.g,r),He(t.b,e.b,r),He(t.a,e.a,r))},array:function(t,e,r){return t.map((function(t,n){return He(t,e[n],r)}))}}),Ye=6/29,We=3*Ye*Ye,Xe=Math.PI/180,Ze=180/Math.PI;function Je(t){return t>.008856451679035631?Math.pow(t,1/3):t/We+4/29}function Ke(t){return t>Ye?t*t*t:We*(t-4/29)}function Qe(t){return 255*(t<=.0031308?12.92*t:1.055*Math.pow(t,1/2.4)-.055)}function $e(t){return(t/=255)<=.04045?t/12.92:Math.pow((t+.055)/1.055,2.4)}function tr(t){var e=$e(t.r),r=$e(t.g),n=$e(t.b),i=Je((.4124564*e+.3575761*r+.1804375*n)/.95047),a=Je((.2126729*e+.7151522*r+.072175*n)/1);return{l:116*a-16,a:500*(i-a),b:200*(a-Je((.0193339*e+.119192*r+.9503041*n)/1.08883)),alpha:t.a}}function er(t){var e=(t.l+16)/116,r=isNaN(t.a)?e:e+t.a/500,n=isNaN(t.b)?e:e-t.b/200;return e=1*Ke(e),r=.95047*Ke(r),n=1.08883*Ke(n),new te(Qe(3.2404542*r-1.5371385*e-.4985314*n),Qe(-.969266*r+1.8760108*e+.041556*n),Qe(.0556434*r-.2040259*e+1.0572252*n),t.alpha)}function rr(t,e,r){var n=e-t;return t+r*(n>180||n<-180?n-360*Math.round(n/360):n)}var nr={forward:tr,reverse:er,interpolate:function(t,e,r){return{l:He(t.l,e.l,r),a:He(t.a,e.a,r),b:He(t.b,e.b,r),alpha:He(t.alpha,e.alpha,r)}}},ir={forward:function(t){var e=tr(t),r=e.l,n=e.a,i=e.b,a=Math.atan2(i,n)*Ze;return{h:a<0?a+360:a,c:Math.sqrt(n*n+i*i),l:r,alpha:t.a}},reverse:function(t){var e=t.h*Xe,r=t.c;return 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if(\"exponential\"===n[0]){var o=n[1];if(\"number\"!=typeof o)return e.error(\"Exponential interpolation requires a numeric base.\",1,1);n={name:\"exponential\",base:o}}else{if(\"cubic-bezier\"!==n[0])return e.error(\"Unknown interpolation type \"+String(n[0]),1,0);var s=n.slice(1);if(4!==s.length||s.some((function(t){return\"number\"!=typeof t||t<0||t>1})))return e.error(\"Cubic bezier interpolation requires four numeric arguments with values between 0 and 1.\",1);n={name:\"cubic-bezier\",controlPoints:s}}if(t.length-1<4)return e.error(\"Expected at least 4 arguments, but found only \"+(t.length-1)+\".\");if((t.length-1)%2!=0)return e.error(\"Expected an even number of arguments.\");if(!(i=e.parse(i,2,Bt)))return null;var l=[],c=null;\"interpolate-hcl\"===r||\"interpolate-lab\"===r?c=Ut:e.expectedType&&\"value\"!==e.expectedType.kind&&(c=e.expectedType);for(var u=0;u<a.length;u+=2){var f=a[u],h=a[u+1],p=u+3,d=u+4;if(\"number\"!=typeof f)return e.error('Input/output pairs for 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a=Ve(e,n),o=e[a],s=e[a+1],l=or.interpolationFactor(this.interpolation,n,o,s),c=r[a].evaluate(t),u=r[a+1].evaluate(t);return\"interpolate\"===this.operator?Ge[this.type.kind.toLowerCase()](c,u,l):\"interpolate-hcl\"===this.operator?ir.reverse(ir.interpolate(ir.forward(c),ir.forward(u),l)):nr.reverse(nr.interpolate(nr.forward(c),nr.forward(u),l))},or.prototype.eachChild=function(t){t(this.input);for(var e=0,r=this.outputs;e<r.length;e+=1){t(r[e])}},or.prototype.outputDefined=function(){return this.outputs.every((function(t){return t.outputDefined()}))},or.prototype.serialize=function(){var t;t=\"linear\"===this.interpolation.name?[\"linear\"]:\"exponential\"===this.interpolation.name?1===this.interpolation.base?[\"linear\"]:[\"exponential\",this.interpolation.base]:[\"cubic-bezier\"].concat(this.interpolation.controlPoints);for(var e=[this.operator,t,this.input.serialize()],r=0;r<this.labels.length;r++)e.push(this.labels[r],this.outputs[r].serialize());return e};var lr=function(t,e){this.type=t,this.args=e};lr.parse=function(t,e){if(t.length<2)return e.error(\"Expectected at least one argument.\");var r=null,n=e.expectedType;n&&\"value\"!==n.kind&&(r=n);for(var i=[],a=0,o=t.slice(1);a<o.length;a+=1){var s=o[a],l=e.parse(s,1+i.length,r,void 0,{typeAnnotation:\"omit\"});if(!l)return null;r=r||l.type,i.push(l)}var c=n&&i.some((function(t){return Jt(n,t.type)}));return new lr(c?qt:r,i)},lr.prototype.evaluate=function(t){for(var e,r=null,n=0,i=0,a=this.args;i<a.length;i+=1){if(n++,(r=a[i].evaluate(t))&&r instanceof ie&&!r.available&&(e||(e=r.name),r=null,n===this.args.length&&(r=e)),null!==r)break}return r},lr.prototype.eachChild=function(t){this.args.forEach(t)},lr.prototype.outputDefined=function(){return this.args.every((function(t){return t.outputDefined()}))},lr.prototype.serialize=function(){var t=[\"coalesce\"];return this.eachChild((function(e){t.push(e.serialize())})),t};var cr=function(t,e){this.type=e.type,this.bindings=[].concat(t),this.result=e};cr.prototype.evaluate=function(t){return this.result.evaluate(t)},cr.prototype.eachChild=function(t){for(var e=0,r=this.bindings;e<r.length;e+=1){t(r[e][1])}t(this.result)},cr.parse=function(t,e){if(t.length<4)return e.error(\"Expected at least 3 arguments, but found \"+(t.length-1)+\" instead.\");for(var r=[],n=1;n<t.length-1;n+=2){var i=t[n];if(\"string\"!=typeof i)return e.error(\"Expected string, but found \"+typeof i+\" instead.\",n);if(/[^a-zA-Z0-9_]/.test(i))return e.error(\"Variable names must contain only alphanumeric characters or '_'.\",n);var a=e.parse(t[n+1],n+1);if(!a)return null;r.push([i,a])}var o=e.parse(t[t.length-1],t.length-1,e.expectedType,r);return o?new cr(r,o):null},cr.prototype.outputDefined=function(){return this.result.outputDefined()},cr.prototype.serialize=function(){for(var t=[\"let\"],e=0,r=this.bindings;e<r.length;e+=1){var n=r[e],i=n[0],a=n[1];t.push(i,a.serialize())}return t.push(this.result.serialize()),t};var ur=function(t,e,r){this.type=t,this.index=e,this.input=r};ur.parse=function(t,e){if(3!==t.length)return e.error(\"Expected 2 arguments, but found \"+(t.length-1)+\" instead.\");var r=e.parse(t[1],1,Bt),n=e.parse(t[2],2,Wt(e.expectedType||qt));if(!r||!n)return null;var i=n.type;return new ur(i.itemType,r,n)},ur.prototype.evaluate=function(t){var e=this.index.evaluate(t),r=this.input.evaluate(t);if(e<0)throw new ue(\"Array index out of bounds: \"+e+\" < 0.\");if(e>=r.length)throw new ue(\"Array index out of bounds: \"+e+\" > \"+(r.length-1)+\".\");if(e!==Math.floor(e))throw new ue(\"Array index must be an integer, but found \"+e+\" instead.\");return r[e]},ur.prototype.eachChild=function(t){t(this.index),t(this.input)},ur.prototype.outputDefined=function(){return!1},ur.prototype.serialize=function(){return[\"at\",this.index.serialize(),this.input.serialize()]};var fr=function(t,e){this.type=jt,this.needle=t,this.haystack=e};fr.parse=function(t,e){if(3!==t.length)return e.error(\"Expected 2 arguments, but found \"+(t.length-1)+\" instead.\");var r=e.parse(t[1],1,qt),n=e.parse(t[2],2,qt);return r&&n?Kt(r.type,[jt,Nt,Bt,Ft,qt])?new fr(r,n):e.error(\"Expected first argument to be of type boolean, string, number or null, but found \"+Xt(r.type)+\" instead\"):null},fr.prototype.evaluate=function(t){var e=this.needle.evaluate(t),r=this.haystack.evaluate(t);if(!r)return!1;if(!Qt(e,[\"boolean\",\"string\",\"number\",\"null\"]))throw new ue(\"Expected first argument to be of type boolean, string, number or null, but found \"+Xt(se(e))+\" instead.\");if(!Qt(r,[\"string\",\"array\"]))throw new ue(\"Expected second argument to be of type array or string, but found \"+Xt(se(r))+\" instead.\");return r.indexOf(e)>=0},fr.prototype.eachChild=function(t){t(this.needle),t(this.haystack)},fr.prototype.outputDefined=function(){return!0},fr.prototype.serialize=function(){return[\"in\",this.needle.serialize(),this.haystack.serialize()]};var hr=function(t,e,r){this.type=Bt,this.needle=t,this.haystack=e,this.fromIndex=r};hr.parse=function(t,e){if(t.length<=2||t.length>=5)return e.error(\"Expected 3 or 4 arguments, but found \"+(t.length-1)+\" instead.\");var r=e.parse(t[1],1,qt),n=e.parse(t[2],2,qt);if(!r||!n)return null;if(!Kt(r.type,[jt,Nt,Bt,Ft,qt]))return e.error(\"Expected first argument to be of type boolean, string, number or null, but found \"+Xt(r.type)+\" instead\");if(4===t.length){var i=e.parse(t[3],3,Bt);return i?new hr(r,n,i):null}return new hr(r,n)},hr.prototype.evaluate=function(t){var e=this.needle.evaluate(t),r=this.haystack.evaluate(t);if(!Qt(e,[\"boolean\",\"string\",\"number\",\"null\"]))throw new ue(\"Expected first argument to be of type boolean, string, number or null, but found \"+Xt(se(e))+\" instead.\");if(!Qt(r,[\"string\",\"array\"]))throw new ue(\"Expected second argument to be of type array or string, but found \"+Xt(se(r))+\" instead.\");if(this.fromIndex){var n=this.fromIndex.evaluate(t);return r.indexOf(e,n)}return r.indexOf(e)},hr.prototype.eachChild=function(t){t(this.needle),t(this.haystack),this.fromIndex&&t(this.fromIndex)},hr.prototype.outputDefined=function(){return!1},hr.prototype.serialize=function(){if(null!=this.fromIndex&&void 0!==this.fromIndex){var t=this.fromIndex.serialize();return[\"index-of\",this.needle.serialize(),this.haystack.serialize(),t]}return[\"index-of\",this.needle.serialize(),this.haystack.serialize()]};var pr=function(t,e,r,n,i,a){this.inputType=t,this.type=e,this.input=r,this.cases=n,this.outputs=i,this.otherwise=a};pr.parse=function(t,e){if(t.length<5)return e.error(\"Expected at least 4 arguments, but found only \"+(t.length-1)+\".\");if(t.length%2!=1)return e.error(\"Expected an even number of 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m?\"value\"!==d.type.kind&&e.concat(1).checkSubtype(r,d.type)?null:new pr(r,n,d,i,a,m):null},pr.prototype.evaluate=function(t){var e=this.input.evaluate(t);return(se(e)===this.inputType&&this.outputs[this.cases[e]]||this.otherwise).evaluate(t)},pr.prototype.eachChild=function(t){t(this.input),this.outputs.forEach(t),t(this.otherwise)},pr.prototype.outputDefined=function(){return this.outputs.every((function(t){return t.outputDefined()}))&&this.otherwise.outputDefined()},pr.prototype.serialize=function(){for(var t=this,e=[\"match\",this.input.serialize()],r=[],n={},i=0,a=Object.keys(this.cases).sort();i<a.length;i+=1){var o=a[i];void 0===(f=n[this.cases[o]])?(n[this.cases[o]]=r.length,r.push([this.cases[o],[o]])):r[f][1].push(o)}for(var s=function(e){return\"number\"===t.inputType.kind?Number(e):e},l=0,c=r;l<c.length;l+=1){var u=c[l],f=u[0],h=u[1];1===h.length?e.push(s(h[0])):e.push(h.map(s)),e.push(this.outputs[outputIndex$1].serialize())}return e.push(this.otherwise.serialize()),e};var dr=function(t,e,r){this.type=t,this.branches=e,this.otherwise=r};dr.parse=function(t,e){if(t.length<4)return e.error(\"Expected at least 3 arguments, but found only \"+(t.length-1)+\".\");if(t.length%2!=0)return e.error(\"Expected an odd number of arguments.\");var r;e.expectedType&&\"value\"!==e.expectedType.kind&&(r=e.expectedType);for(var n=[],i=1;i<t.length-1;i+=2){var a=e.parse(t[i],i,jt);if(!a)return null;var o=e.parse(t[i+1],i+1,r);if(!o)return null;n.push([a,o]),r=r||o.type}var s=e.parse(t[t.length-1],t.length-1,r);return s?new dr(r,n,s):null},dr.prototype.evaluate=function(t){for(var e=0,r=this.branches;e<r.length;e+=1){var n=r[e],i=n[0],a=n[1];if(i.evaluate(t))return a.evaluate(t)}return this.otherwise.evaluate(t)},dr.prototype.eachChild=function(t){for(var e=0,r=this.branches;e<r.length;e+=1){var n=r[e],i=n[0],a=n[1];t(i),t(a)}t(this.otherwise)},dr.prototype.outputDefined=function(){return this.branches.every((function(t){t[0];return 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or?a.interpolation:void 0;return Ir(new Zr(n?\"camera\":\"composite\",t.value,a.labels,o))}Zr.prototype.evaluateWithoutErrorHandling=function(t,e,r,n,i,a){return this._styleExpression.evaluateWithoutErrorHandling(t,e,r,n,i,a)},Zr.prototype.evaluate=function(t,e,r,n,i,a){return this._styleExpression.evaluate(t,e,r,n,i,a)},Zr.prototype.interpolationFactor=function(t,e,r){return this.interpolationType?or.interpolationFactor(this.interpolationType,t,e,r):0};var Kr=function(t,e){this._parameters=t,this._specification=e,It(this,function t(e,r){var n,i,a,o=\"color\"===r.type,s=e.stops&&\"object\"==typeof e.stops[0][0],l=s||void 0!==e.property,c=s||!l,u=e.type||(Rr(r)?\"exponential\":\"interval\");if(o&&((e=It({},e)).stops&&(e.stops=e.stops.map((function(t){return[t[0],te.parse(t[1])]}))),e.default?e.default=te.parse(e.default):e.default=te.parse(r.default)),e.colorSpace&&\"rgb\"!==e.colorSpace&&!ar[e.colorSpace])throw new Error(\"Unknown color space: 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n&&r<n.minimum?[new Ct(e,r,r+\" is less than the minimum value \"+n.minimum)]:\"maximum\"in n&&r>n.maximum?[new Ct(e,r,r+\" is greater than the maximum value \"+n.maximum)]:[]}function en(t){var e,r,n,i=t.valueSpec,a=Ot(t.value.type),o={},s=\"categorical\"!==a&&void 0===t.value.property,l=!s,c=\"array\"===Fr(t.value.stops)&&\"array\"===Fr(t.value.stops[0])&&\"object\"===Fr(t.value.stops[0][0]),u=Qr({key:t.key,value:t.value,valueSpec:t.styleSpec.function,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{stops:function(t){if(\"identity\"===a)return[new Ct(t.key,t.value,'identity function may not have a \"stops\" property')];var e=[],r=t.value;e=e.concat($r({key:t.key,value:r,valueSpec:t.valueSpec,style:t.style,styleSpec:t.styleSpec,arrayElementValidator:f})),\"array\"===Fr(r)&&0===r.length&&e.push(new Ct(t.key,r,\"array must have at least one stop\"));return e},default:function(t){return kn({key:t.key,value:t.value,valueSpec:i,style:t.style,styleSpec:t.styleSpec})}}});return\"identity\"===a&&s&&u.push(new Ct(t.key,t.value,'missing required property \"property\"')),\"identity\"===a||t.value.stops||u.push(new Ct(t.key,t.value,'missing required property \"stops\"')),\"exponential\"===a&&t.valueSpec.expression&&!Rr(t.valueSpec)&&u.push(new Ct(t.key,t.value,\"exponential functions not supported\")),t.styleSpec.$version>=8&&(l&&!zr(t.valueSpec)?u.push(new Ct(t.key,t.value,\"property functions not supported\")):s&&!Dr(t.valueSpec)&&u.push(new Ct(t.key,t.value,\"zoom functions not supported\"))),\"categorical\"!==a&&!c||void 0!==t.value.property||u.push(new Ct(t.key,t.value,'\"property\" property is required')),u;function f(t){var e=[],a=t.value,s=t.key;if(\"array\"!==Fr(a))return[new Ct(s,a,\"array expected, \"+Fr(a)+\" found\")];if(2!==a.length)return[new Ct(s,a,\"array length 2 expected, length \"+a.length+\" found\")];if(c){if(\"object\"!==Fr(a[0]))return[new Ct(s,a,\"object expected, \"+Fr(a[0])+\" found\")];if(void 0===a[0].zoom)return[new Ct(s,a,\"object stop key must have zoom\")];if(void 0===a[0].value)return[new Ct(s,a,\"object stop key must have value\")];if(n&&n>Ot(a[0].zoom))return[new Ct(s,a[0].zoom,\"stop zoom values must appear in ascending order\")];Ot(a[0].zoom)!==n&&(n=Ot(a[0].zoom),r=void 0,o={}),e=e.concat(Qr({key:s+\"[0]\",value:a[0],valueSpec:{zoom:{}},style:t.style,styleSpec:t.styleSpec,objectElementValidators:{zoom:tn,value:h}}))}else e=e.concat(h({key:s+\"[0]\",value:a[0],valueSpec:{},style:t.style,styleSpec:t.styleSpec},a));return Yr(zt(a[1]))?e.concat([new Ct(s+\"[1]\",a[1],\"expressions are not allowed in function stops.\")]):e.concat(kn({key:s+\"[1]\",value:a[1],valueSpec:i,style:t.style,styleSpec:t.styleSpec}))}function h(t,n){var s=Fr(t.value),l=Ot(t.value),c=null!==t.value?t.value:n;if(e){if(s!==e)return[new Ct(t.key,c,s+\" stop domain type must match previous stop domain type \"+e)]}else e=s;if(\"number\"!==s&&\"string\"!==s&&\"boolean\"!==s)return[new Ct(t.key,c,\"stop domain value must be a number, string, or boolean\")];if(\"number\"!==s&&\"categorical\"!==a){var u=\"number expected, \"+s+\" found\";return zr(i)&&void 0===a&&(u+='\\nIf you intended to use a categorical function, specify `\"type\": \"categorical\"`.'),[new Ct(t.key,c,u)]}return\"categorical\"!==a||\"number\"!==s||isFinite(l)&&Math.floor(l)===l?\"categorical\"!==a&&\"number\"===s&&void 0!==r&&l<r?[new Ct(t.key,c,\"stop domain values must appear in ascending order\")]:(r=l,\"categorical\"===a&&l in o?[new Ct(t.key,c,\"stop domain values must be unique\")]:(o[l]=!0,[])):[new Ct(t.key,c,\"integer expected, found \"+l)]}}function rn(t){var e=(\"property\"===t.expressionContext?Jr:Wr)(zt(t.value),t.valueSpec);if(\"error\"===e.result)return e.value.map((function(e){return new Ct(\"\"+t.key+e.key,t.value,e.message)}));var r=e.value.expression||e.value._styleExpression.expression;if(\"property\"===t.expressionContext&&\"text-font\"===t.propertyKey&&!r.outputDefined())return[new Ct(t.key,t.value,'Invalid data expression for \"'+t.propertyKey+'\". Output values must be contained as literals within the expression.')];if(\"property\"===t.expressionContext&&\"layout\"===t.propertyType&&!Be(r))return[new Ct(t.key,t.value,'\"feature-state\" data expressions are not supported with layout properties.')];if(\"filter\"===t.expressionContext&&!Be(r))return[new Ct(t.key,t.value,'\"feature-state\" data expressions are not supported with filters.')];if(t.expressionContext&&0===t.expressionContext.indexOf(\"cluster\")){if(!Ne(r,[\"zoom\",\"feature-state\"]))return[new Ct(t.key,t.value,'\"zoom\" and \"feature-state\" expressions are not supported with cluster properties.')];if(\"cluster-initial\"===t.expressionContext&&!Fe(r))return[new Ct(t.key,t.value,\"Feature data expressions are not supported with initial expression part of cluster properties.\")]}return[]}function nn(t){var e=t.key,r=t.value,n=t.valueSpec,i=[];return Array.isArray(n.values)?-1===n.values.indexOf(Ot(r))&&i.push(new Ct(e,r,\"expected one of [\"+n.values.join(\", \")+\"], 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on={type:\"boolean\",default:!1,transition:!1,\"property-type\":\"data-driven\",expression:{interpolated:!1,parameters:[\"zoom\",\"feature\"]}};function sn(t){if(null==t)return{filter:function(){return!0},needGeometry:!1};an(t)||(t=cn(t));var e=Wr(t,on);if(\"error\"===e.result)throw new Error(e.value.map((function(t){return t.key+\": \"+t.message})).join(\", \"));return{filter:function(t,r,n){return e.value.evaluate(t,r,{},n)},needGeometry:function t(e){if(!Array.isArray(e))return!1;if(\"within\"===e[0])return!0;for(var r=1;r<e.length;r++)if(t(e[r]))return!0;return!1}(t)}}function ln(t,e){return t<e?-1:t>e?1:0}function cn(t){if(!t)return!0;var e,r=t[0];return t.length<=1?\"any\"!==r:\"==\"===r?un(t[1],t[2],\"==\"):\"!=\"===r?pn(un(t[1],t[2],\"==\")):\"<\"===r||\">\"===r||\"<=\"===r||\">=\"===r?un(t[1],t[2],r):\"any\"===r?(e=t.slice(1),[\"any\"].concat(e.map(cn))):\"all\"===r?[\"all\"].concat(t.slice(1).map(cn)):\"none\"===r?[\"all\"].concat(t.slice(1).map(cn).map(pn)):\"in\"===r?fn(t[1],t.slice(2)):\"!in\"===r?pn(fn(t[1],t.slice(2))):\"has\"===r?hn(t[1]):\"!has\"===r?pn(hn(t[1])):\"within\"!==r||t}function un(t,e,r){switch(t){case\"$type\":return[\"filter-type-\"+r,e];case\"$id\":return[\"filter-id-\"+r,e];default:return[\"filter-\"+r,t,e]}}function fn(t,e){if(0===e.length)return!1;switch(t){case\"$type\":return[\"filter-type-in\",[\"literal\",e]];case\"$id\":return[\"filter-id-in\",[\"literal\",e]];default:return e.length>200&&!e.some((function(t){return typeof t!=typeof e[0]}))?[\"filter-in-large\",t,[\"literal\",e.sort(ln)]]:[\"filter-in-small\",t,[\"literal\",e]]}}function hn(t){switch(t){case\"$type\":return!0;case\"$id\":return[\"filter-has-id\"];default:return[\"filter-has\",t]}}function pn(t){return[\"!\",t]}function dn(t){return an(zt(t.value))?rn(It({},t,{expressionContext:\"filter\",valueSpec:{value:\"boolean\"}})):function t(e){var r=e.value,n=e.key;if(\"array\"!==Fr(r))return[new Ct(n,r,\"array expected, \"+Fr(r)+\" found\")];var i,a=e.styleSpec,o=[];if(r.length<1)return[new Ct(n,r,\"filter array must have at least 1 element\")];switch(o=o.concat(nn({key:n+\"[0]\",value:r[0],valueSpec:a.filter_operator,style:e.style,styleSpec:e.styleSpec})),Ot(r[0])){case\"<\":case\"<=\":case\">\":case\">=\":r.length>=2&&\"$type\"===Ot(r[1])&&o.push(new Ct(n,r,'\"$type\" cannot be use with operator \"'+r[0]+'\"'));case\"==\":case\"!=\":3!==r.length&&o.push(new Ct(n,r,'filter array for operator \"'+r[0]+'\" must have 3 elements'));case\"in\":case\"!in\":r.length>=2&&\"string\"!==(i=Fr(r[1]))&&o.push(new Ct(n+\"[1]\",r[1],\"string expected, \"+i+\" 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r=t.key,n=t.style,i=t.styleSpec,a=t.value,o=t.objectKey,s=i[e+\"_\"+t.layerType];if(!s)return[];var l=o.match(/^(.*)-transition$/);if(\"paint\"===e&&l&&s[l[1]]&&s[l[1]].transition)return kn({key:r,value:a,valueSpec:i.transition,style:n,styleSpec:i});var c,u=t.valueSpec||s[o];if(!u)return[new Ct(r,a,'unknown property \"'+o+'\"')];if(\"string\"===Fr(a)&&zr(u)&&!u.tokens&&(c=/^{([^}]+)}$/.exec(a)))return[new Ct(r,a,'\"'+o+'\" does not support interpolation syntax\\nUse an identity property function instead: `{ \"type\": \"identity\", \"property\": '+JSON.stringify(c[1])+\" }`.\")];var f=[];return\"symbol\"===t.layerType&&(\"text-field\"===o&&n&&!n.glyphs&&f.push(new Ct(r,a,'use of \"text-field\" requires a style \"glyphs\" property')),\"text-font\"===o&&Br(zt(a))&&\"identity\"===Ot(a.type)&&f.push(new Ct(r,a,'\"text-font\" does not support identity 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Qn(t,e){return!(!e&&Kn(t))&&!(t>=2304&&t<=3583||t>=3840&&t<=4255||Yn.Khmer(t))}function $n(t){for(var e=0,r=t;e<r.length;e+=1){if(Kn(r[e].charCodeAt(0)))return!0}return!1}var ti=\"deferred\",ei=\"loading\",ri=\"loaded\",ni=\"error\",ii=null,ai=\"unavailable\",oi=null,si=function(t){t&&\"string\"==typeof t&&t.indexOf(\"NetworkError\")>-1&&(ai=ni),ii&&ii(t)};function li(){ci.fire(new At(\"pluginStateChange\",{pluginStatus:ai,pluginURL:oi}))}var ci=new Et,ui=function(){return ai},fi=function(){if(ai!==ti||!oi)throw new Error(\"rtl-text-plugin cannot be downloaded unless a pluginURL is specified\");ai=ei,li(),oi&&xt({url:oi},(function(t){t?si(t):(ai=ri,li())}))},hi={applyArabicShaping:null,processBidirectionalText:null,processStyledBidirectionalText:null,isLoaded:function(){return ai===ri||null!=hi.applyArabicShaping},isLoading:function(){return ai===ei},setState:function(t){ai=t.pluginStatus,oi=t.pluginURL},isParsed:function(){return null!=hi.applyArabicShaping&&null!=hi.processBidirectionalText&&null!=hi.processStyledBidirectionalText},getPluginURL:function(){return oi}},pi=function(t,e){this.zoom=t,e?(this.now=e.now,this.fadeDuration=e.fadeDuration,this.zoomHistory=e.zoomHistory,this.transition=e.transition):(this.now=0,this.fadeDuration=0,this.zoomHistory=new Gn,this.transition={})};pi.prototype.isSupportedScript=function(t){return function(t,e){for(var r=0,n=t;r<n.length;r+=1){if(!Qn(n[r].charCodeAt(0),e))return!1}return!0}(t,hi.isLoaded())},pi.prototype.crossFadingFactor=function(){return 0===this.fadeDuration?1:Math.min((this.now-this.zoomHistory.lastIntegerZoomTime)/this.fadeDuration,1)},pi.prototype.getCrossfadeParameters=function(){var t=this.zoom,e=t-Math.floor(t),r=this.crossFadingFactor();return t>this.zoomHistory.lastIntegerZoom?{fromScale:2,toScale:1,t:e+(1-e)*r}:{fromScale:.5,toScale:1,t:1-(1-r)*e}};var di=function(t,e){this.property=t,this.value=e,this.expression=function(t,e){if(Br(t))return new Kr(t,e);if(Yr(t)){var r=Jr(t,e);if(\"error\"===r.result)throw new Error(r.value.map((function(t){return t.key+\": \"+t.message})).join(\", \"));return r.value}var n=t;return\"string\"==typeof t&&\"color\"===e.type&&(n=te.parse(t)),{kind:\"constant\",evaluate:function(){return n}}}(void 0===e?t.specification.default:e,t.specification)};di.prototype.isDataDriven=function(){return\"source\"===this.expression.kind||\"composite\"===this.expression.kind},di.prototype.possiblyEvaluate=function(t,e,r){return this.property.possiblyEvaluate(this,t,e,r)};var mi=function(t){this.property=t,this.value=new di(t,void 0)};mi.prototype.transitioned=function(t,e){return new vi(this.property,this.value,e,u({},t.transition,this.transition),t.now)},mi.prototype.untransitioned=function(){return new vi(this.property,this.value,null,{},0)};var gi=function(t){this._properties=t,this._values=Object.create(t.defaultTransitionablePropertyValues)};gi.prototype.getValue=function(t){return x(this._values[t].value.value)},gi.prototype.setValue=function(t,e){this._values.hasOwnProperty(t)||(this._values[t]=new mi(this._values[t].property)),this._values[t].value=new di(this._values[t].property,null===e?void 0:x(e))},gi.prototype.getTransition=function(t){return x(this._values[t].transition)},gi.prototype.setTransition=function(t,e){this._values.hasOwnProperty(t)||(this._values[t]=new mi(this._values[t].property)),this._values[t].transition=x(e)||void 0},gi.prototype.serialize=function(){for(var t={},e=0,r=Object.keys(this._values);e<r.length;e+=1){var n=r[e],i=this.getValue(n);void 0!==i&&(t[n]=i);var a=this.getTransition(n);void 0!==a&&(t[n+\"-transition\"]=a)}return t},gi.prototype.transitioned=function(t,e){for(var r=new yi(this._properties),n=0,i=Object.keys(this._values);n<i.length;n+=1){var a=i[n];r._values[a]=this._values[a].transitioned(t,e._values[a])}return r},gi.prototype.untransitioned=function(){for(var t=new yi(this._properties),e=0,r=Object.keys(this._values);e<r.length;e+=1){var n=r[e];t._values[n]=this._values[n].untransitioned()}return t};var vi=function(t,e,r,n,i){this.property=t,this.value=e,this.begin=i+n.delay||0,this.end=this.begin+n.duration||0,t.specification.transition&&(n.delay||n.duration)&&(this.prior=r)};vi.prototype.possiblyEvaluate=function(t,e,r){var n=t.now||0,i=this.value.possiblyEvaluate(t,e,r),a=this.prior;if(a){if(n>this.end)return this.prior=null,i;if(this.value.isDataDriven())return this.prior=null,i;if(n<this.begin)return a.possiblyEvaluate(t,e,r);var o=(n-this.begin)/(this.end-this.begin);return this.property.interpolate(a.possiblyEvaluate(t,e,r),i,function(t){if(t<=0)return 0;if(t>=1)return 1;var e=t*t,r=e*t;return 4*(t<.5?r:3*(t-e)+r-.75)}(o))}return i};var yi=function(t){this._properties=t,this._values=Object.create(t.defaultTransitioningPropertyValues)};yi.prototype.possiblyEvaluate=function(t,e,r){for(var n=new _i(this._properties),i=0,a=Object.keys(this._values);i<a.length;i+=1){var o=a[i];n._values[o]=this._values[o].possiblyEvaluate(t,e,r)}return n},yi.prototype.hasTransition=function(){for(var t=0,e=Object.keys(this._values);t<e.length;t+=1){var r=e[t];if(this._values[r].prior)return!0}return!1};var xi=function(t){this._properties=t,this._values=Object.create(t.defaultPropertyValues)};xi.prototype.getValue=function(t){return x(this._values[t].value)},xi.prototype.setValue=function(t,e){this._values[t]=new di(this._values[t].property,null===e?void 0:x(e))},xi.prototype.serialize=function(){for(var t={},e=0,r=Object.keys(this._values);e<r.length;e+=1){var n=r[e],i=this.getValue(n);void 0!==i&&(t[n]=i)}return t},xi.prototype.possiblyEvaluate=function(t,e,r){for(var n=new _i(this._properties),i=0,a=Object.keys(this._values);i<a.length;i+=1){var o=a[i];n._values[o]=this._values[o].possiblyEvaluate(t,e,r)}return n};var bi=function(t,e,r){this.property=t,this.value=e,this.parameters=r};bi.prototype.isConstant=function(){return\"constant\"===this.value.kind},bi.prototype.constantOr=function(t){return\"constant\"===this.value.kind?this.value.value:t},bi.prototype.evaluate=function(t,e,r,n){return this.property.evaluate(this.value,this.parameters,t,e,r,n)};var _i=function(t){this._properties=t,this._values=Object.create(t.defaultPossiblyEvaluatedValues)};_i.prototype.get=function(t){return this._values[t]};var wi=function(t){this.specification=t};wi.prototype.possiblyEvaluate=function(t,e){return t.expression.evaluate(e)},wi.prototype.interpolate=function(t,e,r){var n=Ge[this.specification.type];return n?n(t,e,r):t};var Ti=function(t,e){this.specification=t,this.overrides=e};Ti.prototype.possiblyEvaluate=function(t,e,r,n){return\"constant\"===t.expression.kind||\"camera\"===t.expression.kind?new bi(this,{kind:\"constant\",value:t.expression.evaluate(e,null,{},r,n)},e):new bi(this,t.expression,e)},Ti.prototype.interpolate=function(t,e,r){if(\"constant\"!==t.value.kind||\"constant\"!==e.value.kind)return t;if(void 0===t.value.value||void 0===e.value.value)return new bi(this,{kind:\"constant\",value:void 0},t.parameters);var n=Ge[this.specification.type];return n?new bi(this,{kind:\"constant\",value:n(t.value.value,e.value.value,r)},t.parameters):t},Ti.prototype.evaluate=function(t,e,r,n,i,a){return\"constant\"===t.kind?t.value:t.evaluate(e,r,n,i,a)};var ki=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.possiblyEvaluate=function(t,e,r,n){if(void 0===t.value)return new bi(this,{kind:\"constant\",value:void 0},e);if(\"constant\"===t.expression.kind){var i=t.expression.evaluate(e,null,{},r,n),a=\"resolvedImage\"===t.property.specification.type&&\"string\"!=typeof i?i.name:i,o=this._calculate(a,a,a,e);return new bi(this,{kind:\"constant\",value:o},e)}if(\"camera\"===t.expression.kind){var s=this._calculate(t.expression.evaluate({zoom:e.zoom-1}),t.expression.evaluate({zoom:e.zoom}),t.expression.evaluate({zoom:e.zoom+1}),e);return new bi(this,{kind:\"constant\",value:s},e)}return new bi(this,t.expression,e)},e.prototype.evaluate=function(t,e,r,n,i,a){if(\"source\"===t.kind){var o=t.evaluate(e,r,n,i,a);return this._calculate(o,o,o,e)}return\"composite\"===t.kind?this._calculate(t.evaluate({zoom:Math.floor(e.zoom)-1},r,n),t.evaluate({zoom:Math.floor(e.zoom)},r,n),t.evaluate({zoom:Math.floor(e.zoom)+1},r,n),e):t.value},e.prototype._calculate=function(t,e,r,n){return n.zoom>n.zoomHistory.lastIntegerZoom?{from:t,to:e}:{from:r,to:e}},e.prototype.interpolate=function(t){return t},e}(Ti),Mi=function(t){this.specification=t};Mi.prototype.possiblyEvaluate=function(t,e,r,n){if(void 0!==t.value){if(\"constant\"===t.expression.kind){var i=t.expression.evaluate(e,null,{},r,n);return this._calculate(i,i,i,e)}return this._calculate(t.expression.evaluate(new pi(Math.floor(e.zoom-1),e)),t.expression.evaluate(new pi(Math.floor(e.zoom),e)),t.expression.evaluate(new pi(Math.floor(e.zoom+1),e)),e)}},Mi.prototype._calculate=function(t,e,r,n){return n.zoom>n.zoomHistory.lastIntegerZoom?{from:t,to:e}:{from:r,to:e}},Mi.prototype.interpolate=function(t){return t};var Ai=function(t){this.specification=t};Ai.prototype.possiblyEvaluate=function(t,e,r,n){return!!t.expression.evaluate(e,null,{},r,n)},Ai.prototype.interpolate=function(){return!1};var Si=function(t){for(var e in this.properties=t,this.defaultPropertyValues={},this.defaultTransitionablePropertyValues={},this.defaultTransitioningPropertyValues={},this.defaultPossiblyEvaluatedValues={},this.overridableProperties=[],t){var r=t[e];r.specification.overridable&&this.overridableProperties.push(e);var n=this.defaultPropertyValues[e]=new di(r,void 0),i=this.defaultTransitionablePropertyValues[e]=new mi(r);this.defaultTransitioningPropertyValues[e]=i.untransitioned(),this.defaultPossiblyEvaluatedValues[e]=n.possiblyEvaluate({})}};Nn(\"DataDrivenProperty\",Ti),Nn(\"DataConstantProperty\",wi),Nn(\"CrossFadedDataDrivenProperty\",ki),Nn(\"CrossFadedProperty\",Mi),Nn(\"ColorRampProperty\",Ai);var Ei=function(t){function e(e,r){if(t.call(this),this.id=e.id,this.type=e.type,this._featureFilter={filter:function(){return!0},needGeometry:!1},\"custom\"!==e.type&&(e=e,this.metadata=e.metadata,this.minzoom=e.minzoom,this.maxzoom=e.maxzoom,\"background\"!==e.type&&(this.source=e.source,this.sourceLayer=e[\"source-layer\"],this.filter=e.filter),r.layout&&(this._unevaluatedLayout=new xi(r.layout)),r.paint)){for(var n in this._transitionablePaint=new gi(r.paint),e.paint)this.setPaintProperty(n,e.paint[n],{validate:!1});for(var i in e.layout)this.setLayoutProperty(i,e.layout[i],{validate:!1});this._transitioningPaint=this._transitionablePaint.untransitioned(),this.paint=new _i(r.paint)}}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getCrossfadeParameters=function(){return this._crossfadeParameters},e.prototype.getLayoutProperty=function(t){return\"visibility\"===t?this.visibility:this._unevaluatedLayout.getValue(t)},e.prototype.setLayoutProperty=function(t,e,r){if(void 0===r&&(r={}),null!=e){var n=\"layers.\"+this.id+\".layout.\"+t;if(this._validate(In,n,t,e,r))return}\"visibility\"!==t?this._unevaluatedLayout.setValue(t,e):this.visibility=e},e.prototype.getPaintProperty=function(t){return g(t,\"-transition\")?this._transitionablePaint.getTransition(t.slice(0,-\"-transition\".length)):this._transitionablePaint.getValue(t)},e.prototype.setPaintProperty=function(t,e,r){if(void 0===r&&(r={}),null!=e){var n=\"layers.\"+this.id+\".paint.\"+t;if(this._validate(Pn,n,t,e,r))return!1}if(g(t,\"-transition\"))return this._transitionablePaint.setTransition(t.slice(0,-\"-transition\".length),e||void 0),!1;var i=this._transitionablePaint._values[t],a=\"cross-faded-data-driven\"===i.property.specification[\"property-type\"],o=i.value.isDataDriven(),s=i.value;this._transitionablePaint.setValue(t,e),this._handleSpecialPaintPropertyUpdate(t);var l=this._transitionablePaint._values[t].value;return l.isDataDriven()||o||a||this._handleOverridablePaintPropertyUpdate(t,s,l)},e.prototype._handleSpecialPaintPropertyUpdate=function(t){},e.prototype._handleOverridablePaintPropertyUpdate=function(t,e,r){return!1},e.prototype.isHidden=function(t){return!!(this.minzoom&&t<this.minzoom)||(!!(this.maxzoom&&t>=this.maxzoom)||\"none\"===this.visibility)},e.prototype.updateTransitions=function(t){this._transitioningPaint=this._transitionablePaint.transitioned(t,this._transitioningPaint)},e.prototype.hasTransition=function(){return this._transitioningPaint.hasTransition()},e.prototype.recalculate=function(t,e){t.getCrossfadeParameters&&(this._crossfadeParameters=t.getCrossfadeParameters()),this._unevaluatedLayout&&(this.layout=this._unevaluatedLayout.possiblyEvaluate(t,void 0,e)),this.paint=this._transitioningPaint.possiblyEvaluate(t,void 0,e)},e.prototype.serialize=function(){var t={id:this.id,type:this.type,source:this.source,\"source-layer\":this.sourceLayer,metadata:this.metadata,minzoom:this.minzoom,maxzoom:this.maxzoom,filter:this.filter,layout:this._unevaluatedLayout&&this._unevaluatedLayout.serialize(),paint:this._transitionablePaint&&this._transitionablePaint.serialize()};return this.visibility&&(t.layout=t.layout||{},t.layout.visibility=this.visibility),y(t,(function(t,e){return!(void 0===t||\"layout\"===e&&!Object.keys(t).length||\"paint\"===e&&!Object.keys(t).length)}))},e.prototype._validate=function(t,e,r,n,i){return void 0===i&&(i={}),(!i||!1!==i.validate)&&On(this,t.call(Ln,{key:e,layerType:this.type,objectKey:r,value:n,styleSpec:Lt,style:{glyphs:!0,sprite:!0}}))},e.prototype.is3D=function(){return!1},e.prototype.isTileClipped=function(){return!1},e.prototype.hasOffscreenPass=function(){return!1},e.prototype.resize=function(){},e.prototype.isStateDependent=function(){for(var t in this.paint._values){var e=this.paint.get(t);if(e instanceof bi&&zr(e.property.specification)&&((\"source\"===e.value.kind||\"composite\"===e.value.kind)&&e.value.isStateDependent))return!0}return!1},e}(Et),Li={Int8:Int8Array,Uint8:Uint8Array,Int16:Int16Array,Uint16:Uint16Array,Int32:Int32Array,Uint32:Uint32Array,Float32:Float32Array},Ci=function(t,e){this._structArray=t,this._pos1=e*this.size,this._pos2=this._pos1/2,this._pos4=this._pos1/4,this._pos8=this._pos1/8},Pi=function(){this.isTransferred=!1,this.capacity=-1,this.resize(0)};function Ii(t,e){void 0===e&&(e=1);var r=0,n=0;return{members:t.map((function(t){var i,a=(i=t.type,Li[i].BYTES_PER_ELEMENT),o=r=Oi(r,Math.max(e,a)),s=t.components||1;return n=Math.max(n,a),r+=a*s,{name:t.name,type:t.type,components:s,offset:o}})),size:Oi(r,Math.max(n,e)),alignment:e}}function Oi(t,e){return Math.ceil(t/e)*e}Pi.serialize=function(t,e){return t._trim(),e&&(t.isTransferred=!0,e.push(t.arrayBuffer)),{length:t.length,arrayBuffer:t.arrayBuffer}},Pi.deserialize=function(t){var e=Object.create(this.prototype);return e.arrayBuffer=t.arrayBuffer,e.length=t.length,e.capacity=t.arrayBuffer.byteLength/e.bytesPerElement,e._refreshViews(),e},Pi.prototype._trim=function(){this.length!==this.capacity&&(this.capacity=this.length,this.arrayBuffer=this.arrayBuffer.slice(0,this.length*this.bytesPerElement),this._refreshViews())},Pi.prototype.clear=function(){this.length=0},Pi.prototype.resize=function(t){this.reserve(t),this.length=t},Pi.prototype.reserve=function(t){if(t>this.capacity){this.capacity=Math.max(t,Math.floor(5*this.capacity),128),this.arrayBuffer=new ArrayBuffer(this.capacity*this.bytesPerElement);var e=this.uint8;this._refreshViews(),e&&this.uint8.set(e)}},Pi.prototype._refreshViews=function(){throw new Error(\"_refreshViews() must be implemented by each concrete StructArray layout\")};var zi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;return this.resize(r+1),this.emplace(r,t,e)},e.prototype.emplace=function(t,e,r){var n=2*t;return this.int16[n+0]=e,this.int16[n+1]=r,t},e}(Pi);zi.prototype.bytesPerElement=4,Nn(\"StructArrayLayout2i4\",zi);var Di=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n){var i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)},e.prototype.emplace=function(t,e,r,n,i){var a=4*t;return this.int16[a+0]=e,this.int16[a+1]=r,this.int16[a+2]=n,this.int16[a+3]=i,t},e}(Pi);Di.prototype.bytesPerElement=8,Nn(\"StructArrayLayout4i8\",Di);var Ri=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)},e.prototype.emplace=function(t,e,r,n,i,a,o){var s=6*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.int16[s+2]=n,this.int16[s+3]=i,this.int16[s+4]=a,this.int16[s+5]=o,t},e}(Pi);Ri.prototype.bytesPerElement=12,Nn(\"StructArrayLayout2i4i12\",Ri);var Fi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)},e.prototype.emplace=function(t,e,r,n,i,a,o){var s=4*t,l=8*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.uint8[l+4]=n,this.uint8[l+5]=i,this.uint8[l+6]=a,this.uint8[l+7]=o,t},e}(Pi);Fi.prototype.bytesPerElement=8,Nn(\"StructArrayLayout2i4ub8\",Fi);var Bi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c){var u=this.length;return this.resize(u+1),this.emplace(u,t,e,r,n,i,a,o,s,l,c)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u){var f=9*t,h=18*t;return this.uint16[f+0]=e,this.uint16[f+1]=r,this.uint16[f+2]=n,this.uint16[f+3]=i,this.uint16[f+4]=a,this.uint16[f+5]=o,this.uint16[f+6]=s,this.uint16[f+7]=l,this.uint8[h+16]=c,this.uint8[h+17]=u,t},e}(Pi);Bi.prototype.bytesPerElement=18,Nn(\"StructArrayLayout8ui2ub18\",Bi);var Ni=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c,u,f){var h=this.length;return this.resize(h+1),this.emplace(h,t,e,r,n,i,a,o,s,l,c,u,f)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u,f,h){var p=12*t;return this.int16[p+0]=e,this.int16[p+1]=r,this.int16[p+2]=n,this.int16[p+3]=i,this.uint16[p+4]=a,this.uint16[p+5]=o,this.uint16[p+6]=s,this.uint16[p+7]=l,this.int16[p+8]=c,this.int16[p+9]=u,this.int16[p+10]=f,this.int16[p+11]=h,t},e}(Pi);Ni.prototype.bytesPerElement=24,Nn(\"StructArrayLayout4i4ui4i24\",Ni);var ji=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=3*t;return this.float32[i+0]=e,this.float32[i+1]=r,this.float32[i+2]=n,t},e}(Pi);ji.prototype.bytesPerElement=12,Nn(\"StructArrayLayout3f12\",ji);var Ui=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t){var e=this.length;return this.resize(e+1),this.emplace(e,t)},e.prototype.emplace=function(t,e){var r=1*t;return this.uint32[r+0]=e,t},e}(Pi);Ui.prototype.bytesPerElement=4,Nn(\"StructArrayLayout1ul4\",Ui);var Vi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l){var c=this.length;return this.resize(c+1),this.emplace(c,t,e,r,n,i,a,o,s,l)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c){var u=10*t,f=5*t;return this.int16[u+0]=e,this.int16[u+1]=r,this.int16[u+2]=n,this.int16[u+3]=i,this.int16[u+4]=a,this.int16[u+5]=o,this.uint32[f+3]=s,this.uint16[u+8]=l,this.uint16[u+9]=c,t},e}(Pi);Vi.prototype.bytesPerElement=20,Nn(\"StructArrayLayout6i1ul2ui20\",Vi);var qi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)},e.prototype.emplace=function(t,e,r,n,i,a,o){var s=6*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.int16[s+2]=n,this.int16[s+3]=i,this.int16[s+4]=a,this.int16[s+5]=o,t},e}(Pi);qi.prototype.bytesPerElement=12,Nn(\"StructArrayLayout2i2i2i12\",qi);var Hi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i){var a=this.length;return this.resize(a+1),this.emplace(a,t,e,r,n,i)},e.prototype.emplace=function(t,e,r,n,i,a){var o=4*t,s=8*t;return this.float32[o+0]=e,this.float32[o+1]=r,this.float32[o+2]=n,this.int16[s+6]=i,this.int16[s+7]=a,t},e}(Pi);Hi.prototype.bytesPerElement=16,Nn(\"StructArrayLayout2f1f2i16\",Hi);var Gi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n){var i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)},e.prototype.emplace=function(t,e,r,n,i){var a=12*t,o=3*t;return this.uint8[a+0]=e,this.uint8[a+1]=r,this.float32[o+1]=n,this.float32[o+2]=i,t},e}(Pi);Gi.prototype.bytesPerElement=12,Nn(\"StructArrayLayout2ub2f12\",Gi);var Yi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=3*t;return this.uint16[i+0]=e,this.uint16[i+1]=r,this.uint16[i+2]=n,t},e}(Pi);Yi.prototype.bytesPerElement=6,Nn(\"StructArrayLayout3ui6\",Yi);var Wi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g){var v=this.length;return this.resize(v+1),this.emplace(v,t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g,v){var y=24*t,x=12*t,b=48*t;return this.int16[y+0]=e,this.int16[y+1]=r,this.uint16[y+2]=n,this.uint16[y+3]=i,this.uint32[x+2]=a,this.uint32[x+3]=o,this.uint32[x+4]=s,this.uint16[y+10]=l,this.uint16[y+11]=c,this.uint16[y+12]=u,this.float32[x+7]=f,this.float32[x+8]=h,this.uint8[b+36]=p,this.uint8[b+37]=d,this.uint8[b+38]=m,this.uint32[x+10]=g,this.int16[y+22]=v,t},e}(Pi);Wi.prototype.bytesPerElement=48,Nn(\"StructArrayLayout2i2ui3ul3ui2f3ub1ul1i48\",Wi);var Xi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g,v,y,x,b,_,w,T,k,M,A,S){var E=this.length;return this.resize(E+1),this.emplace(E,t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g,v,y,x,b,_,w,T,k,M,A,S)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g,v,y,x,b,_,w,T,k,M,A,S,E){var L=34*t,C=17*t;return this.int16[L+0]=e,this.int16[L+1]=r,this.int16[L+2]=n,this.int16[L+3]=i,this.int16[L+4]=a,this.int16[L+5]=o,this.int16[L+6]=s,this.int16[L+7]=l,this.uint16[L+8]=c,this.uint16[L+9]=u,this.uint16[L+10]=f,this.uint16[L+11]=h,this.uint16[L+12]=p,this.uint16[L+13]=d,this.uint16[L+14]=m,this.uint16[L+15]=g,this.uint16[L+16]=v,this.uint16[L+17]=y,this.uint16[L+18]=x,this.uint16[L+19]=b,this.uint16[L+20]=_,this.uint16[L+21]=w,this.uint16[L+22]=T,this.uint32[C+12]=k,this.float32[C+13]=M,this.float32[C+14]=A,this.float32[C+15]=S,this.float32[C+16]=E,t},e}(Pi);Xi.prototype.bytesPerElement=68,Nn(\"StructArrayLayout8i15ui1ul4f68\",Xi);var Zi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t){var e=this.length;return this.resize(e+1),this.emplace(e,t)},e.prototype.emplace=function(t,e){var r=1*t;return this.float32[r+0]=e,t},e}(Pi);Zi.prototype.bytesPerElement=4,Nn(\"StructArrayLayout1f4\",Zi);var Ji=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=3*t;return this.int16[i+0]=e,this.int16[i+1]=r,this.int16[i+2]=n,t},e}(Pi);Ji.prototype.bytesPerElement=6,Nn(\"StructArrayLayout3i6\",Ji);var Ki=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=2*t,a=4*t;return this.uint32[i+0]=e,this.uint16[a+2]=r,this.uint16[a+3]=n,t},e}(Pi);Ki.prototype.bytesPerElement=8,Nn(\"StructArrayLayout1ul2ui8\",Ki);var Qi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;return this.resize(r+1),this.emplace(r,t,e)},e.prototype.emplace=function(t,e,r){var n=2*t;return this.uint16[n+0]=e,this.uint16[n+1]=r,t},e}(Pi);Qi.prototype.bytesPerElement=4,Nn(\"StructArrayLayout2ui4\",Qi);var $i=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t){var e=this.length;return this.resize(e+1),this.emplace(e,t)},e.prototype.emplace=function(t,e){var r=1*t;return this.uint16[r+0]=e,t},e}(Pi);$i.prototype.bytesPerElement=2,Nn(\"StructArrayLayout1ui2\",$i);var ta=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;return this.resize(r+1),this.emplace(r,t,e)},e.prototype.emplace=function(t,e,r){var n=2*t;return this.float32[n+0]=e,this.float32[n+1]=r,t},e}(Pi);ta.prototype.bytesPerElement=8,Nn(\"StructArrayLayout2f8\",ta);var ea=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n){var i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)},e.prototype.emplace=function(t,e,r,n,i){var a=4*t;return this.float32[a+0]=e,this.float32[a+1]=r,this.float32[a+2]=n,this.float32[a+3]=i,t},e}(Pi);ea.prototype.bytesPerElement=16,Nn(\"StructArrayLayout4f16\",ea);var ra=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={anchorPointX:{configurable:!0},anchorPointY:{configurable:!0},x1:{configurable:!0},y1:{configurable:!0},x2:{configurable:!0},y2:{configurable:!0},featureIndex:{configurable:!0},sourceLayerIndex:{configurable:!0},bucketIndex:{configurable:!0},anchorPoint:{configurable:!0}};return r.anchorPointX.get=function(){return this._structArray.int16[this._pos2+0]},r.anchorPointY.get=function(){return this._structArray.int16[this._pos2+1]},r.x1.get=function(){return this._structArray.int16[this._pos2+2]},r.y1.get=function(){return this._structArray.int16[this._pos2+3]},r.x2.get=function(){return this._structArray.int16[this._pos2+4]},r.y2.get=function(){return this._structArray.int16[this._pos2+5]},r.featureIndex.get=function(){return this._structArray.uint32[this._pos4+3]},r.sourceLayerIndex.get=function(){return this._structArray.uint16[this._pos2+8]},r.bucketIndex.get=function(){return this._structArray.uint16[this._pos2+9]},r.anchorPoint.get=function(){return new i(this.anchorPointX,this.anchorPointY)},Object.defineProperties(e.prototype,r),e}(Ci);ra.prototype.size=20;var na=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.get=function(t){return new ra(this,t)},e}(Vi);Nn(\"CollisionBoxArray\",na);var ia=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={anchorX:{configurable:!0},anchorY:{configurable:!0},glyphStartIndex:{configurable:!0},numGlyphs:{configurable:!0},vertexStartIndex:{configurable:!0},lineStartIndex:{configurable:!0},lineLength:{configurable:!0},segment:{configurable:!0},lowerSize:{configurable:!0},upperSize:{configurable:!0},lineOffsetX:{configurable:!0},lineOffsetY:{configurable:!0},writingMode:{configurable:!0},placedOrientation:{configurable:!0},hidden:{configurable:!0},crossTileID:{configurable:!0},associatedIconIndex:{configurable:!0}};return r.anchorX.get=function(){return this._structArray.int16[this._pos2+0]},r.anchorY.get=function(){return this._structArray.int16[this._pos2+1]},r.glyphStartIndex.get=function(){return this._structArray.uint16[this._pos2+2]},r.numGlyphs.get=function(){return this._structArray.uint16[this._pos2+3]},r.vertexStartIndex.get=function(){return this._structArray.uint32[this._pos4+2]},r.lineStartIndex.get=function(){return this._structArray.uint32[this._pos4+3]},r.lineLength.get=function(){return this._structArray.uint32[this._pos4+4]},r.segment.get=function(){return this._structArray.uint16[this._pos2+10]},r.lowerSize.get=function(){return this._structArray.uint16[this._pos2+11]},r.upperSize.get=function(){return this._structArray.uint16[this._pos2+12]},r.lineOffsetX.get=function(){return this._structArray.float32[this._pos4+7]},r.lineOffsetY.get=function(){return this._structArray.float32[this._pos4+8]},r.writingMode.get=function(){return this._structArray.uint8[this._pos1+36]},r.placedOrientation.get=function(){return this._structArray.uint8[this._pos1+37]},r.placedOrientation.set=function(t){this._structArray.uint8[this._pos1+37]=t},r.hidden.get=function(){return this._structArray.uint8[this._pos1+38]},r.hidden.set=function(t){this._structArray.uint8[this._pos1+38]=t},r.crossTileID.get=function(){return this._structArray.uint32[this._pos4+10]},r.crossTileID.set=function(t){this._structArray.uint32[this._pos4+10]=t},r.associatedIconIndex.get=function(){return this._structArray.int16[this._pos2+22]},Object.defineProperties(e.prototype,r),e}(Ci);ia.prototype.size=48;var aa=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.get=function(t){return new ia(this,t)},e}(Wi);Nn(\"PlacedSymbolArray\",aa);var oa=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={anchorX:{configurable:!0},anchorY:{configurable:!0},rightJustifiedTextSymbolIndex:{configurable:!0},centerJustifiedTextSymbolIndex:{configurable:!0},leftJustifiedTextSymbolIndex:{configurable:!0},verticalPlacedTextSymbolIndex:{configurable:!0},placedIconSymbolIndex:{configurable:!0},verticalPlacedIconSymbolIndex:{configurable:!0},key:{configurable:!0},textBoxStartIndex:{configurable:!0},textBoxEndIndex:{configurable:!0},verticalTextBoxStartIndex:{configurable:!0},verticalTextBoxEndIndex:{configurable:!0},iconBoxStartIndex:{configurable:!0},iconBoxEndIndex:{configurable:!0},verticalIconBoxStartIndex:{configurable:!0},verticalIconBoxEndIndex:{configurable:!0},featureIndex:{configurable:!0},numHorizontalGlyphVertices:{configurable:!0},numVerticalGlyphVertices:{configurable:!0},numIconVertices:{configurable:!0},numVerticalIconVertices:{configurable:!0},useRuntimeCollisionCircles:{configurable:!0},crossTileID:{configurable:!0},textBoxScale:{configurable:!0},textOffset0:{configurable:!0},textOffset1:{configurable:!0},collisionCircleDiameter:{configurable:!0}};return r.anchorX.get=function(){return this._structArray.int16[this._pos2+0]},r.anchorY.get=function(){return this._structArray.int16[this._pos2+1]},r.rightJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+2]},r.centerJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+3]},r.leftJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+4]},r.verticalPlacedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+5]},r.placedIconSymbolIndex.get=function(){return this._structArray.int16[this._pos2+6]},r.verticalPlacedIconSymbolIndex.get=function(){return this._structArray.int16[this._pos2+7]},r.key.get=function(){return this._structArray.uint16[this._pos2+8]},r.textBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+9]},r.textBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+10]},r.verticalTextBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+11]},r.verticalTextBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+12]},r.iconBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+13]},r.iconBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+14]},r.verticalIconBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+15]},r.verticalIconBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+16]},r.featureIndex.get=function(){return this._structArray.uint16[this._pos2+17]},r.numHorizontalGlyphVertices.get=function(){return this._structArray.uint16[this._pos2+18]},r.numVerticalGlyphVertices.get=function(){return this._structArray.uint16[this._pos2+19]},r.numIconVertices.get=function(){return this._structArray.uint16[this._pos2+20]},r.numVerticalIconVertices.get=function(){return this._structArray.uint16[this._pos2+21]},r.useRuntimeCollisionCircles.get=function(){return 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c=t.readVarint();n=7&c,a=c>>3}if(a--,1===n||2===n)o+=t.readSVarint(),s+=t.readSVarint(),1===n&&(e&&l.push(e),e=[]),e.push(new i(o,s));else{if(7!==n)throw new Error(\"unknown command \"+n);e&&e.push(e[0].clone())}}return e&&l.push(e),l},ws.prototype.bbox=function(){var t=this._pbf;t.pos=this._geometry;for(var e=t.readVarint()+t.pos,r=1,n=0,i=0,a=0,o=1/0,s=-1/0,l=1/0,c=-1/0;t.pos<e;){if(n<=0){var u=t.readVarint();r=7&u,n=u>>3}if(n--,1===r||2===r)(i+=t.readSVarint())<o&&(o=i),i>s&&(s=i),(a+=t.readSVarint())<l&&(l=a),a>c&&(c=a);else if(7!==r)throw new Error(\"unknown command \"+r)}return[o,l,s,c]},ws.prototype.toGeoJSON=function(t,e,r){var n,i,a=this.extent*Math.pow(2,r),o=this.extent*t,s=this.extent*e,l=this.loadGeometry(),c=ws.types[this.type];function u(t){for(var e=0;e<t.length;e++){var r=t[e],n=180-360*(r.y+s)/a;t[e]=[360*(r.x+o)/a-180,360/Math.PI*Math.atan(Math.exp(n*Math.PI/180))-90]}}switch(this.type){case 1:var f=[];for(n=0;n<l.length;n++)f[n]=l[n][0];u(l=f);break;case 2:for(n=0;n<l.length;n++)u(l[n]);break;case 3:for(l=function(t){var e=t.length;if(e<=1)return[t];for(var r,n,i=[],a=0;a<e;a++){var o=ks(t[a]);0!==o&&(void 0===n&&(n=o<0),n===o<0?(r&&i.push(r),r=[t[a]]):r.push(t[a]))}r&&i.push(r);return i}(l),n=0;n<l.length;n++)for(i=0;i<l[n].length;i++)u(l[n][i])}1===l.length?l=l[0]:c=\"Multi\"+c;var h={type:\"Feature\",geometry:{type:c,coordinates:l},properties:this.properties};return\"id\"in this&&(h.id=this.id),h};var Ms=As;function As(t,e){this.version=1,this.name=null,this.extent=4096,this.length=0,this._pbf=t,this._keys=[],this._values=[],this._features=[],t.readFields(Ss,this,e),this.length=this._features.length}function Ss(t,e,r){15===t?e.version=r.readVarint():1===t?e.name=r.readString():5===t?e.extent=r.readVarint():2===t?e._features.push(r.pos):3===t?e._keys.push(r.readString()):4===t&&e._values.push(function(t){var e=null,r=t.readVarint()+t.pos;for(;t.pos<r;){var n=t.readVarint()>>3;e=1===n?t.readString():2===n?t.readFloat():3===n?t.readDouble():4===n?t.readVarint64():5===n?t.readVarint():6===n?t.readSVarint():7===n?t.readBoolean():null}return e}(r))}function Es(t,e,r){if(3===t){var n=new Ms(r,r.readVarint()+r.pos);n.length&&(e[n.name]=n)}}As.prototype.feature=function(t){if(t<0||t>=this._features.length)throw new Error(\"feature index out of bounds\");this._pbf.pos=this._features[t];var e=this._pbf.readVarint()+this._pbf.pos;return new _s(this._pbf,e,this.extent,this._keys,this._values)};var Ls={VectorTile:function(t,e){this.layers=t.readFields(Es,{},e)},VectorTileFeature:_s,VectorTileLayer:Ms},Cs=Ls.VectorTileFeature.types,Ps=Math.pow(2,13);function Is(t,e,r,n,i,a,o,s){t.emplaceBack(e,r,2*Math.floor(n*Ps)+o,i*Ps*2,a*Ps*2,Math.round(s))}var Os=function(t){this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.hasPattern=!1,this.layoutVertexArray=new Ri,this.indexArray=new Yi,this.programConfigurations=new Ua(bs,t.layers,t.zoom),this.segments=new pa,this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id}))};function zs(t,e){return t.x===e.x&&(t.x<0||t.x>8192)||t.y===e.y&&(t.y<0||t.y>8192)}function Ds(t){return t.every((function(t){return t.x<0}))||t.every((function(t){return t.x>8192}))||t.every((function(t){return t.y<0}))||t.every((function(t){return t.y>8192}))}Os.prototype.populate=function(t,e,r){this.features=[],this.hasPattern=ds(\"fill-extrusion\",this.layers,e);for(var n=0,i=t;n<i.length;n+=1){var a=i[n],o=a.feature,s=a.id,l=a.index,c=a.sourceLayerIndex,u=this.layers[0]._featureFilter.needGeometry,f={type:o.type,id:s,properties:o.properties,geometry:u?Ya(o):[]};if(this.layers[0]._featureFilter.filter(new pi(this.zoom),f,r)){var h={id:s,sourceLayerIndex:c,index:l,geometry:u?f.geometry:Ya(o),properties:o.properties,type:o.type,patterns:{}};void 0!==o.id&&(h.id=o.id),this.hasPattern?this.features.push(ms(\"fill-extrusion\",this.layers,h,this.zoom,e)):this.addFeature(h,h.geometry,l,r,{}),e.featureIndex.insert(o,h.geometry,l,c,this.index,!0)}}},Os.prototype.addFeatures=function(t,e,r){for(var n=0,i=this.features;n<i.length;n+=1){var a=i[n],o=a.geometry;this.addFeature(a,o,a.index,e,r)}},Os.prototype.update=function(t,e,r){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(t,e,this.stateDependentLayers,r)},Os.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length},Os.prototype.uploadPending=function(){return!this.uploaded||this.programConfigurations.needsUpload},Os.prototype.upload=function(t){this.uploaded||(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,bs),this.indexBuffer=t.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(t),this.uploaded=!0},Os.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())},Os.prototype.addFeature=function(t,e,r,n,i){for(var a=0,o=hs(e,500);a<o.length;a+=1){for(var s=o[a],l=0,c=0,u=s;c<u.length;c+=1){l+=u[c].length}for(var f=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray),h=0,p=s;h<p.length;h+=1){var d=p[h];if(0!==d.length&&!Ds(d))for(var m=0,g=0;g<d.length;g++){var v=d[g];if(g>=1){var y=d[g-1];if(!zs(v,y)){f.vertexLength+4>pa.MAX_VERTEX_ARRAY_LENGTH&&(f=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray));var x=v.sub(y)._perp()._unit(),b=y.dist(v);m+b>32768&&(m=0),Is(this.layoutVertexArray,v.x,v.y,x.x,x.y,0,0,m),Is(this.layoutVertexArray,v.x,v.y,x.x,x.y,0,1,m),m+=b,Is(this.layoutVertexArray,y.x,y.y,x.x,x.y,0,0,m),Is(this.layoutVertexArray,y.x,y.y,x.x,x.y,0,1,m);var _=f.vertexLength;this.indexArray.emplaceBack(_,_+2,_+1),this.indexArray.emplaceBack(_+1,_+2,_+3),f.vertexLength+=4,f.primitiveLength+=2}}}}if(f.vertexLength+l>pa.MAX_VERTEX_ARRAY_LENGTH&&(f=this.segments.prepareSegment(l,this.layoutVertexArray,this.indexArray)),\"Polygon\"===Cs[t.type]){for(var w=[],T=[],k=f.vertexLength,M=0,A=s;M<A.length;M+=1){var S=A[M];if(0!==S.length){S!==s[0]&&T.push(w.length/2);for(var E=0;E<S.length;E++){var L=S[E];Is(this.layoutVertexArray,L.x,L.y,0,0,1,1,0),w.push(L.x),w.push(L.y)}}}for(var C=Do(w,T),P=0;P<C.length;P+=3)this.indexArray.emplaceBack(k+C[P],k+C[P+2],k+C[P+1]);f.primitiveLength+=C.length/3,f.vertexLength+=l}}this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,t,r,i,n)},Nn(\"FillExtrusionBucket\",Os,{omit:[\"layers\",\"features\"]});var Rs={paint:new Si({\"fill-extrusion-opacity\":new wi(Lt[\"paint_fill-extrusion\"][\"fill-extrusion-opacity\"]),\"fill-extrusion-color\":new Ti(Lt[\"paint_fill-extrusion\"][\"fill-extrusion-color\"]),\"fill-extrusion-translate\":new wi(Lt[\"paint_fill-extrusion\"][\"fill-extrusion-translate\"]),\"fill-extrusion-translate-anchor\":new wi(Lt[\"paint_fill-extrusion\"][\"fill-extrusion-translate-anchor\"]),\"fill-extrusion-pattern\":new ki(Lt[\"paint_fill-extrusion\"][\"fill-extrusion-pattern\"]),\"fill-extrusion-height\":new Ti(Lt[\"paint_fill-extrusion\"][\"fill-extrusion-height\"]),\"fill-extrusion-base\":new Ti(Lt[\"paint_fill-extrusion\"][\"fill-extrusion-base\"]),\"fill-extrusion-vertical-gradient\":new wi(Lt[\"paint_fill-extrusion\"][\"fill-extrusion-vertical-gradient\"])})},Fs=function(t){function e(e){t.call(this,e,Rs)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.createBucket=function(t){return new Os(t)},e.prototype.queryRadius=function(){return so(this.paint.get(\"fill-extrusion-translate\"))},e.prototype.is3D=function(){return!0},e.prototype.queryIntersectsFeature=function(t,e,r,n,a,o,s,l){var c=lo(t,this.paint.get(\"fill-extrusion-translate\"),this.paint.get(\"fill-extrusion-translate-anchor\"),o.angle,s),u=this.paint.get(\"fill-extrusion-height\").evaluate(e,r),f=this.paint.get(\"fill-extrusion-base\").evaluate(e,r),h=function(t,e,r,n){for(var a=[],o=0,s=t;o<s.length;o+=1){var l=s[o],c=[l.x,l.y,n,1];xo(c,c,e),a.push(new i(c[0]/c[3],c[1]/c[3]))}return a}(c,l,0,0),p=function(t,e,r,n){for(var a=[],o=[],s=n[8]*e,l=n[9]*e,c=n[10]*e,u=n[11]*e,f=n[8]*r,h=n[9]*r,p=n[10]*r,d=n[11]*r,m=0,g=t;m<g.length;m+=1){for(var v=g[m],y=[],x=[],b=0,_=v;b<_.length;b+=1){var w=_[b],T=w.x,k=w.y,M=n[0]*T+n[4]*k+n[12],A=n[1]*T+n[5]*k+n[13],S=n[2]*T+n[6]*k+n[14],E=n[3]*T+n[7]*k+n[15],L=S+c,C=E+u,P=M+f,I=A+h,O=S+p,z=E+d,D=new i((M+s)/C,(A+l)/C);D.z=L/C,y.push(D);var R=new i(P/z,I/z);R.z=O/z,x.push(R)}a.push(y),o.push(x)}return[a,o]}(n,f,u,l);return function(t,e,r){var n=1/0;Ka(r,e)&&(n=Ns(r,e[0]));for(var i=0;i<e.length;i++)for(var a=e[i],o=t[i],s=0;s<a.length-1;s++){var l=a[s],c=a[s+1],u=o[s],f=o[s+1],h=[l,c,f,u,l];Za(r,h)&&(n=Math.min(n,Ns(r,h)))}return n!==1/0&&n}(p[0],p[1],h)},e}(Ei);function Bs(t,e){return t.x*e.x+t.y*e.y}function Ns(t,e){if(1===t.length){for(var r,n=0,i=e[n++];!r||i.equals(r);)if(!(r=e[n++]))return 1/0;for(;n<e.length;n++){var a=e[n],o=t[0],s=r.sub(i),l=a.sub(i),c=o.sub(i),u=Bs(s,s),f=Bs(s,l),h=Bs(l,l),p=Bs(c,s),d=Bs(c,l),m=u*h-f*f,g=(h*p-f*d)/m,v=(u*d-f*p)/m,y=1-g-v,x=i.z*y+r.z*g+a.z*v;if(isFinite(x))return x}return 1/0}for(var b=1/0,_=0,w=e;_<w.length;_+=1){var T=w[_];b=Math.min(b,T.z)}return b}var js=Ii([{name:\"a_pos_normal\",components:2,type:\"Int16\"},{name:\"a_data\",components:4,type:\"Uint8\"}],4).members,Us=Ls.VectorTileFeature.types,Vs=Math.cos(Math.PI/180*37.5),qs=Math.pow(2,14)/.5,Hs=function(t){this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.hasPattern=!1,this.patternFeatures=[],this.layoutVertexArray=new Fi,this.indexArray=new Yi,this.programConfigurations=new Ua(js,t.layers,t.zoom),this.segments=new pa,this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id}))};Hs.prototype.populate=function(t,e,r){this.hasPattern=ds(\"line\",this.layers,e);for(var n=this.layers[0].layout.get(\"line-sort-key\"),i=[],a=0,o=t;a<o.length;a+=1){var s=o[a],l=s.feature,c=s.id,u=s.index,f=s.sourceLayerIndex,h=this.layers[0]._featureFilter.needGeometry,p={type:l.type,id:c,properties:l.properties,geometry:h?Ya(l):[]};if(this.layers[0]._featureFilter.filter(new pi(this.zoom),p,r)){h||(p.geometry=Ya(l));var d=n?n.evaluate(p,{},r):void 0,m={id:c,properties:l.properties,type:l.type,sourceLayerIndex:f,index:u,geometry:p.geometry,patterns:{},sortKey:d};i.push(m)}}n&&i.sort((function(t,e){return t.sortKey-e.sortKey}));for(var g=0,v=i;g<v.length;g+=1){var y=v[g],x=y,b=x.geometry,_=x.index,w=x.sourceLayerIndex;if(this.hasPattern){var T=ms(\"line\",this.layers,y,this.zoom,e);this.patternFeatures.push(T)}else this.addFeature(y,b,_,r,{});var k=t[_].feature;e.featureIndex.insert(k,b,_,w,this.index)}},Hs.prototype.update=function(t,e,r){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(t,e,this.stateDependentLayers,r)},Hs.prototype.addFeatures=function(t,e,r){for(var n=0,i=this.patternFeatures;n<i.length;n+=1){var a=i[n];this.addFeature(a,a.geometry,a.index,e,r)}},Hs.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length},Hs.prototype.uploadPending=function(){return!this.uploaded||this.programConfigurations.needsUpload},Hs.prototype.upload=function(t){this.uploaded||(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,js),this.indexBuffer=t.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(t),this.uploaded=!0},Hs.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())},Hs.prototype.addFeature=function(t,e,r,n,i){for(var a=this.layers[0].layout,o=a.get(\"line-join\").evaluate(t,{}),s=a.get(\"line-cap\"),l=a.get(\"line-miter-limit\"),c=a.get(\"line-round-limit\"),u=0,f=e;u<f.length;u+=1){var h=f[u];this.addLine(h,t,o,s,l,c)}this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,t,r,i,n)},Hs.prototype.addLine=function(t,e,r,n,i,a){if(this.distance=0,this.scaledDistance=0,this.totalDistance=0,e.properties&&e.properties.hasOwnProperty(\"mapbox_clip_start\")&&e.properties.hasOwnProperty(\"mapbox_clip_end\")){this.clipStart=+e.properties.mapbox_clip_start,this.clipEnd=+e.properties.mapbox_clip_end;for(var o=0;o<t.length-1;o++)this.totalDistance+=t[o].dist(t[o+1]);this.updateScaledDistance()}for(var s=\"Polygon\"===Us[e.type],l=t.length;l>=2&&t[l-1].equals(t[l-2]);)l--;for(var c=0;c<l-1&&t[c].equals(t[c+1]);)c++;if(!(l<(s?3:2))){\"bevel\"===r&&(i=1.05);var u,f=this.overscaling<=16?122880/(512*this.overscaling):0,h=this.segments.prepareSegment(10*l,this.layoutVertexArray,this.indexArray),p=void 0,d=void 0,m=void 0,g=void 0;this.e1=this.e2=-1,s&&(u=t[l-2],g=t[c].sub(u)._unit()._perp());for(var v=c;v<l;v++)if(!(d=v===l-1?s?t[c+1]:void 0:t[v+1])||!t[v].equals(d)){g&&(m=g),u&&(p=u),u=t[v],g=d?d.sub(u)._unit()._perp():m;var y=(m=m||g).add(g);0===y.x&&0===y.y||y._unit();var x=m.x*g.x+m.y*g.y,b=y.x*g.x+y.y*g.y,_=0!==b?1/b:1/0,w=2*Math.sqrt(2-2*b),T=b<Vs&&p&&d,k=m.x*g.y-m.y*g.x>0;if(T&&v>c){var M=u.dist(p);if(M>2*f){var A=u.sub(u.sub(p)._mult(f/M)._round());this.updateDistance(p,A),this.addCurrentVertex(A,m,0,0,h),p=A}}var S=p&&d,E=S?r:s?\"butt\":n;if(S&&\"round\"===E&&(_<a?E=\"miter\":_<=2&&(E=\"fakeround\")),\"miter\"===E&&_>i&&(E=\"bevel\"),\"bevel\"===E&&(_>2&&(E=\"flipbevel\"),_<i&&(E=\"miter\")),p&&this.updateDistance(p,u),\"miter\"===E)y._mult(_),this.addCurrentVertex(u,y,0,0,h);else if(\"flipbevel\"===E){if(_>100)y=g.mult(-1);else{var L=_*m.add(g).mag()/m.sub(g).mag();y._perp()._mult(L*(k?-1:1))}this.addCurrentVertex(u,y,0,0,h),this.addCurrentVertex(u,y.mult(-1),0,0,h)}else if(\"bevel\"===E||\"fakeround\"===E){var C=-Math.sqrt(_*_-1),P=k?C:0,I=k?0:C;if(p&&this.addCurrentVertex(u,m,P,I,h),\"fakeround\"===E)for(var O=Math.round(180*w/Math.PI/20),z=1;z<O;z++){var D=z/O;if(.5!==D){var R=D-.5;D+=D*R*(D-1)*((1.0904+x*(x*(3.55645-1.43519*x)-3.2452))*R*R+(.848013+x*(.215638*x-1.06021)))}var F=g.sub(m)._mult(D)._add(m)._unit()._mult(k?-1:1);this.addHalfVertex(u,F.x,F.y,!1,k,0,h)}d&&this.addCurrentVertex(u,g,-P,-I,h)}else if(\"butt\"===E)this.addCurrentVertex(u,y,0,0,h);else if(\"square\"===E){var B=p?1:-1;this.addCurrentVertex(u,y,B,B,h)}else\"round\"===E&&(p&&(this.addCurrentVertex(u,m,0,0,h),this.addCurrentVertex(u,m,1,1,h,!0)),d&&(this.addCurrentVertex(u,g,-1,-1,h,!0),this.addCurrentVertex(u,g,0,0,h)));if(T&&v<l-1){var N=u.dist(d);if(N>2*f){var j=u.add(d.sub(u)._mult(f/N)._round());this.updateDistance(u,j),this.addCurrentVertex(j,g,0,0,h),u=j}}}}},Hs.prototype.addCurrentVertex=function(t,e,r,n,i,a){void 0===a&&(a=!1);var o=e.x+e.y*r,s=e.y-e.x*r,l=-e.x+e.y*n,c=-e.y-e.x*n;this.addHalfVertex(t,o,s,a,!1,r,i),this.addHalfVertex(t,l,c,a,!0,-n,i),this.distance>qs/2&&0===this.totalDistance&&(this.distance=0,this.addCurrentVertex(t,e,r,n,i,a))},Hs.prototype.addHalfVertex=function(t,e,r,n,i,a,o){var s=t.x,l=t.y,c=.5*this.scaledDistance;this.layoutVertexArray.emplaceBack((s<<1)+(n?1:0),(l<<1)+(i?1:0),Math.round(63*e)+128,Math.round(63*r)+128,1+(0===a?0:a<0?-1:1)|(63&c)<<2,c>>6);var u=o.vertexLength++;this.e1>=0&&this.e2>=0&&(this.indexArray.emplaceBack(this.e1,this.e2,u),o.primitiveLength++),i?this.e2=u:this.e1=u},Hs.prototype.updateScaledDistance=function(){this.scaledDistance=this.totalDistance>0?(this.clipStart+(this.clipEnd-this.clipStart)*this.distance/this.totalDistance)*(qs-1):this.distance},Hs.prototype.updateDistance=function(t,e){this.distance+=t.dist(e),this.updateScaledDistance()},Nn(\"LineBucket\",Hs,{omit:[\"layers\",\"patternFeatures\"]});var Gs=new Si({\"line-cap\":new wi(Lt.layout_line[\"line-cap\"]),\"line-join\":new Ti(Lt.layout_line[\"line-join\"]),\"line-miter-limit\":new wi(Lt.layout_line[\"line-miter-limit\"]),\"line-round-limit\":new wi(Lt.layout_line[\"line-round-limit\"]),\"line-sort-key\":new Ti(Lt.layout_line[\"line-sort-key\"])}),Ys={paint:new Si({\"line-opacity\":new Ti(Lt.paint_line[\"line-opacity\"]),\"line-color\":new Ti(Lt.paint_line[\"line-color\"]),\"line-translate\":new wi(Lt.paint_line[\"line-translate\"]),\"line-translate-anchor\":new wi(Lt.paint_line[\"line-translate-anchor\"]),\"line-width\":new Ti(Lt.paint_line[\"line-width\"]),\"line-gap-width\":new Ti(Lt.paint_line[\"line-gap-width\"]),\"line-offset\":new Ti(Lt.paint_line[\"line-offset\"]),\"line-blur\":new Ti(Lt.paint_line[\"line-blur\"]),\"line-dasharray\":new Mi(Lt.paint_line[\"line-dasharray\"]),\"line-pattern\":new ki(Lt.paint_line[\"line-pattern\"]),\"line-gradient\":new Ai(Lt.paint_line[\"line-gradient\"])}),layout:Gs},Ws=new(function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.possiblyEvaluate=function(e,r){return r=new pi(Math.floor(r.zoom),{now:r.now,fadeDuration:r.fadeDuration,zoomHistory:r.zoomHistory,transition:r.transition}),t.prototype.possiblyEvaluate.call(this,e,r)},e.prototype.evaluate=function(e,r,n,i){return r=u({},r,{zoom:Math.floor(r.zoom)}),t.prototype.evaluate.call(this,e,r,n,i)},e}(Ti))(Ys.paint.properties[\"line-width\"].specification);Ws.useIntegerZoom=!0;var Xs=function(t){function e(e){t.call(this,e,Ys)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._handleSpecialPaintPropertyUpdate=function(t){\"line-gradient\"===t&&this._updateGradient()},e.prototype._updateGradient=function(){var t=this._transitionablePaint._values[\"line-gradient\"].value.expression;this.gradient=Co(t,\"lineProgress\"),this.gradientTexture=null},e.prototype.recalculate=function(e,r){t.prototype.recalculate.call(this,e,r),this.paint._values[\"line-floorwidth\"]=Ws.possiblyEvaluate(this._transitioningPaint._values[\"line-width\"].value,e)},e.prototype.createBucket=function(t){return new Hs(t)},e.prototype.queryRadius=function(t){var e=t,r=Zs(oo(\"line-width\",this,e),oo(\"line-gap-width\",this,e)),n=oo(\"line-offset\",this,e);return r/2+Math.abs(n)+so(this.paint.get(\"line-translate\"))},e.prototype.queryIntersectsFeature=function(t,e,r,n,a,o,s){var l=lo(t,this.paint.get(\"line-translate\"),this.paint.get(\"line-translate-anchor\"),o.angle,s),c=s/2*Zs(this.paint.get(\"line-width\").evaluate(e,r),this.paint.get(\"line-gap-width\").evaluate(e,r)),u=this.paint.get(\"line-offset\").evaluate(e,r);return u&&(n=function(t,e){for(var r=[],n=new i(0,0),a=0;a<t.length;a++){for(var o=t[a],s=[],l=0;l<o.length;l++){var c=o[l-1],u=o[l],f=o[l+1],h=0===l?n:u.sub(c)._unit()._perp(),p=l===o.length-1?n:f.sub(u)._unit()._perp(),d=h._add(p)._unit(),m=d.x*p.x+d.y*p.y;d._mult(1/m),s.push(d._mult(e)._add(u))}r.push(s)}return r}(n,u*s)),function(t,e,r){for(var n=0;n<e.length;n++){var i=e[n];if(t.length>=3)for(var a=0;a<i.length;a++)if(io(t,i[a]))return!0;if(Qa(t,i,r))return!0}return!1}(l,n,c)},e.prototype.isTileClipped=function(){return!0},e}(Ei);function Zs(t,e){return e>0?e+2*t:t}var 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el(t,e,r){return t.sections.forEach((function(t){t.text=function(t,e,r){var n=e.layout.get(\"text-transform\").evaluate(r,{});return\"uppercase\"===n?t=t.toLocaleUpperCase():\"lowercase\"===n&&(t=t.toLocaleLowerCase()),hi.applyArabicShaping&&(t=hi.applyArabicShaping(t)),t}(t.text,e,r)})),t}var 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nl=function(t,e,r,n,i){var a,o,s=8*i-n-1,l=(1<<s)-1,c=l>>1,u=-7,f=r?i-1:0,h=r?-1:1,p=t[e+f];for(f+=h,a=p&(1<<-u)-1,p>>=-u,u+=s;u>0;a=256*a+t[e+f],f+=h,u-=8);for(o=a&(1<<-u)-1,a>>=-u,u+=n;u>0;o=256*o+t[e+f],f+=h,u-=8);if(0===a)a=1-c;else{if(a===l)return o?NaN:1/0*(p?-1:1);o+=Math.pow(2,n),a-=c}return(p?-1:1)*o*Math.pow(2,a-n)},il=function(t,e,r,n,i,a){var o,s,l,c=8*a-i-1,u=(1<<c)-1,f=u>>1,h=23===i?Math.pow(2,-24)-Math.pow(2,-77):0,p=n?0:a-1,d=n?1:-1,m=e<0||0===e&&1/e<0?1:0;for(e=Math.abs(e),isNaN(e)||e===1/0?(s=isNaN(e)?1:0,o=u):(o=Math.floor(Math.log(e)/Math.LN2),e*(l=Math.pow(2,-o))<1&&(o--,l*=2),(e+=o+f>=1?h/l:h*Math.pow(2,1-f))*l>=2&&(o++,l/=2),o+f>=u?(s=0,o=u):o+f>=1?(s=(e*l-1)*Math.pow(2,i),o+=f):(s=e*Math.pow(2,f-1)*Math.pow(2,i),o=0));i>=8;t[r+p]=255&s,p+=d,s/=256,i-=8);for(o=o<<i|s,c+=i;c>0;t[r+p]=255&o,p+=d,o/=256,c-=8);t[r+p-d]|=128*m},al=ol;function ol(t){this.buf=ArrayBuffer.isView&&ArrayBuffer.isView(t)?t:new 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e=ll(this);for(t=t||[];this.pos<e;)t.push(this.readSFixed32());return t},readPackedFixed64:function(t){if(this.type!==ol.Bytes)return t.push(this.readFixed64());var e=ll(this);for(t=t||[];this.pos<e;)t.push(this.readFixed64());return t},readPackedSFixed64:function(t){if(this.type!==ol.Bytes)return t.push(this.readSFixed64());var e=ll(this);for(t=t||[];this.pos<e;)t.push(this.readSFixed64());return t},skip:function(t){var e=7&t;if(e===ol.Varint)for(;this.buf[this.pos++]>127;);else if(e===ol.Bytes)this.pos=this.readVarint()+this.pos;else if(e===ol.Fixed32)this.pos+=4;else{if(e!==ol.Fixed64)throw new Error(\"Unimplemented type: \"+e);this.pos+=8}},writeTag:function(t,e){this.writeVarint(t<<3|e)},realloc:function(t){for(var e=this.length||16;e<this.pos+t;)e*=2;if(e!==this.length){var r=new Uint8Array(e);r.set(this.buf),this.buf=r,this.length=e}},finish:function(){return 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r=(7&t)<<4;if(e.buf[e.pos++]|=r|((t>>>=3)?128:0),!t)return;if(e.buf[e.pos++]=127&t|((t>>>=7)?128:0),!t)return;if(e.buf[e.pos++]=127&t|((t>>>=7)?128:0),!t)return;if(e.buf[e.pos++]=127&t|((t>>>=7)?128:0),!t)return;if(e.buf[e.pos++]=127&t|((t>>>=7)?128:0),!t)return;e.buf[e.pos++]=127&t}(n,e)}(t,this):(this.realloc(4),this.buf[this.pos++]=127&t|(t>127?128:0),t<=127||(this.buf[this.pos++]=127&(t>>>=7)|(t>127?128:0),t<=127||(this.buf[this.pos++]=127&(t>>>=7)|(t>127?128:0),t<=127||(this.buf[this.pos++]=t>>>7&127))))},writeSVarint:function(t){this.writeVarint(t<0?2*-t-1:2*t)},writeBoolean:function(t){this.writeVarint(Boolean(t))},writeString:function(t){t=String(t),this.realloc(4*t.length),this.pos++;var e=this.pos;this.pos=function(t,e,r){for(var n,i,a=0;a<e.length;a++){if((n=e.charCodeAt(a))>55295&&n<57344){if(!i){n>56319||a+1===e.length?(t[r++]=239,t[r++]=191,t[r++]=189):i=n;continue}if(n<56320){t[r++]=239,t[r++]=191,t[r++]=189,i=n;continue}n=i-55296<<10|n-56320|65536,i=null}else i&&(t[r++]=239,t[r++]=191,t[r++]=189,i=null);n<128?t[r++]=n:(n<2048?t[r++]=n>>6|192:(n<65536?t[r++]=n>>12|224:(t[r++]=n>>18|240,t[r++]=n>>12&63|128),t[r++]=n>>6&63|128),t[r++]=63&n|128)}return r}(this.buf,t,this.pos);var r=this.pos-e;r>=128&&ul(e,r,this),this.pos=e-1,this.writeVarint(r),this.pos+=r},writeFloat:function(t){this.realloc(4),il(this.buf,t,this.pos,!0,23,4),this.pos+=4},writeDouble:function(t){this.realloc(8),il(this.buf,t,this.pos,!0,52,8),this.pos+=8},writeBytes:function(t){var e=t.length;this.writeVarint(e),this.realloc(e);for(var r=0;r<e;r++)this.buf[this.pos++]=t[r]},writeRawMessage:function(t,e){this.pos++;var r=this.pos;t(e,this);var n=this.pos-r;n>=128&&ul(r,n,this),this.pos=r-1,this.writeVarint(n),this.pos+=n},writeMessage:function(t,e,r){this.writeTag(t,ol.Bytes),this.writeRawMessage(e,r)},writePackedVarint:function(t,e){e.length&&this.writeMessage(t,fl,e)},writePackedSVarint:function(t,e){e.length&&this.writeMessage(t,hl,e)},writePackedBoolean:function(t,e){e.length&&this.writeMessage(t,ml,e)},writePackedFloat:function(t,e){e.length&&this.writeMessage(t,pl,e)},writePackedDouble:function(t,e){e.length&&this.writeMessage(t,dl,e)},writePackedFixed32:function(t,e){e.length&&this.writeMessage(t,gl,e)},writePackedSFixed32:function(t,e){e.length&&this.writeMessage(t,vl,e)},writePackedFixed64:function(t,e){e.length&&this.writeMessage(t,yl,e)},writePackedSFixed64:function(t,e){e.length&&this.writeMessage(t,xl,e)},writeBytesField:function(t,e){this.writeTag(t,ol.Bytes),this.writeBytes(e)},writeFixed32Field:function(t,e){this.writeTag(t,ol.Fixed32),this.writeFixed32(e)},writeSFixed32Field:function(t,e){this.writeTag(t,ol.Fixed32),this.writeSFixed32(e)},writeFixed64Field:function(t,e){this.writeTag(t,ol.Fixed64),this.writeFixed64(e)},writeSFixed64Field:function(t,e){this.writeTag(t,ol.Fixed64),this.writeSFixed64(e)},writeVarintField:function(t,e){this.writeTag(t,ol.Varint),this.writeVarint(e)},writeSVarintField:function(t,e){this.writeTag(t,ol.Varint),this.writeSVarint(e)},writeStringField:function(t,e){this.writeTag(t,ol.Bytes),this.writeString(e)},writeFloatField:function(t,e){this.writeTag(t,ol.Fixed32),this.writeFloat(e)},writeDoubleField:function(t,e){this.writeTag(t,ol.Fixed64),this.writeDouble(e)},writeBooleanField:function(t,e){this.writeVarintField(t,Boolean(e))}};function Tl(t,e,r){1===t&&r.readMessage(kl,e)}function kl(t,e,r){if(3===t){var n=r.readMessage(Ml,{}),i=n.id,a=n.bitmap,o=n.width,s=n.height,l=n.left,c=n.top,u=n.advance;e.push({id:i,bitmap:new So({width:o+6,height:s+6},a),metrics:{width:o,height:s,left:l,top:c,advance:u}})}}function Ml(t,e,r){1===t?e.id=r.readVarint():2===t?e.bitmap=r.readBytes():3===t?e.width=r.readVarint():4===t?e.height=r.readVarint():5===t?e.left=r.readSVarint():6===t?e.top=r.readSVarint():7===t&&(e.advance=r.readVarint())}function Al(t){for(var e=0,r=0,n=0,i=t;n<i.length;n+=1){var a=i[n];e+=a.w*a.h,r=Math.max(r,a.w)}t.sort((function(t,e){return e.h-t.h}));for(var o=[{x:0,y:0,w:Math.max(Math.ceil(Math.sqrt(e/.95)),r),h:1/0}],s=0,l=0,c=0,u=t;c<u.length;c+=1)for(var f=u[c],h=o.length-1;h>=0;h--){var p=o[h];if(!(f.w>p.w||f.h>p.h)){if(f.x=p.x,f.y=p.y,l=Math.max(l,f.y+f.h),s=Math.max(s,f.x+f.w),f.w===p.w&&f.h===p.h){var d=o.pop();h<o.length&&(o[h]=d)}else 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Eo({width:o||1,height:s||1});for(var c in t){var u=t[c],f=r[c].paddedRect;Eo.copy(u.data,l,{x:0,y:0},{x:f.x+1,y:f.y+1},u.data)}for(var h in e){var p=e[h],d=n[h].paddedRect,m=d.x+1,g=d.y+1,v=p.data.width,y=p.data.height;Eo.copy(p.data,l,{x:0,y:0},{x:m,y:g},p.data),Eo.copy(p.data,l,{x:0,y:y-1},{x:m,y:g-1},{width:v,height:1}),Eo.copy(p.data,l,{x:0,y:0},{x:m,y:g+y},{width:v,height:1}),Eo.copy(p.data,l,{x:v-1,y:0},{x:m-1,y:g},{width:1,height:y}),Eo.copy(p.data,l,{x:0,y:0},{x:m+v,y:g},{width:1,height:y})}this.image=l,this.iconPositions=r,this.patternPositions=n};Ll.prototype.addImages=function(t,e,r){for(var n in t){var i=t[n],a={x:0,y:0,w:i.data.width+2,h:i.data.height+2};r.push(a),e[n]=new Sl(a,i),i.hasRenderCallback&&this.haveRenderCallbacks.push(n)}},Ll.prototype.patchUpdatedImages=function(t,e){for(var r in t.dispatchRenderCallbacks(this.haveRenderCallbacks),t.updatedImages)this.patchUpdatedImage(this.iconPositions[r],t.getImage(r),e),this.patchUpdatedImage(this.patternPositions[r],t.getImage(r),e)},Ll.prototype.patchUpdatedImage=function(t,e,r){if(t&&e&&t.version!==e.version){t.version=e.version;var n=t.tl,i=n[0],a=n[1];r.update(e.data,void 0,{x:i,y:a})}},Nn(\"ImagePosition\",Sl),Nn(\"ImageAtlas\",Ll);var Cl={horizontal:1,vertical:2,horizontalOnly:3};var Pl=function(){this.scale=1,this.fontStack=\"\",this.imageName=null};Pl.forText=function(t,e){var r=new Pl;return r.scale=t||1,r.fontStack=e,r},Pl.forImage=function(t){var e=new Pl;return e.imageName=t,e};var Il=function(){this.text=\"\",this.sectionIndex=[],this.sections=[],this.imageSectionID=null};function Ol(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m){var g,v=Il.fromFeature(t,i);f===Cl.vertical&&v.verticalizePunctuation();var y=hi.processBidirectionalText,x=hi.processStyledBidirectionalText;if(y&&1===v.sections.length){g=[];for(var b=0,_=y(v.toString(),jl(v,c,a,e,n,p,d));b<_.length;b+=1){var w=_[b],T=new Il;T.text=w,T.sections=v.sections;for(var k=0;k<w.length;k++)T.sectionIndex.push(0);g.push(T)}}else if(x){g=[];for(var M=0,A=x(v.text,v.sectionIndex,jl(v,c,a,e,n,p,d));M<A.length;M+=1){var S=A[M],E=new Il;E.text=S[0],E.sectionIndex=S[1],E.sections=v.sections,g.push(E)}}else g=function(t,e){for(var r=[],n=t.text,i=0,a=0,o=e;a<o.length;a+=1){var s=o[a];r.push(t.substring(i,s)),i=s}return i<n.length&&r.push(t.substring(i,n.length)),r}(v,jl(v,c,a,e,n,p,d));var L=[],C={positionedLines:L,text:v.toString(),top:u[1],bottom:u[1],left:u[0],right:u[0],writingMode:f,iconsInText:!1,verticalizable:!1};return function(t,e,r,n,i,a,o,s,l,c,u,f){for(var h=0,p=-17,d=0,m=0,g=\"right\"===s?1:\"left\"===s?0:.5,v=0,y=0,x=i;y<x.length;y+=1){var b=x[y];b.trim();var _=b.getMaxScale(),w=24*(_-1),T={positionedGlyphs:[],lineOffset:0};t.positionedLines[v]=T;var k=T.positionedGlyphs,M=0;if(b.length()){for(var A=0;A<b.length();A++){var S=b.getSection(A),E=b.getSectionIndex(A),L=b.getCharCode(A),C=0,P=null,I=null,O=null,z=24,D=!(l===Cl.horizontal||!u&&!Zn(L)||u&&(zl[L]||(Y=L,Yn.Arabic(Y)||Yn[\"Arabic Supplement\"](Y)||Yn[\"Arabic Extended-A\"](Y)||Yn[\"Arabic Presentation Forms-A\"](Y)||Yn[\"Arabic Presentation Forms-B\"](Y))));if(S.imageName){var R=n[S.imageName];if(!R)continue;O=S.imageName,t.iconsInText=t.iconsInText||!0,I=R.paddedRect;var F=R.displaySize;S.scale=24*S.scale/f,P={width:F[0],height:F[1],left:1,top:-3,advance:D?F[1]:F[0]};var B=24-F[1]*S.scale;C=w+B,z=P.advance;var N=D?F[0]*S.scale-24*_:F[1]*S.scale-24*_;N>0&&N>M&&(M=N)}else{var j=r[S.fontStack],U=j&&j[L];if(U&&U.rect)I=U.rect,P=U.metrics;else{var V=e[S.fontStack],q=V&&V[L];if(!q)continue;P=q.metrics}C=24*(_-S.scale)}D?(t.verticalizable=!0,k.push({glyph:L,imageName:O,x:h,y:p+C,vertical:D,scale:S.scale,fontStack:S.fontStack,sectionIndex:E,metrics:P,rect:I}),h+=z*S.scale+c):(k.push({glyph:L,imageName:O,x:h,y:p+C,vertical:D,scale:S.scale,fontStack:S.fontStack,sectionIndex:E,metrics:P,rect:I}),h+=P.advance*S.scale+c)}if(0!==k.length){var H=h-c;d=Math.max(H,d),Vl(k,0,k.length-1,g,M)}h=0;var G=a*_+M;T.lineOffset=Math.max(M,w),p+=G,m=Math.max(G,m),++v}else p+=a,++v}var Y;var W=p- -17,X=Ul(o),Z=X.horizontalAlign,J=X.verticalAlign;(function(t,e,r,n,i,a,o,s,l){var c=(e-r)*i,u=0;u=a!==o?-s*n- -17:(-n*l+.5)*o;for(var f=0,h=t;f<h.length;f+=1)for(var p=h[f],d=0,m=p.positionedGlyphs;d<m.length;d+=1){var g=m[d];g.x+=c,g.y+=u}})(t.positionedLines,g,Z,J,d,m,a,W,i.length),t.top+=-J*W,t.bottom=t.top+W,t.left+=-Z*d,t.right=t.left+d}(C,e,r,n,g,o,s,l,f,c,h,m),!function(t){for(var e=0,r=t;e<r.length;e+=1){if(0!==r[e].positionedGlyphs.length)return!1}return!0}(L)&&C}Il.fromFeature=function(t,e){for(var r=new Il,n=0;n<t.sections.length;n++){var i=t.sections[n];i.image?r.addImageSection(i):r.addTextSection(i,e)}return r},Il.prototype.length=function(){return this.text.length},Il.prototype.getSection=function(t){return this.sections[this.sectionIndex[t]]},Il.prototype.getSectionIndex=function(t){return this.sectionIndex[t]},Il.prototype.getCharCode=function(t){return this.text.charCodeAt(t)},Il.prototype.verticalizePunctuation=function(){this.text=function(t){for(var e=\"\",r=0;r<t.length;r++){var n=t.charCodeAt(r+1)||null,i=t.charCodeAt(r-1)||null;(!n||!Jn(n)||rl[t[r+1]])&&(!i||!Jn(i)||rl[t[r-1]])&&rl[t[r]]?e+=rl[t[r]]:e+=t[r]}return e}(this.text)},Il.prototype.trim=function(){for(var t=0,e=0;e<this.text.length&&zl[this.text.charCodeAt(e)];e++)t++;for(var 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e=this.data,r=this.compare,n=this.length>>1,i=e[t];t<n;){var a=1+(t<<1),o=e[a],s=a+1;if(s<this.length&&r(e[s],o)<0&&(a=s,o=e[s]),r(o,i)>=0)break;e[t]=o,t=a}e[t]=i};var pc=Number.POSITIVE_INFINITY;function dc(t,e){return e[1]!==pc?function(t,e,r){var n=0,i=0;switch(e=Math.abs(e),r=Math.abs(r),t){case\"top-right\":case\"top-left\":case\"top\":i=r-7;break;case\"bottom-right\":case\"bottom-left\":case\"bottom\":i=7-r}switch(t){case\"top-right\":case\"bottom-right\":case\"right\":n=-e;break;case\"top-left\":case\"bottom-left\":case\"left\":n=e}return[n,i]}(t,e[0],e[1]):function(t,e){var r=0,n=0;e<0&&(e=0);var i=e/Math.sqrt(2);switch(t){case\"top-right\":case\"top-left\":n=i-7;break;case\"bottom-right\":case\"bottom-left\":n=7-i;break;case\"bottom\":n=7-e;break;case\"top\":n=e-7}switch(t){case\"top-right\":case\"bottom-right\":r=-i;break;case\"top-left\":case\"bottom-left\":r=i;break;case\"left\":r=e;break;case\"right\":r=-e}return[r,n]}(t,e[0])}function mc(t){switch(t){case\"right\":case\"top-right\":case\"bottom-right\":return\"right\";case\"left\":case\"top-left\":case\"bottom-left\":return\"left\"}return\"center\"}function gc(t,e,r,n,a,o,s,l,c,u,f,h,p,d,m){var g=function(t,e,r,n,a,o,s,l){for(var c=n.layout.get(\"text-rotate\").evaluate(o,{})*Math.PI/180,u=[],f=0,h=e.positionedLines;f<h.length;f+=1)for(var p=h[f],d=0,m=p.positionedGlyphs;d<m.length;d+=1){var g=m[d];if(g.rect){var v=g.rect||{},y=4,x=!0,b=1,_=0,w=(a||l)&&g.vertical,T=g.metrics.advance*g.scale/2;if(l&&e.verticalizable){var k=24*(g.scale-1),M=(24-g.metrics.width*g.scale)/2;_=p.lineOffset/2-(g.imageName?-M:k)}if(g.imageName){var A=s[g.imageName];x=A.sdf,y=1/(b=A.pixelRatio)}var S=a?[g.x+T,g.y]:[0,0],E=a?[0,0]:[g.x+T+r[0],g.y+r[1]-_],L=[0,0];w&&(L=E,E=[0,0]);var C=(g.metrics.left-y)*g.scale-T+E[0],P=(-g.metrics.top-y)*g.scale+E[1],I=C+v.w*g.scale/b,O=P+v.h*g.scale/b,z=new i(C,P),D=new i(I,P),R=new i(C,O),F=new i(I,O);if(w){var B=new i(-T,T- -17),N=-Math.PI/2,j=12-T,U=g.imageName?j:0,V=new i(22-j,-U),q=new(Function.prototype.bind.apply(i,[null].concat(L)));z._rotateAround(N,B)._add(V)._add(q),D._rotateAround(N,B)._add(V)._add(q),R._rotateAround(N,B)._add(V)._add(q),F._rotateAround(N,B)._add(V)._add(q)}if(c){var H=Math.sin(c),G=Math.cos(c),Y=[G,-H,H,G];z._matMult(Y),D._matMult(Y),R._matMult(Y),F._matMult(Y)}var W=new i(0,0),X=new i(0,0);u.push({tl:z,tr:D,bl:R,br:F,tex:v,writingMode:e.writingMode,glyphOffset:S,sectionIndex:g.sectionIndex,isSDF:x,pixelOffsetTL:W,pixelOffsetBR:X,minFontScaleX:0,minFontScaleY:0})}}return u}(0,r,l,a,o,s,n,t.allowVerticalPlacement),v=t.textSizeData,y=null;\"source\"===v.kind?(y=[128*a.layout.get(\"text-size\").evaluate(s,{})])[0]>32640&&_(t.layerIds[0]+': Value for \"text-size\" is >= 255. Reduce your \"text-size\".'):\"composite\"===v.kind&&((y=[128*d.compositeTextSizes[0].evaluate(s,{},m),128*d.compositeTextSizes[1].evaluate(s,{},m)])[0]>32640||y[1]>32640)&&_(t.layerIds[0]+': Value for \"text-size\" is >= 255. Reduce your \"text-size\".'),t.addSymbols(t.text,g,y,l,o,s,u,e,c.lineStartIndex,c.lineLength,p,m);for(var x=0,b=f;x<b.length;x+=1){h[b[x]]=t.text.placedSymbolArray.length-1}return 4*g.length}function vc(t){for(var e in t)return t[e];return null}function yc(t,e,r,n){var i=t.compareText;if(e in i){for(var a=i[e],o=a.length-1;o>=0;o--)if(n.dist(a[o])<r)return!0}else i[e]=[];return i[e].push(n),!1}var xc=Ls.VectorTileFeature.types,bc=[{name:\"a_fade_opacity\",components:1,type:\"Uint8\",offset:0}];function _c(t,e,r,n,i,a,o,s,l,c,u,f,h){var p=s?Math.min(32640,Math.round(s[0])):0,d=s?Math.min(32640,Math.round(s[1])):0;t.emplaceBack(e,r,Math.round(32*n),Math.round(32*i),a,o,(p<<1)+(l?1:0),d,16*c,16*u,256*f,256*h)}function wc(t,e,r){t.emplaceBack(e.x,e.y,r),t.emplaceBack(e.x,e.y,r),t.emplaceBack(e.x,e.y,r),t.emplaceBack(e.x,e.y,r)}function Tc(t){for(var e=0,r=t.sections;e<r.length;e+=1){if($n(r[e].text))return!0}return!1}var kc=function(t){this.layoutVertexArray=new Ni,this.indexArray=new Yi,this.programConfigurations=t,this.segments=new pa,this.dynamicLayoutVertexArray=new ji,this.opacityVertexArray=new Ui,this.placedSymbolArray=new aa};kc.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length&&0===this.indexArray.length&&0===this.dynamicLayoutVertexArray.length&&0===this.opacityVertexArray.length},kc.prototype.upload=function(t,e,r,n){this.isEmpty()||(r&&(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,Js.members),this.indexBuffer=t.createIndexBuffer(this.indexArray,e),this.dynamicLayoutVertexBuffer=t.createVertexBuffer(this.dynamicLayoutVertexArray,Ks.members,!0),this.opacityVertexBuffer=t.createVertexBuffer(this.opacityVertexArray,bc,!0),this.opacityVertexBuffer.itemSize=1),(r||n)&&this.programConfigurations.upload(t))},kc.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy(),this.dynamicLayoutVertexBuffer.destroy(),this.opacityVertexBuffer.destroy())},Nn(\"SymbolBuffers\",kc);var Mc=function(t,e,r){this.layoutVertexArray=new t,this.layoutAttributes=e,this.indexArray=new r,this.segments=new pa,this.collisionVertexArray=new Gi};Mc.prototype.upload=function(t){this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,this.layoutAttributes),this.indexBuffer=t.createIndexBuffer(this.indexArray),this.collisionVertexBuffer=t.createVertexBuffer(this.collisionVertexArray,Qs.members,!0)},Mc.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.segments.destroy(),this.collisionVertexBuffer.destroy())},Nn(\"CollisionBuffers\",Mc);var Ac=function(t){this.collisionBoxArray=t.collisionBoxArray,this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.pixelRatio=t.pixelRatio,this.sourceLayerIndex=t.sourceLayerIndex,this.hasPattern=!1,this.hasRTLText=!1,this.sortKeyRanges=[],this.collisionCircleArray=[],this.placementInvProjMatrix=ho([]),this.placementViewportMatrix=ho([]);var e=this.layers[0]._unevaluatedLayout._values;this.textSizeData=Gl(this.zoom,e[\"text-size\"]),this.iconSizeData=Gl(this.zoom,e[\"icon-size\"]);var r=this.layers[0].layout,n=r.get(\"symbol-sort-key\"),i=r.get(\"symbol-z-order\");this.sortFeaturesByKey=\"viewport-y\"!==i&&void 0!==n.constantOr(1);var a=\"viewport-y\"===i||\"auto\"===i&&!this.sortFeaturesByKey;this.sortFeaturesByY=a&&(r.get(\"text-allow-overlap\")||r.get(\"icon-allow-overlap\")||r.get(\"text-ignore-placement\")||r.get(\"icon-ignore-placement\")),\"point\"===r.get(\"symbol-placement\")&&(this.writingModes=r.get(\"text-writing-mode\").map((function(t){return Cl[t]}))),this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id})),this.sourceID=t.sourceID};Ac.prototype.createArrays=function(){this.text=new kc(new Ua(Js.members,this.layers,this.zoom,(function(t){return/^text/.test(t)}))),this.icon=new kc(new Ua(Js.members,this.layers,this.zoom,(function(t){return/^icon/.test(t)}))),this.glyphOffsetArray=new la,this.lineVertexArray=new ca,this.symbolInstances=new sa},Ac.prototype.calculateGlyphDependencies=function(t,e,r,n,i){for(var a=0;a<t.length;a++)if(e[t.charCodeAt(a)]=!0,(r||n)&&i){var o=rl[t.charAt(a)];o&&(e[o.charCodeAt(0)]=!0)}},Ac.prototype.populate=function(t,e,r){var n=this.layers[0],i=n.layout,a=i.get(\"text-font\"),o=i.get(\"text-field\"),s=i.get(\"icon-image\"),l=(\"constant\"!==o.value.kind||o.value.value instanceof ne&&!o.value.value.isEmpty()||o.value.value.toString().length>0)&&(\"constant\"!==a.value.kind||a.value.value.length>0),c=\"constant\"!==s.value.kind||!!s.value.value||Object.keys(s.parameters).length>0,u=i.get(\"symbol-sort-key\");if(this.features=[],l||c){for(var f=e.iconDependencies,h=e.glyphDependencies,p=e.availableImages,d=new pi(this.zoom),m=0,g=t;m<g.length;m+=1){var v=g[m],y=v.feature,x=v.id,b=v.index,_=v.sourceLayerIndex,w=n._featureFilter.needGeometry,T={type:y.type,id:x,properties:y.properties,geometry:w?Ya(y):[]};if(n._featureFilter.filter(d,T,r)){w||(T.geometry=Ya(y));var k=void 0;if(l){var M=n.getValueAndResolveTokens(\"text-field\",T,r,p),A=ne.factory(M);Tc(A)&&(this.hasRTLText=!0),(!this.hasRTLText||\"unavailable\"===ui()||this.hasRTLText&&hi.isParsed())&&(k=el(A,n,T))}var S=void 0;if(c){var E=n.getValueAndResolveTokens(\"icon-image\",T,r,p);S=E instanceof ie?E:ie.fromString(E)}if(k||S){var L=this.sortFeaturesByKey?u.evaluate(T,{},r):void 0,C={id:x,text:k,icon:S,index:b,sourceLayerIndex:_,geometry:Ya(y),properties:y.properties,type:xc[y.type],sortKey:L};if(this.features.push(C),S&&(f[S.name]=!0),k){var P=a.evaluate(T,{},r).join(\",\"),I=\"map\"===i.get(\"text-rotation-alignment\")&&\"point\"!==i.get(\"symbol-placement\");this.allowVerticalPlacement=this.writingModes&&this.writingModes.indexOf(Cl.vertical)>=0;for(var O=0,z=k.sections;O<z.length;O+=1){var D=z[O];if(D.image)f[D.image.name]=!0;else{var R=Wn(k.toString()),F=D.fontStack||P,B=h[F]=h[F]||{};this.calculateGlyphDependencies(D.text,B,I,this.allowVerticalPlacement,R)}}}}}}\"line\"===i.get(\"symbol-placement\")&&(this.features=function(t){var e={},r={},n=[],i=0;function a(e){n.push(t[e]),i++}function o(t,e,i){var a=r[t];return delete r[t],r[e]=a,n[a].geometry[0].pop(),n[a].geometry[0]=n[a].geometry[0].concat(i[0]),a}function s(t,r,i){var a=e[r];return delete e[r],e[t]=a,n[a].geometry[0].shift(),n[a].geometry[0]=i[0].concat(n[a].geometry[0]),a}function l(t,e,r){var n=r?e[0][e[0].length-1]:e[0][0];return t+\":\"+n.x+\":\"+n.y}for(var c=0;c<t.length;c++){var u=t[c],f=u.geometry,h=u.text?u.text.toString():null;if(h){var p=l(h,f),d=l(h,f,!0);if(p in r&&d in e&&r[p]!==e[d]){var m=s(p,d,f),g=o(p,d,n[m].geometry);delete e[p],delete r[d],r[l(h,n[g].geometry,!0)]=g,n[m].geometry=null}else p in r?o(p,d,f):d in e?s(p,d,f):(a(c),e[p]=i-1,r[d]=i-1)}else a(c)}return n.filter((function(t){return t.geometry}))}(this.features)),this.sortFeaturesByKey&&this.features.sort((function(t,e){return t.sortKey-e.sortKey}))}},Ac.prototype.update=function(t,e,r){this.stateDependentLayers.length&&(this.text.programConfigurations.updatePaintArrays(t,e,this.layers,r),this.icon.programConfigurations.updatePaintArrays(t,e,this.layers,r))},Ac.prototype.isEmpty=function(){return 0===this.symbolInstances.length&&!this.hasRTLText},Ac.prototype.uploadPending=function(){return!this.uploaded||this.text.programConfigurations.needsUpload||this.icon.programConfigurations.needsUpload},Ac.prototype.upload=function(t){!this.uploaded&&this.hasDebugData()&&(this.textCollisionBox.upload(t),this.iconCollisionBox.upload(t)),this.text.upload(t,this.sortFeaturesByY,!this.uploaded,this.text.programConfigurations.needsUpload),this.icon.upload(t,this.sortFeaturesByY,!this.uploaded,this.icon.programConfigurations.needsUpload),this.uploaded=!0},Ac.prototype.destroyDebugData=function(){this.textCollisionBox.destroy(),this.iconCollisionBox.destroy()},Ac.prototype.destroy=function(){this.text.destroy(),this.icon.destroy(),this.hasDebugData()&&this.destroyDebugData()},Ac.prototype.addToLineVertexArray=function(t,e){var r=this.lineVertexArray.length;if(void 0!==t.segment){for(var n=t.dist(e[t.segment+1]),i=t.dist(e[t.segment]),a={},o=t.segment+1;o<e.length;o++)a[o]={x:e[o].x,y:e[o].y,tileUnitDistanceFromAnchor:n},o<e.length-1&&(n+=e[o+1].dist(e[o]));for(var s=t.segment||0;s>=0;s--)a[s]={x:e[s].x,y:e[s].y,tileUnitDistanceFromAnchor:i},s>0&&(i+=e[s-1].dist(e[s]));for(var l=0;l<e.length;l++){var c=a[l];this.lineVertexArray.emplaceBack(c.x,c.y,c.tileUnitDistanceFromAnchor)}}return{lineStartIndex:r,lineLength:this.lineVertexArray.length-r}},Ac.prototype.addSymbols=function(t,e,r,n,i,a,o,s,l,c,u,f){for(var h=t.indexArray,p=t.layoutVertexArray,d=t.segments.prepareSegment(4*e.length,p,h,a.sortKey),m=this.glyphOffsetArray.length,g=d.vertexLength,v=this.allowVerticalPlacement&&o===Cl.vertical?Math.PI/2:0,y=a.text&&a.text.sections,x=0;x<e.length;x++){var b=e[x],_=b.tl,w=b.tr,T=b.bl,k=b.br,M=b.tex,A=b.pixelOffsetTL,S=b.pixelOffsetBR,E=b.minFontScaleX,L=b.minFontScaleY,C=b.glyphOffset,P=b.isSDF,I=b.sectionIndex,O=d.vertexLength,z=C[1];_c(p,s.x,s.y,_.x,z+_.y,M.x,M.y,r,P,A.x,A.y,E,L),_c(p,s.x,s.y,w.x,z+w.y,M.x+M.w,M.y,r,P,S.x,A.y,E,L),_c(p,s.x,s.y,T.x,z+T.y,M.x,M.y+M.h,r,P,A.x,S.y,E,L),_c(p,s.x,s.y,k.x,z+k.y,M.x+M.w,M.y+M.h,r,P,S.x,S.y,E,L),wc(t.dynamicLayoutVertexArray,s,v),h.emplaceBack(O,O+1,O+2),h.emplaceBack(O+1,O+2,O+3),d.vertexLength+=4,d.primitiveLength+=2,this.glyphOffsetArray.emplaceBack(C[0]),x!==e.length-1&&I===e[x+1].sectionIndex||t.programConfigurations.populatePaintArrays(p.length,a,a.index,{},f,y&&y[I])}t.placedSymbolArray.emplaceBack(s.x,s.y,m,this.glyphOffsetArray.length-m,g,l,c,s.segment,r?r[0]:0,r?r[1]:0,n[0],n[1],o,0,!1,0,u)},Ac.prototype._addCollisionDebugVertex=function(t,e,r,n,i,a){return e.emplaceBack(0,0),t.emplaceBack(r.x,r.y,n,i,Math.round(a.x),Math.round(a.y))},Ac.prototype.addCollisionDebugVertices=function(t,e,r,n,a,o,s){var l=a.segments.prepareSegment(4,a.layoutVertexArray,a.indexArray),c=l.vertexLength,u=a.layoutVertexArray,f=a.collisionVertexArray,h=s.anchorX,p=s.anchorY;this._addCollisionDebugVertex(u,f,o,h,p,new i(t,e)),this._addCollisionDebugVertex(u,f,o,h,p,new i(r,e)),this._addCollisionDebugVertex(u,f,o,h,p,new i(r,n)),this._addCollisionDebugVertex(u,f,o,h,p,new i(t,n)),l.vertexLength+=4;var d=a.indexArray;d.emplaceBack(c,c+1),d.emplaceBack(c+1,c+2),d.emplaceBack(c+2,c+3),d.emplaceBack(c+3,c),l.primitiveLength+=4},Ac.prototype.addDebugCollisionBoxes=function(t,e,r,n){for(var i=t;i<e;i++){var a=this.collisionBoxArray.get(i),o=a.x1,s=a.y1,l=a.x2,c=a.y2;this.addCollisionDebugVertices(o,s,l,c,n?this.textCollisionBox:this.iconCollisionBox,a.anchorPoint,r)}},Ac.prototype.generateCollisionDebugBuffers=function(){this.hasDebugData()&&this.destroyDebugData(),this.textCollisionBox=new Mc(qi,$s.members,Qi),this.iconCollisionBox=new Mc(qi,$s.members,Qi);for(var t=0;t<this.symbolInstances.length;t++){var e=this.symbolInstances.get(t);this.addDebugCollisionBoxes(e.textBoxStartIndex,e.textBoxEndIndex,e,!0),this.addDebugCollisionBoxes(e.verticalTextBoxStartIndex,e.verticalTextBoxEndIndex,e,!0),this.addDebugCollisionBoxes(e.iconBoxStartIndex,e.iconBoxEndIndex,e,!1),this.addDebugCollisionBoxes(e.verticalIconBoxStartIndex,e.verticalIconBoxEndIndex,e,!1)}},Ac.prototype._deserializeCollisionBoxesForSymbol=function(t,e,r,n,i,a,o,s,l){for(var c={},u=e;u<r;u++){var f=t.get(u);c.textBox={x1:f.x1,y1:f.y1,x2:f.x2,y2:f.y2,anchorPointX:f.anchorPointX,anchorPointY:f.anchorPointY},c.textFeatureIndex=f.featureIndex;break}for(var h=n;h<i;h++){var p=t.get(h);c.verticalTextBox={x1:p.x1,y1:p.y1,x2:p.x2,y2:p.y2,anchorPointX:p.anchorPointX,anchorPointY:p.anchorPointY},c.verticalTextFeatureIndex=p.featureIndex;break}for(var d=a;d<o;d++){var m=t.get(d);c.iconBox={x1:m.x1,y1:m.y1,x2:m.x2,y2:m.y2,anchorPointX:m.anchorPointX,anchorPointY:m.anchorPointY},c.iconFeatureIndex=m.featureIndex;break}for(var g=s;g<l;g++){var v=t.get(g);c.verticalIconBox={x1:v.x1,y1:v.y1,x2:v.x2,y2:v.y2,anchorPointX:v.anchorPointX,anchorPointY:v.anchorPointY},c.verticalIconFeatureIndex=v.featureIndex;break}return c},Ac.prototype.deserializeCollisionBoxes=function(t){this.collisionArrays=[];for(var e=0;e<this.symbolInstances.length;e++){var r=this.symbolInstances.get(e);this.collisionArrays.push(this._deserializeCollisionBoxesForSymbol(t,r.textBoxStartIndex,r.textBoxEndIndex,r.verticalTextBoxStartIndex,r.verticalTextBoxEndIndex,r.iconBoxStartIndex,r.iconBoxEndIndex,r.verticalIconBoxStartIndex,r.verticalIconBoxEndIndex))}},Ac.prototype.hasTextData=function(){return this.text.segments.get().length>0},Ac.prototype.hasIconData=function(){return this.icon.segments.get().length>0},Ac.prototype.hasDebugData=function(){return this.textCollisionBox&&this.iconCollisionBox},Ac.prototype.hasTextCollisionBoxData=function(){return this.hasDebugData()&&this.textCollisionBox.segments.get().length>0},Ac.prototype.hasIconCollisionBoxData=function(){return this.hasDebugData()&&this.iconCollisionBox.segments.get().length>0},Ac.prototype.addIndicesForPlacedSymbol=function(t,e){for(var r=t.placedSymbolArray.get(e),n=r.vertexStartIndex+4*r.numGlyphs,i=r.vertexStartIndex;i<n;i+=4)t.indexArray.emplaceBack(i,i+1,i+2),t.indexArray.emplaceBack(i+1,i+2,i+3)},Ac.prototype.getSortedSymbolIndexes=function(t){if(this.sortedAngle===t&&void 0!==this.symbolInstanceIndexes)return this.symbolInstanceIndexes;for(var e=Math.sin(t),r=Math.cos(t),n=[],i=[],a=[],o=0;o<this.symbolInstances.length;++o){a.push(o);var s=this.symbolInstances.get(o);n.push(0|Math.round(e*s.anchorX+r*s.anchorY)),i.push(s.featureIndex)}return a.sort((function(t,e){return n[t]-n[e]||i[e]-i[t]})),a},Ac.prototype.addToSortKeyRanges=function(t,e){var r=this.sortKeyRanges[this.sortKeyRanges.length-1];r&&r.sortKey===e?r.symbolInstanceEnd=t+1:this.sortKeyRanges.push({sortKey:e,symbolInstanceStart:t,symbolInstanceEnd:t+1})},Ac.prototype.sortFeatures=function(t){var e=this;if(this.sortFeaturesByY&&this.sortedAngle!==t&&!(this.text.segments.get().length>1||this.icon.segments.get().length>1)){this.symbolInstanceIndexes=this.getSortedSymbolIndexes(t),this.sortedAngle=t,this.text.indexArray.clear(),this.icon.indexArray.clear(),this.featureSortOrder=[];for(var r=0,n=this.symbolInstanceIndexes;r<n.length;r+=1){var i=n[r],a=this.symbolInstances.get(i);this.featureSortOrder.push(a.featureIndex),[a.rightJustifiedTextSymbolIndex,a.centerJustifiedTextSymbolIndex,a.leftJustifiedTextSymbolIndex].forEach((function(t,r,n){t>=0&&n.indexOf(t)===r&&e.addIndicesForPlacedSymbol(e.text,t)})),a.verticalPlacedTextSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.text,a.verticalPlacedTextSymbolIndex),a.placedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,a.placedIconSymbolIndex),a.verticalPlacedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,a.verticalPlacedIconSymbolIndex)}this.text.indexBuffer&&this.text.indexBuffer.updateData(this.text.indexArray),this.icon.indexBuffer&&this.icon.indexBuffer.updateData(this.icon.indexArray)}},Nn(\"SymbolBucket\",Ac,{omit:[\"layers\",\"collisionBoxArray\",\"features\",\"compareText\"]}),Ac.MAX_GLYPHS=65535,Ac.addDynamicAttributes=wc;var Sc=new Si({\"symbol-placement\":new wi(Lt.layout_symbol[\"symbol-placement\"]),\"symbol-spacing\":new wi(Lt.layout_symbol[\"symbol-spacing\"]),\"symbol-avoid-edges\":new wi(Lt.layout_symbol[\"symbol-avoid-edges\"]),\"symbol-sort-key\":new Ti(Lt.layout_symbol[\"symbol-sort-key\"]),\"symbol-z-order\":new wi(Lt.layout_symbol[\"symbol-z-order\"]),\"icon-allow-overlap\":new wi(Lt.layout_symbol[\"icon-allow-overlap\"]),\"icon-ignore-placement\":new wi(Lt.layout_symbol[\"icon-ignore-placement\"]),\"icon-optional\":new wi(Lt.layout_symbol[\"icon-optional\"]),\"icon-rotation-alignment\":new wi(Lt.layout_symbol[\"icon-rotation-alignment\"]),\"icon-size\":new Ti(Lt.layout_symbol[\"icon-size\"]),\"icon-text-fit\":new wi(Lt.layout_symbol[\"icon-text-fit\"]),\"icon-text-fit-padding\":new wi(Lt.layout_symbol[\"icon-text-fit-padding\"]),\"icon-image\":new Ti(Lt.layout_symbol[\"icon-image\"]),\"icon-rotate\":new Ti(Lt.layout_symbol[\"icon-rotate\"]),\"icon-padding\":new wi(Lt.layout_symbol[\"icon-padding\"]),\"icon-keep-upright\":new wi(Lt.layout_symbol[\"icon-keep-upright\"]),\"icon-offset\":new Ti(Lt.layout_symbol[\"icon-offset\"]),\"icon-anchor\":new Ti(Lt.layout_symbol[\"icon-anchor\"]),\"icon-pitch-alignment\":new wi(Lt.layout_symbol[\"icon-pitch-alignment\"]),\"text-pitch-alignment\":new wi(Lt.layout_symbol[\"text-pitch-alignment\"]),\"text-rotation-alignment\":new wi(Lt.layout_symbol[\"text-rotation-alignment\"]),\"text-field\":new Ti(Lt.layout_symbol[\"text-field\"]),\"text-font\":new Ti(Lt.layout_symbol[\"text-font\"]),\"text-size\":new Ti(Lt.layout_symbol[\"text-size\"]),\"text-max-width\":new Ti(Lt.layout_symbol[\"text-max-width\"]),\"text-line-height\":new wi(Lt.layout_symbol[\"text-line-height\"]),\"text-letter-spacing\":new Ti(Lt.layout_symbol[\"text-letter-spacing\"]),\"text-justify\":new Ti(Lt.layout_symbol[\"text-justify\"]),\"text-radial-offset\":new Ti(Lt.layout_symbol[\"text-radial-offset\"]),\"text-variable-anchor\":new wi(Lt.layout_symbol[\"text-variable-anchor\"]),\"text-anchor\":new Ti(Lt.layout_symbol[\"text-anchor\"]),\"text-max-angle\":new wi(Lt.layout_symbol[\"text-max-angle\"]),\"text-writing-mode\":new wi(Lt.layout_symbol[\"text-writing-mode\"]),\"text-rotate\":new Ti(Lt.layout_symbol[\"text-rotate\"]),\"text-padding\":new wi(Lt.layout_symbol[\"text-padding\"]),\"text-keep-upright\":new wi(Lt.layout_symbol[\"text-keep-upright\"]),\"text-transform\":new Ti(Lt.layout_symbol[\"text-transform\"]),\"text-offset\":new Ti(Lt.layout_symbol[\"text-offset\"]),\"text-allow-overlap\":new wi(Lt.layout_symbol[\"text-allow-overlap\"]),\"text-ignore-placement\":new wi(Lt.layout_symbol[\"text-ignore-placement\"]),\"text-optional\":new wi(Lt.layout_symbol[\"text-optional\"])}),Ec={paint:new Si({\"icon-opacity\":new Ti(Lt.paint_symbol[\"icon-opacity\"]),\"icon-color\":new Ti(Lt.paint_symbol[\"icon-color\"]),\"icon-halo-color\":new Ti(Lt.paint_symbol[\"icon-halo-color\"]),\"icon-halo-width\":new Ti(Lt.paint_symbol[\"icon-halo-width\"]),\"icon-halo-blur\":new Ti(Lt.paint_symbol[\"icon-halo-blur\"]),\"icon-translate\":new wi(Lt.paint_symbol[\"icon-translate\"]),\"icon-translate-anchor\":new wi(Lt.paint_symbol[\"icon-translate-anchor\"]),\"text-opacity\":new Ti(Lt.paint_symbol[\"text-opacity\"]),\"text-color\":new Ti(Lt.paint_symbol[\"text-color\"],{runtimeType:Ut,getOverride:function(t){return t.textColor},hasOverride:function(t){return!!t.textColor}}),\"text-halo-color\":new Ti(Lt.paint_symbol[\"text-halo-color\"]),\"text-halo-width\":new Ti(Lt.paint_symbol[\"text-halo-width\"]),\"text-halo-blur\":new Ti(Lt.paint_symbol[\"text-halo-blur\"]),\"text-translate\":new wi(Lt.paint_symbol[\"text-translate\"]),\"text-translate-anchor\":new wi(Lt.paint_symbol[\"text-translate-anchor\"])}),layout:Sc},Lc=function(t){this.type=t.property.overrides?t.property.overrides.runtimeType:Ft,this.defaultValue=t};Lc.prototype.evaluate=function(t){if(t.formattedSection){var e=this.defaultValue.property.overrides;if(e&&e.hasOverride(t.formattedSection))return e.getOverride(t.formattedSection)}return t.feature&&t.featureState?this.defaultValue.evaluate(t.feature,t.featureState):this.defaultValue.property.specification.default},Lc.prototype.eachChild=function(t){this.defaultValue.isConstant()||t(this.defaultValue.value._styleExpression.expression)},Lc.prototype.outputDefined=function(){return!1},Lc.prototype.serialize=function(){return null},Nn(\"FormatSectionOverride\",Lc,{omit:[\"defaultValue\"]});var Cc=function(t){function e(e){t.call(this,e,Ec)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.recalculate=function(e,r){if(t.prototype.recalculate.call(this,e,r),\"auto\"===this.layout.get(\"icon-rotation-alignment\")&&(\"point\"!==this.layout.get(\"symbol-placement\")?this.layout._values[\"icon-rotation-alignment\"]=\"map\":this.layout._values[\"icon-rotation-alignment\"]=\"viewport\"),\"auto\"===this.layout.get(\"text-rotation-alignment\")&&(\"point\"!==this.layout.get(\"symbol-placement\")?this.layout._values[\"text-rotation-alignment\"]=\"map\":this.layout._values[\"text-rotation-alignment\"]=\"viewport\"),\"auto\"===this.layout.get(\"text-pitch-alignment\")&&(this.layout._values[\"text-pitch-alignment\"]=this.layout.get(\"text-rotation-alignment\")),\"auto\"===this.layout.get(\"icon-pitch-alignment\")&&(this.layout._values[\"icon-pitch-alignment\"]=this.layout.get(\"icon-rotation-alignment\")),\"point\"===this.layout.get(\"symbol-placement\")){var n=this.layout.get(\"text-writing-mode\");if(n){for(var i=[],a=0,o=n;a<o.length;a+=1){var s=o[a];i.indexOf(s)<0&&i.push(s)}this.layout._values[\"text-writing-mode\"]=i}else this.layout._values[\"text-writing-mode\"]=[\"horizontal\"]}this._setPaintOverrides()},e.prototype.getValueAndResolveTokens=function(t,e,r,n){var i=this.layout.get(t).evaluate(e,{},r,n),a=this._unevaluatedLayout._values[t];return a.isDataDriven()||Yr(a.value)||!i?i:function(t,e){return e.replace(/{([^{}]+)}/g,(function(e,r){return r in t?String(t[r]):\"\"}))}(e.properties,i)},e.prototype.createBucket=function(t){return new Ac(t)},e.prototype.queryRadius=function(){return 0},e.prototype.queryIntersectsFeature=function(){return!1},e.prototype._setPaintOverrides=function(){for(var t=0,r=Ec.paint.overridableProperties;t<r.length;t+=1){var n=r[t];if(e.hasPaintOverride(this.layout,n)){var i=this.paint.get(n),a=new Lc(i),o=new Gr(a,i.property.specification),s=null;s=\"constant\"===i.value.kind||\"source\"===i.value.kind?new Xr(\"source\",o):new Zr(\"composite\",o,i.value.zoomStops,i.value._interpolationType),this.paint._values[n]=new bi(i.property,s,i.parameters)}}},e.prototype._handleOverridablePaintPropertyUpdate=function(t,r,n){return!(!this.layout||r.isDataDriven()||n.isDataDriven())&&e.hasPaintOverride(this.layout,t)},e.hasPaintOverride=function(t,e){var r=t.get(\"text-field\"),n=Ec.paint.properties[e],i=!1,a=function(t){for(var e=0,r=t;e<r.length;e+=1){var a=r[e];if(n.overrides&&n.overrides.hasOverride(a))return void(i=!0)}};if(\"constant\"===r.value.kind&&r.value.value instanceof ne)a(r.value.value.sections);else if(\"source\"===r.value.kind){var o=function(t){if(!i)if(t instanceof ce&&se(t.value)===Gt){var e=t.value;a(e.sections)}else t instanceof pe?a(t.sections):t.eachChild(o)},s=r.value;s._styleExpression&&o(s._styleExpression.expression)}return i},e}(Ei),Pc={paint:new Si({\"background-color\":new wi(Lt.paint_background[\"background-color\"]),\"background-pattern\":new Mi(Lt.paint_background[\"background-pattern\"]),\"background-opacity\":new wi(Lt.paint_background[\"background-opacity\"])})},Ic=function(t){function e(e){t.call(this,e,Pc)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(Ei),Oc={paint:new Si({\"raster-opacity\":new wi(Lt.paint_raster[\"raster-opacity\"]),\"raster-hue-rotate\":new wi(Lt.paint_raster[\"raster-hue-rotate\"]),\"raster-brightness-min\":new wi(Lt.paint_raster[\"raster-brightness-min\"]),\"raster-brightness-max\":new wi(Lt.paint_raster[\"raster-brightness-max\"]),\"raster-saturation\":new wi(Lt.paint_raster[\"raster-saturation\"]),\"raster-contrast\":new wi(Lt.paint_raster[\"raster-contrast\"]),\"raster-resampling\":new wi(Lt.paint_raster[\"raster-resampling\"]),\"raster-fade-duration\":new wi(Lt.paint_raster[\"raster-fade-duration\"])})},zc=function(t){function e(e){t.call(this,e,Oc)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(Ei);var Dc=function(t){function e(e){t.call(this,e,{}),this.implementation=e}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.is3D=function(){return\"3d\"===this.implementation.renderingMode},e.prototype.hasOffscreenPass=function(){return void 0!==this.implementation.prerender},e.prototype.recalculate=function(){},e.prototype.updateTransitions=function(){},e.prototype.hasTransition=function(){},e.prototype.serialize=function(){},e.prototype.onAdd=function(t){this.implementation.onAdd&&this.implementation.onAdd(t,t.painter.context.gl)},e.prototype.onRemove=function(t){this.implementation.onRemove&&this.implementation.onRemove(t,t.painter.context.gl)},e}(Ei),Rc={circle:_o,heatmap:Po,hillshade:Oo,fill:xs,\"fill-extrusion\":Fs,line:Xs,symbol:Cc,background:Ic,raster:zc};var Fc=self.HTMLImageElement,Bc=self.HTMLCanvasElement,Nc=self.HTMLVideoElement,jc=self.ImageData,Uc=self.ImageBitmap,Vc=function(t,e,r,n){this.context=t,this.format=r,this.texture=t.gl.createTexture(),this.update(e,n)};Vc.prototype.update=function(t,e,r){var n=t.width,i=t.height,a=!(this.size&&this.size[0]===n&&this.size[1]===i||r),o=this.context,s=o.gl;if(this.useMipmap=Boolean(e&&e.useMipmap),s.bindTexture(s.TEXTURE_2D,this.texture),o.pixelStoreUnpackFlipY.set(!1),o.pixelStoreUnpack.set(1),o.pixelStoreUnpackPremultiplyAlpha.set(this.format===s.RGBA&&(!e||!1!==e.premultiply)),a)this.size=[n,i],t instanceof Fc||t instanceof Bc||t instanceof Nc||t instanceof jc||Uc&&t instanceof Uc?s.texImage2D(s.TEXTURE_2D,0,this.format,this.format,s.UNSIGNED_BYTE,t):s.texImage2D(s.TEXTURE_2D,0,this.format,n,i,0,this.format,s.UNSIGNED_BYTE,t.data);else{var l=r||{x:0,y:0},c=l.x,u=l.y;t instanceof Fc||t instanceof Bc||t instanceof Nc||t instanceof jc||Uc&&t instanceof Uc?s.texSubImage2D(s.TEXTURE_2D,0,c,u,s.RGBA,s.UNSIGNED_BYTE,t):s.texSubImage2D(s.TEXTURE_2D,0,c,u,n,i,s.RGBA,s.UNSIGNED_BYTE,t.data)}this.useMipmap&&this.isSizePowerOfTwo()&&s.generateMipmap(s.TEXTURE_2D)},Vc.prototype.bind=function(t,e,r){var n=this.context.gl;n.bindTexture(n.TEXTURE_2D,this.texture),r!==n.LINEAR_MIPMAP_NEAREST||this.isSizePowerOfTwo()||(r=n.LINEAR),t!==this.filter&&(n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MAG_FILTER,t),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MIN_FILTER,r||t),this.filter=t),e!==this.wrap&&(n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_S,e),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_T,e),this.wrap=e)},Vc.prototype.isSizePowerOfTwo=function(){return this.size[0]===this.size[1]&&Math.log(this.size[0])/Math.LN2%1==0},Vc.prototype.destroy=function(){this.context.gl.deleteTexture(this.texture),this.texture=null};var qc=function(t){var e=this;this._callback=t,this._triggered=!1,\"undefined\"!=typeof MessageChannel&&(this._channel=new MessageChannel,this._channel.port2.onmessage=function(){e._triggered=!1,e._callback()})};qc.prototype.trigger=function(){var t=this;this._triggered||(this._triggered=!0,this._channel?this._channel.port1.postMessage(!0):setTimeout((function(){t._triggered=!1,t._callback()}),0))},qc.prototype.remove=function(){delete this._channel,this._callback=function(){}};var Hc=function(t,e,r){this.target=t,this.parent=e,this.mapId=r,this.callbacks={},this.tasks={},this.taskQueue=[],this.cancelCallbacks={},m([\"receive\",\"process\"],this),this.invoker=new qc(this.process),this.target.addEventListener(\"message\",this.receive,!1),this.globalScope=k()?t:self};function Gc(t,e,r){var n=2*Math.PI*6378137/256/Math.pow(2,r);return[t*n-2*Math.PI*6378137/2,e*n-2*Math.PI*6378137/2]}Hc.prototype.send=function(t,e,r,n,i){var a=this;void 0===i&&(i=!1);var o=Math.round(1e18*Math.random()).toString(36).substring(0,10);r&&(this.callbacks[o]=r);var s=S(this.globalScope)?void 0:[];return this.target.postMessage({id:o,type:t,hasCallback:!!r,targetMapId:n,mustQueue:i,sourceMapId:this.mapId,data:qn(e,s)},s),{cancel:function(){r&&delete a.callbacks[o],a.target.postMessage({id:o,type:\"<cancel>\",targetMapId:n,sourceMapId:a.mapId})}}},Hc.prototype.receive=function(t){var e=t.data,r=e.id;if(r&&(!e.targetMapId||this.mapId===e.targetMapId))if(\"<cancel>\"===e.type){delete this.tasks[r];var n=this.cancelCallbacks[r];delete this.cancelCallbacks[r],n&&n()}else k()||e.mustQueue?(this.tasks[r]=e,this.taskQueue.push(r),this.invoker.trigger()):this.processTask(r,e)},Hc.prototype.process=function(){if(this.taskQueue.length){var t=this.taskQueue.shift(),e=this.tasks[t];delete this.tasks[t],this.taskQueue.length&&this.invoker.trigger(),e&&this.processTask(t,e)}},Hc.prototype.processTask=function(t,e){var r=this;if(\"<response>\"===e.type){var n=this.callbacks[t];delete this.callbacks[t],n&&(e.error?n(Hn(e.error)):n(null,Hn(e.data)))}else{var i=!1,a=S(this.globalScope)?void 0:[],o=e.hasCallback?function(e,n){i=!0,delete r.cancelCallbacks[t],r.target.postMessage({id:t,type:\"<response>\",sourceMapId:r.mapId,error:e?qn(e):null,data:qn(n,a)},a)}:function(t){i=!0},s=null,l=Hn(e.data);if(this.parent[e.type])s=this.parent[e.type](e.sourceMapId,l,o);else if(this.parent.getWorkerSource){var c=e.type.split(\".\");s=this.parent.getWorkerSource(e.sourceMapId,c[0],l.source)[c[1]](l,o)}else o(new Error(\"Could not find function \"+e.type));!i&&s&&s.cancel&&(this.cancelCallbacks[t]=s.cancel)}},Hc.prototype.remove=function(){this.invoker.remove(),this.target.removeEventListener(\"message\",this.receive,!1)};var Yc=function(t,e){t&&(e?this.setSouthWest(t).setNorthEast(e):4===t.length?this.setSouthWest([t[0],t[1]]).setNorthEast([t[2],t[3]]):this.setSouthWest(t[0]).setNorthEast(t[1]))};Yc.prototype.setNorthEast=function(t){return this._ne=t instanceof Wc?new Wc(t.lng,t.lat):Wc.convert(t),this},Yc.prototype.setSouthWest=function(t){return this._sw=t instanceof Wc?new Wc(t.lng,t.lat):Wc.convert(t),this},Yc.prototype.extend=function(t){var e,r,n=this._sw,i=this._ne;if(t instanceof Wc)e=t,r=t;else{if(!(t instanceof Yc)){if(Array.isArray(t)){if(4===t.length||t.every(Array.isArray)){var a=t;return this.extend(Yc.convert(a))}var o=t;return this.extend(Wc.convert(o))}return this}if(e=t._sw,r=t._ne,!e||!r)return this}return n||i?(n.lng=Math.min(e.lng,n.lng),n.lat=Math.min(e.lat,n.lat),i.lng=Math.max(r.lng,i.lng),i.lat=Math.max(r.lat,i.lat)):(this._sw=new Wc(e.lng,e.lat),this._ne=new Wc(r.lng,r.lat)),this},Yc.prototype.getCenter=function(){return new Wc((this._sw.lng+this._ne.lng)/2,(this._sw.lat+this._ne.lat)/2)},Yc.prototype.getSouthWest=function(){return this._sw},Yc.prototype.getNorthEast=function(){return this._ne},Yc.prototype.getNorthWest=function(){return new Wc(this.getWest(),this.getNorth())},Yc.prototype.getSouthEast=function(){return new Wc(this.getEast(),this.getSouth())},Yc.prototype.getWest=function(){return this._sw.lng},Yc.prototype.getSouth=function(){return this._sw.lat},Yc.prototype.getEast=function(){return this._ne.lng},Yc.prototype.getNorth=function(){return this._ne.lat},Yc.prototype.toArray=function(){return[this._sw.toArray(),this._ne.toArray()]},Yc.prototype.toString=function(){return\"LngLatBounds(\"+this._sw.toString()+\", \"+this._ne.toString()+\")\"},Yc.prototype.isEmpty=function(){return!(this._sw&&this._ne)},Yc.prototype.contains=function(t){var e=Wc.convert(t),r=e.lng,n=e.lat,i=this._sw.lat<=n&&n<=this._ne.lat,a=this._sw.lng<=r&&r<=this._ne.lng;return this._sw.lng>this._ne.lng&&(a=this._sw.lng>=r&&r>=this._ne.lng),i&&a},Yc.convert=function(t){return!t||t instanceof Yc?t:new Yc(t)};var Wc=function(t,e){if(isNaN(t)||isNaN(e))throw new Error(\"Invalid LngLat object: (\"+t+\", \"+e+\")\");if(this.lng=+t,this.lat=+e,this.lat>90||this.lat<-90)throw new Error(\"Invalid LngLat latitude value: must be between -90 and 90\")};Wc.prototype.wrap=function(){return new Wc(c(this.lng,-180,180),this.lat)},Wc.prototype.toArray=function(){return[this.lng,this.lat]},Wc.prototype.toString=function(){return\"LngLat(\"+this.lng+\", \"+this.lat+\")\"},Wc.prototype.distanceTo=function(t){var e=Math.PI/180,r=this.lat*e,n=t.lat*e,i=Math.sin(r)*Math.sin(n)+Math.cos(r)*Math.cos(n)*Math.cos((t.lng-this.lng)*e);return 6371008.8*Math.acos(Math.min(i,1))},Wc.prototype.toBounds=function(t){void 0===t&&(t=0);var e=360*t/40075017,r=e/Math.cos(Math.PI/180*this.lat);return new Yc(new Wc(this.lng-r,this.lat-e),new Wc(this.lng+r,this.lat+e))},Wc.convert=function(t){if(t instanceof Wc)return t;if(Array.isArray(t)&&(2===t.length||3===t.length))return new Wc(Number(t[0]),Number(t[1]));if(!Array.isArray(t)&&\"object\"==typeof t&&null!==t)return new Wc(Number(\"lng\"in t?t.lng:t.lon),Number(t.lat));throw new Error(\"`LngLatLike` argument must be specified as a LngLat instance, an object {lng: <lng>, lat: <lat>}, an object {lon: <lng>, lat: <lat>}, or an array of [<lng>, <lat>]\")};var Xc=2*Math.PI*6371008.8;function Zc(t){return Xc*Math.cos(t*Math.PI/180)}function Jc(t){return(180+t)/360}function Kc(t){return(180-180/Math.PI*Math.log(Math.tan(Math.PI/4+t*Math.PI/360)))/360}function Qc(t,e){return t/Zc(e)}function $c(t){var e=180-360*t;return 360/Math.PI*Math.atan(Math.exp(e*Math.PI/180))-90}var tu=function(t,e,r){void 0===r&&(r=0),this.x=+t,this.y=+e,this.z=+r};tu.fromLngLat=function(t,e){void 0===e&&(e=0);var r=Wc.convert(t);return new tu(Jc(r.lng),Kc(r.lat),Qc(e,r.lat))},tu.prototype.toLngLat=function(){return new Wc(360*this.x-180,$c(this.y))},tu.prototype.toAltitude=function(){return t=this.z,e=this.y,t*Zc($c(e));var t,e},tu.prototype.meterInMercatorCoordinateUnits=function(){return 1/Xc*(t=$c(this.y),1/Math.cos(t*Math.PI/180));var t};var eu=function(t,e,r){this.z=t,this.x=e,this.y=r,this.key=iu(0,t,t,e,r)};eu.prototype.equals=function(t){return this.z===t.z&&this.x===t.x&&this.y===t.y},eu.prototype.url=function(t,e){var r,n,i,a,o,s=(r=this.x,n=this.y,i=this.z,a=Gc(256*r,256*(n=Math.pow(2,i)-n-1),i),o=Gc(256*(r+1),256*(n+1),i),a[0]+\",\"+a[1]+\",\"+o[0]+\",\"+o[1]),l=function(t,e,r){for(var n,i=\"\",a=t;a>0;a--)i+=(e&(n=1<<a-1)?1:0)+(r&n?2:0);return i}(this.z,this.x,this.y);return t[(this.x+this.y)%t.length].replace(\"{prefix}\",(this.x%16).toString(16)+(this.y%16).toString(16)).replace(\"{z}\",String(this.z)).replace(\"{x}\",String(this.x)).replace(\"{y}\",String(\"tms\"===e?Math.pow(2,this.z)-this.y-1:this.y)).replace(\"{quadkey}\",l).replace(\"{bbox-epsg-3857}\",s)},eu.prototype.getTilePoint=function(t){var e=Math.pow(2,this.z);return new i(8192*(t.x*e-this.x),8192*(t.y*e-this.y))},eu.prototype.toString=function(){return this.z+\"/\"+this.x+\"/\"+this.y};var ru=function(t,e){this.wrap=t,this.canonical=e,this.key=iu(t,e.z,e.z,e.x,e.y)},nu=function(t,e,r,n,i){this.overscaledZ=t,this.wrap=e,this.canonical=new eu(r,+n,+i),this.key=iu(e,t,r,n,i)};function iu(t,e,r,n,i){(t*=2)<0&&(t=-1*t-1);var a=1<<r;return(a*a*t+a*i+n).toString(36)+r.toString(36)+e.toString(36)}nu.prototype.equals=function(t){return this.overscaledZ===t.overscaledZ&&this.wrap===t.wrap&&this.canonical.equals(t.canonical)},nu.prototype.scaledTo=function(t){var e=this.canonical.z-t;return t>this.canonical.z?new nu(t,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y):new nu(t,this.wrap,t,this.canonical.x>>e,this.canonical.y>>e)},nu.prototype.calculateScaledKey=function(t,e){var r=this.canonical.z-t;return t>this.canonical.z?iu(this.wrap*+e,t,this.canonical.z,this.canonical.x,this.canonical.y):iu(this.wrap*+e,t,t,this.canonical.x>>r,this.canonical.y>>r)},nu.prototype.isChildOf=function(t){if(t.wrap!==this.wrap)return!1;var e=this.canonical.z-t.canonical.z;return 0===t.overscaledZ||t.overscaledZ<this.overscaledZ&&t.canonical.x===this.canonical.x>>e&&t.canonical.y===this.canonical.y>>e},nu.prototype.children=function(t){if(this.overscaledZ>=t)return[new nu(this.overscaledZ+1,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y)];var e=this.canonical.z+1,r=2*this.canonical.x,n=2*this.canonical.y;return[new nu(e,this.wrap,e,r,n),new nu(e,this.wrap,e,r+1,n),new nu(e,this.wrap,e,r,n+1),new nu(e,this.wrap,e,r+1,n+1)]},nu.prototype.isLessThan=function(t){return this.wrap<t.wrap||!(this.wrap>t.wrap)&&(this.overscaledZ<t.overscaledZ||!(this.overscaledZ>t.overscaledZ)&&(this.canonical.x<t.canonical.x||!(this.canonical.x>t.canonical.x)&&this.canonical.y<t.canonical.y))},nu.prototype.wrapped=function(){return new nu(this.overscaledZ,0,this.canonical.z,this.canonical.x,this.canonical.y)},nu.prototype.unwrapTo=function(t){return new nu(this.overscaledZ,t,this.canonical.z,this.canonical.x,this.canonical.y)},nu.prototype.overscaleFactor=function(){return Math.pow(2,this.overscaledZ-this.canonical.z)},nu.prototype.toUnwrapped=function(){return new ru(this.wrap,this.canonical)},nu.prototype.toString=function(){return this.overscaledZ+\"/\"+this.canonical.x+\"/\"+this.canonical.y},nu.prototype.getTilePoint=function(t){return this.canonical.getTilePoint(new tu(t.x-this.wrap,t.y))},Nn(\"CanonicalTileID\",eu),Nn(\"OverscaledTileID\",nu,{omit:[\"posMatrix\"]});var au=function(t,e,r){if(this.uid=t,e.height!==e.width)throw new RangeError(\"DEM tiles must be square\");if(r&&\"mapbox\"!==r&&\"terrarium\"!==r)return _('\"'+r+'\" is not a valid encoding type. Valid types include \"mapbox\" and \"terrarium\".');this.stride=e.height;var n=this.dim=e.height-2;this.data=new Uint32Array(e.data.buffer),this.encoding=r||\"mapbox\";for(var i=0;i<n;i++)this.data[this._idx(-1,i)]=this.data[this._idx(0,i)],this.data[this._idx(n,i)]=this.data[this._idx(n-1,i)],this.data[this._idx(i,-1)]=this.data[this._idx(i,0)],this.data[this._idx(i,n)]=this.data[this._idx(i,n-1)];this.data[this._idx(-1,-1)]=this.data[this._idx(0,0)],this.data[this._idx(n,-1)]=this.data[this._idx(n-1,0)],this.data[this._idx(-1,n)]=this.data[this._idx(0,n-1)],this.data[this._idx(n,n)]=this.data[this._idx(n-1,n-1)]};au.prototype.get=function(t,e){var r=new Uint8Array(this.data.buffer),n=4*this._idx(t,e);return(\"terrarium\"===this.encoding?this._unpackTerrarium:this._unpackMapbox)(r[n],r[n+1],r[n+2])},au.prototype.getUnpackVector=function(){return\"terrarium\"===this.encoding?[256,1,1/256,32768]:[6553.6,25.6,.1,1e4]},au.prototype._idx=function(t,e){if(t<-1||t>=this.dim+1||e<-1||e>=this.dim+1)throw new RangeError(\"out of range source coordinates for DEM data\");return(e+1)*this.stride+(t+1)},au.prototype._unpackMapbox=function(t,e,r){return(256*t*256+256*e+r)/10-1e4},au.prototype._unpackTerrarium=function(t,e,r){return 256*t+e+r/256-32768},au.prototype.getPixels=function(){return new Eo({width:this.stride,height:this.stride},new Uint8Array(this.data.buffer))},au.prototype.backfillBorder=function(t,e,r){if(this.dim!==t.dim)throw new Error(\"dem dimension mismatch\");var n=e*this.dim,i=e*this.dim+this.dim,a=r*this.dim,o=r*this.dim+this.dim;switch(e){case-1:n=i-1;break;case 1:i=n+1}switch(r){case-1:a=o-1;break;case 1:o=a+1}for(var s=-e*this.dim,l=-r*this.dim,c=a;c<o;c++)for(var u=n;u<i;u++)this.data[this._idx(u,c)]=t.data[this._idx(u+s,c+l)]},Nn(\"DEMData\",au);var ou=function(t){this._stringToNumber={},this._numberToString=[];for(var e=0;e<t.length;e++){var r=t[e];this._stringToNumber[r]=e,this._numberToString[e]=r}};ou.prototype.encode=function(t){return this._stringToNumber[t]},ou.prototype.decode=function(t){return this._numberToString[t]};var su=function(t,e,r,n,i){this.type=\"Feature\",this._vectorTileFeature=t,t._z=e,t._x=r,t._y=n,this.properties=t.properties,this.id=i},lu={geometry:{configurable:!0}};lu.geometry.get=function(){return void 0===this._geometry&&(this._geometry=this._vectorTileFeature.toGeoJSON(this._vectorTileFeature._x,this._vectorTileFeature._y,this._vectorTileFeature._z).geometry),this._geometry},lu.geometry.set=function(t){this._geometry=t},su.prototype.toJSON=function(){var t={geometry:this.geometry};for(var e in this)\"_geometry\"!==e&&\"_vectorTileFeature\"!==e&&(t[e]=this[e]);return t},Object.defineProperties(su.prototype,lu);var cu=function(){this.state={},this.stateChanges={},this.deletedStates={}};cu.prototype.updateState=function(t,e,r){var n=String(e);if(this.stateChanges[t]=this.stateChanges[t]||{},this.stateChanges[t][n]=this.stateChanges[t][n]||{},u(this.stateChanges[t][n],r),null===this.deletedStates[t])for(var i in this.deletedStates[t]={},this.state[t])i!==n&&(this.deletedStates[t][i]=null);else if(this.deletedStates[t]&&null===this.deletedStates[t][n])for(var a in this.deletedStates[t][n]={},this.state[t][n])r[a]||(this.deletedStates[t][n][a]=null);else for(var o in r){this.deletedStates[t]&&this.deletedStates[t][n]&&null===this.deletedStates[t][n][o]&&delete this.deletedStates[t][n][o]}},cu.prototype.removeFeatureState=function(t,e,r){if(!(null===this.deletedStates[t])){var n=String(e);if(this.deletedStates[t]=this.deletedStates[t]||{},r&&void 0!==e)null!==this.deletedStates[t][n]&&(this.deletedStates[t][n]=this.deletedStates[t][n]||{},this.deletedStates[t][n][r]=null);else if(void 0!==e){if(this.stateChanges[t]&&this.stateChanges[t][n])for(r in this.deletedStates[t][n]={},this.stateChanges[t][n])this.deletedStates[t][n][r]=null;else this.deletedStates[t][n]=null}else this.deletedStates[t]=null}},cu.prototype.getState=function(t,e){var r=String(e),n=this.state[t]||{},i=this.stateChanges[t]||{},a=u({},n[r],i[r]);if(null===this.deletedStates[t])return{};if(this.deletedStates[t]){var o=this.deletedStates[t][e];if(null===o)return{};for(var s in o)delete a[s]}return a},cu.prototype.initializeTileState=function(t,e){t.setFeatureState(this.state,e)},cu.prototype.coalesceChanges=function(t,e){var r={};for(var n in this.stateChanges){this.state[n]=this.state[n]||{};var i={};for(var a in this.stateChanges[n])this.state[n][a]||(this.state[n][a]={}),u(this.state[n][a],this.stateChanges[n][a]),i[a]=this.state[n][a];r[n]=i}for(var o in this.deletedStates){this.state[o]=this.state[o]||{};var s={};if(null===this.deletedStates[o])for(var l in this.state[o])s[l]={},this.state[o][l]={};else for(var c in this.deletedStates[o]){if(null===this.deletedStates[o][c])this.state[o][c]={};else for(var f=0,h=Object.keys(this.deletedStates[o][c]);f<h.length;f+=1){var p=h[f];delete this.state[o][c][p]}s[c]=this.state[o][c]}r[o]=r[o]||{},u(r[o],s)}if(this.stateChanges={},this.deletedStates={},0!==Object.keys(r).length)for(var d in t){t[d].setFeatureState(r,e)}};var uu=function(t,e){this.tileID=t,this.x=t.canonical.x,this.y=t.canonical.y,this.z=t.canonical.z,this.grid=new zn(8192,16,0),this.grid3D=new zn(8192,16,0),this.featureIndexArray=new fa,this.promoteId=e};function fu(t,e,r,n,i){return v(t,(function(t,a){var o=e instanceof _i?e.get(a):null;return o&&o.evaluate?o.evaluate(r,n,i):o}))}function hu(t){for(var e=1/0,r=1/0,n=-1/0,i=-1/0,a=0,o=t;a<o.length;a+=1){var s=o[a];e=Math.min(e,s.x),r=Math.min(r,s.y),n=Math.max(n,s.x),i=Math.max(i,s.y)}return{minX:e,minY:r,maxX:n,maxY:i}}function pu(t,e){return e-t}uu.prototype.insert=function(t,e,r,n,i,a){var o=this.featureIndexArray.length;this.featureIndexArray.emplaceBack(r,n,i);for(var s=a?this.grid3D:this.grid,l=0;l<e.length;l++){for(var c=e[l],u=[1/0,1/0,-1/0,-1/0],f=0;f<c.length;f++){var h=c[f];u[0]=Math.min(u[0],h.x),u[1]=Math.min(u[1],h.y),u[2]=Math.max(u[2],h.x),u[3]=Math.max(u[3],h.y)}u[0]<8192&&u[1]<8192&&u[2]>=0&&u[3]>=0&&s.insert(o,u[0],u[1],u[2],u[3])}},uu.prototype.loadVTLayers=function(){return this.vtLayers||(this.vtLayers=new Ls.VectorTile(new al(this.rawTileData)).layers,this.sourceLayerCoder=new ou(this.vtLayers?Object.keys(this.vtLayers).sort():[\"_geojsonTileLayer\"])),this.vtLayers},uu.prototype.query=function(t,e,r,n){var a=this;this.loadVTLayers();for(var o=t.params||{},s=8192/t.tileSize/t.scale,l=sn(o.filter),c=t.queryGeometry,u=t.queryPadding*s,f=hu(c),h=this.grid.query(f.minX-u,f.minY-u,f.maxX+u,f.maxY+u),p=hu(t.cameraQueryGeometry),d=this.grid3D.query(p.minX-u,p.minY-u,p.maxX+u,p.maxY+u,(function(e,r,n,a){return function(t,e,r,n,a){for(var o=0,s=t;o<s.length;o+=1){var l=s[o];if(e<=l.x&&r<=l.y&&n>=l.x&&a>=l.y)return!0}var c=[new i(e,r),new i(e,a),new i(n,a),new i(n,r)];if(t.length>2)for(var u=0,f=c;u<f.length;u+=1){if(io(t,f[u]))return!0}for(var h=0;h<t.length-1;h++){if(ao(t[h],t[h+1],c))return!0}return!1}(t.cameraQueryGeometry,e-u,r-u,n+u,a+u)})),m=0,g=d;m<g.length;m+=1){var v=g[m];h.push(v)}h.sort(pu);for(var y,x={},b=function(i){var u=h[i];if(u!==y){y=u;var f=a.featureIndexArray.get(u),p=null;a.loadMatchingFeature(x,f.bucketIndex,f.sourceLayerIndex,f.featureIndex,l,o.layers,o.availableImages,e,r,n,(function(e,r,n){return p||(p=Ya(e)),r.queryIntersectsFeature(c,e,n,p,a.z,t.transform,s,t.pixelPosMatrix)}))}},_=0;_<h.length;_++)b(_);return x},uu.prototype.loadMatchingFeature=function(t,e,r,n,i,a,o,s,l,c,u){var f=this.bucketLayerIDs[e];if(!a||function(t,e){for(var r=0;r<t.length;r++)if(e.indexOf(t[r])>=0)return!0;return!1}(a,f)){var h=this.sourceLayerCoder.decode(r),p=this.vtLayers[h].feature(n);if(i.filter(new pi(this.tileID.overscaledZ),p))for(var d=this.getId(p,h),m=0;m<f.length;m++){var g=f[m];if(!(a&&a.indexOf(g)<0)){var v=s[g];if(v){var y={};void 0!==d&&c&&(y=c.getState(v.sourceLayer||\"_geojsonTileLayer\",d));var x=l[g];x.paint=fu(x.paint,v.paint,p,y,o),x.layout=fu(x.layout,v.layout,p,y,o);var b=!u||u(p,v,y);if(b){var _=new su(p,this.z,this.x,this.y,d);_.layer=x;var w=t[g];void 0===w&&(w=t[g]=[]),w.push({featureIndex:n,feature:_,intersectionZ:b})}}}}}},uu.prototype.lookupSymbolFeatures=function(t,e,r,n,i,a,o,s){var l={};this.loadVTLayers();for(var c=sn(i),u=0,f=t;u<f.length;u+=1){var h=f[u];this.loadMatchingFeature(l,r,n,h,c,a,o,s,e)}return l},uu.prototype.hasLayer=function(t){for(var e=0,r=this.bucketLayerIDs;e<r.length;e+=1)for(var n=0,i=r[e];n<i.length;n+=1){if(t===i[n])return!0}return!1},uu.prototype.getId=function(t,e){var r=t.id;if(this.promoteId){var n=\"string\"==typeof this.promoteId?this.promoteId:this.promoteId[e];\"boolean\"==typeof(r=t.properties[n])&&(r=Number(r))}return r},Nn(\"FeatureIndex\",uu,{omit:[\"rawTileData\",\"sourceLayerCoder\"]});var du=function(t,e){this.tileID=t,this.uid=h(),this.uses=0,this.tileSize=e,this.buckets={},this.expirationTime=null,this.queryPadding=0,this.hasSymbolBuckets=!1,this.hasRTLText=!1,this.dependencies={},this.expiredRequestCount=0,this.state=\"loading\"};du.prototype.registerFadeDuration=function(t){var e=t+this.timeAdded;e<R.now()||this.fadeEndTime&&e<this.fadeEndTime||(this.fadeEndTime=e)},du.prototype.wasRequested=function(){return\"errored\"===this.state||\"loaded\"===this.state||\"reloading\"===this.state},du.prototype.loadVectorData=function(t,e,r){if(this.hasData()&&this.unloadVectorData(),this.state=\"loaded\",t){for(var n in t.featureIndex&&(this.latestFeatureIndex=t.featureIndex,t.rawTileData?(this.latestRawTileData=t.rawTileData,this.latestFeatureIndex.rawTileData=t.rawTileData):this.latestRawTileData&&(this.latestFeatureIndex.rawTileData=this.latestRawTileData)),this.collisionBoxArray=t.collisionBoxArray,this.buckets=function(t,e){var r={};if(!e)return r;for(var n=function(){var t=a[i],n=t.layerIds.map((function(t){return e.getLayer(t)})).filter(Boolean);if(0!==n.length){t.layers=n,t.stateDependentLayerIds&&(t.stateDependentLayers=t.stateDependentLayerIds.map((function(t){return n.filter((function(e){return e.id===t}))[0]})));for(var o=0,s=n;o<s.length;o+=1){var l=s[o];r[l.id]=t}}},i=0,a=t;i<a.length;i+=1)n();return r}(t.buckets,e.style),this.hasSymbolBuckets=!1,this.buckets){var i=this.buckets[n];if(i instanceof Ac){if(this.hasSymbolBuckets=!0,!r)break;i.justReloaded=!0}}if(this.hasRTLText=!1,this.hasSymbolBuckets)for(var a in this.buckets){var o=this.buckets[a];if(o instanceof Ac&&o.hasRTLText){this.hasRTLText=!0,hi.isLoading()||hi.isLoaded()||\"deferred\"!==ui()||fi();break}}for(var s in this.queryPadding=0,this.buckets){var l=this.buckets[s];this.queryPadding=Math.max(this.queryPadding,e.style.getLayer(s).queryRadius(l))}t.imageAtlas&&(this.imageAtlas=t.imageAtlas),t.glyphAtlasImage&&(this.glyphAtlasImage=t.glyphAtlasImage)}else this.collisionBoxArray=new na},du.prototype.unloadVectorData=function(){for(var t in this.buckets)this.buckets[t].destroy();this.buckets={},this.imageAtlasTexture&&this.imageAtlasTexture.destroy(),this.imageAtlas&&(this.imageAtlas=null),this.glyphAtlasTexture&&this.glyphAtlasTexture.destroy(),this.latestFeatureIndex=null,this.state=\"unloaded\"},du.prototype.getBucket=function(t){return this.buckets[t.id]},du.prototype.upload=function(t){for(var e in this.buckets){var r=this.buckets[e];r.uploadPending()&&r.upload(t)}var n=t.gl;this.imageAtlas&&!this.imageAtlas.uploaded&&(this.imageAtlasTexture=new Vc(t,this.imageAtlas.image,n.RGBA),this.imageAtlas.uploaded=!0),this.glyphAtlasImage&&(this.glyphAtlasTexture=new Vc(t,this.glyphAtlasImage,n.ALPHA),this.glyphAtlasImage=null)},du.prototype.prepare=function(t){this.imageAtlas&&this.imageAtlas.patchUpdatedImages(t,this.imageAtlasTexture)},du.prototype.queryRenderedFeatures=function(t,e,r,n,i,a,o,s,l,c){return this.latestFeatureIndex&&this.latestFeatureIndex.rawTileData?this.latestFeatureIndex.query({queryGeometry:n,cameraQueryGeometry:i,scale:a,tileSize:this.tileSize,pixelPosMatrix:c,transform:s,params:o,queryPadding:this.queryPadding*l},t,e,r):{}},du.prototype.querySourceFeatures=function(t,e){var r=this.latestFeatureIndex;if(r&&r.rawTileData){var n=r.loadVTLayers(),i=e?e.sourceLayer:\"\",a=n._geojsonTileLayer||n[i];if(a)for(var o=sn(e&&e.filter),s=this.tileID.canonical,l=s.z,c=s.x,u=s.y,f={z:l,x:c,y:u},h=0;h<a.length;h++){var p=a.feature(h);if(o.filter(new pi(this.tileID.overscaledZ),p)){var d=r.getId(p,i),m=new su(p,l,c,u,d);m.tile=f,t.push(m)}}}},du.prototype.hasData=function(){return\"loaded\"===this.state||\"reloading\"===this.state||\"expired\"===this.state},du.prototype.patternsLoaded=function(){return this.imageAtlas&&!!Object.keys(this.imageAtlas.patternPositions).length},du.prototype.setExpiryData=function(t){var e=this.expirationTime;if(t.cacheControl){var r=M(t.cacheControl);r[\"max-age\"]&&(this.expirationTime=Date.now()+1e3*r[\"max-age\"])}else t.expires&&(this.expirationTime=new Date(t.expires).getTime());if(this.expirationTime){var n=Date.now(),i=!1;if(this.expirationTime>n)i=!1;else if(e)if(this.expirationTime<e)i=!0;else{var a=this.expirationTime-e;a?this.expirationTime=n+Math.max(a,3e4):i=!0}else i=!0;i?(this.expiredRequestCount++,this.state=\"expired\"):this.expiredRequestCount=0}},du.prototype.getExpiryTimeout=function(){if(this.expirationTime)return this.expiredRequestCount?1e3*(1<<Math.min(this.expiredRequestCount-1,31)):Math.min(this.expirationTime-(new Date).getTime(),Math.pow(2,31)-1)},du.prototype.setFeatureState=function(t,e){if(this.latestFeatureIndex&&this.latestFeatureIndex.rawTileData&&0!==Object.keys(t).length){var r=this.latestFeatureIndex.loadVTLayers();for(var n in this.buckets)if(e.style.hasLayer(n)){var i=this.buckets[n],a=i.layers[0].sourceLayer||\"_geojsonTileLayer\",o=r[a],s=t[a];if(o&&s&&0!==Object.keys(s).length){i.update(s,o,this.imageAtlas&&this.imageAtlas.patternPositions||{});var l=e&&e.style&&e.style.getLayer(n);l&&(this.queryPadding=Math.max(this.queryPadding,l.queryRadius(i)))}}}},du.prototype.holdingForFade=function(){return void 0!==this.symbolFadeHoldUntil},du.prototype.symbolFadeFinished=function(){return!this.symbolFadeHoldUntil||this.symbolFadeHoldUntil<R.now()},du.prototype.clearFadeHold=function(){this.symbolFadeHoldUntil=void 0},du.prototype.setHoldDuration=function(t){this.symbolFadeHoldUntil=R.now()+t},du.prototype.setDependencies=function(t,e){for(var r={},n=0,i=e;n<i.length;n+=1){r[i[n]]=!0}this.dependencies[t]=r},du.prototype.hasDependency=function(t,e){for(var r=0,n=t;r<n.length;r+=1){var i=n[r],a=this.dependencies[i];if(a)for(var o=0,s=e;o<s.length;o+=1){if(a[s[o]])return!0}}return!1};var 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r},t.makeRequest=yt,t.mapObject=v,t.mercatorXfromLng=Jc,t.mercatorYfromLat=Kc,t.mercatorZfromAltitude=Qc,t.mul=mo,t.multiply=po,t.mvt=Ls,t.normalize=function(t,e){var r=e[0],n=e[1],i=e[2],a=r*r+n*n+i*i;return a>0&&(a=1/Math.sqrt(a)),t[0]=e[0]*a,t[1]=e[1]*a,t[2]=e[2]*a,t},t.number=He,t.offscreenCanvasSupported=pt,t.ortho=function(t,e,r,n,i,a,o){var s=1/(e-r),l=1/(n-i),c=1/(a-o);return t[0]=-2*s,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=-2*l,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=2*c,t[11]=0,t[12]=(e+r)*s,t[13]=(i+n)*l,t[14]=(o+a)*c,t[15]=1,t},t.parseGlyphPBF=function(t){return new al(t).readFields(Tl,[])},t.pbf=al,t.performSymbolLayout=function(t,e,r,n,i,a,o){t.createArrays();var s=512*t.overscaling;t.tilePixelRatio=8192/s,t.compareText={},t.iconsNeedLinear=!1;var l=t.layers[0].layout,c=t.layers[0]._unevaluatedLayout._values,u={};if(\"composite\"===t.textSizeData.kind){var f=t.textSizeData,h=f.minZoom,p=f.maxZoom;u.compositeTextSizes=[c[\"text-size\"].possiblyEvaluate(new 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g=(c+1)%2;(0===c?r-i<=d:n-i<=m)&&(o.push(f),o.push(p-1),o.push(g)),(0===c?r+i>=d:n+i>=m)&&(o.push(p+1),o.push(u),o.push(g))}}return s}(this.ids,this.coords,t,e,r,this.nodeSize)};var j={minZoom:0,maxZoom:16,radius:40,extent:512,nodeSize:64,log:!1,generateId:!1,reduce:null,map:function(t){return t}},U=function(t){this.options=X(Object.create(j),t),this.trees=new Array(this.options.maxZoom+1)};function V(t,e,r,n,i){return{x:t,y:e,zoom:1/0,id:r,parentId:-1,numPoints:n,properties:i}}function q(t,e){var r=t.geometry.coordinates,n=r[0],i=r[1];return{x:Y(n),y:W(i),zoom:1/0,index:e,parentId:-1}}function H(t){return{type:\"Feature\",id:t.id,properties:G(t),geometry:{type:\"Point\",coordinates:[(n=t.x,360*(n-.5)),(e=t.y,r=(180-360*e)*Math.PI/180,360*Math.atan(Math.exp(r))/Math.PI-90)]}};var e,r,n}function G(t){var e=t.numPoints,r=e>=1e4?Math.round(e/1e3)+\"k\":e>=1e3?Math.round(e/100)/10+\"k\":e;return 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r=0;r<e.length;r+=3)t.minX=Math.min(t.minX,e[r]),t.minY=Math.min(t.minY,e[r+1]),t.maxX=Math.max(t.maxX,e[r]),t.maxY=Math.max(t.maxY,e[r+1])}function tt(t,e,r,n){if(e.geometry){var i=e.geometry.coordinates,a=e.geometry.type,o=Math.pow(r.tolerance/((1<<r.maxZoom)*r.extent),2),s=[],l=e.id;if(r.promoteId?l=e.properties[r.promoteId]:r.generateId&&(l=n||0),\"Point\"===a)et(i,s);else if(\"MultiPoint\"===a)for(var c=0;c<i.length;c++)et(i[c],s);else if(\"LineString\"===a)rt(i,s,o,!1);else if(\"MultiLineString\"===a){if(r.lineMetrics){for(c=0;c<i.length;c++)s=[],rt(i[c],s,o,!1),t.push(Q(l,\"LineString\",s,e.properties));return}nt(i,s,o,!1)}else if(\"Polygon\"===a)nt(i,s,o,!0);else{if(\"MultiPolygon\"!==a){if(\"GeometryCollection\"===a){for(c=0;c<e.geometry.geometries.length;c++)tt(t,{id:l,geometry:e.geometry.geometries[c],properties:e.properties},r,n);return}throw new Error(\"Input data is not a valid GeoJSON object.\")}for(c=0;c<i.length;c++){var 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u=t[c],f=u.geometry,h=u.type,p=0===i?u.minX:u.minY,d=0===i?u.maxX:u.maxY;if(p>=r&&d<n)l.push(u);else if(!(d<r||p>=n)){var m=[];if(\"Point\"===h||\"MultiPoint\"===h)st(f,m,r,n,i);else if(\"LineString\"===h)lt(f,m,r,n,i,!1,s.lineMetrics);else if(\"MultiLineString\"===h)ut(f,m,r,n,i,!1);else if(\"Polygon\"===h)ut(f,m,r,n,i,!0);else if(\"MultiPolygon\"===h)for(var g=0;g<f.length;g++){var v=[];ut(f[g],v,r,n,i,!0),v.length&&m.push(v)}if(m.length){if(s.lineMetrics&&\"LineString\"===h){for(g=0;g<m.length;g++)l.push(Q(u.id,h,m[g],u.tags));continue}\"LineString\"!==h&&\"MultiLineString\"!==h||(1===m.length?(h=\"LineString\",m=m[0]):h=\"MultiLineString\"),\"Point\"!==h&&\"MultiPoint\"!==h||(h=3===m.length?\"Point\":\"MultiPoint\"),l.push(Q(u.id,h,m,u.tags))}}}return l.length?l:null}function st(t,e,r,n,i){for(var a=0;a<t.length;a+=3){var o=t[a+i];o>=r&&o<=n&&(e.push(t[a]),e.push(t[a+1]),e.push(t[a+2]))}}function lt(t,e,r,n,i,a,o){for(var 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r=[],n=0;n<t.length;n++){var i,a=t[n],o=a.type;if(\"Point\"===o||\"MultiPoint\"===o||\"LineString\"===o)i=mt(a.geometry,e);else if(\"MultiLineString\"===o||\"Polygon\"===o){i=[];for(var s=0;s<a.geometry.length;s++)i.push(mt(a.geometry[s],e))}else if(\"MultiPolygon\"===o)for(i=[],s=0;s<a.geometry.length;s++){for(var l=[],c=0;c<a.geometry[s].length;c++)l.push(mt(a.geometry[s][c],e));i.push(l)}r.push(Q(a.id,o,i,a.tags))}return r}function mt(t,e){var r=[];r.size=t.size,void 0!==t.start&&(r.start=t.start,r.end=t.end);for(var n=0;n<t.length;n+=3)r.push(t[n]+e,t[n+1],t[n+2]);return r}function gt(t,e){if(t.transformed)return t;var r,n,i,a=1<<t.z,o=t.x,s=t.y;for(r=0;r<t.features.length;r++){var l=t.features[r],c=l.geometry,u=l.type;if(l.geometry=[],1===u)for(n=0;n<c.length;n+=2)l.geometry.push(vt(c[n],c[n+1],e,a,o,s));else for(n=0;n<c.length;n++){var f=[];for(i=0;i<c[n].length;i+=2)f.push(vt(c[n][i],c[n][i+1],e,a,o,s));l.geometry.push(f)}}return t.transformed=!0,t}function 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u={},e.tags)u[f]=e.tags[f];u.mapbox_clip_start=i.start/i.size,u.mapbox_clip_end=i.end/i.size}var h={geometry:o,type:\"Polygon\"===a||\"MultiPolygon\"===a?3:\"LineString\"===a||\"MultiLineString\"===a?2:1,tags:u};null!==e.id&&(h.id=e.id),t.features.push(h)}}function bt(t,e,r,n,i,a){var o=n*n;if(n>0&&e.size<(i?o:n))r.numPoints+=e.length/3;else{for(var s=[],l=0;l<e.length;l+=3)(0===n||e[l+2]>o)&&(r.numSimplified++,s.push(e[l]),s.push(e[l+1])),r.numPoints++;i&&function(t,e){for(var r=0,n=0,i=t.length,a=i-2;n<i;a=n,n+=2)r+=(t[n]-t[a])*(t[n+1]+t[a+1]);if(r>0===e)for(n=0,i=t.length;n<i/2;n+=2){var o=t[n],s=t[n+1];t[n]=t[i-2-n],t[n+1]=t[i-1-n],t[i-2-n]=o,t[i-1-n]=s}}(s,a),t.push(s)}}function _t(t,e){var r=(e=this.options=function(t,e){for(var r in e)t[r]=e[r];return t}(Object.create(this.options),e)).debug;if(r&&console.time(\"preprocess data\"),e.maxZoom<0||e.maxZoom>24)throw new Error(\"maxZoom should be in the 0-24 range\");if(e.promoteId&&e.generateId)throw new Error(\"promoteId and generateId cannot be used together.\");var n=function(t,e){var r=[];if(\"FeatureCollection\"===t.type)for(var n=0;n<t.features.length;n++)tt(r,t.features[n],e,n);else\"Feature\"===t.type?tt(r,t,e):tt(r,{geometry:t},e);return r}(t,e);this.tiles={},this.tileCoords=[],r&&(console.timeEnd(\"preprocess data\"),console.log(\"index: maxZoom: %d, maxPoints: %d\",e.indexMaxZoom,e.indexMaxPoints),console.time(\"generate tiles\"),this.stats={},this.total=0),(n=function(t,e){var r=e.buffer/e.extent,n=t,i=ot(t,1,-1-r,r,0,-1,2,e),a=ot(t,1,1-r,2+r,0,-1,2,e);return(i||a)&&(n=ot(t,1,-r,1+r,0,-1,2,e)||[],i&&(n=dt(i,1).concat(n)),a&&(n=n.concat(dt(a,-1)))),n}(n,e)).length&&this.splitTile(n,0,0,0),r&&(n.length&&console.log(\"features: %d, points: %d\",this.tiles[0].numFeatures,this.tiles[0].numPoints),console.timeEnd(\"generate tiles\"),console.log(\"tiles generated:\",this.total,JSON.stringify(this.stats)))}function wt(t,e,r){return 32*((1<<t)*r+e)+t}function Tt(t,e){var r=t.tileID.canonical;if(!this._geoJSONIndex)return e(null,null);var n=this._geoJSONIndex.getTile(r.z,r.x,r.y);if(!n)return e(null,null);var i=new g(n.features),a=_(i);0===a.byteOffset&&a.byteLength===a.buffer.byteLength||(a=new Uint8Array(a)),e(null,{vectorTile:i,rawData:a.buffer})}U.prototype.load=function(t){var e=this.options,r=e.log,n=e.minZoom,i=e.maxZoom,a=e.nodeSize;r&&console.time(\"total time\");var o=\"prepare \"+t.length+\" points\";r&&console.time(o),this.points=t;for(var s=[],l=0;l<t.length;l++)t[l].geometry&&s.push(q(t[l],l));this.trees[i+1]=new N(s,Z,J,a,Float32Array),r&&console.timeEnd(o);for(var c=i;c>=n;c--){var u=+Date.now();s=this._cluster(s,c),this.trees[c]=new N(s,Z,J,a,Float32Array),r&&console.log(\"z%d: %d clusters in %dms\",c,s.length,+Date.now()-u)}return r&&console.timeEnd(\"total time\"),this},U.prototype.getClusters=function(t,e){var r=((t[0]+180)%360+360)%360-180,n=Math.max(-90,Math.min(90,t[1])),i=180===t[2]?180:((t[2]+180)%360+360)%360-180,a=Math.max(-90,Math.min(90,t[3]));if(t[2]-t[0]>=360)r=-180,i=180;else if(r>i){var o=this.getClusters([r,n,180,a],e),s=this.getClusters([-180,n,i,a],e);return o.concat(s)}for(var l=this.trees[this._limitZoom(e)],c=[],u=0,f=l.range(Y(r),W(a),Y(i),W(n));u<f.length;u+=1){var h=f[u],p=l.points[h];c.push(p.numPoints?H(p):this.points[p.index])}return c},U.prototype.getChildren=function(t){var e=this._getOriginId(t),r=this._getOriginZoom(t),n=\"No cluster with the specified id.\",i=this.trees[r];if(!i)throw new Error(n);var a=i.points[e];if(!a)throw new Error(n);for(var o=this.options.radius/(this.options.extent*Math.pow(2,r-1)),s=[],l=0,c=i.within(a.x,a.y,o);l<c.length;l+=1){var u=c[l],f=i.points[u];f.parentId===t&&s.push(f.numPoints?H(f):this.points[f.index])}if(0===s.length)throw new Error(n);return s},U.prototype.getLeaves=function(t,e,r){e=e||10,r=r||0;var n=[];return this._appendLeaves(n,t,e,r,0),n},U.prototype.getTile=function(t,e,r){var n=this.trees[this._limitZoom(t)],i=Math.pow(2,t),a=this.options,o=a.extent,s=a.radius/o,l=(r-s)/i,c=(r+1+s)/i,u={features:[]};return this._addTileFeatures(n.range((e-s)/i,l,(e+1+s)/i,c),n.points,e,r,i,u),0===e&&this._addTileFeatures(n.range(1-s/i,l,1,c),n.points,i,r,i,u),e===i-1&&this._addTileFeatures(n.range(0,l,s/i,c),n.points,-1,r,i,u),u.features.length?u:null},U.prototype.getClusterExpansionZoom=function(t){for(var e=this._getOriginZoom(t)-1;e<=this.options.maxZoom;){var r=this.getChildren(t);if(e++,1!==r.length)break;t=r[0].properties.cluster_id}return e},U.prototype._appendLeaves=function(t,e,r,n,i){for(var a=0,o=this.getChildren(e);a<o.length;a+=1){var s=o[a],l=s.properties;if(l&&l.cluster?i+l.point_count<=n?i+=l.point_count:i=this._appendLeaves(t,l.cluster_id,r,n,i):i<n?i++:t.push(s),t.length===r)break}return i},U.prototype._addTileFeatures=function(t,e,r,n,i,a){for(var o=0,s=t;o<s.length;o+=1){var l=e[s[o]],c=l.numPoints,u={type:1,geometry:[[Math.round(this.options.extent*(l.x*i-r)),Math.round(this.options.extent*(l.y*i-n))]],tags:c?G(l):this.points[l.index].properties},f=void 0;c?f=l.id:this.options.generateId?f=l.index:this.points[l.index].id&&(f=this.points[l.index].id),void 0!==f&&(u.id=f),a.features.push(u)}},U.prototype._limitZoom=function(t){return Math.max(this.options.minZoom,Math.min(t,this.options.maxZoom+1))},U.prototype._cluster=function(t,e){for(var r=[],n=this.options,i=n.radius,a=n.extent,o=n.reduce,s=i/(a*Math.pow(2,e)),l=0;l<t.length;l++){var c=t[l];if(!(c.zoom<=e)){c.zoom=e;for(var u=this.trees[e+1],f=u.within(c.x,c.y,s),h=c.numPoints||1,p=c.x*h,d=c.y*h,m=o&&h>1?this._map(c,!0):null,g=(l<<5)+(e+1)+this.points.length,v=0,y=f;v<y.length;v+=1){var x=y[v],b=u.points[x];if(!(b.zoom<=e)){b.zoom=e;var _=b.numPoints||1;p+=b.x*_,d+=b.y*_,h+=_,b.parentId=g,o&&(m||(m=this._map(c,!0)),o(m,this._map(b)))}}1===h?r.push(c):(c.parentId=g,r.push(V(p/h,d/h,g,h,m)))}}return r},U.prototype._getOriginId=function(t){return t-this.points.length>>5},U.prototype._getOriginZoom=function(t){return(t-this.points.length)%32},U.prototype._map=function(t,e){if(t.numPoints)return e?X({},t.properties):t.properties;var r=this.points[t.index].properties,n=this.options.map(r);return e&&n===r?X({},n):n},_t.prototype.options={maxZoom:14,indexMaxZoom:5,indexMaxPoints:1e5,tolerance:3,extent:4096,buffer:64,lineMetrics:!1,promoteId:null,generateId:!1,debug:0},_t.prototype.splitTile=function(t,e,r,n,i,a,o){for(var s=[t,e,r,n],l=this.options,c=l.debug;s.length;){n=s.pop(),r=s.pop(),e=s.pop(),t=s.pop();var u=1<<e,f=wt(e,r,n),h=this.tiles[f];if(!h&&(c>1&&console.time(\"creation\"),h=this.tiles[f]=yt(t,e,r,n,l),this.tileCoords.push({z:e,x:r,y:n}),c)){c>1&&(console.log(\"tile z%d-%d-%d (features: %d, points: %d, simplified: %d)\",e,r,n,h.numFeatures,h.numPoints,h.numSimplified),console.timeEnd(\"creation\"));var p=\"z\"+e;this.stats[p]=(this.stats[p]||0)+1,this.total++}if(h.source=t,i){if(e===l.maxZoom||e===i)continue;var d=1<<i-e;if(r!==Math.floor(a/d)||n!==Math.floor(o/d))continue}else if(e===l.indexMaxZoom||h.numPoints<=l.indexMaxPoints)continue;if(h.source=null,0!==t.length){c>1&&console.time(\"clipping\");var m,g,v,y,x,b,_=.5*l.buffer/l.extent,w=.5-_,T=.5+_,k=1+_;m=g=v=y=null,x=ot(t,u,r-_,r+T,0,h.minX,h.maxX,l),b=ot(t,u,r+w,r+k,0,h.minX,h.maxX,l),t=null,x&&(m=ot(x,u,n-_,n+T,1,h.minY,h.maxY,l),g=ot(x,u,n+w,n+k,1,h.minY,h.maxY,l),x=null),b&&(v=ot(b,u,n-_,n+T,1,h.minY,h.maxY,l),y=ot(b,u,n+w,n+k,1,h.minY,h.maxY,l),b=null),c>1&&console.timeEnd(\"clipping\"),s.push(m||[],e+1,2*r,2*n),s.push(g||[],e+1,2*r,2*n+1),s.push(v||[],e+1,2*r+1,2*n),s.push(y||[],e+1,2*r+1,2*n+1)}}},_t.prototype.getTile=function(t,e,r){var n=this.options,i=n.extent,a=n.debug;if(t<0||t>24)return null;var o=1<<t,s=wt(t,e=(e%o+o)%o,r);if(this.tiles[s])return gt(this.tiles[s],i);a>1&&console.log(\"drilling down to z%d-%d-%d\",t,e,r);for(var l,c=t,u=e,f=r;!l&&c>0;)c--,u=Math.floor(u/2),f=Math.floor(f/2),l=this.tiles[wt(c,u,f)];return l&&l.source?(a>1&&console.log(\"found parent tile z%d-%d-%d\",c,u,f),a>1&&console.time(\"drilling down\"),this.splitTile(l.source,c,u,f,t,e,r),a>1&&console.timeEnd(\"drilling down\"),this.tiles[s]?gt(this.tiles[s],i):null):null};var kt=function(e){function r(t,r,n,i){e.call(this,t,r,n,Tt),i&&(this.loadGeoJSON=i)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.loadData=function(t,e){this._pendingCallback&&this._pendingCallback(null,{abandoned:!0}),this._pendingCallback=e,this._pendingLoadDataParams=t,this._state&&\"Idle\"!==this._state?this._state=\"NeedsLoadData\":(this._state=\"Coalescing\",this._loadData())},r.prototype._loadData=function(){var e=this;if(this._pendingCallback&&this._pendingLoadDataParams){var r=this._pendingCallback,n=this._pendingLoadDataParams;delete this._pendingCallback,delete this._pendingLoadDataParams;var i=!!(n&&n.request&&n.request.collectResourceTiming)&&new t.RequestPerformance(n.request);this.loadGeoJSON(n,(function(a,o){if(a||!o)return r(a);if(\"object\"!=typeof o)return r(new Error(\"Input data given to '\"+n.source+\"' is not a valid GeoJSON object.\"));f(o,!0);try{e._geoJSONIndex=n.cluster?new U(function(e){var r=e.superclusterOptions,n=e.clusterProperties;if(!n||!r)return r;for(var i={},a={},o={accumulated:null,zoom:0},s={properties:null},l=Object.keys(n),c=0,u=l;c<u.length;c+=1){var f=u[c],h=n[f],p=h[0],d=h[1],m=t.createExpression(d),g=t.createExpression(\"string\"==typeof p?[p,[\"accumulated\"],[\"get\",f]]:p);i[f]=m.value,a[f]=g.value}return r.map=function(t){s.properties=t;for(var e={},r=0,n=l;r<n.length;r+=1){var a=n[r];e[a]=i[a].evaluate(o,s)}return e},r.reduce=function(t,e){s.properties=e;for(var r=0,n=l;r<n.length;r+=1){var i=n[r];o.accumulated=t[i],t[i]=a[i].evaluate(o,s)}},r}(n)).load(o.features):function(t,e){return new _t(t,e)}(o,n.geojsonVtOptions)}catch(a){return r(a)}e.loaded={};var s={};if(i){var l=i.finish();l&&(s.resourceTiming={},s.resourceTiming[n.source]=JSON.parse(JSON.stringify(l)))}r(null,s)}))}},r.prototype.coalesce=function(){\"Coalescing\"===this._state?this._state=\"Idle\":\"NeedsLoadData\"===this._state&&(this._state=\"Coalescing\",this._loadData())},r.prototype.reloadTile=function(t,r){var n=this.loaded,i=t.uid;return n&&n[i]?e.prototype.reloadTile.call(this,t,r):this.loadTile(t,r)},r.prototype.loadGeoJSON=function(e,r){if(e.request)t.getJSON(e.request,r);else{if(\"string\"!=typeof e.data)return r(new Error(\"Input data given to '\"+e.source+\"' is not a valid GeoJSON object.\"));try{return r(null,JSON.parse(e.data))}catch(t){return r(new Error(\"Input data given to '\"+e.source+\"' is not a valid GeoJSON 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registered.\");t.plugin.applyArabicShaping=e.applyArabicShaping,t.plugin.processBidirectionalText=e.processBidirectionalText,t.plugin.processStyledBidirectionalText=e.processStyledBidirectionalText}};return Mt.prototype.setReferrer=function(t,e){this.referrer=e},Mt.prototype.setImages=function(t,e,r){for(var n in this.availableImages[t]=e,this.workerSources[t]){var i=this.workerSources[t][n];for(var a in 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t.Point(r.clientX-n.left-e.clientLeft,r.clientY-n.top-e.clientTop)},r.touchPos=function(e,r){for(var n=e.getBoundingClientRect(),i=[],a=0;a<r.length;a++)i.push(new t.Point(r[a].clientX-n.left-e.clientLeft,r[a].clientY-n.top-e.clientTop));return i},r.mouseButton=function(e){return void 0!==t.window.InstallTrigger&&2===e.button&&e.ctrlKey&&t.window.navigator.platform.toUpperCase().indexOf(\"MAC\")>=0?0:e.button},r.remove=function(t){t.parentNode&&t.parentNode.removeChild(t)};var h=function(e){function r(){e.call(this),this.images={},this.updatedImages={},this.callbackDispatchedThisFrame={},this.loaded=!1,this.requestors=[],this.patterns={},this.atlasImage=new t.RGBAImage({width:1,height:1}),this.dirty=!0}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.isLoaded=function(){return this.loaded},r.prototype.setLoaded=function(t){if(this.loaded!==t&&(this.loaded=t,t)){for(var e=0,r=this.requestors;e<r.length;e+=1){var n=r[e],i=n.ids,a=n.callback;this._notify(i,a)}this.requestors=[]}},r.prototype.getImage=function(t){return this.images[t]},r.prototype.addImage=function(t,e){this._validate(t,e)&&(this.images[t]=e)},r.prototype._validate=function(e,r){var n=!0;return this._validateStretch(r.stretchX,r.data&&r.data.width)||(this.fire(new t.ErrorEvent(new Error('Image \"'+e+'\" has invalid \"stretchX\" value'))),n=!1),this._validateStretch(r.stretchY,r.data&&r.data.height)||(this.fire(new t.ErrorEvent(new Error('Image \"'+e+'\" has invalid \"stretchY\" value'))),n=!1),this._validateContent(r.content,r)||(this.fire(new t.ErrorEvent(new Error('Image \"'+e+'\" has invalid \"content\" value'))),n=!1),n},r.prototype._validateStretch=function(t,e){if(!t)return!0;for(var r=0,n=0,i=t;n<i.length;n+=1){var a=i[n];if(a[0]<r||a[1]<a[0]||e<a[1])return!1;r=a[1]}return!0},r.prototype._validateContent=function(t,e){return!t||4===t.length&&(!(t[0]<0||e.data.width<t[0])&&(!(t[1]<0||e.data.height<t[1])&&(!(t[2]<0||e.data.width<t[2])&&(!(t[3]<0||e.data.height<t[3])&&(!(t[2]<t[0])&&!(t[3]<t[1]))))))},r.prototype.updateImage=function(t,e){var r=this.images[t];e.version=r.version+1,this.images[t]=e,this.updatedImages[t]=!0},r.prototype.removeImage=function(t){var e=this.images[t];delete this.images[t],delete this.patterns[t],e.userImage&&e.userImage.onRemove&&e.userImage.onRemove()},r.prototype.listImages=function(){return Object.keys(this.images)},r.prototype.getImages=function(t,e){var r=!0;if(!this.isLoaded())for(var n=0,i=t;n<i.length;n+=1){var a=i[n];this.images[a]||(r=!1)}this.isLoaded()||r?this._notify(t,e):this.requestors.push({ids:t,callback:e})},r.prototype._notify=function(e,r){for(var n={},i=0,a=e;i<a.length;i+=1){var o=a[i];this.images[o]||this.fire(new t.Event(\"styleimagemissing\",{id:o}));var s=this.images[o];s?n[o]={data:s.data.clone(),pixelRatio:s.pixelRatio,sdf:s.sdf,version:s.version,stretchX:s.stretchX,stretchY:s.stretchY,content:s.content,hasRenderCallback:Boolean(s.userImage&&s.userImage.render)}:t.warnOnce('Image \"'+o+'\" could not be loaded. Please make sure you have added the image with map.addImage() or a \"sprite\" property in your style. You can provide missing images by listening for the \"styleimagemissing\" map event.')}r(null,n)},r.prototype.getPixelSize=function(){var t=this.atlasImage;return{width:t.width,height:t.height}},r.prototype.getPattern=function(e){var r=this.patterns[e],n=this.getImage(e);if(!n)return null;if(r&&r.position.version===n.version)return r.position;if(r)r.position.version=n.version;else{var i={w:n.data.width+2,h:n.data.height+2,x:0,y:0},a=new t.ImagePosition(i,n);this.patterns[e]={bin:i,position:a}}return this._updatePatternAtlas(),this.patterns[e].position},r.prototype.bind=function(e){var r=e.gl;this.atlasTexture?this.dirty&&(this.atlasTexture.update(this.atlasImage),this.dirty=!1):this.atlasTexture=new t.Texture(e,this.atlasImage,r.RGBA),this.atlasTexture.bind(r.LINEAR,r.CLAMP_TO_EDGE)},r.prototype._updatePatternAtlas=function(){var e=[];for(var r in this.patterns)e.push(this.patterns[r].bin);var n=t.potpack(e),i=n.w,a=n.h,o=this.atlasImage;for(var s in o.resize({width:i||1,height:a||1}),this.patterns){var l=this.patterns[s].bin,c=l.x+1,u=l.y+1,f=this.images[s].data,h=f.width,p=f.height;t.RGBAImage.copy(f,o,{x:0,y:0},{x:c,y:u},{width:h,height:p}),t.RGBAImage.copy(f,o,{x:0,y:p-1},{x:c,y:u-1},{width:h,height:1}),t.RGBAImage.copy(f,o,{x:0,y:0},{x:c,y:u+p},{width:h,height:1}),t.RGBAImage.copy(f,o,{x:h-1,y:0},{x:c-1,y:u},{width:1,height:p}),t.RGBAImage.copy(f,o,{x:0,y:0},{x:c+h,y:u},{width:1,height:p})}this.dirty=!0},r.prototype.beginFrame=function(){this.callbackDispatchedThisFrame={}},r.prototype.dispatchRenderCallbacks=function(t){for(var e=0,r=t;e<r.length;e+=1){var n=r[e];if(!this.callbackDispatchedThisFrame[n]){this.callbackDispatchedThisFrame[n]=!0;var i=this.images[n];f(i)&&this.updateImage(n,i)}}},r}(t.Evented);var p=g,d=g,m=1e20;function g(t,e,r,n,i,a){this.fontSize=t||24,this.buffer=void 0===e?3:e,this.cutoff=n||.25,this.fontFamily=i||\"sans-serif\",this.fontWeight=a||\"normal\",this.radius=r||8;var o=this.size=this.fontSize+2*this.buffer;this.canvas=document.createElement(\"canvas\"),this.canvas.width=this.canvas.height=o,this.ctx=this.canvas.getContext(\"2d\"),this.ctx.font=this.fontWeight+\" \"+this.fontSize+\"px \"+this.fontFamily,this.ctx.textBaseline=\"middle\",this.ctx.fillStyle=\"black\",this.gridOuter=new Float64Array(o*o),this.gridInner=new Float64Array(o*o),this.f=new Float64Array(o),this.d=new Float64Array(o),this.z=new Float64Array(o+1),this.v=new Int16Array(o),this.middle=Math.round(o/2*(navigator.userAgent.indexOf(\"Gecko/\")>=0?1.2:1))}function v(t,e,r,n,i,a,o){for(var s=0;s<e;s++){for(var l=0;l<r;l++)n[l]=t[l*e+s];for(y(n,i,a,o,r),l=0;l<r;l++)t[l*e+s]=i[l]}for(l=0;l<r;l++){for(s=0;s<e;s++)n[s]=t[l*e+s];for(y(n,i,a,o,e),s=0;s<e;s++)t[l*e+s]=Math.sqrt(i[s])}}function y(t,e,r,n,i){r[0]=0,n[0]=-m,n[1]=+m;for(var a=1,o=0;a<i;a++){for(var s=(t[a]+a*a-(t[r[o]]+r[o]*r[o]))/(2*a-2*r[o]);s<=n[o];)o--,s=(t[a]+a*a-(t[r[o]]+r[o]*r[o]))/(2*a-2*r[o]);r[++o]=a,n[o]=s,n[o+1]=+m}for(a=0,o=0;a<i;a++){for(;n[o+1]<a;)o++;e[a]=(a-r[o])*(a-r[o])+t[r[o]]}}g.prototype.draw=function(t){this.ctx.clearRect(0,0,this.size,this.size),this.ctx.fillText(t,this.buffer,this.middle);for(var e=this.ctx.getImageData(0,0,this.size,this.size),r=new Uint8ClampedArray(this.size*this.size),n=0;n<this.size*this.size;n++){var i=e.data[4*n+3]/255;this.gridOuter[n]=1===i?0:0===i?m:Math.pow(Math.max(0,.5-i),2),this.gridInner[n]=1===i?m:0===i?0:Math.pow(Math.max(0,i-.5),2)}for(v(this.gridOuter,this.size,this.size,this.f,this.d,this.v,this.z),v(this.gridInner,this.size,this.size,this.f,this.d,this.v,this.z),n=0;n<this.size*this.size;n++){var a=this.gridOuter[n]-this.gridInner[n];r[n]=Math.max(0,Math.min(255,Math.round(255-255*(a/this.radius+this.cutoff))))}return r},p.default=d;var x=function(t,e){this.requestManager=t,this.localIdeographFontFamily=e,this.entries={}};x.prototype.setURL=function(t){this.url=t},x.prototype.getGlyphs=function(e,r){var n=this,i=[];for(var a in e)for(var o=0,s=e[a];o<s.length;o+=1){var l=s[o];i.push({stack:a,id:l})}t.asyncAll(i,(function(t,e){var r=t.stack,i=t.id,a=n.entries[r];a||(a=n.entries[r]={glyphs:{},requests:{},ranges:{}});var o=a.glyphs[i];if(void 0===o){if(o=n._tinySDF(a,r,i))return a.glyphs[i]=o,void e(null,{stack:r,id:i,glyph:o});var s=Math.floor(i/256);if(256*s>65535)e(new Error(\"glyphs > 65535 not supported\"));else if(a.ranges[s])e(null,{stack:r,id:i,glyph:o});else{var l=a.requests[s];l||(l=a.requests[s]=[],x.loadGlyphRange(r,s,n.url,n.requestManager,(function(t,e){if(e){for(var r in e)n._doesCharSupportLocalGlyph(+r)||(a.glyphs[+r]=e[+r]);a.ranges[s]=!0}for(var i=0,o=l;i<o.length;i+=1){(0,o[i])(t,e)}delete a.requests[s]}))),l.push((function(t,n){t?e(t):n&&e(null,{stack:r,id:i,glyph:n[i]||null})}))}}else e(null,{stack:r,id:i,glyph:o})}),(function(t,e){if(t)r(t);else if(e){for(var n={},i=0,a=e;i<a.length;i+=1){var o=a[i],s=o.stack,l=o.id,c=o.glyph;(n[s]||(n[s]={}))[l]=c&&{id:c.id,bitmap:c.bitmap.clone(),metrics:c.metrics}}r(null,n)}}))},x.prototype._doesCharSupportLocalGlyph=function(e){return!!this.localIdeographFontFamily&&(t.isChar[\"CJK Unified Ideographs\"](e)||t.isChar[\"Hangul Syllables\"](e)||t.isChar.Hiragana(e)||t.isChar.Katakana(e))},x.prototype._tinySDF=function(e,r,n){var i=this.localIdeographFontFamily;if(i&&this._doesCharSupportLocalGlyph(n)){var a=e.tinySDF;if(!a){var o=\"400\";/bold/i.test(r)?o=\"900\":/medium/i.test(r)?o=\"500\":/light/i.test(r)&&(o=\"200\"),a=e.tinySDF=new x.TinySDF(24,3,8,.25,i,o)}return{id:n,bitmap:new t.AlphaImage({width:30,height:30},a.draw(String.fromCharCode(n))),metrics:{width:24,height:24,left:0,top:-8,advance:24}}}},x.loadGlyphRange=function(e,r,n,i,a){var o=256*r,s=o+255,l=i.transformRequest(i.normalizeGlyphsURL(n).replace(\"{fontstack}\",e).replace(\"{range}\",o+\"-\"+s),t.ResourceType.Glyphs);t.getArrayBuffer(l,(function(e,r){if(e)a(e);else if(r){for(var n={},i=0,o=t.parseGlyphPBF(r);i<o.length;i+=1){var s=o[i];n[s.id]=s}a(null,n)}}))},x.TinySDF=p;var b=function(){this.specification=t.styleSpec.light.position};b.prototype.possiblyEvaluate=function(e,r){return t.sphericalToCartesian(e.expression.evaluate(r))},b.prototype.interpolate=function(e,r,n){return{x:t.number(e.x,r.x,n),y:t.number(e.y,r.y,n),z:t.number(e.z,r.z,n)}};var _=new t.Properties({anchor:new t.DataConstantProperty(t.styleSpec.light.anchor),position:new b,color:new t.DataConstantProperty(t.styleSpec.light.color),intensity:new t.DataConstantProperty(t.styleSpec.light.intensity)}),w=function(e){function r(r){e.call(this),this._transitionable=new t.Transitionable(_),this.setLight(r),this._transitioning=this._transitionable.untransitioned()}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.getLight=function(){return this._transitionable.serialize()},r.prototype.setLight=function(e,r){if(void 0===r&&(r={}),!this._validate(t.validateLight,e,r))for(var n in e){var i=e[n];t.endsWith(n,\"-transition\")?this._transitionable.setTransition(n.slice(0,-\"-transition\".length),i):this._transitionable.setValue(n,i)}},r.prototype.updateTransitions=function(t){this._transitioning=this._transitionable.transitioned(t,this._transitioning)},r.prototype.hasTransition=function(){return this._transitioning.hasTransition()},r.prototype.recalculate=function(t){this.properties=this._transitioning.possiblyEvaluate(t)},r.prototype._validate=function(e,r,n){return(!n||!1!==n.validate)&&t.emitValidationErrors(this,e.call(t.validateStyle,t.extend({value:r,style:{glyphs:!0,sprite:!0},styleSpec:t.styleSpec})))},r}(t.Evented),T=function(t,e){this.width=t,this.height=e,this.nextRow=0,this.data=new Uint8Array(this.width*this.height),this.dashEntry={}};T.prototype.getDash=function(t,e){var r=t.join(\",\")+String(e);return this.dashEntry[r]||(this.dashEntry[r]=this.addDash(t,e)),this.dashEntry[r]},T.prototype.getDashRanges=function(t,e,r){var n=[],i=t.length%2==1?-t[t.length-1]*r:0,a=t[0]*r,o=!0;n.push({left:i,right:a,isDash:o,zeroLength:0===t[0]});for(var s=t[0],l=1;l<t.length;l++){o=!o;var c=t[l];i=s*r,a=(s+=c)*r,n.push({left:i,right:a,isDash:o,zeroLength:0===c})}return n},T.prototype.addRoundDash=function(t,e,r){for(var n=e/2,i=-r;i<=r;i++)for(var a=this.nextRow+r+i,o=this.width*a,s=0,l=t[s],c=0;c<this.width;c++){c/l.right>1&&(l=t[++s]);var u=Math.abs(c-l.left),f=Math.abs(c-l.right),h=Math.min(u,f),p=void 0,d=i/r*(n+1);if(l.isDash){var m=n-Math.abs(d);p=Math.sqrt(h*h+m*m)}else p=n-Math.sqrt(h*h+d*d);this.data[o+c]=Math.max(0,Math.min(255,p+128))}},T.prototype.addRegularDash=function(t){for(var e=t.length-1;e>=0;--e){var r=t[e],n=t[e+1];r.zeroLength?t.splice(e,1):n&&n.isDash===r.isDash&&(n.left=r.left,t.splice(e,1))}var i=t[0],a=t[t.length-1];i.isDash===a.isDash&&(i.left=a.left-this.width,a.right=i.right+this.width);for(var o=this.width*this.nextRow,s=0,l=t[s],c=0;c<this.width;c++){c/l.right>1&&(l=t[++s]);var u=Math.abs(c-l.left),f=Math.abs(c-l.right),h=Math.min(u,f),p=l.isDash?h:-h;this.data[o+c]=Math.max(0,Math.min(255,p+128))}},T.prototype.addDash=function(e,r){var n=r?7:0,i=2*n+1;if(this.nextRow+i>this.height)return t.warnOnce(\"LineAtlas out of space\"),null;for(var a=0,o=0;o<e.length;o++)a+=e[o];if(0!==a){var s=this.width/a,l=this.getDashRanges(e,this.width,s);r?this.addRoundDash(l,s,n):this.addRegularDash(l)}var c={y:(this.nextRow+n+.5)/this.height,height:2*n/this.height,width:a};return this.nextRow+=i,this.dirty=!0,c},T.prototype.bind=function(t){var e=t.gl;this.texture?(e.bindTexture(e.TEXTURE_2D,this.texture),this.dirty&&(this.dirty=!1,e.texSubImage2D(e.TEXTURE_2D,0,0,0,this.width,this.height,e.ALPHA,e.UNSIGNED_BYTE,this.data))):(this.texture=e.createTexture(),e.bindTexture(e.TEXTURE_2D,this.texture),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_S,e.REPEAT),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_T,e.REPEAT),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MIN_FILTER,e.LINEAR),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MAG_FILTER,e.LINEAR),e.texImage2D(e.TEXTURE_2D,0,e.ALPHA,this.width,this.height,0,e.ALPHA,e.UNSIGNED_BYTE,this.data))};var k=function e(r,n){this.workerPool=r,this.actors=[],this.currentActor=0,this.id=t.uniqueId();for(var i=this.workerPool.acquire(this.id),a=0;a<i.length;a++){var o=i[a],s=new e.Actor(o,n,this.id);s.name=\"Worker \"+a,this.actors.push(s)}};function M(e,r,n){var i=function(i,a){if(i)return n(i);if(a){var o=t.pick(t.extend(a,e),[\"tiles\",\"minzoom\",\"maxzoom\",\"attribution\",\"mapbox_logo\",\"bounds\",\"scheme\",\"tileSize\",\"encoding\"]);a.vector_layers&&(o.vectorLayers=a.vector_layers,o.vectorLayerIds=o.vectorLayers.map((function(t){return t.id}))),o.tiles=r.canonicalizeTileset(o,e.url),n(null,o)}};return e.url?t.getJSON(r.transformRequest(r.normalizeSourceURL(e.url),t.ResourceType.Source),i):t.browser.frame((function(){return i(null,e)}))}k.prototype.broadcast=function(e,r,n){n=n||function(){},t.asyncAll(this.actors,(function(t,n){t.send(e,r,n)}),n)},k.prototype.getActor=function(){return this.currentActor=(this.currentActor+1)%this.actors.length,this.actors[this.currentActor]},k.prototype.remove=function(){this.actors.forEach((function(t){t.remove()})),this.actors=[],this.workerPool.release(this.id)},k.Actor=t.Actor;var A=function(e,r,n){this.bounds=t.LngLatBounds.convert(this.validateBounds(e)),this.minzoom=r||0,this.maxzoom=n||24};A.prototype.validateBounds=function(t){return Array.isArray(t)&&4===t.length?[Math.max(-180,t[0]),Math.max(-90,t[1]),Math.min(180,t[2]),Math.min(90,t[3])]:[-180,-90,180,90]},A.prototype.contains=function(e){var r=Math.pow(2,e.z),n=Math.floor(t.mercatorXfromLng(this.bounds.getWest())*r),i=Math.floor(t.mercatorYfromLat(this.bounds.getNorth())*r),a=Math.ceil(t.mercatorXfromLng(this.bounds.getEast())*r),o=Math.ceil(t.mercatorYfromLat(this.bounds.getSouth())*r);return e.x>=n&&e.x<a&&e.y>=i&&e.y<o};var S=function(e){function r(r,n,i,a){if(e.call(this),this.id=r,this.dispatcher=i,this.type=\"vector\",this.minzoom=0,this.maxzoom=22,this.scheme=\"xyz\",this.tileSize=512,this.reparseOverscaled=!0,this.isTileClipped=!0,this._loaded=!1,t.extend(this,t.pick(n,[\"url\",\"scheme\",\"tileSize\",\"promoteId\"])),this._options=t.extend({type:\"vector\"},n),this._collectResourceTiming=n.collectResourceTiming,512!==this.tileSize)throw new Error(\"vector tile sources must have a tileSize of 512\");this.setEventedParent(a)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1,this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this._tileJSONRequest=M(this._options,this.map._requestManager,(function(r,n){e._tileJSONRequest=null,e._loaded=!0,r?e.fire(new t.ErrorEvent(r)):n&&(t.extend(e,n),n.bounds&&(e.tileBounds=new A(n.bounds,e.minzoom,e.maxzoom)),t.postTurnstileEvent(n.tiles,e.map._requestManager._customAccessToken),t.postMapLoadEvent(n.tiles,e.map._getMapId(),e.map._requestManager._skuToken,e.map._requestManager._customAccessToken),e.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})),e.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"content\"})))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.hasTile=function(t){return!this.tileBounds||this.tileBounds.contains(t.canonical)},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.onRemove=function(){this._tileJSONRequest&&(this._tileJSONRequest.cancel(),this._tileJSONRequest=null)},r.prototype.serialize=function(){return t.extend({},this._options)},r.prototype.loadTile=function(e,r){var n=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme)),i={request:this.map._requestManager.transformRequest(n,t.ResourceType.Tile),uid:e.uid,tileID:e.tileID,zoom:e.tileID.overscaledZ,tileSize:this.tileSize*e.tileID.overscaleFactor(),type:this.type,source:this.id,pixelRatio:t.browser.devicePixelRatio,showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId};function a(n,i){return delete e.request,e.aborted?r(null):n&&404!==n.status?r(n):(i&&i.resourceTiming&&(e.resourceTiming=i.resourceTiming),this.map._refreshExpiredTiles&&i&&e.setExpiryData(i),e.loadVectorData(i,this.map.painter),t.cacheEntryPossiblyAdded(this.dispatcher),r(null),void(e.reloadCallback&&(this.loadTile(e,e.reloadCallback),e.reloadCallback=null)))}i.request.collectResourceTiming=this._collectResourceTiming,e.actor&&\"expired\"!==e.state?\"loading\"===e.state?e.reloadCallback=r:e.request=e.actor.send(\"reloadTile\",i,a.bind(this)):(e.actor=this.dispatcher.getActor(),e.request=e.actor.send(\"loadTile\",i,a.bind(this)))},r.prototype.abortTile=function(t){t.request&&(t.request.cancel(),delete t.request),t.actor&&t.actor.send(\"abortTile\",{uid:t.uid,type:this.type,source:this.id},void 0)},r.prototype.unloadTile=function(t){t.unloadVectorData(),t.actor&&t.actor.send(\"removeTile\",{uid:t.uid,type:this.type,source:this.id},void 0)},r.prototype.hasTransition=function(){return!1},r}(t.Evented),E=function(e){function r(r,n,i,a){e.call(this),this.id=r,this.dispatcher=i,this.setEventedParent(a),this.type=\"raster\",this.minzoom=0,this.maxzoom=22,this.roundZoom=!0,this.scheme=\"xyz\",this.tileSize=512,this._loaded=!1,this._options=t.extend({type:\"raster\"},n),t.extend(this,t.pick(n,[\"url\",\"scheme\",\"tileSize\"]))}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1,this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this._tileJSONRequest=M(this._options,this.map._requestManager,(function(r,n){e._tileJSONRequest=null,e._loaded=!0,r?e.fire(new t.ErrorEvent(r)):n&&(t.extend(e,n),n.bounds&&(e.tileBounds=new A(n.bounds,e.minzoom,e.maxzoom)),t.postTurnstileEvent(n.tiles),t.postMapLoadEvent(n.tiles,e.map._getMapId(),e.map._requestManager._skuToken),e.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})),e.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"content\"})))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.onRemove=function(){this._tileJSONRequest&&(this._tileJSONRequest.cancel(),this._tileJSONRequest=null)},r.prototype.serialize=function(){return t.extend({},this._options)},r.prototype.hasTile=function(t){return!this.tileBounds||this.tileBounds.contains(t.canonical)},r.prototype.loadTile=function(e,r){var n=this,i=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme),this.tileSize);e.request=t.getImage(this.map._requestManager.transformRequest(i,t.ResourceType.Tile),(function(i,a){if(delete e.request,e.aborted)e.state=\"unloaded\",r(null);else if(i)e.state=\"errored\",r(i);else if(a){n.map._refreshExpiredTiles&&e.setExpiryData(a),delete a.cacheControl,delete a.expires;var o=n.map.painter.context,s=o.gl;e.texture=n.map.painter.getTileTexture(a.width),e.texture?e.texture.update(a,{useMipmap:!0}):(e.texture=new t.Texture(o,a,s.RGBA,{useMipmap:!0}),e.texture.bind(s.LINEAR,s.CLAMP_TO_EDGE,s.LINEAR_MIPMAP_NEAREST),o.extTextureFilterAnisotropic&&s.texParameterf(s.TEXTURE_2D,o.extTextureFilterAnisotropic.TEXTURE_MAX_ANISOTROPY_EXT,o.extTextureFilterAnisotropicMax)),e.state=\"loaded\",t.cacheEntryPossiblyAdded(n.dispatcher),r(null)}}))},r.prototype.abortTile=function(t,e){t.request&&(t.request.cancel(),delete t.request),e()},r.prototype.unloadTile=function(t,e){t.texture&&this.map.painter.saveTileTexture(t.texture),e()},r.prototype.hasTransition=function(){return!1},r}(t.Evented),L=function(e){function r(r,n,i,a){e.call(this,r,n,i,a),this.type=\"raster-dem\",this.maxzoom=22,this._options=t.extend({type:\"raster-dem\"},n),this.encoding=n.encoding||\"mapbox\"}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.serialize=function(){return{type:\"raster-dem\",url:this.url,tileSize:this.tileSize,tiles:this.tiles,bounds:this.bounds,encoding:this.encoding}},r.prototype.loadTile=function(e,r){var n=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme),this.tileSize);function i(t,n){t&&(e.state=\"errored\",r(t)),n&&(e.dem=n,e.needsHillshadePrepare=!0,e.state=\"loaded\",r(null))}e.request=t.getImage(this.map._requestManager.transformRequest(n,t.ResourceType.Tile),function(n,a){if(delete e.request,e.aborted)e.state=\"unloaded\",r(null);else if(n)e.state=\"errored\",r(n);else if(a){this.map._refreshExpiredTiles&&e.setExpiryData(a),delete a.cacheControl,delete a.expires;var o=t.window.ImageBitmap&&a instanceof t.window.ImageBitmap&&t.offscreenCanvasSupported()?a:t.browser.getImageData(a,1),s={uid:e.uid,coord:e.tileID,source:this.id,rawImageData:o,encoding:this.encoding};e.actor&&\"expired\"!==e.state||(e.actor=this.dispatcher.getActor(),e.actor.send(\"loadDEMTile\",s,i.bind(this)))}}.bind(this)),e.neighboringTiles=this._getNeighboringTiles(e.tileID)},r.prototype._getNeighboringTiles=function(e){var r=e.canonical,n=Math.pow(2,r.z),i=(r.x-1+n)%n,a=0===r.x?e.wrap-1:e.wrap,o=(r.x+1+n)%n,s=r.x+1===n?e.wrap+1:e.wrap,l={};return l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y).key]={backfilled:!1},r.y>0&&(l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y-1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,e.wrap,r.z,r.x,r.y-1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y-1).key]={backfilled:!1}),r.y+1<n&&(l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y+1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,e.wrap,r.z,r.x,r.y+1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y+1).key]={backfilled:!1}),l},r.prototype.unloadTile=function(t){t.demTexture&&this.map.painter.saveTileTexture(t.demTexture),t.fbo&&(t.fbo.destroy(),delete t.fbo),t.dem&&delete t.dem,delete t.neighboringTiles,t.state=\"unloaded\",t.actor&&t.actor.send(\"removeDEMTile\",{uid:t.uid,source:this.id})},r}(E),C=function(e){function r(r,n,i,a){e.call(this),this.id=r,this.type=\"geojson\",this.minzoom=0,this.maxzoom=18,this.tileSize=512,this.isTileClipped=!0,this.reparseOverscaled=!0,this._removed=!1,this._loaded=!1,this.actor=i.getActor(),this.setEventedParent(a),this._data=n.data,this._options=t.extend({},n),this._collectResourceTiming=n.collectResourceTiming,this._resourceTiming=[],void 0!==n.maxzoom&&(this.maxzoom=n.maxzoom),n.type&&(this.type=n.type),n.attribution&&(this.attribution=n.attribution),this.promoteId=n.promoteId;var o=t.EXTENT/this.tileSize;this.workerOptions=t.extend({source:this.id,cluster:n.cluster||!1,geojsonVtOptions:{buffer:(void 0!==n.buffer?n.buffer:128)*o,tolerance:(void 0!==n.tolerance?n.tolerance:.375)*o,extent:t.EXTENT,maxZoom:this.maxzoom,lineMetrics:n.lineMetrics||!1,generateId:n.generateId||!1},superclusterOptions:{maxZoom:void 0!==n.clusterMaxZoom?Math.min(n.clusterMaxZoom,this.maxzoom-1):this.maxzoom-1,extent:t.EXTENT,radius:(n.clusterRadius||50)*o,log:!1,generateId:n.generateId||!1},clusterProperties:n.clusterProperties},n.workerOptions)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this._updateWorkerData((function(r){if(r)e.fire(new t.ErrorEvent(r));else{var n={dataType:\"source\",sourceDataType:\"metadata\"};e._collectResourceTiming&&e._resourceTiming&&e._resourceTiming.length>0&&(n.resourceTiming=e._resourceTiming,e._resourceTiming=[]),e.fire(new t.Event(\"data\",n))}}))},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.setData=function(e){var r=this;return this._data=e,this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this._updateWorkerData((function(e){if(e)r.fire(new t.ErrorEvent(e));else{var n={dataType:\"source\",sourceDataType:\"content\"};r._collectResourceTiming&&r._resourceTiming&&r._resourceTiming.length>0&&(n.resourceTiming=r._resourceTiming,r._resourceTiming=[]),r.fire(new t.Event(\"data\",n))}})),this},r.prototype.getClusterExpansionZoom=function(t,e){return this.actor.send(\"geojson.getClusterExpansionZoom\",{clusterId:t,source:this.id},e),this},r.prototype.getClusterChildren=function(t,e){return this.actor.send(\"geojson.getClusterChildren\",{clusterId:t,source:this.id},e),this},r.prototype.getClusterLeaves=function(t,e,r,n){return this.actor.send(\"geojson.getClusterLeaves\",{source:this.id,clusterId:t,limit:e,offset:r},n),this},r.prototype._updateWorkerData=function(e){var r=this;this._loaded=!1;var n=t.extend({},this.workerOptions),i=this._data;\"string\"==typeof i?(n.request=this.map._requestManager.transformRequest(t.browser.resolveURL(i),t.ResourceType.Source),n.request.collectResourceTiming=this._collectResourceTiming):n.data=JSON.stringify(i),this.actor.send(this.type+\".loadData\",n,(function(t,i){r._removed||i&&i.abandoned||(r._loaded=!0,i&&i.resourceTiming&&i.resourceTiming[r.id]&&(r._resourceTiming=i.resourceTiming[r.id].slice(0)),r.actor.send(r.type+\".coalesce\",{source:n.source},null),e(t))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.loadTile=function(e,r){var n=this,i=e.actor?\"reloadTile\":\"loadTile\";e.actor=this.actor;var a={type:this.type,uid:e.uid,tileID:e.tileID,zoom:e.tileID.overscaledZ,maxZoom:this.maxzoom,tileSize:this.tileSize,source:this.id,pixelRatio:t.browser.devicePixelRatio,showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId};e.request=this.actor.send(i,a,(function(t,a){return delete e.request,e.unloadVectorData(),e.aborted?r(null):t?r(t):(e.loadVectorData(a,n.map.painter,\"reloadTile\"===i),r(null))}))},r.prototype.abortTile=function(t){t.request&&(t.request.cancel(),delete t.request),t.aborted=!0},r.prototype.unloadTile=function(t){t.unloadVectorData(),this.actor.send(\"removeTile\",{uid:t.uid,type:this.type,source:this.id})},r.prototype.onRemove=function(){this._removed=!0,this.actor.send(\"removeSource\",{type:this.type,source:this.id})},r.prototype.serialize=function(){return t.extend({},this._options,{type:this.type,data:this._data})},r.prototype.hasTransition=function(){return!1},r}(t.Evented),P=t.createLayout([{name:\"a_pos\",type:\"Int16\",components:2},{name:\"a_texture_pos\",type:\"Int16\",components:2}]),I=function(e){function r(t,r,n,i){e.call(this),this.id=t,this.dispatcher=n,this.coordinates=r.coordinates,this.type=\"image\",this.minzoom=0,this.maxzoom=22,this.tileSize=512,this.tiles={},this._loaded=!1,this.setEventedParent(i),this.options=r}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(e,r){var n=this;this._loaded=!1,this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this.url=this.options.url,t.getImage(this.map._requestManager.transformRequest(this.url,t.ResourceType.Image),(function(i,a){n._loaded=!0,i?n.fire(new t.ErrorEvent(i)):a&&(n.image=a,e&&(n.coordinates=e),r&&r(),n._finishLoading())}))},r.prototype.loaded=function(){return this._loaded},r.prototype.updateImage=function(t){var e=this;return this.image&&t.url?(this.options.url=t.url,this.load(t.coordinates,(function(){e.texture=null})),this):this},r.prototype._finishLoading=function(){this.map&&(this.setCoordinates(this.coordinates),this.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})))},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.setCoordinates=function(e){var r=this;this.coordinates=e;var n=e.map(t.MercatorCoordinate.fromLngLat);this.tileID=function(e){for(var r=1/0,n=1/0,i=-1/0,a=-1/0,o=0,s=e;o<s.length;o+=1){var l=s[o];r=Math.min(r,l.x),n=Math.min(n,l.y),i=Math.max(i,l.x),a=Math.max(a,l.y)}var c=i-r,u=a-n,f=Math.max(c,u),h=Math.max(0,Math.floor(-Math.log(f)/Math.LN2)),p=Math.pow(2,h);return new t.CanonicalTileID(h,Math.floor((r+i)/2*p),Math.floor((n+a)/2*p))}(n),this.minzoom=this.maxzoom=this.tileID.z;var i=n.map((function(t){return r.tileID.getTilePoint(t)._round()}));return this._boundsArray=new t.StructArrayLayout4i8,this._boundsArray.emplaceBack(i[0].x,i[0].y,0,0),this._boundsArray.emplaceBack(i[1].x,i[1].y,t.EXTENT,0),this._boundsArray.emplaceBack(i[3].x,i[3].y,0,t.EXTENT),this._boundsArray.emplaceBack(i[2].x,i[2].y,t.EXTENT,t.EXTENT),this.boundsBuffer&&(this.boundsBuffer.destroy(),delete this.boundsBuffer),this.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"content\"})),this},r.prototype.prepare=function(){if(0!==Object.keys(this.tiles).length&&this.image){var e=this.map.painter.context,r=e.gl;for(var n in this.boundsBuffer||(this.boundsBuffer=e.createVertexBuffer(this._boundsArray,P.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture||(this.texture=new t.Texture(e,this.image,r.RGBA),this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE)),this.tiles){var i=this.tiles[n];\"loaded\"!==i.state&&(i.state=\"loaded\",i.texture=this.texture)}}},r.prototype.loadTile=function(t,e){this.tileID&&this.tileID.equals(t.tileID.canonical)?(this.tiles[String(t.tileID.wrap)]=t,t.buckets={},e(null)):(t.state=\"errored\",e(null))},r.prototype.serialize=function(){return{type:\"image\",url:this.options.url,coordinates:this.coordinates}},r.prototype.hasTransition=function(){return!1},r}(t.Evented);var O=function(e){function r(t,r,n,i){e.call(this,t,r,n,i),this.roundZoom=!0,this.type=\"video\",this.options=r}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1;var r=this.options;this.urls=[];for(var n=0,i=r.urls;n<i.length;n+=1){var a=i[n];this.urls.push(this.map._requestManager.transformRequest(a,t.ResourceType.Source).url)}t.getVideo(this.urls,(function(r,n){e._loaded=!0,r?e.fire(new t.ErrorEvent(r)):n&&(e.video=n,e.video.loop=!0,e.video.addEventListener(\"playing\",(function(){e.map.triggerRepaint()})),e.map&&e.video.play(),e._finishLoading())}))},r.prototype.pause=function(){this.video&&this.video.pause()},r.prototype.play=function(){this.video&&this.video.play()},r.prototype.seek=function(e){if(this.video){var r=this.video.seekable;e<r.start(0)||e>r.end(0)?this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+this.id,null,\"Playback for this video can be set only between the \"+r.start(0)+\" and \"+r.end(0)+\"-second mark.\"))):this.video.currentTime=e}},r.prototype.getVideo=function(){return this.video},r.prototype.onAdd=function(t){this.map||(this.map=t,this.load(),this.video&&(this.video.play(),this.setCoordinates(this.coordinates)))},r.prototype.prepare=function(){if(!(0===Object.keys(this.tiles).length||this.video.readyState<2)){var e=this.map.painter.context,r=e.gl;for(var n in this.boundsBuffer||(this.boundsBuffer=e.createVertexBuffer(this._boundsArray,P.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture?this.video.paused||(this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE),r.texSubImage2D(r.TEXTURE_2D,0,0,0,r.RGBA,r.UNSIGNED_BYTE,this.video)):(this.texture=new t.Texture(e,this.video,r.RGBA),this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE)),this.tiles){var i=this.tiles[n];\"loaded\"!==i.state&&(i.state=\"loaded\",i.texture=this.texture)}}},r.prototype.serialize=function(){return{type:\"video\",urls:this.urls,coordinates:this.coordinates}},r.prototype.hasTransition=function(){return this.video&&!this.video.paused},r}(I),z=function(e){function r(r,n,i,a){e.call(this,r,n,i,a),n.coordinates?Array.isArray(n.coordinates)&&4===n.coordinates.length&&!n.coordinates.some((function(t){return!Array.isArray(t)||2!==t.length||t.some((function(t){return\"number\"!=typeof t}))}))||this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'\"coordinates\" property must be an array of 4 longitude/latitude array pairs'))):this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'missing required property \"coordinates\"'))),n.animate&&\"boolean\"!=typeof n.animate&&this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'optional \"animate\" property must be a boolean value'))),n.canvas?\"string\"==typeof n.canvas||n.canvas instanceof t.window.HTMLCanvasElement||this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'\"canvas\" must be either a string representing the ID of the canvas element from which to read, or an HTMLCanvasElement instance'))):this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'missing required property \"canvas\"'))),this.options=n,this.animate=void 0===n.animate||n.animate}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){this._loaded=!0,this.canvas||(this.canvas=this.options.canvas instanceof t.window.HTMLCanvasElement?this.options.canvas:t.window.document.getElementById(this.options.canvas)),this.width=this.canvas.width,this.height=this.canvas.height,this._hasInvalidDimensions()?this.fire(new t.ErrorEvent(new Error(\"Canvas dimensions cannot be less than or equal to zero.\"))):(this.play=function(){this._playing=!0,this.map.triggerRepaint()},this.pause=function(){this._playing&&(this.prepare(),this._playing=!1)},this._finishLoading())},r.prototype.getCanvas=function(){return this.canvas},r.prototype.onAdd=function(t){this.map=t,this.load(),this.canvas&&this.animate&&this.play()},r.prototype.onRemove=function(){this.pause()},r.prototype.prepare=function(){var e=!1;if(this.canvas.width!==this.width&&(this.width=this.canvas.width,e=!0),this.canvas.height!==this.height&&(this.height=this.canvas.height,e=!0),!this._hasInvalidDimensions()&&0!==Object.keys(this.tiles).length){var r=this.map.painter.context,n=r.gl;for(var i in this.boundsBuffer||(this.boundsBuffer=r.createVertexBuffer(this._boundsArray,P.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture?(e||this._playing)&&this.texture.update(this.canvas,{premultiply:!0}):this.texture=new t.Texture(r,this.canvas,n.RGBA,{premultiply:!0}),this.tiles){var a=this.tiles[i];\"loaded\"!==a.state&&(a.state=\"loaded\",a.texture=this.texture)}}},r.prototype.serialize=function(){return{type:\"canvas\",coordinates:this.coordinates}},r.prototype.hasTransition=function(){return this._playing},r.prototype._hasInvalidDimensions=function(){for(var t=0,e=[this.canvas.width,this.canvas.height];t<e.length;t+=1){var 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e=this.gl;e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,t,0),this.current=t,this.dirty=!1}},e}(_t),Tt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.set=function(t){if(t!==this.current||this.dirty){this.context.bindFramebuffer.set(this.parent);var e=this.gl;e.framebufferRenderbuffer(e.FRAMEBUFFER,e.DEPTH_ATTACHMENT,e.RENDERBUFFER,t),this.current=t,this.dirty=!1}},e}(_t),kt=function(t,e,r,n){this.context=t,this.width=e,this.height=r;var i=t.gl,a=this.framebuffer=i.createFramebuffer();this.colorAttachment=new wt(t,a),n&&(this.depthAttachment=new Tt(t,a))};kt.prototype.destroy=function(){var t=this.context.gl,e=this.colorAttachment.get();if(e&&t.deleteTexture(e),this.depthAttachment){var r=this.depthAttachment.get();r&&t.deleteRenderbuffer(r)}t.deleteFramebuffer(this.framebuffer)};var Mt=function(t,e,r){this.func=t,this.mask=e,this.range=r};Mt.ReadOnly=!1,Mt.ReadWrite=!0,Mt.disabled=new Mt(519,Mt.ReadOnly,[0,1]);var At=function(t,e,r,n,i,a){this.test=t,this.ref=e,this.mask=r,this.fail=n,this.depthFail=i,this.pass=a};At.disabled=new At({func:519,mask:0},0,0,7680,7680,7680);var St=function(t,e,r){this.blendFunction=t,this.blendColor=e,this.mask=r};St.disabled=new St(St.Replace=[1,0],t.Color.transparent,[!1,!1,!1,!1]),St.unblended=new St(St.Replace,t.Color.transparent,[!0,!0,!0,!0]),St.alphaBlended=new St([1,771],t.Color.transparent,[!0,!0,!0,!0]);var Et=function(t,e,r){this.enable=t,this.mode=e,this.frontFace=r};Et.disabled=new Et(!1,1029,2305),Et.backCCW=new Et(!0,1029,2305);var Lt=function(t){this.gl=t,this.extVertexArrayObject=this.gl.getExtension(\"OES_vertex_array_object\"),this.clearColor=new H(this),this.clearDepth=new G(this),this.clearStencil=new Y(this),this.colorMask=new W(this),this.depthMask=new X(this),this.stencilMask=new Z(this),this.stencilFunc=new J(this),this.stencilOp=new K(this),this.stencilTest=new Q(this),this.depthRange=new $(this),this.depthTest=new tt(this),this.depthFunc=new et(this),this.blend=new rt(this),this.blendFunc=new nt(this),this.blendColor=new it(this),this.blendEquation=new at(this),this.cullFace=new ot(this),this.cullFaceSide=new st(this),this.frontFace=new lt(this),this.program=new ct(this),this.activeTexture=new ut(this),this.viewport=new ft(this),this.bindFramebuffer=new ht(this),this.bindRenderbuffer=new pt(this),this.bindTexture=new dt(this),this.bindVertexBuffer=new mt(this),this.bindElementBuffer=new gt(this),this.bindVertexArrayOES=this.extVertexArrayObject&&new vt(this),this.pixelStoreUnpack=new yt(this),this.pixelStoreUnpackPremultiplyAlpha=new xt(this),this.pixelStoreUnpackFlipY=new bt(this),this.extTextureFilterAnisotropic=t.getExtension(\"EXT_texture_filter_anisotropic\")||t.getExtension(\"MOZ_EXT_texture_filter_anisotropic\")||t.getExtension(\"WEBKIT_EXT_texture_filter_anisotropic\"),this.extTextureFilterAnisotropic&&(this.extTextureFilterAnisotropicMax=t.getParameter(this.extTextureFilterAnisotropic.MAX_TEXTURE_MAX_ANISOTROPY_EXT)),this.extTextureHalfFloat=t.getExtension(\"OES_texture_half_float\"),this.extTextureHalfFloat&&(t.getExtension(\"OES_texture_half_float_linear\"),this.extRenderToTextureHalfFloat=t.getExtension(\"EXT_color_buffer_half_float\")),this.extTimerQuery=t.getExtension(\"EXT_disjoint_timer_query\")};Lt.prototype.setDefault=function(){this.unbindVAO(),this.clearColor.setDefault(),this.clearDepth.setDefault(),this.clearStencil.setDefault(),this.colorMask.setDefault(),this.depthMask.setDefault(),this.stencilMask.setDefault(),this.stencilFunc.setDefault(),this.stencilOp.setDefault(),this.stencilTest.setDefault(),this.depthRange.setDefault(),this.depthTest.setDefault(),this.depthFunc.setDefault(),this.blend.setDefault(),this.blendFunc.setDefault(),this.blendColor.setDefault(),this.blendEquation.setDefault(),this.cullFace.setDefault(),this.cullFaceSide.setDefault(),this.frontFace.setDefault(),this.program.setDefault(),this.activeTexture.setDefault(),this.bindFramebuffer.setDefault(),this.pixelStoreUnpack.setDefault(),this.pixelStoreUnpackPremultiplyAlpha.setDefault(),this.pixelStoreUnpackFlipY.setDefault()},Lt.prototype.setDirty=function(){this.clearColor.dirty=!0,this.clearDepth.dirty=!0,this.clearStencil.dirty=!0,this.colorMask.dirty=!0,this.depthMask.dirty=!0,this.stencilMask.dirty=!0,this.stencilFunc.dirty=!0,this.stencilOp.dirty=!0,this.stencilTest.dirty=!0,this.depthRange.dirty=!0,this.depthTest.dirty=!0,this.depthFunc.dirty=!0,this.blend.dirty=!0,this.blendFunc.dirty=!0,this.blendColor.dirty=!0,this.blendEquation.dirty=!0,this.cullFace.dirty=!0,this.cullFaceSide.dirty=!0,this.frontFace.dirty=!0,this.program.dirty=!0,this.activeTexture.dirty=!0,this.viewport.dirty=!0,this.bindFramebuffer.dirty=!0,this.bindRenderbuffer.dirty=!0,this.bindTexture.dirty=!0,this.bindVertexBuffer.dirty=!0,this.bindElementBuffer.dirty=!0,this.extVertexArrayObject&&(this.bindVertexArrayOES.dirty=!0),this.pixelStoreUnpack.dirty=!0,this.pixelStoreUnpackPremultiplyAlpha.dirty=!0,this.pixelStoreUnpackFlipY.dirty=!0},Lt.prototype.createIndexBuffer=function(t,e){return new j(this,t,e)},Lt.prototype.createVertexBuffer=function(t,e,r){return new V(this,t,e,r)},Lt.prototype.createRenderbuffer=function(t,e,r){var n=this.gl,i=n.createRenderbuffer();return this.bindRenderbuffer.set(i),n.renderbufferStorage(n.RENDERBUFFER,t,e,r),this.bindRenderbuffer.set(null),i},Lt.prototype.createFramebuffer=function(t,e,r){return new kt(this,t,e,r)},Lt.prototype.clear=function(t){var e=t.color,r=t.depth,n=this.gl,i=0;e&&(i|=n.COLOR_BUFFER_BIT,this.clearColor.set(e),this.colorMask.set([!0,!0,!0,!0])),void 0!==r&&(i|=n.DEPTH_BUFFER_BIT,this.depthRange.set([0,1]),this.clearDepth.set(r),this.depthMask.set(!0)),n.clear(i)},Lt.prototype.setCullFace=function(t){!1===t.enable?this.cullFace.set(!1):(this.cullFace.set(!0),this.cullFaceSide.set(t.mode),this.frontFace.set(t.frontFace))},Lt.prototype.setDepthMode=function(t){t.func!==this.gl.ALWAYS||t.mask?(this.depthTest.set(!0),this.depthFunc.set(t.func),this.depthMask.set(t.mask),this.depthRange.set(t.range)):this.depthTest.set(!1)},Lt.prototype.setStencilMode=function(t){t.test.func!==this.gl.ALWAYS||t.mask?(this.stencilTest.set(!0),this.stencilMask.set(t.mask),this.stencilOp.set([t.fail,t.depthFail,t.pass]),this.stencilFunc.set({func:t.test.func,ref:t.ref,mask:t.test.mask})):this.stencilTest.set(!1)},Lt.prototype.setColorMode=function(e){t.deepEqual(e.blendFunction,St.Replace)?this.blend.set(!1):(this.blend.set(!0),this.blendFunc.set(e.blendFunction),this.blendColor.set(e.blendColor)),this.colorMask.set(e.mask)},Lt.prototype.unbindVAO=function(){this.extVertexArrayObject&&this.bindVertexArrayOES.set(null)};var Ct=function(e){function r(r,n,i){var a=this;e.call(this),this.id=r,this.dispatcher=i,this.on(\"data\",(function(t){\"source\"===t.dataType&&\"metadata\"===t.sourceDataType&&(a._sourceLoaded=!0),a._sourceLoaded&&!a._paused&&\"source\"===t.dataType&&\"content\"===t.sourceDataType&&(a.reload(),a.transform&&a.update(a.transform))})),this.on(\"error\",(function(){a._sourceErrored=!0})),this._source=function(e,r,n,i){var a=new D[r.type](e,r,n,i);if(a.id!==e)throw new Error(\"Expected Source id to be \"+e+\" instead of \"+a.id);return t.bindAll([\"load\",\"abort\",\"unload\",\"serialize\",\"prepare\"],a),a}(r,n,i,this),this._tiles={},this._cache=new N(0,this._unloadTile.bind(this)),this._timers={},this._cacheTimers={},this._maxTileCacheSize=null,this._loadedParentTiles={},this._coveredTiles={},this._state=new t.SourceFeatureState}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.onAdd=function(t){this.map=t,this._maxTileCacheSize=t?t._maxTileCacheSize:null,this._source&&this._source.onAdd&&this._source.onAdd(t)},r.prototype.onRemove=function(t){this._source&&this._source.onRemove&&this._source.onRemove(t)},r.prototype.loaded=function(){if(this._sourceErrored)return!0;if(!this._sourceLoaded)return!1;if(!this._source.loaded())return!1;for(var t in this._tiles){var e=this._tiles[t];if(\"loaded\"!==e.state&&\"errored\"!==e.state)return!1}return!0},r.prototype.getSource=function(){return this._source},r.prototype.pause=function(){this._paused=!0},r.prototype.resume=function(){if(this._paused){var t=this._shouldReloadOnResume;this._paused=!1,this._shouldReloadOnResume=!1,t&&this.reload(),this.transform&&this.update(this.transform)}},r.prototype._loadTile=function(t,e){return this._source.loadTile(t,e)},r.prototype._unloadTile=function(t){if(this._source.unloadTile)return this._source.unloadTile(t,(function(){}))},r.prototype._abortTile=function(t){if(this._source.abortTile)return this._source.abortTile(t,(function(){}))},r.prototype.serialize=function(){return this._source.serialize()},r.prototype.prepare=function(t){for(var e in this._source.prepare&&this._source.prepare(),this._state.coalesceChanges(this._tiles,this.map?this.map.painter:null),this._tiles){var r=this._tiles[e];r.upload(t),r.prepare(this.map.style.imageManager)}},r.prototype.getIds=function(){return t.values(this._tiles).map((function(t){return t.tileID})).sort(Pt).map((function(t){return t.key}))},r.prototype.getRenderableIds=function(e){var r=this,n=[];for(var i in this._tiles)this._isIdRenderable(i,e)&&n.push(this._tiles[i]);return e?n.sort((function(e,n){var i=e.tileID,a=n.tileID,o=new t.Point(i.canonical.x,i.canonical.y)._rotate(r.transform.angle),s=new t.Point(a.canonical.x,a.canonical.y)._rotate(r.transform.angle);return i.overscaledZ-a.overscaledZ||s.y-o.y||s.x-o.x})).map((function(t){return t.tileID.key})):n.map((function(t){return t.tileID})).sort(Pt).map((function(t){return t.key}))},r.prototype.hasRenderableParent=function(t){var e=this.findLoadedParent(t,0);return!!e&&this._isIdRenderable(e.tileID.key)},r.prototype._isIdRenderable=function(t,e){return this._tiles[t]&&this._tiles[t].hasData()&&!this._coveredTiles[t]&&(e||!this._tiles[t].holdingForFade())},r.prototype.reload=function(){if(this._paused)this._shouldReloadOnResume=!0;else for(var t in this._cache.reset(),this._tiles)\"errored\"!==this._tiles[t].state&&this._reloadTile(t,\"reloading\")},r.prototype._reloadTile=function(t,e){var r=this._tiles[t];r&&(\"loading\"!==r.state&&(r.state=e),this._loadTile(r,this._tileLoaded.bind(this,r,t,e)))},r.prototype._tileLoaded=function(e,r,n,i){if(i)return e.state=\"errored\",void(404!==i.status?this._source.fire(new t.ErrorEvent(i,{tile:e})):this.update(this.transform));e.timeAdded=t.browser.now(),\"expired\"===n&&(e.refreshedUponExpiration=!0),this._setTileReloadTimer(r,e),\"raster-dem\"===this.getSource().type&&e.dem&&this._backfillDEM(e),this._state.initializeTileState(e,this.map?this.map.painter:null),this._source.fire(new t.Event(\"data\",{dataType:\"source\",tile:e,coord:e.tileID}))},r.prototype._backfillDEM=function(t){for(var e=this.getRenderableIds(),r=0;r<e.length;r++){var n=e[r];if(t.neighboringTiles&&t.neighboringTiles[n]){var i=this.getTileByID(n);a(t,i),a(i,t)}}function a(t,e){t.needsHillshadePrepare=!0;var r=e.tileID.canonical.x-t.tileID.canonical.x,n=e.tileID.canonical.y-t.tileID.canonical.y,i=Math.pow(2,t.tileID.canonical.z),a=e.tileID.key;0===r&&0===n||Math.abs(n)>1||(Math.abs(r)>1&&(1===Math.abs(r+i)?r+=i:1===Math.abs(r-i)&&(r-=i)),e.dem&&t.dem&&(t.dem.backfillBorder(e.dem,r,n),t.neighboringTiles&&t.neighboringTiles[a]&&(t.neighboringTiles[a].backfilled=!0)))}},r.prototype.getTile=function(t){return this.getTileByID(t.key)},r.prototype.getTileByID=function(t){return this._tiles[t]},r.prototype._retainLoadedChildren=function(t,e,r,n){for(var i in this._tiles){var a=this._tiles[i];if(!(n[i]||!a.hasData()||a.tileID.overscaledZ<=e||a.tileID.overscaledZ>r)){for(var o=a.tileID;a&&a.tileID.overscaledZ>e+1;){var s=a.tileID.scaledTo(a.tileID.overscaledZ-1);(a=this._tiles[s.key])&&a.hasData()&&(o=s)}for(var l=o;l.overscaledZ>e;)if(t[(l=l.scaledTo(l.overscaledZ-1)).key]){n[o.key]=o;break}}}},r.prototype.findLoadedParent=function(t,e){if(t.key in this._loadedParentTiles){var r=this._loadedParentTiles[t.key];return r&&r.tileID.overscaledZ>=e?r:null}for(var n=t.overscaledZ-1;n>=e;n--){var i=t.scaledTo(n),a=this._getLoadedTile(i);if(a)return a}},r.prototype._getLoadedTile=function(t){var e=this._tiles[t.key];return e&&e.hasData()?e:this._cache.getByKey(t.wrapped().key)},r.prototype.updateCacheSize=function(t){var e=(Math.ceil(t.width/this._source.tileSize)+1)*(Math.ceil(t.height/this._source.tileSize)+1),r=Math.floor(5*e),n=\"number\"==typeof this._maxTileCacheSize?Math.min(this._maxTileCacheSize,r):r;this._cache.setMaxSize(n)},r.prototype.handleWrapJump=function(t){var e=(t-(void 0===this._prevLng?t:this._prevLng))/360,r=Math.round(e);if(this._prevLng=t,r){var n={};for(var i in this._tiles){var a=this._tiles[i];a.tileID=a.tileID.unwrapTo(a.tileID.wrap+r),n[a.tileID.key]=a}for(var o in this._tiles=n,this._timers)clearTimeout(this._timers[o]),delete this._timers[o];for(var s in this._tiles){var l=this._tiles[s];this._setTileReloadTimer(s,l)}}},r.prototype.update=function(e){var n=this;if(this.transform=e,this._sourceLoaded&&!this._paused){var i;this.updateCacheSize(e),this.handleWrapJump(this.transform.center.lng),this._coveredTiles={},this.used?this._source.tileID?i=e.getVisibleUnwrappedCoordinates(this._source.tileID).map((function(e){return new t.OverscaledTileID(e.canonical.z,e.wrap,e.canonical.z,e.canonical.x,e.canonical.y)})):(i=e.coveringTiles({tileSize:this._source.tileSize,minzoom:this._source.minzoom,maxzoom:this._source.maxzoom,roundZoom:this._source.roundZoom,reparseOverscaled:this._source.reparseOverscaled}),this._source.hasTile&&(i=i.filter((function(t){return n._source.hasTile(t)})))):i=[];var a=e.coveringZoomLevel(this._source),o=Math.max(a-r.maxOverzooming,this._source.minzoom),s=Math.max(a+r.maxUnderzooming,this._source.minzoom),l=this._updateRetainedTiles(i,a);if(It(this._source.type)){for(var c={},u={},f=0,h=Object.keys(l);f<h.length;f+=1){var p=h[f],d=l[p],m=this._tiles[p];if(m&&!(m.fadeEndTime&&m.fadeEndTime<=t.browser.now())){var g=this.findLoadedParent(d,o);g&&(this._addTile(g.tileID),c[g.tileID.key]=g.tileID),u[p]=d}}for(var v in this._retainLoadedChildren(u,a,s,l),c)l[v]||(this._coveredTiles[v]=!0,l[v]=c[v])}for(var y in l)this._tiles[y].clearFadeHold();for(var x=0,b=t.keysDifference(this._tiles,l);x<b.length;x+=1){var _=b[x],w=this._tiles[_];w.hasSymbolBuckets&&!w.holdingForFade()?w.setHoldDuration(this.map._fadeDuration):w.hasSymbolBuckets&&!w.symbolFadeFinished()||this._removeTile(_)}this._updateLoadedParentTileCache()}},r.prototype.releaseSymbolFadeTiles=function(){for(var t in this._tiles)this._tiles[t].holdingForFade()&&this._removeTile(t)},r.prototype._updateRetainedTiles=function(t,e){for(var n={},i={},a=Math.max(e-r.maxOverzooming,this._source.minzoom),o=Math.max(e+r.maxUnderzooming,this._source.minzoom),s={},l=0,c=t;l<c.length;l+=1){var u=c[l],f=this._addTile(u);n[u.key]=u,f.hasData()||e<this._source.maxzoom&&(s[u.key]=u)}this._retainLoadedChildren(s,e,o,n);for(var h=0,p=t;h<p.length;h+=1){var d=p[h],m=this._tiles[d.key];if(!m.hasData()){if(e+1>this._source.maxzoom){var g=d.children(this._source.maxzoom)[0],v=this.getTile(g);if(v&&v.hasData()){n[g.key]=g;continue}}else{var y=d.children(this._source.maxzoom);if(n[y[0].key]&&n[y[1].key]&&n[y[2].key]&&n[y[3].key])continue}for(var x=m.wasRequested(),b=d.overscaledZ-1;b>=a;--b){var _=d.scaledTo(b);if(i[_.key])break;if(i[_.key]=!0,!(m=this.getTile(_))&&x&&(m=this._addTile(_)),m&&(n[_.key]=_,x=m.wasRequested(),m.hasData()))break}}}return n},r.prototype._updateLoadedParentTileCache=function(){for(var t in this._loadedParentTiles={},this._tiles){for(var e=[],r=void 0,n=this._tiles[t].tileID;n.overscaledZ>0;){if(n.key in this._loadedParentTiles){r=this._loadedParentTiles[n.key];break}e.push(n.key);var i=n.scaledTo(n.overscaledZ-1);if(r=this._getLoadedTile(i))break;n=i}for(var a=0,o=e;a<o.length;a+=1){var s=o[a];this._loadedParentTiles[s]=r}}},r.prototype._addTile=function(e){var r=this._tiles[e.key];if(r)return r;(r=this._cache.getAndRemove(e))&&(this._setTileReloadTimer(e.key,r),r.tileID=e,this._state.initializeTileState(r,this.map?this.map.painter:null),this._cacheTimers[e.key]&&(clearTimeout(this._cacheTimers[e.key]),delete this._cacheTimers[e.key],this._setTileReloadTimer(e.key,r)));var n=Boolean(r);return n||(r=new t.Tile(e,this._source.tileSize*e.overscaleFactor()),this._loadTile(r,this._tileLoaded.bind(this,r,e.key,r.state))),r?(r.uses++,this._tiles[e.key]=r,n||this._source.fire(new t.Event(\"dataloading\",{tile:r,coord:r.tileID,dataType:\"source\"})),r):null},r.prototype._setTileReloadTimer=function(t,e){var r=this;t in this._timers&&(clearTimeout(this._timers[t]),delete this._timers[t]);var n=e.getExpiryTimeout();n&&(this._timers[t]=setTimeout((function(){r._reloadTile(t,\"expired\"),delete r._timers[t]}),n))},r.prototype._removeTile=function(t){var e=this._tiles[t];e&&(e.uses--,delete this._tiles[t],this._timers[t]&&(clearTimeout(this._timers[t]),delete this._timers[t]),e.uses>0||(e.hasData()&&\"reloading\"!==e.state?this._cache.add(e.tileID,e,e.getExpiryTimeout()):(e.aborted=!0,this._abortTile(e),this._unloadTile(e))))},r.prototype.clearTiles=function(){for(var t in this._shouldReloadOnResume=!1,this._paused=!1,this._tiles)this._removeTile(t);this._cache.reset()},r.prototype.tilesIn=function(e,r,n){var i=this,a=[],o=this.transform;if(!o)return a;for(var s=n?o.getCameraQueryGeometry(e):e,l=e.map((function(t){return o.pointCoordinate(t)})),c=s.map((function(t){return o.pointCoordinate(t)})),u=this.getIds(),f=1/0,h=1/0,p=-1/0,d=-1/0,m=0,g=c;m<g.length;m+=1){var v=g[m];f=Math.min(f,v.x),h=Math.min(h,v.y),p=Math.max(p,v.x),d=Math.max(d,v.y)}for(var y=function(e){var n=i._tiles[u[e]];if(!n.holdingForFade()){var s=n.tileID,m=Math.pow(2,o.zoom-n.tileID.overscaledZ),g=r*n.queryPadding*t.EXTENT/n.tileSize/m,v=[s.getTilePoint(new t.MercatorCoordinate(f,h)),s.getTilePoint(new t.MercatorCoordinate(p,d))];if(v[0].x-g<t.EXTENT&&v[0].y-g<t.EXTENT&&v[1].x+g>=0&&v[1].y+g>=0){var y=l.map((function(t){return s.getTilePoint(t)})),x=c.map((function(t){return s.getTilePoint(t)}));a.push({tile:n,tileID:s,queryGeometry:y,cameraQueryGeometry:x,scale:m})}}},x=0;x<u.length;x++)y(x);return a},r.prototype.getVisibleCoordinates=function(t){for(var e=this,r=this.getRenderableIds(t).map((function(t){return e._tiles[t].tileID})),n=0,i=r;n<i.length;n+=1){var a=i[n];a.posMatrix=this.transform.calculatePosMatrix(a.toUnwrapped())}return r},r.prototype.hasTransition=function(){if(this._source.hasTransition())return!0;if(It(this._source.type))for(var e in this._tiles){var r=this._tiles[e];if(void 0!==r.fadeEndTime&&r.fadeEndTime>=t.browser.now())return!0}return!1},r.prototype.setFeatureState=function(t,e,r){t=t||\"_geojsonTileLayer\",this._state.updateState(t,e,r)},r.prototype.removeFeatureState=function(t,e,r){t=t||\"_geojsonTileLayer\",this._state.removeFeatureState(t,e,r)},r.prototype.getFeatureState=function(t,e){return t=t||\"_geojsonTileLayer\",this._state.getState(t,e)},r.prototype.setDependencies=function(t,e,r){var n=this._tiles[t];n&&n.setDependencies(e,r)},r.prototype.reloadTilesForDependencies=function(t,e){for(var r in this._tiles){this._tiles[r].hasDependency(t,e)&&this._reloadTile(r,\"reloading\")}this._cache.filter((function(r){return!r.hasDependency(t,e)}))},r}(t.Evented);function Pt(t,e){var r=Math.abs(2*t.wrap)-+(t.wrap<0),n=Math.abs(2*e.wrap)-+(e.wrap<0);return t.overscaledZ-e.overscaledZ||n-r||e.canonical.y-t.canonical.y||e.canonical.x-t.canonical.x}function It(t){return\"raster\"===t||\"image\"===t||\"video\"===t}function Ot(){return new t.window.Worker(Zi.workerUrl)}Ct.maxOverzooming=10,Ct.maxUnderzooming=3;var zt=\"mapboxgl_preloaded_worker_pool\",Dt=function(){this.active={}};Dt.prototype.acquire=function(t){if(!this.workers)for(this.workers=[];this.workers.length<Dt.workerCount;)this.workers.push(new Ot);return this.active[t]=!0,this.workers.slice()},Dt.prototype.release=function(t){delete this.active[t],0===this.numActive()&&(this.workers.forEach((function(t){t.terminate()})),this.workers=null)},Dt.prototype.isPreloaded=function(){return!!this.active[zt]},Dt.prototype.numActive=function(){return Object.keys(this.active).length};var Rt,Ft=Math.floor(t.browser.hardwareConcurrency/2);function Bt(){return Rt||(Rt=new Dt),Rt}function Nt(e,r){var n={};for(var i in e)\"ref\"!==i&&(n[i]=e[i]);return t.refProperties.forEach((function(t){t in r&&(n[t]=r[t])})),n}function jt(t){t=t.slice();for(var e=Object.create(null),r=0;r<t.length;r++)e[t[r].id]=t[r];for(var n=0;n<t.length;n++)\"ref\"in t[n]&&(t[n]=Nt(t[n],e[t[n].ref]));return t}Dt.workerCount=Math.max(Math.min(Ft,6),1);var Ut={setStyle:\"setStyle\",addLayer:\"addLayer\",removeLayer:\"removeLayer\",setPaintProperty:\"setPaintProperty\",setLayoutProperty:\"setLayoutProperty\",setFilter:\"setFilter\",addSource:\"addSource\",removeSource:\"removeSource\",setGeoJSONSourceData:\"setGeoJSONSourceData\",setLayerZoomRange:\"setLayerZoomRange\",setLayerProperty:\"setLayerProperty\",setCenter:\"setCenter\",setZoom:\"setZoom\",setBearing:\"setBearing\",setPitch:\"setPitch\",setSprite:\"setSprite\",setGlyphs:\"setGlyphs\",setTransition:\"setTransition\",setLight:\"setLight\"};function Vt(t,e,r){r.push({command:Ut.addSource,args:[t,e[t]]})}function qt(t,e,r){e.push({command:Ut.removeSource,args:[t]}),r[t]=!0}function Ht(t,e,r,n){qt(t,r,n),Vt(t,e,r)}function Gt(e,r,n){var i;for(i in e[n])if(e[n].hasOwnProperty(i)&&\"data\"!==i&&!t.deepEqual(e[n][i],r[n][i]))return!1;for(i in r[n])if(r[n].hasOwnProperty(i)&&\"data\"!==i&&!t.deepEqual(e[n][i],r[n][i]))return!1;return!0}function Yt(e,r,n,i,a,o){var s;for(s in r=r||{},e=e||{})e.hasOwnProperty(s)&&(t.deepEqual(e[s],r[s])||n.push({command:o,args:[i,s,r[s],a]}));for(s in r)r.hasOwnProperty(s)&&!e.hasOwnProperty(s)&&(t.deepEqual(e[s],r[s])||n.push({command:o,args:[i,s,r[s],a]}))}function Wt(t){return t.id}function Xt(t,e){return t[e.id]=e,t}function Zt(e,r){if(!e)return[{command:Ut.setStyle,args:[r]}];var n=[];try{if(!t.deepEqual(e.version,r.version))return[{command:Ut.setStyle,args:[r]}];t.deepEqual(e.center,r.center)||n.push({command:Ut.setCenter,args:[r.center]}),t.deepEqual(e.zoom,r.zoom)||n.push({command:Ut.setZoom,args:[r.zoom]}),t.deepEqual(e.bearing,r.bearing)||n.push({command:Ut.setBearing,args:[r.bearing]}),t.deepEqual(e.pitch,r.pitch)||n.push({command:Ut.setPitch,args:[r.pitch]}),t.deepEqual(e.sprite,r.sprite)||n.push({command:Ut.setSprite,args:[r.sprite]}),t.deepEqual(e.glyphs,r.glyphs)||n.push({command:Ut.setGlyphs,args:[r.glyphs]}),t.deepEqual(e.transition,r.transition)||n.push({command:Ut.setTransition,args:[r.transition]}),t.deepEqual(e.light,r.light)||n.push({command:Ut.setLight,args:[r.light]});var i={},a=[];!function(e,r,n,i){var a;for(a in r=r||{},e=e||{})e.hasOwnProperty(a)&&(r.hasOwnProperty(a)||qt(a,n,i));for(a in r)r.hasOwnProperty(a)&&(e.hasOwnProperty(a)?t.deepEqual(e[a],r[a])||(\"geojson\"===e[a].type&&\"geojson\"===r[a].type&&Gt(e,r,a)?n.push({command:Ut.setGeoJSONSourceData,args:[a,r[a].data]}):Ht(a,r,n,i)):Vt(a,r,n))}(e.sources,r.sources,a,i);var o=[];e.layers&&e.layers.forEach((function(t){i[t.source]?n.push({command:Ut.removeLayer,args:[t.id]}):o.push(t)})),n=n.concat(a),function(e,r,n){r=r||[];var i,a,o,s,l,c,u,f=(e=e||[]).map(Wt),h=r.map(Wt),p=e.reduce(Xt,{}),d=r.reduce(Xt,{}),m=f.slice(),g=Object.create(null);for(i=0,a=0;i<f.length;i++)o=f[i],d.hasOwnProperty(o)?a++:(n.push({command:Ut.removeLayer,args:[o]}),m.splice(m.indexOf(o,a),1));for(i=0,a=0;i<h.length;i++)o=h[h.length-1-i],m[m.length-1-i]!==o&&(p.hasOwnProperty(o)?(n.push({command:Ut.removeLayer,args:[o]}),m.splice(m.lastIndexOf(o,m.length-a),1)):a++,c=m[m.length-i],n.push({command:Ut.addLayer,args:[d[o],c]}),m.splice(m.length-i,0,o),g[o]=!0);for(i=0;i<h.length;i++)if(s=p[o=h[i]],l=d[o],!g[o]&&!t.deepEqual(s,l))if(t.deepEqual(s.source,l.source)&&t.deepEqual(s[\"source-layer\"],l[\"source-layer\"])&&t.deepEqual(s.type,l.type)){for(u in Yt(s.layout,l.layout,n,o,null,Ut.setLayoutProperty),Yt(s.paint,l.paint,n,o,null,Ut.setPaintProperty),t.deepEqual(s.filter,l.filter)||n.push({command:Ut.setFilter,args:[o,l.filter]}),t.deepEqual(s.minzoom,l.minzoom)&&t.deepEqual(s.maxzoom,l.maxzoom)||n.push({command:Ut.setLayerZoomRange,args:[o,l.minzoom,l.maxzoom]}),s)s.hasOwnProperty(u)&&\"layout\"!==u&&\"paint\"!==u&&\"filter\"!==u&&\"metadata\"!==u&&\"minzoom\"!==u&&\"maxzoom\"!==u&&(0===u.indexOf(\"paint.\")?Yt(s[u],l[u],n,o,u.slice(6),Ut.setPaintProperty):t.deepEqual(s[u],l[u])||n.push({command:Ut.setLayerProperty,args:[o,u,l[u]]}));for(u in l)l.hasOwnProperty(u)&&!s.hasOwnProperty(u)&&\"layout\"!==u&&\"paint\"!==u&&\"filter\"!==u&&\"metadata\"!==u&&\"minzoom\"!==u&&\"maxzoom\"!==u&&(0===u.indexOf(\"paint.\")?Yt(s[u],l[u],n,o,u.slice(6),Ut.setPaintProperty):t.deepEqual(s[u],l[u])||n.push({command:Ut.setLayerProperty,args:[o,u,l[u]]}))}else n.push({command:Ut.removeLayer,args:[o]}),c=m[m.lastIndexOf(o)+1],n.push({command:Ut.addLayer,args:[l,c]})}(o,r.layers,n)}catch(t){console.warn(\"Unable to compute style diff:\",t),n=[{command:Ut.setStyle,args:[r]}]}return n}var Jt=function(t,e){this.reset(t,e)};Jt.prototype.reset=function(t,e){this.points=t||[],this._distances=[0];for(var r=1;r<this.points.length;r++)this._distances[r]=this._distances[r-1]+this.points[r].dist(this.points[r-1]);this.length=this._distances[this._distances.length-1],this.padding=Math.min(e||0,.5*this.length),this.paddedLength=this.length-2*this.padding},Jt.prototype.lerp=function(e){if(1===this.points.length)return this.points[0];e=t.clamp(e,0,1);for(var r=1,n=this._distances[r],i=e*this.paddedLength+this.padding;n<i&&r<this._distances.length;)n=this._distances[++r];var a=r-1,o=this._distances[a],s=n-o,l=s>0?(i-o)/s:0;return this.points[a].mult(1-l).add(this.points[r].mult(l))};var Kt=function(t,e,r){var n=this.boxCells=[],i=this.circleCells=[];this.xCellCount=Math.ceil(t/r),this.yCellCount=Math.ceil(e/r);for(var a=0;a<this.xCellCount*this.yCellCount;a++)n.push([]),i.push([]);this.circleKeys=[],this.boxKeys=[],this.bboxes=[],this.circles=[],this.width=t,this.height=e,this.xScale=this.xCellCount/t,this.yScale=this.yCellCount/e,this.boxUid=0,this.circleUid=0};function Qt(e,r,n,i,a){var o=t.create();return r?(t.scale(o,o,[1/a,1/a,1]),n||t.rotateZ(o,o,i.angle)):t.multiply(o,i.labelPlaneMatrix,e),o}function $t(e,r,n,i,a){if(r){var o=t.clone(e);return t.scale(o,o,[a,a,1]),n||t.rotateZ(o,o,-i.angle),o}return i.glCoordMatrix}function te(e,r){var n=[e.x,e.y,0,1];fe(n,n,r);var i=n[3];return{point:new t.Point(n[0]/i,n[1]/i),signedDistanceFromCamera:i}}function ee(t,e){return.5+t/e*.5}function re(t,e){var r=t[0]/t[3],n=t[1]/t[3];return r>=-e[0]&&r<=e[0]&&n>=-e[1]&&n<=e[1]}function ne(e,r,n,i,a,o,s,l){var c=i?e.textSizeData:e.iconSizeData,u=t.evaluateSizeForZoom(c,n.transform.zoom),f=[256/n.width*2+1,256/n.height*2+1],h=i?e.text.dynamicLayoutVertexArray:e.icon.dynamicLayoutVertexArray;h.clear();for(var p=e.lineVertexArray,d=i?e.text.placedSymbolArray:e.icon.placedSymbolArray,m=n.transform.width/n.transform.height,g=!1,v=0;v<d.length;v++){var y=d.get(v);if(y.hidden||y.writingMode===t.WritingMode.vertical&&!g)ue(y.numGlyphs,h);else{g=!1;var x=[y.anchorX,y.anchorY,0,1];if(t.transformMat4(x,x,r),re(x,f)){var b=x[3],_=ee(n.transform.cameraToCenterDistance,b),w=t.evaluateSizeForFeature(c,u,y),T=s?w/_:w*_,k=new t.Point(y.anchorX,y.anchorY),M=te(k,a).point,A={},S=oe(y,T,!1,l,r,a,o,e.glyphOffsetArray,p,h,M,k,A,m);g=S.useVertical,(S.notEnoughRoom||g||S.needsFlipping&&oe(y,T,!0,l,r,a,o,e.glyphOffsetArray,p,h,M,k,A,m).notEnoughRoom)&&ue(y.numGlyphs,h)}else ue(y.numGlyphs,h)}}i?e.text.dynamicLayoutVertexBuffer.updateData(h):e.icon.dynamicLayoutVertexBuffer.updateData(h)}function ie(t,e,r,n,i,a,o,s,l,c,u){var f=s.glyphStartIndex+s.numGlyphs,h=s.lineStartIndex,p=s.lineStartIndex+s.lineLength,d=e.getoffsetX(s.glyphStartIndex),m=e.getoffsetX(f-1),g=le(t*d,r,n,i,a,o,s.segment,h,p,l,c,u);if(!g)return null;var v=le(t*m,r,n,i,a,o,s.segment,h,p,l,c,u);return v?{first:g,last:v}:null}function ae(e,r,n,i){if(e===t.WritingMode.horizontal&&Math.abs(n.y-r.y)>Math.abs(n.x-r.x)*i)return{useVertical:!0};return(e===t.WritingMode.vertical?r.y<n.y:r.x>n.x)?{needsFlipping:!0}:null}function oe(e,r,n,i,a,o,s,l,c,u,f,h,p,d){var m,g=r/24,v=e.lineOffsetX*g,y=e.lineOffsetY*g;if(e.numGlyphs>1){var x=e.glyphStartIndex+e.numGlyphs,b=e.lineStartIndex,_=e.lineStartIndex+e.lineLength,w=ie(g,l,v,y,n,f,h,e,c,o,p);if(!w)return{notEnoughRoom:!0};var T=te(w.first.point,s).point,k=te(w.last.point,s).point;if(i&&!n){var M=ae(e.writingMode,T,k,d);if(M)return M}m=[w.first];for(var A=e.glyphStartIndex+1;A<x-1;A++)m.push(le(g*l.getoffsetX(A),v,y,n,f,h,e.segment,b,_,c,o,p));m.push(w.last)}else{if(i&&!n){var S=te(h,a).point,E=e.lineStartIndex+e.segment+1,L=new t.Point(c.getx(E),c.gety(E)),C=te(L,a),P=C.signedDistanceFromCamera>0?C.point:se(h,L,S,1,a),I=ae(e.writingMode,S,P,d);if(I)return I}var O=le(g*l.getoffsetX(e.glyphStartIndex),v,y,n,f,h,e.segment,e.lineStartIndex,e.lineStartIndex+e.lineLength,c,o,p);if(!O)return{notEnoughRoom:!0};m=[O]}for(var z=0,D=m;z<D.length;z+=1){var R=D[z];t.addDynamicAttributes(u,R.point,R.angle)}return{}}function se(t,e,r,n,i){var a=te(t.add(t.sub(e)._unit()),i).point,o=r.sub(a);return r.add(o._mult(n/o.mag()))}function le(e,r,n,i,a,o,s,l,c,u,f,h){var p=i?e-r:e+r,d=p>0?1:-1,m=0;i&&(d*=-1,m=Math.PI),d<0&&(m+=Math.PI);for(var g=d>0?l+s:l+s+1,v=a,y=a,x=0,b=0,_=Math.abs(p),w=[];x+b<=_;){if((g+=d)<l||g>=c)return null;if(y=v,w.push(v),void 0===(v=h[g])){var T=new t.Point(u.getx(g),u.gety(g)),k=te(T,f);if(k.signedDistanceFromCamera>0)v=h[g]=k.point;else{var M=g-d;v=se(0===x?o:new t.Point(u.getx(M),u.gety(M)),T,y,_-x+1,f)}}x+=b,b=y.dist(v)}var A=(_-x)/b,S=v.sub(y),E=S.mult(A)._add(y);E._add(S._unit()._perp()._mult(n*d));var L=m+Math.atan2(v.y-y.y,v.x-y.x);return w.push(E),{point:E,angle:L,path:w}}Kt.prototype.keysLength=function(){return this.boxKeys.length+this.circleKeys.length},Kt.prototype.insert=function(t,e,r,n,i){this._forEachCell(e,r,n,i,this._insertBoxCell,this.boxUid++),this.boxKeys.push(t),this.bboxes.push(e),this.bboxes.push(r),this.bboxes.push(n),this.bboxes.push(i)},Kt.prototype.insertCircle=function(t,e,r,n){this._forEachCell(e-n,r-n,e+n,r+n,this._insertCircleCell,this.circleUid++),this.circleKeys.push(t),this.circles.push(e),this.circles.push(r),this.circles.push(n)},Kt.prototype._insertBoxCell=function(t,e,r,n,i,a){this.boxCells[i].push(a)},Kt.prototype._insertCircleCell=function(t,e,r,n,i,a){this.circleCells[i].push(a)},Kt.prototype._query=function(t,e,r,n,i,a){if(r<0||t>this.width||n<0||e>this.height)return!i&&[];var o=[];if(t<=0&&e<=0&&this.width<=r&&this.height<=n){if(i)return!0;for(var s=0;s<this.boxKeys.length;s++)o.push({key:this.boxKeys[s],x1:this.bboxes[4*s],y1:this.bboxes[4*s+1],x2:this.bboxes[4*s+2],y2:this.bboxes[4*s+3]});for(var l=0;l<this.circleKeys.length;l++){var c=this.circles[3*l],u=this.circles[3*l+1],f=this.circles[3*l+2];o.push({key:this.circleKeys[l],x1:c-f,y1:u-f,x2:c+f,y2:u+f})}return a?o.filter(a):o}var h={hitTest:i,seenUids:{box:{},circle:{}}};return this._forEachCell(t,e,r,n,this._queryCell,o,h,a),i?o.length>0:o},Kt.prototype._queryCircle=function(t,e,r,n,i){var a=t-r,o=t+r,s=e-r,l=e+r;if(o<0||a>this.width||l<0||s>this.height)return!n&&[];var c=[],u={hitTest:n,circle:{x:t,y:e,radius:r},seenUids:{box:{},circle:{}}};return this._forEachCell(a,s,o,l,this._queryCellCircle,c,u,i),n?c.length>0:c},Kt.prototype.query=function(t,e,r,n,i){return this._query(t,e,r,n,!1,i)},Kt.prototype.hitTest=function(t,e,r,n,i){return this._query(t,e,r,n,!0,i)},Kt.prototype.hitTestCircle=function(t,e,r,n){return this._queryCircle(t,e,r,!0,n)},Kt.prototype._queryCell=function(t,e,r,n,i,a,o,s){var l=o.seenUids,c=this.boxCells[i];if(null!==c)for(var u=this.bboxes,f=0,h=c;f<h.length;f+=1){var p=h[f];if(!l.box[p]){l.box[p]=!0;var d=4*p;if(t<=u[d+2]&&e<=u[d+3]&&r>=u[d+0]&&n>=u[d+1]&&(!s||s(this.boxKeys[p]))){if(o.hitTest)return a.push(!0),!0;a.push({key:this.boxKeys[p],x1:u[d],y1:u[d+1],x2:u[d+2],y2:u[d+3]})}}}var m=this.circleCells[i];if(null!==m)for(var g=this.circles,v=0,y=m;v<y.length;v+=1){var x=y[v];if(!l.circle[x]){l.circle[x]=!0;var b=3*x;if(this._circleAndRectCollide(g[b],g[b+1],g[b+2],t,e,r,n)&&(!s||s(this.circleKeys[x]))){if(o.hitTest)return a.push(!0),!0;var _=g[b],w=g[b+1],T=g[b+2];a.push({key:this.circleKeys[x],x1:_-T,y1:w-T,x2:_+T,y2:w+T})}}}},Kt.prototype._queryCellCircle=function(t,e,r,n,i,a,o,s){var l=o.circle,c=o.seenUids,u=this.boxCells[i];if(null!==u)for(var f=this.bboxes,h=0,p=u;h<p.length;h+=1){var d=p[h];if(!c.box[d]){c.box[d]=!0;var m=4*d;if(this._circleAndRectCollide(l.x,l.y,l.radius,f[m+0],f[m+1],f[m+2],f[m+3])&&(!s||s(this.boxKeys[d])))return a.push(!0),!0}}var g=this.circleCells[i];if(null!==g)for(var v=this.circles,y=0,x=g;y<x.length;y+=1){var b=x[y];if(!c.circle[b]){c.circle[b]=!0;var _=3*b;if(this._circlesCollide(v[_],v[_+1],v[_+2],l.x,l.y,l.radius)&&(!s||s(this.circleKeys[b])))return a.push(!0),!0}}},Kt.prototype._forEachCell=function(t,e,r,n,i,a,o,s){for(var l=this._convertToXCellCoord(t),c=this._convertToYCellCoord(e),u=this._convertToXCellCoord(r),f=this._convertToYCellCoord(n),h=l;h<=u;h++)for(var p=c;p<=f;p++){var d=this.xCellCount*p+h;if(i.call(this,t,e,r,n,d,a,o,s))return}},Kt.prototype._convertToXCellCoord=function(t){return Math.max(0,Math.min(this.xCellCount-1,Math.floor(t*this.xScale)))},Kt.prototype._convertToYCellCoord=function(t){return Math.max(0,Math.min(this.yCellCount-1,Math.floor(t*this.yScale)))},Kt.prototype._circlesCollide=function(t,e,r,n,i,a){var o=n-t,s=i-e,l=r+a;return l*l>o*o+s*s},Kt.prototype._circleAndRectCollide=function(t,e,r,n,i,a,o){var s=(a-n)/2,l=Math.abs(t-(n+s));if(l>s+r)return!1;var c=(o-i)/2,u=Math.abs(e-(i+c));if(u>c+r)return!1;if(l<=s||u<=c)return!0;var f=l-s,h=u-c;return f*f+h*h<=r*r};var ce=new Float32Array([-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0]);function ue(t,e){for(var r=0;r<t;r++){var n=e.length;e.resize(n+4),e.float32.set(ce,3*n)}}function fe(t,e,r){var n=e[0],i=e[1];return t[0]=r[0]*n+r[4]*i+r[12],t[1]=r[1]*n+r[5]*i+r[13],t[3]=r[3]*n+r[7]*i+r[15],t}var he=function(t,e,r){void 0===e&&(e=new Kt(t.width+200,t.height+200,25)),void 0===r&&(r=new Kt(t.width+200,t.height+200,25)),this.transform=t,this.grid=e,this.ignoredGrid=r,this.pitchfactor=Math.cos(t._pitch)*t.cameraToCenterDistance,this.screenRightBoundary=t.width+100,this.screenBottomBoundary=t.height+100,this.gridRightBoundary=t.width+200,this.gridBottomBoundary=t.height+200};function pe(e,r,n){return r*(t.EXTENT/(e.tileSize*Math.pow(2,n-e.tileID.overscaledZ)))}he.prototype.placeCollisionBox=function(t,e,r,n,i){var a=this.projectAndGetPerspectiveRatio(n,t.anchorPointX,t.anchorPointY),o=r*a.perspectiveRatio,s=t.x1*o+a.point.x,l=t.y1*o+a.point.y,c=t.x2*o+a.point.x,u=t.y2*o+a.point.y;return!this.isInsideGrid(s,l,c,u)||!e&&this.grid.hitTest(s,l,c,u,i)?{box:[],offscreen:!1}:{box:[s,l,c,u],offscreen:this.isOffscreen(s,l,c,u)}},he.prototype.placeCollisionCircles=function(e,r,n,i,a,o,s,l,c,u,f,h,p){var d=[],m=new t.Point(r.anchorX,r.anchorY),g=te(m,o),v=ee(this.transform.cameraToCenterDistance,g.signedDistanceFromCamera),y=(u?a/v:a*v)/t.ONE_EM,x=te(m,s).point,b=ie(y,i,r.lineOffsetX*y,r.lineOffsetY*y,!1,x,m,r,n,s,{}),_=!1,w=!1,T=!0;if(b){for(var k=.5*h*v+p,M=new t.Point(-100,-100),A=new t.Point(this.screenRightBoundary,this.screenBottomBoundary),S=new Jt,E=b.first,L=b.last,C=[],P=E.path.length-1;P>=1;P--)C.push(E.path[P]);for(var I=1;I<L.path.length;I++)C.push(L.path[I]);var O=2.5*k;if(l){var z=C.map((function(t){return te(t,l)}));C=z.some((function(t){return t.signedDistanceFromCamera<=0}))?[]:z.map((function(t){return t.point}))}var D=[];if(C.length>0){for(var R=C[0].clone(),F=C[0].clone(),B=1;B<C.length;B++)R.x=Math.min(R.x,C[B].x),R.y=Math.min(R.y,C[B].y),F.x=Math.max(F.x,C[B].x),F.y=Math.max(F.y,C[B].y);D=R.x>=M.x&&F.x<=A.x&&R.y>=M.y&&F.y<=A.y?[C]:F.x<M.x||R.x>A.x||F.y<M.y||R.y>A.y?[]:t.clipLine([C],M.x,M.y,A.x,A.y)}for(var N=0,j=D;N<j.length;N+=1){var U=j[N];S.reset(U,.25*k);var V=0;V=S.length<=.5*k?1:Math.ceil(S.paddedLength/O)+1;for(var q=0;q<V;q++){var H=q/Math.max(V-1,1),G=S.lerp(H),Y=G.x+100,W=G.y+100;d.push(Y,W,k,0);var X=Y-k,Z=W-k,J=Y+k,K=W+k;if(T=T&&this.isOffscreen(X,Z,J,K),w=w||this.isInsideGrid(X,Z,J,K),!e&&this.grid.hitTestCircle(Y,W,k,f)&&(_=!0,!c))return{circles:[],offscreen:!1,collisionDetected:_}}}}return{circles:!c&&_||!w?[]:d,offscreen:T,collisionDetected:_}},he.prototype.queryRenderedSymbols=function(e){if(0===e.length||0===this.grid.keysLength()&&0===this.ignoredGrid.keysLength())return{};for(var r=[],n=1/0,i=1/0,a=-1/0,o=-1/0,s=0,l=e;s<l.length;s+=1){var c=l[s],u=new t.Point(c.x+100,c.y+100);n=Math.min(n,u.x),i=Math.min(i,u.y),a=Math.max(a,u.x),o=Math.max(o,u.y),r.push(u)}for(var f={},h={},p=0,d=this.grid.query(n,i,a,o).concat(this.ignoredGrid.query(n,i,a,o));p<d.length;p+=1){var m=d[p],g=m.key;if(void 0===f[g.bucketInstanceId]&&(f[g.bucketInstanceId]={}),!f[g.bucketInstanceId][g.featureIndex]){var v=[new t.Point(m.x1,m.y1),new t.Point(m.x2,m.y1),new t.Point(m.x2,m.y2),new t.Point(m.x1,m.y2)];t.polygonIntersectsPolygon(r,v)&&(f[g.bucketInstanceId][g.featureIndex]=!0,void 0===h[g.bucketInstanceId]&&(h[g.bucketInstanceId]=[]),h[g.bucketInstanceId].push(g.featureIndex))}}return h},he.prototype.insertCollisionBox=function(t,e,r,n,i){var a={bucketInstanceId:r,featureIndex:n,collisionGroupID:i};(e?this.ignoredGrid:this.grid).insert(a,t[0],t[1],t[2],t[3])},he.prototype.insertCollisionCircles=function(t,e,r,n,i){for(var a=e?this.ignoredGrid:this.grid,o={bucketInstanceId:r,featureIndex:n,collisionGroupID:i},s=0;s<t.length;s+=4)a.insertCircle(o,t[s],t[s+1],t[s+2])},he.prototype.projectAndGetPerspectiveRatio=function(e,r,n){var i=[r,n,0,1];return fe(i,i,e),{point:new t.Point((i[0]/i[3]+1)/2*this.transform.width+100,(-i[1]/i[3]+1)/2*this.transform.height+100),perspectiveRatio:.5+this.transform.cameraToCenterDistance/i[3]*.5}},he.prototype.isOffscreen=function(t,e,r,n){return r<100||t>=this.screenRightBoundary||n<100||e>this.screenBottomBoundary},he.prototype.isInsideGrid=function(t,e,r,n){return r>=0&&t<this.gridRightBoundary&&n>=0&&e<this.gridBottomBoundary},he.prototype.getViewportMatrix=function(){var e=t.identity([]);return t.translate(e,e,[-100,-100,0]),e};var de=function(t,e,r,n){this.opacity=t?Math.max(0,Math.min(1,t.opacity+(t.placed?e:-e))):n&&r?1:0,this.placed=r};de.prototype.isHidden=function(){return 0===this.opacity&&!this.placed};var me=function(t,e,r,n,i){this.text=new de(t?t.text:null,e,r,i),this.icon=new de(t?t.icon:null,e,n,i)};me.prototype.isHidden=function(){return this.text.isHidden()&&this.icon.isHidden()};var ge=function(t,e,r){this.text=t,this.icon=e,this.skipFade=r},ve=function(){this.invProjMatrix=t.create(),this.viewportMatrix=t.create(),this.circles=[]},ye=function(t,e,r,n,i){this.bucketInstanceId=t,this.featureIndex=e,this.sourceLayerIndex=r,this.bucketIndex=n,this.tileID=i},xe=function(t){this.crossSourceCollisions=t,this.maxGroupID=0,this.collisionGroups={}};function be(e,r,n,i,a){var o=t.getAnchorAlignment(e),s=-(o.horizontalAlign-.5)*r,l=-(o.verticalAlign-.5)*n,c=t.evaluateVariableOffset(e,i);return new t.Point(s+c[0]*a,l+c[1]*a)}function _e(e,r,n,i,a,o){var s=e.x1,l=e.x2,c=e.y1,u=e.y2,f=e.anchorPointX,h=e.anchorPointY,p=new t.Point(r,n);return i&&p._rotate(a?o:-o),{x1:s+p.x,y1:c+p.y,x2:l+p.x,y2:u+p.y,anchorPointX:f,anchorPointY:h}}xe.prototype.get=function(t){if(this.crossSourceCollisions)return{ID:0,predicate:null};if(!this.collisionGroups[t]){var e=++this.maxGroupID;this.collisionGroups[t]={ID:e,predicate:function(t){return t.collisionGroupID===e}}}return this.collisionGroups[t]};var we=function(t,e,r,n){this.transform=t.clone(),this.collisionIndex=new he(this.transform),this.placements={},this.opacities={},this.variableOffsets={},this.stale=!1,this.commitTime=0,this.fadeDuration=e,this.retainedQueryData={},this.collisionGroups=new xe(r),this.collisionCircleArrays={},this.prevPlacement=n,n&&(n.prevPlacement=void 0),this.placedOrientations={}};function Te(t,e,r,n,i){t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0)}we.prototype.getBucketParts=function(e,r,n,i){var a=n.getBucket(r),o=n.latestFeatureIndex;if(a&&o&&r.id===a.layerIds[0]){var s=n.collisionBoxArray,l=a.layers[0].layout,c=Math.pow(2,this.transform.zoom-n.tileID.overscaledZ),u=n.tileSize/t.EXTENT,f=this.transform.calculatePosMatrix(n.tileID.toUnwrapped()),h=\"map\"===l.get(\"text-pitch-alignment\"),p=\"map\"===l.get(\"text-rotation-alignment\"),d=pe(n,1,this.transform.zoom),m=Qt(f,h,p,this.transform,d),g=null;if(h){var v=$t(f,h,p,this.transform,d);g=t.multiply([],this.transform.labelPlaneMatrix,v)}this.retainedQueryData[a.bucketInstanceId]=new ye(a.bucketInstanceId,o,a.sourceLayerIndex,a.index,n.tileID);var y={bucket:a,layout:l,posMatrix:f,textLabelPlaneMatrix:m,labelToScreenMatrix:g,scale:c,textPixelRatio:u,holdingForFade:n.holdingForFade(),collisionBoxArray:s,partiallyEvaluatedTextSize:t.evaluateSizeForZoom(a.textSizeData,this.transform.zoom),collisionGroup:this.collisionGroups.get(a.sourceID)};if(i)for(var x=0,b=a.sortKeyRanges;x<b.length;x+=1){var _=b[x],w=_.sortKey,T=_.symbolInstanceStart,k=_.symbolInstanceEnd;e.push({sortKey:w,symbolInstanceStart:T,symbolInstanceEnd:k,parameters:y})}else e.push({symbolInstanceStart:0,symbolInstanceEnd:a.symbolInstances.length,parameters:y})}},we.prototype.attemptAnchorPlacement=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d){var m,g=[f.textOffset0,f.textOffset1],v=be(t,r,n,g,i),y=this.collisionIndex.placeCollisionBox(_e(e,v.x,v.y,a,o,this.transform.angle),u,s,l,c.predicate);if(d&&0===this.collisionIndex.placeCollisionBox(_e(d,v.x,v.y,a,o,this.transform.angle),u,s,l,c.predicate).box.length)return;if(y.box.length>0)return this.prevPlacement&&this.prevPlacement.variableOffsets[f.crossTileID]&&this.prevPlacement.placements[f.crossTileID]&&this.prevPlacement.placements[f.crossTileID].text&&(m=this.prevPlacement.variableOffsets[f.crossTileID].anchor),this.variableOffsets[f.crossTileID]={textOffset:g,width:r,height:n,anchor:t,textBoxScale:i,prevAnchor:m},this.markUsedJustification(h,t,f,p),h.allowVerticalPlacement&&(this.markUsedOrientation(h,p,f),this.placedOrientations[f.crossTileID]=p),{shift:v,placedGlyphBoxes:y}},we.prototype.placeLayerBucketPart=function(e,r,n){var i=this,a=e.parameters,o=a.bucket,s=a.layout,l=a.posMatrix,c=a.textLabelPlaneMatrix,u=a.labelToScreenMatrix,f=a.textPixelRatio,h=a.holdingForFade,p=a.collisionBoxArray,d=a.partiallyEvaluatedTextSize,m=a.collisionGroup,g=s.get(\"text-optional\"),v=s.get(\"icon-optional\"),y=s.get(\"text-allow-overlap\"),x=s.get(\"icon-allow-overlap\"),b=\"map\"===s.get(\"text-rotation-alignment\"),_=\"map\"===s.get(\"text-pitch-alignment\"),w=\"none\"!==s.get(\"icon-text-fit\"),T=\"viewport-y\"===s.get(\"symbol-z-order\"),k=y&&(x||!o.hasIconData()||v),M=x&&(y||!o.hasTextData()||g);!o.collisionArrays&&p&&o.deserializeCollisionBoxes(p);var A=function(e,a){if(!r[e.crossTileID])if(h)i.placements[e.crossTileID]=new ge(!1,!1,!1);else{var p,T=!1,A=!1,S=!0,E=null,L={box:null,offscreen:null},C={box:null,offscreen:null},P=null,I=null,O=0,z=0,D=0;a.textFeatureIndex?O=a.textFeatureIndex:e.useRuntimeCollisionCircles&&(O=e.featureIndex),a.verticalTextFeatureIndex&&(z=a.verticalTextFeatureIndex);var R=a.textBox;if(R){var F=function(r){var n=t.WritingMode.horizontal;if(o.allowVerticalPlacement&&!r&&i.prevPlacement){var a=i.prevPlacement.placedOrientations[e.crossTileID];a&&(i.placedOrientations[e.crossTileID]=a,n=a,i.markUsedOrientation(o,n,e))}return n},B=function(r,n){if(o.allowVerticalPlacement&&e.numVerticalGlyphVertices>0&&a.verticalTextBox)for(var i=0,s=o.writingModes;i<s.length;i+=1){if(s[i]===t.WritingMode.vertical?(L=n(),C=L):L=r(),L&&L.box&&L.box.length)break}else L=r()};if(s.get(\"text-variable-anchor\")){var N=s.get(\"text-variable-anchor\");if(i.prevPlacement&&i.prevPlacement.variableOffsets[e.crossTileID]){var j=i.prevPlacement.variableOffsets[e.crossTileID];N.indexOf(j.anchor)>0&&(N=N.filter((function(t){return t!==j.anchor}))).unshift(j.anchor)}var U=function(t,r,n){for(var a=t.x2-t.x1,s=t.y2-t.y1,c=e.textBoxScale,u=w&&!x?r:null,h={box:[],offscreen:!1},p=y?2*N.length:N.length,d=0;d<p;++d){var g=N[d%N.length],v=d>=N.length,k=i.attemptAnchorPlacement(g,t,a,s,c,b,_,f,l,m,v,e,o,n,u);if(k&&(h=k.placedGlyphBoxes)&&h.box&&h.box.length){T=!0,E=k.shift;break}}return h};B((function(){return U(R,a.iconBox,t.WritingMode.horizontal)}),(function(){var r=a.verticalTextBox,n=L&&L.box&&L.box.length;return o.allowVerticalPlacement&&!n&&e.numVerticalGlyphVertices>0&&r?U(r,a.verticalIconBox,t.WritingMode.vertical):{box:null,offscreen:null}})),L&&(T=L.box,S=L.offscreen);var V=F(L&&L.box);if(!T&&i.prevPlacement){var q=i.prevPlacement.variableOffsets[e.crossTileID];q&&(i.variableOffsets[e.crossTileID]=q,i.markUsedJustification(o,q.anchor,e,V))}}else{var H=function(t,r){var n=i.collisionIndex.placeCollisionBox(t,y,f,l,m.predicate);return n&&n.box&&n.box.length&&(i.markUsedOrientation(o,r,e),i.placedOrientations[e.crossTileID]=r),n};B((function(){return H(R,t.WritingMode.horizontal)}),(function(){var r=a.verticalTextBox;return o.allowVerticalPlacement&&e.numVerticalGlyphVertices>0&&r?H(r,t.WritingMode.vertical):{box:null,offscreen:null}})),F(L&&L.box&&L.box.length)}}if(T=(p=L)&&p.box&&p.box.length>0,S=p&&p.offscreen,e.useRuntimeCollisionCircles){var G=o.text.placedSymbolArray.get(e.centerJustifiedTextSymbolIndex),Y=t.evaluateSizeForFeature(o.textSizeData,d,G),W=s.get(\"text-padding\"),X=e.collisionCircleDiameter;P=i.collisionIndex.placeCollisionCircles(y,G,o.lineVertexArray,o.glyphOffsetArray,Y,l,c,u,n,_,m.predicate,X,W),T=y||P.circles.length>0&&!P.collisionDetected,S=S&&P.offscreen}if(a.iconFeatureIndex&&(D=a.iconFeatureIndex),a.iconBox){var Z=function(t){var e=w&&E?_e(t,E.x,E.y,b,_,i.transform.angle):t;return i.collisionIndex.placeCollisionBox(e,x,f,l,m.predicate)};A=C&&C.box&&C.box.length&&a.verticalIconBox?(I=Z(a.verticalIconBox)).box.length>0:(I=Z(a.iconBox)).box.length>0,S=S&&I.offscreen}var J=g||0===e.numHorizontalGlyphVertices&&0===e.numVerticalGlyphVertices,K=v||0===e.numIconVertices;if(J||K?K?J||(A=A&&T):T=A&&T:A=T=A&&T,T&&p&&p.box&&(C&&C.box&&z?i.collisionIndex.insertCollisionBox(p.box,s.get(\"text-ignore-placement\"),o.bucketInstanceId,z,m.ID):i.collisionIndex.insertCollisionBox(p.box,s.get(\"text-ignore-placement\"),o.bucketInstanceId,O,m.ID)),A&&I&&i.collisionIndex.insertCollisionBox(I.box,s.get(\"icon-ignore-placement\"),o.bucketInstanceId,D,m.ID),P&&(T&&i.collisionIndex.insertCollisionCircles(P.circles,s.get(\"text-ignore-placement\"),o.bucketInstanceId,O,m.ID),n)){var Q=o.bucketInstanceId,$=i.collisionCircleArrays[Q];void 0===$&&($=i.collisionCircleArrays[Q]=new ve);for(var tt=0;tt<P.circles.length;tt+=4)$.circles.push(P.circles[tt+0]),$.circles.push(P.circles[tt+1]),$.circles.push(P.circles[tt+2]),$.circles.push(P.collisionDetected?1:0)}i.placements[e.crossTileID]=new ge(T||k,A||M,S||o.justReloaded),r[e.crossTileID]=!0}};if(T)for(var S=o.getSortedSymbolIndexes(this.transform.angle),E=S.length-1;E>=0;--E){var L=S[E];A(o.symbolInstances.get(L),o.collisionArrays[L])}else for(var C=e.symbolInstanceStart;C<e.symbolInstanceEnd;C++)A(o.symbolInstances.get(C),o.collisionArrays[C]);if(n&&o.bucketInstanceId in this.collisionCircleArrays){var P=this.collisionCircleArrays[o.bucketInstanceId];t.invert(P.invProjMatrix,l),P.viewportMatrix=this.collisionIndex.getViewportMatrix()}o.justReloaded=!1},we.prototype.markUsedJustification=function(e,r,n,i){var a,o={left:n.leftJustifiedTextSymbolIndex,center:n.centerJustifiedTextSymbolIndex,right:n.rightJustifiedTextSymbolIndex};a=i===t.WritingMode.vertical?n.verticalPlacedTextSymbolIndex:o[t.getAnchorJustification(r)];for(var s=0,l=[n.leftJustifiedTextSymbolIndex,n.centerJustifiedTextSymbolIndex,n.rightJustifiedTextSymbolIndex,n.verticalPlacedTextSymbolIndex];s<l.length;s+=1){var c=l[s];c>=0&&(e.text.placedSymbolArray.get(c).crossTileID=a>=0&&c!==a?0:n.crossTileID)}},we.prototype.markUsedOrientation=function(e,r,n){for(var i=r===t.WritingMode.horizontal||r===t.WritingMode.horizontalOnly?r:0,a=r===t.WritingMode.vertical?r:0,o=0,s=[n.leftJustifiedTextSymbolIndex,n.centerJustifiedTextSymbolIndex,n.rightJustifiedTextSymbolIndex];o<s.length;o+=1){var l=s[o];e.text.placedSymbolArray.get(l).placedOrientation=i}n.verticalPlacedTextSymbolIndex&&(e.text.placedSymbolArray.get(n.verticalPlacedTextSymbolIndex).placedOrientation=a)},we.prototype.commit=function(t){this.commitTime=t,this.zoomAtLastRecencyCheck=this.transform.zoom;var e=this.prevPlacement,r=!1;this.prevZoomAdjustment=e?e.zoomAdjustment(this.transform.zoom):0;var n=e?e.symbolFadeChange(t):1,i=e?e.opacities:{},a=e?e.variableOffsets:{},o=e?e.placedOrientations:{};for(var s in this.placements){var l=this.placements[s],c=i[s];c?(this.opacities[s]=new me(c,n,l.text,l.icon),r=r||l.text!==c.text.placed||l.icon!==c.icon.placed):(this.opacities[s]=new me(null,n,l.text,l.icon,l.skipFade),r=r||l.text||l.icon)}for(var u in i){var f=i[u];if(!this.opacities[u]){var h=new me(f,n,!1,!1);h.isHidden()||(this.opacities[u]=h,r=r||f.text.placed||f.icon.placed)}}for(var p in a)this.variableOffsets[p]||!this.opacities[p]||this.opacities[p].isHidden()||(this.variableOffsets[p]=a[p]);for(var d in o)this.placedOrientations[d]||!this.opacities[d]||this.opacities[d].isHidden()||(this.placedOrientations[d]=o[d]);r?this.lastPlacementChangeTime=t:\"number\"!=typeof this.lastPlacementChangeTime&&(this.lastPlacementChangeTime=e?e.lastPlacementChangeTime:t)},we.prototype.updateLayerOpacities=function(t,e){for(var r={},n=0,i=e;n<i.length;n+=1){var a=i[n],o=a.getBucket(t);o&&a.latestFeatureIndex&&t.id===o.layerIds[0]&&this.updateBucketOpacities(o,r,a.collisionBoxArray)}},we.prototype.updateBucketOpacities=function(e,r,n){var i=this;e.hasTextData()&&e.text.opacityVertexArray.clear(),e.hasIconData()&&e.icon.opacityVertexArray.clear(),e.hasIconCollisionBoxData()&&e.iconCollisionBox.collisionVertexArray.clear(),e.hasTextCollisionBoxData()&&e.textCollisionBox.collisionVertexArray.clear();var a=e.layers[0].layout,o=new me(null,0,!1,!1,!0),s=a.get(\"text-allow-overlap\"),l=a.get(\"icon-allow-overlap\"),c=a.get(\"text-variable-anchor\"),u=\"map\"===a.get(\"text-rotation-alignment\"),f=\"map\"===a.get(\"text-pitch-alignment\"),h=\"none\"!==a.get(\"icon-text-fit\"),p=new me(null,0,s&&(l||!e.hasIconData()||a.get(\"icon-optional\")),l&&(s||!e.hasTextData()||a.get(\"text-optional\")),!0);!e.collisionArrays&&n&&(e.hasIconCollisionBoxData()||e.hasTextCollisionBoxData())&&e.deserializeCollisionBoxes(n);for(var d=function(t,e,r){for(var n=0;n<e/4;n++)t.opacityVertexArray.emplaceBack(r)},m=function(n){var a=e.symbolInstances.get(n),s=a.numHorizontalGlyphVertices,l=a.numVerticalGlyphVertices,m=a.crossTileID,g=r[m],v=i.opacities[m];g?v=o:v||(v=p,i.opacities[m]=v),r[m]=!0;var y=s>0||l>0,x=a.numIconVertices>0,b=i.placedOrientations[a.crossTileID],_=b===t.WritingMode.vertical,w=b===t.WritingMode.horizontal||b===t.WritingMode.horizontalOnly;if(y){var T=Pe(v.text),k=_?Ie:T;d(e.text,s,k);var M=w?Ie:T;d(e.text,l,M);var A=v.text.isHidden();[a.rightJustifiedTextSymbolIndex,a.centerJustifiedTextSymbolIndex,a.leftJustifiedTextSymbolIndex].forEach((function(t){t>=0&&(e.text.placedSymbolArray.get(t).hidden=A||_?1:0)})),a.verticalPlacedTextSymbolIndex>=0&&(e.text.placedSymbolArray.get(a.verticalPlacedTextSymbolIndex).hidden=A||w?1:0);var S=i.variableOffsets[a.crossTileID];S&&i.markUsedJustification(e,S.anchor,a,b);var E=i.placedOrientations[a.crossTileID];E&&(i.markUsedJustification(e,\"left\",a,E),i.markUsedOrientation(e,E,a))}if(x){var L=Pe(v.icon),C=!(h&&a.verticalPlacedIconSymbolIndex&&_);if(a.placedIconSymbolIndex>=0){var P=C?L:Ie;d(e.icon,a.numIconVertices,P),e.icon.placedSymbolArray.get(a.placedIconSymbolIndex).hidden=v.icon.isHidden()}if(a.verticalPlacedIconSymbolIndex>=0){var I=C?Ie:L;d(e.icon,a.numVerticalIconVertices,I),e.icon.placedSymbolArray.get(a.verticalPlacedIconSymbolIndex).hidden=v.icon.isHidden()}}if(e.hasIconCollisionBoxData()||e.hasTextCollisionBoxData()){var O=e.collisionArrays[n];if(O){var z=new t.Point(0,0);if(O.textBox||O.verticalTextBox){var D=!0;if(c){var R=i.variableOffsets[m];R?(z=be(R.anchor,R.width,R.height,R.textOffset,R.textBoxScale),u&&z._rotate(f?i.transform.angle:-i.transform.angle)):D=!1}O.textBox&&Te(e.textCollisionBox.collisionVertexArray,v.text.placed,!D||_,z.x,z.y),O.verticalTextBox&&Te(e.textCollisionBox.collisionVertexArray,v.text.placed,!D||w,z.x,z.y)}var F=Boolean(!w&&O.verticalIconBox);O.iconBox&&Te(e.iconCollisionBox.collisionVertexArray,v.icon.placed,F,h?z.x:0,h?z.y:0),O.verticalIconBox&&Te(e.iconCollisionBox.collisionVertexArray,v.icon.placed,!F,h?z.x:0,h?z.y:0)}}},g=0;g<e.symbolInstances.length;g++)m(g);if(e.sortFeatures(this.transform.angle),this.retainedQueryData[e.bucketInstanceId]&&(this.retainedQueryData[e.bucketInstanceId].featureSortOrder=e.featureSortOrder),e.hasTextData()&&e.text.opacityVertexBuffer&&e.text.opacityVertexBuffer.updateData(e.text.opacityVertexArray),e.hasIconData()&&e.icon.opacityVertexBuffer&&e.icon.opacityVertexBuffer.updateData(e.icon.opacityVertexArray),e.hasIconCollisionBoxData()&&e.iconCollisionBox.collisionVertexBuffer&&e.iconCollisionBox.collisionVertexBuffer.updateData(e.iconCollisionBox.collisionVertexArray),e.hasTextCollisionBoxData()&&e.textCollisionBox.collisionVertexBuffer&&e.textCollisionBox.collisionVertexBuffer.updateData(e.textCollisionBox.collisionVertexArray),e.bucketInstanceId in this.collisionCircleArrays){var v=this.collisionCircleArrays[e.bucketInstanceId];e.placementInvProjMatrix=v.invProjMatrix,e.placementViewportMatrix=v.viewportMatrix,e.collisionCircleArray=v.circles,delete this.collisionCircleArrays[e.bucketInstanceId]}},we.prototype.symbolFadeChange=function(t){return 0===this.fadeDuration?1:(t-this.commitTime)/this.fadeDuration+this.prevZoomAdjustment},we.prototype.zoomAdjustment=function(t){return Math.max(0,(this.transform.zoom-t)/1.5)},we.prototype.hasTransitions=function(t){return this.stale||t-this.lastPlacementChangeTime<this.fadeDuration},we.prototype.stillRecent=function(t,e){var r=this.zoomAtLastRecencyCheck===e?1-this.zoomAdjustment(e):1;return this.zoomAtLastRecencyCheck=e,this.commitTime+this.fadeDuration*r>t},we.prototype.setStale=function(){this.stale=!0};var ke=Math.pow(2,25),Me=Math.pow(2,24),Ae=Math.pow(2,17),Se=Math.pow(2,16),Ee=Math.pow(2,9),Le=Math.pow(2,8),Ce=Math.pow(2,1);function Pe(t){if(0===t.opacity&&!t.placed)return 0;if(1===t.opacity&&t.placed)return 4294967295;var e=t.placed?1:0,r=Math.floor(127*t.opacity);return r*ke+e*Me+r*Ae+e*Se+r*Ee+e*Le+r*Ce+e}var Ie=0,Oe=function(t){this._sortAcrossTiles=\"viewport-y\"!==t.layout.get(\"symbol-z-order\")&&void 0!==t.layout.get(\"symbol-sort-key\").constantOr(1),this._currentTileIndex=0,this._currentPartIndex=0,this._seenCrossTileIDs={},this._bucketParts=[]};Oe.prototype.continuePlacement=function(t,e,r,n,i){for(var a=this._bucketParts;this._currentTileIndex<t.length;){var o=t[this._currentTileIndex];if(e.getBucketParts(a,n,o,this._sortAcrossTiles),this._currentTileIndex++,i())return!0}for(this._sortAcrossTiles&&(this._sortAcrossTiles=!1,a.sort((function(t,e){return t.sortKey-e.sortKey})));this._currentPartIndex<a.length;){var s=a[this._currentPartIndex];if(e.placeLayerBucketPart(s,this._seenCrossTileIDs,r),this._currentPartIndex++,i())return!0}return!1};var ze=function(t,e,r,n,i,a,o){this.placement=new we(t,i,a,o),this._currentPlacementIndex=e.length-1,this._forceFullPlacement=r,this._showCollisionBoxes=n,this._done=!1};ze.prototype.isDone=function(){return this._done},ze.prototype.continuePlacement=function(e,r,n){for(var i=this,a=t.browser.now(),o=function(){var e=t.browser.now()-a;return!i._forceFullPlacement&&e>2};this._currentPlacementIndex>=0;){var s=r[e[this._currentPlacementIndex]],l=this.placement.collisionIndex.transform.zoom;if(\"symbol\"===s.type&&(!s.minzoom||s.minzoom<=l)&&(!s.maxzoom||s.maxzoom>l)){if(this._inProgressLayer||(this._inProgressLayer=new Oe(s)),this._inProgressLayer.continuePlacement(n[s.source],this.placement,this._showCollisionBoxes,s,o))return;delete this._inProgressLayer}this._currentPlacementIndex--}this._done=!0},ze.prototype.commit=function(t){return this.placement.commit(t),this.placement};var De=512/t.EXTENT/2,Re=function(t,e,r){this.tileID=t,this.indexedSymbolInstances={},this.bucketInstanceId=r;for(var n=0;n<e.length;n++){var i=e.get(n),a=i.key;this.indexedSymbolInstances[a]||(this.indexedSymbolInstances[a]=[]),this.indexedSymbolInstances[a].push({crossTileID:i.crossTileID,coord:this.getScaledCoordinates(i,t)})}};Re.prototype.getScaledCoordinates=function(e,r){var n=r.canonical.z-this.tileID.canonical.z,i=De/Math.pow(2,n);return{x:Math.floor((r.canonical.x*t.EXTENT+e.anchorX)*i),y:Math.floor((r.canonical.y*t.EXTENT+e.anchorY)*i)}},Re.prototype.findMatches=function(t,e,r){for(var n=this.tileID.canonical.z<e.canonical.z?1:Math.pow(2,this.tileID.canonical.z-e.canonical.z),i=0;i<t.length;i++){var a=t.get(i);if(!a.crossTileID){var o=this.indexedSymbolInstances[a.key];if(o)for(var s=this.getScaledCoordinates(a,e),l=0,c=o;l<c.length;l+=1){var u=c[l];if(Math.abs(u.coord.x-s.x)<=n&&Math.abs(u.coord.y-s.y)<=n&&!r[u.crossTileID]){r[u.crossTileID]=!0,a.crossTileID=u.crossTileID;break}}}}};var Fe=function(){this.maxCrossTileID=0};Fe.prototype.generate=function(){return++this.maxCrossTileID};var Be=function(){this.indexes={},this.usedCrossTileIDs={},this.lng=0};Be.prototype.handleWrapJump=function(t){var e=Math.round((t-this.lng)/360);if(0!==e)for(var r in this.indexes){var n=this.indexes[r],i={};for(var a in n){var o=n[a];o.tileID=o.tileID.unwrapTo(o.tileID.wrap+e),i[o.tileID.key]=o}this.indexes[r]=i}this.lng=t},Be.prototype.addBucket=function(t,e,r){if(this.indexes[t.overscaledZ]&&this.indexes[t.overscaledZ][t.key]){if(this.indexes[t.overscaledZ][t.key].bucketInstanceId===e.bucketInstanceId)return!1;this.removeBucketCrossTileIDs(t.overscaledZ,this.indexes[t.overscaledZ][t.key])}for(var n=0;n<e.symbolInstances.length;n++){e.symbolInstances.get(n).crossTileID=0}this.usedCrossTileIDs[t.overscaledZ]||(this.usedCrossTileIDs[t.overscaledZ]={});var i=this.usedCrossTileIDs[t.overscaledZ];for(var a in this.indexes){var o=this.indexes[a];if(Number(a)>t.overscaledZ)for(var s in o){var l=o[s];l.tileID.isChildOf(t)&&l.findMatches(e.symbolInstances,t,i)}else{var c=o[t.scaledTo(Number(a)).key];c&&c.findMatches(e.symbolInstances,t,i)}}for(var u=0;u<e.symbolInstances.length;u++){var f=e.symbolInstances.get(u);f.crossTileID||(f.crossTileID=r.generate(),i[f.crossTileID]=!0)}return void 0===this.indexes[t.overscaledZ]&&(this.indexes[t.overscaledZ]={}),this.indexes[t.overscaledZ][t.key]=new Re(t,e.symbolInstances,e.bucketInstanceId),!0},Be.prototype.removeBucketCrossTileIDs=function(t,e){for(var r in e.indexedSymbolInstances)for(var n=0,i=e.indexedSymbolInstances[r];n<i.length;n+=1){var a=i[n];delete this.usedCrossTileIDs[t][a.crossTileID]}},Be.prototype.removeStaleBuckets=function(t){var e=!1;for(var r in this.indexes){var n=this.indexes[r];for(var i in n)t[n[i].bucketInstanceId]||(this.removeBucketCrossTileIDs(r,n[i]),delete n[i],e=!0)}return e};var Ne=function(){this.layerIndexes={},this.crossTileIDs=new Fe,this.maxBucketInstanceId=0,this.bucketsInCurrentPlacement={}};Ne.prototype.addLayer=function(t,e,r){var n=this.layerIndexes[t.id];void 0===n&&(n=this.layerIndexes[t.id]=new Be);var i=!1,a={};n.handleWrapJump(r);for(var o=0,s=e;o<s.length;o+=1){var l=s[o],c=l.getBucket(t);c&&t.id===c.layerIds[0]&&(c.bucketInstanceId||(c.bucketInstanceId=++this.maxBucketInstanceId),n.addBucket(l.tileID,c,this.crossTileIDs)&&(i=!0),a[c.bucketInstanceId]=!0)}return n.removeStaleBuckets(a)&&(i=!0),i},Ne.prototype.pruneUnusedLayers=function(t){var e={};for(var r in t.forEach((function(t){e[t]=!0})),this.layerIndexes)e[r]||delete this.layerIndexes[r]};var je=function(e,r){return t.emitValidationErrors(e,r&&r.filter((function(t){return\"source.canvas\"!==t.identifier})))},Ue=t.pick(Ut,[\"addLayer\",\"removeLayer\",\"setPaintProperty\",\"setLayoutProperty\",\"setFilter\",\"addSource\",\"removeSource\",\"setLayerZoomRange\",\"setLight\",\"setTransition\",\"setGeoJSONSourceData\"]),Ve=t.pick(Ut,[\"setCenter\",\"setZoom\",\"setBearing\",\"setPitch\"]),qe=function(){var e={},r=t.styleSpec.$version;for(var n in t.styleSpec.$root){var i=t.styleSpec.$root[n];if(i.required){var a=null;null!=(a=\"version\"===n?r:\"array\"===i.type?[]:{})&&(e[n]=a)}}return e}(),He=function(e){function r(n,i){var a=this;void 0===i&&(i={}),e.call(this),this.map=n,this.dispatcher=new k(Bt(),this),this.imageManager=new h,this.imageManager.setEventedParent(this),this.glyphManager=new x(n._requestManager,i.localIdeographFontFamily),this.lineAtlas=new T(256,512),this.crossTileSymbolIndex=new Ne,this._layers={},this._serializedLayers={},this._order=[],this.sourceCaches={},this.zoomHistory=new t.ZoomHistory,this._loaded=!1,this._availableImages=[],this._resetUpdates(),this.dispatcher.broadcast(\"setReferrer\",t.getReferrer());var o=this;this._rtlTextPluginCallback=r.registerForPluginStateChange((function(e){var r={pluginStatus:e.pluginStatus,pluginURL:e.pluginURL};o.dispatcher.broadcast(\"syncRTLPluginState\",r,(function(e,r){if((t.triggerPluginCompletionEvent(e),r)&&r.every((function(t){return t})))for(var n in o.sourceCaches)o.sourceCaches[n].reload()}))})),this.on(\"data\",(function(t){if(\"source\"===t.dataType&&\"metadata\"===t.sourceDataType){var e=a.sourceCaches[t.sourceId];if(e){var r=e.getSource();if(r&&r.vectorLayerIds)for(var n in a._layers){var i=a._layers[n];i.source===r.id&&a._validateLayer(i)}}}}))}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.loadURL=function(e,r){var n=this;void 0===r&&(r={}),this.fire(new t.Event(\"dataloading\",{dataType:\"style\"}));var i=\"boolean\"==typeof r.validate?r.validate:!t.isMapboxURL(e);e=this.map._requestManager.normalizeStyleURL(e,r.accessToken);var a=this.map._requestManager.transformRequest(e,t.ResourceType.Style);this._request=t.getJSON(a,(function(e,r){n._request=null,e?n.fire(new t.ErrorEvent(e)):r&&n._load(r,i)}))},r.prototype.loadJSON=function(e,r){var n=this;void 0===r&&(r={}),this.fire(new t.Event(\"dataloading\",{dataType:\"style\"})),this._request=t.browser.frame((function(){n._request=null,n._load(e,!1!==r.validate)}))},r.prototype.loadEmpty=function(){this.fire(new t.Event(\"dataloading\",{dataType:\"style\"})),this._load(qe,!1)},r.prototype._load=function(e,r){if(!r||!je(this,t.validateStyle(e))){for(var n in this._loaded=!0,this.stylesheet=e,e.sources)this.addSource(n,e.sources[n],{validate:!1});e.sprite?this._loadSprite(e.sprite):this.imageManager.setLoaded(!0),this.glyphManager.setURL(e.glyphs);var i=jt(this.stylesheet.layers);this._order=i.map((function(t){return t.id})),this._layers={},this._serializedLayers={};for(var a=0,o=i;a<o.length;a+=1){var s=o[a];(s=t.createStyleLayer(s)).setEventedParent(this,{layer:{id:s.id}}),this._layers[s.id]=s,this._serializedLayers[s.id]=s.serialize()}this.dispatcher.broadcast(\"setLayers\",this._serializeLayers(this._order)),this.light=new w(this.stylesheet.light),this.fire(new t.Event(\"data\",{dataType:\"style\"})),this.fire(new t.Event(\"style.load\"))}},r.prototype._loadSprite=function(e){var r=this;this._spriteRequest=function(e,r,n){var i,a,o,s=t.browser.devicePixelRatio>1?\"@2x\":\"\",l=t.getJSON(r.transformRequest(r.normalizeSpriteURL(e,s,\".json\"),t.ResourceType.SpriteJSON),(function(t,e){l=null,o||(o=t,i=e,u())})),c=t.getImage(r.transformRequest(r.normalizeSpriteURL(e,s,\".png\"),t.ResourceType.SpriteImage),(function(t,e){c=null,o||(o=t,a=e,u())}));function u(){if(o)n(o);else if(i&&a){var e=t.browser.getImageData(a),r={};for(var s in i){var l=i[s],c=l.width,u=l.height,f=l.x,h=l.y,p=l.sdf,d=l.pixelRatio,m=l.stretchX,g=l.stretchY,v=l.content,y=new t.RGBAImage({width:c,height:u});t.RGBAImage.copy(e,y,{x:f,y:h},{x:0,y:0},{width:c,height:u}),r[s]={data:y,pixelRatio:d,sdf:p,stretchX:m,stretchY:g,content:v}}n(null,r)}}return{cancel:function(){l&&(l.cancel(),l=null),c&&(c.cancel(),c=null)}}}(e,this.map._requestManager,(function(e,n){if(r._spriteRequest=null,e)r.fire(new t.ErrorEvent(e));else if(n)for(var i in n)r.imageManager.addImage(i,n[i]);r.imageManager.setLoaded(!0),r._availableImages=r.imageManager.listImages(),r.dispatcher.broadcast(\"setImages\",r._availableImages),r.fire(new t.Event(\"data\",{dataType:\"style\"}))}))},r.prototype._validateLayer=function(e){var r=this.sourceCaches[e.source];if(r){var n=e.sourceLayer;if(n){var i=r.getSource();(\"geojson\"===i.type||i.vectorLayerIds&&-1===i.vectorLayerIds.indexOf(n))&&this.fire(new t.ErrorEvent(new Error('Source layer \"'+n+'\" does not exist on source \"'+i.id+'\" as specified by style layer \"'+e.id+'\"')))}}},r.prototype.loaded=function(){if(!this._loaded)return!1;if(Object.keys(this._updatedSources).length)return!1;for(var t in this.sourceCaches)if(!this.sourceCaches[t].loaded())return!1;return!!this.imageManager.isLoaded()},r.prototype._serializeLayers=function(t){for(var e=[],r=0,n=t;r<n.length;r+=1){var i=n[r],a=this._layers[i];\"custom\"!==a.type&&e.push(a.serialize())}return e},r.prototype.hasTransitions=function(){if(this.light&&this.light.hasTransition())return!0;for(var t in this.sourceCaches)if(this.sourceCaches[t].hasTransition())return!0;for(var e in this._layers)if(this._layers[e].hasTransition())return!0;return!1},r.prototype._checkLoaded=function(){if(!this._loaded)throw new Error(\"Style is not done loading\")},r.prototype.update=function(e){if(this._loaded){var r=this._changed;if(this._changed){var n=Object.keys(this._updatedLayers),i=Object.keys(this._removedLayers);for(var a in(n.length||i.length)&&this._updateWorkerLayers(n,i),this._updatedSources){var o=this._updatedSources[a];\"reload\"===o?this._reloadSource(a):\"clear\"===o&&this._clearSource(a)}for(var s in this._updateTilesForChangedImages(),this._updatedPaintProps)this._layers[s].updateTransitions(e);this.light.updateTransitions(e),this._resetUpdates()}for(var l in this.sourceCaches)this.sourceCaches[l].used=!1;for(var c=0,u=this._order;c<u.length;c+=1){var f=u[c],h=this._layers[f];h.recalculate(e,this._availableImages),!h.isHidden(e.zoom)&&h.source&&(this.sourceCaches[h.source].used=!0)}this.light.recalculate(e),this.z=e.zoom,r&&this.fire(new t.Event(\"data\",{dataType:\"style\"}))}},r.prototype._updateTilesForChangedImages=function(){var t=Object.keys(this._changedImages);if(t.length){for(var e in this.sourceCaches)this.sourceCaches[e].reloadTilesForDependencies([\"icons\",\"patterns\"],t);this._changedImages={}}},r.prototype._updateWorkerLayers=function(t,e){this.dispatcher.broadcast(\"updateLayers\",{layers:this._serializeLayers(t),removedIds:e})},r.prototype._resetUpdates=function(){this._changed=!1,this._updatedLayers={},this._removedLayers={},this._updatedSources={},this._updatedPaintProps={},this._changedImages={}},r.prototype.setState=function(e){var r=this;if(this._checkLoaded(),je(this,t.validateStyle(e)))return!1;(e=t.clone$1(e)).layers=jt(e.layers);var n=Zt(this.serialize(),e).filter((function(t){return!(t.command in Ve)}));if(0===n.length)return!1;var i=n.filter((function(t){return!(t.command in Ue)}));if(i.length>0)throw new Error(\"Unimplemented: \"+i.map((function(t){return t.command})).join(\", \")+\".\");return n.forEach((function(t){\"setTransition\"!==t.command&&r[t.command].apply(r,t.args)})),this.stylesheet=e,!0},r.prototype.addImage=function(e,r){if(this.getImage(e))return this.fire(new t.ErrorEvent(new Error(\"An image with this name already exists.\")));this.imageManager.addImage(e,r),this._availableImages=this.imageManager.listImages(),this._changedImages[e]=!0,this._changed=!0,this.fire(new t.Event(\"data\",{dataType:\"style\"}))},r.prototype.updateImage=function(t,e){this.imageManager.updateImage(t,e)},r.prototype.getImage=function(t){return this.imageManager.getImage(t)},r.prototype.removeImage=function(e){if(!this.getImage(e))return this.fire(new t.ErrorEvent(new Error(\"No image with this name exists.\")));this.imageManager.removeImage(e),this._availableImages=this.imageManager.listImages(),this._changedImages[e]=!0,this._changed=!0,this.fire(new t.Event(\"data\",{dataType:\"style\"}))},r.prototype.listImages=function(){return this._checkLoaded(),this.imageManager.listImages()},r.prototype.addSource=function(e,r,n){var i=this;if(void 0===n&&(n={}),this._checkLoaded(),void 0!==this.sourceCaches[e])throw new Error(\"There is already a source with this ID\");if(!r.type)throw new Error(\"The type property must be defined, but the only the following properties were given: \"+Object.keys(r).join(\", \")+\".\");if(!([\"vector\",\"raster\",\"geojson\",\"video\",\"image\"].indexOf(r.type)>=0)||!this._validate(t.validateStyle.source,\"sources.\"+e,r,null,n)){this.map&&this.map._collectResourceTiming&&(r.collectResourceTiming=!0);var a=this.sourceCaches[e]=new Ct(e,r,this.dispatcher);a.style=this,a.setEventedParent(this,(function(){return{isSourceLoaded:i.loaded(),source:a.serialize(),sourceId:e}})),a.onAdd(this.map),this._changed=!0}},r.prototype.removeSource=function(e){if(this._checkLoaded(),void 0===this.sourceCaches[e])throw new Error(\"There is no source with this ID\");for(var r in this._layers)if(this._layers[r].source===e)return this.fire(new t.ErrorEvent(new Error('Source \"'+e+'\" cannot be removed while layer \"'+r+'\" is using it.')));var n=this.sourceCaches[e];delete this.sourceCaches[e],delete this._updatedSources[e],n.fire(new t.Event(\"data\",{sourceDataType:\"metadata\",dataType:\"source\",sourceId:e})),n.setEventedParent(null),n.clearTiles(),n.onRemove&&n.onRemove(this.map),this._changed=!0},r.prototype.setGeoJSONSourceData=function(t,e){this._checkLoaded(),this.sourceCaches[t].getSource().setData(e),this._changed=!0},r.prototype.getSource=function(t){return this.sourceCaches[t]&&this.sourceCaches[t].getSource()},r.prototype.addLayer=function(e,r,n){void 0===n&&(n={}),this._checkLoaded();var i=e.id;if(this.getLayer(i))this.fire(new t.ErrorEvent(new Error('Layer with id \"'+i+'\" already exists on this map')));else{var a;if(\"custom\"===e.type){if(je(this,t.validateCustomStyleLayer(e)))return;a=t.createStyleLayer(e)}else{if(\"object\"==typeof e.source&&(this.addSource(i,e.source),e=t.clone$1(e),e=t.extend(e,{source:i})),this._validate(t.validateStyle.layer,\"layers.\"+i,e,{arrayIndex:-1},n))return;a=t.createStyleLayer(e),this._validateLayer(a),a.setEventedParent(this,{layer:{id:i}}),this._serializedLayers[a.id]=a.serialize()}var o=r?this._order.indexOf(r):this._order.length;if(r&&-1===o)this.fire(new t.ErrorEvent(new Error('Layer with id \"'+r+'\" does not exist on this map.')));else{if(this._order.splice(o,0,i),this._layerOrderChanged=!0,this._layers[i]=a,this._removedLayers[i]&&a.source&&\"custom\"!==a.type){var s=this._removedLayers[i];delete this._removedLayers[i],s.type!==a.type?this._updatedSources[a.source]=\"clear\":(this._updatedSources[a.source]=\"reload\",this.sourceCaches[a.source].pause())}this._updateLayer(a),a.onAdd&&a.onAdd(this.map)}}},r.prototype.moveLayer=function(e,r){if(this._checkLoaded(),this._changed=!0,this._layers[e]){if(e!==r){var n=this._order.indexOf(e);this._order.splice(n,1);var i=r?this._order.indexOf(r):this._order.length;r&&-1===i?this.fire(new t.ErrorEvent(new Error('Layer with id \"'+r+'\" does not exist on this map.'))):(this._order.splice(i,0,e),this._layerOrderChanged=!0)}}else this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be moved.\")))},r.prototype.removeLayer=function(e){this._checkLoaded();var r=this._layers[e];if(r){r.setEventedParent(null);var n=this._order.indexOf(e);this._order.splice(n,1),this._layerOrderChanged=!0,this._changed=!0,this._removedLayers[e]=r,delete this._layers[e],delete this._serializedLayers[e],delete this._updatedLayers[e],delete this._updatedPaintProps[e],r.onRemove&&r.onRemove(this.map)}else this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be removed.\")))},r.prototype.getLayer=function(t){return this._layers[t]},r.prototype.hasLayer=function(t){return t in this._layers},r.prototype.setLayerZoomRange=function(e,r,n){this._checkLoaded();var i=this.getLayer(e);i?i.minzoom===r&&i.maxzoom===n||(null!=r&&(i.minzoom=r),null!=n&&(i.maxzoom=n),this._updateLayer(i)):this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot have zoom extent.\")))},r.prototype.setFilter=function(e,r,n){void 0===n&&(n={}),this._checkLoaded();var i=this.getLayer(e);if(i){if(!t.deepEqual(i.filter,r))return null==r?(i.filter=void 0,void this._updateLayer(i)):void(this._validate(t.validateStyle.filter,\"layers.\"+i.id+\".filter\",r,null,n)||(i.filter=t.clone$1(r),this._updateLayer(i)))}else this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be filtered.\")))},r.prototype.getFilter=function(e){return t.clone$1(this.getLayer(e).filter)},r.prototype.setLayoutProperty=function(e,r,n,i){void 0===i&&(i={}),this._checkLoaded();var a=this.getLayer(e);a?t.deepEqual(a.getLayoutProperty(r),n)||(a.setLayoutProperty(r,n,i),this._updateLayer(a)):this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be styled.\")))},r.prototype.getLayoutProperty=function(e,r){var n=this.getLayer(e);if(n)return n.getLayoutProperty(r);this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style.\")))},r.prototype.setPaintProperty=function(e,r,n,i){void 0===i&&(i={}),this._checkLoaded();var a=this.getLayer(e);a?t.deepEqual(a.getPaintProperty(r),n)||(a.setPaintProperty(r,n,i)&&this._updateLayer(a),this._changed=!0,this._updatedPaintProps[e]=!0):this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be styled.\")))},r.prototype.getPaintProperty=function(t,e){return this.getLayer(t).getPaintProperty(e)},r.prototype.setFeatureState=function(e,r){this._checkLoaded();var n=e.source,i=e.sourceLayer,a=this.sourceCaches[n];if(void 0!==a){var o=a.getSource().type;\"geojson\"===o&&i?this.fire(new t.ErrorEvent(new Error(\"GeoJSON sources cannot have a sourceLayer parameter.\"))):\"vector\"!==o||i?(void 0===e.id&&this.fire(new t.ErrorEvent(new Error(\"The feature id parameter must be provided.\"))),a.setFeatureState(i,e.id,r)):this.fire(new t.ErrorEvent(new Error(\"The sourceLayer parameter must be provided for vector source types.\")))}else this.fire(new t.ErrorEvent(new Error(\"The source '\"+n+\"' does not exist in the map's style.\")))},r.prototype.removeFeatureState=function(e,r){this._checkLoaded();var n=e.source,i=this.sourceCaches[n];if(void 0!==i){var a=i.getSource().type,o=\"vector\"===a?e.sourceLayer:void 0;\"vector\"!==a||o?r&&\"string\"!=typeof e.id&&\"number\"!=typeof e.id?this.fire(new t.ErrorEvent(new Error(\"A feature id is requred to remove its specific state property.\"))):i.removeFeatureState(o,e.id,r):this.fire(new t.ErrorEvent(new Error(\"The sourceLayer parameter must be provided for vector source types.\")))}else this.fire(new t.ErrorEvent(new Error(\"The source '\"+n+\"' does not exist in the map's style.\")))},r.prototype.getFeatureState=function(e){this._checkLoaded();var r=e.source,n=e.sourceLayer,i=this.sourceCaches[r];if(void 0!==i){if(\"vector\"!==i.getSource().type||n)return void 0===e.id&&this.fire(new t.ErrorEvent(new Error(\"The feature id parameter must be provided.\"))),i.getFeatureState(n,e.id);this.fire(new t.ErrorEvent(new Error(\"The sourceLayer parameter must be provided for vector source types.\")))}else this.fire(new t.ErrorEvent(new Error(\"The source '\"+r+\"' does not exist in the map's style.\")))},r.prototype.getTransition=function(){return t.extend({duration:300,delay:0},this.stylesheet&&this.stylesheet.transition)},r.prototype.serialize=function(){return t.filterObject({version:this.stylesheet.version,name:this.stylesheet.name,metadata:this.stylesheet.metadata,light:this.stylesheet.light,center:this.stylesheet.center,zoom:this.stylesheet.zoom,bearing:this.stylesheet.bearing,pitch:this.stylesheet.pitch,sprite:this.stylesheet.sprite,glyphs:this.stylesheet.glyphs,transition:this.stylesheet.transition,sources:t.mapObject(this.sourceCaches,(function(t){return t.serialize()})),layers:this._serializeLayers(this._order)},(function(t){return void 0!==t}))},r.prototype._updateLayer=function(t){this._updatedLayers[t.id]=!0,t.source&&!this._updatedSources[t.source]&&\"raster\"!==this.sourceCaches[t.source].getSource().type&&(this._updatedSources[t.source]=\"reload\",this.sourceCaches[t.source].pause()),this._changed=!0},r.prototype._flattenAndSortRenderedFeatures=function(t){for(var e=this,r=function(t){return\"fill-extrusion\"===e._layers[t].type},n={},i=[],a=this._order.length-1;a>=0;a--){var o=this._order[a];if(r(o)){n[o]=a;for(var s=0,l=t;s<l.length;s+=1){var c=l[s][o];if(c)for(var u=0,f=c;u<f.length;u+=1){var h=f[u];i.push(h)}}}}i.sort((function(t,e){return e.intersectionZ-t.intersectionZ}));for(var p=[],d=this._order.length-1;d>=0;d--){var m=this._order[d];if(r(m))for(var g=i.length-1;g>=0;g--){var v=i[g].feature;if(n[v.layer.id]<d)break;p.push(v),i.pop()}else for(var y=0,x=t;y<x.length;y+=1){var b=x[y][m];if(b)for(var _=0,w=b;_<w.length;_+=1){var T=w[_];p.push(T.feature)}}}return p},r.prototype.queryRenderedFeatures=function(e,r,n){r&&r.filter&&this._validate(t.validateStyle.filter,\"queryRenderedFeatures.filter\",r.filter,null,r);var i={};if(r&&r.layers){if(!Array.isArray(r.layers))return this.fire(new t.ErrorEvent(new Error(\"parameters.layers must be an Array.\"))),[];for(var a=0,o=r.layers;a<o.length;a+=1){var s=o[a],l=this._layers[s];if(!l)return this.fire(new t.ErrorEvent(new Error(\"The layer '\"+s+\"' does not exist in the map's style and cannot be queried for features.\"))),[];i[l.source]=!0}}var c=[];for(var u in r.availableImages=this._availableImages,this.sourceCaches)r.layers&&!i[u]||c.push(F(this.sourceCaches[u],this._layers,this._serializedLayers,e,r,n));return this.placement&&c.push(function(t,e,r,n,i,a,o){for(var s={},l=a.queryRenderedSymbols(n),c=[],u=0,f=Object.keys(l).map(Number);u<f.length;u+=1){var h=f[u];c.push(o[h])}c.sort(B);for(var p=function(){var r=m[d],n=r.featureIndex.lookupSymbolFeatures(l[r.bucketInstanceId],e,r.bucketIndex,r.sourceLayerIndex,i.filter,i.layers,i.availableImages,t);for(var a in n){var o=s[a]=s[a]||[],c=n[a];c.sort((function(t,e){var n=r.featureSortOrder;if(n){var i=n.indexOf(t.featureIndex);return n.indexOf(e.featureIndex)-i}return e.featureIndex-t.featureIndex}));for(var u=0,f=c;u<f.length;u+=1){var h=f[u];o.push(h)}}},d=0,m=c;d<m.length;d+=1)p();var g=function(e){s[e].forEach((function(n){var i=n.feature,a=t[e],o=r[a.source].getFeatureState(i.layer[\"source-layer\"],i.id);i.source=i.layer.source,i.layer[\"source-layer\"]&&(i.sourceLayer=i.layer[\"source-layer\"]),i.state=o}))};for(var v in s)g(v);return s}(this._layers,this._serializedLayers,this.sourceCaches,e,r,this.placement.collisionIndex,this.placement.retainedQueryData)),this._flattenAndSortRenderedFeatures(c)},r.prototype.querySourceFeatures=function(e,r){r&&r.filter&&this._validate(t.validateStyle.filter,\"querySourceFeatures.filter\",r.filter,null,r);var n=this.sourceCaches[e];return n?function(t,e){for(var r=t.getRenderableIds().map((function(e){return t.getTileByID(e)})),n=[],i={},a=0;a<r.length;a++){var o=r[a],s=o.tileID.canonical.key;i[s]||(i[s]=!0,o.querySourceFeatures(n,e))}return n}(n,r):[]},r.prototype.addSourceType=function(t,e,n){return r.getSourceType(t)?n(new Error('A source type called \"'+t+'\" already exists.')):(r.setSourceType(t,e),e.workerSourceURL?void this.dispatcher.broadcast(\"loadWorkerSource\",{name:t,url:e.workerSourceURL},n):n(null,null))},r.prototype.getLight=function(){return this.light.getLight()},r.prototype.setLight=function(e,r){void 0===r&&(r={}),this._checkLoaded();var n=this.light.getLight(),i=!1;for(var a in e)if(!t.deepEqual(e[a],n[a])){i=!0;break}if(i){var o={now:t.browser.now(),transition:t.extend({duration:300,delay:0},this.stylesheet.transition)};this.light.setLight(e,r),this.light.updateTransitions(o)}},r.prototype._validate=function(e,r,n,i,a){return void 0===a&&(a={}),(!a||!1!==a.validate)&&je(this,e.call(t.validateStyle,t.extend({key:r,style:this.serialize(),value:n,styleSpec:t.styleSpec},i)))},r.prototype._remove=function(){for(var e in this._request&&(this._request.cancel(),this._request=null),this._spriteRequest&&(this._spriteRequest.cancel(),this._spriteRequest=null),t.evented.off(\"pluginStateChange\",this._rtlTextPluginCallback),this._layers){this._layers[e].setEventedParent(null)}for(var r in this.sourceCaches)this.sourceCaches[r].clearTiles(),this.sourceCaches[r].setEventedParent(null);this.imageManager.setEventedParent(null),this.setEventedParent(null),this.dispatcher.remove()},r.prototype._clearSource=function(t){this.sourceCaches[t].clearTiles()},r.prototype._reloadSource=function(t){this.sourceCaches[t].resume(),this.sourceCaches[t].reload()},r.prototype._updateSources=function(t){for(var e in this.sourceCaches)this.sourceCaches[e].update(t)},r.prototype._generateCollisionBoxes=function(){for(var t in this.sourceCaches)this._reloadSource(t)},r.prototype._updatePlacement=function(e,r,n,i,a){void 0===a&&(a=!1);for(var o=!1,s=!1,l={},c=0,u=this._order;c<u.length;c+=1){var f=u[c],h=this._layers[f];if(\"symbol\"===h.type){if(!l[h.source]){var p=this.sourceCaches[h.source];l[h.source]=p.getRenderableIds(!0).map((function(t){return p.getTileByID(t)})).sort((function(t,e){return e.tileID.overscaledZ-t.tileID.overscaledZ||(t.tileID.isLessThan(e.tileID)?-1:1)}))}var d=this.crossTileSymbolIndex.addLayer(h,l[h.source],e.center.lng);o=o||d}}if(this.crossTileSymbolIndex.pruneUnusedLayers(this._order),((a=a||this._layerOrderChanged||0===n)||!this.pauseablePlacement||this.pauseablePlacement.isDone()&&!this.placement.stillRecent(t.browser.now(),e.zoom))&&(this.pauseablePlacement=new ze(e,this._order,a,r,n,i,this.placement),this._layerOrderChanged=!1),this.pauseablePlacement.isDone()?this.placement.setStale():(this.pauseablePlacement.continuePlacement(this._order,this._layers,l),this.pauseablePlacement.isDone()&&(this.placement=this.pauseablePlacement.commit(t.browser.now()),s=!0),o&&this.pauseablePlacement.placement.setStale()),s||o)for(var m=0,g=this._order;m<g.length;m+=1){var v=g[m],y=this._layers[v];\"symbol\"===y.type&&this.placement.updateLayerOpacities(y,l[y.source])}return!this.pauseablePlacement.isDone()||this.placement.hasTransitions(t.browser.now())},r.prototype._releaseSymbolFadeTiles=function(){for(var t in this.sourceCaches)this.sourceCaches[t].releaseSymbolFadeTiles()},r.prototype.getImages=function(t,e,r){this.imageManager.getImages(e.icons,r),this._updateTilesForChangedImages();var n=this.sourceCaches[e.source];n&&n.setDependencies(e.tileID.key,e.type,e.icons)},r.prototype.getGlyphs=function(t,e,r){this.glyphManager.getGlyphs(e.stacks,r)},r.prototype.getResource=function(e,r,n){return t.makeRequest(r,n)},r}(t.Evented);He.getSourceType=function(t){return D[t]},He.setSourceType=function(t,e){D[t]=e},He.registerForPluginStateChange=t.registerForPluginStateChange;var Ge=t.createLayout([{name:\"a_pos\",type:\"Int16\",components:2}]),Ye=yr(\"#ifdef GL_ES\\nprecision mediump float;\\n#else\\n#if !defined(lowp)\\n#define lowp\\n#endif\\n#if !defined(mediump)\\n#define mediump\\n#endif\\n#if !defined(highp)\\n#define highp\\n#endif\\n#endif\",\"#ifdef GL_ES\\nprecision highp float;\\n#else\\n#if !defined(lowp)\\n#define lowp\\n#endif\\n#if !defined(mediump)\\n#define mediump\\n#endif\\n#if !defined(highp)\\n#define highp\\n#endif\\n#endif\\nvec2 unpack_float(const float packedValue) {int packedIntValue=int(packedValue);int v0=packedIntValue/256;return vec2(v0,packedIntValue-v0*256);}vec2 unpack_opacity(const float packedOpacity) {int intOpacity=int(packedOpacity)/2;return vec2(float(intOpacity)/127.0,mod(packedOpacity,2.0));}vec4 decode_color(const vec2 encodedColor) {return vec4(unpack_float(encodedColor[0])/255.0,unpack_float(encodedColor[1])/255.0\\n);}float unpack_mix_vec2(const vec2 packedValue,const float t) {return mix(packedValue[0],packedValue[1],t);}vec4 unpack_mix_color(const vec4 packedColors,const float t) {vec4 minColor=decode_color(vec2(packedColors[0],packedColors[1]));vec4 maxColor=decode_color(vec2(packedColors[2],packedColors[3]));return mix(minColor,maxColor,t);}vec2 get_pattern_pos(const vec2 pixel_coord_upper,const vec2 pixel_coord_lower,const vec2 pattern_size,const float tile_units_to_pixels,const vec2 pos) {vec2 offset=mod(mod(mod(pixel_coord_upper,pattern_size)*256.0,pattern_size)*256.0+pixel_coord_lower,pattern_size);return (tile_units_to_pixels*pos+offset)/pattern_size;}\"),We=yr(\"uniform vec4 u_color;uniform float u_opacity;void main() {gl_FragColor=u_color*u_opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);}\"),Xe=yr(\"uniform vec2 u_pattern_tl_a;uniform vec2 u_pattern_br_a;uniform vec2 u_pattern_tl_b;uniform vec2 u_pattern_br_b;uniform vec2 u_texsize;uniform float u_mix;uniform float u_opacity;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;void main() {vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(u_pattern_tl_a/u_texsize,u_pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(u_pattern_tl_b/u_texsize,u_pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_mix)*u_opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_pattern_size_a;uniform vec2 u_pattern_size_b;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_scale_a;uniform float u_scale_b;uniform float u_tile_units_to_pixels;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,u_scale_a*u_pattern_size_a,u_tile_units_to_pixels,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,u_scale_b*u_pattern_size_b,u_tile_units_to_pixels,a_pos);}\"),Ze=yr(\"varying vec3 v_data;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define mediump float radius\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define highp vec4 stroke_color\\n#pragma mapbox: define mediump float stroke_width\\n#pragma mapbox: define lowp float stroke_opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize mediump float radius\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize highp vec4 stroke_color\\n#pragma mapbox: initialize mediump float stroke_width\\n#pragma mapbox: initialize lowp float stroke_opacity\\nvec2 extrude=v_data.xy;float extrude_length=length(extrude);lowp float antialiasblur=v_data.z;float antialiased_blur=-max(blur,antialiasblur);float opacity_t=smoothstep(0.0,antialiased_blur,extrude_length-1.0);float color_t=stroke_width < 0.01 ? 0.0 : smoothstep(antialiased_blur,0.0,extrude_length-radius/(radius+stroke_width));gl_FragColor=opacity_t*mix(color*opacity,stroke_color*stroke_opacity,color_t);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform bool u_scale_with_map;uniform bool u_pitch_with_map;uniform vec2 u_extrude_scale;uniform lowp float u_device_pixel_ratio;uniform highp float u_camera_to_center_distance;attribute vec2 a_pos;varying vec3 v_data;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define mediump float radius\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define highp vec4 stroke_color\\n#pragma mapbox: define mediump float stroke_width\\n#pragma mapbox: define lowp float stroke_opacity\\nvoid main(void) {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize mediump float radius\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize highp vec4 stroke_color\\n#pragma mapbox: initialize mediump float stroke_width\\n#pragma mapbox: initialize lowp float stroke_opacity\\nvec2 extrude=vec2(mod(a_pos,2.0)*2.0-1.0);vec2 circle_center=floor(a_pos*0.5);if (u_pitch_with_map) {vec2 corner_position=circle_center;if (u_scale_with_map) {corner_position+=extrude*(radius+stroke_width)*u_extrude_scale;} else {vec4 projected_center=u_matrix*vec4(circle_center,0,1);corner_position+=extrude*(radius+stroke_width)*u_extrude_scale*(projected_center.w/u_camera_to_center_distance);}gl_Position=u_matrix*vec4(corner_position,0,1);} else {gl_Position=u_matrix*vec4(circle_center,0,1);if (u_scale_with_map) {gl_Position.xy+=extrude*(radius+stroke_width)*u_extrude_scale*u_camera_to_center_distance;} else {gl_Position.xy+=extrude*(radius+stroke_width)*u_extrude_scale*gl_Position.w;}}lowp float antialiasblur=1.0/u_device_pixel_ratio/(radius+stroke_width);v_data=vec3(extrude.x,extrude.y,antialiasblur);}\"),Je=yr(\"void main() {gl_FragColor=vec4(1.0);}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);}\"),Ke=yr(\"uniform highp float u_intensity;varying vec2 v_extrude;\\n#pragma mapbox: define highp float weight\\n#define GAUSS_COEF 0.3989422804014327\\nvoid main() {\\n#pragma mapbox: initialize highp float weight\\nfloat d=-0.5*3.0*3.0*dot(v_extrude,v_extrude);float val=weight*u_intensity*GAUSS_COEF*exp(d);gl_FragColor=vec4(val,1.0,1.0,1.0);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform float u_extrude_scale;uniform float u_opacity;uniform float u_intensity;attribute vec2 a_pos;varying vec2 v_extrude;\\n#pragma mapbox: define highp float weight\\n#pragma mapbox: define mediump float radius\\nconst highp float ZERO=1.0/255.0/16.0;\\n#define GAUSS_COEF 0.3989422804014327\\nvoid main(void) {\\n#pragma mapbox: initialize highp float weight\\n#pragma mapbox: initialize mediump float radius\\nvec2 unscaled_extrude=vec2(mod(a_pos,2.0)*2.0-1.0);float S=sqrt(-2.0*log(ZERO/weight/u_intensity/GAUSS_COEF))/3.0;v_extrude=S*unscaled_extrude;vec2 extrude=v_extrude*radius*u_extrude_scale;vec4 pos=vec4(floor(a_pos*0.5)+extrude,0,1);gl_Position=u_matrix*pos;}\"),Qe=yr(\"uniform sampler2D u_image;uniform sampler2D u_color_ramp;uniform float u_opacity;varying vec2 v_pos;void main() {float t=texture2D(u_image,v_pos).r;vec4 color=texture2D(u_color_ramp,vec2(t,0.5));gl_FragColor=color*u_opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(0.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_world;attribute vec2 a_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos*u_world,0,1);v_pos.x=a_pos.x;v_pos.y=1.0-a_pos.y;}\"),$e=yr(\"varying float v_placed;varying float v_notUsed;void main() {float alpha=0.5;gl_FragColor=vec4(1.0,0.0,0.0,1.0)*alpha;if (v_placed > 0.5) {gl_FragColor=vec4(0.0,0.0,1.0,0.5)*alpha;}if (v_notUsed > 0.5) {gl_FragColor*=.1;}}\",\"attribute vec2 a_pos;attribute vec2 a_anchor_pos;attribute vec2 a_extrude;attribute vec2 a_placed;attribute vec2 a_shift;uniform mat4 u_matrix;uniform vec2 u_extrude_scale;uniform float u_camera_to_center_distance;varying float v_placed;varying float v_notUsed;void main() {vec4 projectedPoint=u_matrix*vec4(a_anchor_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);gl_Position=u_matrix*vec4(a_pos,0.0,1.0);gl_Position.xy+=(a_extrude+a_shift)*u_extrude_scale*gl_Position.w*collision_perspective_ratio;v_placed=a_placed.x;v_notUsed=a_placed.y;}\"),tr=yr(\"varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;void main() {float alpha=0.5*min(v_perspective_ratio,1.0);float stroke_radius=0.9*max(v_perspective_ratio,1.0);float distance_to_center=length(v_extrude);float distance_to_edge=abs(distance_to_center-v_radius);float opacity_t=smoothstep(-stroke_radius,0.0,-distance_to_edge);vec4 color=mix(vec4(0.0,0.0,1.0,0.5),vec4(1.0,0.0,0.0,1.0),v_collision);gl_FragColor=color*alpha*opacity_t;}\",\"attribute vec2 a_pos;attribute float a_radius;attribute vec2 a_flags;uniform mat4 u_matrix;uniform mat4 u_inv_matrix;uniform vec2 u_viewport_size;uniform float u_camera_to_center_distance;varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;vec3 toTilePosition(vec2 screenPos) {vec4 rayStart=u_inv_matrix*vec4(screenPos,-1.0,1.0);vec4 rayEnd  =u_inv_matrix*vec4(screenPos, 1.0,1.0);rayStart.xyz/=rayStart.w;rayEnd.xyz  /=rayEnd.w;highp float t=(0.0-rayStart.z)/(rayEnd.z-rayStart.z);return mix(rayStart.xyz,rayEnd.xyz,t);}void main() {vec2 quadCenterPos=a_pos;float radius=a_radius;float collision=a_flags.x;float vertexIdx=a_flags.y;vec2 quadVertexOffset=vec2(mix(-1.0,1.0,float(vertexIdx >=2.0)),mix(-1.0,1.0,float(vertexIdx >=1.0 && vertexIdx <=2.0)));vec2 quadVertexExtent=quadVertexOffset*radius;vec3 tilePos=toTilePosition(quadCenterPos);vec4 clipPos=u_matrix*vec4(tilePos,1.0);highp float camera_to_anchor_distance=clipPos.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);float padding_factor=1.2;v_radius=radius;v_extrude=quadVertexExtent*padding_factor;v_perspective_ratio=collision_perspective_ratio;v_collision=collision;gl_Position=vec4(clipPos.xyz/clipPos.w,1.0)+vec4(quadVertexExtent*padding_factor/u_viewport_size*2.0,0.0,0.0);}\"),er=yr(\"uniform highp vec4 u_color;uniform sampler2D u_overlay;varying vec2 v_uv;void main() {vec4 overlay_color=texture2D(u_overlay,v_uv);gl_FragColor=mix(u_color,overlay_color,overlay_color.a);}\",\"attribute vec2 a_pos;varying vec2 v_uv;uniform mat4 u_matrix;uniform float u_overlay_scale;void main() {v_uv=a_pos/8192.0;gl_Position=u_matrix*vec4(a_pos*u_overlay_scale,0,1);}\"),rr=yr(\"#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float opacity\\ngl_FragColor=color*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float opacity\\ngl_Position=u_matrix*vec4(a_pos,0,1);}\"),nr=yr(\"varying vec2 v_pos;\\n#pragma mapbox: define highp vec4 outline_color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 outline_color\\n#pragma mapbox: initialize lowp float opacity\\nfloat dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=outline_color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;uniform vec2 u_world;varying vec2 v_pos;\\n#pragma mapbox: define highp vec4 outline_color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 outline_color\\n#pragma mapbox: initialize lowp float opacity\\ngl_Position=u_matrix*vec4(a_pos,0,1);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}\"),ir=yr(\"uniform vec2 u_texsize;uniform sampler2D u_image;uniform float u_fade;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);float dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=mix(color1,color2,u_fade)*alpha*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_world;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;gl_Position=u_matrix*vec4(a_pos,0,1);vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,a_pos);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}\"),ar=yr(\"uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_fade)*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileZoomRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileZoomRatio,a_pos);}\"),or=yr(\"varying vec4 v_color;void main() {gl_FragColor=v_color;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;uniform float u_vertical_gradient;uniform lowp float u_opacity;attribute vec2 a_pos;attribute vec4 a_normal_ed;varying vec4 v_color;\\n#pragma mapbox: define highp float base\\n#pragma mapbox: define highp float height\\n#pragma mapbox: define highp vec4 color\\nvoid main() {\\n#pragma mapbox: initialize highp float base\\n#pragma mapbox: initialize highp float height\\n#pragma mapbox: initialize highp vec4 color\\nvec3 normal=a_normal_ed.xyz;base=max(0.0,base);height=max(0.0,height);float t=mod(normal.x,2.0);gl_Position=u_matrix*vec4(a_pos,t > 0.0 ? height : base,1);float colorvalue=color.r*0.2126+color.g*0.7152+color.b*0.0722;v_color=vec4(0.0,0.0,0.0,1.0);vec4 ambientlight=vec4(0.03,0.03,0.03,1.0);color+=ambientlight;float directional=clamp(dot(normal/16384.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((1.0-colorvalue+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_color.r+=clamp(color.r*directional*u_lightcolor.r,mix(0.0,0.3,1.0-u_lightcolor.r),1.0);v_color.g+=clamp(color.g*directional*u_lightcolor.g,mix(0.0,0.3,1.0-u_lightcolor.g),1.0);v_color.b+=clamp(color.b*directional*u_lightcolor.b,mix(0.0,0.3,1.0-u_lightcolor.b),1.0);v_color*=u_opacity;}\"),sr=yr(\"uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\\n#pragma mapbox: define lowp float base\\n#pragma mapbox: define lowp float height\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float base\\n#pragma mapbox: initialize lowp float height\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);vec4 mixedColor=mix(color1,color2,u_fade);gl_FragColor=mixedColor*v_lighting;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_height_factor;uniform vec3 u_scale;uniform float u_vertical_gradient;uniform lowp float u_opacity;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;attribute vec2 a_pos;attribute vec4 a_normal_ed;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\\n#pragma mapbox: define lowp float base\\n#pragma mapbox: define lowp float height\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float base\\n#pragma mapbox: initialize lowp float height\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec3 normal=a_normal_ed.xyz;float edgedistance=a_normal_ed.w;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;base=max(0.0,base);height=max(0.0,height);float t=mod(normal.x,2.0);float z=t > 0.0 ? height : base;gl_Position=u_matrix*vec4(a_pos,z,1);vec2 pos=normal.x==1.0 && normal.y==0.0 && normal.z==16384.0\\n? a_pos\\n: vec2(edgedistance,z*u_height_factor);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,pos);v_lighting=vec4(0.0,0.0,0.0,1.0);float directional=clamp(dot(normal/16383.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((0.5+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_lighting.rgb+=clamp(directional*u_lightcolor,mix(vec3(0.0),vec3(0.3),1.0-u_lightcolor),vec3(1.0));v_lighting*=u_opacity;}\"),lr=yr(\"#ifdef GL_ES\\nprecision highp float;\\n#endif\\nuniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_dimension;uniform float u_zoom;uniform float u_maxzoom;uniform vec4 u_unpack;float getElevation(vec2 coord,float bias) {vec4 data=texture2D(u_image,coord)*255.0;data.a=-1.0;return dot(data,u_unpack)/4.0;}void main() {vec2 epsilon=1.0/u_dimension;float a=getElevation(v_pos+vec2(-epsilon.x,-epsilon.y),0.0);float b=getElevation(v_pos+vec2(0,-epsilon.y),0.0);float c=getElevation(v_pos+vec2(epsilon.x,-epsilon.y),0.0);float d=getElevation(v_pos+vec2(-epsilon.x,0),0.0);float e=getElevation(v_pos,0.0);float f=getElevation(v_pos+vec2(epsilon.x,0),0.0);float g=getElevation(v_pos+vec2(-epsilon.x,epsilon.y),0.0);float h=getElevation(v_pos+vec2(0,epsilon.y),0.0);float i=getElevation(v_pos+vec2(epsilon.x,epsilon.y),0.0);float exaggeration=u_zoom < 2.0 ? 0.4 : u_zoom < 4.5 ? 0.35 : 0.3;vec2 deriv=vec2((c+f+f+i)-(a+d+d+g),(g+h+h+i)-(a+b+b+c))/ pow(2.0,(u_zoom-u_maxzoom)*exaggeration+19.2562-u_zoom);gl_FragColor=clamp(vec4(deriv.x/2.0+0.5,deriv.y/2.0+0.5,1.0,1.0),0.0,1.0);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_dimension;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);highp vec2 epsilon=1.0/u_dimension;float scale=(u_dimension.x-2.0)/u_dimension.x;v_pos=(a_texture_pos/8192.0)*scale+epsilon;}\"),cr=yr(\"uniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_latrange;uniform vec2 u_light;uniform vec4 u_shadow;uniform vec4 u_highlight;uniform vec4 u_accent;\\n#define PI 3.141592653589793\\nvoid main() {vec4 pixel=texture2D(u_image,v_pos);vec2 deriv=((pixel.rg*2.0)-1.0);float scaleFactor=cos(radians((u_latrange[0]-u_latrange[1])*(1.0-v_pos.y)+u_latrange[1]));float slope=atan(1.25*length(deriv)/scaleFactor);float aspect=deriv.x !=0.0 ? atan(deriv.y,-deriv.x) : PI/2.0*(deriv.y > 0.0 ? 1.0 :-1.0);float intensity=u_light.x;float azimuth=u_light.y+PI;float base=1.875-intensity*1.75;float maxValue=0.5*PI;float scaledSlope=intensity !=0.5 ? ((pow(base,slope)-1.0)/(pow(base,maxValue)-1.0))*maxValue : slope;float accent=cos(scaledSlope);vec4 accent_color=(1.0-accent)*u_accent*clamp(intensity*2.0,0.0,1.0);float shade=abs(mod((aspect+azimuth)/PI+0.5,2.0)-1.0);vec4 shade_color=mix(u_shadow,u_highlight,shade)*sin(scaledSlope)*clamp(intensity*2.0,0.0,1.0);gl_FragColor=accent_color*(1.0-shade_color.a)+shade_color;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos=a_texture_pos/8192.0;}\"),ur=yr(\"uniform lowp float u_device_pixel_ratio;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);gl_FragColor=color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define scale 0.015873016\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform vec2 u_units_to_pixels;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp float v_linesofar;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float width\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float width\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;v_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*2.0;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_width2=vec2(outset,inset);}\"),fr=yr(\"uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;varying highp float v_lineprogress;\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);vec4 color=texture2D(u_image,vec2(v_lineprogress,0.5));gl_FragColor=color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define MAX_LINE_DISTANCE 32767.0\\n#define scale 0.015873016\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_units_to_pixels;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp float v_lineprogress;\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float width\\nvoid main() {\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float width\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;v_lineprogress=(floor(a_data.z/4.0)+a_data.w*64.0)*2.0/MAX_LINE_DISTANCE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_width2=vec2(outset,inset);}\"),hr=yr(\"uniform lowp float u_device_pixel_ratio;uniform vec2 u_texsize;uniform float u_fade;uniform mediump vec3 u_scale;uniform sampler2D u_image;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;vec2 pattern_size_a=vec2(display_size_a.x*fromScale/tileZoomRatio,display_size_a.y);vec2 pattern_size_b=vec2(display_size_b.x*toScale/tileZoomRatio,display_size_b.y);float aspect_a=display_size_a.y/v_width;float aspect_b=display_size_b.y/v_width;float dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float x_a=mod(v_linesofar/pattern_size_a.x*aspect_a,1.0);float x_b=mod(v_linesofar/pattern_size_b.x*aspect_b,1.0);float y=0.5*v_normal.y+0.5;vec2 texel_size=1.0/u_texsize;vec2 pos_a=mix(pattern_tl_a*texel_size-texel_size,pattern_br_a*texel_size+texel_size,vec2(x_a,y));vec2 pos_b=mix(pattern_tl_b*texel_size-texel_size,pattern_br_b*texel_size+texel_size,vec2(x_b,y));vec4 color=mix(texture2D(u_image,pos_a),texture2D(u_image,pos_b),u_fade);gl_FragColor=color*alpha*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define scale 0.015873016\\n#define LINE_DISTANCE_SCALE 2.0\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform vec2 u_units_to_pixels;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define mediump float width\\n#pragma mapbox: define lowp float floorwidth\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize mediump float width\\n#pragma mapbox: initialize lowp float floorwidth\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_linesofar=a_linesofar;v_width2=vec2(outset,inset);v_width=floorwidth;}\"),pr=yr(\"uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;uniform float u_sdfgamma;uniform float u_mix;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float width\\n#pragma mapbox: define lowp float floorwidth\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float width\\n#pragma mapbox: initialize lowp float floorwidth\\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float sdfdist_a=texture2D(u_image,v_tex_a).a;float sdfdist_b=texture2D(u_image,v_tex_b).a;float sdfdist=mix(sdfdist_a,sdfdist_b,u_mix);alpha*=smoothstep(0.5-u_sdfgamma/floorwidth,0.5+u_sdfgamma/floorwidth,sdfdist);gl_FragColor=color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define scale 0.015873016\\n#define LINE_DISTANCE_SCALE 2.0\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_patternscale_a;uniform float u_tex_y_a;uniform vec2 u_patternscale_b;uniform float u_tex_y_b;uniform vec2 u_units_to_pixels;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float width\\n#pragma mapbox: define lowp float floorwidth\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float width\\n#pragma mapbox: initialize lowp float floorwidth\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_tex_a=vec2(a_linesofar*u_patternscale_a.x/floorwidth,normal.y*u_patternscale_a.y+u_tex_y_a);v_tex_b=vec2(a_linesofar*u_patternscale_b.x/floorwidth,normal.y*u_patternscale_b.y+u_tex_y_b);v_width2=vec2(outset,inset);}\"),dr=yr(\"uniform float u_fade_t;uniform float u_opacity;uniform sampler2D u_image0;uniform sampler2D u_image1;varying vec2 v_pos0;varying vec2 v_pos1;uniform float u_brightness_low;uniform float u_brightness_high;uniform float u_saturation_factor;uniform float u_contrast_factor;uniform vec3 u_spin_weights;void main() {vec4 color0=texture2D(u_image0,v_pos0);vec4 color1=texture2D(u_image1,v_pos1);if (color0.a > 0.0) {color0.rgb=color0.rgb/color0.a;}if (color1.a > 0.0) {color1.rgb=color1.rgb/color1.a;}vec4 color=mix(color0,color1,u_fade_t);color.a*=u_opacity;vec3 rgb=color.rgb;rgb=vec3(dot(rgb,u_spin_weights.xyz),dot(rgb,u_spin_weights.zxy),dot(rgb,u_spin_weights.yzx));float average=(color.r+color.g+color.b)/3.0;rgb+=(average-rgb)*u_saturation_factor;rgb=(rgb-0.5)*u_contrast_factor+0.5;vec3 u_high_vec=vec3(u_brightness_low,u_brightness_low,u_brightness_low);vec3 u_low_vec=vec3(u_brightness_high,u_brightness_high,u_brightness_high);gl_FragColor=vec4(mix(u_high_vec,u_low_vec,rgb)*color.a,color.a);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_tl_parent;uniform float u_scale_parent;uniform float u_buffer_scale;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos0;varying vec2 v_pos1;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos0=(((a_texture_pos/8192.0)-0.5)/u_buffer_scale )+0.5;v_pos1=(v_pos0*u_scale_parent)+u_tl_parent;}\"),mr=yr(\"uniform sampler2D u_texture;varying vec2 v_tex;varying float v_fade_opacity;\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\nlowp float alpha=opacity*v_fade_opacity;gl_FragColor=texture2D(u_texture,v_tex)*alpha;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform highp float u_camera_to_center_distance;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform float u_fade_change;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform vec2 u_texsize;varying vec2 v_tex;varying float v_fade_opacity;\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;vec2 a_minFontScale=a_pixeloffset.zw/256.0;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\\ncamera_to_anchor_distance/u_camera_to_center_distance :\\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*max(a_minFontScale,fontScale)+a_pxoffset/16.0),0.0,1.0);v_tex=a_tex/u_texsize;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;v_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));}\"),gr=yr(\"#define SDF_PX 8.0\\nuniform bool u_is_halo;uniform sampler2D u_texture;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;uniform bool u_is_text;varying vec2 v_data0;varying vec3 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nfloat EDGE_GAMMA=0.105/u_device_pixel_ratio;vec2 tex=v_data0.xy;float gamma_scale=v_data1.x;float size=v_data1.y;float fade_opacity=v_data1[2];float fontScale=u_is_text ? size/24.0 : size;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;varying vec2 v_data0;varying vec3 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\\ncamera_to_anchor_distance/u_camera_to_center_distance :\\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale+a_pxoffset),0.0,1.0);float gamma_scale=gl_Position.w;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));v_data0=a_tex/u_texsize;v_data1=vec3(gamma_scale,size,interpolated_fade_opacity);}\"),vr=yr(\"#define SDF_PX 8.0\\n#define SDF 1.0\\n#define ICON 0.0\\nuniform bool u_is_halo;uniform sampler2D u_texture;uniform sampler2D u_texture_icon;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;varying vec4 v_data0;varying vec4 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nfloat fade_opacity=v_data1[2];if (v_data1.w==ICON) {vec2 tex_icon=v_data0.zw;lowp float alpha=opacity*fade_opacity;gl_FragColor=texture2D(u_texture_icon,tex_icon)*alpha;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\nreturn;}vec2 tex=v_data0.xy;float EDGE_GAMMA=0.105/u_device_pixel_ratio;float gamma_scale=v_data1.x;float size=v_data1.y;float fontScale=size/24.0;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;uniform vec2 u_texsize_icon;varying vec4 v_data0;varying vec4 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);float is_sdf=a_size[0]-2.0*a_size_min;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\\ncamera_to_anchor_distance/u_camera_to_center_distance :\\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=size/24.0;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale),0.0,1.0);float gamma_scale=gl_Position.w;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));v_data0.xy=a_tex/u_texsize;v_data0.zw=a_tex/u_texsize_icon;v_data1=vec4(gamma_scale,size,interpolated_fade_opacity,is_sdf);}\");function yr(t,e){var r=/#pragma mapbox: ([\\w]+) ([\\w]+) ([\\w]+) ([\\w]+)/g,n={};return{fragmentSource:t=t.replace(r,(function(t,e,r,i,a){return n[a]=!0,\"define\"===e?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\nvarying \"+r+\" \"+i+\" \"+a+\";\\n#else\\nuniform \"+r+\" \"+i+\" u_\"+a+\";\\n#endif\\n\":\"\\n#ifdef HAS_UNIFORM_u_\"+a+\"\\n    \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\"})),vertexSource:e=e.replace(r,(function(t,e,r,i,a){var o=\"float\"===i?\"vec2\":\"vec4\",s=a.match(/color/)?\"color\":o;return n[a]?\"define\"===e?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\nuniform lowp float u_\"+a+\"_t;\\nattribute \"+r+\" \"+o+\" a_\"+a+\";\\nvarying \"+r+\" \"+i+\" \"+a+\";\\n#else\\nuniform \"+r+\" \"+i+\" u_\"+a+\";\\n#endif\\n\":\"vec4\"===s?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\n    \"+a+\" = a_\"+a+\";\\n#else\\n    \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\":\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\n    \"+a+\" = unpack_mix_\"+s+\"(a_\"+a+\", u_\"+a+\"_t);\\n#else\\n    \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\":\"define\"===e?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\nuniform lowp float u_\"+a+\"_t;\\nattribute \"+r+\" \"+o+\" a_\"+a+\";\\n#else\\nuniform \"+r+\" \"+i+\" u_\"+a+\";\\n#endif\\n\":\"vec4\"===s?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\n    \"+r+\" \"+i+\" \"+a+\" = a_\"+a+\";\\n#else\\n    \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\":\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\n    \"+r+\" \"+i+\" \"+a+\" = unpack_mix_\"+s+\"(a_\"+a+\", u_\"+a+\"_t);\\n#else\\n    \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\"}))}}var xr=Object.freeze({__proto__:null,prelude:Ye,background:We,backgroundPattern:Xe,circle:Ze,clippingMask:Je,heatmap:Ke,heatmapTexture:Qe,collisionBox:$e,collisionCircle:tr,debug:er,fill:rr,fillOutline:nr,fillOutlinePattern:ir,fillPattern:ar,fillExtrusion:or,fillExtrusionPattern:sr,hillshadePrepare:lr,hillshade:cr,line:ur,lineGradient:fr,linePattern:hr,lineSDF:pr,raster:dr,symbolIcon:mr,symbolSDF:gr,symbolTextAndIcon:vr}),br=function(){this.boundProgram=null,this.boundLayoutVertexBuffer=null,this.boundPaintVertexBuffers=[],this.boundIndexBuffer=null,this.boundVertexOffset=null,this.boundDynamicVertexBuffer=null,this.vao=null};br.prototype.bind=function(t,e,r,n,i,a,o,s){this.context=t;for(var l=this.boundPaintVertexBuffers.length!==n.length,c=0;!l&&c<n.length;c++)this.boundPaintVertexBuffers[c]!==n[c]&&(l=!0);var u=!this.vao||this.boundProgram!==e||this.boundLayoutVertexBuffer!==r||l||this.boundIndexBuffer!==i||this.boundVertexOffset!==a||this.boundDynamicVertexBuffer!==o||this.boundDynamicVertexBuffer2!==s;!t.extVertexArrayObject||u?this.freshBind(e,r,n,i,a,o,s):(t.bindVertexArrayOES.set(this.vao),o&&o.bind(),i&&i.dynamicDraw&&i.bind(),s&&s.bind())},br.prototype.freshBind=function(t,e,r,n,i,a,o){var s,l=t.numAttributes,c=this.context,u=c.gl;if(c.extVertexArrayObject)this.vao&&this.destroy(),this.vao=c.extVertexArrayObject.createVertexArrayOES(),c.bindVertexArrayOES.set(this.vao),s=0,this.boundProgram=t,this.boundLayoutVertexBuffer=e,this.boundPaintVertexBuffers=r,this.boundIndexBuffer=n,this.boundVertexOffset=i,this.boundDynamicVertexBuffer=a,this.boundDynamicVertexBuffer2=o;else{s=c.currentNumAttributes||0;for(var f=l;f<s;f++)u.disableVertexAttribArray(f)}e.enableAttributes(u,t);for(var h=0,p=r;h<p.length;h+=1){p[h].enableAttributes(u,t)}a&&a.enableAttributes(u,t),o&&o.enableAttributes(u,t),e.bind(),e.setVertexAttribPointers(u,t,i);for(var d=0,m=r;d<m.length;d+=1){var g=m[d];g.bind(),g.setVertexAttribPointers(u,t,i)}a&&(a.bind(),a.setVertexAttribPointers(u,t,i)),n&&n.bind(),o&&(o.bind(),o.setVertexAttribPointers(u,t,i)),c.currentNumAttributes=l},br.prototype.destroy=function(){this.vao&&(this.context.extVertexArrayObject.deleteVertexArrayOES(this.vao),this.vao=null)};var _r=function(t,e,r,n,i){var a=t.gl;this.program=a.createProgram();var o=r?r.defines():[];i&&o.push(\"#define OVERDRAW_INSPECTOR;\");var s=o.concat(Ye.fragmentSource,e.fragmentSource).join(\"\\n\"),l=o.concat(Ye.vertexSource,e.vertexSource).join(\"\\n\"),c=a.createShader(a.FRAGMENT_SHADER);if(a.isContextLost())this.failedToCreate=!0;else{a.shaderSource(c,s),a.compileShader(c),a.attachShader(this.program,c);var u=a.createShader(a.VERTEX_SHADER);if(a.isContextLost())this.failedToCreate=!0;else{a.shaderSource(u,l),a.compileShader(u),a.attachShader(this.program,u);for(var f=r?r.layoutAttributes:[],h=0;h<f.length;h++)a.bindAttribLocation(this.program,h,f[h].name);a.linkProgram(this.program),a.deleteShader(u),a.deleteShader(c),this.numAttributes=a.getProgramParameter(this.program,a.ACTIVE_ATTRIBUTES),this.attributes={};for(var p={},d=0;d<this.numAttributes;d++){var m=a.getActiveAttrib(this.program,d);m&&(this.attributes[m.name]=a.getAttribLocation(this.program,m.name))}for(var g=a.getProgramParameter(this.program,a.ACTIVE_UNIFORMS),v=0;v<g;v++){var y=a.getActiveUniform(this.program,v);y&&(p[y.name]=a.getUniformLocation(this.program,y.name))}this.fixedUniforms=n(t,p),this.binderUniforms=r?r.getUniforms(t,p):[]}}};function wr(t,e,r){var n=1/pe(r,1,e.transform.tileZoom),i=Math.pow(2,r.tileID.overscaledZ),a=r.tileSize*Math.pow(2,e.transform.tileZoom)/i,o=a*(r.tileID.canonical.x+r.tileID.wrap*i),s=a*r.tileID.canonical.y;return{u_image:0,u_texsize:r.imageAtlasTexture.size,u_scale:[n,t.fromScale,t.toScale],u_fade:t.t,u_pixel_coord_upper:[o>>16,s>>16],u_pixel_coord_lower:[65535&o,65535&s]}}_r.prototype.draw=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m){var g,v=t.gl;if(!this.failedToCreate){for(var y in t.program.set(this.program),t.setDepthMode(r),t.setStencilMode(n),t.setColorMode(i),t.setCullFace(a),this.fixedUniforms)this.fixedUniforms[y].set(o[y]);p&&p.setUniforms(t,this.binderUniforms,f,{zoom:h});for(var x=(g={},g[v.LINES]=2,g[v.TRIANGLES]=3,g[v.LINE_STRIP]=1,g)[e],b=0,_=u.get();b<_.length;b+=1){var w=_[b],T=w.vaos||(w.vaos={});(T[s]||(T[s]=new br)).bind(t,this,l,p?p.getPaintVertexBuffers():[],c,w.vertexOffset,d,m),v.drawElements(e,w.primitiveLength*x,v.UNSIGNED_SHORT,w.primitiveOffset*x*2)}}};var Tr=function(e,r,n,i){var a=r.style.light,o=a.properties.get(\"position\"),s=[o.x,o.y,o.z],l=t.create$1();\"viewport\"===a.properties.get(\"anchor\")&&t.fromRotation(l,-r.transform.angle),t.transformMat3(s,s,l);var c=a.properties.get(\"color\");return{u_matrix:e,u_lightpos:s,u_lightintensity:a.properties.get(\"intensity\"),u_lightcolor:[c.r,c.g,c.b],u_vertical_gradient:+n,u_opacity:i}},kr=function(e,r,n,i,a,o,s){return t.extend(Tr(e,r,n,i),wr(o,r,s),{u_height_factor:-Math.pow(2,a.overscaledZ)/s.tileSize/8})},Mr=function(t){return{u_matrix:t}},Ar=function(e,r,n,i){return t.extend(Mr(e),wr(n,r,i))},Sr=function(t,e){return{u_matrix:t,u_world:e}},Er=function(e,r,n,i,a){return t.extend(Ar(e,r,n,i),{u_world:a})},Lr=function(e,r,n,i){var a,o,s=e.transform;if(\"map\"===i.paint.get(\"circle-pitch-alignment\")){var l=pe(n,1,s.zoom);a=!0,o=[l,l]}else a=!1,o=s.pixelsToGLUnits;return{u_camera_to_center_distance:s.cameraToCenterDistance,u_scale_with_map:+(\"map\"===i.paint.get(\"circle-pitch-scale\")),u_matrix:e.translatePosMatrix(r.posMatrix,n,i.paint.get(\"circle-translate\"),i.paint.get(\"circle-translate-anchor\")),u_pitch_with_map:+a,u_device_pixel_ratio:t.browser.devicePixelRatio,u_extrude_scale:o}},Cr=function(t,e,r){var n=pe(r,1,e.zoom),i=Math.pow(2,e.zoom-r.tileID.overscaledZ),a=r.tileID.overscaleFactor();return{u_matrix:t,u_camera_to_center_distance:e.cameraToCenterDistance,u_pixels_to_tile_units:n,u_extrude_scale:[e.pixelsToGLUnits[0]/(n*i),e.pixelsToGLUnits[1]/(n*i)],u_overscale_factor:a}},Pr=function(t,e,r){return{u_matrix:t,u_inv_matrix:e,u_camera_to_center_distance:r.cameraToCenterDistance,u_viewport_size:[r.width,r.height]}},Ir=function(t,e,r){return void 0===r&&(r=1),{u_matrix:t,u_color:e,u_overlay:0,u_overlay_scale:r}},Or=function(t){return{u_matrix:t}},zr=function(t,e,r,n){return{u_matrix:t,u_extrude_scale:pe(e,1,r),u_intensity:n}};function Dr(e,r){var n=Math.pow(2,r.canonical.z),i=r.canonical.y;return[new t.MercatorCoordinate(0,i/n).toLngLat().lat,new t.MercatorCoordinate(0,(i+1)/n).toLngLat().lat]}var Rr=function(e,r,n){var i=e.transform;return{u_matrix:Ur(e,r,n),u_ratio:1/pe(r,1,i.zoom),u_device_pixel_ratio:t.browser.devicePixelRatio,u_units_to_pixels:[1/i.pixelsToGLUnits[0],1/i.pixelsToGLUnits[1]]}},Fr=function(e,r,n){return t.extend(Rr(e,r,n),{u_image:0})},Br=function(e,r,n,i){var a=e.transform,o=jr(r,a);return{u_matrix:Ur(e,r,n),u_texsize:r.imageAtlasTexture.size,u_ratio:1/pe(r,1,a.zoom),u_device_pixel_ratio:t.browser.devicePixelRatio,u_image:0,u_scale:[o,i.fromScale,i.toScale],u_fade:i.t,u_units_to_pixels:[1/a.pixelsToGLUnits[0],1/a.pixelsToGLUnits[1]]}},Nr=function(e,r,n,i,a){var o=e.transform,s=e.lineAtlas,l=jr(r,o),c=\"round\"===n.layout.get(\"line-cap\"),u=s.getDash(i.from,c),f=s.getDash(i.to,c),h=u.width*a.fromScale,p=f.width*a.toScale;return t.extend(Rr(e,r,n),{u_patternscale_a:[l/h,-u.height/2],u_patternscale_b:[l/p,-f.height/2],u_sdfgamma:s.width/(256*Math.min(h,p)*t.browser.devicePixelRatio)/2,u_image:0,u_tex_y_a:u.y,u_tex_y_b:f.y,u_mix:a.t})};function jr(t,e){return 1/pe(t,1,e.tileZoom)}function Ur(t,e,r){return t.translatePosMatrix(e.tileID.posMatrix,e,r.paint.get(\"line-translate\"),r.paint.get(\"line-translate-anchor\"))}var Vr=function(t,e,r,n,i){return{u_matrix:t,u_tl_parent:e,u_scale_parent:r,u_buffer_scale:1,u_fade_t:n.mix,u_opacity:n.opacity*i.paint.get(\"raster-opacity\"),u_image0:0,u_image1:1,u_brightness_low:i.paint.get(\"raster-brightness-min\"),u_brightness_high:i.paint.get(\"raster-brightness-max\"),u_saturation_factor:(o=i.paint.get(\"raster-saturation\"),o>0?1-1/(1.001-o):-o),u_contrast_factor:(a=i.paint.get(\"raster-contrast\"),a>0?1/(1-a):1+a),u_spin_weights:qr(i.paint.get(\"raster-hue-rotate\"))};var a,o};function qr(t){t*=Math.PI/180;var e=Math.sin(t),r=Math.cos(t);return[(2*r+1)/3,(-Math.sqrt(3)*e-r+1)/3,(Math.sqrt(3)*e-r+1)/3]}var Hr,Gr=function(t,e,r,n,i,a,o,s,l,c){var u=i.transform;return{u_is_size_zoom_constant:+(\"constant\"===t||\"source\"===t),u_is_size_feature_constant:+(\"constant\"===t||\"camera\"===t),u_size_t:e?e.uSizeT:0,u_size:e?e.uSize:0,u_camera_to_center_distance:u.cameraToCenterDistance,u_pitch:u.pitch/360*2*Math.PI,u_rotate_symbol:+r,u_aspect_ratio:u.width/u.height,u_fade_change:i.options.fadeDuration?i.symbolFadeChange:1,u_matrix:a,u_label_plane_matrix:o,u_coord_matrix:s,u_is_text:+l,u_pitch_with_map:+n,u_texsize:c,u_texture:0}},Yr=function(e,r,n,i,a,o,s,l,c,u,f){var h=a.transform;return t.extend(Gr(e,r,n,i,a,o,s,l,c,u),{u_gamma_scale:i?Math.cos(h._pitch)*h.cameraToCenterDistance:1,u_device_pixel_ratio:t.browser.devicePixelRatio,u_is_halo:+f})},Wr=function(e,r,n,i,a,o,s,l,c,u){return t.extend(Yr(e,r,n,i,a,o,s,l,!0,c,!0),{u_texsize_icon:u,u_texture_icon:1})},Xr=function(t,e,r){return{u_matrix:t,u_opacity:e,u_color:r}},Zr=function(e,r,n,i,a,o){return t.extend(function(t,e,r,n){var i=r.imageManager.getPattern(t.from.toString()),a=r.imageManager.getPattern(t.to.toString()),o=r.imageManager.getPixelSize(),s=o.width,l=o.height,c=Math.pow(2,n.tileID.overscaledZ),u=n.tileSize*Math.pow(2,r.transform.tileZoom)/c,f=u*(n.tileID.canonical.x+n.tileID.wrap*c),h=u*n.tileID.canonical.y;return{u_image:0,u_pattern_tl_a:i.tl,u_pattern_br_a:i.br,u_pattern_tl_b:a.tl,u_pattern_br_b:a.br,u_texsize:[s,l],u_mix:e.t,u_pattern_size_a:i.displaySize,u_pattern_size_b:a.displaySize,u_scale_a:e.fromScale,u_scale_b:e.toScale,u_tile_units_to_pixels:1/pe(n,1,r.transform.tileZoom),u_pixel_coord_upper:[f>>16,h>>16],u_pixel_coord_lower:[65535&f,65535&h]}}(i,o,n,a),{u_matrix:e,u_opacity:r})},Jr={fillExtrusion:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_lightpos:new t.Uniform3f(e,r.u_lightpos),u_lightintensity:new t.Uniform1f(e,r.u_lightintensity),u_lightcolor:new t.Uniform3f(e,r.u_lightcolor),u_vertical_gradient:new t.Uniform1f(e,r.u_vertical_gradient),u_opacity:new t.Uniform1f(e,r.u_opacity)}},fillExtrusionPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_lightpos:new t.Uniform3f(e,r.u_lightpos),u_lightintensity:new t.Uniform1f(e,r.u_lightintensity),u_lightcolor:new t.Uniform3f(e,r.u_lightcolor),u_vertical_gradient:new t.Uniform1f(e,r.u_vertical_gradient),u_height_factor:new t.Uniform1f(e,r.u_height_factor),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade),u_opacity:new t.Uniform1f(e,r.u_opacity)}},fill:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},fillPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},fillOutline:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world)}},fillOutlinePattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},circle:function(e,r){return{u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_scale_with_map:new t.Uniform1i(e,r.u_scale_with_map),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_extrude_scale:new t.Uniform2f(e,r.u_extrude_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},collisionBox:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pixels_to_tile_units:new t.Uniform1f(e,r.u_pixels_to_tile_units),u_extrude_scale:new t.Uniform2f(e,r.u_extrude_scale),u_overscale_factor:new t.Uniform1f(e,r.u_overscale_factor)}},collisionCircle:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_inv_matrix:new t.UniformMatrix4f(e,r.u_inv_matrix),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_viewport_size:new t.Uniform2f(e,r.u_viewport_size)}},debug:function(e,r){return{u_color:new t.UniformColor(e,r.u_color),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_overlay:new t.Uniform1i(e,r.u_overlay),u_overlay_scale:new t.Uniform1f(e,r.u_overlay_scale)}},clippingMask:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},heatmap:function(e,r){return{u_extrude_scale:new t.Uniform1f(e,r.u_extrude_scale),u_intensity:new t.Uniform1f(e,r.u_intensity),u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},heatmapTexture:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world),u_image:new t.Uniform1i(e,r.u_image),u_color_ramp:new t.Uniform1i(e,r.u_color_ramp),u_opacity:new t.Uniform1f(e,r.u_opacity)}},hillshade:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_latrange:new t.Uniform2f(e,r.u_latrange),u_light:new t.Uniform2f(e,r.u_light),u_shadow:new t.UniformColor(e,r.u_shadow),u_highlight:new t.UniformColor(e,r.u_highlight),u_accent:new t.UniformColor(e,r.u_accent)}},hillshadePrepare:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_dimension:new t.Uniform2f(e,r.u_dimension),u_zoom:new t.Uniform1f(e,r.u_zoom),u_maxzoom:new t.Uniform1f(e,r.u_maxzoom),u_unpack:new t.Uniform4f(e,r.u_unpack)}},line:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels)}},lineGradient:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_image:new t.Uniform1i(e,r.u_image)}},linePattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_texsize:new t.Uniform2f(e,r.u_texsize),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_image:new t.Uniform1i(e,r.u_image),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},lineSDF:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_patternscale_a:new t.Uniform2f(e,r.u_patternscale_a),u_patternscale_b:new t.Uniform2f(e,r.u_patternscale_b),u_sdfgamma:new t.Uniform1f(e,r.u_sdfgamma),u_image:new t.Uniform1i(e,r.u_image),u_tex_y_a:new t.Uniform1f(e,r.u_tex_y_a),u_tex_y_b:new t.Uniform1f(e,r.u_tex_y_b),u_mix:new t.Uniform1f(e,r.u_mix)}},raster:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_tl_parent:new t.Uniform2f(e,r.u_tl_parent),u_scale_parent:new t.Uniform1f(e,r.u_scale_parent),u_buffer_scale:new t.Uniform1f(e,r.u_buffer_scale),u_fade_t:new t.Uniform1f(e,r.u_fade_t),u_opacity:new t.Uniform1f(e,r.u_opacity),u_image0:new t.Uniform1i(e,r.u_image0),u_image1:new t.Uniform1i(e,r.u_image1),u_brightness_low:new t.Uniform1f(e,r.u_brightness_low),u_brightness_high:new t.Uniform1f(e,r.u_brightness_high),u_saturation_factor:new t.Uniform1f(e,r.u_saturation_factor),u_contrast_factor:new t.Uniform1f(e,r.u_contrast_factor),u_spin_weights:new t.Uniform3f(e,r.u_spin_weights)}},symbolIcon:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texture:new t.Uniform1i(e,r.u_texture)}},symbolSDF:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texture:new t.Uniform1i(e,r.u_texture),u_gamma_scale:new t.Uniform1f(e,r.u_gamma_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_is_halo:new t.Uniform1i(e,r.u_is_halo)}},symbolTextAndIcon:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texsize_icon:new t.Uniform2f(e,r.u_texsize_icon),u_texture:new t.Uniform1i(e,r.u_texture),u_texture_icon:new t.Uniform1i(e,r.u_texture_icon),u_gamma_scale:new t.Uniform1f(e,r.u_gamma_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_is_halo:new t.Uniform1i(e,r.u_is_halo)}},background:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_opacity:new t.Uniform1f(e,r.u_opacity),u_color:new t.UniformColor(e,r.u_color)}},backgroundPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_opacity:new t.Uniform1f(e,r.u_opacity),u_image:new t.Uniform1i(e,r.u_image),u_pattern_tl_a:new t.Uniform2f(e,r.u_pattern_tl_a),u_pattern_br_a:new t.Uniform2f(e,r.u_pattern_br_a),u_pattern_tl_b:new t.Uniform2f(e,r.u_pattern_tl_b),u_pattern_br_b:new t.Uniform2f(e,r.u_pattern_br_b),u_texsize:new t.Uniform2f(e,r.u_texsize),u_mix:new t.Uniform1f(e,r.u_mix),u_pattern_size_a:new t.Uniform2f(e,r.u_pattern_size_a),u_pattern_size_b:new t.Uniform2f(e,r.u_pattern_size_b),u_scale_a:new t.Uniform1f(e,r.u_scale_a),u_scale_b:new t.Uniform1f(e,r.u_scale_b),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_tile_units_to_pixels:new t.Uniform1f(e,r.u_tile_units_to_pixels)}}};function Kr(e,r,n,i,a,o,s){for(var l=e.context,c=l.gl,u=e.useProgram(\"collisionBox\"),f=[],h=0,p=0,d=0;d<i.length;d++){var m=i[d],g=r.getTile(m),v=g.getBucket(n);if(v){var y=m.posMatrix;0===a[0]&&0===a[1]||(y=e.translatePosMatrix(m.posMatrix,g,a,o));var x=s?v.textCollisionBox:v.iconCollisionBox,b=v.collisionCircleArray;if(b.length>0){var _=t.create(),w=y;t.mul(_,v.placementInvProjMatrix,e.transform.glCoordMatrix),t.mul(_,_,v.placementViewportMatrix),f.push({circleArray:b,circleOffset:p,transform:w,invTransform:_}),p=h+=b.length/4}x&&u.draw(l,c.LINES,Mt.disabled,At.disabled,e.colorModeForRenderPass(),Et.disabled,Cr(y,e.transform,g),n.id,x.layoutVertexBuffer,x.indexBuffer,x.segments,null,e.transform.zoom,null,null,x.collisionVertexBuffer)}}if(s&&f.length){var T=e.useProgram(\"collisionCircle\"),k=new t.StructArrayLayout2f1f2i16;k.resize(4*h),k._trim();for(var M=0,A=0,S=f;A<S.length;A+=1)for(var E=S[A],L=0;L<E.circleArray.length/4;L++){var C=4*L,P=E.circleArray[C+0],I=E.circleArray[C+1],O=E.circleArray[C+2],z=E.circleArray[C+3];k.emplace(M++,P,I,O,z,0),k.emplace(M++,P,I,O,z,1),k.emplace(M++,P,I,O,z,2),k.emplace(M++,P,I,O,z,3)}(!Hr||Hr.length<2*h)&&(Hr=function(e){var r=2*e,n=new t.StructArrayLayout3ui6;n.resize(r),n._trim();for(var i=0;i<r;i++){var a=6*i;n.uint16[a+0]=4*i+0,n.uint16[a+1]=4*i+1,n.uint16[a+2]=4*i+2,n.uint16[a+3]=4*i+2,n.uint16[a+4]=4*i+3,n.uint16[a+5]=4*i+0}return n}(h));for(var D=l.createIndexBuffer(Hr,!0),R=l.createVertexBuffer(k,t.collisionCircleLayout.members,!0),F=0,B=f;F<B.length;F+=1){var N=B[F],j=Pr(N.transform,N.invTransform,e.transform);T.draw(l,c.TRIANGLES,Mt.disabled,At.disabled,e.colorModeForRenderPass(),Et.disabled,j,n.id,R,D,t.SegmentVector.simpleSegment(0,2*N.circleOffset,N.circleArray.length,N.circleArray.length/2),null,e.transform.zoom,null,null,null)}R.destroy(),D.destroy()}}var Qr=t.identity(new Float32Array(16));function $r(e,r,n,i,a,o){var s=t.getAnchorAlignment(e),l=-(s.horizontalAlign-.5)*r,c=-(s.verticalAlign-.5)*n,u=t.evaluateVariableOffset(e,i);return new t.Point((l/a+u[0])*o,(c/a+u[1])*o)}function tn(e,r,n,i,a,o,s,l,c,u,f){var h=e.text.placedSymbolArray,p=e.text.dynamicLayoutVertexArray,d=e.icon.dynamicLayoutVertexArray,m={};p.clear();for(var g=0;g<h.length;g++){var v=h.get(g),y=e.allowVerticalPlacement&&!v.placedOrientation,x=v.hidden||!v.crossTileID||y?null:i[v.crossTileID];if(x){var b=new t.Point(v.anchorX,v.anchorY),_=te(b,n?l:s),w=ee(o.cameraToCenterDistance,_.signedDistanceFromCamera),T=a.evaluateSizeForFeature(e.textSizeData,u,v)*w/t.ONE_EM;n&&(T*=e.tilePixelRatio/c);for(var k=x.width,M=x.height,A=$r(x.anchor,k,M,x.textOffset,x.textBoxScale,T),S=n?te(b.add(A),s).point:_.point.add(r?A.rotate(-o.angle):A),E=e.allowVerticalPlacement&&v.placedOrientation===t.WritingMode.vertical?Math.PI/2:0,L=0;L<v.numGlyphs;L++)t.addDynamicAttributes(p,S,E);f&&v.associatedIconIndex>=0&&(m[v.associatedIconIndex]={shiftedAnchor:S,angle:E})}else ue(v.numGlyphs,p)}if(f){d.clear();for(var C=e.icon.placedSymbolArray,P=0;P<C.length;P++){var I=C.get(P);if(I.hidden)ue(I.numGlyphs,d);else{var O=m[P];if(O)for(var z=0;z<I.numGlyphs;z++)t.addDynamicAttributes(d,O.shiftedAnchor,O.angle);else ue(I.numGlyphs,d)}}e.icon.dynamicLayoutVertexBuffer.updateData(d)}e.text.dynamicLayoutVertexBuffer.updateData(p)}function en(t,e,r){return r.iconsInText&&e?\"symbolTextAndIcon\":t?\"symbolSDF\":\"symbolIcon\"}function rn(e,r,n,i,a,o,s,l,c,u,f,h){for(var p=e.context,d=p.gl,m=e.transform,g=\"map\"===l,v=\"map\"===c,y=g&&\"point\"!==n.layout.get(\"symbol-placement\"),x=g&&!v&&!y,b=void 0!==n.layout.get(\"symbol-sort-key\").constantOr(1),_=e.depthModeForSublayer(0,Mt.ReadOnly),w=n.layout.get(\"text-variable-anchor\"),T=[],k=0,M=i;k<M.length;k+=1){var A=M[k],S=r.getTile(A),E=S.getBucket(n);if(E){var L=a?E.text:E.icon;if(L&&L.segments.get().length){var C=L.programConfigurations.get(n.id),P=a||E.sdfIcons,I=a?E.textSizeData:E.iconSizeData,O=v||0!==m.pitch,z=e.useProgram(en(P,a,E),C),D=t.evaluateSizeForZoom(I,m.zoom),R=void 0,F=[0,0],B=void 0,N=void 0,j=null,U=void 0;if(a){if(B=S.glyphAtlasTexture,N=d.LINEAR,R=S.glyphAtlasTexture.size,E.iconsInText){F=S.imageAtlasTexture.size,j=S.imageAtlasTexture;var V=\"composite\"===I.kind||\"camera\"===I.kind;U=O||e.options.rotating||e.options.zooming||V?d.LINEAR:d.NEAREST}}else{var q=1!==n.layout.get(\"icon-size\").constantOr(0)||E.iconsNeedLinear;B=S.imageAtlasTexture,N=P||e.options.rotating||e.options.zooming||q||O?d.LINEAR:d.NEAREST,R=S.imageAtlasTexture.size}var H=pe(S,1,e.transform.zoom),G=Qt(A.posMatrix,v,g,e.transform,H),Y=$t(A.posMatrix,v,g,e.transform,H),W=w&&E.hasTextData(),X=\"none\"!==n.layout.get(\"icon-text-fit\")&&W&&E.hasIconData();y&&ne(E,A.posMatrix,e,a,G,Y,v,u);var Z=e.translatePosMatrix(A.posMatrix,S,o,s),J=y||a&&w||X?Qr:G,K=e.translatePosMatrix(Y,S,o,s,!0),Q=P&&0!==n.paint.get(a?\"text-halo-width\":\"icon-halo-width\").constantOr(1),$={program:z,buffers:L,uniformValues:P?E.iconsInText?Wr(I.kind,D,x,v,e,Z,J,K,R,F):Yr(I.kind,D,x,v,e,Z,J,K,a,R,!0):Gr(I.kind,D,x,v,e,Z,J,K,a,R),atlasTexture:B,atlasTextureIcon:j,atlasInterpolation:N,atlasInterpolationIcon:U,isSDF:P,hasHalo:Q};if(b)for(var tt=0,et=L.segments.get();tt<et.length;tt+=1){var rt=et[tt];T.push({segments:new t.SegmentVector([rt]),sortKey:rt.sortKey,state:$})}else T.push({segments:L.segments,sortKey:0,state:$})}}}b&&T.sort((function(t,e){return t.sortKey-e.sortKey}));for(var nt=0,it=T;nt<it.length;nt+=1){var at=it[nt],ot=at.state;if(p.activeTexture.set(d.TEXTURE0),ot.atlasTexture.bind(ot.atlasInterpolation,d.CLAMP_TO_EDGE),ot.atlasTextureIcon&&(p.activeTexture.set(d.TEXTURE1),ot.atlasTextureIcon&&ot.atlasTextureIcon.bind(ot.atlasInterpolationIcon,d.CLAMP_TO_EDGE)),ot.isSDF){var st=ot.uniformValues;ot.hasHalo&&(st.u_is_halo=1,nn(ot.buffers,at.segments,n,e,ot.program,_,f,h,st)),st.u_is_halo=0}nn(ot.buffers,at.segments,n,e,ot.program,_,f,h,ot.uniformValues)}}function nn(t,e,r,n,i,a,o,s,l){var c=n.context,u=c.gl;i.draw(c,u.TRIANGLES,a,o,s,Et.disabled,l,r.id,t.layoutVertexBuffer,t.indexBuffer,e,r.paint,n.transform.zoom,t.programConfigurations.get(r.id),t.dynamicLayoutVertexBuffer,t.opacityVertexBuffer)}function an(t,e,r,n,i,a,o){var s,l,c,u,f,h=t.context.gl,p=r.paint.get(\"fill-pattern\"),d=p&&p.constantOr(1),m=r.getCrossfadeParameters();o?(l=d&&!r.getPaintProperty(\"fill-outline-color\")?\"fillOutlinePattern\":\"fillOutline\",s=h.LINES):(l=d?\"fillPattern\":\"fill\",s=h.TRIANGLES);for(var g=0,v=n;g<v.length;g+=1){var y=v[g],x=e.getTile(y);if(!d||x.patternsLoaded()){var b=x.getBucket(r);if(b){var _=b.programConfigurations.get(r.id),w=t.useProgram(l,_);d&&(t.context.activeTexture.set(h.TEXTURE0),x.imageAtlasTexture.bind(h.LINEAR,h.CLAMP_TO_EDGE),_.updatePaintBuffers(m));var T=p.constantOr(null);if(T&&x.imageAtlas){var k=x.imageAtlas,M=k.patternPositions[T.to.toString()],A=k.patternPositions[T.from.toString()];M&&A&&_.setConstantPatternPositions(M,A)}var S=t.translatePosMatrix(y.posMatrix,x,r.paint.get(\"fill-translate\"),r.paint.get(\"fill-translate-anchor\"));if(o){u=b.indexBuffer2,f=b.segments2;var E=[h.drawingBufferWidth,h.drawingBufferHeight];c=\"fillOutlinePattern\"===l&&d?Er(S,t,m,x,E):Sr(S,E)}else u=b.indexBuffer,f=b.segments,c=d?Ar(S,t,m,x):Mr(S);w.draw(t.context,s,i,t.stencilModeForClipping(y),a,Et.disabled,c,r.id,b.layoutVertexBuffer,u,f,r.paint,t.transform.zoom,_)}}}}function on(t,e,r,n,i,a,o){for(var s=t.context,l=s.gl,c=r.paint.get(\"fill-extrusion-pattern\"),u=c.constantOr(1),f=r.getCrossfadeParameters(),h=r.paint.get(\"fill-extrusion-opacity\"),p=0,d=n;p<d.length;p+=1){var m=d[p],g=e.getTile(m),v=g.getBucket(r);if(v){var y=v.programConfigurations.get(r.id),x=t.useProgram(u?\"fillExtrusionPattern\":\"fillExtrusion\",y);u&&(t.context.activeTexture.set(l.TEXTURE0),g.imageAtlasTexture.bind(l.LINEAR,l.CLAMP_TO_EDGE),y.updatePaintBuffers(f));var b=c.constantOr(null);if(b&&g.imageAtlas){var _=g.imageAtlas,w=_.patternPositions[b.to.toString()],T=_.patternPositions[b.from.toString()];w&&T&&y.setConstantPatternPositions(w,T)}var k=t.translatePosMatrix(m.posMatrix,g,r.paint.get(\"fill-extrusion-translate\"),r.paint.get(\"fill-extrusion-translate-anchor\")),M=r.paint.get(\"fill-extrusion-vertical-gradient\"),A=u?kr(k,t,M,h,m,f,g):Tr(k,t,M,h);x.draw(s,s.gl.TRIANGLES,i,a,o,Et.backCCW,A,r.id,v.layoutVertexBuffer,v.indexBuffer,v.segments,r.paint,t.transform.zoom,y)}}}function sn(t,e,r,n,i,a){var o=t.context,s=o.gl,l=e.fbo;if(l){var c=t.useProgram(\"hillshade\");o.activeTexture.set(s.TEXTURE0),s.bindTexture(s.TEXTURE_2D,l.colorAttachment.get());var u=function(t,e,r){var n=r.paint.get(\"hillshade-shadow-color\"),i=r.paint.get(\"hillshade-highlight-color\"),a=r.paint.get(\"hillshade-accent-color\"),o=r.paint.get(\"hillshade-illumination-direction\")*(Math.PI/180);\"viewport\"===r.paint.get(\"hillshade-illumination-anchor\")&&(o-=t.transform.angle);var s=!t.options.moving;return{u_matrix:t.transform.calculatePosMatrix(e.tileID.toUnwrapped(),s),u_image:0,u_latrange:Dr(t,e.tileID),u_light:[r.paint.get(\"hillshade-exaggeration\"),o],u_shadow:n,u_highlight:i,u_accent:a}}(t,e,r);c.draw(o,s.TRIANGLES,n,i,a,Et.disabled,u,r.id,t.rasterBoundsBuffer,t.quadTriangleIndexBuffer,t.rasterBoundsSegments)}}function ln(e,r,n,i,a,o,s){var l=e.context,c=l.gl,u=r.dem;if(u&&u.data){var f=u.dim,h=u.stride,p=u.getPixels();if(l.activeTexture.set(c.TEXTURE1),l.pixelStoreUnpackPremultiplyAlpha.set(!1),r.demTexture=r.demTexture||e.getTileTexture(h),r.demTexture){var d=r.demTexture;d.update(p,{premultiply:!1}),d.bind(c.NEAREST,c.CLAMP_TO_EDGE)}else r.demTexture=new t.Texture(l,p,c.RGBA,{premultiply:!1}),r.demTexture.bind(c.NEAREST,c.CLAMP_TO_EDGE);l.activeTexture.set(c.TEXTURE0);var m=r.fbo;if(!m){var g=new t.Texture(l,{width:f,height:f,data:null},c.RGBA);g.bind(c.LINEAR,c.CLAMP_TO_EDGE),(m=r.fbo=l.createFramebuffer(f,f,!0)).colorAttachment.set(g.texture)}l.bindFramebuffer.set(m.framebuffer),l.viewport.set([0,0,f,f]),e.useProgram(\"hillshadePrepare\").draw(l,c.TRIANGLES,a,o,s,Et.disabled,function(e,r,n){var i=r.stride,a=t.create();return t.ortho(a,0,t.EXTENT,-t.EXTENT,0,0,1),t.translate(a,a,[0,-t.EXTENT,0]),{u_matrix:a,u_image:1,u_dimension:[i,i],u_zoom:e.overscaledZ,u_maxzoom:n,u_unpack:r.getUnpackVector()}}(r.tileID,u,i),n.id,e.rasterBoundsBuffer,e.quadTriangleIndexBuffer,e.rasterBoundsSegments),r.needsHillshadePrepare=!1}}function cn(e,r,n,i,a){var o=i.paint.get(\"raster-fade-duration\");if(o>0){var s=t.browser.now(),l=(s-e.timeAdded)/o,c=r?(s-r.timeAdded)/o:-1,u=n.getSource(),f=a.coveringZoomLevel({tileSize:u.tileSize,roundZoom:u.roundZoom}),h=!r||Math.abs(r.tileID.overscaledZ-f)>Math.abs(e.tileID.overscaledZ-f),p=h&&e.refreshedUponExpiration?1:t.clamp(h?l:1-c,0,1);return e.refreshedUponExpiration&&l>=1&&(e.refreshedUponExpiration=!1),r?{opacity:1,mix:1-p}:{opacity:p,mix:0}}return{opacity:1,mix:0}}var un=new t.Color(1,0,0,1),fn=new t.Color(0,1,0,1),hn=new t.Color(0,0,1,1),pn=new t.Color(1,0,1,1),dn=new t.Color(0,1,1,1);function mn(t){var e=t.transform.padding;gn(t,t.transform.height-(e.top||0),3,un),gn(t,e.bottom||0,3,fn),vn(t,e.left||0,3,hn),vn(t,t.transform.width-(e.right||0),3,pn);var r=t.transform.centerPoint;!function(t,e,r,n){yn(t,e-1,r-10,2,20,n),yn(t,e-10,r-1,20,2,n)}(t,r.x,t.transform.height-r.y,dn)}function gn(t,e,r,n){yn(t,0,e+r/2,t.transform.width,r,n)}function vn(t,e,r,n){yn(t,e-r/2,0,r,t.transform.height,n)}function yn(e,r,n,i,a,o){var s=e.context,l=s.gl;l.enable(l.SCISSOR_TEST),l.scissor(r*t.browser.devicePixelRatio,n*t.browser.devicePixelRatio,i*t.browser.devicePixelRatio,a*t.browser.devicePixelRatio),s.clear({color:o}),l.disable(l.SCISSOR_TEST)}function xn(e,r,n){var i=e.context,a=i.gl,o=n.posMatrix,s=e.useProgram(\"debug\"),l=Mt.disabled,c=At.disabled,u=e.colorModeForRenderPass();i.activeTexture.set(a.TEXTURE0),e.emptyTexture.bind(a.LINEAR,a.CLAMP_TO_EDGE),s.draw(i,a.LINE_STRIP,l,c,u,Et.disabled,Ir(o,t.Color.red),\"$debug\",e.debugBuffer,e.tileBorderIndexBuffer,e.debugSegments);var f=r.getTileByID(n.key).latestRawTileData,h=f&&f.byteLength||0,p=Math.floor(h/1024),d=r.getTile(n).tileSize,m=512/Math.min(d,512)*(n.overscaledZ/e.transform.zoom)*.5,g=n.canonical.toString();n.overscaledZ!==n.canonical.z&&(g+=\" => \"+n.overscaledZ),function(t,e){t.initDebugOverlayCanvas();var r=t.debugOverlayCanvas,n=t.context.gl,i=t.debugOverlayCanvas.getContext(\"2d\");i.clearRect(0,0,r.width,r.height),i.shadowColor=\"white\",i.shadowBlur=2,i.lineWidth=1.5,i.strokeStyle=\"white\",i.textBaseline=\"top\",i.font=\"bold 36px Open Sans, sans-serif\",i.fillText(e,5,5),i.strokeText(e,5,5),t.debugOverlayTexture.update(r),t.debugOverlayTexture.bind(n.LINEAR,n.CLAMP_TO_EDGE)}(e,g+\" \"+p+\"kb\"),s.draw(i,a.TRIANGLES,l,c,St.alphaBlended,Et.disabled,Ir(o,t.Color.transparent,m),\"$debug\",e.debugBuffer,e.quadTriangleIndexBuffer,e.debugSegments)}var bn={symbol:function(e,r,n,i,a){if(\"translucent\"===e.renderPass){var o=At.disabled,s=e.colorModeForRenderPass();n.layout.get(\"text-variable-anchor\")&&function(e,r,n,i,a,o,s){for(var l=r.transform,c=\"map\"===a,u=\"map\"===o,f=0,h=e;f<h.length;f+=1){var p=h[f],d=i.getTile(p),m=d.getBucket(n);if(m&&m.text&&m.text.segments.get().length){var g=m.textSizeData,v=t.evaluateSizeForZoom(g,l.zoom),y=pe(d,1,r.transform.zoom),x=Qt(p.posMatrix,u,c,r.transform,y),b=\"none\"!==n.layout.get(\"icon-text-fit\")&&m.hasIconData();if(v){var _=Math.pow(2,l.zoom-d.tileID.overscaledZ);tn(m,c,u,s,t.symbolSize,l,x,p.posMatrix,_,v,b)}}}}(i,e,n,r,n.layout.get(\"text-rotation-alignment\"),n.layout.get(\"text-pitch-alignment\"),a),0!==n.paint.get(\"icon-opacity\").constantOr(1)&&rn(e,r,n,i,!1,n.paint.get(\"icon-translate\"),n.paint.get(\"icon-translate-anchor\"),n.layout.get(\"icon-rotation-alignment\"),n.layout.get(\"icon-pitch-alignment\"),n.layout.get(\"icon-keep-upright\"),o,s),0!==n.paint.get(\"text-opacity\").constantOr(1)&&rn(e,r,n,i,!0,n.paint.get(\"text-translate\"),n.paint.get(\"text-translate-anchor\"),n.layout.get(\"text-rotation-alignment\"),n.layout.get(\"text-pitch-alignment\"),n.layout.get(\"text-keep-upright\"),o,s),r.map.showCollisionBoxes&&(Kr(e,r,n,i,n.paint.get(\"text-translate\"),n.paint.get(\"text-translate-anchor\"),!0),Kr(e,r,n,i,n.paint.get(\"icon-translate\"),n.paint.get(\"icon-translate-anchor\"),!1))}},circle:function(e,r,n,i){if(\"translucent\"===e.renderPass){var a=n.paint.get(\"circle-opacity\"),o=n.paint.get(\"circle-stroke-width\"),s=n.paint.get(\"circle-stroke-opacity\"),l=void 0!==n.layout.get(\"circle-sort-key\").constantOr(1);if(0!==a.constantOr(1)||0!==o.constantOr(1)&&0!==s.constantOr(1)){for(var c=e.context,u=c.gl,f=e.depthModeForSublayer(0,Mt.ReadOnly),h=At.disabled,p=e.colorModeForRenderPass(),d=[],m=0;m<i.length;m++){var g=i[m],v=r.getTile(g),y=v.getBucket(n);if(y){var x=y.programConfigurations.get(n.id),b={programConfiguration:x,program:e.useProgram(\"circle\",x),layoutVertexBuffer:y.layoutVertexBuffer,indexBuffer:y.indexBuffer,uniformValues:Lr(e,g,v,n)};if(l)for(var _=0,w=y.segments.get();_<w.length;_+=1){var T=w[_];d.push({segments:new t.SegmentVector([T]),sortKey:T.sortKey,state:b})}else d.push({segments:y.segments,sortKey:0,state:b})}}l&&d.sort((function(t,e){return t.sortKey-e.sortKey}));for(var k=0,M=d;k<M.length;k+=1){var A=M[k],S=A.state,E=S.programConfiguration,L=S.program,C=S.layoutVertexBuffer,P=S.indexBuffer,I=S.uniformValues,O=A.segments;L.draw(c,u.TRIANGLES,f,h,p,Et.disabled,I,n.id,C,P,O,n.paint,e.transform.zoom,E)}}}},heatmap:function(e,r,n,i){if(0!==n.paint.get(\"heatmap-opacity\"))if(\"offscreen\"===e.renderPass){var a=e.context,o=a.gl,s=At.disabled,l=new St([o.ONE,o.ONE],t.Color.transparent,[!0,!0,!0,!0]);!function(t,e,r){var n=t.gl;t.activeTexture.set(n.TEXTURE1),t.viewport.set([0,0,e.width/4,e.height/4]);var i=r.heatmapFbo;if(i)n.bindTexture(n.TEXTURE_2D,i.colorAttachment.get()),t.bindFramebuffer.set(i.framebuffer);else{var a=n.createTexture();n.bindTexture(n.TEXTURE_2D,a),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_S,n.CLAMP_TO_EDGE),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_T,n.CLAMP_TO_EDGE),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MIN_FILTER,n.LINEAR),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MAG_FILTER,n.LINEAR),i=r.heatmapFbo=t.createFramebuffer(e.width/4,e.height/4,!1),function(t,e,r,n){var i=t.gl,a=t.extRenderToTextureHalfFloat?t.extTextureHalfFloat.HALF_FLOAT_OES:i.UNSIGNED_BYTE;i.texImage2D(i.TEXTURE_2D,0,i.RGBA,e.width/4,e.height/4,0,i.RGBA,a,null),n.colorAttachment.set(r)}(t,e,a,i)}}(a,e,n),a.clear({color:t.Color.transparent});for(var c=0;c<i.length;c++){var u=i[c];if(!r.hasRenderableParent(u)){var f=r.getTile(u),h=f.getBucket(n);if(h){var p=h.programConfigurations.get(n.id),d=e.useProgram(\"heatmap\",p),m=e.transform.zoom;d.draw(a,o.TRIANGLES,Mt.disabled,s,l,Et.disabled,zr(u.posMatrix,f,m,n.paint.get(\"heatmap-intensity\")),n.id,h.layoutVertexBuffer,h.indexBuffer,h.segments,n.paint,e.transform.zoom,p)}}}a.viewport.set([0,0,e.width,e.height])}else\"translucent\"===e.renderPass&&(e.context.setColorMode(e.colorModeForRenderPass()),function(e,r){var n=e.context,i=n.gl,a=r.heatmapFbo;if(!a)return;n.activeTexture.set(i.TEXTURE0),i.bindTexture(i.TEXTURE_2D,a.colorAttachment.get()),n.activeTexture.set(i.TEXTURE1);var o=r.colorRampTexture;o||(o=r.colorRampTexture=new t.Texture(n,r.colorRamp,i.RGBA));o.bind(i.LINEAR,i.CLAMP_TO_EDGE),e.useProgram(\"heatmapTexture\").draw(n,i.TRIANGLES,Mt.disabled,At.disabled,e.colorModeForRenderPass(),Et.disabled,function(e,r,n,i){var a=t.create();t.ortho(a,0,e.width,e.height,0,0,1);var o=e.context.gl;return{u_matrix:a,u_world:[o.drawingBufferWidth,o.drawingBufferHeight],u_image:n,u_color_ramp:i,u_opacity:r.paint.get(\"heatmap-opacity\")}}(e,r,0,1),r.id,e.viewportBuffer,e.quadTriangleIndexBuffer,e.viewportSegments,r.paint,e.transform.zoom)}(e,n))},line:function(e,r,n,i){if(\"translucent\"===e.renderPass){var a=n.paint.get(\"line-opacity\"),o=n.paint.get(\"line-width\");if(0!==a.constantOr(1)&&0!==o.constantOr(1)){var s=e.depthModeForSublayer(0,Mt.ReadOnly),l=e.colorModeForRenderPass(),c=n.paint.get(\"line-dasharray\"),u=n.paint.get(\"line-pattern\"),f=u.constantOr(1),h=n.paint.get(\"line-gradient\"),p=n.getCrossfadeParameters(),d=f?\"linePattern\":c?\"lineSDF\":h?\"lineGradient\":\"line\",m=e.context,g=m.gl,v=!0;if(h){m.activeTexture.set(g.TEXTURE0);var y=n.gradientTexture;if(!n.gradient)return;y||(y=n.gradientTexture=new t.Texture(m,n.gradient,g.RGBA)),y.bind(g.LINEAR,g.CLAMP_TO_EDGE)}for(var x=0,b=i;x<b.length;x+=1){var _=b[x],w=r.getTile(_);if(!f||w.patternsLoaded()){var T=w.getBucket(n);if(T){var k=T.programConfigurations.get(n.id),M=e.context.program.get(),A=e.useProgram(d,k),S=v||A.program!==M,E=u.constantOr(null);if(E&&w.imageAtlas){var L=w.imageAtlas,C=L.patternPositions[E.to.toString()],P=L.patternPositions[E.from.toString()];C&&P&&k.setConstantPatternPositions(C,P)}var I=f?Br(e,w,n,p):c?Nr(e,w,n,c,p):h?Fr(e,w,n):Rr(e,w,n);f?(m.activeTexture.set(g.TEXTURE0),w.imageAtlasTexture.bind(g.LINEAR,g.CLAMP_TO_EDGE),k.updatePaintBuffers(p)):c&&(S||e.lineAtlas.dirty)&&(m.activeTexture.set(g.TEXTURE0),e.lineAtlas.bind(m)),A.draw(m,g.TRIANGLES,s,e.stencilModeForClipping(_),l,Et.disabled,I,n.id,T.layoutVertexBuffer,T.indexBuffer,T.segments,n.paint,e.transform.zoom,k),v=!1}}}}}},fill:function(e,r,n,i){var a=n.paint.get(\"fill-color\"),o=n.paint.get(\"fill-opacity\");if(0!==o.constantOr(1)){var s=e.colorModeForRenderPass(),l=n.paint.get(\"fill-pattern\"),c=e.opaquePassEnabledForLayer()&&!l.constantOr(1)&&1===a.constantOr(t.Color.transparent).a&&1===o.constantOr(0)?\"opaque\":\"translucent\";if(e.renderPass===c){var u=e.depthModeForSublayer(1,\"opaque\"===e.renderPass?Mt.ReadWrite:Mt.ReadOnly);an(e,r,n,i,u,s,!1)}if(\"translucent\"===e.renderPass&&n.paint.get(\"fill-antialias\")){var f=e.depthModeForSublayer(n.getPaintProperty(\"fill-outline-color\")?2:0,Mt.ReadOnly);an(e,r,n,i,f,s,!0)}}},\"fill-extrusion\":function(t,e,r,n){var i=r.paint.get(\"fill-extrusion-opacity\");if(0!==i&&\"translucent\"===t.renderPass){var a=new Mt(t.context.gl.LEQUAL,Mt.ReadWrite,t.depthRangeFor3D);if(1!==i||r.paint.get(\"fill-extrusion-pattern\").constantOr(1))on(t,e,r,n,a,At.disabled,St.disabled),on(t,e,r,n,a,t.stencilModeFor3D(),t.colorModeForRenderPass());else{var o=t.colorModeForRenderPass();on(t,e,r,n,a,At.disabled,o)}}},hillshade:function(t,e,r,n){if(\"offscreen\"===t.renderPass||\"translucent\"===t.renderPass){for(var i=t.context,a=e.getSource().maxzoom,o=t.depthModeForSublayer(0,Mt.ReadOnly),s=t.colorModeForRenderPass(),l=\"translucent\"===t.renderPass?t.stencilConfigForOverlap(n):[{},n],c=l[0],u=0,f=l[1];u<f.length;u+=1){var h=f[u],p=e.getTile(h);p.needsHillshadePrepare&&\"offscreen\"===t.renderPass?ln(t,p,r,a,o,At.disabled,s):\"translucent\"===t.renderPass&&sn(t,p,r,o,c[h.overscaledZ],s)}i.viewport.set([0,0,t.width,t.height])}},raster:function(t,e,r,n){if(\"translucent\"===t.renderPass&&0!==r.paint.get(\"raster-opacity\")&&n.length)for(var i=t.context,a=i.gl,o=e.getSource(),s=t.useProgram(\"raster\"),l=t.colorModeForRenderPass(),c=o instanceof I?[{},n]:t.stencilConfigForOverlap(n),u=c[0],f=c[1],h=f[f.length-1].overscaledZ,p=!t.options.moving,d=0,m=f;d<m.length;d+=1){var g=m[d],v=t.depthModeForSublayer(g.overscaledZ-h,1===r.paint.get(\"raster-opacity\")?Mt.ReadWrite:Mt.ReadOnly,a.LESS),y=e.getTile(g),x=t.transform.calculatePosMatrix(g.toUnwrapped(),p);y.registerFadeDuration(r.paint.get(\"raster-fade-duration\"));var b=e.findLoadedParent(g,0),_=cn(y,b,e,r,t.transform),w=void 0,T=void 0,k=\"nearest\"===r.paint.get(\"raster-resampling\")?a.NEAREST:a.LINEAR;i.activeTexture.set(a.TEXTURE0),y.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST),i.activeTexture.set(a.TEXTURE1),b?(b.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST),w=Math.pow(2,b.tileID.overscaledZ-y.tileID.overscaledZ),T=[y.tileID.canonical.x*w%1,y.tileID.canonical.y*w%1]):y.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST);var M=Vr(x,T||[0,0],w||1,_,r);o instanceof I?s.draw(i,a.TRIANGLES,v,At.disabled,l,Et.disabled,M,r.id,o.boundsBuffer,t.quadTriangleIndexBuffer,o.boundsSegments):s.draw(i,a.TRIANGLES,v,u[g.overscaledZ],l,Et.disabled,M,r.id,t.rasterBoundsBuffer,t.quadTriangleIndexBuffer,t.rasterBoundsSegments)}},background:function(t,e,r){var n=r.paint.get(\"background-color\"),i=r.paint.get(\"background-opacity\");if(0!==i){var a=t.context,o=a.gl,s=t.transform,l=s.tileSize,c=r.paint.get(\"background-pattern\");if(!t.isPatternMissing(c)){var u=!c&&1===n.a&&1===i&&t.opaquePassEnabledForLayer()?\"opaque\":\"translucent\";if(t.renderPass===u){var f=At.disabled,h=t.depthModeForSublayer(0,\"opaque\"===u?Mt.ReadWrite:Mt.ReadOnly),p=t.colorModeForRenderPass(),d=t.useProgram(c?\"backgroundPattern\":\"background\"),m=s.coveringTiles({tileSize:l});c&&(a.activeTexture.set(o.TEXTURE0),t.imageManager.bind(t.context));for(var g=r.getCrossfadeParameters(),v=0,y=m;v<y.length;v+=1){var x=y[v],b=t.transform.calculatePosMatrix(x.toUnwrapped()),_=c?Zr(b,i,t,c,{tileID:x,tileSize:l},g):Xr(b,i,n);d.draw(a,o.TRIANGLES,h,f,p,Et.disabled,_,r.id,t.tileExtentBuffer,t.quadTriangleIndexBuffer,t.tileExtentSegments)}}}}},debug:function(t,e,r){for(var n=0;n<r.length;n++)xn(t,e,r[n])},custom:function(t,e,r){var n=t.context,i=r.implementation;if(\"offscreen\"===t.renderPass){var a=i.prerender;a&&(t.setCustomLayerDefaults(),n.setColorMode(t.colorModeForRenderPass()),a.call(i,n.gl,t.transform.customLayerMatrix()),n.setDirty(),t.setBaseState())}else if(\"translucent\"===t.renderPass){t.setCustomLayerDefaults(),n.setColorMode(t.colorModeForRenderPass()),n.setStencilMode(At.disabled);var o=\"3d\"===i.renderingMode?new Mt(t.context.gl.LEQUAL,Mt.ReadWrite,t.depthRangeFor3D):t.depthModeForSublayer(0,Mt.ReadOnly);n.setDepthMode(o),i.render(n.gl,t.transform.customLayerMatrix()),n.setDirty(),t.setBaseState(),n.bindFramebuffer.set(null)}}},_n=function(t,e){this.context=new Lt(t),this.transform=e,this._tileTextures={},this.setup(),this.numSublayers=Ct.maxUnderzooming+Ct.maxOverzooming+1,this.depthEpsilon=1/Math.pow(2,16),this.crossTileSymbolIndex=new Ne,this.gpuTimers={}};_n.prototype.resize=function(e,r){if(this.width=e*t.browser.devicePixelRatio,this.height=r*t.browser.devicePixelRatio,this.context.viewport.set([0,0,this.width,this.height]),this.style)for(var n=0,i=this.style._order;n<i.length;n+=1){var a=i[n];this.style._layers[a].resize()}},_n.prototype.setup=function(){var e=this.context,r=new t.StructArrayLayout2i4;r.emplaceBack(0,0),r.emplaceBack(t.EXTENT,0),r.emplaceBack(0,t.EXTENT),r.emplaceBack(t.EXTENT,t.EXTENT),this.tileExtentBuffer=e.createVertexBuffer(r,Ge.members),this.tileExtentSegments=t.SegmentVector.simpleSegment(0,0,4,2);var n=new t.StructArrayLayout2i4;n.emplaceBack(0,0),n.emplaceBack(t.EXTENT,0),n.emplaceBack(0,t.EXTENT),n.emplaceBack(t.EXTENT,t.EXTENT),this.debugBuffer=e.createVertexBuffer(n,Ge.members),this.debugSegments=t.SegmentVector.simpleSegment(0,0,4,5);var i=new t.StructArrayLayout4i8;i.emplaceBack(0,0,0,0),i.emplaceBack(t.EXTENT,0,t.EXTENT,0),i.emplaceBack(0,t.EXTENT,0,t.EXTENT),i.emplaceBack(t.EXTENT,t.EXTENT,t.EXTENT,t.EXTENT),this.rasterBoundsBuffer=e.createVertexBuffer(i,P.members),this.rasterBoundsSegments=t.SegmentVector.simpleSegment(0,0,4,2);var a=new t.StructArrayLayout2i4;a.emplaceBack(0,0),a.emplaceBack(1,0),a.emplaceBack(0,1),a.emplaceBack(1,1),this.viewportBuffer=e.createVertexBuffer(a,Ge.members),this.viewportSegments=t.SegmentVector.simpleSegment(0,0,4,2);var o=new t.StructArrayLayout1ui2;o.emplaceBack(0),o.emplaceBack(1),o.emplaceBack(3),o.emplaceBack(2),o.emplaceBack(0),this.tileBorderIndexBuffer=e.createIndexBuffer(o);var s=new t.StructArrayLayout3ui6;s.emplaceBack(0,1,2),s.emplaceBack(2,1,3),this.quadTriangleIndexBuffer=e.createIndexBuffer(s),this.emptyTexture=new t.Texture(e,{width:1,height:1,data:new Uint8Array([0,0,0,0])},e.gl.RGBA);var l=this.context.gl;this.stencilClearMode=new At({func:l.ALWAYS,mask:0},0,255,l.ZERO,l.ZERO,l.ZERO)},_n.prototype.clearStencil=function(){var e=this.context,r=e.gl;this.nextStencilID=1,this.currentStencilSource=void 0;var n=t.create();t.ortho(n,0,this.width,this.height,0,0,1),t.scale(n,n,[r.drawingBufferWidth,r.drawingBufferHeight,0]),this.useProgram(\"clippingMask\").draw(e,r.TRIANGLES,Mt.disabled,this.stencilClearMode,St.disabled,Et.disabled,Or(n),\"$clipping\",this.viewportBuffer,this.quadTriangleIndexBuffer,this.viewportSegments)},_n.prototype._renderTileClippingMasks=function(t,e){if(this.currentStencilSource!==t.source&&t.isTileClipped()&&e&&e.length){this.currentStencilSource=t.source;var r=this.context,n=r.gl;this.nextStencilID+e.length>256&&this.clearStencil(),r.setColorMode(St.disabled),r.setDepthMode(Mt.disabled);var i=this.useProgram(\"clippingMask\");this._tileClippingMaskIDs={};for(var a=0,o=e;a<o.length;a+=1){var s=o[a],l=this._tileClippingMaskIDs[s.key]=this.nextStencilID++;i.draw(r,n.TRIANGLES,Mt.disabled,new At({func:n.ALWAYS,mask:0},l,255,n.KEEP,n.KEEP,n.REPLACE),St.disabled,Et.disabled,Or(s.posMatrix),\"$clipping\",this.tileExtentBuffer,this.quadTriangleIndexBuffer,this.tileExtentSegments)}}},_n.prototype.stencilModeFor3D=function(){this.currentStencilSource=void 0,this.nextStencilID+1>256&&this.clearStencil();var t=this.nextStencilID++,e=this.context.gl;return new At({func:e.NOTEQUAL,mask:255},t,255,e.KEEP,e.KEEP,e.REPLACE)},_n.prototype.stencilModeForClipping=function(t){var e=this.context.gl;return new At({func:e.EQUAL,mask:255},this._tileClippingMaskIDs[t.key],0,e.KEEP,e.KEEP,e.REPLACE)},_n.prototype.stencilConfigForOverlap=function(t){var e,r=this.context.gl,n=t.sort((function(t,e){return e.overscaledZ-t.overscaledZ})),i=n[n.length-1].overscaledZ,a=n[0].overscaledZ-i+1;if(a>1){this.currentStencilSource=void 0,this.nextStencilID+a>256&&this.clearStencil();for(var o={},s=0;s<a;s++)o[s+i]=new At({func:r.GEQUAL,mask:255},s+this.nextStencilID,255,r.KEEP,r.KEEP,r.REPLACE);return this.nextStencilID+=a,[o,n]}return[(e={},e[i]=At.disabled,e),n]},_n.prototype.colorModeForRenderPass=function(){var e=this.context.gl;if(this._showOverdrawInspector){return new St([e.CONSTANT_COLOR,e.ONE],new t.Color(1/8,1/8,1/8,0),[!0,!0,!0,!0])}return\"opaque\"===this.renderPass?St.unblended:St.alphaBlended},_n.prototype.depthModeForSublayer=function(t,e,r){if(!this.opaquePassEnabledForLayer())return Mt.disabled;var n=1-((1+this.currentLayer)*this.numSublayers+t)*this.depthEpsilon;return new Mt(r||this.context.gl.LEQUAL,e,[n,n])},_n.prototype.opaquePassEnabledForLayer=function(){return this.currentLayer<this.opaquePassCutoff},_n.prototype.render=function(e,r){var n=this;this.style=e,this.options=r,this.lineAtlas=e.lineAtlas,this.imageManager=e.imageManager,this.glyphManager=e.glyphManager,this.symbolFadeChange=e.placement.symbolFadeChange(t.browser.now()),this.imageManager.beginFrame();var i=this.style._order,a=this.style.sourceCaches;for(var o in a){var s=a[o];s.used&&s.prepare(this.context)}var l,c,u={},f={},h={};for(var p in a){var d=a[p];u[p]=d.getVisibleCoordinates(),f[p]=u[p].slice().reverse(),h[p]=d.getVisibleCoordinates(!0).reverse()}this.opaquePassCutoff=1/0;for(var m=0;m<i.length;m++){var g=i[m];if(this.style._layers[g].is3D()){this.opaquePassCutoff=m;break}}this.renderPass=\"offscreen\";for(var v=0,y=i;v<y.length;v+=1){var x=y[v],b=this.style._layers[x];if(b.hasOffscreenPass()&&!b.isHidden(this.transform.zoom)){var _=f[b.source];(\"custom\"===b.type||_.length)&&this.renderLayer(this,a[b.source],b,_)}}for(this.context.bindFramebuffer.set(null),this.context.clear({color:r.showOverdrawInspector?t.Color.black:t.Color.transparent,depth:1}),this.clearStencil(),this._showOverdrawInspector=r.showOverdrawInspector,this.depthRangeFor3D=[0,1-(e._order.length+2)*this.numSublayers*this.depthEpsilon],this.renderPass=\"opaque\",this.currentLayer=i.length-1;this.currentLayer>=0;this.currentLayer--){var w=this.style._layers[i[this.currentLayer]],T=a[w.source],k=u[w.source];this._renderTileClippingMasks(w,k),this.renderLayer(this,T,w,k)}for(this.renderPass=\"translucent\",this.currentLayer=0;this.currentLayer<i.length;this.currentLayer++){var M=this.style._layers[i[this.currentLayer]],A=a[M.source],S=(\"symbol\"===M.type?h:f)[M.source];this._renderTileClippingMasks(M,u[M.source]),this.renderLayer(this,A,M,S)}this.options.showTileBoundaries&&(t.values(this.style._layers).forEach((function(t){t.source&&!t.isHidden(n.transform.zoom)&&(t.source!==(c&&c.id)&&(c=n.style.sourceCaches[t.source]),(!l||l.getSource().maxzoom<c.getSource().maxzoom)&&(l=c))})),l&&bn.debug(this,l,l.getVisibleCoordinates()));this.options.showPadding&&mn(this),this.context.setDefault()},_n.prototype.renderLayer=function(t,e,r,n){r.isHidden(this.transform.zoom)||(\"background\"===r.type||\"custom\"===r.type||n.length)&&(this.id=r.id,this.gpuTimingStart(r),bn[r.type](t,e,r,n,this.style.placement.variableOffsets),this.gpuTimingEnd())},_n.prototype.gpuTimingStart=function(t){if(this.options.gpuTiming){var e=this.context.extTimerQuery,r=this.gpuTimers[t.id];r||(r=this.gpuTimers[t.id]={calls:0,cpuTime:0,query:e.createQueryEXT()}),r.calls++,e.beginQueryEXT(e.TIME_ELAPSED_EXT,r.query)}},_n.prototype.gpuTimingEnd=function(){if(this.options.gpuTiming){var t=this.context.extTimerQuery;t.endQueryEXT(t.TIME_ELAPSED_EXT)}},_n.prototype.collectGpuTimers=function(){var t=this.gpuTimers;return this.gpuTimers={},t},_n.prototype.queryGpuTimers=function(t){var e={};for(var r in t){var n=t[r],i=this.context.extTimerQuery,a=i.getQueryObjectEXT(n.query,i.QUERY_RESULT_EXT)/1e6;i.deleteQueryEXT(n.query),e[r]=a}return e},_n.prototype.translatePosMatrix=function(e,r,n,i,a){if(!n[0]&&!n[1])return e;var o=a?\"map\"===i?this.transform.angle:0:\"viewport\"===i?-this.transform.angle:0;if(o){var s=Math.sin(o),l=Math.cos(o);n=[n[0]*l-n[1]*s,n[0]*s+n[1]*l]}var c=[a?n[0]:pe(r,n[0],this.transform.zoom),a?n[1]:pe(r,n[1],this.transform.zoom),0],u=new Float32Array(16);return t.translate(u,e,c),u},_n.prototype.saveTileTexture=function(t){var e=this._tileTextures[t.size[0]];e?e.push(t):this._tileTextures[t.size[0]]=[t]},_n.prototype.getTileTexture=function(t){var e=this._tileTextures[t];return e&&e.length>0?e.pop():null},_n.prototype.isPatternMissing=function(t){if(!t)return!1;if(!t.from||!t.to)return!0;var e=this.imageManager.getPattern(t.from.toString()),r=this.imageManager.getPattern(t.to.toString());return!e||!r},_n.prototype.useProgram=function(t,e){this.cache=this.cache||{};var r=\"\"+t+(e?e.cacheKey:\"\")+(this._showOverdrawInspector?\"/overdraw\":\"\");return this.cache[r]||(this.cache[r]=new _r(this.context,xr[t],e,Jr[t],this._showOverdrawInspector)),this.cache[r]},_n.prototype.setCustomLayerDefaults=function(){this.context.unbindVAO(),this.context.cullFace.setDefault(),this.context.activeTexture.setDefault(),this.context.pixelStoreUnpack.setDefault(),this.context.pixelStoreUnpackPremultiplyAlpha.setDefault(),this.context.pixelStoreUnpackFlipY.setDefault()},_n.prototype.setBaseState=function(){var t=this.context.gl;this.context.cullFace.set(!1),this.context.viewport.set([0,0,this.width,this.height]),this.context.blendEquation.set(t.FUNC_ADD)},_n.prototype.initDebugOverlayCanvas=function(){if(null==this.debugOverlayCanvas){this.debugOverlayCanvas=t.window.document.createElement(\"canvas\"),this.debugOverlayCanvas.width=512,this.debugOverlayCanvas.height=512;var e=this.context.gl;this.debugOverlayTexture=new t.Texture(this.context,this.debugOverlayCanvas,e.RGBA)}},_n.prototype.destroy=function(){this.emptyTexture.destroy(),this.debugOverlayTexture&&this.debugOverlayTexture.destroy()};var wn=function(t,e){this.points=t,this.planes=e};wn.fromInvProjectionMatrix=function(e,r,n){var i=Math.pow(2,n),a=[[-1,1,-1,1],[1,1,-1,1],[1,-1,-1,1],[-1,-1,-1,1],[-1,1,1,1],[1,1,1,1],[1,-1,1,1],[-1,-1,1,1]].map((function(r){return t.transformMat4([],r,e)})).map((function(e){return t.scale$1([],e,1/e[3]/r*i)})),o=[[0,1,2],[6,5,4],[0,3,7],[2,1,5],[3,2,6],[0,4,5]].map((function(e){var r=t.sub([],a[e[0]],a[e[1]]),n=t.sub([],a[e[2]],a[e[1]]),i=t.normalize([],t.cross([],r,n)),o=-t.dot(i,a[e[1]]);return i.concat(o)}));return new wn(a,o)};var Tn=function(e,r){this.min=e,this.max=r,this.center=t.scale$2([],t.add([],this.min,this.max),.5)};Tn.prototype.quadrant=function(e){for(var r=[e%2==0,e<2],n=t.clone$2(this.min),i=t.clone$2(this.max),a=0;a<r.length;a++)n[a]=r[a]?this.min[a]:this.center[a],i[a]=r[a]?this.center[a]:this.max[a];return i[2]=this.max[2],new Tn(n,i)},Tn.prototype.distanceX=function(t){return Math.max(Math.min(this.max[0],t[0]),this.min[0])-t[0]},Tn.prototype.distanceY=function(t){return Math.max(Math.min(this.max[1],t[1]),this.min[1])-t[1]},Tn.prototype.intersects=function(e){for(var r=[[this.min[0],this.min[1],0,1],[this.max[0],this.min[1],0,1],[this.max[0],this.max[1],0,1],[this.min[0],this.max[1],0,1]],n=!0,i=0;i<e.planes.length;i++){for(var a=e.planes[i],o=0,s=0;s<r.length;s++)o+=t.dot$1(a,r[s])>=0;if(0===o)return 0;o!==r.length&&(n=!1)}if(n)return 2;for(var l=0;l<3;l++){for(var c=Number.MAX_VALUE,u=-Number.MAX_VALUE,f=0;f<e.points.length;f++){var h=e.points[f][l]-this.min[l];c=Math.min(c,h),u=Math.max(u,h)}if(u<0||c>this.max[l]-this.min[l])return 0}return 1};var kn=function(t,e,r,n){if(void 0===t&&(t=0),void 0===e&&(e=0),void 0===r&&(r=0),void 0===n&&(n=0),isNaN(t)||t<0||isNaN(e)||e<0||isNaN(r)||r<0||isNaN(n)||n<0)throw new Error(\"Invalid value for edge-insets, top, bottom, left and right must all be numbers\");this.top=t,this.bottom=e,this.left=r,this.right=n};kn.prototype.interpolate=function(e,r,n){return null!=r.top&&null!=e.top&&(this.top=t.number(e.top,r.top,n)),null!=r.bottom&&null!=e.bottom&&(this.bottom=t.number(e.bottom,r.bottom,n)),null!=r.left&&null!=e.left&&(this.left=t.number(e.left,r.left,n)),null!=r.right&&null!=e.right&&(this.right=t.number(e.right,r.right,n)),this},kn.prototype.getCenter=function(e,r){var n=t.clamp((this.left+e-this.right)/2,0,e),i=t.clamp((this.top+r-this.bottom)/2,0,r);return new t.Point(n,i)},kn.prototype.equals=function(t){return this.top===t.top&&this.bottom===t.bottom&&this.left===t.left&&this.right===t.right},kn.prototype.clone=function(){return new kn(this.top,this.bottom,this.left,this.right)},kn.prototype.toJSON=function(){return{top:this.top,bottom:this.bottom,left:this.left,right:this.right}};var Mn=function(e,r,n,i,a){this.tileSize=512,this.maxValidLatitude=85.051129,this._renderWorldCopies=void 0===a||a,this._minZoom=e||0,this._maxZoom=r||22,this._minPitch=null==n?0:n,this._maxPitch=null==i?60:i,this.setMaxBounds(),this.width=0,this.height=0,this._center=new t.LngLat(0,0),this.zoom=0,this.angle=0,this._fov=.6435011087932844,this._pitch=0,this._unmodified=!0,this._edgeInsets=new kn,this._posMatrixCache={},this._alignedPosMatrixCache={}},An={minZoom:{configurable:!0},maxZoom:{configurable:!0},minPitch:{configurable:!0},maxPitch:{configurable:!0},renderWorldCopies:{configurable:!0},worldSize:{configurable:!0},centerOffset:{configurable:!0},size:{configurable:!0},bearing:{configurable:!0},pitch:{configurable:!0},fov:{configurable:!0},zoom:{configurable:!0},center:{configurable:!0},padding:{configurable:!0},centerPoint:{configurable:!0},unmodified:{configurable:!0},point:{configurable:!0}};Mn.prototype.clone=function(){var t=new Mn(this._minZoom,this._maxZoom,this._minPitch,this.maxPitch,this._renderWorldCopies);return t.tileSize=this.tileSize,t.latRange=this.latRange,t.width=this.width,t.height=this.height,t._center=this._center,t.zoom=this.zoom,t.angle=this.angle,t._fov=this._fov,t._pitch=this._pitch,t._unmodified=this._unmodified,t._edgeInsets=this._edgeInsets.clone(),t._calcMatrices(),t},An.minZoom.get=function(){return this._minZoom},An.minZoom.set=function(t){this._minZoom!==t&&(this._minZoom=t,this.zoom=Math.max(this.zoom,t))},An.maxZoom.get=function(){return this._maxZoom},An.maxZoom.set=function(t){this._maxZoom!==t&&(this._maxZoom=t,this.zoom=Math.min(this.zoom,t))},An.minPitch.get=function(){return this._minPitch},An.minPitch.set=function(t){this._minPitch!==t&&(this._minPitch=t,this.pitch=Math.max(this.pitch,t))},An.maxPitch.get=function(){return this._maxPitch},An.maxPitch.set=function(t){this._maxPitch!==t&&(this._maxPitch=t,this.pitch=Math.min(this.pitch,t))},An.renderWorldCopies.get=function(){return this._renderWorldCopies},An.renderWorldCopies.set=function(t){void 0===t?t=!0:null===t&&(t=!1),this._renderWorldCopies=t},An.worldSize.get=function(){return this.tileSize*this.scale},An.centerOffset.get=function(){return this.centerPoint._sub(this.size._div(2))},An.size.get=function(){return new t.Point(this.width,this.height)},An.bearing.get=function(){return-this.angle/Math.PI*180},An.bearing.set=function(e){var r=-t.wrap(e,-180,180)*Math.PI/180;this.angle!==r&&(this._unmodified=!1,this.angle=r,this._calcMatrices(),this.rotationMatrix=t.create$2(),t.rotate(this.rotationMatrix,this.rotationMatrix,this.angle))},An.pitch.get=function(){return this._pitch/Math.PI*180},An.pitch.set=function(e){var r=t.clamp(e,this.minPitch,this.maxPitch)/180*Math.PI;this._pitch!==r&&(this._unmodified=!1,this._pitch=r,this._calcMatrices())},An.fov.get=function(){return this._fov/Math.PI*180},An.fov.set=function(t){t=Math.max(.01,Math.min(60,t)),this._fov!==t&&(this._unmodified=!1,this._fov=t/180*Math.PI,this._calcMatrices())},An.zoom.get=function(){return this._zoom},An.zoom.set=function(t){var e=Math.min(Math.max(t,this.minZoom),this.maxZoom);this._zoom!==e&&(this._unmodified=!1,this._zoom=e,this.scale=this.zoomScale(e),this.tileZoom=Math.floor(e),this.zoomFraction=e-this.tileZoom,this._constrain(),this._calcMatrices())},An.center.get=function(){return this._center},An.center.set=function(t){t.lat===this._center.lat&&t.lng===this._center.lng||(this._unmodified=!1,this._center=t,this._constrain(),this._calcMatrices())},An.padding.get=function(){return this._edgeInsets.toJSON()},An.padding.set=function(t){this._edgeInsets.equals(t)||(this._unmodified=!1,this._edgeInsets.interpolate(this._edgeInsets,t,1),this._calcMatrices())},An.centerPoint.get=function(){return this._edgeInsets.getCenter(this.width,this.height)},Mn.prototype.isPaddingEqual=function(t){return this._edgeInsets.equals(t)},Mn.prototype.interpolatePadding=function(t,e,r){this._unmodified=!1,this._edgeInsets.interpolate(t,e,r),this._constrain(),this._calcMatrices()},Mn.prototype.coveringZoomLevel=function(t){var e=(t.roundZoom?Math.round:Math.floor)(this.zoom+this.scaleZoom(this.tileSize/t.tileSize));return Math.max(0,e)},Mn.prototype.getVisibleUnwrappedCoordinates=function(e){var r=[new t.UnwrappedTileID(0,e)];if(this._renderWorldCopies)for(var n=this.pointCoordinate(new t.Point(0,0)),i=this.pointCoordinate(new t.Point(this.width,0)),a=this.pointCoordinate(new t.Point(this.width,this.height)),o=this.pointCoordinate(new t.Point(0,this.height)),s=Math.floor(Math.min(n.x,i.x,a.x,o.x)),l=Math.floor(Math.max(n.x,i.x,a.x,o.x)),c=s-1;c<=l+1;c++)0!==c&&r.push(new t.UnwrappedTileID(c,e));return r},Mn.prototype.coveringTiles=function(e){var r=this.coveringZoomLevel(e),n=r;if(void 0!==e.minzoom&&r<e.minzoom)return[];void 0!==e.maxzoom&&r>e.maxzoom&&(r=e.maxzoom);var i=t.MercatorCoordinate.fromLngLat(this.center),a=Math.pow(2,r),o=[a*i.x,a*i.y,0],s=wn.fromInvProjectionMatrix(this.invProjMatrix,this.worldSize,r),l=e.minzoom||0;this.pitch<=60&&this._edgeInsets.top<.1&&(l=r);var c=function(t){return{aabb:new Tn([t*a,0,0],[(t+1)*a,a,0]),zoom:0,x:0,y:0,wrap:t,fullyVisible:!1}},u=[],f=[],h=r,p=e.reparseOverscaled?n:r;if(this._renderWorldCopies)for(var d=1;d<=3;d++)u.push(c(-d)),u.push(c(d));for(u.push(c(0));u.length>0;){var m=u.pop(),g=m.x,v=m.y,y=m.fullyVisible;if(!y){var x=m.aabb.intersects(s);if(0===x)continue;y=2===x}var b=m.aabb.distanceX(o),_=m.aabb.distanceY(o),w=Math.max(Math.abs(b),Math.abs(_)),T=3+(1<<h-m.zoom)-2;if(m.zoom===h||w>T&&m.zoom>=l)f.push({tileID:new t.OverscaledTileID(m.zoom===h?p:m.zoom,m.wrap,m.zoom,g,v),distanceSq:t.sqrLen([o[0]-.5-g,o[1]-.5-v])});else for(var k=0;k<4;k++){var M=(g<<1)+k%2,A=(v<<1)+(k>>1);u.push({aabb:m.aabb.quadrant(k),zoom:m.zoom+1,x:M,y:A,wrap:m.wrap,fullyVisible:y})}}return f.sort((function(t,e){return t.distanceSq-e.distanceSq})).map((function(t){return t.tileID}))},Mn.prototype.resize=function(t,e){this.width=t,this.height=e,this.pixelsToGLUnits=[2/t,-2/e],this._constrain(),this._calcMatrices()},An.unmodified.get=function(){return this._unmodified},Mn.prototype.zoomScale=function(t){return Math.pow(2,t)},Mn.prototype.scaleZoom=function(t){return Math.log(t)/Math.LN2},Mn.prototype.project=function(e){var r=t.clamp(e.lat,-this.maxValidLatitude,this.maxValidLatitude);return new t.Point(t.mercatorXfromLng(e.lng)*this.worldSize,t.mercatorYfromLat(r)*this.worldSize)},Mn.prototype.unproject=function(e){return new t.MercatorCoordinate(e.x/this.worldSize,e.y/this.worldSize).toLngLat()},An.point.get=function(){return this.project(this.center)},Mn.prototype.setLocationAtPoint=function(e,r){var n=this.pointCoordinate(r),i=this.pointCoordinate(this.centerPoint),a=this.locationCoordinate(e),o=new t.MercatorCoordinate(a.x-(n.x-i.x),a.y-(n.y-i.y));this.center=this.coordinateLocation(o),this._renderWorldCopies&&(this.center=this.center.wrap())},Mn.prototype.locationPoint=function(t){return this.coordinatePoint(this.locationCoordinate(t))},Mn.prototype.pointLocation=function(t){return this.coordinateLocation(this.pointCoordinate(t))},Mn.prototype.locationCoordinate=function(e){return t.MercatorCoordinate.fromLngLat(e)},Mn.prototype.coordinateLocation=function(t){return t.toLngLat()},Mn.prototype.pointCoordinate=function(e){var r=[e.x,e.y,0,1],n=[e.x,e.y,1,1];t.transformMat4(r,r,this.pixelMatrixInverse),t.transformMat4(n,n,this.pixelMatrixInverse);var i=r[3],a=n[3],o=r[0]/i,s=n[0]/a,l=r[1]/i,c=n[1]/a,u=r[2]/i,f=n[2]/a,h=u===f?0:(0-u)/(f-u);return new t.MercatorCoordinate(t.number(o,s,h)/this.worldSize,t.number(l,c,h)/this.worldSize)},Mn.prototype.coordinatePoint=function(e){var r=[e.x*this.worldSize,e.y*this.worldSize,0,1];return t.transformMat4(r,r,this.pixelMatrix),new t.Point(r[0]/r[3],r[1]/r[3])},Mn.prototype.getBounds=function(){return(new t.LngLatBounds).extend(this.pointLocation(new t.Point(0,0))).extend(this.pointLocation(new t.Point(this.width,0))).extend(this.pointLocation(new t.Point(this.width,this.height))).extend(this.pointLocation(new t.Point(0,this.height)))},Mn.prototype.getMaxBounds=function(){return this.latRange&&2===this.latRange.length&&this.lngRange&&2===this.lngRange.length?new t.LngLatBounds([this.lngRange[0],this.latRange[0]],[this.lngRange[1],this.latRange[1]]):null},Mn.prototype.setMaxBounds=function(t){t?(this.lngRange=[t.getWest(),t.getEast()],this.latRange=[t.getSouth(),t.getNorth()],this._constrain()):(this.lngRange=null,this.latRange=[-this.maxValidLatitude,this.maxValidLatitude])},Mn.prototype.calculatePosMatrix=function(e,r){void 0===r&&(r=!1);var n=e.key,i=r?this._alignedPosMatrixCache:this._posMatrixCache;if(i[n])return i[n];var a=e.canonical,o=this.worldSize/this.zoomScale(a.z),s=a.x+Math.pow(2,a.z)*e.wrap,l=t.identity(new Float64Array(16));return t.translate(l,l,[s*o,a.y*o,0]),t.scale(l,l,[o/t.EXTENT,o/t.EXTENT,1]),t.multiply(l,r?this.alignedProjMatrix:this.projMatrix,l),i[n]=new Float32Array(l),i[n]},Mn.prototype.customLayerMatrix=function(){return this.mercatorMatrix.slice()},Mn.prototype._constrain=function(){if(this.center&&this.width&&this.height&&!this._constraining){this._constraining=!0;var e,r,n,i,a=-90,o=90,s=-180,l=180,c=this.size,u=this._unmodified;if(this.latRange){var f=this.latRange;a=t.mercatorYfromLat(f[1])*this.worldSize,e=(o=t.mercatorYfromLat(f[0])*this.worldSize)-a<c.y?c.y/(o-a):0}if(this.lngRange){var h=this.lngRange;s=t.mercatorXfromLng(h[0])*this.worldSize,r=(l=t.mercatorXfromLng(h[1])*this.worldSize)-s<c.x?c.x/(l-s):0}var p=this.point,d=Math.max(r||0,e||0);if(d)return this.center=this.unproject(new t.Point(r?(l+s)/2:p.x,e?(o+a)/2:p.y)),this.zoom+=this.scaleZoom(d),this._unmodified=u,void(this._constraining=!1);if(this.latRange){var m=p.y,g=c.y/2;m-g<a&&(i=a+g),m+g>o&&(i=o-g)}if(this.lngRange){var v=p.x,y=c.x/2;v-y<s&&(n=s+y),v+y>l&&(n=l-y)}void 0===n&&void 0===i||(this.center=this.unproject(new t.Point(void 0!==n?n:p.x,void 0!==i?i:p.y))),this._unmodified=u,this._constraining=!1}},Mn.prototype._calcMatrices=function(){if(this.height){var e=this._fov/2,r=this.centerOffset;this.cameraToCenterDistance=.5/Math.tan(e)*this.height;var n=Math.PI/2+this._pitch,i=this._fov*(.5+r.y/this.height),a=Math.sin(i)*this.cameraToCenterDistance/Math.sin(t.clamp(Math.PI-n-i,.01,Math.PI-.01)),o=this.point,s=o.x,l=o.y,c=1.01*(Math.cos(Math.PI/2-this._pitch)*a+this.cameraToCenterDistance),u=this.height/50,f=new Float64Array(16);t.perspective(f,this._fov,this.width/this.height,u,c),f[8]=2*-r.x/this.width,f[9]=2*r.y/this.height,t.scale(f,f,[1,-1,1]),t.translate(f,f,[0,0,-this.cameraToCenterDistance]),t.rotateX(f,f,this._pitch),t.rotateZ(f,f,this.angle),t.translate(f,f,[-s,-l,0]),this.mercatorMatrix=t.scale([],f,[this.worldSize,this.worldSize,this.worldSize]),t.scale(f,f,[1,1,t.mercatorZfromAltitude(1,this.center.lat)*this.worldSize,1]),this.projMatrix=f,this.invProjMatrix=t.invert([],this.projMatrix);var h=this.width%2/2,p=this.height%2/2,d=Math.cos(this.angle),m=Math.sin(this.angle),g=s-Math.round(s)+d*h+m*p,v=l-Math.round(l)+d*p+m*h,y=new Float64Array(f);if(t.translate(y,y,[g>.5?g-1:g,v>.5?v-1:v,0]),this.alignedProjMatrix=y,f=t.create(),t.scale(f,f,[this.width/2,-this.height/2,1]),t.translate(f,f,[1,-1,0]),this.labelPlaneMatrix=f,f=t.create(),t.scale(f,f,[1,-1,1]),t.translate(f,f,[-1,-1,0]),t.scale(f,f,[2/this.width,2/this.height,1]),this.glCoordMatrix=f,this.pixelMatrix=t.multiply(new Float64Array(16),this.labelPlaneMatrix,this.projMatrix),!(f=t.invert(new Float64Array(16),this.pixelMatrix)))throw new Error(\"failed to invert matrix\");this.pixelMatrixInverse=f,this._posMatrixCache={},this._alignedPosMatrixCache={}}},Mn.prototype.maxPitchScaleFactor=function(){if(!this.pixelMatrixInverse)return 1;var e=this.pointCoordinate(new t.Point(0,0)),r=[e.x*this.worldSize,e.y*this.worldSize,0,1];return t.transformMat4(r,r,this.pixelMatrix)[3]/this.cameraToCenterDistance},Mn.prototype.getCameraPoint=function(){var e=this._pitch,r=Math.tan(e)*(this.cameraToCenterDistance||1);return this.centerPoint.add(new t.Point(0,r))},Mn.prototype.getCameraQueryGeometry=function(e){var r=this.getCameraPoint();if(1===e.length)return[e[0],r];for(var n=r.x,i=r.y,a=r.x,o=r.y,s=0,l=e;s<l.length;s+=1){var c=l[s];n=Math.min(n,c.x),i=Math.min(i,c.y),a=Math.max(a,c.x),o=Math.max(o,c.y)}return[new t.Point(n,i),new t.Point(a,i),new t.Point(a,o),new t.Point(n,o),new t.Point(n,i)]},Object.defineProperties(Mn.prototype,An);var Sn=function(e){var r,n,i,a,o;this._hashName=e&&encodeURIComponent(e),t.bindAll([\"_getCurrentHash\",\"_onHashChange\",\"_updateHash\"],this),this._updateHash=(r=this._updateHashUnthrottled.bind(this),n=300,i=!1,a=null,o=function(){a=null,i&&(r(),a=setTimeout(o,n),i=!1)},function(){return i=!0,a||o(),a})};Sn.prototype.addTo=function(e){return this._map=e,t.window.addEventListener(\"hashchange\",this._onHashChange,!1),this._map.on(\"moveend\",this._updateHash),this},Sn.prototype.remove=function(){return t.window.removeEventListener(\"hashchange\",this._onHashChange,!1),this._map.off(\"moveend\",this._updateHash),clearTimeout(this._updateHash()),delete this._map,this},Sn.prototype.getHashString=function(e){var r=this._map.getCenter(),n=Math.round(100*this._map.getZoom())/100,i=Math.ceil((n*Math.LN2+Math.log(512/360/.5))/Math.LN10),a=Math.pow(10,i),o=Math.round(r.lng*a)/a,s=Math.round(r.lat*a)/a,l=this._map.getBearing(),c=this._map.getPitch(),u=\"\";if(u+=e?\"/\"+o+\"/\"+s+\"/\"+n:n+\"/\"+s+\"/\"+o,(l||c)&&(u+=\"/\"+Math.round(10*l)/10),c&&(u+=\"/\"+Math.round(c)),this._hashName){var f=this._hashName,h=!1,p=t.window.location.hash.slice(1).split(\"&\").map((function(t){var e=t.split(\"=\")[0];return e===f?(h=!0,e+\"=\"+u):t})).filter((function(t){return t}));return h||p.push(f+\"=\"+u),\"#\"+p.join(\"&\")}return\"#\"+u},Sn.prototype._getCurrentHash=function(){var e,r=this,n=t.window.location.hash.replace(\"#\",\"\");return this._hashName?(n.split(\"&\").map((function(t){return t.split(\"=\")})).forEach((function(t){t[0]===r._hashName&&(e=t)})),(e&&e[1]||\"\").split(\"/\")):n.split(\"/\")},Sn.prototype._onHashChange=function(){var t=this._getCurrentHash();if(t.length>=3&&!t.some((function(t){return isNaN(t)}))){var e=this._map.dragRotate.isEnabled()&&this._map.touchZoomRotate.isEnabled()?+(t[3]||0):this._map.getBearing();return this._map.jumpTo({center:[+t[2],+t[1]],zoom:+t[0],bearing:e,pitch:+(t[4]||0)}),!0}return!1},Sn.prototype._updateHashUnthrottled=function(){var e=this.getHashString();try{t.window.history.replaceState(t.window.history.state,\"\",e)}catch(t){}};var En={linearity:.3,easing:t.bezier(0,0,.3,1)},Ln=t.extend({deceleration:2500,maxSpeed:1400},En),Cn=t.extend({deceleration:20,maxSpeed:1400},En),Pn=t.extend({deceleration:1e3,maxSpeed:360},En),In=t.extend({deceleration:1e3,maxSpeed:90},En),On=function(t){this._map=t,this.clear()};function zn(t,e){(!t.duration||t.duration<e.duration)&&(t.duration=e.duration,t.easing=e.easing)}function Dn(e,r,n){var i=n.maxSpeed,a=n.linearity,o=n.deceleration,s=t.clamp(e*a/(r/1e3),-i,i),l=Math.abs(s)/(o*a);return{easing:n.easing,duration:1e3*l,amount:s*(l/2)}}On.prototype.clear=function(){this._inertiaBuffer=[]},On.prototype.record=function(e){this._drainInertiaBuffer(),this._inertiaBuffer.push({time:t.browser.now(),settings:e})},On.prototype._drainInertiaBuffer=function(){for(var e=this._inertiaBuffer,r=t.browser.now();e.length>0&&r-e[0].time>160;)e.shift()},On.prototype._onMoveEnd=function(e){if(this._drainInertiaBuffer(),!(this._inertiaBuffer.length<2)){for(var r={zoom:0,bearing:0,pitch:0,pan:new t.Point(0,0),pinchAround:void 0,around:void 0},n=0,i=this._inertiaBuffer;n<i.length;n+=1){var a=i[n].settings;r.zoom+=a.zoomDelta||0,r.bearing+=a.bearingDelta||0,r.pitch+=a.pitchDelta||0,a.panDelta&&r.pan._add(a.panDelta),a.around&&(r.around=a.around),a.pinchAround&&(r.pinchAround=a.pinchAround)}var o=this._inertiaBuffer[this._inertiaBuffer.length-1].time-this._inertiaBuffer[0].time,s={};if(r.pan.mag()){var l=Dn(r.pan.mag(),o,t.extend({},Ln,e||{}));s.offset=r.pan.mult(l.amount/r.pan.mag()),s.center=this._map.transform.center,zn(s,l)}if(r.zoom){var c=Dn(r.zoom,o,Cn);s.zoom=this._map.transform.zoom+c.amount,zn(s,c)}if(r.bearing){var u=Dn(r.bearing,o,Pn);s.bearing=this._map.transform.bearing+t.clamp(u.amount,-179,179),zn(s,u)}if(r.pitch){var f=Dn(r.pitch,o,In);s.pitch=this._map.transform.pitch+f.amount,zn(s,f)}if(s.zoom||s.bearing){var h=void 0===r.pinchAround?r.around:r.pinchAround;s.around=h?this._map.unproject(h):this._map.getCenter()}return this.clear(),t.extend(s,{noMoveStart:!0})}};var Rn=function(e){function n(n,i,a,o){void 0===o&&(o={});var s=r.mousePos(i.getCanvasContainer(),a),l=i.unproject(s);e.call(this,n,t.extend({point:s,lngLat:l,originalEvent:a},o)),this._defaultPrevented=!1,this.target=i}e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n;var i={defaultPrevented:{configurable:!0}};return n.prototype.preventDefault=function(){this._defaultPrevented=!0},i.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(n.prototype,i),n}(t.Event),Fn=function(e){function n(n,i,a){var o=\"touchend\"===n?a.changedTouches:a.touches,s=r.touchPos(i.getCanvasContainer(),o),l=s.map((function(t){return i.unproject(t)})),c=s.reduce((function(t,e,r,n){return t.add(e.div(n.length))}),new t.Point(0,0)),u=i.unproject(c);e.call(this,n,{points:s,point:c,lngLats:l,lngLat:u,originalEvent:a}),this._defaultPrevented=!1}e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n;var i={defaultPrevented:{configurable:!0}};return n.prototype.preventDefault=function(){this._defaultPrevented=!0},i.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(n.prototype,i),n}(t.Event),Bn=function(t){function e(e,r,n){t.call(this,e,{originalEvent:n}),this._defaultPrevented=!1}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={defaultPrevented:{configurable:!0}};return e.prototype.preventDefault=function(){this._defaultPrevented=!0},r.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(e.prototype,r),e}(t.Event),Nn=function(t,e){this._map=t,this._clickTolerance=e.clickTolerance};Nn.prototype.reset=function(){delete this._mousedownPos},Nn.prototype.wheel=function(t){return this._firePreventable(new Bn(t.type,this._map,t))},Nn.prototype.mousedown=function(t,e){return this._mousedownPos=e,this._firePreventable(new Rn(t.type,this._map,t))},Nn.prototype.mouseup=function(t){this._map.fire(new Rn(t.type,this._map,t))},Nn.prototype.click=function(t,e){this._mousedownPos&&this._mousedownPos.dist(e)>=this._clickTolerance||this._map.fire(new Rn(t.type,this._map,t))},Nn.prototype.dblclick=function(t){return this._firePreventable(new Rn(t.type,this._map,t))},Nn.prototype.mouseover=function(t){this._map.fire(new Rn(t.type,this._map,t))},Nn.prototype.mouseout=function(t){this._map.fire(new Rn(t.type,this._map,t))},Nn.prototype.touchstart=function(t){return this._firePreventable(new Fn(t.type,this._map,t))},Nn.prototype.touchmove=function(t){this._map.fire(new Fn(t.type,this._map,t))},Nn.prototype.touchend=function(t){this._map.fire(new Fn(t.type,this._map,t))},Nn.prototype.touchcancel=function(t){this._map.fire(new Fn(t.type,this._map,t))},Nn.prototype._firePreventable=function(t){if(this._map.fire(t),t.defaultPrevented)return{}},Nn.prototype.isEnabled=function(){return!0},Nn.prototype.isActive=function(){return!1},Nn.prototype.enable=function(){},Nn.prototype.disable=function(){};var jn=function(t){this._map=t};jn.prototype.reset=function(){this._delayContextMenu=!1,delete this._contextMenuEvent},jn.prototype.mousemove=function(t){this._map.fire(new Rn(t.type,this._map,t))},jn.prototype.mousedown=function(){this._delayContextMenu=!0},jn.prototype.mouseup=function(){this._delayContextMenu=!1,this._contextMenuEvent&&(this._map.fire(new Rn(\"contextmenu\",this._map,this._contextMenuEvent)),delete this._contextMenuEvent)},jn.prototype.contextmenu=function(t){this._delayContextMenu?this._contextMenuEvent=t:this._map.fire(new Rn(t.type,this._map,t)),this._map.listens(\"contextmenu\")&&t.preventDefault()},jn.prototype.isEnabled=function(){return!0},jn.prototype.isActive=function(){return!1},jn.prototype.enable=function(){},jn.prototype.disable=function(){};var Un=function(t,e){this._map=t,this._el=t.getCanvasContainer(),this._container=t.getContainer(),this._clickTolerance=e.clickTolerance||1};function Vn(t,e){for(var r={},n=0;n<t.length;n++)r[t[n].identifier]=e[n];return r}Un.prototype.isEnabled=function(){return!!this._enabled},Un.prototype.isActive=function(){return!!this._active},Un.prototype.enable=function(){this.isEnabled()||(this._enabled=!0)},Un.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},Un.prototype.mousedown=function(t,e){this.isEnabled()&&t.shiftKey&&0===t.button&&(r.disableDrag(),this._startPos=this._lastPos=e,this._active=!0)},Un.prototype.mousemoveWindow=function(t,e){if(this._active){var n=e;if(!(this._lastPos.equals(n)||!this._box&&n.dist(this._startPos)<this._clickTolerance)){var i=this._startPos;this._lastPos=n,this._box||(this._box=r.create(\"div\",\"mapboxgl-boxzoom\",this._container),this._container.classList.add(\"mapboxgl-crosshair\"),this._fireEvent(\"boxzoomstart\",t));var a=Math.min(i.x,n.x),o=Math.max(i.x,n.x),s=Math.min(i.y,n.y),l=Math.max(i.y,n.y);r.setTransform(this._box,\"translate(\"+a+\"px,\"+s+\"px)\"),this._box.style.width=o-a+\"px\",this._box.style.height=l-s+\"px\"}}},Un.prototype.mouseupWindow=function(e,n){var i=this;if(this._active&&0===e.button){var a=this._startPos,o=n;if(this.reset(),r.suppressClick(),a.x!==o.x||a.y!==o.y)return this._map.fire(new t.Event(\"boxzoomend\",{originalEvent:e})),{cameraAnimation:function(t){return t.fitScreenCoordinates(a,o,i._map.getBearing(),{linear:!0})}};this._fireEvent(\"boxzoomcancel\",e)}},Un.prototype.keydown=function(t){this._active&&27===t.keyCode&&(this.reset(),this._fireEvent(\"boxzoomcancel\",t))},Un.prototype.reset=function(){this._active=!1,this._container.classList.remove(\"mapboxgl-crosshair\"),this._box&&(r.remove(this._box),this._box=null),r.enableDrag(),delete this._startPos,delete this._lastPos},Un.prototype._fireEvent=function(e,r){return this._map.fire(new t.Event(e,{originalEvent:r}))};var qn=function(t){this.reset(),this.numTouches=t.numTouches};qn.prototype.reset=function(){delete this.centroid,delete this.startTime,delete this.touches,this.aborted=!1},qn.prototype.touchstart=function(e,r,n){(this.centroid||n.length>this.numTouches)&&(this.aborted=!0),this.aborted||(void 0===this.startTime&&(this.startTime=e.timeStamp),n.length===this.numTouches&&(this.centroid=function(e){for(var r=new t.Point(0,0),n=0,i=e;n<i.length;n+=1){var a=i[n];r._add(a)}return r.div(e.length)}(r),this.touches=Vn(n,r)))},qn.prototype.touchmove=function(t,e,r){if(!this.aborted&&this.centroid){var n=Vn(r,e);for(var i in this.touches){var a=this.touches[i],o=n[i];(!o||o.dist(a)>30)&&(this.aborted=!0)}}},qn.prototype.touchend=function(t,e,r){if((!this.centroid||t.timeStamp-this.startTime>500)&&(this.aborted=!0),0===r.length){var n=!this.aborted&&this.centroid;if(this.reset(),n)return n}};var Hn=function(t){this.singleTap=new qn(t),this.numTaps=t.numTaps,this.reset()};Hn.prototype.reset=function(){this.lastTime=1/0,delete this.lastTap,this.count=0,this.singleTap.reset()},Hn.prototype.touchstart=function(t,e,r){this.singleTap.touchstart(t,e,r)},Hn.prototype.touchmove=function(t,e,r){this.singleTap.touchmove(t,e,r)},Hn.prototype.touchend=function(t,e,r){var n=this.singleTap.touchend(t,e,r);if(n){var i=t.timeStamp-this.lastTime<500,a=!this.lastTap||this.lastTap.dist(n)<30;if(i&&a||this.reset(),this.count++,this.lastTime=t.timeStamp,this.lastTap=n,this.count===this.numTaps)return this.reset(),n}};var Gn=function(){this._zoomIn=new Hn({numTouches:1,numTaps:2}),this._zoomOut=new Hn({numTouches:2,numTaps:1}),this.reset()};Gn.prototype.reset=function(){this._active=!1,this._zoomIn.reset(),this._zoomOut.reset()},Gn.prototype.touchstart=function(t,e,r){this._zoomIn.touchstart(t,e,r),this._zoomOut.touchstart(t,e,r)},Gn.prototype.touchmove=function(t,e,r){this._zoomIn.touchmove(t,e,r),this._zoomOut.touchmove(t,e,r)},Gn.prototype.touchend=function(t,e,r){var n=this,i=this._zoomIn.touchend(t,e,r),a=this._zoomOut.touchend(t,e,r);return i?(this._active=!0,t.preventDefault(),setTimeout((function(){return n.reset()}),0),{cameraAnimation:function(e){return e.easeTo({duration:300,zoom:e.getZoom()+1,around:e.unproject(i)},{originalEvent:t})}}):a?(this._active=!0,t.preventDefault(),setTimeout((function(){return n.reset()}),0),{cameraAnimation:function(e){return e.easeTo({duration:300,zoom:e.getZoom()-1,around:e.unproject(a)},{originalEvent:t})}}):void 0},Gn.prototype.touchcancel=function(){this.reset()},Gn.prototype.enable=function(){this._enabled=!0},Gn.prototype.disable=function(){this._enabled=!1,this.reset()},Gn.prototype.isEnabled=function(){return this._enabled},Gn.prototype.isActive=function(){return this._active};var Yn=function(t){this.reset(),this._clickTolerance=t.clickTolerance||1};Yn.prototype.reset=function(){this._active=!1,this._moved=!1,delete this._lastPoint,delete this._eventButton},Yn.prototype._correctButton=function(t,e){return!1},Yn.prototype._move=function(t,e){return{}},Yn.prototype.mousedown=function(t,e){if(!this._lastPoint){var n=r.mouseButton(t);this._correctButton(t,n)&&(this._lastPoint=e,this._eventButton=n)}},Yn.prototype.mousemoveWindow=function(t,e){var r=this._lastPoint;if(r&&(t.preventDefault(),this._moved||!(e.dist(r)<this._clickTolerance)))return this._moved=!0,this._lastPoint=e,this._move(r,e)},Yn.prototype.mouseupWindow=function(t){r.mouseButton(t)===this._eventButton&&(this._moved&&r.suppressClick(),this.reset())},Yn.prototype.enable=function(){this._enabled=!0},Yn.prototype.disable=function(){this._enabled=!1,this.reset()},Yn.prototype.isEnabled=function(){return this._enabled},Yn.prototype.isActive=function(){return this._active};var Wn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.mousedown=function(e,r){t.prototype.mousedown.call(this,e,r),this._lastPoint&&(this._active=!0)},e.prototype._correctButton=function(t,e){return 0===e&&!t.ctrlKey},e.prototype._move=function(t,e){return{around:e,panDelta:e.sub(t)}},e}(Yn),Xn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._correctButton=function(t,e){return 0===e&&t.ctrlKey||2===e},e.prototype._move=function(t,e){var r=.8*(e.x-t.x);if(r)return this._active=!0,{bearingDelta:r}},e.prototype.contextmenu=function(t){t.preventDefault()},e}(Yn),Zn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._correctButton=function(t,e){return 0===e&&t.ctrlKey||2===e},e.prototype._move=function(t,e){var r=-.5*(e.y-t.y);if(r)return this._active=!0,{pitchDelta:r}},e.prototype.contextmenu=function(t){t.preventDefault()},e}(Yn),Jn=function(t){this._minTouches=1,this._clickTolerance=t.clickTolerance||1,this.reset()};Jn.prototype.reset=function(){this._active=!1,this._touches={},this._sum=new t.Point(0,0)},Jn.prototype.touchstart=function(t,e,r){return this._calculateTransform(t,e,r)},Jn.prototype.touchmove=function(t,e,r){if(this._active)return t.preventDefault(),this._calculateTransform(t,e,r)},Jn.prototype.touchend=function(t,e,r){this._calculateTransform(t,e,r),this._active&&r.length<this._minTouches&&this.reset()},Jn.prototype.touchcancel=function(){this.reset()},Jn.prototype._calculateTransform=function(e,r,n){n.length>0&&(this._active=!0);var i=Vn(n,r),a=new t.Point(0,0),o=new t.Point(0,0),s=0;for(var l in i){var c=i[l],u=this._touches[l];u&&(a._add(c),o._add(c.sub(u)),s++,i[l]=c)}if(this._touches=i,!(s<this._minTouches)&&o.mag()){var f=o.div(s);if(this._sum._add(f),!(this._sum.mag()<this._clickTolerance))return{around:a.div(s),panDelta:f}}},Jn.prototype.enable=function(){this._enabled=!0},Jn.prototype.disable=function(){this._enabled=!1,this.reset()},Jn.prototype.isEnabled=function(){return this._enabled},Jn.prototype.isActive=function(){return this._active};var Kn=function(){this.reset()};function Qn(t,e,r){for(var n=0;n<t.length;n++)if(t[n].identifier===r)return e[n]}Kn.prototype.reset=function(){this._active=!1,delete this._firstTwoTouches},Kn.prototype._start=function(t){},Kn.prototype._move=function(t,e,r){return{}},Kn.prototype.touchstart=function(t,e,r){this._firstTwoTouches||r.length<2||(this._firstTwoTouches=[r[0].identifier,r[1].identifier],this._start([e[0],e[1]]))},Kn.prototype.touchmove=function(t,e,r){if(this._firstTwoTouches){t.preventDefault();var n=this._firstTwoTouches,i=n[0],a=n[1],o=Qn(r,e,i),s=Qn(r,e,a);if(o&&s){var l=this._aroundCenter?null:o.add(s).div(2);return this._move([o,s],l,t)}}},Kn.prototype.touchend=function(t,e,n){if(this._firstTwoTouches){var i=this._firstTwoTouches,a=i[0],o=i[1],s=Qn(n,e,a),l=Qn(n,e,o);s&&l||(this._active&&r.suppressClick(),this.reset())}},Kn.prototype.touchcancel=function(){this.reset()},Kn.prototype.enable=function(t){this._enabled=!0,this._aroundCenter=!!t&&\"center\"===t.around},Kn.prototype.disable=function(){this._enabled=!1,this.reset()},Kn.prototype.isEnabled=function(){return this._enabled},Kn.prototype.isActive=function(){return this._active};function $n(t,e){return Math.log(t/e)/Math.LN2}var ti=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),delete this._distance,delete this._startDistance},e.prototype._start=function(t){this._startDistance=this._distance=t[0].dist(t[1])},e.prototype._move=function(t,e){var r=this._distance;if(this._distance=t[0].dist(t[1]),this._active||!(Math.abs($n(this._distance,this._startDistance))<.1))return this._active=!0,{zoomDelta:$n(this._distance,r),pinchAround:e}},e}(Kn);function ei(t,e){return 180*t.angleWith(e)/Math.PI}var ri=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),delete this._minDiameter,delete this._startVector,delete this._vector},e.prototype._start=function(t){this._startVector=this._vector=t[0].sub(t[1]),this._minDiameter=t[0].dist(t[1])},e.prototype._move=function(t,e){var r=this._vector;if(this._vector=t[0].sub(t[1]),this._active||!this._isBelowThreshold(this._vector))return this._active=!0,{bearingDelta:ei(this._vector,r),pinchAround:e}},e.prototype._isBelowThreshold=function(t){this._minDiameter=Math.min(this._minDiameter,t.mag());var e=25/(Math.PI*this._minDiameter)*360,r=ei(t,this._startVector);return Math.abs(r)<e},e}(Kn);function ni(t){return Math.abs(t.y)>Math.abs(t.x)}var ii=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),this._valid=void 0,delete this._firstMove,delete this._lastPoints},e.prototype._start=function(t){this._lastPoints=t,ni(t[0].sub(t[1]))&&(this._valid=!1)},e.prototype._move=function(t,e,r){var n=t[0].sub(this._lastPoints[0]),i=t[1].sub(this._lastPoints[1]);if(this._valid=this.gestureBeginsVertically(n,i,r.timeStamp),this._valid){this._lastPoints=t,this._active=!0;return{pitchDelta:-.5*((n.y+i.y)/2)}}},e.prototype.gestureBeginsVertically=function(t,e,r){if(void 0!==this._valid)return this._valid;var n=t.mag()>=2,i=e.mag()>=2;if(n||i){if(!n||!i)return void 0===this._firstMove&&(this._firstMove=r),r-this._firstMove<100&&void 0;var a=t.y>0==e.y>0;return ni(t)&&ni(e)&&a}},e}(Kn),ai={panStep:100,bearingStep:15,pitchStep:10},oi=function(){var t=ai;this._panStep=t.panStep,this._bearingStep=t.bearingStep,this._pitchStep=t.pitchStep};function si(t){return t*(2-t)}oi.prototype.reset=function(){this._active=!1},oi.prototype.keydown=function(t){var e=this;if(!(t.altKey||t.ctrlKey||t.metaKey)){var r=0,n=0,i=0,a=0,o=0;switch(t.keyCode){case 61:case 107:case 171:case 187:r=1;break;case 189:case 109:case 173:r=-1;break;case 37:t.shiftKey?n=-1:(t.preventDefault(),a=-1);break;case 39:t.shiftKey?n=1:(t.preventDefault(),a=1);break;case 38:t.shiftKey?i=1:(t.preventDefault(),o=-1);break;case 40:t.shiftKey?i=-1:(t.preventDefault(),o=1);break;default:return}return{cameraAnimation:function(s){var l=s.getZoom();s.easeTo({duration:300,easeId:\"keyboardHandler\",easing:si,zoom:r?Math.round(l)+r*(t.shiftKey?2:1):l,bearing:s.getBearing()+n*e._bearingStep,pitch:s.getPitch()+i*e._pitchStep,offset:[-a*e._panStep,-o*e._panStep],center:s.getCenter()},{originalEvent:t})}}}},oi.prototype.enable=function(){this._enabled=!0},oi.prototype.disable=function(){this._enabled=!1,this.reset()},oi.prototype.isEnabled=function(){return this._enabled},oi.prototype.isActive=function(){return this._active};var li=function(e,r){this._map=e,this._el=e.getCanvasContainer(),this._handler=r,this._delta=0,this._defaultZoomRate=.01,this._wheelZoomRate=1/450,t.bindAll([\"_onWheel\",\"_onTimeout\",\"_onScrollFrame\",\"_onScrollFinished\"],this)};li.prototype.setZoomRate=function(t){this._defaultZoomRate=t},li.prototype.setWheelZoomRate=function(t){this._wheelZoomRate=t},li.prototype.isEnabled=function(){return!!this._enabled},li.prototype.isActive=function(){return!!this._active||void 0!==this._finishTimeout},li.prototype.isZooming=function(){return!!this._zooming},li.prototype.enable=function(t){this.isEnabled()||(this._enabled=!0,this._aroundCenter=t&&\"center\"===t.around)},li.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},li.prototype.wheel=function(e){if(this.isEnabled()){var r=e.deltaMode===t.window.WheelEvent.DOM_DELTA_LINE?40*e.deltaY:e.deltaY,n=t.browser.now(),i=n-(this._lastWheelEventTime||0);this._lastWheelEventTime=n,0!==r&&r%4.000244140625==0?this._type=\"wheel\":0!==r&&Math.abs(r)<4?this._type=\"trackpad\":i>400?(this._type=null,this._lastValue=r,this._timeout=setTimeout(this._onTimeout,40,e)):this._type||(this._type=Math.abs(i*r)<200?\"trackpad\":\"wheel\",this._timeout&&(clearTimeout(this._timeout),this._timeout=null,r+=this._lastValue)),e.shiftKey&&r&&(r/=4),this._type&&(this._lastWheelEvent=e,this._delta-=r,this._active||this._start(e)),e.preventDefault()}},li.prototype._onTimeout=function(t){this._type=\"wheel\",this._delta-=this._lastValue,this._active||this._start(t)},li.prototype._start=function(e){if(this._delta){this._frameId&&(this._frameId=null),this._active=!0,this.isZooming()||(this._zooming=!0),this._finishTimeout&&(clearTimeout(this._finishTimeout),delete this._finishTimeout);var n=r.mousePos(this._el,e);this._around=t.LngLat.convert(this._aroundCenter?this._map.getCenter():this._map.unproject(n)),this._aroundPoint=this._map.transform.locationPoint(this._around),this._frameId||(this._frameId=!0,this._handler._triggerRenderFrame())}},li.prototype.renderFrame=function(){return this._onScrollFrame()},li.prototype._onScrollFrame=function(){var e=this;if(this._frameId&&(this._frameId=null,this.isActive())){var r=this._map.transform;if(0!==this._delta){var n=\"wheel\"===this._type&&Math.abs(this._delta)>4.000244140625?this._wheelZoomRate:this._defaultZoomRate,i=2/(1+Math.exp(-Math.abs(this._delta*n)));this._delta<0&&0!==i&&(i=1/i);var a=\"number\"==typeof this._targetZoom?r.zoomScale(this._targetZoom):r.scale;this._targetZoom=Math.min(r.maxZoom,Math.max(r.minZoom,r.scaleZoom(a*i))),\"wheel\"===this._type&&(this._startZoom=r.zoom,this._easing=this._smoothOutEasing(200)),this._delta=0}var o,s=\"number\"==typeof this._targetZoom?this._targetZoom:r.zoom,l=this._startZoom,c=this._easing,u=!1;if(\"wheel\"===this._type&&l&&c){var f=Math.min((t.browser.now()-this._lastWheelEventTime)/200,1),h=c(f);o=t.number(l,s,h),f<1?this._frameId||(this._frameId=!0):u=!0}else o=s,u=!0;return this._active=!0,u&&(this._active=!1,this._finishTimeout=setTimeout((function(){e._zooming=!1,e._handler._triggerRenderFrame(),delete e._targetZoom,delete e._finishTimeout}),200)),{noInertia:!0,needsRenderFrame:!u,zoomDelta:o-r.zoom,around:this._aroundPoint,originalEvent:this._lastWheelEvent}}},li.prototype._smoothOutEasing=function(e){var r=t.ease;if(this._prevEase){var n=this._prevEase,i=(t.browser.now()-n.start)/n.duration,a=n.easing(i+.01)-n.easing(i),o=.27/Math.sqrt(a*a+1e-4)*.01,s=Math.sqrt(.0729-o*o);r=t.bezier(o,s,.25,1)}return this._prevEase={start:t.browser.now(),duration:e,easing:r},r},li.prototype.reset=function(){this._active=!1};var ci=function(t,e){this._clickZoom=t,this._tapZoom=e};ci.prototype.enable=function(){this._clickZoom.enable(),this._tapZoom.enable()},ci.prototype.disable=function(){this._clickZoom.disable(),this._tapZoom.disable()},ci.prototype.isEnabled=function(){return this._clickZoom.isEnabled()&&this._tapZoom.isEnabled()},ci.prototype.isActive=function(){return this._clickZoom.isActive()||this._tapZoom.isActive()};var ui=function(){this.reset()};ui.prototype.reset=function(){this._active=!1},ui.prototype.dblclick=function(t,e){return t.preventDefault(),{cameraAnimation:function(r){r.easeTo({duration:300,zoom:r.getZoom()+(t.shiftKey?-1:1),around:r.unproject(e)},{originalEvent:t})}}},ui.prototype.enable=function(){this._enabled=!0},ui.prototype.disable=function(){this._enabled=!1,this.reset()},ui.prototype.isEnabled=function(){return this._enabled},ui.prototype.isActive=function(){return this._active};var fi=function(){this._tap=new Hn({numTouches:1,numTaps:1}),this.reset()};fi.prototype.reset=function(){this._active=!1,delete this._swipePoint,delete this._swipeTouch,delete this._tapTime,this._tap.reset()},fi.prototype.touchstart=function(t,e,r){this._swipePoint||(this._tapTime&&t.timeStamp-this._tapTime>500&&this.reset(),this._tapTime?r.length>0&&(this._swipePoint=e[0],this._swipeTouch=r[0].identifier):this._tap.touchstart(t,e,r))},fi.prototype.touchmove=function(t,e,r){if(this._tapTime){if(this._swipePoint){if(r[0].identifier!==this._swipeTouch)return;var n=e[0],i=n.y-this._swipePoint.y;return this._swipePoint=n,t.preventDefault(),this._active=!0,{zoomDelta:i/128}}}else this._tap.touchmove(t,e,r)},fi.prototype.touchend=function(t,e,r){this._tapTime?this._swipePoint&&0===r.length&&this.reset():this._tap.touchend(t,e,r)&&(this._tapTime=t.timeStamp)},fi.prototype.touchcancel=function(){this.reset()},fi.prototype.enable=function(){this._enabled=!0},fi.prototype.disable=function(){this._enabled=!1,this.reset()},fi.prototype.isEnabled=function(){return this._enabled},fi.prototype.isActive=function(){return this._active};var hi=function(t,e,r){this._el=t,this._mousePan=e,this._touchPan=r};hi.prototype.enable=function(t){this._inertiaOptions=t||{},this._mousePan.enable(),this._touchPan.enable(),this._el.classList.add(\"mapboxgl-touch-drag-pan\")},hi.prototype.disable=function(){this._mousePan.disable(),this._touchPan.disable(),this._el.classList.remove(\"mapboxgl-touch-drag-pan\")},hi.prototype.isEnabled=function(){return this._mousePan.isEnabled()&&this._touchPan.isEnabled()},hi.prototype.isActive=function(){return this._mousePan.isActive()||this._touchPan.isActive()};var pi=function(t,e,r){this._pitchWithRotate=t.pitchWithRotate,this._mouseRotate=e,this._mousePitch=r};pi.prototype.enable=function(){this._mouseRotate.enable(),this._pitchWithRotate&&this._mousePitch.enable()},pi.prototype.disable=function(){this._mouseRotate.disable(),this._mousePitch.disable()},pi.prototype.isEnabled=function(){return this._mouseRotate.isEnabled()&&(!this._pitchWithRotate||this._mousePitch.isEnabled())},pi.prototype.isActive=function(){return this._mouseRotate.isActive()||this._mousePitch.isActive()};var di=function(t,e,r,n){this._el=t,this._touchZoom=e,this._touchRotate=r,this._tapDragZoom=n,this._rotationDisabled=!1,this._enabled=!0};di.prototype.enable=function(t){this._touchZoom.enable(t),this._rotationDisabled||this._touchRotate.enable(t),this._tapDragZoom.enable(),this._el.classList.add(\"mapboxgl-touch-zoom-rotate\")},di.prototype.disable=function(){this._touchZoom.disable(),this._touchRotate.disable(),this._tapDragZoom.disable(),this._el.classList.remove(\"mapboxgl-touch-zoom-rotate\")},di.prototype.isEnabled=function(){return this._touchZoom.isEnabled()&&(this._rotationDisabled||this._touchRotate.isEnabled())&&this._tapDragZoom.isEnabled()},di.prototype.isActive=function(){return this._touchZoom.isActive()||this._touchRotate.isActive()||this._tapDragZoom.isActive()},di.prototype.disableRotation=function(){this._rotationDisabled=!0,this._touchRotate.disable()},di.prototype.enableRotation=function(){this._rotationDisabled=!1,this._touchZoom.isEnabled()&&this._touchRotate.enable()};var mi=function(t){return t.zoom||t.drag||t.pitch||t.rotate},gi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(t.Event);function vi(t){return t.panDelta&&t.panDelta.mag()||t.zoomDelta||t.bearingDelta||t.pitchDelta}var yi=function(e,n){this._map=e,this._el=this._map.getCanvasContainer(),this._handlers=[],this._handlersById={},this._changes=[],this._inertia=new On(e),this._bearingSnap=n.bearingSnap,this._previousActiveHandlers={},this._eventsInProgress={},this._addDefaultHandlers(n),t.bindAll([\"handleEvent\",\"handleWindowEvent\"],this);var i=this._el;this._listeners=[[i,\"touchstart\",{passive:!1}],[i,\"touchmove\",{passive:!1}],[i,\"touchend\",void 0],[i,\"touchcancel\",void 0],[i,\"mousedown\",void 0],[i,\"mousemove\",void 0],[i,\"mouseup\",void 0],[t.window.document,\"mousemove\",{capture:!0}],[t.window.document,\"mouseup\",void 0],[i,\"mouseover\",void 0],[i,\"mouseout\",void 0],[i,\"dblclick\",void 0],[i,\"click\",void 0],[i,\"keydown\",{capture:!1}],[i,\"keyup\",void 0],[i,\"wheel\",{passive:!1}],[i,\"contextmenu\",void 0],[t.window,\"blur\",void 0]];for(var a=0,o=this._listeners;a<o.length;a+=1){var s=o[a],l=s[0],c=s[1],u=s[2];r.addEventListener(l,c,l===t.window.document?this.handleWindowEvent:this.handleEvent,u)}};yi.prototype.destroy=function(){for(var e=0,n=this._listeners;e<n.length;e+=1){var i=n[e],a=i[0],o=i[1],s=i[2];r.removeEventListener(a,o,a===t.window.document?this.handleWindowEvent:this.handleEvent,s)}},yi.prototype._addDefaultHandlers=function(t){var e=this._map,r=e.getCanvasContainer();this._add(\"mapEvent\",new Nn(e,t));var n=e.boxZoom=new Un(e,t);this._add(\"boxZoom\",n);var i=new Gn,a=new ui;e.doubleClickZoom=new ci(a,i),this._add(\"tapZoom\",i),this._add(\"clickZoom\",a);var o=new fi;this._add(\"tapDragZoom\",o);var s=e.touchPitch=new ii;this._add(\"touchPitch\",s);var l=new Xn(t),c=new Zn(t);e.dragRotate=new pi(t,l,c),this._add(\"mouseRotate\",l,[\"mousePitch\"]),this._add(\"mousePitch\",c,[\"mouseRotate\"]);var u=new Wn(t),f=new Jn(t);e.dragPan=new hi(r,u,f),this._add(\"mousePan\",u),this._add(\"touchPan\",f,[\"touchZoom\",\"touchRotate\"]);var h=new ri,p=new ti;e.touchZoomRotate=new di(r,p,h,o),this._add(\"touchRotate\",h,[\"touchPan\",\"touchZoom\"]),this._add(\"touchZoom\",p,[\"touchPan\",\"touchRotate\"]);var d=e.scrollZoom=new li(e,this);this._add(\"scrollZoom\",d,[\"mousePan\"]);var m=e.keyboard=new oi;this._add(\"keyboard\",m),this._add(\"blockableMapEvent\",new jn(e));for(var g=0,v=[\"boxZoom\",\"doubleClickZoom\",\"tapDragZoom\",\"touchPitch\",\"dragRotate\",\"dragPan\",\"touchZoomRotate\",\"scrollZoom\",\"keyboard\"];g<v.length;g+=1){var y=v[g];t.interactive&&t[y]&&e[y].enable(t[y])}},yi.prototype._add=function(t,e,r){this._handlers.push({handlerName:t,handler:e,allowed:r}),this._handlersById[t]=e},yi.prototype.stop=function(){if(!this._updatingCamera){for(var t=0,e=this._handlers;t<e.length;t+=1){e[t].handler.reset()}this._inertia.clear(),this._fireEvents({},{}),this._changes=[]}},yi.prototype.isActive=function(){for(var t=0,e=this._handlers;t<e.length;t+=1){if(e[t].handler.isActive())return!0}return!1},yi.prototype.isZooming=function(){return!!this._eventsInProgress.zoom||this._map.scrollZoom.isZooming()},yi.prototype.isRotating=function(){return!!this._eventsInProgress.rotate},yi.prototype.isMoving=function(){return Boolean(mi(this._eventsInProgress))||this.isZooming()},yi.prototype._blockedByActive=function(t,e,r){for(var n in t)if(n!==r&&(!e||e.indexOf(n)<0))return!0;return!1},yi.prototype.handleWindowEvent=function(t){this.handleEvent(t,t.type+\"Window\")},yi.prototype._getMapTouches=function(t){for(var e=[],r=0,n=t;r<n.length;r+=1){var i=n[r],a=i.target;this._el.contains(a)&&e.push(i)}return e},yi.prototype.handleEvent=function(t,e){if(\"blur\"!==t.type){this._updatingCamera=!0;for(var n=\"renderFrame\"===t.type?void 0:t,i={needsRenderFrame:!1},a={},o={},s=t.touches?this._getMapTouches(t.touches):void 0,l=s?r.touchPos(this._el,s):r.mousePos(this._el,t),c=0,u=this._handlers;c<u.length;c+=1){var f=u[c],h=f.handlerName,p=f.handler,d=f.allowed;if(p.isEnabled()){var m=void 0;this._blockedByActive(o,d,h)?p.reset():p[e||t.type]&&(m=p[e||t.type](t,l,s),this.mergeHandlerResult(i,a,m,h,n),m&&m.needsRenderFrame&&this._triggerRenderFrame()),(m||p.isActive())&&(o[h]=p)}}var g={};for(var v in this._previousActiveHandlers)o[v]||(g[v]=n);this._previousActiveHandlers=o,(Object.keys(g).length||vi(i))&&(this._changes.push([i,a,g]),this._triggerRenderFrame()),(Object.keys(o).length||vi(i))&&this._map._stop(!0),this._updatingCamera=!1;var y=i.cameraAnimation;y&&(this._inertia.clear(),this._fireEvents({},{}),this._changes=[],y(this._map))}else this.stop()},yi.prototype.mergeHandlerResult=function(e,r,n,i,a){if(n){t.extend(e,n);var o={handlerName:i,originalEvent:n.originalEvent||a};void 0!==n.zoomDelta&&(r.zoom=o),void 0!==n.panDelta&&(r.drag=o),void 0!==n.pitchDelta&&(r.pitch=o),void 0!==n.bearingDelta&&(r.rotate=o)}},yi.prototype._applyChanges=function(){for(var e={},r={},n={},i=0,a=this._changes;i<a.length;i+=1){var o=a[i],s=o[0],l=o[1],c=o[2];s.panDelta&&(e.panDelta=(e.panDelta||new t.Point(0,0))._add(s.panDelta)),s.zoomDelta&&(e.zoomDelta=(e.zoomDelta||0)+s.zoomDelta),s.bearingDelta&&(e.bearingDelta=(e.bearingDelta||0)+s.bearingDelta),s.pitchDelta&&(e.pitchDelta=(e.pitchDelta||0)+s.pitchDelta),void 0!==s.around&&(e.around=s.around),void 0!==s.pinchAround&&(e.pinchAround=s.pinchAround),s.noInertia&&(e.noInertia=s.noInertia),t.extend(r,l),t.extend(n,c)}this._updateMapTransform(e,r,n),this._changes=[]},yi.prototype._updateMapTransform=function(t,e,r){var n=this._map,i=n.transform;if(!vi(t))return this._fireEvents(e,r);var a=t.panDelta,o=t.zoomDelta,s=t.bearingDelta,l=t.pitchDelta,c=t.around,u=t.pinchAround;void 0!==u&&(c=u),n._stop(!0),c=c||n.transform.centerPoint;var f=i.pointLocation(a?c.sub(a):c);s&&(i.bearing+=s),l&&(i.pitch+=l),o&&(i.zoom+=o),i.setLocationAtPoint(f,c),this._map._update(),t.noInertia||this._inertia.record(t),this._fireEvents(e,r)},yi.prototype._fireEvents=function(e,r){var n=this,i=mi(this._eventsInProgress),a=mi(e),o={};for(var s in e){var l=e[s].originalEvent;this._eventsInProgress[s]||(o[s+\"start\"]=l),this._eventsInProgress[s]=e[s]}for(var c in!i&&a&&this._fireEvent(\"movestart\",a.originalEvent),o)this._fireEvent(c,o[c]);for(var u in e.rotate&&(this._bearingChanged=!0),a&&this._fireEvent(\"move\",a.originalEvent),e){var f=e[u].originalEvent;this._fireEvent(u,f)}var h,p={};for(var d in this._eventsInProgress){var m=this._eventsInProgress[d],g=m.handlerName,v=m.originalEvent;this._handlersById[g].isActive()||(delete this._eventsInProgress[d],h=r[g]||v,p[d+\"end\"]=h)}for(var y in p)this._fireEvent(y,p[y]);var x=mi(this._eventsInProgress);if((i||a)&&!x){this._updatingCamera=!0;var b=this._inertia._onMoveEnd(this._map.dragPan._inertiaOptions),_=function(t){return 0!==t&&-n._bearingSnap<t&&t<n._bearingSnap};b?(_(b.bearing||this._map.getBearing())&&(b.bearing=0),this._map.easeTo(b,{originalEvent:h})):(this._map.fire(new t.Event(\"moveend\",{originalEvent:h})),_(this._map.getBearing())&&this._map.resetNorth()),this._bearingChanged=!1,this._updatingCamera=!1}},yi.prototype._fireEvent=function(e,r){this._map.fire(new t.Event(e,r?{originalEvent:r}:{}))},yi.prototype._triggerRenderFrame=function(){var t=this;void 0===this._frameId&&(this._frameId=this._map._requestRenderFrame((function(e){delete t._frameId,t.handleEvent(new gi(\"renderFrame\",{timeStamp:e})),t._applyChanges()})))};var xi=function(e){function r(r,n){e.call(this),this._moving=!1,this._zooming=!1,this.transform=r,this._bearingSnap=n.bearingSnap,t.bindAll([\"_renderFrameCallback\"],this)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.getCenter=function(){return new t.LngLat(this.transform.center.lng,this.transform.center.lat)},r.prototype.setCenter=function(t,e){return this.jumpTo({center:t},e)},r.prototype.panBy=function(e,r,n){return e=t.Point.convert(e).mult(-1),this.panTo(this.transform.center,t.extend({offset:e},r),n)},r.prototype.panTo=function(e,r,n){return this.easeTo(t.extend({center:e},r),n)},r.prototype.getZoom=function(){return this.transform.zoom},r.prototype.setZoom=function(t,e){return this.jumpTo({zoom:t},e),this},r.prototype.zoomTo=function(e,r,n){return this.easeTo(t.extend({zoom:e},r),n)},r.prototype.zoomIn=function(t,e){return this.zoomTo(this.getZoom()+1,t,e),this},r.prototype.zoomOut=function(t,e){return this.zoomTo(this.getZoom()-1,t,e),this},r.prototype.getBearing=function(){return this.transform.bearing},r.prototype.setBearing=function(t,e){return this.jumpTo({bearing:t},e),this},r.prototype.getPadding=function(){return this.transform.padding},r.prototype.setPadding=function(t,e){return this.jumpTo({padding:t},e),this},r.prototype.rotateTo=function(e,r,n){return this.easeTo(t.extend({bearing:e},r),n)},r.prototype.resetNorth=function(e,r){return this.rotateTo(0,t.extend({duration:1e3},e),r),this},r.prototype.resetNorthPitch=function(e,r){return this.easeTo(t.extend({bearing:0,pitch:0,duration:1e3},e),r),this},r.prototype.snapToNorth=function(t,e){return Math.abs(this.getBearing())<this._bearingSnap?this.resetNorth(t,e):this},r.prototype.getPitch=function(){return this.transform.pitch},r.prototype.setPitch=function(t,e){return this.jumpTo({pitch:t},e),this},r.prototype.cameraForBounds=function(e,r){return e=t.LngLatBounds.convert(e),this._cameraForBoxAndBearing(e.getNorthWest(),e.getSouthEast(),0,r)},r.prototype._cameraForBoxAndBearing=function(e,r,n,i){var a={top:0,bottom:0,right:0,left:0};if(\"number\"==typeof(i=t.extend({padding:a,offset:[0,0],maxZoom:this.transform.maxZoom},i)).padding){var o=i.padding;i.padding={top:o,bottom:o,right:o,left:o}}i.padding=t.extend(a,i.padding);var s=this.transform,l=s.padding,c=s.project(t.LngLat.convert(e)),u=s.project(t.LngLat.convert(r)),f=c.rotate(-n*Math.PI/180),h=u.rotate(-n*Math.PI/180),p=new t.Point(Math.max(f.x,h.x),Math.max(f.y,h.y)),d=new t.Point(Math.min(f.x,h.x),Math.min(f.y,h.y)),m=p.sub(d),g=(s.width-(l.left+l.right+i.padding.left+i.padding.right))/m.x,v=(s.height-(l.top+l.bottom+i.padding.top+i.padding.bottom))/m.y;if(!(v<0||g<0)){var y=Math.min(s.scaleZoom(s.scale*Math.min(g,v)),i.maxZoom),x=t.Point.convert(i.offset),b=(i.padding.left-i.padding.right)/2,_=(i.padding.top-i.padding.bottom)/2,w=new t.Point(x.x+b,x.y+_).mult(s.scale/s.zoomScale(y));return{center:s.unproject(c.add(u).div(2).sub(w)),zoom:y,bearing:n}}t.warnOnce(\"Map cannot fit within canvas with the given bounds, padding, and/or offset.\")},r.prototype.fitBounds=function(t,e,r){return this._fitInternal(this.cameraForBounds(t,e),e,r)},r.prototype.fitScreenCoordinates=function(e,r,n,i,a){return this._fitInternal(this._cameraForBoxAndBearing(this.transform.pointLocation(t.Point.convert(e)),this.transform.pointLocation(t.Point.convert(r)),n,i),i,a)},r.prototype._fitInternal=function(e,r,n){return e?(delete(r=t.extend(e,r)).padding,r.linear?this.easeTo(r,n):this.flyTo(r,n)):this},r.prototype.jumpTo=function(e,r){this.stop();var n=this.transform,i=!1,a=!1,o=!1;return\"zoom\"in e&&n.zoom!==+e.zoom&&(i=!0,n.zoom=+e.zoom),void 0!==e.center&&(n.center=t.LngLat.convert(e.center)),\"bearing\"in e&&n.bearing!==+e.bearing&&(a=!0,n.bearing=+e.bearing),\"pitch\"in e&&n.pitch!==+e.pitch&&(o=!0,n.pitch=+e.pitch),null==e.padding||n.isPaddingEqual(e.padding)||(n.padding=e.padding),this.fire(new t.Event(\"movestart\",r)).fire(new t.Event(\"move\",r)),i&&this.fire(new t.Event(\"zoomstart\",r)).fire(new t.Event(\"zoom\",r)).fire(new t.Event(\"zoomend\",r)),a&&this.fire(new t.Event(\"rotatestart\",r)).fire(new t.Event(\"rotate\",r)).fire(new t.Event(\"rotateend\",r)),o&&this.fire(new t.Event(\"pitchstart\",r)).fire(new t.Event(\"pitch\",r)).fire(new t.Event(\"pitchend\",r)),this.fire(new t.Event(\"moveend\",r))},r.prototype.easeTo=function(e,r){var n=this;this._stop(!1,e.easeId),(!1===(e=t.extend({offset:[0,0],duration:500,easing:t.ease},e)).animate||!e.essential&&t.browser.prefersReducedMotion)&&(e.duration=0);var i=this.transform,a=this.getZoom(),o=this.getBearing(),s=this.getPitch(),l=this.getPadding(),c=\"zoom\"in e?+e.zoom:a,u=\"bearing\"in e?this._normalizeBearing(e.bearing,o):o,f=\"pitch\"in e?+e.pitch:s,h=\"padding\"in e?e.padding:i.padding,p=t.Point.convert(e.offset),d=i.centerPoint.add(p),m=i.pointLocation(d),g=t.LngLat.convert(e.center||m);this._normalizeCenter(g);var v,y,x=i.project(m),b=i.project(g).sub(x),_=i.zoomScale(c-a);e.around&&(v=t.LngLat.convert(e.around),y=i.locationPoint(v));var w={moving:this._moving,zooming:this._zooming,rotating:this._rotating,pitching:this._pitching};return this._zooming=this._zooming||c!==a,this._rotating=this._rotating||o!==u,this._pitching=this._pitching||f!==s,this._padding=!i.isPaddingEqual(h),this._easeId=e.easeId,this._prepareEase(r,e.noMoveStart,w),clearTimeout(this._easeEndTimeoutID),this._ease((function(e){if(n._zooming&&(i.zoom=t.number(a,c,e)),n._rotating&&(i.bearing=t.number(o,u,e)),n._pitching&&(i.pitch=t.number(s,f,e)),n._padding&&(i.interpolatePadding(l,h,e),d=i.centerPoint.add(p)),v)i.setLocationAtPoint(v,y);else{var m=i.zoomScale(i.zoom-a),g=c>a?Math.min(2,_):Math.max(.5,_),w=Math.pow(g,1-e),T=i.unproject(x.add(b.mult(e*w)).mult(m));i.setLocationAtPoint(i.renderWorldCopies?T.wrap():T,d)}n._fireMoveEvents(r)}),(function(t){n._afterEase(r,t)}),e),this},r.prototype._prepareEase=function(e,r,n){void 0===n&&(n={}),this._moving=!0,r||n.moving||this.fire(new t.Event(\"movestart\",e)),this._zooming&&!n.zooming&&this.fire(new t.Event(\"zoomstart\",e)),this._rotating&&!n.rotating&&this.fire(new t.Event(\"rotatestart\",e)),this._pitching&&!n.pitching&&this.fire(new t.Event(\"pitchstart\",e))},r.prototype._fireMoveEvents=function(e){this.fire(new t.Event(\"move\",e)),this._zooming&&this.fire(new t.Event(\"zoom\",e)),this._rotating&&this.fire(new t.Event(\"rotate\",e)),this._pitching&&this.fire(new t.Event(\"pitch\",e))},r.prototype._afterEase=function(e,r){if(!this._easeId||!r||this._easeId!==r){delete this._easeId;var n=this._zooming,i=this._rotating,a=this._pitching;this._moving=!1,this._zooming=!1,this._rotating=!1,this._pitching=!1,this._padding=!1,n&&this.fire(new t.Event(\"zoomend\",e)),i&&this.fire(new t.Event(\"rotateend\",e)),a&&this.fire(new t.Event(\"pitchend\",e)),this.fire(new t.Event(\"moveend\",e))}},r.prototype.flyTo=function(e,r){var n=this;if(!e.essential&&t.browser.prefersReducedMotion){var i=t.pick(e,[\"center\",\"zoom\",\"bearing\",\"pitch\",\"around\"]);return this.jumpTo(i,r)}this.stop(),e=t.extend({offset:[0,0],speed:1.2,curve:1.42,easing:t.ease},e);var a=this.transform,o=this.getZoom(),s=this.getBearing(),l=this.getPitch(),c=this.getPadding(),u=\"zoom\"in e?t.clamp(+e.zoom,a.minZoom,a.maxZoom):o,f=\"bearing\"in e?this._normalizeBearing(e.bearing,s):s,h=\"pitch\"in e?+e.pitch:l,p=\"padding\"in e?e.padding:a.padding,d=a.zoomScale(u-o),m=t.Point.convert(e.offset),g=a.centerPoint.add(m),v=a.pointLocation(g),y=t.LngLat.convert(e.center||v);this._normalizeCenter(y);var x=a.project(v),b=a.project(y).sub(x),_=e.curve,w=Math.max(a.width,a.height),T=w/d,k=b.mag();if(\"minZoom\"in e){var M=t.clamp(Math.min(e.minZoom,o,u),a.minZoom,a.maxZoom),A=w/a.zoomScale(M-o);_=Math.sqrt(A/k*2)}var S=_*_;function E(t){var e=(T*T-w*w+(t?-1:1)*S*S*k*k)/(2*(t?T:w)*S*k);return Math.log(Math.sqrt(e*e+1)-e)}function L(t){return(Math.exp(t)-Math.exp(-t))/2}function C(t){return(Math.exp(t)+Math.exp(-t))/2}var P=E(0),I=function(t){return C(P)/C(P+_*t)},O=function(t){return w*((C(P)*(L(e=P+_*t)/C(e))-L(P))/S)/k;var e},z=(E(1)-P)/_;if(Math.abs(k)<1e-6||!isFinite(z)){if(Math.abs(w-T)<1e-6)return this.easeTo(e,r);var D=T<w?-1:1;z=Math.abs(Math.log(T/w))/_,O=function(){return 0},I=function(t){return Math.exp(D*_*t)}}if(\"duration\"in e)e.duration=+e.duration;else{var R=\"screenSpeed\"in e?+e.screenSpeed/_:+e.speed;e.duration=1e3*z/R}return e.maxDuration&&e.duration>e.maxDuration&&(e.duration=0),this._zooming=!0,this._rotating=s!==f,this._pitching=h!==l,this._padding=!a.isPaddingEqual(p),this._prepareEase(r,!1),this._ease((function(e){var i=e*z,d=1/I(i);a.zoom=1===e?u:o+a.scaleZoom(d),n._rotating&&(a.bearing=t.number(s,f,e)),n._pitching&&(a.pitch=t.number(l,h,e)),n._padding&&(a.interpolatePadding(c,p,e),g=a.centerPoint.add(m));var v=1===e?y:a.unproject(x.add(b.mult(O(i))).mult(d));a.setLocationAtPoint(a.renderWorldCopies?v.wrap():v,g),n._fireMoveEvents(r)}),(function(){return n._afterEase(r)}),e),this},r.prototype.isEasing=function(){return!!this._easeFrameId},r.prototype.stop=function(){return this._stop()},r.prototype._stop=function(t,e){if(this._easeFrameId&&(this._cancelRenderFrame(this._easeFrameId),delete this._easeFrameId,delete this._onEaseFrame),this._onEaseEnd){var r=this._onEaseEnd;delete this._onEaseEnd,r.call(this,e)}if(!t){var n=this.handlers;n&&n.stop()}return this},r.prototype._ease=function(e,r,n){!1===n.animate||0===n.duration?(e(1),r()):(this._easeStart=t.browser.now(),this._easeOptions=n,this._onEaseFrame=e,this._onEaseEnd=r,this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback))},r.prototype._renderFrameCallback=function(){var e=Math.min((t.browser.now()-this._easeStart)/this._easeOptions.duration,1);this._onEaseFrame(this._easeOptions.easing(e)),e<1?this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback):this.stop()},r.prototype._normalizeBearing=function(e,r){e=t.wrap(e,-180,180);var n=Math.abs(e-r);return Math.abs(e-360-r)<n&&(e-=360),Math.abs(e+360-r)<n&&(e+=360),e},r.prototype._normalizeCenter=function(t){var e=this.transform;if(e.renderWorldCopies&&!e.lngRange){var r=t.lng-e.center.lng;t.lng+=r>180?-360:r<-180?360:0}},r}(t.Evented),bi=function(e){void 0===e&&(e={}),this.options=e,t.bindAll([\"_updateEditLink\",\"_updateData\",\"_updateCompact\"],this)};bi.prototype.getDefaultPosition=function(){return\"bottom-right\"},bi.prototype.onAdd=function(t){var e=this.options&&this.options.compact;return this._map=t,this._container=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-attrib\"),this._innerContainer=r.create(\"div\",\"mapboxgl-ctrl-attrib-inner\",this._container),e&&this._container.classList.add(\"mapboxgl-compact\"),this._updateAttributions(),this._updateEditLink(),this._map.on(\"styledata\",this._updateData),this._map.on(\"sourcedata\",this._updateData),this._map.on(\"moveend\",this._updateEditLink),void 0===e&&(this._map.on(\"resize\",this._updateCompact),this._updateCompact()),this._container},bi.prototype.onRemove=function(){r.remove(this._container),this._map.off(\"styledata\",this._updateData),this._map.off(\"sourcedata\",this._updateData),this._map.off(\"moveend\",this._updateEditLink),this._map.off(\"resize\",this._updateCompact),this._map=void 0,this._attribHTML=void 0},bi.prototype._updateEditLink=function(){var e=this._editLink;e||(e=this._editLink=this._container.querySelector(\".mapbox-improve-map\"));var r=[{key:\"owner\",value:this.styleOwner},{key:\"id\",value:this.styleId},{key:\"access_token\",value:this._map._requestManager._customAccessToken||t.config.ACCESS_TOKEN}];if(e){var n=r.reduce((function(t,e,n){return e.value&&(t+=e.key+\"=\"+e.value+(n<r.length-1?\"&\":\"\")),t}),\"?\");e.href=t.config.FEEDBACK_URL+\"/\"+n+(this._map._hash?this._map._hash.getHashString(!0):\"\"),e.rel=\"noopener nofollow\"}},bi.prototype._updateData=function(t){!t||\"metadata\"!==t.sourceDataType&&\"style\"!==t.dataType||(this._updateAttributions(),this._updateEditLink())},bi.prototype._updateAttributions=function(){if(this._map.style){var t=[];if(this.options.customAttribution&&(Array.isArray(this.options.customAttribution)?t=t.concat(this.options.customAttribution.map((function(t){return\"string\"!=typeof t?\"\":t}))):\"string\"==typeof this.options.customAttribution&&t.push(this.options.customAttribution)),this._map.style.stylesheet){var e=this._map.style.stylesheet;this.styleOwner=e.owner,this.styleId=e.id}var r=this._map.style.sourceCaches;for(var n in r){var i=r[n];if(i.used){var a=i.getSource();a.attribution&&t.indexOf(a.attribution)<0&&t.push(a.attribution)}}t.sort((function(t,e){return t.length-e.length}));var o=(t=t.filter((function(e,r){for(var n=r+1;n<t.length;n++)if(t[n].indexOf(e)>=0)return!1;return!0}))).join(\" | \");o!==this._attribHTML&&(this._attribHTML=o,t.length?(this._innerContainer.innerHTML=o,this._container.classList.remove(\"mapboxgl-attrib-empty\")):this._container.classList.add(\"mapboxgl-attrib-empty\"),this._editLink=null)}},bi.prototype._updateCompact=function(){this._map.getCanvasContainer().offsetWidth<=640?this._container.classList.add(\"mapboxgl-compact\"):this._container.classList.remove(\"mapboxgl-compact\")};var _i=function(){t.bindAll([\"_updateLogo\"],this),t.bindAll([\"_updateCompact\"],this)};_i.prototype.onAdd=function(t){this._map=t,this._container=r.create(\"div\",\"mapboxgl-ctrl\");var e=r.create(\"a\",\"mapboxgl-ctrl-logo\");return e.target=\"_blank\",e.rel=\"noopener nofollow\",e.href=\"https://www.mapbox.com/\",e.setAttribute(\"aria-label\",this._map._getUIString(\"LogoControl.Title\")),e.setAttribute(\"rel\",\"noopener nofollow\"),this._container.appendChild(e),this._container.style.display=\"none\",this._map.on(\"sourcedata\",this._updateLogo),this._updateLogo(),this._map.on(\"resize\",this._updateCompact),this._updateCompact(),this._container},_i.prototype.onRemove=function(){r.remove(this._container),this._map.off(\"sourcedata\",this._updateLogo),this._map.off(\"resize\",this._updateCompact)},_i.prototype.getDefaultPosition=function(){return\"bottom-left\"},_i.prototype._updateLogo=function(t){t&&\"metadata\"!==t.sourceDataType||(this._container.style.display=this._logoRequired()?\"block\":\"none\")},_i.prototype._logoRequired=function(){if(this._map.style){var t=this._map.style.sourceCaches;for(var e in t){if(t[e].getSource().mapbox_logo)return!0}return!1}},_i.prototype._updateCompact=function(){var t=this._container.children;if(t.length){var e=t[0];this._map.getCanvasContainer().offsetWidth<250?e.classList.add(\"mapboxgl-compact\"):e.classList.remove(\"mapboxgl-compact\")}};var wi=function(){this._queue=[],this._id=0,this._cleared=!1,this._currentlyRunning=!1};wi.prototype.add=function(t){var e=++this._id;return this._queue.push({callback:t,id:e,cancelled:!1}),e},wi.prototype.remove=function(t){for(var e=this._currentlyRunning,r=0,n=e?this._queue.concat(e):this._queue;r<n.length;r+=1){var i=n[r];if(i.id===t)return void(i.cancelled=!0)}},wi.prototype.run=function(t){void 0===t&&(t=0);var e=this._currentlyRunning=this._queue;this._queue=[];for(var r=0,n=e;r<n.length;r+=1){var i=n[r];if(!i.cancelled&&(i.callback(t),this._cleared))break}this._cleared=!1,this._currentlyRunning=!1},wi.prototype.clear=function(){this._currentlyRunning&&(this._cleared=!0),this._queue=[]};var Ti={\"FullscreenControl.Enter\":\"Enter fullscreen\",\"FullscreenControl.Exit\":\"Exit fullscreen\",\"GeolocateControl.FindMyLocation\":\"Find my location\",\"GeolocateControl.LocationNotAvailable\":\"Location not available\",\"LogoControl.Title\":\"Mapbox logo\",\"NavigationControl.ResetBearing\":\"Reset bearing to north\",\"NavigationControl.ZoomIn\":\"Zoom in\",\"NavigationControl.ZoomOut\":\"Zoom out\",\"ScaleControl.Feet\":\"ft\",\"ScaleControl.Meters\":\"m\",\"ScaleControl.Kilometers\":\"km\",\"ScaleControl.Miles\":\"mi\",\"ScaleControl.NauticalMiles\":\"nm\"},ki=t.window.HTMLImageElement,Mi=t.window.HTMLElement,Ai=t.window.ImageBitmap,Si={center:[0,0],zoom:0,bearing:0,pitch:0,minZoom:-2,maxZoom:22,minPitch:0,maxPitch:60,interactive:!0,scrollZoom:!0,boxZoom:!0,dragRotate:!0,dragPan:!0,keyboard:!0,doubleClickZoom:!0,touchZoomRotate:!0,touchPitch:!0,bearingSnap:7,clickTolerance:3,pitchWithRotate:!0,hash:!1,attributionControl:!0,failIfMajorPerformanceCaveat:!1,preserveDrawingBuffer:!1,trackResize:!0,renderWorldCopies:!0,refreshExpiredTiles:!0,maxTileCacheSize:null,localIdeographFontFamily:\"sans-serif\",transformRequest:null,accessToken:null,fadeDuration:300,crossSourceCollisions:!0},Ei=function(n){function i(e){var r=this;if(null!=(e=t.extend({},Si,e)).minZoom&&null!=e.maxZoom&&e.minZoom>e.maxZoom)throw new Error(\"maxZoom must be greater than or equal to minZoom\");if(null!=e.minPitch&&null!=e.maxPitch&&e.minPitch>e.maxPitch)throw new Error(\"maxPitch must be greater than or equal to minPitch\");if(null!=e.minPitch&&e.minPitch<0)throw new Error(\"minPitch must be greater than or equal to 0\");if(null!=e.maxPitch&&e.maxPitch>60)throw new Error(\"maxPitch must be less than or equal to 60\");var i=new Mn(e.minZoom,e.maxZoom,e.minPitch,e.maxPitch,e.renderWorldCopies);if(n.call(this,i,e),this._interactive=e.interactive,this._maxTileCacheSize=e.maxTileCacheSize,this._failIfMajorPerformanceCaveat=e.failIfMajorPerformanceCaveat,this._preserveDrawingBuffer=e.preserveDrawingBuffer,this._antialias=e.antialias,this._trackResize=e.trackResize,this._bearingSnap=e.bearingSnap,this._refreshExpiredTiles=e.refreshExpiredTiles,this._fadeDuration=e.fadeDuration,this._crossSourceCollisions=e.crossSourceCollisions,this._crossFadingFactor=1,this._collectResourceTiming=e.collectResourceTiming,this._renderTaskQueue=new wi,this._controls=[],this._mapId=t.uniqueId(),this._locale=t.extend({},Ti,e.locale),this._requestManager=new t.RequestManager(e.transformRequest,e.accessToken),\"string\"==typeof e.container){if(this._container=t.window.document.getElementById(e.container),!this._container)throw new Error(\"Container '\"+e.container+\"' not found.\")}else{if(!(e.container instanceof Mi))throw new Error(\"Invalid type: 'container' must be a String or HTMLElement.\");this._container=e.container}if(e.maxBounds&&this.setMaxBounds(e.maxBounds),t.bindAll([\"_onWindowOnline\",\"_onWindowResize\",\"_contextLost\",\"_contextRestored\"],this),this._setupContainer(),this._setupPainter(),void 0===this.painter)throw new Error(\"Failed to initialize WebGL.\");this.on(\"move\",(function(){return r._update(!1)})),this.on(\"moveend\",(function(){return r._update(!1)})),this.on(\"zoom\",(function(){return r._update(!0)})),void 0!==t.window&&(t.window.addEventListener(\"online\",this._onWindowOnline,!1),t.window.addEventListener(\"resize\",this._onWindowResize,!1)),this.handlers=new yi(this,e);var a=\"string\"==typeof e.hash&&e.hash||void 0;this._hash=e.hash&&new Sn(a).addTo(this),this._hash&&this._hash._onHashChange()||(this.jumpTo({center:e.center,zoom:e.zoom,bearing:e.bearing,pitch:e.pitch}),e.bounds&&(this.resize(),this.fitBounds(e.bounds,t.extend({},e.fitBoundsOptions,{duration:0})))),this.resize(),this._localIdeographFontFamily=e.localIdeographFontFamily,e.style&&this.setStyle(e.style,{localIdeographFontFamily:e.localIdeographFontFamily}),e.attributionControl&&this.addControl(new bi({customAttribution:e.customAttribution})),this.addControl(new _i,e.logoPosition),this.on(\"style.load\",(function(){r.transform.unmodified&&r.jumpTo(r.style.stylesheet)})),this.on(\"data\",(function(e){r._update(\"style\"===e.dataType),r.fire(new t.Event(e.dataType+\"data\",e))})),this.on(\"dataloading\",(function(e){r.fire(new t.Event(e.dataType+\"dataloading\",e))}))}n&&(i.__proto__=n),i.prototype=Object.create(n&&n.prototype),i.prototype.constructor=i;var a={showTileBoundaries:{configurable:!0},showPadding:{configurable:!0},showCollisionBoxes:{configurable:!0},showOverdrawInspector:{configurable:!0},repaint:{configurable:!0},vertices:{configurable:!0},version:{configurable:!0}};return i.prototype._getMapId=function(){return this._mapId},i.prototype.addControl=function(e,r){if(void 0===r&&e.getDefaultPosition&&(r=e.getDefaultPosition()),void 0===r&&(r=\"top-right\"),!e||!e.onAdd)return this.fire(new t.ErrorEvent(new Error(\"Invalid argument to map.addControl(). Argument must be a control with onAdd and onRemove methods.\")));var n=e.onAdd(this);this._controls.push(e);var i=this._controlPositions[r];return-1!==r.indexOf(\"bottom\")?i.insertBefore(n,i.firstChild):i.appendChild(n),this},i.prototype.removeControl=function(e){if(!e||!e.onRemove)return this.fire(new t.ErrorEvent(new Error(\"Invalid argument to map.removeControl(). Argument must be a control with onAdd and onRemove methods.\")));var r=this._controls.indexOf(e);return r>-1&&this._controls.splice(r,1),e.onRemove(this),this},i.prototype.resize=function(e){var r=this._containerDimensions(),n=r[0],i=r[1];this._resizeCanvas(n,i),this.transform.resize(n,i),this.painter.resize(n,i);var a=!this._moving;return a&&(this.stop(),this.fire(new t.Event(\"movestart\",e)).fire(new t.Event(\"move\",e))),this.fire(new t.Event(\"resize\",e)),a&&this.fire(new t.Event(\"moveend\",e)),this},i.prototype.getBounds=function(){return this.transform.getBounds()},i.prototype.getMaxBounds=function(){return this.transform.getMaxBounds()},i.prototype.setMaxBounds=function(e){return this.transform.setMaxBounds(t.LngLatBounds.convert(e)),this._update()},i.prototype.setMinZoom=function(t){if((t=null==t?-2:t)>=-2&&t<=this.transform.maxZoom)return this.transform.minZoom=t,this._update(),this.getZoom()<t&&this.setZoom(t),this;throw new Error(\"minZoom must be between -2 and the current maxZoom, inclusive\")},i.prototype.getMinZoom=function(){return this.transform.minZoom},i.prototype.setMaxZoom=function(t){if((t=null==t?22:t)>=this.transform.minZoom)return this.transform.maxZoom=t,this._update(),this.getZoom()>t&&this.setZoom(t),this;throw new Error(\"maxZoom must be greater than the current minZoom\")},i.prototype.getMaxZoom=function(){return this.transform.maxZoom},i.prototype.setMinPitch=function(t){if((t=null==t?0:t)<0)throw new Error(\"minPitch must be greater than or equal to 0\");if(t>=0&&t<=this.transform.maxPitch)return this.transform.minPitch=t,this._update(),this.getPitch()<t&&this.setPitch(t),this;throw new Error(\"minPitch must be between 0 and the current maxPitch, inclusive\")},i.prototype.getMinPitch=function(){return this.transform.minPitch},i.prototype.setMaxPitch=function(t){if((t=null==t?60:t)>60)throw new Error(\"maxPitch must be less than or equal to 60\");if(t>=this.transform.minPitch)return this.transform.maxPitch=t,this._update(),this.getPitch()>t&&this.setPitch(t),this;throw new Error(\"maxPitch must be greater than the current minPitch\")},i.prototype.getMaxPitch=function(){return this.transform.maxPitch},i.prototype.getRenderWorldCopies=function(){return this.transform.renderWorldCopies},i.prototype.setRenderWorldCopies=function(t){return this.transform.renderWorldCopies=t,this._update()},i.prototype.project=function(e){return this.transform.locationPoint(t.LngLat.convert(e))},i.prototype.unproject=function(e){return this.transform.pointLocation(t.Point.convert(e))},i.prototype.isMoving=function(){return this._moving||this.handlers.isMoving()},i.prototype.isZooming=function(){return this._zooming||this.handlers.isZooming()},i.prototype.isRotating=function(){return this._rotating||this.handlers.isRotating()},i.prototype._createDelegatedListener=function(t,e,r){var n,i=this;if(\"mouseenter\"===t||\"mouseover\"===t){var a=!1;return{layer:e,listener:r,delegates:{mousemove:function(n){var o=i.getLayer(e)?i.queryRenderedFeatures(n.point,{layers:[e]}):[];o.length?a||(a=!0,r.call(i,new Rn(t,i,n.originalEvent,{features:o}))):a=!1},mouseout:function(){a=!1}}}}if(\"mouseleave\"===t||\"mouseout\"===t){var o=!1;return{layer:e,listener:r,delegates:{mousemove:function(n){(i.getLayer(e)?i.queryRenderedFeatures(n.point,{layers:[e]}):[]).length?o=!0:o&&(o=!1,r.call(i,new Rn(t,i,n.originalEvent)))},mouseout:function(e){o&&(o=!1,r.call(i,new Rn(t,i,e.originalEvent)))}}}}return{layer:e,listener:r,delegates:(n={},n[t]=function(t){var n=i.getLayer(e)?i.queryRenderedFeatures(t.point,{layers:[e]}):[];n.length&&(t.features=n,r.call(i,t),delete t.features)},n)}},i.prototype.on=function(t,e,r){if(void 0===r)return n.prototype.on.call(this,t,e);var i=this._createDelegatedListener(t,e,r);for(var a in this._delegatedListeners=this._delegatedListeners||{},this._delegatedListeners[t]=this._delegatedListeners[t]||[],this._delegatedListeners[t].push(i),i.delegates)this.on(a,i.delegates[a]);return this},i.prototype.once=function(t,e,r){if(void 0===r)return n.prototype.once.call(this,t,e);var i=this._createDelegatedListener(t,e,r);for(var a in i.delegates)this.once(a,i.delegates[a]);return this},i.prototype.off=function(t,e,r){var i=this;if(void 0===r)return n.prototype.off.call(this,t,e);return this._delegatedListeners&&this._delegatedListeners[t]&&function(n){for(var a=n[t],o=0;o<a.length;o++){var s=a[o];if(s.layer===e&&s.listener===r){for(var l in s.delegates)i.off(l,s.delegates[l]);return a.splice(o,1),i}}}(this._delegatedListeners),this},i.prototype.queryRenderedFeatures=function(e,r){if(!this.style)return[];var n;if(void 0!==r||void 0===e||e instanceof t.Point||Array.isArray(e)||(r=e,e=void 0),r=r||{},(e=e||[[0,0],[this.transform.width,this.transform.height]])instanceof t.Point||\"number\"==typeof e[0])n=[t.Point.convert(e)];else{var i=t.Point.convert(e[0]),a=t.Point.convert(e[1]);n=[i,new t.Point(a.x,i.y),a,new t.Point(i.x,a.y),i]}return this.style.queryRenderedFeatures(n,r,this.transform)},i.prototype.querySourceFeatures=function(t,e){return this.style.querySourceFeatures(t,e)},i.prototype.setStyle=function(e,r){return!1!==(r=t.extend({},{localIdeographFontFamily:this._localIdeographFontFamily},r)).diff&&r.localIdeographFontFamily===this._localIdeographFontFamily&&this.style&&e?(this._diffStyle(e,r),this):(this._localIdeographFontFamily=r.localIdeographFontFamily,this._updateStyle(e,r))},i.prototype._getUIString=function(t){var e=this._locale[t];if(null==e)throw new Error(\"Missing UI string '\"+t+\"'\");return e},i.prototype._updateStyle=function(t,e){return this.style&&(this.style.setEventedParent(null),this.style._remove()),t?(this.style=new He(this,e||{}),this.style.setEventedParent(this,{style:this.style}),\"string\"==typeof t?this.style.loadURL(t):this.style.loadJSON(t),this):(delete this.style,this)},i.prototype._lazyInitEmptyStyle=function(){this.style||(this.style=new He(this,{}),this.style.setEventedParent(this,{style:this.style}),this.style.loadEmpty())},i.prototype._diffStyle=function(e,r){var n=this;if(\"string\"==typeof e){var i=this._requestManager.normalizeStyleURL(e),a=this._requestManager.transformRequest(i,t.ResourceType.Style);t.getJSON(a,(function(e,i){e?n.fire(new t.ErrorEvent(e)):i&&n._updateDiff(i,r)}))}else\"object\"==typeof e&&this._updateDiff(e,r)},i.prototype._updateDiff=function(e,r){try{this.style.setState(e)&&this._update(!0)}catch(n){t.warnOnce(\"Unable to perform style diff: \"+(n.message||n.error||n)+\".  Rebuilding the style from scratch.\"),this._updateStyle(e,r)}},i.prototype.getStyle=function(){if(this.style)return this.style.serialize()},i.prototype.isStyleLoaded=function(){return this.style?this.style.loaded():t.warnOnce(\"There is no style added to the map.\")},i.prototype.addSource=function(t,e){return this._lazyInitEmptyStyle(),this.style.addSource(t,e),this._update(!0)},i.prototype.isSourceLoaded=function(e){var r=this.style&&this.style.sourceCaches[e];if(void 0!==r)return r.loaded();this.fire(new t.ErrorEvent(new Error(\"There is no source with ID '\"+e+\"'\")))},i.prototype.areTilesLoaded=function(){var t=this.style&&this.style.sourceCaches;for(var e in t){var r=t[e]._tiles;for(var n in r){var i=r[n];if(\"loaded\"!==i.state&&\"errored\"!==i.state)return!1}}return!0},i.prototype.addSourceType=function(t,e,r){return this._lazyInitEmptyStyle(),this.style.addSourceType(t,e,r)},i.prototype.removeSource=function(t){return this.style.removeSource(t),this._update(!0)},i.prototype.getSource=function(t){return this.style.getSource(t)},i.prototype.addImage=function(e,r,n){void 0===n&&(n={});var i=n.pixelRatio;void 0===i&&(i=1);var a=n.sdf;void 0===a&&(a=!1);var o=n.stretchX,s=n.stretchY,l=n.content;this._lazyInitEmptyStyle();if(r instanceof ki||Ai&&r instanceof Ai){var c=t.browser.getImageData(r),u=c.width,f=c.height,h=c.data;this.style.addImage(e,{data:new t.RGBAImage({width:u,height:f},h),pixelRatio:i,stretchX:o,stretchY:s,content:l,sdf:a,version:0})}else{if(void 0===r.width||void 0===r.height)return this.fire(new t.ErrorEvent(new Error(\"Invalid arguments to map.addImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`\")));var p=r.width,d=r.height,m=r.data,g=r;this.style.addImage(e,{data:new t.RGBAImage({width:p,height:d},new Uint8Array(m)),pixelRatio:i,stretchX:o,stretchY:s,content:l,sdf:a,version:0,userImage:g}),g.onAdd&&g.onAdd(this,e)}},i.prototype.updateImage=function(e,r){var n=this.style.getImage(e);if(!n)return this.fire(new t.ErrorEvent(new Error(\"The map has no image with that id. If you are adding a new image use `map.addImage(...)` instead.\")));var i=r instanceof ki||Ai&&r instanceof Ai?t.browser.getImageData(r):r,a=i.width,o=i.height,s=i.data;if(void 0===a||void 0===o)return this.fire(new t.ErrorEvent(new Error(\"Invalid arguments to map.updateImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`\")));if(a!==n.data.width||o!==n.data.height)return this.fire(new t.ErrorEvent(new Error(\"The width and height of the updated image must be that same as the previous version of the image\")));var l=!(r instanceof ki||Ai&&r instanceof Ai);n.data.replace(s,l),this.style.updateImage(e,n)},i.prototype.hasImage=function(e){return e?!!this.style.getImage(e):(this.fire(new t.ErrorEvent(new Error(\"Missing required image id\"))),!1)},i.prototype.removeImage=function(t){this.style.removeImage(t)},i.prototype.loadImage=function(e,r){t.getImage(this._requestManager.transformRequest(e,t.ResourceType.Image),r)},i.prototype.listImages=function(){return this.style.listImages()},i.prototype.addLayer=function(t,e){return this._lazyInitEmptyStyle(),this.style.addLayer(t,e),this._update(!0)},i.prototype.moveLayer=function(t,e){return this.style.moveLayer(t,e),this._update(!0)},i.prototype.removeLayer=function(t){return this.style.removeLayer(t),this._update(!0)},i.prototype.getLayer=function(t){return this.style.getLayer(t)},i.prototype.setLayerZoomRange=function(t,e,r){return this.style.setLayerZoomRange(t,e,r),this._update(!0)},i.prototype.setFilter=function(t,e,r){return void 0===r&&(r={}),this.style.setFilter(t,e,r),this._update(!0)},i.prototype.getFilter=function(t){return this.style.getFilter(t)},i.prototype.setPaintProperty=function(t,e,r,n){return void 0===n&&(n={}),this.style.setPaintProperty(t,e,r,n),this._update(!0)},i.prototype.getPaintProperty=function(t,e){return this.style.getPaintProperty(t,e)},i.prototype.setLayoutProperty=function(t,e,r,n){return void 0===n&&(n={}),this.style.setLayoutProperty(t,e,r,n),this._update(!0)},i.prototype.getLayoutProperty=function(t,e){return this.style.getLayoutProperty(t,e)},i.prototype.setLight=function(t,e){return void 0===e&&(e={}),this._lazyInitEmptyStyle(),this.style.setLight(t,e),this._update(!0)},i.prototype.getLight=function(){return this.style.getLight()},i.prototype.setFeatureState=function(t,e){return this.style.setFeatureState(t,e),this._update()},i.prototype.removeFeatureState=function(t,e){return this.style.removeFeatureState(t,e),this._update()},i.prototype.getFeatureState=function(t){return this.style.getFeatureState(t)},i.prototype.getContainer=function(){return this._container},i.prototype.getCanvasContainer=function(){return this._canvasContainer},i.prototype.getCanvas=function(){return this._canvas},i.prototype._containerDimensions=function(){var t=0,e=0;return this._container&&(t=this._container.clientWidth||400,e=this._container.clientHeight||300),[t,e]},i.prototype._detectMissingCSS=function(){\"rgb(250, 128, 114)\"!==t.window.getComputedStyle(this._missingCSSCanary).getPropertyValue(\"background-color\")&&t.warnOnce(\"This page appears to be missing CSS declarations for Mapbox GL JS, which may cause the map to display incorrectly. Please ensure your page includes mapbox-gl.css, as described in https://www.mapbox.com/mapbox-gl-js/api/.\")},i.prototype._setupContainer=function(){var t=this._container;t.classList.add(\"mapboxgl-map\"),(this._missingCSSCanary=r.create(\"div\",\"mapboxgl-canary\",t)).style.visibility=\"hidden\",this._detectMissingCSS();var e=this._canvasContainer=r.create(\"div\",\"mapboxgl-canvas-container\",t);this._interactive&&e.classList.add(\"mapboxgl-interactive\"),this._canvas=r.create(\"canvas\",\"mapboxgl-canvas\",e),this._canvas.addEventListener(\"webglcontextlost\",this._contextLost,!1),this._canvas.addEventListener(\"webglcontextrestored\",this._contextRestored,!1),this._canvas.setAttribute(\"tabindex\",\"0\"),this._canvas.setAttribute(\"aria-label\",\"Map\");var n=this._containerDimensions();this._resizeCanvas(n[0],n[1]);var i=this._controlContainer=r.create(\"div\",\"mapboxgl-control-container\",t),a=this._controlPositions={};[\"top-left\",\"top-right\",\"bottom-left\",\"bottom-right\"].forEach((function(t){a[t]=r.create(\"div\",\"mapboxgl-ctrl-\"+t,i)}))},i.prototype._resizeCanvas=function(e,r){var n=t.browser.devicePixelRatio||1;this._canvas.width=n*e,this._canvas.height=n*r,this._canvas.style.width=e+\"px\",this._canvas.style.height=r+\"px\"},i.prototype._setupPainter=function(){var r=t.extend({},e.webGLContextAttributes,{failIfMajorPerformanceCaveat:this._failIfMajorPerformanceCaveat,preserveDrawingBuffer:this._preserveDrawingBuffer,antialias:this._antialias||!1}),n=this._canvas.getContext(\"webgl\",r)||this._canvas.getContext(\"experimental-webgl\",r);n?(this.painter=new _n(n,this.transform),t.webpSupported.testSupport(n)):this.fire(new t.ErrorEvent(new Error(\"Failed to initialize WebGL\")))},i.prototype._contextLost=function(e){e.preventDefault(),this._frame&&(this._frame.cancel(),this._frame=null),this.fire(new t.Event(\"webglcontextlost\",{originalEvent:e}))},i.prototype._contextRestored=function(e){this._setupPainter(),this.resize(),this._update(),this.fire(new t.Event(\"webglcontextrestored\",{originalEvent:e}))},i.prototype.loaded=function(){return!this._styleDirty&&!this._sourcesDirty&&!!this.style&&this.style.loaded()},i.prototype._update=function(t){return this.style?(this._styleDirty=this._styleDirty||t,this._sourcesDirty=!0,this.triggerRepaint(),this):this},i.prototype._requestRenderFrame=function(t){return this._update(),this._renderTaskQueue.add(t)},i.prototype._cancelRenderFrame=function(t){this._renderTaskQueue.remove(t)},i.prototype._render=function(e){var r,n=this,i=0,a=this.painter.context.extTimerQuery;if(this.listens(\"gpu-timing-frame\")&&(r=a.createQueryEXT(),a.beginQueryEXT(a.TIME_ELAPSED_EXT,r),i=t.browser.now()),this.painter.context.setDirty(),this.painter.setBaseState(),this._renderTaskQueue.run(e),!this._removed){var o=!1;if(this.style&&this._styleDirty){this._styleDirty=!1;var s=this.transform.zoom,l=t.browser.now();this.style.zoomHistory.update(s,l);var c=new t.EvaluationParameters(s,{now:l,fadeDuration:this._fadeDuration,zoomHistory:this.style.zoomHistory,transition:this.style.getTransition()}),u=c.crossFadingFactor();1===u&&u===this._crossFadingFactor||(o=!0,this._crossFadingFactor=u),this.style.update(c)}if(this.style&&this._sourcesDirty&&(this._sourcesDirty=!1,this.style._updateSources(this.transform)),this._placementDirty=this.style&&this.style._updatePlacement(this.painter.transform,this.showCollisionBoxes,this._fadeDuration,this._crossSourceCollisions),this.painter.render(this.style,{showTileBoundaries:this.showTileBoundaries,showOverdrawInspector:this._showOverdrawInspector,rotating:this.isRotating(),zooming:this.isZooming(),moving:this.isMoving(),fadeDuration:this._fadeDuration,showPadding:this.showPadding,gpuTiming:!!this.listens(\"gpu-timing-layer\")}),this.fire(new t.Event(\"render\")),this.loaded()&&!this._loaded&&(this._loaded=!0,this.fire(new t.Event(\"load\"))),this.style&&(this.style.hasTransitions()||o)&&(this._styleDirty=!0),this.style&&!this._placementDirty&&this.style._releaseSymbolFadeTiles(),this.listens(\"gpu-timing-frame\")){var f=t.browser.now()-i;a.endQueryEXT(a.TIME_ELAPSED_EXT,r),setTimeout((function(){var e=a.getQueryObjectEXT(r,a.QUERY_RESULT_EXT)/1e6;a.deleteQueryEXT(r),n.fire(new t.Event(\"gpu-timing-frame\",{cpuTime:f,gpuTime:e}))}),50)}if(this.listens(\"gpu-timing-layer\")){var h=this.painter.collectGpuTimers();setTimeout((function(){var e=n.painter.queryGpuTimers(h);n.fire(new t.Event(\"gpu-timing-layer\",{layerTimes:e}))}),50)}return this._sourcesDirty||this._styleDirty||this._placementDirty||this._repaint?this.triggerRepaint():!this.isMoving()&&this.loaded()&&(this._fullyLoaded||(this._fullyLoaded=!0),this.fire(new t.Event(\"idle\"))),this}},i.prototype.remove=function(){this._hash&&this._hash.remove();for(var e=0,r=this._controls;e<r.length;e+=1){r[e].onRemove(this)}this._controls=[],this._frame&&(this._frame.cancel(),this._frame=null),this._renderTaskQueue.clear(),this.painter.destroy(),this.handlers.destroy(),delete this.handlers,this.setStyle(null),void 0!==t.window&&(t.window.removeEventListener(\"resize\",this._onWindowResize,!1),t.window.removeEventListener(\"online\",this._onWindowOnline,!1));var n=this.painter.context.gl.getExtension(\"WEBGL_lose_context\");n&&n.loseContext(),Li(this._canvasContainer),Li(this._controlContainer),Li(this._missingCSSCanary),this._container.classList.remove(\"mapboxgl-map\"),this._removed=!0,this.fire(new t.Event(\"remove\"))},i.prototype.triggerRepaint=function(){var e=this;this.style&&!this._frame&&(this._frame=t.browser.frame((function(t){e._frame=null,e._render(t)})))},i.prototype._onWindowOnline=function(){this._update()},i.prototype._onWindowResize=function(t){this._trackResize&&this.resize({originalEvent:t})._update()},a.showTileBoundaries.get=function(){return!!this._showTileBoundaries},a.showTileBoundaries.set=function(t){this._showTileBoundaries!==t&&(this._showTileBoundaries=t,this._update())},a.showPadding.get=function(){return!!this._showPadding},a.showPadding.set=function(t){this._showPadding!==t&&(this._showPadding=t,this._update())},a.showCollisionBoxes.get=function(){return!!this._showCollisionBoxes},a.showCollisionBoxes.set=function(t){this._showCollisionBoxes!==t&&(this._showCollisionBoxes=t,t?this.style._generateCollisionBoxes():this._update())},a.showOverdrawInspector.get=function(){return!!this._showOverdrawInspector},a.showOverdrawInspector.set=function(t){this._showOverdrawInspector!==t&&(this._showOverdrawInspector=t,this._update())},a.repaint.get=function(){return!!this._repaint},a.repaint.set=function(t){this._repaint!==t&&(this._repaint=t,this.triggerRepaint())},a.vertices.get=function(){return!!this._vertices},a.vertices.set=function(t){this._vertices=t,this._update()},i.prototype._setCacheLimits=function(e,r){t.setCacheLimits(e,r)},a.version.get=function(){return t.version},Object.defineProperties(i.prototype,a),i}(xi);function Li(t){t.parentNode&&t.parentNode.removeChild(t)}var Ci={showCompass:!0,showZoom:!0,visualizePitch:!1},Pi=function(e){var n=this;this.options=t.extend({},Ci,e),this._container=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-group\"),this._container.addEventListener(\"contextmenu\",(function(t){return t.preventDefault()})),this.options.showZoom&&(t.bindAll([\"_setButtonTitle\",\"_updateZoomButtons\"],this),this._zoomInButton=this._createButton(\"mapboxgl-ctrl-zoom-in\",(function(t){return n._map.zoomIn({},{originalEvent:t})})),r.create(\"span\",\"mapboxgl-ctrl-icon\",this._zoomInButton).setAttribute(\"aria-hidden\",!0),this._zoomOutButton=this._createButton(\"mapboxgl-ctrl-zoom-out\",(function(t){return n._map.zoomOut({},{originalEvent:t})})),r.create(\"span\",\"mapboxgl-ctrl-icon\",this._zoomOutButton).setAttribute(\"aria-hidden\",!0)),this.options.showCompass&&(t.bindAll([\"_rotateCompassArrow\"],this),this._compass=this._createButton(\"mapboxgl-ctrl-compass\",(function(t){n.options.visualizePitch?n._map.resetNorthPitch({},{originalEvent:t}):n._map.resetNorth({},{originalEvent:t})})),this._compassIcon=r.create(\"span\",\"mapboxgl-ctrl-icon\",this._compass),this._compassIcon.setAttribute(\"aria-hidden\",!0))};Pi.prototype._updateZoomButtons=function(){var t=this._map.getZoom();this._zoomInButton.disabled=t===this._map.getMaxZoom(),this._zoomOutButton.disabled=t===this._map.getMinZoom()},Pi.prototype._rotateCompassArrow=function(){var t=this.options.visualizePitch?\"scale(\"+1/Math.pow(Math.cos(this._map.transform.pitch*(Math.PI/180)),.5)+\") rotateX(\"+this._map.transform.pitch+\"deg) rotateZ(\"+this._map.transform.angle*(180/Math.PI)+\"deg)\":\"rotate(\"+this._map.transform.angle*(180/Math.PI)+\"deg)\";this._compassIcon.style.transform=t},Pi.prototype.onAdd=function(t){return this._map=t,this.options.showZoom&&(this._setButtonTitle(this._zoomInButton,\"ZoomIn\"),this._setButtonTitle(this._zoomOutButton,\"ZoomOut\"),this._map.on(\"zoom\",this._updateZoomButtons),this._updateZoomButtons()),this.options.showCompass&&(this._setButtonTitle(this._compass,\"ResetBearing\"),this.options.visualizePitch&&this._map.on(\"pitch\",this._rotateCompassArrow),this._map.on(\"rotate\",this._rotateCompassArrow),this._rotateCompassArrow(),this._handler=new Ii(this._map,this._compass,this.options.visualizePitch)),this._container},Pi.prototype.onRemove=function(){r.remove(this._container),this.options.showZoom&&this._map.off(\"zoom\",this._updateZoomButtons),this.options.showCompass&&(this.options.visualizePitch&&this._map.off(\"pitch\",this._rotateCompassArrow),this._map.off(\"rotate\",this._rotateCompassArrow),this._handler.off(),delete this._handler),delete this._map},Pi.prototype._createButton=function(t,e){var n=r.create(\"button\",t,this._container);return n.type=\"button\",n.addEventListener(\"click\",e),n},Pi.prototype._setButtonTitle=function(t,e){var r=this._map._getUIString(\"NavigationControl.\"+e);t.title=r,t.setAttribute(\"aria-label\",r)};var Ii=function(e,n,i){void 0===i&&(i=!1),this._clickTolerance=10,this.element=n,this.mouseRotate=new Xn({clickTolerance:e.dragRotate._mouseRotate._clickTolerance}),this.map=e,i&&(this.mousePitch=new Zn({clickTolerance:e.dragRotate._mousePitch._clickTolerance})),t.bindAll([\"mousedown\",\"mousemove\",\"mouseup\",\"touchstart\",\"touchmove\",\"touchend\",\"reset\"],this),r.addEventListener(n,\"mousedown\",this.mousedown),r.addEventListener(n,\"touchstart\",this.touchstart,{passive:!1}),r.addEventListener(n,\"touchmove\",this.touchmove),r.addEventListener(n,\"touchend\",this.touchend),r.addEventListener(n,\"touchcancel\",this.reset)};function Oi(e,r,n){if(e=new t.LngLat(e.lng,e.lat),r){var i=new t.LngLat(e.lng-360,e.lat),a=new t.LngLat(e.lng+360,e.lat),o=n.locationPoint(e).distSqr(r);n.locationPoint(i).distSqr(r)<o?e=i:n.locationPoint(a).distSqr(r)<o&&(e=a)}for(;Math.abs(e.lng-n.center.lng)>180;){var s=n.locationPoint(e);if(s.x>=0&&s.y>=0&&s.x<=n.width&&s.y<=n.height)break;e.lng>n.center.lng?e.lng-=360:e.lng+=360}return e}Ii.prototype.down=function(t,e){this.mouseRotate.mousedown(t,e),this.mousePitch&&this.mousePitch.mousedown(t,e),r.disableDrag()},Ii.prototype.move=function(t,e){var r=this.map,n=this.mouseRotate.mousemoveWindow(t,e);if(n&&n.bearingDelta&&r.setBearing(r.getBearing()+n.bearingDelta),this.mousePitch){var i=this.mousePitch.mousemoveWindow(t,e);i&&i.pitchDelta&&r.setPitch(r.getPitch()+i.pitchDelta)}},Ii.prototype.off=function(){var t=this.element;r.removeEventListener(t,\"mousedown\",this.mousedown),r.removeEventListener(t,\"touchstart\",this.touchstart,{passive:!1}),r.removeEventListener(t,\"touchmove\",this.touchmove),r.removeEventListener(t,\"touchend\",this.touchend),r.removeEventListener(t,\"touchcancel\",this.reset),this.offTemp()},Ii.prototype.offTemp=function(){r.enableDrag(),r.removeEventListener(t.window,\"mousemove\",this.mousemove),r.removeEventListener(t.window,\"mouseup\",this.mouseup)},Ii.prototype.mousedown=function(e){this.down(t.extend({},e,{ctrlKey:!0,preventDefault:function(){return e.preventDefault()}}),r.mousePos(this.element,e)),r.addEventListener(t.window,\"mousemove\",this.mousemove),r.addEventListener(t.window,\"mouseup\",this.mouseup)},Ii.prototype.mousemove=function(t){this.move(t,r.mousePos(this.element,t))},Ii.prototype.mouseup=function(t){this.mouseRotate.mouseupWindow(t),this.mousePitch&&this.mousePitch.mouseupWindow(t),this.offTemp()},Ii.prototype.touchstart=function(t){1!==t.targetTouches.length?this.reset():(this._startPos=this._lastPos=r.touchPos(this.element,t.targetTouches)[0],this.down({type:\"mousedown\",button:0,ctrlKey:!0,preventDefault:function(){return t.preventDefault()}},this._startPos))},Ii.prototype.touchmove=function(t){1!==t.targetTouches.length?this.reset():(this._lastPos=r.touchPos(this.element,t.targetTouches)[0],this.move({preventDefault:function(){return t.preventDefault()}},this._lastPos))},Ii.prototype.touchend=function(t){0===t.targetTouches.length&&this._startPos&&this._lastPos&&this._startPos.dist(this._lastPos)<this._clickTolerance&&this.element.click(),this.reset()},Ii.prototype.reset=function(){this.mouseRotate.reset(),this.mousePitch&&this.mousePitch.reset(),delete this._startPos,delete this._lastPos,this.offTemp()};var zi={center:\"translate(-50%,-50%)\",top:\"translate(-50%,0)\",\"top-left\":\"translate(0,0)\",\"top-right\":\"translate(-100%,0)\",bottom:\"translate(-50%,-100%)\",\"bottom-left\":\"translate(0,-100%)\",\"bottom-right\":\"translate(-100%,-100%)\",left:\"translate(0,-50%)\",right:\"translate(-100%,-50%)\"};function Di(t,e,r){var n=t.classList;for(var i in zi)n.remove(\"mapboxgl-\"+r+\"-anchor-\"+i);n.add(\"mapboxgl-\"+r+\"-anchor-\"+e)}var Ri,Fi=function(e){function n(n,i){var a=this;if(e.call(this),(n instanceof t.window.HTMLElement||i)&&(n=t.extend({element:n},i)),t.bindAll([\"_update\",\"_onMove\",\"_onUp\",\"_addDragHandler\",\"_onMapClick\",\"_onKeyPress\"],this),this._anchor=n&&n.anchor||\"center\",this._color=n&&n.color||\"#3FB1CE\",this._draggable=n&&n.draggable||!1,this._state=\"inactive\",this._rotation=n&&n.rotation||0,this._rotationAlignment=n&&n.rotationAlignment||\"auto\",this._pitchAlignment=n&&n.pitchAlignment&&\"auto\"!==n.pitchAlignment?n.pitchAlignment:this._rotationAlignment,n&&n.element)this._element=n.element,this._offset=t.Point.convert(n&&n.offset||[0,0]);else{this._defaultMarker=!0,this._element=r.create(\"div\"),this._element.setAttribute(\"aria-label\",\"Map marker\");var o=r.createNS(\"http://www.w3.org/2000/svg\",\"svg\");o.setAttributeNS(null,\"display\",\"block\"),o.setAttributeNS(null,\"height\",\"41px\"),o.setAttributeNS(null,\"width\",\"27px\"),o.setAttributeNS(null,\"viewBox\",\"0 0 27 41\");var s=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");s.setAttributeNS(null,\"stroke\",\"none\"),s.setAttributeNS(null,\"stroke-width\",\"1\"),s.setAttributeNS(null,\"fill\",\"none\"),s.setAttributeNS(null,\"fill-rule\",\"evenodd\");var l=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");l.setAttributeNS(null,\"fill-rule\",\"nonzero\");var c=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");c.setAttributeNS(null,\"transform\",\"translate(3.0, 29.0)\"),c.setAttributeNS(null,\"fill\",\"#000000\");for(var u=0,f=[{rx:\"10.5\",ry:\"5.25002273\"},{rx:\"10.5\",ry:\"5.25002273\"},{rx:\"9.5\",ry:\"4.77275007\"},{rx:\"8.5\",ry:\"4.29549936\"},{rx:\"7.5\",ry:\"3.81822308\"},{rx:\"6.5\",ry:\"3.34094679\"},{rx:\"5.5\",ry:\"2.86367051\"},{rx:\"4.5\",ry:\"2.38636864\"}];u<f.length;u+=1){var h=f[u],p=r.createNS(\"http://www.w3.org/2000/svg\",\"ellipse\");p.setAttributeNS(null,\"opacity\",\"0.04\"),p.setAttributeNS(null,\"cx\",\"10.5\"),p.setAttributeNS(null,\"cy\",\"5.80029008\"),p.setAttributeNS(null,\"rx\",h.rx),p.setAttributeNS(null,\"ry\",h.ry),c.appendChild(p)}var d=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");d.setAttributeNS(null,\"fill\",this._color);var m=r.createNS(\"http://www.w3.org/2000/svg\",\"path\");m.setAttributeNS(null,\"d\",\"M27,13.5 C27,19.074644 20.250001,27.000002 14.75,34.500002 C14.016665,35.500004 12.983335,35.500004 12.25,34.500002 C6.7499993,27.000002 0,19.222562 0,13.5 C0,6.0441559 6.0441559,0 13.5,0 C20.955844,0 27,6.0441559 27,13.5 Z\"),d.appendChild(m);var g=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");g.setAttributeNS(null,\"opacity\",\"0.25\"),g.setAttributeNS(null,\"fill\",\"#000000\");var v=r.createNS(\"http://www.w3.org/2000/svg\",\"path\");v.setAttributeNS(null,\"d\",\"M13.5,0 C6.0441559,0 0,6.0441559 0,13.5 C0,19.222562 6.7499993,27 12.25,34.5 C13,35.522727 14.016664,35.500004 14.75,34.5 C20.250001,27 27,19.074644 27,13.5 C27,6.0441559 20.955844,0 13.5,0 Z M13.5,1 C20.415404,1 26,6.584596 26,13.5 C26,15.898657 24.495584,19.181431 22.220703,22.738281 C19.945823,26.295132 16.705119,30.142167 13.943359,33.908203 C13.743445,34.180814 13.612715,34.322738 13.5,34.441406 C13.387285,34.322738 13.256555,34.180814 13.056641,33.908203 C10.284481,30.127985 7.4148684,26.314159 5.015625,22.773438 C2.6163816,19.232715 1,15.953538 1,13.5 C1,6.584596 6.584596,1 13.5,1 Z\"),g.appendChild(v);var y=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");y.setAttributeNS(null,\"transform\",\"translate(6.0, 7.0)\"),y.setAttributeNS(null,\"fill\",\"#FFFFFF\");var x=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");x.setAttributeNS(null,\"transform\",\"translate(8.0, 8.0)\");var b=r.createNS(\"http://www.w3.org/2000/svg\",\"circle\");b.setAttributeNS(null,\"fill\",\"#000000\"),b.setAttributeNS(null,\"opacity\",\"0.25\"),b.setAttributeNS(null,\"cx\",\"5.5\"),b.setAttributeNS(null,\"cy\",\"5.5\"),b.setAttributeNS(null,\"r\",\"5.4999962\");var _=r.createNS(\"http://www.w3.org/2000/svg\",\"circle\");_.setAttributeNS(null,\"fill\",\"#FFFFFF\"),_.setAttributeNS(null,\"cx\",\"5.5\"),_.setAttributeNS(null,\"cy\",\"5.5\"),_.setAttributeNS(null,\"r\",\"5.4999962\"),x.appendChild(b),x.appendChild(_),l.appendChild(c),l.appendChild(d),l.appendChild(g),l.appendChild(y),l.appendChild(x),o.appendChild(l),this._element.appendChild(o),this._offset=t.Point.convert(n&&n.offset||[0,-14])}this._element.classList.add(\"mapboxgl-marker\"),this._element.addEventListener(\"dragstart\",(function(t){t.preventDefault()})),this._element.addEventListener(\"mousedown\",(function(t){t.preventDefault()})),this._element.addEventListener(\"focus\",(function(){var t=a._map.getContainer();t.scrollTop=0,t.scrollLeft=0})),Di(this._element,this._anchor,\"marker\"),this._popup=null}return e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n,n.prototype.addTo=function(t){return this.remove(),this._map=t,t.getCanvasContainer().appendChild(this._element),t.on(\"move\",this._update),t.on(\"moveend\",this._update),this.setDraggable(this._draggable),this._update(),this._map.on(\"click\",this._onMapClick),this},n.prototype.remove=function(){return this._map&&(this._map.off(\"click\",this._onMapClick),this._map.off(\"move\",this._update),this._map.off(\"moveend\",this._update),this._map.off(\"mousedown\",this._addDragHandler),this._map.off(\"touchstart\",this._addDragHandler),this._map.off(\"mouseup\",this._onUp),this._map.off(\"touchend\",this._onUp),this._map.off(\"mousemove\",this._onMove),this._map.off(\"touchmove\",this._onMove),delete this._map),r.remove(this._element),this._popup&&this._popup.remove(),this},n.prototype.getLngLat=function(){return this._lngLat},n.prototype.setLngLat=function(e){return this._lngLat=t.LngLat.convert(e),this._pos=null,this._popup&&this._popup.setLngLat(this._lngLat),this._update(),this},n.prototype.getElement=function(){return this._element},n.prototype.setPopup=function(t){if(this._popup&&(this._popup.remove(),this._popup=null,this._element.removeEventListener(\"keypress\",this._onKeyPress),this._originalTabIndex||this._element.removeAttribute(\"tabindex\")),t){if(!(\"offset\"in t.options)){var e=Math.sqrt(Math.pow(13.5,2)/2);t.options.offset=this._defaultMarker?{top:[0,0],\"top-left\":[0,0],\"top-right\":[0,0],bottom:[0,-38.1],\"bottom-left\":[e,-1*(24.6+e)],\"bottom-right\":[-e,-1*(24.6+e)],left:[13.5,-24.6],right:[-13.5,-24.6]}:this._offset}this._popup=t,this._lngLat&&this._popup.setLngLat(this._lngLat),this._originalTabIndex=this._element.getAttribute(\"tabindex\"),this._originalTabIndex||this._element.setAttribute(\"tabindex\",\"0\"),this._element.addEventListener(\"keypress\",this._onKeyPress)}return this},n.prototype._onKeyPress=function(t){var e=t.code,r=t.charCode||t.keyCode;\"Space\"!==e&&\"Enter\"!==e&&32!==r&&13!==r||this.togglePopup()},n.prototype._onMapClick=function(t){var e=t.originalEvent.target,r=this._element;this._popup&&(e===r||r.contains(e))&&this.togglePopup()},n.prototype.getPopup=function(){return this._popup},n.prototype.togglePopup=function(){var t=this._popup;return t?(t.isOpen()?t.remove():t.addTo(this._map),this):this},n.prototype._update=function(t){if(this._map){this._map.transform.renderWorldCopies&&(this._lngLat=Oi(this._lngLat,this._pos,this._map.transform)),this._pos=this._map.project(this._lngLat)._add(this._offset);var e=\"\";\"viewport\"===this._rotationAlignment||\"auto\"===this._rotationAlignment?e=\"rotateZ(\"+this._rotation+\"deg)\":\"map\"===this._rotationAlignment&&(e=\"rotateZ(\"+(this._rotation-this._map.getBearing())+\"deg)\");var n=\"\";\"viewport\"===this._pitchAlignment||\"auto\"===this._pitchAlignment?n=\"rotateX(0deg)\":\"map\"===this._pitchAlignment&&(n=\"rotateX(\"+this._map.getPitch()+\"deg)\"),t&&\"moveend\"!==t.type||(this._pos=this._pos.round()),r.setTransform(this._element,zi[this._anchor]+\" translate(\"+this._pos.x+\"px, \"+this._pos.y+\"px) \"+n+\" \"+e)}},n.prototype.getOffset=function(){return this._offset},n.prototype.setOffset=function(e){return this._offset=t.Point.convert(e),this._update(),this},n.prototype._onMove=function(e){this._pos=e.point.sub(this._positionDelta),this._lngLat=this._map.unproject(this._pos),this.setLngLat(this._lngLat),this._element.style.pointerEvents=\"none\",\"pending\"===this._state&&(this._state=\"active\",this.fire(new t.Event(\"dragstart\"))),this.fire(new t.Event(\"drag\"))},n.prototype._onUp=function(){this._element.style.pointerEvents=\"auto\",this._positionDelta=null,this._map.off(\"mousemove\",this._onMove),this._map.off(\"touchmove\",this._onMove),\"active\"===this._state&&this.fire(new t.Event(\"dragend\")),this._state=\"inactive\"},n.prototype._addDragHandler=function(t){this._element.contains(t.originalEvent.target)&&(t.preventDefault(),this._positionDelta=t.point.sub(this._pos).add(this._offset),this._state=\"pending\",this._map.on(\"mousemove\",this._onMove),this._map.on(\"touchmove\",this._onMove),this._map.once(\"mouseup\",this._onUp),this._map.once(\"touchend\",this._onUp))},n.prototype.setDraggable=function(t){return this._draggable=!!t,this._map&&(t?(this._map.on(\"mousedown\",this._addDragHandler),this._map.on(\"touchstart\",this._addDragHandler)):(this._map.off(\"mousedown\",this._addDragHandler),this._map.off(\"touchstart\",this._addDragHandler))),this},n.prototype.isDraggable=function(){return this._draggable},n.prototype.setRotation=function(t){return this._rotation=t||0,this._update(),this},n.prototype.getRotation=function(){return this._rotation},n.prototype.setRotationAlignment=function(t){return this._rotationAlignment=t||\"auto\",this._update(),this},n.prototype.getRotationAlignment=function(){return this._rotationAlignment},n.prototype.setPitchAlignment=function(t){return this._pitchAlignment=t&&\"auto\"!==t?t:this._rotationAlignment,this._update(),this},n.prototype.getPitchAlignment=function(){return this._pitchAlignment},n}(t.Evented),Bi={positionOptions:{enableHighAccuracy:!1,maximumAge:0,timeout:6e3},fitBoundsOptions:{maxZoom:15},trackUserLocation:!1,showAccuracyCircle:!0,showUserLocation:!0};var Ni=0,ji=!1,Ui=function(e){function n(r){e.call(this),this.options=t.extend({},Bi,r),t.bindAll([\"_onSuccess\",\"_onError\",\"_onZoom\",\"_finish\",\"_setupUI\",\"_updateCamera\",\"_updateMarker\"],this)}return e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n,n.prototype.onAdd=function(e){var n;return this._map=e,this._container=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-group\"),n=this._setupUI,void 0!==Ri?n(Ri):void 0!==t.window.navigator.permissions?t.window.navigator.permissions.query({name:\"geolocation\"}).then((function(t){Ri=\"denied\"!==t.state,n(Ri)})):(Ri=!!t.window.navigator.geolocation,n(Ri)),this._container},n.prototype.onRemove=function(){void 0!==this._geolocationWatchID&&(t.window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0),this.options.showUserLocation&&this._userLocationDotMarker&&this._userLocationDotMarker.remove(),this.options.showAccuracyCircle&&this._accuracyCircleMarker&&this._accuracyCircleMarker.remove(),r.remove(this._container),this._map.off(\"zoom\",this._onZoom),this._map=void 0,Ni=0,ji=!1},n.prototype._isOutOfMapMaxBounds=function(t){var e=this._map.getMaxBounds(),r=t.coords;return e&&(r.longitude<e.getWest()||r.longitude>e.getEast()||r.latitude<e.getSouth()||r.latitude>e.getNorth())},n.prototype._setErrorState=function(){switch(this._watchState){case\"WAITING_ACTIVE\":this._watchState=\"ACTIVE_ERROR\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active-error\");break;case\"ACTIVE_LOCK\":this._watchState=\"ACTIVE_ERROR\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\");break;case\"BACKGROUND\":this._watchState=\"BACKGROUND_ERROR\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\")}},n.prototype._onSuccess=function(e){if(this._map){if(this._isOutOfMapMaxBounds(e))return this._setErrorState(),this.fire(new t.Event(\"outofmaxbounds\",e)),this._updateMarker(),void this._finish();if(this.options.trackUserLocation)switch(this._lastKnownPosition=e,this._watchState){case\"WAITING_ACTIVE\":case\"ACTIVE_LOCK\":case\"ACTIVE_ERROR\":this._watchState=\"ACTIVE_LOCK\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active\");break;case\"BACKGROUND\":case\"BACKGROUND_ERROR\":this._watchState=\"BACKGROUND\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background\")}this.options.showUserLocation&&\"OFF\"!==this._watchState&&this._updateMarker(e),this.options.trackUserLocation&&\"ACTIVE_LOCK\"!==this._watchState||this._updateCamera(e),this.options.showUserLocation&&this._dotElement.classList.remove(\"mapboxgl-user-location-dot-stale\"),this.fire(new t.Event(\"geolocate\",e)),this._finish()}},n.prototype._updateCamera=function(e){var r=new t.LngLat(e.coords.longitude,e.coords.latitude),n=e.coords.accuracy,i=this._map.getBearing(),a=t.extend({bearing:i},this.options.fitBoundsOptions);this._map.fitBounds(r.toBounds(n),a,{geolocateSource:!0})},n.prototype._updateMarker=function(e){if(e){var r=new t.LngLat(e.coords.longitude,e.coords.latitude);this._accuracyCircleMarker.setLngLat(r).addTo(this._map),this._userLocationDotMarker.setLngLat(r).addTo(this._map),this._accuracy=e.coords.accuracy,this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()}else this._userLocationDotMarker.remove(),this._accuracyCircleMarker.remove()},n.prototype._updateCircleRadius=function(){var t=this._map._container.clientHeight/2,e=this._map.unproject([0,t]),r=this._map.unproject([1,t]),n=e.distanceTo(r),i=Math.ceil(2*this._accuracy/n);this._circleElement.style.width=i+\"px\",this._circleElement.style.height=i+\"px\"},n.prototype._onZoom=function(){this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()},n.prototype._onError=function(e){if(this._map){if(this.options.trackUserLocation)if(1===e.code){this._watchState=\"OFF\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background-error\"),this._geolocateButton.disabled=!0;var r=this._map._getUIString(\"GeolocateControl.LocationNotAvailable\");this._geolocateButton.title=r,this._geolocateButton.setAttribute(\"aria-label\",r),void 0!==this._geolocationWatchID&&this._clearWatch()}else{if(3===e.code&&ji)return;this._setErrorState()}\"OFF\"!==this._watchState&&this.options.showUserLocation&&this._dotElement.classList.add(\"mapboxgl-user-location-dot-stale\"),this.fire(new t.Event(\"error\",e)),this._finish()}},n.prototype._finish=function(){this._timeoutId&&clearTimeout(this._timeoutId),this._timeoutId=void 0},n.prototype._setupUI=function(e){var n=this;if(this._container.addEventListener(\"contextmenu\",(function(t){return t.preventDefault()})),this._geolocateButton=r.create(\"button\",\"mapboxgl-ctrl-geolocate\",this._container),r.create(\"span\",\"mapboxgl-ctrl-icon\",this._geolocateButton).setAttribute(\"aria-hidden\",!0),this._geolocateButton.type=\"button\",!1===e){t.warnOnce(\"Geolocation support is not available so the GeolocateControl will be disabled.\");var i=this._map._getUIString(\"GeolocateControl.LocationNotAvailable\");this._geolocateButton.disabled=!0,this._geolocateButton.title=i,this._geolocateButton.setAttribute(\"aria-label\",i)}else{var a=this._map._getUIString(\"GeolocateControl.FindMyLocation\");this._geolocateButton.title=a,this._geolocateButton.setAttribute(\"aria-label\",a)}this.options.trackUserLocation&&(this._geolocateButton.setAttribute(\"aria-pressed\",\"false\"),this._watchState=\"OFF\"),this.options.showUserLocation&&(this._dotElement=r.create(\"div\",\"mapboxgl-user-location-dot\"),this._userLocationDotMarker=new Fi(this._dotElement),this._circleElement=r.create(\"div\",\"mapboxgl-user-location-accuracy-circle\"),this._accuracyCircleMarker=new Fi({element:this._circleElement,pitchAlignment:\"map\"}),this.options.trackUserLocation&&(this._watchState=\"OFF\"),this._map.on(\"zoom\",this._onZoom)),this._geolocateButton.addEventListener(\"click\",this.trigger.bind(this)),this._setup=!0,this.options.trackUserLocation&&this._map.on(\"movestart\",(function(e){var r=e.originalEvent&&\"resize\"===e.originalEvent.type;e.geolocateSource||\"ACTIVE_LOCK\"!==n._watchState||r||(n._watchState=\"BACKGROUND\",n._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background\"),n._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),n.fire(new t.Event(\"trackuserlocationend\")))}))},n.prototype.trigger=function(){if(!this._setup)return t.warnOnce(\"Geolocate control triggered before added to a map\"),!1;if(this.options.trackUserLocation){switch(this._watchState){case\"OFF\":this._watchState=\"WAITING_ACTIVE\",this.fire(new t.Event(\"trackuserlocationstart\"));break;case\"WAITING_ACTIVE\":case\"ACTIVE_LOCK\":case\"ACTIVE_ERROR\":case\"BACKGROUND_ERROR\":Ni--,ji=!1,this._watchState=\"OFF\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background-error\"),this.fire(new t.Event(\"trackuserlocationend\"));break;case\"BACKGROUND\":this._watchState=\"ACTIVE_LOCK\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._lastKnownPosition&&this._updateCamera(this._lastKnownPosition),this.fire(new t.Event(\"trackuserlocationstart\"))}switch(this._watchState){case\"WAITING_ACTIVE\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active\");break;case\"ACTIVE_LOCK\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active\");break;case\"ACTIVE_ERROR\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active-error\");break;case\"BACKGROUND\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background\");break;case\"BACKGROUND_ERROR\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background-error\")}if(\"OFF\"===this._watchState&&void 0!==this._geolocationWatchID)this._clearWatch();else if(void 0===this._geolocationWatchID){var e;this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.setAttribute(\"aria-pressed\",\"true\"),++Ni>1?(e={maximumAge:6e5,timeout:0},ji=!0):(e=this.options.positionOptions,ji=!1),this._geolocationWatchID=t.window.navigator.geolocation.watchPosition(this._onSuccess,this._onError,e)}}else t.window.navigator.geolocation.getCurrentPosition(this._onSuccess,this._onError,this.options.positionOptions),this._timeoutId=setTimeout(this._finish,1e4);return!0},n.prototype._clearWatch=function(){t.window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0,this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.setAttribute(\"aria-pressed\",\"false\"),this.options.showUserLocation&&this._updateMarker(null)},n}(t.Evented),Vi={maxWidth:100,unit:\"metric\"},qi=function(e){this.options=t.extend({},Vi,e),t.bindAll([\"_onMove\",\"setUnit\"],this)};function Hi(t,e,r){var n=r&&r.maxWidth||100,i=t._container.clientHeight/2,a=t.unproject([0,i]),o=t.unproject([n,i]),s=a.distanceTo(o);if(r&&\"imperial\"===r.unit){var l=3.2808*s;if(l>5280)Gi(e,n,l/5280,t._getUIString(\"ScaleControl.Miles\"));else Gi(e,n,l,t._getUIString(\"ScaleControl.Feet\"))}else if(r&&\"nautical\"===r.unit){Gi(e,n,s/1852,t._getUIString(\"ScaleControl.NauticalMiles\"))}else s>=1e3?Gi(e,n,s/1e3,t._getUIString(\"ScaleControl.Kilometers\")):Gi(e,n,s,t._getUIString(\"ScaleControl.Meters\"))}function Gi(t,e,r,n){var i,a,o,s=(i=r,a=Math.pow(10,(\"\"+Math.floor(i)).length-1),o=(o=i/a)>=10?10:o>=5?5:o>=3?3:o>=2?2:o>=1?1:function(t){var e=Math.pow(10,Math.ceil(-Math.log(t)/Math.LN10));return Math.round(t*e)/e}(o),a*o),l=s/r;t.style.width=e*l+\"px\",t.innerHTML=s+\"&nbsp;\"+n}qi.prototype.getDefaultPosition=function(){return\"bottom-left\"},qi.prototype._onMove=function(){Hi(this._map,this._container,this.options)},qi.prototype.onAdd=function(t){return this._map=t,this._container=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-scale\",t.getContainer()),this._map.on(\"move\",this._onMove),this._onMove(),this._container},qi.prototype.onRemove=function(){r.remove(this._container),this._map.off(\"move\",this._onMove),this._map=void 0},qi.prototype.setUnit=function(t){this.options.unit=t,Hi(this._map,this._container,this.options)};var Yi=function(e){this._fullscreen=!1,e&&e.container&&(e.container instanceof t.window.HTMLElement?this._container=e.container:t.warnOnce(\"Full screen control 'container' must be a DOM element.\")),t.bindAll([\"_onClickFullscreen\",\"_changeIcon\"],this),\"onfullscreenchange\"in t.window.document?this._fullscreenchange=\"fullscreenchange\":\"onmozfullscreenchange\"in t.window.document?this._fullscreenchange=\"mozfullscreenchange\":\"onwebkitfullscreenchange\"in t.window.document?this._fullscreenchange=\"webkitfullscreenchange\":\"onmsfullscreenchange\"in t.window.document&&(this._fullscreenchange=\"MSFullscreenChange\")};Yi.prototype.onAdd=function(e){return this._map=e,this._container||(this._container=this._map.getContainer()),this._controlContainer=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-group\"),this._checkFullscreenSupport()?this._setupUI():(this._controlContainer.style.display=\"none\",t.warnOnce(\"This device does not support fullscreen mode.\")),this._controlContainer},Yi.prototype.onRemove=function(){r.remove(this._controlContainer),this._map=null,t.window.document.removeEventListener(this._fullscreenchange,this._changeIcon)},Yi.prototype._checkFullscreenSupport=function(){return!!(t.window.document.fullscreenEnabled||t.window.document.mozFullScreenEnabled||t.window.document.msFullscreenEnabled||t.window.document.webkitFullscreenEnabled)},Yi.prototype._setupUI=function(){var e=this._fullscreenButton=r.create(\"button\",\"mapboxgl-ctrl-fullscreen\",this._controlContainer);r.create(\"span\",\"mapboxgl-ctrl-icon\",e).setAttribute(\"aria-hidden\",!0),e.type=\"button\",this._updateTitle(),this._fullscreenButton.addEventListener(\"click\",this._onClickFullscreen),t.window.document.addEventListener(this._fullscreenchange,this._changeIcon)},Yi.prototype._updateTitle=function(){var t=this._getTitle();this._fullscreenButton.setAttribute(\"aria-label\",t),this._fullscreenButton.title=t},Yi.prototype._getTitle=function(){return this._map._getUIString(this._isFullscreen()?\"FullscreenControl.Exit\":\"FullscreenControl.Enter\")},Yi.prototype._isFullscreen=function(){return this._fullscreen},Yi.prototype._changeIcon=function(){(t.window.document.fullscreenElement||t.window.document.mozFullScreenElement||t.window.document.webkitFullscreenElement||t.window.document.msFullscreenElement)===this._container!==this._fullscreen&&(this._fullscreen=!this._fullscreen,this._fullscreenButton.classList.toggle(\"mapboxgl-ctrl-shrink\"),this._fullscreenButton.classList.toggle(\"mapboxgl-ctrl-fullscreen\"),this._updateTitle())},Yi.prototype._onClickFullscreen=function(){this._isFullscreen()?t.window.document.exitFullscreen?t.window.document.exitFullscreen():t.window.document.mozCancelFullScreen?t.window.document.mozCancelFullScreen():t.window.document.msExitFullscreen?t.window.document.msExitFullscreen():t.window.document.webkitCancelFullScreen&&t.window.document.webkitCancelFullScreen():this._container.requestFullscreen?this._container.requestFullscreen():this._container.mozRequestFullScreen?this._container.mozRequestFullScreen():this._container.msRequestFullscreen?this._container.msRequestFullscreen():this._container.webkitRequestFullscreen&&this._container.webkitRequestFullscreen()};var Wi={closeButton:!0,closeOnClick:!0,className:\"\",maxWidth:\"240px\"},Xi=function(e){function n(r){e.call(this),this.options=t.extend(Object.create(Wi),r),t.bindAll([\"_update\",\"_onClose\",\"remove\",\"_onMouseMove\",\"_onMouseUp\",\"_onDrag\"],this)}return e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n,n.prototype.addTo=function(e){return this._map&&this.remove(),this._map=e,this.options.closeOnClick&&this._map.on(\"click\",this._onClose),this.options.closeOnMove&&this._map.on(\"move\",this._onClose),this._map.on(\"remove\",this.remove),this._update(),this._trackPointer?(this._map.on(\"mousemove\",this._onMouseMove),this._map.on(\"mouseup\",this._onMouseUp),this._container&&this._container.classList.add(\"mapboxgl-popup-track-pointer\"),this._map._canvasContainer.classList.add(\"mapboxgl-track-pointer\")):this._map.on(\"move\",this._update),this.fire(new t.Event(\"open\")),this},n.prototype.isOpen=function(){return!!this._map},n.prototype.remove=function(){return this._content&&r.remove(this._content),this._container&&(r.remove(this._container),delete this._container),this._map&&(this._map.off(\"move\",this._update),this._map.off(\"move\",this._onClose),this._map.off(\"click\",this._onClose),this._map.off(\"remove\",this.remove),this._map.off(\"mousemove\",this._onMouseMove),this._map.off(\"mouseup\",this._onMouseUp),this._map.off(\"drag\",this._onDrag),delete this._map),this.fire(new t.Event(\"close\")),this},n.prototype.getLngLat=function(){return this._lngLat},n.prototype.setLngLat=function(e){return this._lngLat=t.LngLat.convert(e),this._pos=null,this._trackPointer=!1,this._update(),this._map&&(this._map.on(\"move\",this._update),this._map.off(\"mousemove\",this._onMouseMove),this._container&&this._container.classList.remove(\"mapboxgl-popup-track-pointer\"),this._map._canvasContainer.classList.remove(\"mapboxgl-track-pointer\")),this},n.prototype.trackPointer=function(){return this._trackPointer=!0,this._pos=null,this._update(),this._map&&(this._map.off(\"move\",this._update),this._map.on(\"mousemove\",this._onMouseMove),this._map.on(\"drag\",this._onDrag),this._container&&this._container.classList.add(\"mapboxgl-popup-track-pointer\"),this._map._canvasContainer.classList.add(\"mapboxgl-track-pointer\")),this},n.prototype.getElement=function(){return this._container},n.prototype.setText=function(e){return this.setDOMContent(t.window.document.createTextNode(e))},n.prototype.setHTML=function(e){var r,n=t.window.document.createDocumentFragment(),i=t.window.document.createElement(\"body\");for(i.innerHTML=e;r=i.firstChild;)n.appendChild(r);return this.setDOMContent(n)},n.prototype.getMaxWidth=function(){return this._container&&this._container.style.maxWidth},n.prototype.setMaxWidth=function(t){return this.options.maxWidth=t,this._update(),this},n.prototype.setDOMContent=function(t){return this._createContent(),this._content.appendChild(t),this._update(),this},n.prototype.addClassName=function(t){this._container&&this._container.classList.add(t)},n.prototype.removeClassName=function(t){this._container&&this._container.classList.remove(t)},n.prototype.toggleClassName=function(t){if(this._container)return this._container.classList.toggle(t)},n.prototype._createContent=function(){this._content&&r.remove(this._content),this._content=r.create(\"div\",\"mapboxgl-popup-content\",this._container),this.options.closeButton&&(this._closeButton=r.create(\"button\",\"mapboxgl-popup-close-button\",this._content),this._closeButton.type=\"button\",this._closeButton.setAttribute(\"aria-label\",\"Close popup\"),this._closeButton.innerHTML=\"&#215;\",this._closeButton.addEventListener(\"click\",this._onClose))},n.prototype._onMouseUp=function(t){this._update(t.point)},n.prototype._onMouseMove=function(t){this._update(t.point)},n.prototype._onDrag=function(t){this._update(t.point)},n.prototype._update=function(e){var n=this,i=this._lngLat||this._trackPointer;if(this._map&&i&&this._content&&(this._container||(this._container=r.create(\"div\",\"mapboxgl-popup\",this._map.getContainer()),this._tip=r.create(\"div\",\"mapboxgl-popup-tip\",this._container),this._container.appendChild(this._content),this.options.className&&this.options.className.split(\" \").forEach((function(t){return n._container.classList.add(t)})),this._trackPointer&&this._container.classList.add(\"mapboxgl-popup-track-pointer\")),this.options.maxWidth&&this._container.style.maxWidth!==this.options.maxWidth&&(this._container.style.maxWidth=this.options.maxWidth),this._map.transform.renderWorldCopies&&!this._trackPointer&&(this._lngLat=Oi(this._lngLat,this._pos,this._map.transform)),!this._trackPointer||e)){var a=this._pos=this._trackPointer&&e?e:this._map.project(this._lngLat),o=this.options.anchor,s=function e(r){if(r){if(\"number\"==typeof r){var n=Math.round(Math.sqrt(.5*Math.pow(r,2)));return{center:new t.Point(0,0),top:new t.Point(0,r),\"top-left\":new t.Point(n,n),\"top-right\":new t.Point(-n,n),bottom:new t.Point(0,-r),\"bottom-left\":new t.Point(n,-n),\"bottom-right\":new t.Point(-n,-n),left:new t.Point(r,0),right:new t.Point(-r,0)}}if(r instanceof t.Point||Array.isArray(r)){var i=t.Point.convert(r);return{center:i,top:i,\"top-left\":i,\"top-right\":i,bottom:i,\"bottom-left\":i,\"bottom-right\":i,left:i,right:i}}return{center:t.Point.convert(r.center||[0,0]),top:t.Point.convert(r.top||[0,0]),\"top-left\":t.Point.convert(r[\"top-left\"]||[0,0]),\"top-right\":t.Point.convert(r[\"top-right\"]||[0,0]),bottom:t.Point.convert(r.bottom||[0,0]),\"bottom-left\":t.Point.convert(r[\"bottom-left\"]||[0,0]),\"bottom-right\":t.Point.convert(r[\"bottom-right\"]||[0,0]),left:t.Point.convert(r.left||[0,0]),right:t.Point.convert(r.right||[0,0])}}return e(new t.Point(0,0))}(this.options.offset);if(!o){var l,c=this._container.offsetWidth,u=this._container.offsetHeight;l=a.y+s.bottom.y<u?[\"top\"]:a.y>this._map.transform.height-u?[\"bottom\"]:[],a.x<c/2?l.push(\"left\"):a.x>this._map.transform.width-c/2&&l.push(\"right\"),o=0===l.length?\"bottom\":l.join(\"-\")}var f=a.add(s[o]).round();r.setTransform(this._container,zi[o]+\" translate(\"+f.x+\"px,\"+f.y+\"px)\"),Di(this._container,o,\"popup\")}},n.prototype._onClose=function(){this.remove()},n}(t.Evented);var Zi={version:t.version,supported:e,setRTLTextPlugin:t.setRTLTextPlugin,getRTLTextPluginStatus:t.getRTLTextPluginStatus,Map:Ei,NavigationControl:Pi,GeolocateControl:Ui,AttributionControl:bi,ScaleControl:qi,FullscreenControl:Yi,Popup:Xi,Marker:Fi,Style:He,LngLat:t.LngLat,LngLatBounds:t.LngLatBounds,Point:t.Point,MercatorCoordinate:t.MercatorCoordinate,Evented:t.Evented,config:t.config,prewarm:function(){Bt().acquire(zt)},clearPrewarmedResources:function(){var t=Rt;t&&(t.isPreloaded()&&1===t.numActive()?(t.release(zt),Rt=null):console.warn(\"Could not clear WebWorkers since there are active Map instances that still reference it. The pre-warmed WebWorker pool can only be cleared when all map instances have been removed with map.remove()\"))},get accessToken(){return t.config.ACCESS_TOKEN},set accessToken(e){t.config.ACCESS_TOKEN=e},get baseApiUrl(){return t.config.API_URL},set baseApiUrl(e){t.config.API_URL=e},get workerCount(){return Dt.workerCount},set workerCount(t){Dt.workerCount=t},get maxParallelImageRequests(){return t.config.MAX_PARALLEL_IMAGE_REQUESTS},set maxParallelImageRequests(e){t.config.MAX_PARALLEL_IMAGE_REQUESTS=e},clearStorage:function(e){t.clearTileCache(e)},workerUrl:\"\"};return Zi})),r}))},{}],458:[function(t,e,r){\"use strict\";e.exports=function(t){for(var e=1<<t+1,r=new Array(e),n=0;n<e;++n)r[n]=a(t,n);return r};var n=t(\"convex-hull\");function i(t,e,r){for(var n=new Array(t),i=0;i<t;++i)n[i]=0,i===e&&(n[i]+=.5),i===r&&(n[i]+=.5);return n}function a(t,e){if(0===e||e===(1<<t+1)-1)return[];for(var r=[],a=[],o=0;o<=t;++o)if(e&1<<o){r.push(i(t,o-1,o-1)),a.push(null);for(var s=0;s<=t;++s)~e&1<<s&&(r.push(i(t,o-1,s-1)),a.push([o,s]))}var l=n(r),c=[];t:for(o=0;o<l.length;++o){var u=l[o],f=[];for(s=0;s<u.length;++s){if(!a[u[s]])continue t;f.push(a[u[s]].slice())}c.push(f)}return c}},{\"convex-hull\":141}],459:[function(t,e,r){var n=t(\"./normalize\"),i=t(\"gl-mat4/create\"),a=t(\"gl-mat4/clone\"),o=t(\"gl-mat4/determinant\"),s=t(\"gl-mat4/invert\"),l=t(\"gl-mat4/transpose\"),c={length:t(\"gl-vec3/length\"),normalize:t(\"gl-vec3/normalize\"),dot:t(\"gl-vec3/dot\"),cross:t(\"gl-vec3/cross\")},u=i(),f=i(),h=[0,0,0,0],p=[[0,0,0],[0,0,0],[0,0,0]],d=[0,0,0];function m(t,e,r,n,i){t[0]=e[0]*n+r[0]*i,t[1]=e[1]*n+r[1]*i,t[2]=e[2]*n+r[2]*i}e.exports=function(t,e,r,i,g,v){if(e||(e=[0,0,0]),r||(r=[0,0,0]),i||(i=[0,0,0]),g||(g=[0,0,0,1]),v||(v=[0,0,0,1]),!n(u,t))return!1;if(a(f,u),f[3]=0,f[7]=0,f[11]=0,f[15]=1,Math.abs(o(f)<1e-8))return!1;var y,x,b,_,w,T,k,M=u[3],A=u[7],S=u[11],E=u[12],L=u[13],C=u[14],P=u[15];if(0!==M||0!==A||0!==S){if(h[0]=M,h[1]=A,h[2]=S,h[3]=P,!s(f,f))return!1;l(f,f),y=g,b=f,_=(x=h)[0],w=x[1],T=x[2],k=x[3],y[0]=b[0]*_+b[4]*w+b[8]*T+b[12]*k,y[1]=b[1]*_+b[5]*w+b[9]*T+b[13]*k,y[2]=b[2]*_+b[6]*w+b[10]*T+b[14]*k,y[3]=b[3]*_+b[7]*w+b[11]*T+b[15]*k}else g[0]=g[1]=g[2]=0,g[3]=1;if(e[0]=E,e[1]=L,e[2]=C,function(t,e){t[0][0]=e[0],t[0][1]=e[1],t[0][2]=e[2],t[1][0]=e[4],t[1][1]=e[5],t[1][2]=e[6],t[2][0]=e[8],t[2][1]=e[9],t[2][2]=e[10]}(p,u),r[0]=c.length(p[0]),c.normalize(p[0],p[0]),i[0]=c.dot(p[0],p[1]),m(p[1],p[1],p[0],1,-i[0]),r[1]=c.length(p[1]),c.normalize(p[1],p[1]),i[0]/=r[1],i[1]=c.dot(p[0],p[2]),m(p[2],p[2],p[0],1,-i[1]),i[2]=c.dot(p[1],p[2]),m(p[2],p[2],p[1],1,-i[2]),r[2]=c.length(p[2]),c.normalize(p[2],p[2]),i[1]/=r[2],i[2]/=r[2],c.cross(d,p[1],p[2]),c.dot(p[0],d)<0)for(var I=0;I<3;I++)r[I]*=-1,p[I][0]*=-1,p[I][1]*=-1,p[I][2]*=-1;return v[0]=.5*Math.sqrt(Math.max(1+p[0][0]-p[1][1]-p[2][2],0)),v[1]=.5*Math.sqrt(Math.max(1-p[0][0]+p[1][1]-p[2][2],0)),v[2]=.5*Math.sqrt(Math.max(1-p[0][0]-p[1][1]+p[2][2],0)),v[3]=.5*Math.sqrt(Math.max(1+p[0][0]+p[1][1]+p[2][2],0)),p[2][1]>p[1][2]&&(v[0]=-v[0]),p[0][2]>p[2][0]&&(v[1]=-v[1]),p[1][0]>p[0][1]&&(v[2]=-v[2]),!0}},{\"./normalize\":460,\"gl-mat4/clone\":280,\"gl-mat4/create\":282,\"gl-mat4/determinant\":283,\"gl-mat4/invert\":295,\"gl-mat4/transpose\":308,\"gl-vec3/cross\":364,\"gl-vec3/dot\":369,\"gl-vec3/length\":379,\"gl-vec3/normalize\":386}],460:[function(t,e,r){e.exports=function(t,e){var r=e[15];if(0===r)return!1;for(var n=1/r,i=0;i<16;i++)t[i]=e[i]*n;return!0}},{}],461:[function(t,e,r){var n=t(\"gl-vec3/lerp\"),i=t(\"mat4-recompose\"),a=t(\"mat4-decompose\"),o=t(\"gl-mat4/determinant\"),s=t(\"quat-slerp\"),l=f(),c=f(),u=f();function f(){return{translate:h(),scale:h(1),skew:h(),perspective:[0,0,0,1],quaternion:[0,0,0,1]}}function 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n.identity(t),n.fromRotationTranslation(t,s,e),t[3]=o[0],t[7]=o[1],t[11]=o[2],t[15]=o[3],n.identity(i),0!==a[2]&&(i[9]=a[2],n.multiply(t,t,i)),0!==a[1]&&(i[9]=0,i[8]=a[1],n.multiply(t,t,i)),0!==a[0]&&(i[8]=0,i[4]=a[0],n.multiply(t,t,i)),n.scale(t,t,r),t}},{\"gl-mat4/create\":282,\"gl-mat4/fromRotationTranslation\":286,\"gl-mat4/identity\":293,\"gl-mat4/multiply\":297,\"gl-mat4/scale\":305,\"gl-mat4/translate\":307}],463:[function(t,e,r){\"use strict\";e.exports=Math.log2||function(t){return Math.log(t)*Math.LOG2E}},{}],464:[function(t,e,r){\"use strict\";var n=t(\"binary-search-bounds\"),i=t(\"mat4-interpolate\"),a=t(\"gl-mat4/invert\"),o=t(\"gl-mat4/rotateX\"),s=t(\"gl-mat4/rotateY\"),l=t(\"gl-mat4/rotateZ\"),c=t(\"gl-mat4/lookAt\"),u=t(\"gl-mat4/translate\"),f=(t(\"gl-mat4/scale\"),t(\"gl-vec3/normalize\")),h=[0,0,0];function 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-       "/*!\n",
-       " * pad-left <https://github.com/jonschlinkert/pad-left>\n",
-       " *\n",
-       " * Copyright (c) 2014-2015, Jon Schlinkert.\n",
-       " * Licensed under the MIT license.\n",
-       " */\n",
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-       "/*\n",
-       " * @copyright 2016 Sean Connelly (@voidqk), http://syntheti.cc\n",
-       " * @license MIT\n",
-       " * @preserve Project Home: https://github.com/voidqk/polybooljs\n",
-       " */\n",
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f=e.linesIntersect(o,s,c,u);if(!1===f){if(!e.pointsCollinear(o,s,c))return!1;if(e.pointsSame(o,u)||e.pointsSame(s,c))return!1;var h=e.pointsSame(o,c),p=e.pointsSame(s,u);if(h&&p)return n;var d=!h&&e.pointBetween(o,c,u),m=!p&&e.pointBetween(s,c,u);if(h)return m?l(n,s):l(t,u),n;d&&(p||(m?l(n,s):l(t,u)),l(n,o))}else 0===f.alongA&&(-1===f.alongB?l(t,c):0===f.alongB?l(t,f.pt):1===f.alongB&&l(t,u)),0===f.alongB&&(-1===f.alongA?l(n,o):0===f.alongA?l(n,f.pt):1===f.alongA&&l(n,s));return!1}for(var f=[];!a.isEmpty();){var h=a.getHead();if(r&&r.vert(h.pt[0]),h.isStart){r&&r.segmentNew(h.seg,h.primary);var p=c(h),d=p.before?p.before.ev:null,m=p.after?p.after.ev:null;function g(){if(d){var t=u(h,d);if(t)return t}return!!m&&u(h,m)}r&&r.tempStatus(h.seg,!!d&&d.seg,!!m&&m.seg);var v,y=g();if(y){var x;if(t)(x=null===h.seg.myFill.below||h.seg.myFill.above!==h.seg.myFill.below)&&(y.seg.myFill.above=!y.seg.myFill.above);else 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p=s.index,d=s.matches_pt1?o:a,m=s.matches_head,g=n[p],v=m?g[0]:g[g.length-1],y=m?g[1]:g[g.length-2],x=m?g[g.length-1]:g[0],b=m?g[g.length-2]:g[1];return e.pointsCollinear(y,v,d)&&(m?(r&&r.chainRemoveHead(s.index,d),g.shift()):(r&&r.chainRemoveTail(s.index,d),g.pop()),v=y),e.pointsSame(x,d)?(n.splice(p,1),e.pointsCollinear(b,x,v)&&(m?(r&&r.chainRemoveTail(s.index,v),g.pop()):(r&&r.chainRemoveHead(s.index,v),g.shift())),r&&r.chainClose(s.index),void i.push(g)):void(m?(r&&r.chainAddHead(s.index,d),g.unshift(d)):(r&&r.chainAddTail(s.index,d),g.push(d)))}var _=s.index,w=l.index;r&&r.chainConnect(_,w);var T=n[_].length<n[w].length;s.matches_head?l.matches_head?T?(M(_),A(_,w)):(M(w),A(w,_)):A(w,_):l.matches_head?A(_,w):T?(M(_),A(w,_)):(M(w),A(_,w))}function k(t,e,r){return c.index=t,c.matches_head=e,c.matches_pt1=r,c===s?(c=l,!1):(c=null,!0)}function M(t){r&&r.chainReverse(t),n[t].reverse()}function A(t,i){var a=n[t],o=n[i],s=a[a.length-1],l=a[a.length-2],c=o[0],u=o[1];e.pointsCollinear(l,s,c)&&(r&&r.chainRemoveTail(t,s),a.pop(),s=l),e.pointsCollinear(s,c,u)&&(r&&r.chainRemoveHead(i,c),o.shift()),r&&r.chainJoin(t,i),n[t]=a.concat(o),n.splice(i,1)}})),i}},{}],507:[function(t,e,r){function n(t,e,r){var n=[];return t.forEach((function(t){var i=(t.myFill.above?8:0)+(t.myFill.below?4:0)+(t.otherFill&&t.otherFill.above?2:0)+(t.otherFill&&t.otherFill.below?1:0);0!==e[i]&&n.push({id:r?r.segmentId():-1,start:t.start,end:t.end,myFill:{above:1===e[i],below:2===e[i]},otherFill:null})})),r&&r.selected(n),n}var i={union:function(t,e){return n(t,[0,2,1,0,2,2,0,0,1,0,1,0,0,0,0,0],e)},intersect:function(t,e){return n(t,[0,0,0,0,0,2,0,2,0,0,1,1,0,2,1,0],e)},difference:function(t,e){return n(t,[0,0,0,0,2,0,2,0,1,1,0,0,0,1,2,0],e)},differenceRev:function(t,e){return n(t,[0,2,1,0,0,0,1,1,0,2,0,2,0,0,0,0],e)},xor:function(t,e){return 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n=e,i=0;i<r.length;)if(t[n++]!==r[i++])return!1;return!0},r.str2arr=function(t,e){var r=[],n=0;if(e&&\"hex\"===e)for(;n<t.length;)r.push(parseInt(t.slice(n,n+2),16)),n+=2;else for(;n<t.length;n++)r.push(255&t.charCodeAt(n));return r},r.readUInt16LE=function(t,e){return t[e]|t[e+1]<<8},r.readUInt16BE=function(t,e){return t[e+1]|t[e]<<8},r.readUInt32LE=function(t,e){return t[e]|t[e+1]<<8|t[e+2]<<16|16777216*t[e+3]},r.readUInt32BE=function(t,e){return t[e+3]|t[e+2]<<8|t[e+1]<<16|16777216*t[e]},o.prototype=Object.create(Error.prototype),o.prototype.constructor=o,r.ProbeError=o},{stream:565,\"stream-parser\":581}],510:[function(t,e,r){\"use strict\";function n(t,e){var r=new Error(t);return r.code=e,r}function i(t){try{return decodeURIComponent(escape(t))}catch(e){return t}}function a(t,e,r){this.input=t.subarray(e,r),this.start=e;var i=String.fromCharCode.apply(null,this.input.subarray(0,4));if(\"II*\\0\"!==i&&\"MM\\0*\"!==i)throw n(\"invalid TIFF signature\",\"EBADDATA\");this.big_endian=\"M\"===i[0]}a.prototype.each=function(t){this.aborted=!1;var e=this.read_uint32(4);for(this.ifds_to_read=[{id:0,offset:e}];this.ifds_to_read.length>0&&!this.aborted;){var r=this.ifds_to_read.shift();r.offset&&this.scan_ifd(r.id,r.offset,t)}},a.prototype.read_uint16=function(t){var e=this.input;if(t+2>e.length)throw n(\"unexpected EOF\",\"EBADDATA\");return this.big_endian?256*e[t]+e[t+1]:e[t]+256*e[t+1]},a.prototype.read_uint32=function(t){var e=this.input;if(t+4>e.length)throw n(\"unexpected EOF\",\"EBADDATA\");return this.big_endian?16777216*e[t]+65536*e[t+1]+256*e[t+2]+e[t+3]:e[t]+256*e[t+1]+65536*e[t+2]+16777216*e[t+3]},a.prototype.is_subifd_link=function(t,e){return 0===t&&34665===e||0===t&&34853===e||34665===t&&40965===e},a.prototype.exif_format_length=function(t){switch(t){case 1:case 2:case 6:case 7:return 1;case 3:case 8:return 2;case 4:case 9:case 11:return 4;case 5:case 10:case 12:return 8;default:return 0}},a.prototype.exif_format_read=function(t,e){var r;switch(t){case 1:case 2:return r=this.input[e];case 6:return(r=this.input[e])|33554430*(128&r);case 3:return r=this.read_uint16(e);case 8:return(r=this.read_uint16(e))|131070*(32768&r);case 4:return r=this.read_uint32(e);case 9:return 0|(r=this.read_uint32(e));case 5:case 10:case 11:case 12:case 7:default:return null}},a.prototype.scan_ifd=function(t,e,r){var a=this.read_uint16(e);e+=2;for(var o=0;o<a;o++){var s=this.read_uint16(e),l=this.read_uint16(e+2),c=this.read_uint32(e+4),u=this.exif_format_length(l),f=c*u,h=f<=4?e+8:this.read_uint32(e+8),p=!1;if(h+f>this.input.length)throw n(\"unexpected EOF\",\"EBADDATA\");for(var d=[],m=h,g=0;g<c;g++,m+=u){var v=this.exif_format_read(l,m);if(null===v){d=null;break}d.push(v)}if(Array.isArray(d)&&2===l&&(d=i(String.fromCharCode.apply(null,d)))&&\"\\0\"===d[d.length-1]&&(d=d.slice(0,-1)),this.is_subifd_link(t,s)&&Array.isArray(d)&&Number.isInteger(d[0])&&d[0]>0&&(this.ifds_to_read.push({id:s,offset:d[0]}),p=!0),!1===r({is_big_endian:this.big_endian,ifd:t,tag:s,format:l,count:c,entry_offset:e+this.start,data_length:f,data_offset:h+this.start,value:d,is_subifd_link:p}))return void(this.aborted=!0);e+=12}0===t&&this.ifds_to_read.push({id:1,offset:this.read_uint32(e)})},e.exports.ExifParser=a,e.exports.get_orientation=function(t){var e=0;try{return new a(t,0,t.length).each((function(t){if(0===t.ifd&&274===t.tag&&Array.isArray(t.value))return e=t.value[0],!1})),e}catch(t){return-1}}},{}],511:[function(t,e,r){\"use strict\";var n=t(\"./common\").readUInt16BE,i=t(\"./common\").readUInt32BE;function a(t,e){if(t.length<4+e)return null;var r=i(t,e);return t.length<r+e||r<8?null:{boxtype:String.fromCharCode.apply(null,t.slice(e+4,e+8)),data:t.slice(e+8,e+r),end:e+r}}function o(t,e){for(var r=0;;){var n=a(t,r);if(!n)break;switch(n.boxtype){case\"ispe\":e.sizes.push({width:i(n.data,4),height:i(n.data,8)});break;case\"irot\":e.transforms.push({type:\"irot\",value:3&n.data[0]});break;case\"imir\":e.transforms.push({type:\"imir\",value:1&n.data[0]})}r=n.end}}function s(t,e,r){for(var n=0,i=0;i<r;i++)n=256*n+(t[e+i]||0);return n}function l(t,e){for(var r=t[4]>>4&15,i=15&t[4],a=t[5]>>4&15,o=n(t,6),l=8,c=0;c<o;c++){var u=n(t,l),f=n(t,l+=2),h=s(t,l+=2,a),p=n(t,l+=a);if(l+=2,0===f&&1===p){var d=s(t,l,r),m=s(t,l+r,i);e.item_loc[u]={length:m,offset:d+h}}l+=p*(r+i)}}function c(t,e){for(var r=n(t,4),i=6,o=0;o<r;o++){var s=a(t,i);if(!s)break;if(\"infe\"===s.boxtype){for(var l=n(s.data,4),c=\"\",u=8;u<s.data.length&&s.data[u];u++)c+=String.fromCharCode(s.data[u]);e.item_inf[c]=l}i=s.end}}function u(t,e){for(var r=0;;){var n=a(t,r);if(!n)break;\"ipco\"===n.boxtype&&o(n.data,e),r=n.end}}e.exports.unbox=a,e.exports.readSizeFromMeta=function(t){var e={sizes:[],transforms:[],item_inf:{},item_loc:{}};if(function(t,e){for(var r=4;;){var n=a(t,r);if(!n)break;\"iprp\"===n.boxtype&&u(n.data,e),\"iloc\"===n.boxtype&&l(n.data,e),\"iinf\"===n.boxtype&&c(n.data,e),r=n.end}}(t,e),e.sizes.length){var r,n,i,o=(r=e.sizes,n=r.reduce((function(t,e){return t.width>e.width||t.width===e.width&&t.height>e.height?t:e})),i=r.reduce((function(t,e){return t.height>e.height||t.height===e.height&&t.width>e.width?t:e})),n.width>i.height||n.width===i.height&&n.height>i.width?n:i),s=1;e.transforms.forEach((function(t){var e={1:6,2:5,3:8,4:7,5:4,6:3,7:2,8:1},r={1:4,2:3,3:2,4:1,5:6,6:5,7:8,8:7};if(\"imir\"===t.type&&(s=0===t.value?r[s]:e[s=e[s=r[s]]]),\"irot\"===t.type)for(var n=0;n<t.value;n++)s=e[s]}));var f=null;return e.item_inf.Exif&&(f=e.item_loc[e.item_inf.Exif]),{width:o.width,height:o.height,orientation:e.transforms.length?s:null,variants:e.sizes,exif_location:f}}},e.exports.getMimeType=function(t){var e=String.fromCharCode.apply(null,t.slice(0,4)),r={};r[e]=!0;for(var n=8;n<t.length;n+=4)r[String.fromCharCode.apply(null,t.slice(n,n+4))]=!0;if(r.mif1||r.msf1||r.miaf)return\"avif\"===e||\"avis\"===e||\"avio\"===e?{type:\"avif\",mime:\"image/avif\"}:\"heic\"===e||\"heix\"===e?{type:\"heic\",mime:\"image/heic\"}:\"hevc\"===e||\"hevx\"===e?{type:\"heic\",mime:\"image/heic-sequence\"}:r.avif||r.avis?{type:\"avif\",mime:\"image/avif\"}:r.heic||r.heix||r.hevc||r.hevx||r.heis?r.msf1?{type:\"heif\",mime:\"image/heif-sequence\"}:{type:\"heif\",mime:\"image/heif\"}:{type:\"avif\",mime:\"image/avif\"}}},{\"./common\":509}],512:[function(t,e,r){\"use strict\";var n=t(\"../common\").str2arr,i=t(\"../common\").sliceEq,a=t(\"../common\").readUInt32BE,o=t(\"../miaf_utils\"),s=t(\"../exif_utils\"),l=n(\"ftyp\");e.exports=function(t){if(i(t,4,l)){var e=o.unbox(t,0);if(e){var r=o.getMimeType(e.data);if(r){for(var n,c=e.end;;){var u=o.unbox(t,c);if(!u)break;if(c=u.end,\"mdat\"===u.boxtype)return;if(\"meta\"===u.boxtype){n=u.data;break}}if(n){var f=o.readSizeFromMeta(n);if(f){var h={width:f.width,height:f.height,type:r.type,mime:r.mime,wUnits:\"px\",hUnits:\"px\"};if(f.variants.length>1&&(h.variants=f.variants),f.orientation&&(h.orientation=f.orientation),f.exif_location&&f.exif_location.offset+f.exif_location.length<=t.length){var p=a(t,f.exif_location.offset),d=t.slice(f.exif_location.offset+p+4,f.exif_location.offset+f.exif_location.length),m=s.get_orientation(d);m>0&&(h.orientation=m)}return h}}}}}}},{\"../common\":509,\"../exif_utils\":510,\"../miaf_utils\":511}],513:[function(t,e,r){\"use strict\";var n=t(\"../common\").str2arr,i=t(\"../common\").sliceEq,a=t(\"../common\").readUInt16LE,o=n(\"BM\");e.exports=function(t){if(!(t.length<26)&&i(t,0,o))return{width:a(t,18),height:a(t,22),type:\"bmp\",mime:\"image/bmp\",wUnits:\"px\",hUnits:\"px\"}}},{\"../common\":509}],514:[function(t,e,r){\"use strict\";var n=t(\"../common\").str2arr,i=t(\"../common\").sliceEq,a=t(\"../common\").readUInt16LE,o=n(\"GIF87a\"),s=n(\"GIF89a\");e.exports=function(t){if(!(t.length<10)&&(i(t,0,o)||i(t,0,s)))return{width:a(t,6),height:a(t,8),type:\"gif\",mime:\"image/gif\",wUnits:\"px\",hUnits:\"px\"}}},{\"../common\":509}],515:[function(t,e,r){\"use strict\";var n=t(\"../common\").readUInt16LE;e.exports=function(t){var e=n(t,0),r=n(t,2),i=n(t,4);if(0===e&&1===r&&i){for(var a=[],o={width:0,height:0},s=0;s<i;s++){var 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n=t(\"color-normalize\"),i=t(\"array-bounds\"),a=t(\"object-assign\"),o=t(\"glslify\"),s=t(\"pick-by-alias\"),l=t(\"flatten-vertex-data\"),c=t(\"earcut\"),u=t(\"array-normalize\"),f=t(\"to-float32\"),h=f.float32,p=f.fract32,d=t(\"es6-weak-map\"),m=t(\"parse-rect\"),g=t(\"array-find-index\");function v(t,e){if(!(this instanceof v))return new v(t,e);if(\"function\"==typeof t?(e||(e={}),e.regl=t):e=t,e.length&&(e.positions=e),!(t=e.regl).hasExtension(\"ANGLE_instanced_arrays\"))throw Error(\"regl-error2d: `ANGLE_instanced_arrays` extension should be enabled\");this.gl=t._gl,this.regl=t,this.passes=[],this.shaders=v.shaders.has(t)?v.shaders.get(t):v.shaders.set(t,v.createShaders(t)).get(t),this.update(e)}e.exports=v,v.dashMult=2,v.maxPatternLength=256,v.precisionThreshold=3e6,v.maxPoints=1e4,v.maxLines=2048,v.shaders=new d,v.createShaders=function(t){var e,r=t.buffer({usage:\"static\",type:\"float\",data:[0,1,0,0,1,1,1,0]}),n={primitive:\"triangle strip\",instances:t.prop(\"count\"),count:4,offset:0,uniforms:{miterMode:function(t,e){return\"round\"===e.join?2:1},miterLimit:t.prop(\"miterLimit\"),scale:t.prop(\"scale\"),scaleFract:t.prop(\"scaleFract\"),translateFract:t.prop(\"translateFract\"),translate:t.prop(\"translate\"),thickness:t.prop(\"thickness\"),dashPattern:t.prop(\"dashTexture\"),opacity:t.prop(\"opacity\"),pixelRatio:t.context(\"pixelRatio\"),id:t.prop(\"id\"),dashSize:t.prop(\"dashLength\"),viewport:function(t,e){return[e.viewport.x,e.viewport.y,t.viewportWidth,t.viewportHeight]},depth:t.prop(\"depth\")},blend:{enable:!0,color:[0,0,0,0],equation:{rgb:\"add\",alpha:\"add\"},func:{srcRGB:\"src alpha\",dstRGB:\"one minus src alpha\",srcAlpha:\"one minus dst alpha\",dstAlpha:\"one\"}},depth:{enable:function(t,e){return!e.overlay}},stencil:{enable:!1},scissor:{enable:!0,box:t.prop(\"viewport\")},viewport:t.prop(\"viewport\")},i=t(a({vert:o([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec2 aCoord, bCoord, aCoordFract, bCoordFract;\\nattribute vec4 color;\\nattribute float lineEnd, lineTop;\\n\\nuniform vec2 scale, scaleFract, translate, translateFract;\\nuniform float thickness, pixelRatio, id, depth;\\nuniform vec4 viewport;\\n\\nvarying vec4 fragColor;\\nvarying vec2 tangent;\\n\\nvec2 project(vec2 position, vec2 positionFract, vec2 scale, vec2 scaleFract, vec2 translate, vec2 translateFract) {\\n\\t// the order is important\\n\\treturn position * scale + translate\\n       + positionFract * scale + translateFract\\n       + position * scaleFract\\n       + positionFract * scaleFract;\\n}\\n\\nvoid main() {\\n\\tfloat lineStart = 1. - lineEnd;\\n\\tfloat lineOffset = lineTop * 2. - 1.;\\n\\n\\tvec2 diff = (bCoord + bCoordFract - aCoord - aCoordFract);\\n\\ttangent = normalize(diff * scale * viewport.zw);\\n\\tvec2 normal = vec2(-tangent.y, tangent.x);\\n\\n\\tvec2 position = project(aCoord, aCoordFract, scale, scaleFract, translate, translateFract) * lineStart\\n\\t\\t+ project(bCoord, bCoordFract, scale, scaleFract, translate, translateFract) * lineEnd\\n\\n\\t\\t+ thickness * normal * .5 * lineOffset / viewport.zw;\\n\\n\\tgl_Position = vec4(position * 2.0 - 1.0, depth, 1);\\n\\n\\tfragColor = color / 255.;\\n}\\n\"]),frag:o([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform sampler2D dashPattern;\\n\\nuniform float dashSize, pixelRatio, thickness, opacity, id;\\n\\nvarying vec4 fragColor;\\nvarying vec2 tangent;\\n\\nvoid main() {\\n\\tfloat alpha = 1.;\\n\\n\\tfloat t = fract(dot(tangent, gl_FragCoord.xy) / dashSize) * .5 + .25;\\n\\tfloat dash = texture2D(dashPattern, vec2(t, .5)).r;\\n\\n\\tgl_FragColor = fragColor;\\n\\tgl_FragColor.a *= alpha * opacity * dash;\\n}\\n\"]),attributes:{lineEnd:{buffer:r,divisor:0,stride:8,offset:0},lineTop:{buffer:r,divisor:0,stride:8,offset:4},aCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:8,divisor:1},bCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:16,divisor:1},aCoordFract:{buffer:t.prop(\"positionFractBuffer\"),stride:8,offset:8,divisor:1},bCoordFract:{buffer:t.prop(\"positionFractBuffer\"),stride:8,offset:16,divisor:1},color:{buffer:t.prop(\"colorBuffer\"),stride:4,offset:0,divisor:1}}},n));try{e=t(a({cull:{enable:!0,face:\"back\"},vert:o([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec2 aCoord, bCoord, nextCoord, prevCoord;\\nattribute vec4 aColor, bColor;\\nattribute float lineEnd, lineTop;\\n\\nuniform vec2 scale, translate;\\nuniform float thickness, pixelRatio, id, depth;\\nuniform vec4 viewport;\\nuniform float miterLimit, miterMode;\\n\\nvarying vec4 fragColor;\\nvarying vec4 startCutoff, endCutoff;\\nvarying vec2 tangent;\\nvarying vec2 startCoord, endCoord;\\nvarying float enableStartMiter, enableEndMiter;\\n\\nconst float REVERSE_THRESHOLD = -.875;\\nconst float MIN_DIFF = 1e-6;\\n\\n// TODO: possible optimizations: avoid overcalculating all for vertices and calc just one instead\\n// TODO: precalculate dot products, normalize things beforehead etc.\\n// TODO: refactor to rectangular algorithm\\n\\nfloat distToLine(vec2 p, vec2 a, vec2 b) {\\n\\tvec2 diff = b - a;\\n\\tvec2 perp = normalize(vec2(-diff.y, diff.x));\\n\\treturn dot(p - a, perp);\\n}\\n\\nbool isNaN( float val ){\\n  return ( val < 0.0 || 0.0 < val || val == 0.0 ) ? false : true;\\n}\\n\\nvoid main() {\\n\\tvec2 aCoord = aCoord, bCoord = bCoord, prevCoord = prevCoord, nextCoord = nextCoord;\\n\\n  vec2 adjustedScale;\\n  adjustedScale.x = (abs(scale.x) < MIN_DIFF) ? MIN_DIFF : scale.x;\\n  adjustedScale.y = (abs(scale.y) < MIN_DIFF) ? MIN_DIFF : scale.y;\\n\\n  vec2 scaleRatio = adjustedScale * viewport.zw;\\n\\tvec2 normalWidth = thickness / scaleRatio;\\n\\n\\tfloat lineStart = 1. - lineEnd;\\n\\tfloat lineBot = 1. - lineTop;\\n\\n\\tfragColor = (lineStart * aColor + lineEnd * bColor) / 255.;\\n\\n\\tif (isNaN(aCoord.x) || isNaN(aCoord.y) || isNaN(bCoord.x) || isNaN(bCoord.y)) return;\\n\\n\\tif (aCoord == prevCoord) prevCoord = aCoord + normalize(bCoord - aCoord);\\n\\tif (bCoord == nextCoord) nextCoord = bCoord - normalize(bCoord - aCoord);\\n\\n\\tvec2 prevDiff = aCoord - prevCoord;\\n\\tvec2 currDiff = bCoord - aCoord;\\n\\tvec2 nextDiff = nextCoord - bCoord;\\n\\n\\tvec2 prevTangent = normalize(prevDiff * scaleRatio);\\n\\tvec2 currTangent = normalize(currDiff * scaleRatio);\\n\\tvec2 nextTangent = normalize(nextDiff * scaleRatio);\\n\\n\\tvec2 prevNormal = vec2(-prevTangent.y, prevTangent.x);\\n\\tvec2 currNormal = vec2(-currTangent.y, currTangent.x);\\n\\tvec2 nextNormal = vec2(-nextTangent.y, nextTangent.x);\\n\\n\\tvec2 startJoinDirection = normalize(prevTangent - currTangent);\\n\\tvec2 endJoinDirection = normalize(currTangent - nextTangent);\\n\\n\\t// collapsed/unidirectional segment cases\\n\\t// FIXME: there should be more elegant solution\\n\\tvec2 prevTanDiff = abs(prevTangent - currTangent);\\n\\tvec2 nextTanDiff = abs(nextTangent - currTangent);\\n\\tif (max(prevTanDiff.x, prevTanDiff.y) < MIN_DIFF) {\\n\\t\\tstartJoinDirection = currNormal;\\n\\t}\\n\\tif (max(nextTanDiff.x, nextTanDiff.y) < MIN_DIFF) {\\n\\t\\tendJoinDirection = currNormal;\\n\\t}\\n\\tif (aCoord == bCoord) {\\n\\t\\tendJoinDirection = startJoinDirection;\\n\\t\\tcurrNormal = prevNormal;\\n\\t\\tcurrTangent = prevTangent;\\n\\t}\\n\\n\\ttangent = currTangent;\\n\\n\\t//calculate join shifts relative to normals\\n\\tfloat startJoinShift = dot(currNormal, startJoinDirection);\\n\\tfloat endJoinShift = dot(currNormal, endJoinDirection);\\n\\n\\tfloat startMiterRatio = abs(1. / startJoinShift);\\n\\tfloat endMiterRatio = abs(1. / endJoinShift);\\n\\n\\tvec2 startJoin = startJoinDirection * startMiterRatio;\\n\\tvec2 endJoin = endJoinDirection * endMiterRatio;\\n\\n\\tvec2 startTopJoin, startBotJoin, endTopJoin, endBotJoin;\\n\\tstartTopJoin = sign(startJoinShift) * startJoin * .5;\\n\\tstartBotJoin = -startTopJoin;\\n\\n\\tendTopJoin = sign(endJoinShift) * endJoin * .5;\\n\\tendBotJoin = -endTopJoin;\\n\\n\\tvec2 aTopCoord = aCoord + normalWidth * startTopJoin;\\n\\tvec2 bTopCoord = bCoord + normalWidth * endTopJoin;\\n\\tvec2 aBotCoord = aCoord + normalWidth * startBotJoin;\\n\\tvec2 bBotCoord = bCoord + normalWidth * endBotJoin;\\n\\n\\t//miter anti-clipping\\n\\tfloat baClipping = distToLine(bCoord, aCoord, aBotCoord) / dot(normalize(normalWidth * endBotJoin), normalize(normalWidth.yx * vec2(-startBotJoin.y, startBotJoin.x)));\\n\\tfloat abClipping = distToLine(aCoord, bCoord, bTopCoord) / dot(normalize(normalWidth * startBotJoin), normalize(normalWidth.yx * vec2(-endBotJoin.y, endBotJoin.x)));\\n\\n\\t//prevent close to reverse direction switch\\n\\tbool prevReverse = dot(currTangent, prevTangent) <= REVERSE_THRESHOLD && abs(dot(currTangent, prevNormal)) * min(length(prevDiff), length(currDiff)) <  length(normalWidth * currNormal);\\n\\tbool nextReverse = dot(currTangent, nextTangent) <= REVERSE_THRESHOLD && abs(dot(currTangent, nextNormal)) * min(length(nextDiff), length(currDiff)) <  length(normalWidth * currNormal);\\n\\n\\tif (prevReverse) {\\n\\t\\t//make join rectangular\\n\\t\\tvec2 miterShift = normalWidth * startJoinDirection * miterLimit * .5;\\n\\t\\tfloat normalAdjust = 1. - min(miterLimit / startMiterRatio, 1.);\\n\\t\\taBotCoord = aCoord + miterShift - normalAdjust * normalWidth * currNormal * .5;\\n\\t\\taTopCoord = aCoord + miterShift + normalAdjust * normalWidth * currNormal * .5;\\n\\t}\\n\\telse if (!nextReverse && baClipping > 0. && baClipping < length(normalWidth * endBotJoin)) {\\n\\t\\t//handle miter clipping\\n\\t\\tbTopCoord -= normalWidth * endTopJoin;\\n\\t\\tbTopCoord += normalize(endTopJoin * normalWidth) * baClipping;\\n\\t}\\n\\n\\tif (nextReverse) {\\n\\t\\t//make join rectangular\\n\\t\\tvec2 miterShift = normalWidth * endJoinDirection * miterLimit * .5;\\n\\t\\tfloat normalAdjust = 1. - min(miterLimit / endMiterRatio, 1.);\\n\\t\\tbBotCoord = bCoord + miterShift - normalAdjust * normalWidth * currNormal * .5;\\n\\t\\tbTopCoord = bCoord + miterShift + normalAdjust * normalWidth * currNormal * .5;\\n\\t}\\n\\telse if (!prevReverse && abClipping > 0. && abClipping < length(normalWidth * startBotJoin)) {\\n\\t\\t//handle miter clipping\\n\\t\\taBotCoord -= normalWidth * startBotJoin;\\n\\t\\taBotCoord += normalize(startBotJoin * normalWidth) * abClipping;\\n\\t}\\n\\n\\tvec2 aTopPosition = (aTopCoord) * adjustedScale + translate;\\n\\tvec2 aBotPosition = (aBotCoord) * adjustedScale + translate;\\n\\n\\tvec2 bTopPosition = (bTopCoord) * adjustedScale + translate;\\n\\tvec2 bBotPosition = (bBotCoord) * adjustedScale + translate;\\n\\n\\t//position is normalized 0..1 coord on the screen\\n\\tvec2 position = (aTopPosition * lineTop + aBotPosition * lineBot) * lineStart + (bTopPosition * lineTop + bBotPosition * lineBot) * lineEnd;\\n\\n\\tstartCoord = aCoord * scaleRatio + translate * viewport.zw + viewport.xy;\\n\\tendCoord = bCoord * scaleRatio + translate * viewport.zw + viewport.xy;\\n\\n\\tgl_Position = vec4(position  * 2.0 - 1.0, depth, 1);\\n\\n\\tenableStartMiter = step(dot(currTangent, prevTangent), .5);\\n\\tenableEndMiter = step(dot(currTangent, nextTangent), .5);\\n\\n\\t//bevel miter cutoffs\\n\\tif (miterMode == 1.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tvec2 startMiterWidth = vec2(startJoinDirection) * thickness * miterLimit * .5;\\n\\t\\t\\tstartCutoff = vec4(aCoord, aCoord);\\n\\t\\t\\tstartCutoff.zw += vec2(-startJoinDirection.y, startJoinDirection.x) / scaleRatio;\\n\\t\\t\\tstartCutoff = startCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tstartCutoff += viewport.xyxy;\\n\\t\\t\\tstartCutoff += startMiterWidth.xyxy;\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tvec2 endMiterWidth = vec2(endJoinDirection) * thickness * miterLimit * .5;\\n\\t\\t\\tendCutoff = vec4(bCoord, bCoord);\\n\\t\\t\\tendCutoff.zw += vec2(-endJoinDirection.y, endJoinDirection.x)  / scaleRatio;\\n\\t\\t\\tendCutoff = endCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tendCutoff += viewport.xyxy;\\n\\t\\t\\tendCutoff += endMiterWidth.xyxy;\\n\\t\\t}\\n\\t}\\n\\n\\t//round miter cutoffs\\n\\telse if (miterMode == 2.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tvec2 startMiterWidth = vec2(startJoinDirection) * thickness * abs(dot(startJoinDirection, currNormal)) * .5;\\n\\t\\t\\tstartCutoff = vec4(aCoord, aCoord);\\n\\t\\t\\tstartCutoff.zw += vec2(-startJoinDirection.y, startJoinDirection.x) / scaleRatio;\\n\\t\\t\\tstartCutoff = startCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tstartCutoff += viewport.xyxy;\\n\\t\\t\\tstartCutoff += startMiterWidth.xyxy;\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tvec2 endMiterWidth = vec2(endJoinDirection) * thickness * abs(dot(endJoinDirection, currNormal)) * .5;\\n\\t\\t\\tendCutoff = vec4(bCoord, bCoord);\\n\\t\\t\\tendCutoff.zw += vec2(-endJoinDirection.y, endJoinDirection.x)  / scaleRatio;\\n\\t\\t\\tendCutoff = endCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tendCutoff += viewport.xyxy;\\n\\t\\t\\tendCutoff += endMiterWidth.xyxy;\\n\\t\\t}\\n\\t}\\n}\\n\"]),frag:o([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform sampler2D dashPattern;\\nuniform float dashSize, pixelRatio, thickness, opacity, id, miterMode;\\n\\nvarying vec4 fragColor;\\nvarying vec2 tangent;\\nvarying vec4 startCutoff, endCutoff;\\nvarying vec2 startCoord, endCoord;\\nvarying float enableStartMiter, enableEndMiter;\\n\\nfloat distToLine(vec2 p, vec2 a, vec2 b) {\\n\\tvec2 diff = b - a;\\n\\tvec2 perp = normalize(vec2(-diff.y, diff.x));\\n\\treturn dot(p - a, perp);\\n}\\n\\nvoid main() {\\n\\tfloat alpha = 1., distToStart, distToEnd;\\n\\tfloat cutoff = thickness * .5;\\n\\n\\t//bevel miter\\n\\tif (miterMode == 1.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tdistToStart = distToLine(gl_FragCoord.xy, startCutoff.xy, startCutoff.zw);\\n\\t\\t\\tif (distToStart < -1.) {\\n\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\treturn;\\n\\t\\t\\t}\\n\\t\\t\\talpha *= min(max(distToStart + 1., 0.), 1.);\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tdistToEnd = distToLine(gl_FragCoord.xy, endCutoff.xy, endCutoff.zw);\\n\\t\\t\\tif (distToEnd < -1.) {\\n\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\treturn;\\n\\t\\t\\t}\\n\\t\\t\\talpha *= min(max(distToEnd + 1., 0.), 1.);\\n\\t\\t}\\n\\t}\\n\\n\\t// round miter\\n\\telse if (miterMode == 2.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tdistToStart = distToLine(gl_FragCoord.xy, startCutoff.xy, startCutoff.zw);\\n\\t\\t\\tif (distToStart < 0.) {\\n\\t\\t\\t\\tfloat radius = length(gl_FragCoord.xy - startCoord);\\n\\n\\t\\t\\t\\tif(radius > cutoff + .5) {\\n\\t\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\t\\treturn;\\n\\t\\t\\t\\t}\\n\\n\\t\\t\\t\\talpha -= smoothstep(cutoff - .5, cutoff + .5, radius);\\n\\t\\t\\t}\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tdistToEnd = distToLine(gl_FragCoord.xy, endCutoff.xy, endCutoff.zw);\\n\\t\\t\\tif (distToEnd < 0.) {\\n\\t\\t\\t\\tfloat radius = length(gl_FragCoord.xy - endCoord);\\n\\n\\t\\t\\t\\tif(radius > cutoff + .5) {\\n\\t\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\t\\treturn;\\n\\t\\t\\t\\t}\\n\\n\\t\\t\\t\\talpha -= smoothstep(cutoff - .5, cutoff + .5, radius);\\n\\t\\t\\t}\\n\\t\\t}\\n\\t}\\n\\n\\tfloat t = fract(dot(tangent, gl_FragCoord.xy) / dashSize) * .5 + .25;\\n\\tfloat dash = texture2D(dashPattern, vec2(t, .5)).r;\\n\\n\\tgl_FragColor = fragColor;\\n\\tgl_FragColor.a *= alpha * opacity * dash;\\n}\\n\"]),attributes:{lineEnd:{buffer:r,divisor:0,stride:8,offset:0},lineTop:{buffer:r,divisor:0,stride:8,offset:4},aColor:{buffer:t.prop(\"colorBuffer\"),stride:4,offset:0,divisor:1},bColor:{buffer:t.prop(\"colorBuffer\"),stride:4,offset:4,divisor:1},prevCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:0,divisor:1},aCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:8,divisor:1},bCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:16,divisor:1},nextCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:24,divisor:1}}},n))}catch(t){e=i}return{fill:t({primitive:\"triangle\",elements:function(t,e){return e.triangles},offset:0,vert:o([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position, positionFract;\\n\\nuniform vec4 color;\\nuniform vec2 scale, scaleFract, translate, translateFract;\\nuniform float pixelRatio, id;\\nuniform vec4 viewport;\\nuniform float opacity;\\n\\nvarying vec4 fragColor;\\n\\nconst float MAX_LINES = 256.;\\n\\nvoid main() {\\n\\tfloat depth = (MAX_LINES - 4. - id) / (MAX_LINES);\\n\\n\\tvec2 position = position * scale + translate\\n       + positionFract * scale + translateFract\\n       + position * scaleFract\\n       + positionFract * scaleFract;\\n\\n\\tgl_Position = vec4(position * 2.0 - 1.0, depth, 1);\\n\\n\\tfragColor = color / 255.;\\n\\tfragColor.a *= opacity;\\n}\\n\"]),frag:o([\"precision highp float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n\\tgl_FragColor = fragColor;\\n}\\n\"]),uniforms:{scale:t.prop(\"scale\"),color:t.prop(\"fill\"),scaleFract:t.prop(\"scaleFract\"),translateFract:t.prop(\"translateFract\"),translate:t.prop(\"translate\"),opacity:t.prop(\"opacity\"),pixelRatio:t.context(\"pixelRatio\"),id:t.prop(\"id\"),viewport:function(t,e){return[e.viewport.x,e.viewport.y,t.viewportWidth,t.viewportHeight]}},attributes:{position:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:8},positionFract:{buffer:t.prop(\"positionFractBuffer\"),stride:8,offset:8}},blend:n.blend,depth:{enable:!1},scissor:n.scissor,stencil:n.stencil,viewport:n.viewport}),rect:i,miter:e}},v.defaults={dashes:null,join:\"miter\",miterLimit:1,thickness:10,cap:\"square\",color:\"black\",opacity:1,overlay:!1,viewport:null,range:null,close:!1,fill:null},v.prototype.render=function(){for(var t,e=[],r=arguments.length;r--;)e[r]=arguments[r];e.length&&(t=this).update.apply(t,e),this.draw()},v.prototype.draw=function(){for(var t=this,e=[],r=arguments.length;r--;)e[r]=arguments[r];return(e.length?e:this.passes).forEach((function(e,r){var n;if(e&&Array.isArray(e))return(n=t).draw.apply(n,e);\"number\"==typeof e&&(e=t.passes[e]),e&&e.count>1&&e.opacity&&(t.regl._refresh(),e.fill&&e.triangles&&e.triangles.length>2&&t.shaders.fill(e),e.thickness&&(e.scale[0]*e.viewport.width>v.precisionThreshold||e.scale[1]*e.viewport.height>v.precisionThreshold||\"rect\"===e.join||!e.join&&(e.thickness<=2||e.count>=v.maxPoints)?t.shaders.rect(e):t.shaders.miter(e)))})),this},v.prototype.update=function(t){var e=this;if(t){null!=t.length?\"number\"==typeof t[0]&&(t=[{positions:t}]):Array.isArray(t)||(t=[t]);var r=this.regl,o=this.gl;if(t.forEach((function(t,f){var d=e.passes[f];if(void 0!==t)if(null!==t){if(\"number\"==typeof t[0]&&(t={positions:t}),t=s(t,{positions:\"positions points data coords\",thickness:\"thickness lineWidth lineWidths line-width linewidth width stroke-width strokewidth strokeWidth\",join:\"lineJoin linejoin 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fragColor, colorAmt);\\n    color.a *= opacity;\\n\\n    gl_FragColor = color;\\n  }\\n\\n}\\n\"]),s.vert=h([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute float x, y, xFract, yFract;\\nattribute float size, borderSize;\\nattribute vec4 colorId, borderColorId;\\nattribute float isActive;\\n\\nuniform vec2 scale, scaleFract, translate, translateFract, paletteSize;\\nuniform float pixelRatio;\\nuniform bool constPointSize;\\nuniform sampler2D palette;\\n\\nconst float maxSize = 100.;\\nconst float borderLevel = .5;\\n\\nvarying vec4 fragColor, fragBorderColor;\\nvarying float fragPointSize, fragBorderRadius, fragWidth, fragBorderColorLevel, fragColorLevel;\\n\\nfloat pointSizeScale = (constPointSize) ? 2. : pixelRatio;\\n\\nbool isDirect = (paletteSize.x < 1.);\\n\\nvec4 getColor(vec4 id) {\\n  return isDirect ? id / 255. : texture2D(palette,\\n    vec2(\\n      (id.x + .5) / paletteSize.x,\\n      (id.y + .5) / paletteSize.y\\n    )\\n  );\\n}\\n\\nvoid main() {\\n  // ignore inactive points\\n  if (isActive == 0.) return;\\n\\n  vec2 position = vec2(x, y);\\n  vec2 positionFract = vec2(xFract, yFract);\\n\\n  vec4 color = getColor(colorId);\\n  vec4 borderColor = getColor(borderColorId);\\n\\n  float size = size * maxSize / 255.;\\n  float borderSize = borderSize * maxSize / 255.;\\n\\n  gl_PointSize = 2. * size * pointSizeScale;\\n  fragPointSize = size * pixelRatio;\\n\\n  vec2 pos = (position + translate) * scale\\n      + (positionFract + translateFract) * scale\\n      + (position + translate) * scaleFract\\n      + (positionFract + translateFract) * scaleFract;\\n\\n  gl_Position = vec4(pos * 2. - 1., 0., 1.);\\n\\n  fragColor = color;\\n  fragBorderColor = borderColor;\\n  fragWidth = 1. / gl_PointSize;\\n\\n  fragBorderColorLevel = clamp(borderLevel - borderLevel * borderSize / size, 0., 1.);\\n  fragColorLevel = clamp(borderLevel + (1. - borderLevel) * borderSize / size, 0., 1.);\\n}\"]),this.drawMarker=t(s);var l=f({},o);l.frag=h([\"precision highp float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragColor, fragBorderColor;\\n\\nuniform float opacity;\\nvarying float fragBorderRadius, fragWidth;\\n\\nfloat smoothStep(float edge0, float edge1, float x) {\\n\\tfloat t;\\n\\tt = clamp((x - edge0) / (edge1 - edge0), 0.0, 1.0);\\n\\treturn t * t * (3.0 - 2.0 * t);\\n}\\n\\nvoid main() {\\n\\tfloat radius, alpha = 1.0, delta = fragWidth;\\n\\n\\tradius = length(2.0 * gl_PointCoord.xy - 1.0);\\n\\n\\tif (radius > 1.0 + delta) {\\n\\t\\tdiscard;\\n\\t}\\n\\n\\talpha -= smoothstep(1.0 - delta, 1.0 + delta, radius);\\n\\n\\tfloat borderRadius = fragBorderRadius;\\n\\tfloat ratio = smoothstep(borderRadius - delta, borderRadius + delta, radius);\\n\\tvec4 color = mix(fragColor, fragBorderColor, ratio);\\n\\tcolor.a *= alpha * opacity;\\n\\tgl_FragColor = color;\\n}\\n\"]),l.vert=h([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute float x, y, xFract, yFract;\\nattribute float size, borderSize;\\nattribute vec4 colorId, borderColorId;\\nattribute float isActive;\\n\\nuniform vec2 scale, scaleFract, translate, translateFract;\\nuniform float pixelRatio;\\nuniform bool constPointSize;\\nuniform sampler2D palette;\\nuniform vec2 paletteSize;\\n\\nconst float maxSize = 100.;\\n\\nvarying vec4 fragColor, fragBorderColor;\\nvarying float fragBorderRadius, fragWidth;\\n\\nfloat pointSizeScale = (constPointSize) ? 2. : pixelRatio;\\n\\nbool isDirect = (paletteSize.x < 1.);\\n\\nvec4 getColor(vec4 id) {\\n  return isDirect ? id / 255. : texture2D(palette,\\n    vec2(\\n      (id.x + .5) / paletteSize.x,\\n      (id.y + .5) / paletteSize.y\\n    )\\n  );\\n}\\n\\nvoid main() {\\n  // ignore inactive points\\n  if (isActive == 0.) return;\\n\\n  vec2 position = vec2(x, y);\\n  vec2 positionFract = vec2(xFract, yFract);\\n\\n  vec4 color = getColor(colorId);\\n  vec4 borderColor = getColor(borderColorId);\\n\\n  float size = size * maxSize / 255.;\\n  float borderSize = borderSize * maxSize / 255.;\\n\\n  gl_PointSize = (size + borderSize) * pointSizeScale;\\n\\n  vec2 pos = (position + translate) * scale\\n      + (positionFract + translateFract) * scale\\n      + (position + translate) * scaleFract\\n      + (positionFract + translateFract) * scaleFract;\\n\\n  gl_Position = vec4(pos * 2. - 1., 0., 1.);\\n\\n  fragBorderRadius = 1. - 2. * borderSize / (size + borderSize);\\n  fragColor = color;\\n  fragBorderColor = borderColor.a == 0. || borderSize == 0. ? vec4(color.rgb, 0.) : borderColor;\\n  fragWidth = 1. / gl_PointSize;\\n}\\n\"]),g&&(l.frag=l.frag.replace(\"smoothstep\",\"smoothStep\"),s.frag=s.frag.replace(\"smoothstep\",\"smoothStep\")),this.drawCircle=t(l)}b.defaults={color:\"black\",borderColor:\"transparent\",borderSize:0,size:12,opacity:1,marker:void 0,viewport:null,range:null,pixelSize:null,count:0,offset:0,bounds:null,positions:[],snap:1e4},b.prototype.render=function(){return arguments.length&&this.update.apply(this,arguments),this.draw(),this},b.prototype.draw=function(){for(var t=this,e=arguments.length,r=new Array(e),n=0;n<e;n++)r[n]=arguments[n];var i=this.groups;if(1===r.length&&Array.isArray(r[0])&&(null===r[0][0]||Array.isArray(r[0][0]))&&(r=r[0]),this.regl._refresh(),r.length)for(var a=0;a<r.length;a++)this.drawItem(a,r[a]);else i.forEach((function(e,r){t.drawItem(r)}));return this},b.prototype.drawItem=function(t,e){var r=this.groups,n=r[t];if(\"number\"==typeof e&&(t=e,n=r[e],e=null),n&&n.count&&n.opacity){n.activation[0]&&this.drawCircle(this.getMarkerDrawOptions(0,n,e));for(var a=[],o=1;o<n.activation.length;o++)n.activation[o]&&(!0===n.activation[o]||n.activation[o].data.length)&&a.push.apply(a,i(this.getMarkerDrawOptions(o,n,e)));a.length&&this.drawMarker(a)}},b.prototype.getMarkerDrawOptions=function(t,e,r){var i=e.range,a=e.tree,o=e.viewport,s=e.activation,l=e.selectionBuffer,c=e.count;this.regl;if(!a)return r?[f({},e,{markerTexture:this.markerTextures[t],activation:s[t],count:r.length,elements:r,offset:0})]:[f({},e,{markerTexture:this.markerTextures[t],activation:s[t],offset:0})];var u=[],h=a.range(i,{lod:!0,px:[(i[2]-i[0])/o.width,(i[3]-i[1])/o.height]});if(r){for(var p=s[t].data,d=new Uint8Array(c),m=0;m<r.length;m++){var g=r[m];d[g]=p?p[g]:1}l.subdata(d)}for(var v=h.length;v--;){var y=n(h[v],2),x=y[0],b=y[1];u.push(f({},e,{markerTexture:this.markerTextures[t],activation:r?l:s[t],offset:x,count:b-x}))}return u},b.prototype.update=function(){for(var t=this,e=arguments.length,r=new Array(e),n=0;n<e;n++)r[n]=arguments[n];if(r.length){1===r.length&&Array.isArray(r[0])&&(r=r[0]);var i=this.groups,a=this.gl,o=this.regl,s=this.maxSize,c=this.maxColors,h=this.palette;this.groups=i=r.map((function(e,r){var n=i[r];if(void 0===e)return n;null===e?e={positions:null}:\"function\"==typeof e?e={ondraw:e}:\"number\"==typeof e[0]&&(e={positions:e}),null===(e=p(e,{positions:\"positions data 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t.x.length?e.xAttr={buffer:o.buffer(t.x),offset:0,stride:4,count:t.x.length}:e.xAttr={buffer:t.x.buffer,offset:4*t.x.offset||0,stride:4*(t.x.stride||1),count:t.x.count},t.y.length?e.yAttr={buffer:o.buffer(t.y),offset:0,stride:4,count:t.y.length}:e.yAttr={buffer:t.y.buffer,offset:4*t.y.offset||0,stride:4*(t.y.stride||1),count:t.y.count},e.count=Math.max(e.xAttr.count,e.yAttr.count),t;t=m(t,\"float64\");var c=e.count=Math.floor(t.length/2),f=e.bounds=c?l(t,2):null;if(r.range||e.range||(delete e.range,r.range=f),r.marker||e.marker||(delete e.marker,r.marker=null),n&&(!0===n||c>n)?e.tree=u(t,{bounds:f}):n&&n.length&&(e.tree=n),e.tree){var h={primitive:\"points\",usage:\"static\",data:e.tree,type:\"uint32\"};e.elements?e.elements(h):e.elements=o.elements(h)}return i({data:v.float(t),usage:\"dynamic\"}),a({data:v.fract(t),usage:\"dynamic\"}),s({data:new Uint8Array(c),type:\"uint8\",usage:\"stream\"}),t}},{marker:function(e,r,n){var i=r.activation;if(i.forEach((function(t){return t&&t.destroy&&t.destroy()})),i.length=0,e&&\"number\"!=typeof e[0]){for(var a=[],s=0,l=Math.min(e.length,r.count);s<l;s++){var c=t.addMarker(e[s]);a[c]||(a[c]=new Uint8Array(r.count)),a[c][s]=1}for(var u=0;u<a.length;u++)if(a[u]){var f={data:a[u],type:\"uint8\",usage:\"static\"};i[u]?i[u](f):i[u]=o.buffer(f),i[u].data=a[u]}}else{i[t.addMarker(e)]=!0}return e},range:function(t,e,r){var n=e.bounds;if(n)return t||(t=n),e.scale=[1/(t[2]-t[0]),1/(t[3]-t[1])],e.translate=[-t[0],-t[1]],e.scaleFract=v.fract(e.scale),e.translateFract=v.fract(e.translate),t},viewport:function(t){return y(t||[a.drawingBufferWidth,a.drawingBufferHeight])}}]),g){var _=n,w=_.count,T=_.size,k=_.borderSize,M=_.sizeBuffer,A=new Uint8Array(2*w);if(T.length||k.length)for(var S=0;S<w;S++)A[2*S]=Math.round(255*(null==T[S]?T:T[S])/s),A[2*S+1]=Math.round(255*(null==k[S]?k:k[S])/s);M({data:A,usage:\"dynamic\"})}if(x){var E,L=n,C=L.count,P=L.color,I=L.borderColor,O=L.colorBuffer;if(t.tooManyColors){if(P.length||I.length){E=new Uint8Array(8*C);for(var z=0;z<C;z++){var D=P[z];E[8*z]=h[4*D],E[8*z+1]=h[4*D+1],E[8*z+2]=h[4*D+2],E[8*z+3]=h[4*D+3];var R=I[z];E[8*z+4]=h[4*R],E[8*z+5]=h[4*R+1],E[8*z+6]=h[4*R+2],E[8*z+7]=h[4*R+3]}}}else if(P.length||I.length){E=new Uint8Array(4*C+2);for(var F=0;F<C;F++)null!=P[F]&&(E[4*F]=P[F]%c,E[4*F+1]=Math.floor(P[F]/c)),null!=I[F]&&(E[4*F+2]=I[F]%c,E[4*F+3]=Math.floor(I[F]/c))}O({data:E||new Uint8Array(0),type:\"uint8\",usage:\"dynamic\"})}return n}))}},b.prototype.addMarker=function(t){var e,r=this.markerTextures,n=this.regl,i=this.markerCache,a=null==t?0:i.indexOf(t);if(a>=0)return a;if(t instanceof Uint8Array||t instanceof Uint8ClampedArray)e=t;else{e=new Uint8Array(t.length);for(var o=0,s=t.length;o<s;o++)e[o]=255*t[o]}var l=Math.floor(Math.sqrt(e.length));return a=r.length,i.push(t),r.push(n.texture({channels:1,data:e,radius:l,mag:\"linear\",min:\"linear\"})),a},b.prototype.updateColor=function(t){var e=this.paletteIds,r=this.palette,n=this.maxColors;Array.isArray(t)||(t=[t]);var i=[];if(\"number\"==typeof t[0]){var a=[];if(Array.isArray(t))for(var o=0;o<t.length;o+=4)a.push(t.slice(o,o+4));else for(var l=0;l<t.length;l+=4)a.push(t.subarray(l,l+4));t=a}for(var u=0;u<t.length;u++){var f=t[u];f=s(f,\"uint8\");var h=c(f,!1);if(null==e[h]){var p=r.length;e[h]=Math.floor(p/4),r[p]=f[0],r[p+1]=f[1],r[p+2]=f[2],r[p+3]=f[3]}i[u]=e[h]}return!this.tooManyColors&&r.length>4*n&&(this.tooManyColors=!0),this.updatePalette(r),1===i.length?i[0]:i},b.prototype.updatePalette=function(t){if(!this.tooManyColors){var e=this.maxColors,r=this.paletteTexture,n=Math.ceil(.25*t.length/e);if(n>1)for(var i=.25*(t=t.slice()).length%e;i<n*e;i++)t.push(0,0,0,0);r.height<n&&r.resize(e,n),r.subimage({width:Math.min(.25*t.length,e),height:n,data:t},0,0)}},b.prototype.destroy=function(){return this.groups.forEach((function(t){t.sizeBuffer.destroy(),t.positionBuffer.destroy(),t.positionFractBuffer.destroy(),t.colorBuffer.destroy(),t.activation.forEach((function(t){return t&&t.destroy&&t.destroy()})),t.selectionBuffer.destroy(),t.elements&&t.elements.destroy()})),this.groups.length=0,this.paletteTexture.destroy(),this.markerTextures.forEach((function(t){return t&&t.destroy&&t.destroy()})),this};var _=t(\"object-assign\");e.exports=function(t,e){var r=new x(t,e),n=r.render.bind(r);return _(n,{render:n,update:r.update.bind(r),draw:r.draw.bind(r),destroy:r.destroy.bind(r),regl:r.regl,gl:r.gl,canvas:r.gl.canvas,groups:r.groups,markers:r.markerCache,palette:r.palette}),n}},{\"@plotly/point-cluster\":59,\"array-bounds\":76,\"color-id\":129,\"color-normalize\":131,\"flatten-vertex-data\":248,glslify:438,\"is-iexplorer\":450,\"object-assign\":483,\"parse-rect\":488,\"pick-by-alias\":494,\"to-float32\":595,\"update-diff\":617}],535:[function(t,e,r){\"use strict\";var n=t(\"regl-scatter2d\"),i=t(\"pick-by-alias\"),a=t(\"array-bounds\"),o=t(\"raf\"),s=t(\"array-range\"),l=t(\"parse-rect\"),c=t(\"flatten-vertex-data\");function u(t,e){if(!(this instanceof u))return new u(t,e);this.traces=[],this.passes={},this.regl=t,this.scatter=n(t),this.canvas=this.scatter.canvas}function f(t,e,r){return(null!=t.id?t.id:t)<<16|(255&e)<<8|255&r}function h(t,e,r){var n,i,a,o,s=t[e],l=t[r];return s.length>2?(s[0],s[2],n=s[1],i=s[3]):s.length?(n=s[0],i=s[1]):(s.x,n=s.y,s.x+s.width,i=s.y+s.height),l.length>2?(a=l[0],o=l[2],l[1],l[3]):l.length?(a=l[0],o=l[1]):(a=l.x,l.y,o=l.x+l.width,l.y+l.height),[a,n,o,i]}function p(t){if(\"number\"==typeof t)return[t,t,t,t];if(2===t.length)return[t[0],t[1],t[0],t[1]];var e=l(t);return[e.x,e.y,e.x+e.width,e.y+e.height]}e.exports=u,u.prototype.render=function(){for(var t,e=this,r=[],n=arguments.length;n--;)r[n]=arguments[n];return r.length&&(t=this).update.apply(t,r),this.regl.attributes.preserveDrawingBuffer?this.draw():(this.dirty?null==this.planned&&(this.planned=o((function(){e.draw(),e.dirty=!0,e.planned=null}))):(this.draw(),this.dirty=!0,o((function(){e.dirty=!1}))),this)},u.prototype.update=function(){for(var t,e=[],r=arguments.length;r--;)e[r]=arguments[r];if(e.length){for(var n=0;n<e.length;n++)this.updateItem(n,e[n]);this.traces=this.traces.filter(Boolean);for(var i=[],a=0,o=0;o<this.traces.length;o++){for(var s=this.traces[o],l=this.traces[o].passes,c=0;c<l.length;c++)i.push(this.passes[l[c]]);s.passOffset=a,a+=s.passes.length}return(t=this.scatter).update.apply(t,i),this}},u.prototype.updateItem=function(t,e){var r=this.regl;if(null===e)return this.traces[t]=null,this;if(!e)return this;var n,o=i(e,{data:\"data items columns rows values dimensions samples x\",snap:\"snap cluster\",size:\"sizes size radius\",color:\"colors color fill fill-color fillColor\",opacity:\"opacity alpha transparency 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A.viewport=[x+k*_+_*S[0],b+T*w+w*S[1],x+(k+1)*_-_*S[2],b+(T+1)*w-w*S[3]]}o.color&&(A.color=s.color),o.size&&(A.size=s.size),o.marker&&(A.marker=s.marker),o.borderSize&&(A.borderSize=s.borderSize),o.borderColor&&(A.borderColor=s.borderColor),o.opacity&&(A.opacity=s.opacity),o.range&&(A.range=n?h(s.range,T,k):s.range||A.bounds),s.passes.push(M)}return this},u.prototype.draw=function(){for(var t,e=[],r=arguments.length;r--;)e[r]=arguments[r];if(e.length){for(var n=[],i=0;i<e.length;i++)if(\"number\"==typeof e[i]){var a=this.traces[e[i]],o=a.passes,l=a.passOffset;n.push.apply(n,s(l,l+o.length))}else if(e[i].length){var c=e[i],u=this.traces[i],f=u.passes,h=u.passOffset;f=f.map((function(t,e){n[h+e]=c}))}(t=this.scatter).draw.apply(t,n)}else this.scatter.draw();return this},u.prototype.destroy=function(){return this.traces.forEach((function(t){t.buffer&&t.buffer.destroy&&t.buffer.destroy()})),this.traces=null,this.passes=null,this.scatter.destroy(),this}},{\"array-bounds\":76,\"array-range\":79,\"flatten-vertex-data\":248,\"parse-rect\":488,\"pick-by-alias\":494,raf:526,\"regl-scatter2d\":534}],536:[function(t,e,r){!function(t,n){\"object\"==typeof r&&void 0!==e?e.exports=n():t.createREGL=n()}(this,(function(){function t(t,e){this.id=V++,this.type=t,this.data=e}function e(t){return\"[\"+function t(e){if(0===e.length)return[];var r=e.charAt(0),n=e.charAt(e.length-1);if(1<e.length&&r===n&&('\"'===r||\"'\"===r))return['\"'+e.substr(1,e.length-2).replace(/\\\\/g,\"\\\\\\\\\").replace(/\"/g,'\\\\\"')+'\"'];if(r=/\\[(false|true|null|\\d+|'[^']*'|\"[^\"]*\")\\]/.exec(e))return 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n.baseCalendar,i(a.prototype,{name:\"Ethiopian\",jdEpoch:1724220.5,daysPerMonth:[30,30,30,30,30,30,30,30,30,30,30,30,5],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Ethiopian\",epochs:[\"BEE\",\"EE\"],monthNames:[\"Meskerem\",\"Tikemet\",\"Hidar\",\"Tahesas\",\"Tir\",\"Yekatit\",\"Megabit\",\"Miazia\",\"Genbot\",\"Sene\",\"Hamle\",\"Nehase\",\"Pagume\"],monthNamesShort:[\"Mes\",\"Tik\",\"Hid\",\"Tah\",\"Tir\",\"Yek\",\"Meg\",\"Mia\",\"Gen\",\"Sen\",\"Ham\",\"Neh\",\"Pag\"],dayNames:[\"Ehud\",\"Segno\",\"Maksegno\",\"Irob\",\"Hamus\",\"Arb\",\"Kidame\"],dayNamesShort:[\"Ehu\",\"Seg\",\"Mak\",\"Iro\",\"Ham\",\"Arb\",\"Kid\"],dayNamesMin:[\"Eh\",\"Se\",\"Ma\",\"Ir\",\"Ha\",\"Ar\",\"Ki\"],digits:null,dateFormat:\"dd/mm/yyyy\",firstDay:0,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return(t=e.year()+(e.year()<0?1:0))%4==3||t%4==-1},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear||n.regionalOptions[\"\"].invalidYear),13},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(13===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return(t=i.year())<0&&t++,i.day()+30*(i.month()-1)+365*(t-1)+Math.floor(t/4)+this.jdEpoch-1},fromJD:function(t){var e=Math.floor(t)+.5-this.jdEpoch,r=Math.floor((e-Math.floor((e+366)/1461))/365)+1;r<=0&&r--,e=Math.floor(t)+.5-this.newDate(r,1,1).toJD();var n=Math.floor(e/30)+1,i=e-30*(n-1)+1;return this.newDate(r,n,i)}}),n.calendars.ethiopian=a},{\"../main\":640,\"object-assign\":483}],630:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}function o(t,e){return t-e*Math.floor(t/e)}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"Hebrew\",jdEpoch:347995.5,daysPerMonth:[30,29,30,29,30,29,30,29,30,29,30,29,29],hasYearZero:!1,minMonth:1,firstMonth:7,minDay:1,regionalOptions:{\"\":{name:\"Hebrew\",epochs:[\"BAM\",\"AM\"],monthNames:[\"Nisan\",\"Iyar\",\"Sivan\",\"Tammuz\",\"Av\",\"Elul\",\"Tishrei\",\"Cheshvan\",\"Kislev\",\"Tevet\",\"Shevat\",\"Adar\",\"Adar II\"],monthNamesShort:[\"Nis\",\"Iya\",\"Siv\",\"Tam\",\"Av\",\"Elu\",\"Tis\",\"Che\",\"Kis\",\"Tev\",\"She\",\"Ada\",\"Ad2\"],dayNames:[\"Yom Rishon\",\"Yom Sheni\",\"Yom Shlishi\",\"Yom Revi'i\",\"Yom Chamishi\",\"Yom Shishi\",\"Yom Shabbat\"],dayNamesShort:[\"Ris\",\"She\",\"Shl\",\"Rev\",\"Cha\",\"Shi\",\"Sha\"],dayNamesMin:[\"Ri\",\"She\",\"Shl\",\"Re\",\"Ch\",\"Shi\",\"Sha\"],digits:null,dateFormat:\"dd/mm/yyyy\",firstDay:0,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return this._leapYear(e.year())},_leapYear:function(t){return o(7*(t=t<0?t+1:t)+1,19)<7},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),this._leapYear(t.year?t.year():t)?13:12},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){return t=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year(),this.toJD(-1===t?1:t+1,7,1)-this.toJD(t,7,1)},daysInMonth:function(t,e){return t.year&&(e=t.month(),t=t.year()),this._validate(t,e,this.minDay,n.local.invalidMonth),12===e&&this.leapYear(t)||8===e&&5===o(this.daysInYear(t),10)?30:9===e&&3===o(this.daysInYear(t),10)?29:this.daysPerMonth[e-1]},weekDay:function(t,e,r){return 6!==this.dayOfWeek(t,e,r)},extraInfo:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return{yearType:(this.leapYear(i)?\"embolismic\":\"common\")+\" \"+[\"deficient\",\"regular\",\"complete\"][this.daysInYear(i)%10-3]}},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);t=i.year(),e=i.month(),r=i.day();var a=t<=0?t+1:t,o=this.jdEpoch+this._delay1(a)+this._delay2(a)+r+1;if(e<7){for(var s=7;s<=this.monthsInYear(t);s++)o+=this.daysInMonth(t,s);for(s=1;s<e;s++)o+=this.daysInMonth(t,s)}else for(s=7;s<e;s++)o+=this.daysInMonth(t,s);return o},_delay1:function(t){var e=Math.floor((235*t-234)/19),r=12084+13753*e,n=29*e+Math.floor(r/25920);return o(3*(n+1),7)<3&&n++,n},_delay2:function(t){var e=this._delay1(t-1),r=this._delay1(t);return this._delay1(t+1)-r==356?2:r-e==382?1:0},fromJD:function(t){t=Math.floor(t)+.5;for(var e=Math.floor(98496*(t-this.jdEpoch)/35975351)-1;t>=this.toJD(-1===e?1:e+1,7,1);)e++;for(var r=t<this.toJD(e,1,1)?7:1;t>this.toJD(e,r,this.daysInMonth(e,r));)r++;var n=t-this.toJD(e,r,1)+1;return this.newDate(e,r,n)}}),n.calendars.hebrew=a},{\"../main\":640,\"object-assign\":483}],631:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"Islamic\",jdEpoch:1948439.5,daysPerMonth:[30,29,30,29,30,29,30,29,30,29,30,29],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Islamic\",epochs:[\"BH\",\"AH\"],monthNames:[\"Muharram\",\"Safar\",\"Rabi' al-awwal\",\"Rabi' al-thani\",\"Jumada al-awwal\",\"Jumada al-thani\",\"Rajab\",\"Sha'aban\",\"Ramadan\",\"Shawwal\",\"Dhu al-Qi'dah\",\"Dhu al-Hijjah\"],monthNamesShort:[\"Muh\",\"Saf\",\"Rab1\",\"Rab2\",\"Jum1\",\"Jum2\",\"Raj\",\"Sha'\",\"Ram\",\"Shaw\",\"DhuQ\",\"DhuH\"],dayNames:[\"Yawm al-ahad\",\"Yawm al-ithnayn\",\"Yawm ath-thulaathaa'\",\"Yawm al-arbi'aa'\",\"Yawm al-kham\\u012bs\",\"Yawm al-jum'a\",\"Yawm as-sabt\"],dayNamesShort:[\"Aha\",\"Ith\",\"Thu\",\"Arb\",\"Kha\",\"Jum\",\"Sab\"],dayNamesMin:[\"Ah\",\"It\",\"Th\",\"Ar\",\"Kh\",\"Ju\",\"Sa\"],digits:null,dateFormat:\"yyyy/mm/dd\",firstDay:6,isRTL:!1}},leapYear:function(t){return(11*this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year()+14)%30<11},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){return this.leapYear(t)?355:354},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(12===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return 5!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return t=i.year(),e=i.month(),t=t<=0?t+1:t,(r=i.day())+Math.ceil(29.5*(e-1))+354*(t-1)+Math.floor((3+11*t)/30)+this.jdEpoch-1},fromJD:function(t){t=Math.floor(t)+.5;var e=Math.floor((30*(t-this.jdEpoch)+10646)/10631);e=e<=0?e-1:e;var r=Math.min(12,Math.ceil((t-29-this.toJD(e,1,1))/29.5)+1),n=t-this.toJD(e,r,1)+1;return this.newDate(e,r,n)}}),n.calendars.islamic=a},{\"../main\":640,\"object-assign\":483}],632:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"Julian\",jdEpoch:1721423.5,daysPerMonth:[31,28,31,30,31,30,31,31,30,31,30,31],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Julian\",epochs:[\"BC\",\"AD\"],monthNames:[\"January\",\"February\",\"March\",\"April\",\"May\",\"June\",\"July\",\"August\",\"September\",\"October\",\"November\",\"December\"],monthNamesShort:[\"Jan\",\"Feb\",\"Mar\",\"Apr\",\"May\",\"Jun\",\"Jul\",\"Aug\",\"Sep\",\"Oct\",\"Nov\",\"Dec\"],dayNames:[\"Sunday\",\"Monday\",\"Tuesday\",\"Wednesday\",\"Thursday\",\"Friday\",\"Saturday\"],dayNamesShort:[\"Sun\",\"Mon\",\"Tue\",\"Wed\",\"Thu\",\"Fri\",\"Sat\"],dayNamesMin:[\"Su\",\"Mo\",\"Tu\",\"We\",\"Th\",\"Fr\",\"Sa\"],digits:null,dateFormat:\"mm/dd/yyyy\",firstDay:0,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return(t=e.year()<0?e.year()+1:e.year())%4==0},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(4-(n.dayOfWeek()||7),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(2===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return t=i.year(),e=i.month(),r=i.day(),t<0&&t++,e<=2&&(t--,e+=12),Math.floor(365.25*(t+4716))+Math.floor(30.6001*(e+1))+r-1524.5},fromJD:function(t){var e=Math.floor(t+.5)+1524,r=Math.floor((e-122.1)/365.25),n=Math.floor(365.25*r),i=Math.floor((e-n)/30.6001),a=i-Math.floor(i<14?1:13),o=r-Math.floor(a>2?4716:4715),s=e-n-Math.floor(30.6001*i);return o<=0&&o--,this.newDate(o,a,s)}}),n.calendars.julian=a},{\"../main\":640,\"object-assign\":483}],633:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}function o(t,e){return t-e*Math.floor(t/e)}function s(t,e){return o(t-1,e)+1}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"Mayan\",jdEpoch:584282.5,hasYearZero:!0,minMonth:0,firstMonth:0,minDay:0,regionalOptions:{\"\":{name:\"Mayan\",epochs:[\"\",\"\"],monthNames:[\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\",\"12\",\"13\",\"14\",\"15\",\"16\",\"17\"],monthNamesShort:[\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\",\"12\",\"13\",\"14\",\"15\",\"16\",\"17\"],dayNames:[\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\",\"12\",\"13\",\"14\",\"15\",\"16\",\"17\",\"18\",\"19\"],dayNamesShort:[\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\",\"12\",\"13\",\"14\",\"15\",\"16\",\"17\",\"18\",\"19\"],dayNamesMin:[\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\",\"12\",\"13\",\"14\",\"15\",\"16\",\"17\",\"18\",\"19\"],digits:null,dateFormat:\"YYYY.m.d\",firstDay:0,isRTL:!1,haabMonths:[\"Pop\",\"Uo\",\"Zip\",\"Zotz\",\"Tzec\",\"Xul\",\"Yaxkin\",\"Mol\",\"Chen\",\"Yax\",\"Zac\",\"Ceh\",\"Mac\",\"Kankin\",\"Muan\",\"Pax\",\"Kayab\",\"Cumku\",\"Uayeb\"],tzolkinMonths:[\"Imix\",\"Ik\",\"Akbal\",\"Kan\",\"Chicchan\",\"Cimi\",\"Manik\",\"Lamat\",\"Muluc\",\"Oc\",\"Chuen\",\"Eb\",\"Ben\",\"Ix\",\"Men\",\"Cib\",\"Caban\",\"Etznab\",\"Cauac\",\"Ahau\"]}},leapYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),!1},formatYear:function(t){t=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year();var e=Math.floor(t/400);return t%=400,t+=t<0?400:0,e+\".\"+Math.floor(t/20)+\".\"+t%20},forYear:function(t){if((t=t.split(\".\")).length<3)throw\"Invalid Mayan year\";for(var e=0,r=0;r<t.length;r++){var n=parseInt(t[r],10);if(Math.abs(n)>19||r>0&&n<0)throw\"Invalid Mayan year\";e=20*e+n}return e},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),18},weekOfYear:function(t,e,r){return this._validate(t,e,r,n.local.invalidDate),0},daysInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),360},daysInMonth:function(t,e){return this._validate(t,e,this.minDay,n.local.invalidMonth),20},daysInWeek:function(){return 5},dayOfWeek:function(t,e,r){return this._validate(t,e,r,n.local.invalidDate).day()},weekDay:function(t,e,r){return this._validate(t,e,r,n.local.invalidDate),!0},extraInfo:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate).toJD(),a=this._toHaab(i),o=this._toTzolkin(i);return{haabMonthName:this.local.haabMonths[a[0]-1],haabMonth:a[0],haabDay:a[1],tzolkinDayName:this.local.tzolkinMonths[o[0]-1],tzolkinDay:o[0],tzolkinTrecena:o[1]}},_toHaab:function(t){var e=o((t-=this.jdEpoch)+8+340,365);return[Math.floor(e/20)+1,o(e,20)]},_toTzolkin:function(t){return[s((t-=this.jdEpoch)+20,20),s(t+4,13)]},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return i.day()+20*i.month()+360*i.year()+this.jdEpoch},fromJD:function(t){t=Math.floor(t)+.5-this.jdEpoch;var e=Math.floor(t/360);t%=360,t+=t<0?360:0;var r=Math.floor(t/20),n=t%20;return this.newDate(e,r,n)}}),n.calendars.mayan=a},{\"../main\":640,\"object-assign\":483}],634:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new n.baseCalendar;var o=n.instance(\"gregorian\");i(a.prototype,{name:\"Nanakshahi\",jdEpoch:2257673.5,daysPerMonth:[31,31,31,31,31,30,30,30,30,30,30,30],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Nanakshahi\",epochs:[\"BN\",\"AN\"],monthNames:[\"Chet\",\"Vaisakh\",\"Jeth\",\"Harh\",\"Sawan\",\"Bhadon\",\"Assu\",\"Katak\",\"Maghar\",\"Poh\",\"Magh\",\"Phagun\"],monthNamesShort:[\"Che\",\"Vai\",\"Jet\",\"Har\",\"Saw\",\"Bha\",\"Ass\",\"Kat\",\"Mgr\",\"Poh\",\"Mgh\",\"Pha\"],dayNames:[\"Somvaar\",\"Mangalvar\",\"Budhvaar\",\"Veervaar\",\"Shukarvaar\",\"Sanicharvaar\",\"Etvaar\"],dayNamesShort:[\"Som\",\"Mangal\",\"Budh\",\"Veer\",\"Shukar\",\"Sanichar\",\"Et\"],dayNamesMin:[\"So\",\"Ma\",\"Bu\",\"Ve\",\"Sh\",\"Sa\",\"Et\"],digits:null,dateFormat:\"dd-mm-yyyy\",firstDay:0,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear||n.regionalOptions[\"\"].invalidYear);return o.leapYear(e.year()+(e.year()<1?1:0)+1469)},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(1-(n.dayOfWeek()||7),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(12===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidMonth);(t=i.year())<0&&t++;for(var a=i.day(),s=1;s<i.month();s++)a+=this.daysPerMonth[s-1];return a+o.toJD(t+1468,3,13)},fromJD:function(t){t=Math.floor(t+.5);for(var e=Math.floor((t-(this.jdEpoch-1))/366);t>=this.toJD(e+1,1,1);)e++;for(var r=t-Math.floor(this.toJD(e,1,1)+.5)+1,n=1;r>this.daysInMonth(e,n);)r-=this.daysInMonth(e,n),n++;return this.newDate(e,n,r)}}),n.calendars.nanakshahi=a},{\"../main\":640,\"object-assign\":483}],635:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"Nepali\",jdEpoch:1700709.5,daysPerMonth:[31,31,32,32,31,30,30,29,30,29,30,30],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,daysPerYear:365,regionalOptions:{\"\":{name:\"Nepali\",epochs:[\"BBS\",\"ABS\"],monthNames:[\"Baisakh\",\"Jestha\",\"Ashadh\",\"Shrawan\",\"Bhadra\",\"Ashwin\",\"Kartik\",\"Mangsir\",\"Paush\",\"Mangh\",\"Falgun\",\"Chaitra\"],monthNamesShort:[\"Bai\",\"Je\",\"As\",\"Shra\",\"Bha\",\"Ash\",\"Kar\",\"Mang\",\"Pau\",\"Ma\",\"Fal\",\"Chai\"],dayNames:[\"Aaitabaar\",\"Sombaar\",\"Manglbaar\",\"Budhabaar\",\"Bihibaar\",\"Shukrabaar\",\"Shanibaar\"],dayNamesShort:[\"Aaita\",\"Som\",\"Mangl\",\"Budha\",\"Bihi\",\"Shukra\",\"Shani\"],dayNamesMin:[\"Aai\",\"So\",\"Man\",\"Bu\",\"Bi\",\"Shu\",\"Sha\"],digits:null,dateFormat:\"dd/mm/yyyy\",firstDay:1,isRTL:!1}},leapYear:function(t){return 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9===e?(o+=r-this.NEPALI_CALENDAR_DATA[l][0])<0&&(o+=a.daysInYear(c)):o+=this.NEPALI_CALENDAR_DATA[l][9]-this.NEPALI_CALENDAR_DATA[l][0],a.newDate(c,1,1).add(o,\"d\").toJD()},fromJD:function(t){var e=n.instance().fromJD(t),r=e.year(),i=e.dayOfYear(),a=r+56;this._createMissingCalendarData(a);for(var o=9,s=this.NEPALI_CALENDAR_DATA[a][0],l=this.NEPALI_CALENDAR_DATA[a][o]-s+1;i>l;)++o>12&&(o=1,a++),l+=this.NEPALI_CALENDAR_DATA[a][o];var c=this.NEPALI_CALENDAR_DATA[a][o]-(l-i);return this.newDate(a,o,c)},_createMissingCalendarData:function(t){var e=this.daysPerMonth.slice(0);e.unshift(17);for(var r=t-1;r<t+2;r++)void 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e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return 682*((e.year()-(e.year()>0?474:473))%2820+474+38)%2816<682},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-(n.dayOfWeek()+1)%7,\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(12===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return 5!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);t=i.year(),e=i.month(),r=i.day();var a=t-(t>=0?474:473),s=474+o(a,2820);return r+(e<=7?31*(e-1):30*(e-1)+6)+Math.floor((682*s-110)/2816)+365*(s-1)+1029983*Math.floor(a/2820)+this.jdEpoch-1},fromJD:function(t){var e=(t=Math.floor(t)+.5)-this.toJD(475,1,1),r=Math.floor(e/1029983),n=o(e,1029983),i=2820;if(1029982!==n){var a=Math.floor(n/366),s=o(n,366);i=Math.floor((2134*a+2816*s+2815)/1028522)+a+1}var l=i+2820*r+474;l=l<=0?l-1:l;var 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i=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);t=this._t2gYear(i.year());return a.weekOfYear(t,i.month(),i.day())},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(2===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);t=this._t2gYear(i.year());return a.toJD(t,i.month(),i.day())},fromJD:function(t){var e=a.fromJD(t),r=this._g2tYear(e.year());return this.newDate(r,e.month(),e.day())},_t2gYear:function(t){return t+this.yearsOffset+(t>=-this.yearsOffset&&t<=-1?1:0)},_g2tYear:function(t){return t-this.yearsOffset-(t>=1&&t<=this.yearsOffset?1:0)}}),n.calendars.taiwan=o},{\"../main\":640,\"object-assign\":483}],638:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\"),a=n.instance();function 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n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"UmmAlQura\",hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Umm al-Qura\",epochs:[\"BH\",\"AH\"],monthNames:[\"Al-Muharram\",\"Safar\",\"Rabi' al-awwal\",\"Rabi' Al-Thani\",\"Jumada Al-Awwal\",\"Jumada Al-Thani\",\"Rajab\",\"Sha'aban\",\"Ramadan\",\"Shawwal\",\"Dhu al-Qi'dah\",\"Dhu al-Hijjah\"],monthNamesShort:[\"Muh\",\"Saf\",\"Rab1\",\"Rab2\",\"Jum1\",\"Jum2\",\"Raj\",\"Sha'\",\"Ram\",\"Shaw\",\"DhuQ\",\"DhuH\"],dayNames:[\"Yawm al-Ahad\",\"Yawm al-Ithnain\",\"Yawm al-Thal\\u0101th\\u0101\\u2019\",\"Yawm al-Arba\\u2018\\u0101\\u2019\",\"Yawm al-Kham\\u012bs\",\"Yawm al-Jum\\u2018a\",\"Yawm al-Sabt\"],dayNamesMin:[\"Ah\",\"Ith\",\"Th\",\"Ar\",\"Kh\",\"Ju\",\"Sa\"],digits:null,dateFormat:\"yyyy/mm/dd\",firstDay:6,isRTL:!0}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return 355===this.daysInYear(e.year())},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){for(var e=0,r=1;r<=12;r++)e+=this.daysInMonth(t,r);return e},daysInMonth:function(t,e){for(var r=this._validate(t,e,this.minDay,n.local.invalidMonth).toJD()-24e5+.5,i=0,a=0;a<o.length;a++){if(o[a]>r)return o[i]-o[i-1];i++}return 30},weekDay:function(t,e,r){return 5!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate),a=12*(i.year()-1)+i.month()-15292;return i.day()+o[a-1]-1+24e5-.5},fromJD:function(t){for(var e=t-24e5+.5,r=0,n=0;n<o.length&&!(o[n]>e);n++)r++;var i=r+15292,a=Math.floor((i-1)/12),s=a+1,l=i-12*a,c=e-o[r-1]+1;return this.newDate(s,l,c)},isValid:function(t,e,r){var i=n.baseCalendar.prototype.isValid.apply(this,arguments);return i&&(i=(t=null!=t.year?t.year:t)>=1276&&t<=1500),i},_validate:function(t,e,r,i){var a=n.baseCalendar.prototype._validate.apply(this,arguments);if(a.year<1276||a.year>1500)throw i.replace(/\\{0\\}/,this.local.name);return a}}),n.calendars.ummalqura=a;var 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n=t(\"object-assign\");function i(){this.regionalOptions=[],this.regionalOptions[\"\"]={invalidCalendar:\"Calendar {0} not found\",invalidDate:\"Invalid {0} date\",invalidMonth:\"Invalid {0} month\",invalidYear:\"Invalid {0} year\",differentCalendars:\"Cannot mix {0} and {1} dates\"},this.local=this.regionalOptions[\"\"],this.calendars={},this._localCals={}}function a(t,e,r,n){if(this._calendar=t,this._year=e,this._month=r,this._day=n,0===this._calendar._validateLevel&&!this._calendar.isValid(this._year,this._month,this._day))throw(c.local.invalidDate||c.regionalOptions[\"\"].invalidDate).replace(/\\{0\\}/,this._calendar.local.name)}function o(t,e){return\"000000\".substring(0,e-(t=\"\"+t).length)+t}function s(){this.shortYearCutoff=\"+10\"}function l(t){this.local=this.regionalOptions[t]||this.regionalOptions[\"\"]}n(i.prototype,{instance:function(t,e){t=(t||\"gregorian\").toLowerCase(),e=e||\"\";var r=this._localCals[t+\"-\"+e];if(!r&&this.calendars[t]&&(r=new this.calendars[t](e),this._localCals[t+\"-\"+e]=r),!r)throw(this.local.invalidCalendar||this.regionalOptions[\"\"].invalidCalendar).replace(/\\{0\\}/,t);return r},newDate:function(t,e,r,n,i){return(n=(null!=t&&t.year?t.calendar():\"string\"==typeof n?this.instance(n,i):n)||this.instance()).newDate(t,e,r)},substituteDigits:function(t){return function(e){return(e+\"\").replace(/[0-9]/g,(function(e){return t[e]}))}},substituteChineseDigits:function(t,e){return function(r){for(var n=\"\",i=0;r>0;){var a=r%10;n=(0===a?\"\":t[a]+e[i])+n,i++,r=Math.floor(r/10)}return 0===n.indexOf(t[1]+e[1])&&(n=n.substr(1)),n||t[0]}}}),n(a.prototype,{newDate:function(t,e,r){return this._calendar.newDate(null==t?this:t,e,r)},year:function(t){return 0===arguments.length?this._year:this.set(t,\"y\")},month:function(t){return 0===arguments.length?this._month:this.set(t,\"m\")},day:function(t){return 0===arguments.length?this._day:this.set(t,\"d\")},date:function(t,e,r){if(!this._calendar.isValid(t,e,r))throw(c.local.invalidDate||c.regionalOptions[\"\"].invalidDate).replace(/\\{0\\}/,this._calendar.local.name);return this._year=t,this._month=e,this._day=r,this},leapYear:function(){return this._calendar.leapYear(this)},epoch:function(){return this._calendar.epoch(this)},formatYear:function(){return this._calendar.formatYear(this)},monthOfYear:function(){return this._calendar.monthOfYear(this)},weekOfYear:function(){return this._calendar.weekOfYear(this)},daysInYear:function(){return this._calendar.daysInYear(this)},dayOfYear:function(){return this._calendar.dayOfYear(this)},daysInMonth:function(){return this._calendar.daysInMonth(this)},dayOfWeek:function(){return this._calendar.dayOfWeek(this)},weekDay:function(){return this._calendar.weekDay(this)},extraInfo:function(){return this._calendar.extraInfo(this)},add:function(t,e){return this._calendar.add(this,t,e)},set:function(t,e){return this._calendar.set(this,t,e)},compareTo:function(t){if(this._calendar.name!==t._calendar.name)throw(c.local.differentCalendars||c.regionalOptions[\"\"].differentCalendars).replace(/\\{0\\}/,this._calendar.local.name).replace(/\\{1\\}/,t._calendar.local.name);var e=this._year!==t._year?this._year-t._year:this._month!==t._month?this.monthOfYear()-t.monthOfYear():this._day-t._day;return 0===e?0:e<0?-1:1},calendar:function(){return this._calendar},toJD:function(){return this._calendar.toJD(this)},fromJD:function(t){return this._calendar.fromJD(t)},toJSDate:function(){return this._calendar.toJSDate(this)},fromJSDate:function(t){return this._calendar.fromJSDate(t)},toString:function(){return(this.year()<0?\"-\":\"\")+o(Math.abs(this.year()),4)+\"-\"+o(this.month(),2)+\"-\"+o(this.day(),2)}}),n(s.prototype,{_validateLevel:0,newDate:function(t,e,r){return null==t?this.today():(t.year&&(this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[\"\"].invalidDate),r=t.day(),e=t.month(),t=t.year()),new a(this,t,e,r))},today:function(){return this.fromJSDate(new Date)},epoch:function(t){return this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[\"\"].invalidYear).year()<0?this.local.epochs[0]:this.local.epochs[1]},formatYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[\"\"].invalidYear);return(e.year()<0?\"-\":\"\")+o(Math.abs(e.year()),4)},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[\"\"].invalidYear),12},monthOfYear:function(t,e){var r=this._validate(t,e,this.minDay,c.local.invalidMonth||c.regionalOptions[\"\"].invalidMonth);return(r.month()+this.monthsInYear(r)-this.firstMonth)%this.monthsInYear(r)+this.minMonth},fromMonthOfYear:function(t,e){var r=(e+this.firstMonth-2*this.minMonth)%this.monthsInYear(t)+this.minMonth;return this._validate(t,r,this.minDay,c.local.invalidMonth||c.regionalOptions[\"\"].invalidMonth),r},daysInYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[\"\"].invalidYear);return this.leapYear(e)?366:365},dayOfYear:function(t,e,r){var n=this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[\"\"].invalidDate);return n.toJD()-this.newDate(n.year(),this.fromMonthOfYear(n.year(),this.minMonth),this.minDay).toJD()+1},daysInWeek:function(){return 7},dayOfWeek:function(t,e,r){var n=this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[\"\"].invalidDate);return(Math.floor(this.toJD(n))+2)%this.daysInWeek()},extraInfo:function(t,e,r){return this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[\"\"].invalidDate),{}},add:function(t,e,r){return this._validate(t,this.minMonth,this.minDay,c.local.invalidDate||c.regionalOptions[\"\"].invalidDate),this._correctAdd(t,this._add(t,e,r),e,r)},_add:function(t,e,r){if(this._validateLevel++,\"d\"===r||\"w\"===r){var n=t.toJD()+e*(\"w\"===r?this.daysInWeek():1),i=t.calendar().fromJD(n);return this._validateLevel--,[i.year(),i.month(),i.day()]}try{var a=t.year()+(\"y\"===r?e:0),o=t.monthOfYear()+(\"m\"===r?e:0);i=t.day();\"y\"===r?(t.month()!==this.fromMonthOfYear(a,o)&&(o=this.newDate(a,t.month(),this.minDay).monthOfYear()),o=Math.min(o,this.monthsInYear(a)),i=Math.min(i,this.daysInMonth(a,this.fromMonthOfYear(a,o)))):\"m\"===r&&(!function(t){for(;o<t.minMonth;)a--,o+=t.monthsInYear(a);for(var e=t.monthsInYear(a);o>e-1+t.minMonth;)a++,o-=e,e=t.monthsInYear(a)}(this),i=Math.min(i,this.daysInMonth(a,this.fromMonthOfYear(a,o))));var s=[a,this.fromMonthOfYear(a,o),i];return this._validateLevel--,s}catch(t){throw 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r,s,l=o(t,e),c=l.on,u=l.off.concat(l.explicitOff),f={},h=t._fullLayout.annotations;if(!c.length&&!u.length)return;for(r=0;r<c.length;r++)(s=a(t.layout,\"annotations\",h[c[r]])).modifyItem(\"visible\",!0),n.extendFlat(f,s.getUpdateObj());for(r=0;r<u.length;r++)(s=a(t.layout,\"annotations\",h[u[r]])).modifyItem(\"visible\",!1),n.extendFlat(f,s.getUpdateObj());return i.call(\"update\",t,{},f)}}},{\"../../lib\":799,\"../../plot_api/plot_template\":838,\"../../registry\":927}],648:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../color\");e.exports=function(t,e,r,a){a(\"opacity\");var o=a(\"bgcolor\"),s=a(\"bordercolor\"),l=i.opacity(s);a(\"borderpad\");var c=a(\"borderwidth\"),u=a(\"showarrow\");if(a(\"text\",u?\" \":r._dfltTitle.annotation),a(\"textangle\"),n.coerceFont(a,\"font\",r.font),a(\"width\"),a(\"align\"),a(\"height\")&&a(\"valign\"),u){var f,h,p=a(\"arrowside\");-1!==p.indexOf(\"end\")&&(f=a(\"arrowhead\"),h=a(\"arrowsize\")),-1!==p.indexOf(\"start\")&&(a(\"startarrowhead\",f),a(\"startarrowsize\",h)),a(\"arrowcolor\",l?e.bordercolor:i.defaultLine),a(\"arrowwidth\",2*(l&&c||1)),a(\"standoff\"),a(\"startstandoff\")}var d=a(\"hovertext\"),m=r.hoverlabel||{};if(d){var g=a(\"hoverlabel.bgcolor\",m.bgcolor||(i.opacity(o)?i.rgb(o):i.defaultLine)),v=a(\"hoverlabel.bordercolor\",m.bordercolor||i.contrast(g));n.coerceFont(a,\"hoverlabel.font\",{family:m.font.family,size:m.font.size,color:m.font.color||v})}a(\"captureevents\",!!d)}},{\"../../lib\":799,\"../color\":662}],649:[function(t,e,r){\"use strict\";var n=t(\"fast-isnumeric\"),i=t(\"../../lib/to_log_range\");e.exports=function(t,e,r,a){e=e||{};var o=\"log\"===r&&\"linear\"===e.type,s=\"linear\"===r&&\"log\"===e.type;if(o||s)for(var l,c,u=t._fullLayout.annotations,f=e._id.charAt(0),h=0;h<u.length;h++)l=u[h],c=\"annotations[\"+h+\"].\",l[f+\"ref\"]===e._id&&p(f),l[\"a\"+f+\"ref\"]===e._id&&p(\"a\"+f);function p(t){var r=l[t],s=null;s=o?i(r,e.range):Math.pow(10,r),n(s)||(s=null),a(c+t,s)}}},{\"../../lib/to_log_range\":826,\"fast-isnumeric\":245}],650:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../../plots/cartesian/axes\"),a=t(\"../../plots/array_container_defaults\"),o=t(\"./common_defaults\"),s=t(\"./attributes\");function l(t,e,r){function a(r,i){return n.coerce(t,e,s,r,i)}var l=a(\"visible\"),c=a(\"clicktoshow\");if(l||c){o(t,e,r,a);for(var u=e.showarrow,f=[\"x\",\"y\"],h=[-10,-30],p={_fullLayout:r},d=0;d<2;d++){var m=f[d],g=i.coerceRef(t,e,p,m,\"\",\"paper\");if(\"paper\"!==g)i.getFromId(p,g)._annIndices.push(e._index);if(i.coercePosition(e,p,a,g,m,.5),u){var v=\"a\"+m,y=i.coerceRef(t,e,p,v,\"pixel\",[\"pixel\",\"paper\"]);\"pixel\"!==y&&y!==g&&(y=e[v]=\"pixel\");var x=\"pixel\"===y?h[d]:.4;i.coercePosition(e,p,a,y,v,x)}a(m+\"anchor\"),a(m+\"shift\")}if(n.noneOrAll(t,e,[\"x\",\"y\"]),u&&n.noneOrAll(t,e,[\"ax\",\"ay\"]),c){var b=a(\"xclick\"),_=a(\"yclick\");e._xclick=void 0===b?e.x:i.cleanPosition(b,p,e.xref),e._yclick=void 0===_?e.y:i.cleanPosition(_,p,e.yref)}}}e.exports=function(t,e){a(t,e,{name:\"annotations\",handleItemDefaults:l})}},{\"../../lib\":799,\"../../plots/array_container_defaults\":844,\"../../plots/cartesian/axes\":849,\"./attributes\":645,\"./common_defaults\":648}],651:[function(t,e,r){\"use strict\";var n=t(\"@plotly/d3\"),i=t(\"../../registry\"),a=t(\"../../plots/plots\"),o=t(\"../../lib\"),s=o.strTranslate,l=t(\"../../plots/cartesian/axes\"),c=t(\"../color\"),u=t(\"../drawing\"),f=t(\"../fx\"),h=t(\"../../lib/svg_text_utils\"),p=t(\"../../lib/setcursor\"),d=t(\"../dragelement\"),m=t(\"../../plot_api/plot_template\").arrayEditor,g=t(\"./draw_arrow_head\");function v(t,e){var r=t._fullLayout.annotations[e]||{},n=l.getFromId(t,r.xref),i=l.getFromId(t,r.yref);n&&n.setScale(),i&&i.setScale(),x(t,r,e,!1,n,i)}function y(t,e,r,n,i){var a=i[r],o=i[r+\"ref\"],s=-1!==r.indexOf(\"y\"),c=\"domain\"===l.getRefType(o),u=s?n.h:n.w;return t?c?a+(s?-e:e)/t._length:t.p2r(t.r2p(a)+e):a+(s?-e:e)/u}function x(t,e,r,a,v,x){var b,_,w=t._fullLayout,T=t._fullLayout._size,k=t._context.edits;a?(b=\"annotation-\"+a,_=a+\".annotations\"):(b=\"annotation\",_=\"annotations\");var M=m(t.layout,_,e),A=M.modifyBase,S=M.modifyItem,E=M.getUpdateObj;w._infolayer.selectAll(\".\"+b+'[data-index=\"'+r+'\"]').remove();var L=\"clip\"+w._uid+\"_ann\"+r;if(e._input&&!1!==e.visible){var C={x:{},y:{}},P=+e.textangle||0,I=w._infolayer.append(\"g\").classed(b,!0).attr(\"data-index\",String(r)).style(\"opacity\",e.opacity),O=I.append(\"g\").classed(\"annotation-text-g\",!0),z=k[e.showarrow?\"annotationTail\":\"annotationPosition\"],D=e.captureevents||k.annotationText||z,R=O.append(\"g\").style(\"pointer-events\",D?\"all\":null).call(p,\"pointer\").on(\"click\",(function(){t._dragging=!1,t.emit(\"plotly_clickannotation\",Y(n.event))}));e.hovertext&&R.on(\"mouseover\",(function(){var r=e.hoverlabel,n=r.font,i=this.getBoundingClientRect(),a=t.getBoundingClientRect();f.loneHover({x0:i.left-a.left,x1:i.right-a.left,y:(i.top+i.bottom)/2-a.top,text:e.hovertext,color:r.bgcolor,borderColor:r.bordercolor,fontFamily:n.family,fontSize:n.size,fontColor:n.color},{container:w._hoverlayer.node(),outerContainer:w._paper.node(),gd:t})})).on(\"mouseout\",(function(){f.loneUnhover(w._hoverlayer.node())}));var F=e.borderwidth,B=e.borderpad,N=F+B,j=R.append(\"rect\").attr(\"class\",\"bg\").style(\"stroke-width\",F+\"px\").call(c.stroke,e.bordercolor).call(c.fill,e.bgcolor),U=e.width||e.height,V=w._topclips.selectAll(\"#\"+L).data(U?[0]:[]);V.enter().append(\"clipPath\").classed(\"annclip\",!0).attr(\"id\",L).append(\"rect\"),V.exit().remove();var q=e.font,H=w._meta?o.templateString(e.text,w._meta):e.text,G=R.append(\"text\").classed(\"annotation-text\",!0).text(H);k.annotationText?G.call(h.makeEditable,{delegate:R,gd:t}).call(W).on(\"edit\",(function(r){e.text=r,this.call(W),S(\"text\",r),v&&v.autorange&&A(v._name+\".autorange\",!0),x&&x.autorange&&A(x._name+\".autorange\",!0),i.call(\"_guiRelayout\",t,E())})):G.call(W)}else n.selectAll(\"#\"+L).remove();function Y(t){var n={index:r,annotation:e._input,fullAnnotation:e,event:t};return a&&(n.subplotId=a),n}function W(r){return r.call(u.font,q).attr({\"text-anchor\":{left:\"start\",right:\"end\"}[e.align]||\"middle\"}),h.convertToTspans(r,t,X),r}function X(){var r=G.selectAll(\"a\");1===r.size()&&r.text()===G.text()&&R.insert(\"a\",\":first-child\").attr({\"xlink:xlink:href\":r.attr(\"xlink:href\"),\"xlink:xlink:show\":r.attr(\"xlink:show\")}).style({cursor:\"pointer\"}).node().appendChild(j.node());var n=R.select(\".annotation-text-math-group\"),f=!n.empty(),m=u.bBox((f?n:G).node()),b=m.width,_=m.height,M=e.width||b,D=e.height||_,B=Math.round(M+2*N),q=Math.round(D+2*N);function H(t,e){return\"auto\"===e&&(e=t<1/3?\"left\":t>2/3?\"right\":\"center\"),{center:0,middle:0,left:.5,bottom:-.5,right:-.5,top:.5}[e]}for(var W=!1,X=[\"x\",\"y\"],Z=0;Z<X.length;Z++){var J,K,Q,$,tt,et=X[Z],rt=e[et+\"ref\"]||et,nt=e[\"a\"+et+\"ref\"],it={x:v,y:x}[et],at=(P+(\"x\"===et?0:-90))*Math.PI/180,ot=B*Math.cos(at),st=q*Math.sin(at),lt=Math.abs(ot)+Math.abs(st),ct=e[et+\"anchor\"],ut=e[et+\"shift\"]*(\"x\"===et?1:-1),ft=C[et],ht=l.getRefType(rt);if(it&&\"domain\"!==ht){var pt=it.r2fraction(e[et]);(pt<0||pt>1)&&(nt===rt?((pt=it.r2fraction(e[\"a\"+et]))<0||pt>1)&&(W=!0):W=!0),J=it._offset+it.r2p(e[et]),$=.5}else{var dt=\"domain\"===ht;\"x\"===et?(Q=e[et],J=dt?it._offset+it._length*Q:J=T.l+T.w*Q):(Q=1-e[et],J=dt?it._offset+it._length*Q:J=T.t+T.h*Q),$=e.showarrow?.5:Q}if(e.showarrow){ft.head=J;var mt=e[\"a\"+et];if(tt=ot*H(.5,e.xanchor)-st*H(.5,e.yanchor),nt===rt){var gt=l.getRefType(nt);\"domain\"===gt?(\"y\"===et&&(mt=1-mt),ft.tail=it._offset+it._length*mt):\"paper\"===gt?\"y\"===et?(mt=1-mt,ft.tail=T.t+T.h*mt):ft.tail=T.l+T.w*mt:ft.tail=it._offset+it.r2p(mt),K=tt}else ft.tail=J+mt,K=tt+mt;ft.text=ft.tail+tt;var vt=w[\"x\"===et?\"width\":\"height\"];if(\"paper\"===rt&&(ft.head=o.constrain(ft.head,1,vt-1)),\"pixel\"===nt){var yt=-Math.max(ft.tail-3,ft.text),xt=Math.min(ft.tail+3,ft.text)-vt;yt>0?(ft.tail+=yt,ft.text+=yt):xt>0&&(ft.tail-=xt,ft.text-=xt)}ft.tail+=ut,ft.head+=ut}else K=tt=lt*H($,ct),ft.text=J+tt;ft.text+=ut,tt+=ut,K+=ut,e[\"_\"+et+\"padplus\"]=lt/2+K,e[\"_\"+et+\"padminus\"]=lt/2-K,e[\"_\"+et+\"size\"]=lt,e[\"_\"+et+\"shift\"]=tt}if(W)R.remove();else{var bt=0,_t=0;if(\"left\"!==e.align&&(bt=(M-b)*(\"center\"===e.align?.5:1)),\"top\"!==e.valign&&(_t=(D-_)*(\"middle\"===e.valign?.5:1)),f)n.select(\"svg\").attr({x:N+bt-1,y:N+_t}).call(u.setClipUrl,U?L:null,t);else{var wt=N+_t-m.top,Tt=N+bt-m.left;G.call(h.positionText,Tt,wt).call(u.setClipUrl,U?L:null,t)}V.select(\"rect\").call(u.setRect,N,N,M,D),j.call(u.setRect,F/2,F/2,B-F,q-F),R.call(u.setTranslate,Math.round(C.x.text-B/2),Math.round(C.y.text-q/2)),O.attr({transform:\"rotate(\"+P+\",\"+C.x.text+\",\"+C.y.text+\")\"});var kt,Mt=function(r,n){I.selectAll(\".annotation-arrow-g\").remove();var l=C.x.head,f=C.y.head,h=C.x.tail+r,p=C.y.tail+n,m=C.x.text+r,b=C.y.text+n,_=o.rotationXYMatrix(P,m,b),w=o.apply2DTransform(_),M=o.apply2DTransform2(_),L=+j.attr(\"width\"),z=+j.attr(\"height\"),D=m-.5*L,F=D+L,B=b-.5*z,N=B+z,U=[[D,B,D,N],[D,N,F,N],[F,N,F,B],[F,B,D,B]].map(M);if(!U.reduce((function(t,e){return t^!!o.segmentsIntersect(l,f,l+1e6,f+1e6,e[0],e[1],e[2],e[3])}),!1)){U.forEach((function(t){var e=o.segmentsIntersect(h,p,l,f,t[0],t[1],t[2],t[3]);e&&(h=e.x,p=e.y)}));var V=e.arrowwidth,q=e.arrowcolor,H=e.arrowside,G=I.append(\"g\").style({opacity:c.opacity(q)}).classed(\"annotation-arrow-g\",!0),Y=G.append(\"path\").attr(\"d\",\"M\"+h+\",\"+p+\"L\"+l+\",\"+f).style(\"stroke-width\",V+\"px\").call(c.stroke,c.rgb(q));if(g(Y,H,e),k.annotationPosition&&Y.node().parentNode&&!a){var W=l,X=f;if(e.standoff){var Z=Math.sqrt(Math.pow(l-h,2)+Math.pow(f-p,2));W+=e.standoff*(h-l)/Z,X+=e.standoff*(p-f)/Z}var 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n=t(\"../annotations/attributes\"),i=t(\"../../plot_api/edit_types\").overrideAll,a=t(\"../../plot_api/plot_template\").templatedArray;e.exports=i(a(\"annotation\",{visible:n.visible,x:{valType:\"any\"},y:{valType:\"any\"},z:{valType:\"any\"},ax:{valType:\"number\"},ay:{valType:\"number\"},xanchor:n.xanchor,xshift:n.xshift,yanchor:n.yanchor,yshift:n.yshift,text:n.text,textangle:n.textangle,font:n.font,width:n.width,height:n.height,opacity:n.opacity,align:n.align,valign:n.valign,bgcolor:n.bgcolor,bordercolor:n.bordercolor,borderpad:n.borderpad,borderwidth:n.borderwidth,showarrow:n.showarrow,arrowcolor:n.arrowcolor,arrowhead:n.arrowhead,startarrowhead:n.startarrowhead,arrowside:n.arrowside,arrowsize:n.arrowsize,startarrowsize:n.startarrowsize,arrowwidth:n.arrowwidth,standoff:n.standoff,startstandoff:n.startstandoff,hovertext:n.hovertext,hoverlabel:n.hoverlabel,captureevents:n.captureevents}),\"calc\",\"from-root\")},{\"../../plot_api/edit_types\":831,\"../../plot_api/plot_template\":838,\"../annotations/attributes\":645}],655:[function(t,e,r){\"use 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n.coerce(t,e,s,r,i)}function c(t){var n=t+\"axis\",a={_fullLayout:{}};return a._fullLayout[n]=r[n],i.coercePosition(e,a,l,t,t,.5)}l(\"visible\")&&(o(t,e,a.fullLayout,l),c(\"x\"),c(\"y\"),c(\"z\"),n.noneOrAll(t,e,[\"x\",\"y\",\"z\"]),e.xref=\"x\",e.yref=\"y\",e.zref=\"z\",l(\"xanchor\"),l(\"yanchor\"),l(\"xshift\"),l(\"yshift\"),e.showarrow&&(e.axref=\"pixel\",e.ayref=\"pixel\",l(\"ax\",-10),l(\"ay\",-30),n.noneOrAll(t,e,[\"ax\",\"ay\"])))}e.exports=function(t,e,r){a(t,e,{name:\"annotations\",handleItemDefaults:l,fullLayout:r.fullLayout})}},{\"../../lib\":799,\"../../plots/array_container_defaults\":844,\"../../plots/cartesian/axes\":849,\"../annotations/common_defaults\":648,\"./attributes\":654}],657:[function(t,e,r){\"use strict\";var n=t(\"../annotations/draw\").drawRaw,i=t(\"../../plots/gl3d/project\"),a=[\"x\",\"y\",\"z\"];e.exports=function(t){for(var e=t.fullSceneLayout,r=t.dataScale,o=e.annotations,s=0;s<o.length;s++){for(var l=o[s],c=!1,u=0;u<3;u++){var 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This does not set the calendar for\",\"interpreting data on this axis, that's specified in the trace\",\"or via the global `layout.calendar`\"].join(\" \"));e.exports={moduleType:\"component\",name:\"calendars\",schema:{traces:{scatter:g,bar:g,box:g,heatmap:g,contour:g,histogram:g,histogram2d:g,histogram2dcontour:g,scatter3d:v,surface:v,mesh3d:v,scattergl:g,ohlc:m,candlestick:m},layout:{calendar:p([\"Sets the default calendar system to use for interpreting and\",\"displaying dates throughout the plot.\"].join(\" \"))},subplots:{xaxis:{calendar:y},yaxis:{calendar:y},scene:{xaxis:{calendar:y},yaxis:{calendar:y},zaxis:{calendar:y}},polar:{radialaxis:{calendar:y}}},transforms:{filter:{valuecalendar:p([\"WARNING: All transforms are deprecated and may be removed from the API in next major version.\",\"Sets the calendar system to use for `value`, if it is a date.\"].join(\" \")),targetcalendar:p([\"WARNING: All transforms are deprecated and may be removed from the API in next major version.\",\"Sets the calendar system to use for `target`, if it is an\",\"array of dates. If `target` is a string (eg *x*) we use the\",\"corresponding trace attribute (eg `xcalendar`) if it exists,\",\"even if `targetcalendar` is provided.\"].join(\" \"))}}},layoutAttributes:l,handleDefaults:c,handleTraceDefaults:function(t,e,r,n){for(var i=0;i<r.length;i++)c(t,e,r[i]+\"calendar\",n.calendar)},CANONICAL_SUNDAY:{chinese:\"2000-01-02\",coptic:\"2000-01-03\",discworld:\"2000-01-03\",ethiopian:\"2000-01-05\",hebrew:\"5000-01-01\",islamic:\"1000-01-02\",julian:\"2000-01-03\",mayan:\"5000-01-01\",nanakshahi:\"1000-01-05\",nepali:\"2000-01-05\",persian:\"1000-01-01\",jalali:\"1000-01-01\",taiwan:\"1000-01-04\",thai:\"2000-01-04\",ummalqura:\"1400-01-06\"},CANONICAL_TICK:{chinese:\"2000-01-01\",coptic:\"2000-01-01\",discworld:\"2000-01-01\",ethiopian:\"2000-01-01\",hebrew:\"5000-01-01\",islamic:\"1000-01-01\",julian:\"2000-01-01\",mayan:\"5000-01-01\",nanakshahi:\"1000-01-01\",nepali:\"2000-01-01\",persian:\"1000-01-01\",jalali:\"1000-01-01\",taiwan:\"1000-01-01\",thai:\"2000-01-01\",ummalqura:\"1400-01-01\"},DFLTRANGE:{chinese:[\"2000-01-01\",\"2001-01-01\"],coptic:[\"1700-01-01\",\"1701-01-01\"],discworld:[\"1800-01-01\",\"1801-01-01\"],ethiopian:[\"2000-01-01\",\"2001-01-01\"],hebrew:[\"5700-01-01\",\"5701-01-01\"],islamic:[\"1400-01-01\",\"1401-01-01\"],julian:[\"2000-01-01\",\"2001-01-01\"],mayan:[\"5200-01-01\",\"5201-01-01\"],nanakshahi:[\"0500-01-01\",\"0501-01-01\"],nepali:[\"2000-01-01\",\"2001-01-01\"],persian:[\"1400-01-01\",\"1401-01-01\"],jalali:[\"1400-01-01\",\"1401-01-01\"],taiwan:[\"0100-01-01\",\"0101-01-01\"],thai:[\"2500-01-01\",\"2501-01-01\"],ummalqura:[\"1400-01-01\",\"1401-01-01\"]},getCal:h,worldCalFmt:function(t,e,r){for(var 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F,N,j,U,V,q,H,G,Y,W,X,Z,J,K=[],Q=[],$={hLinePoint:null,vLinePoint:null},tt=!1;if(Array.isArray(e))for(S=\"array\",j=0;j<e.length;j++)(V=t.calcdata[e[j].curveNumber||0])&&(q=V[0].trace,\"skip\"!==V[0].trace.hoverinfo&&(Q.push(V),\"h\"===q.orientation&&(tt=!0)));else{for(U=0;U<t.calcdata.length;U++)V=t.calcdata[U],\"skip\"!==(q=V[0].trace).hoverinfo&&v.isTraceInSubplots(q,s)&&(Q.push(V),\"h\"===q.orientation&&(tt=!0));var et,rt;if(!e.target)et=\"xpx\"in e?e.xpx:b[0]._length/2,rt=\"ypx\"in e?e.ypx:_[0]._length/2;else{if(!1===c.triggerHandler(t,\"plotly_beforehover\",e))return;var nt=e.composedPath&&e.composedPath()[0];nt||(nt=e.target);var it=nt.getBoundingClientRect();et=e.clientX-it.left,rt=e.clientY-it.top,l._calcInverseTransform(t);var at=o.apply3DTransform(l._invTransform)(et,rt);if(et=at[0],rt=at[1],et<0||et>b[0]._length||rt<0||rt>_[0]._length)return d.unhoverRaw(t,e)}if(e.pointerX=et+b[0]._offset,e.pointerY=rt+_[0]._offset,F=\"xval\"in e?v.flat(s,e.xval):v.p2c(b,et),N=\"yval\"in e?v.flat(s,e.yval):v.p2c(_,rt),!i(F[0])||!i(N[0]))return o.warn(\"Fx.hover failed\",e,t),d.unhoverRaw(t,e)}var ot=1/0;function st(t,r){for(U=0;U<Q.length;U++)if((V=Q[U])&&V[0]&&V[0].trace&&!0===(q=V[0].trace).visible&&0!==q._length&&-1===[\"carpet\",\"contourcarpet\"].indexOf(q._module.name)){if(\"splom\"===q.type?H=s[G=0]:(H=v.getSubplot(q),G=s.indexOf(H)),Y=S,v.isUnifiedHover(Y)&&(Y=Y.charAt(0)),Z={cd:V,trace:q,xa:b[G],ya:_[G],maxHoverDistance:C,maxSpikeDistance:I,index:!1,distance:Math.min(ot,C),spikeDistance:1/0,xSpike:void 0,ySpike:void 0,color:p.defaultLine,name:q.name,x0:void 0,x1:void 0,y0:void 0,y1:void 0,xLabelVal:void 0,yLabelVal:void 0,zLabelVal:void 0,text:void 0},l[H]&&(Z.subplot=l[H]._subplot),l._splomScenes&&l._splomScenes[q.uid]&&(Z.scene=l._splomScenes[q.uid]),J=K.length,\"array\"===Y){var n=e[U];\"pointNumber\"in n?(Z.index=n.pointNumber,Y=\"closest\"):(Y=\"\",\"xval\"in n&&(W=n.xval,Y=\"x\"),\"yval\"in n&&(X=n.yval,Y=Y?\"closest\":\"y\"))}else void 0!==t&&void 0!==r?(W=t,X=r):(W=F[G],X=N[G]);if(0!==C)if(q._module&&q._module.hoverPoints){var a=q._module.hoverPoints(Z,W,X,Y,{finiteRange:!0,hoverLayer:l._hoverlayer});if(a)for(var c,u=0;u<a.length;u++)c=a[u],i(c.x0)&&i(c.y0)&&K.push(z(c,S))}else o.log(\"Unrecognized trace type in hover:\",q);if(\"closest\"===S&&K.length>J&&(K.splice(0,J),ot=K[0].distance),m&&0!==I&&0===K.length){Z.distance=I,Z.index=!1;var f=q._module.hoverPoints(Z,W,X,\"closest\",{hoverLayer:l._hoverlayer});if(f&&(f=f.filter((function(t){return t.spikeDistance<=I}))),f&&f.length){var h,d=f.filter((function(t){return t.xa.showspikes&&\"hovered data\"!==t.xa.spikesnap}));if(d.length){var g=d[0];i(g.x0)&&i(g.y0)&&(h=ct(g),(!$.vLinePoint||$.vLinePoint.spikeDistance>h.spikeDistance)&&($.vLinePoint=h))}var y=f.filter((function(t){return t.ya.showspikes&&\"hovered data\"!==t.ya.spikesnap}));if(y.length){var x=y[0];i(x.x0)&&i(x.y0)&&(h=ct(x),(!$.hLinePoint||$.hLinePoint.spikeDistance>h.spikeDistance)&&($.hLinePoint=h))}}}}}function lt(t,e){for(var r,n=null,i=1/0,a=0;a<t.length;a++)(r=t[a].spikeDistance)<=i&&r<=e&&(n=t[a],i=r);return n}function ct(t){return t?{xa:t.xa,ya:t.ya,x:void 0!==t.xSpike?t.xSpike:(t.x0+t.x1)/2,y:void 0!==t.ySpike?t.ySpike:(t.y0+t.y1)/2,distance:t.distance,spikeDistance:t.spikeDistance,curveNumber:t.trace.index,color:t.color,pointNumber:t.index}:null}st();var ut={fullLayout:l,container:l._hoverlayer,outerContainer:l._paperdiv,event:e},ft=t._spikepoints,ht={vLinePoint:$.vLinePoint,hLinePoint:$.hLinePoint};if(t._spikepoints=ht,m&&0!==I&&0!==K.length){var pt=lt(K.filter((function(t){return t.ya.showspikes})),I);$.hLinePoint=ct(pt);var dt=lt(K.filter((function(t){return t.xa.showspikes})),I);$.vLinePoint=ct(dt)}if(0===K.length){var mt=d.unhoverRaw(t,e);return!m||null===$.hLinePoint&&null===$.vLinePoint||R(ft)&&D(t,$,ut),mt}m&&R(ft)&&D(t,$,ut);var gt=function(){K.sort((function(t,e){return t.distance-e.distance})),K=function(t,e){for(var r=e.charAt(0),n=[],i=[],a=[],o=0;o<t.length;o++){var s=t[o];g.traceIs(s.trace,\"bar-like\")||g.traceIs(s.trace,\"box-violin\")?a.push(s):s.trace[r+\"period\"]?i.push(s):n.push(s)}return n.concat(i).concat(a)}(K,S)};if(gt(),v.isXYhover(Y)&&0!==K[0].length&&\"splom\"!==K[0].trace.type){var vt=K[0],yt=(K=E[vt.trace.type]?K.filter((function(t){return t.trace.index===vt.trace.index})):[vt]).length,xt=B(\"x\",vt,l),bt=B(\"y\",vt,l);st(xt,bt);var _t,wt=[],Tt={},kt=0,Mt=function(t){var e=E[t.trace.type]?L(t):t.trace.index;if(Tt[e]){var r=Tt[e]-1,n=wt[r];r>0&&Math.abs(t.distance)<Math.abs(n.distance)&&(wt[r]=t)}else kt++,Tt[e]=kt,wt.push(t)};for(_t=0;_t<yt;_t++)Mt(K[_t]);for(_t=K.length-1;_t>yt-1;_t--)Mt(K[_t]);K=wt,gt()}var At=t._hoverdata,St=[];for(j=0;j<K.length;j++){var Et=K[j],Lt=v.makeEventData(Et,Et.trace,Et.cd);if(!1!==Et.hovertemplate){var Ct=!1;Et.cd[Et.index]&&Et.cd[Et.index].ht&&(Ct=Et.cd[Et.index].ht),Et.hovertemplate=Ct||Et.trace.hovertemplate||!1}Et.eventData=[Lt],St.push(Lt)}t._hoverdata=St;var Pt=\"y\"===S&&(Q.length>1||K.length>1)||\"closest\"===S&&tt&&K.length>1,It=p.combine(l.plot_bgcolor||p.background,l.paper_bgcolor),Ot={hovermode:S,rotateLabels:Pt,bgColor:It,container:l._hoverlayer,outerContainer:l._paperdiv,commonLabelOpts:l.hoverlabel,hoverdistance:l.hoverdistance},zt=P(K,Ot,t);v.isUnifiedHover(S)||(!function(t,e,r){var n,i,a,o,s,l,c,u=0,f=1,h=t.size(),p=new Array(h),d=0;function m(t){var e=t[0],r=t[t.length-1];if(i=e.pmin-e.pos-e.dp+e.size,a=r.pos+r.dp+r.size-e.pmax,i>.01){for(s=t.length-1;s>=0;s--)t[s].dp+=i;n=!1}if(!(a<.01)){if(i<-.01){for(s=t.length-1;s>=0;s--)t[s].dp-=a;n=!1}if(n){var c=0;for(o=0;o<t.length;o++)(l=t[o]).pos+l.dp+l.size>e.pmax&&c++;for(o=t.length-1;o>=0&&!(c<=0);o--)(l=t[o]).pos>e.pmax-1&&(l.del=!0,c--);for(o=0;o<t.length&&!(c<=0);o++)if((l=t[o]).pos<e.pmin+1)for(l.del=!0,c--,a=2*l.size,s=t.length-1;s>=0;s--)t[s].dp-=a;for(o=t.length-1;o>=0&&!(c<=0);o--)(l=t[o]).pos+l.dp+l.size>e.pmax&&(l.del=!0,c--)}}}t.each((function(t){var n=t[e],i=\"x\"===n._id.charAt(0),a=n.range;0===d&&a&&a[0]>a[1]!==i&&(f=-1),p[d++]=[{datum:t,traceIndex:t.trace.index,dp:0,pos:t.pos,posref:t.posref,size:t.by*(i?T:1)/2,pmin:0,pmax:i?r.width:r.height}]})),p.sort((function(t,e){return t[0].posref-e[0].posref||f*(e[0].traceIndex-t[0].traceIndex)}));for(;!n&&u<=h;){for(u++,n=!0,o=0;o<p.length-1;){var g=p[o],v=p[o+1],y=g[g.length-1],x=v[0];if((i=y.pos+y.dp+y.size-x.pos-x.dp+x.size)>.01&&y.pmin===x.pmin&&y.pmax===x.pmax){for(s=v.length-1;s>=0;s--)v[s].dp+=i;for(g.push.apply(g,v),p.splice(o+1,1),c=0,s=g.length-1;s>=0;s--)c+=g[s].dp;for(a=c/g.length,s=g.length-1;s>=0;s--)g[s].dp-=a;n=!1}else o++}p.forEach(m)}for(o=p.length-1;o>=0;o--){var b=p[o];for(s=b.length-1;s>=0;s--){var _=b[s],w=_.datum;w.offset=_.dp,w.del=_.del}}}(zt,Pt?\"xa\":\"ya\",l),O(zt,Pt,l._invScaleX,l._invScaleY));if(e.target&&e.target.tagName){var Dt=g.getComponentMethod(\"annotations\",\"hasClickToShow\")(t,St);f(n.select(e.target),Dt?\"pointer\":\"\")}if(!e.target||a||!function(t,e,r){if(!r||r.length!==t._hoverdata.length)return!0;for(var n=r.length-1;n>=0;n--){var i=r[n],a=t._hoverdata[n];if(i.curveNumber!==a.curveNumber||String(i.pointNumber)!==String(a.pointNumber)||String(i.pointNumbers)!==String(a.pointNumbers))return!0}return!1}(t,0,At))return;At&&t.emit(\"plotly_unhover\",{event:e,points:At});t.emit(\"plotly_hover\",{event:e,points:t._hoverdata,xaxes:b,yaxes:_,xvals:F,yvals:N})}(t,e,r,a)}))},r.loneHover=function(t,e){var r=!0;Array.isArray(t)||(r=!1,t=[t]);var i=t.map((function(t){return{color:t.color||p.defaultLine,x0:t.x0||t.x||0,x1:t.x1||t.x||0,y0:t.y0||t.y||0,y1:t.y1||t.y||0,xLabel:t.xLabel,yLabel:t.yLabel,zLabel:t.zLabel,text:t.text,name:t.name,idealAlign:t.idealAlign,borderColor:t.borderColor,fontFamily:t.fontFamily,fontSize:t.fontSize,fontColor:t.fontColor,nameLength:t.nameLength,textAlign:t.textAlign,trace:t.trace||{index:0,hoverinfo:\"\"},xa:{_offset:0},ya:{_offset:0},index:0,hovertemplate:t.hovertemplate||!1,eventData:t.eventData||!1,hovertemplateLabels:t.hovertemplateLabels||!1}})),a=n.select(e.container),o=e.outerContainer?n.select(e.outerContainer):a,s={hovermode:\"closest\",rotateLabels:!1,bgColor:e.bgColor||p.background,container:a,outerContainer:o},l=P(i,s,e.gd),c=0,u=0;l.sort((function(t,e){return t.y0-e.y0})).each((function(t,r){var n=t.y0-t.by/2;t.offset=n-5<c?c-n+5:0,c=n+t.by+t.offset,r===e.anchorIndex&&(u=t.offset)})).each((function(t){t.offset-=u}));var f=e.gd._fullLayout._invScaleX,h=e.gd._fullLayout._invScaleY;return O(l,s.rotateLabels,f,h),r?l:l.node()};var C=/<extra>([\\s\\S]*)<\\/extra>/;function P(t,e,r){var i=r._fullLayout,a=e.hovermode,c=e.rotateLabels,f=e.bgColor,d=e.container,m=e.outerContainer,g=e.commonLabelOpts||{},w=e.fontFamily||y.HOVERFONT,T=e.fontSize||y.HOVERFONTSIZE,k=t[0],M=k.xa,E=k.ya,C=a.charAt(0),P=k[C+\"Label\"],O=m.node().getBoundingClientRect(),z=O.top,D=O.width,R=O.height,F=void 0!==P&&k.distance<=e.hoverdistance&&(\"x\"===a||\"y\"===a);if(F){var B,N,j=!0;for(B=0;B<t.length;B++)if(j&&void 0===t[B].zLabel&&(j=!1),N=t[B].hoverinfo||t[B].trace.hoverinfo){var U=Array.isArray(N)?N:N.split(\"+\");if(-1===U.indexOf(\"all\")&&-1===U.indexOf(a)){F=!1;break}}j&&(F=!1)}var V=d.selectAll(\"g.axistext\").data(F?[0]:[]);if(V.enter().append(\"g\").classed(\"axistext\",!0),V.exit().remove(),V.each((function(){var t=n.select(this),e=o.ensureSingle(t,\"path\",\"\",(function(t){t.style({\"stroke-width\":\"1px\"})})),l=o.ensureSingle(t,\"text\",\"\",(function(t){t.attr(\"data-notex\",1)})),c=g.bgcolor||p.defaultLine,f=g.bordercolor||p.contrast(c),d=p.contrast(c),m={family:g.font.family||w,size:g.font.size||T,color:g.font.color||d};e.style({fill:c,stroke:f}),l.text(P).call(h.font,m).call(u.positionText,0,0).call(u.convertToTspans,r),t.attr(\"transform\",\"\");var v,y,x=l.node().getBoundingClientRect();if(\"x\"===a){var b=\"top\"===M.side?\"-\":\"\";l.attr(\"text-anchor\",\"middle\").call(u.positionText,0,\"top\"===M.side?z-x.bottom-A-S:z-x.top+A+S),v=M._offset+(k.x0+k.x1)/2,y=E._offset+(\"top\"===M.side?0:E._length);var _=x.width/2+S;v<_?(v=_,e.attr(\"d\",\"M-\"+(_-A)+\",0L-\"+(_-2*A)+\",\"+b+A+\"H\"+(S+x.width/2)+\"v\"+b+(2*S+x.height)+\"H-\"+_+\"V\"+b+A+\"Z\")):v>i.width-_?(v=i.width-_,e.attr(\"d\",\"M\"+(_-A)+\",0L\"+_+\",\"+b+A+\"v\"+b+(2*S+x.height)+\"H-\"+_+\"V\"+b+A+\"H\"+(_-2*A)+\"Z\")):e.attr(\"d\",\"M0,0L\"+A+\",\"+b+A+\"H\"+(S+x.width/2)+\"v\"+b+(2*S+x.height)+\"H-\"+(S+x.width/2)+\"V\"+b+A+\"H-\"+A+\"Z\")}else{var L,C,I;\"right\"===E.side?(L=\"start\",C=1,I=\"\",v=M._offset+M._length):(L=\"end\",C=-1,I=\"-\",v=M._offset),y=E._offset+(k.y0+k.y1)/2,l.attr(\"text-anchor\",L),e.attr(\"d\",\"M0,0L\"+I+A+\",\"+A+\"V\"+(S+x.height/2)+\"h\"+I+(2*S+x.width)+\"V-\"+(S+x.height/2)+\"H\"+I+A+\"V-\"+A+\"Z\");var O,D=x.height/2,R=z-x.top-D,F=\"clip\"+i._uid+\"commonlabel\"+E._id;if(v<x.width+2*S+A){O=\"M-\"+(A+S)+\"-\"+D+\"h-\"+(x.width-S)+\"V\"+D+\"h\"+(x.width-S)+\"Z\";var B=x.width-v+S;u.positionText(l,B,R),\"end\"===L&&l.selectAll(\"tspan\").each((function(){var t=n.select(this),e=h.tester.append(\"text\").text(t.text()).call(h.font,m),r=e.node().getBoundingClientRect();Math.round(r.width)<Math.round(x.width)&&t.attr(\"x\",B-r.width),e.remove()}))}else u.positionText(l,C*(S+A),R),O=null;var N=i._topclips.selectAll(\"#\"+F).data(O?[0]:[]);N.enter().append(\"clipPath\").attr(\"id\",F).append(\"path\"),N.exit().remove(),N.select(\"path\").attr(\"d\",O),h.setClipUrl(l,O?F:null,r)}t.attr(\"transform\",s(v,y))})),v.isUnifiedHover(a)){if(d.selectAll(\"g.hovertext\").remove(),0===t.length)return;var q={showlegend:!0,legend:{title:{text:P,font:i.hoverlabel.font},font:i.hoverlabel.font,bgcolor:i.hoverlabel.bgcolor,bordercolor:i.hoverlabel.bordercolor,borderwidth:1,tracegroupgap:7,traceorder:i.legend?i.legend.traceorder:void 0,orientation:\"v\"}},H={};x(q,H,r._fullData);var G=H.legend;G.entries=[];for(var Y=0;Y<t.length;Y++){var W=I(t[Y],!0,a,i,P),X=W[0],Z=W[1],J=t[Y];J.name=Z,J.text=\"\"!==Z?Z+\" : \"+X:X;var 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t=n.select(this);t.append(\"rect\").call(p.fill,p.addOpacity(f,.8)),t.append(\"text\").classed(\"name\",!0),t.append(\"path\").style(\"stroke-width\",\"1px\"),t.append(\"text\").classed(\"nums\",!0).call(h.font,w,T)})),ot.exit().remove(),ot.each((function(t){var e=n.select(this).attr(\"transform\",\"\"),o=t.color;Array.isArray(o)&&(o=o[t.eventData[0].pointNumber]);var d=t.bgcolor||o,m=p.combine(p.opacity(d)?d:p.defaultLine,f),g=p.combine(p.opacity(o)?o:p.defaultLine,f),v=t.borderColor||p.contrast(m),y=I(t,F,a,i,P,e),x=y[0],b=y[1],k=e.select(\"text.nums\").call(h.font,t.fontFamily||w,t.fontSize||T,t.fontColor||v).text(x).attr(\"data-notex\",1).call(u.positionText,0,0).call(u.convertToTspans,r),M=e.select(\"text.name\"),E=0,L=0;if(b&&b!==x){M.call(h.font,t.fontFamily||w,t.fontSize||T,g).text(b).attr(\"data-notex\",1).call(u.positionText,0,0).call(u.convertToTspans,r);var C=M.node().getBoundingClientRect();E=C.width+2*S,L=C.height+2*S}else 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n=t(\"@plotly/d3\"),i=t(\"../../lib\"),a=t(\"../dragelement\"),o=t(\"./helpers\"),s=t(\"./layout_attributes\"),l=t(\"./hover\");e.exports={moduleType:\"component\",name:\"fx\",constants:t(\"./constants\"),schema:{layout:s},attributes:t(\"./attributes\"),layoutAttributes:s,supplyLayoutGlobalDefaults:t(\"./layout_global_defaults\"),supplyDefaults:t(\"./defaults\"),supplyLayoutDefaults:t(\"./layout_defaults\"),calc:t(\"./calc\"),getDistanceFunction:o.getDistanceFunction,getClosest:o.getClosest,inbox:o.inbox,quadrature:o.quadrature,appendArrayPointValue:o.appendArrayPointValue,castHoverOption:function(t,e,r){return i.castOption(t,e,\"hoverlabel.\"+r)},castHoverinfo:function(t,e,r){return i.castOption(t,r,\"hoverinfo\",(function(r){return i.coerceHoverinfo({hoverinfo:r},{_module:t._module},e)}))},hover:l.hover,unhover:a.unhover,loneHover:l.loneHover,loneUnhover:function(t){var 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n=t(\"@plotly/d3\"),i=t(\"../../registry\"),a=t(\"../../lib\"),o=a.strTranslate,s=t(\"../drawing\"),l=t(\"../color\"),c=t(\"../colorscale/helpers\").extractOpts,u=t(\"../../traces/scatter/subtypes\"),f=t(\"../../traces/pie/style_one\"),h=t(\"../../traces/pie/helpers\").castOption,p=t(\"./constants\");function d(t,e){return(e?\"radial\":\"horizontal\")+(t?\"\":\"reversed\")}function m(t){var e=t[0].trace,r=e.contours,n=u.hasLines(e),i=u.hasMarkers(e),a=e.visible&&e.fill&&\"none\"!==e.fill,o=!1,s=!1;if(r){var l=r.coloring;\"lines\"===l?o=!0:n=\"none\"===l||\"heatmap\"===l||r.showlines,\"constraint\"===r.type?a=\"=\"!==r._operation:\"fill\"!==l&&\"heatmap\"!==l||(s=!0)}return{showMarker:i,showLine:n,showFill:a,showGradientLine:o,showGradientFill:s,anyLine:n||o,anyFill:a||s}}function g(t,e,r){return t&&a.isArrayOrTypedArray(t)?e:t>r?r:t}e.exports=function(t,e,r){var v=e._fullLayout;r||(r=v.legend);var 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r=e.currentTarget;r.setAttribute(\"data-attr\",\"zoom\"),r.setAttribute(\"data-val\",\"reset\"),f(t,e),r.setAttribute(\"data-attr\",\"resetLastSave\"),p(t,e),x(t,\"geo\"),x(t,\"mapbox\")}},u.toggleSpikelines={name:\"toggleSpikelines\",title:function(t){return c(t,\"Toggle Spike Lines\")},icon:o.spikeline,attr:\"_cartesianSpikesEnabled\",val:\"on\",click:function(t){var e=t._fullLayout,r=e._cartesianSpikesEnabled;e._cartesianSpikesEnabled=\"on\"===r?\"off\":\"on\",n.call(\"_guiRelayout\",t,function(t){for(var e=\"on\"===t._fullLayout._cartesianSpikesEnabled,r=a.list(t,null,!0),n={},i=0;i<r.length;i++){var o=r[i];n[o._name+\".showspikes\"]=!!e||o._showSpikeInitial}return n}(t))}},u.resetViewMapbox={name:\"resetViewMapbox\",_cat:\"resetView\",title:function(t){return c(t,\"Reset view\")},attr:\"reset\",icon:o.home,click:function(t){x(t,\"mapbox\")}},u.zoomInMapbox={name:\"zoomInMapbox\",_cat:\"zoomin\",title:function(t){return c(t,\"Zoom 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n=t(\"../../lib\"),i=t(\"../color\"),a=t(\"../../plot_api/plot_template\"),o=t(\"./attributes\");e.exports=function(t,e){var r=t.modebar||{},s=a.newContainer(e,\"modebar\");function l(t,e){return n.coerce(r,s,o,t,e)}l(\"orientation\"),l(\"bgcolor\",i.addOpacity(e.paper_bgcolor,.5));var c=i.contrast(i.rgb(e.modebar.bgcolor));l(\"color\",i.addOpacity(c,.3)),l(\"activecolor\",i.addOpacity(c,.7)),l(\"uirevision\",e.uirevision),l(\"add\"),l(\"remove\")}},{\"../../lib\":799,\"../../plot_api/plot_template\":838,\"../color\":662,\"./attributes\":721}],725:[function(t,e,r){\"use strict\";e.exports={moduleType:\"component\",name:\"modebar\",layoutAttributes:t(\"./attributes\"),supplyLayoutDefaults:t(\"./defaults\"),manage:t(\"./manage\")}},{\"./attributes\":721,\"./defaults\":724,\"./manage\":726}],726:[function(t,e,r){\"use strict\";var n=t(\"../../plots/cartesian/axis_ids\"),i=t(\"../../traces/scatter/subtypes\"),a=t(\"../../registry\"),o=t(\"../fx/helpers\").isUnifiedHover,s=t(\"./modebar\"),l=t(\"./buttons\"),c=t(\"./constants\").DRAW_MODES;e.exports=function(t){var e=t._fullLayout,r=t._context,u=e._modeBar;if(r.displayModeBar||r.watermark){if(!Array.isArray(r.modeBarButtonsToRemove))throw new Error([\"*modeBarButtonsToRemove* configuration options\",\"must be an array.\"].join(\" \"));if(!Array.isArray(r.modeBarButtonsToAdd))throw new Error([\"*modeBarButtonsToAdd* configuration options\",\"must be an array.\"].join(\" \"));var f,h=r.modeBarButtons;f=Array.isArray(h)&&h.length?function(t){for(var e=0;e<t.length;e++)for(var r=t[e],n=0;n<r.length;n++){var i=r[n];if(\"string\"==typeof i){if(void 0===l[i])throw new Error([\"*modeBarButtons* configuration options\",\"invalid button name\"].join(\" \"));t[e][n]=l[i]}}return t}(h):!r.displayModeBar&&r.watermark?[]:function(t){var e=t._fullLayout,r=t._fullData,s=t._context;function u(t,e){if(\"string\"==typeof e){if(e.toLowerCase()===t.toLowerCase())return!0}else{var r=e.name,n=e._cat||e.name;if(r===t||n===t.toLowerCase())return!0}return!1}var f=e.modebar.add;\"string\"==typeof f&&(f=[f]);var h=e.modebar.remove;\"string\"==typeof h&&(h=[h]);var p=s.modeBarButtonsToAdd.concat(f.filter((function(t){for(var e=0;e<s.modeBarButtonsToRemove.length;e++)if(u(t,s.modeBarButtonsToRemove[e]))return!1;return!0}))),d=s.modeBarButtonsToRemove.concat(h.filter((function(t){for(var e=0;e<s.modeBarButtonsToAdd.length;e++)if(u(t,s.modeBarButtonsToAdd[e]))return!1;return!0}))),m=e._has(\"cartesian\"),g=e._has(\"gl3d\"),v=e._has(\"geo\"),y=e._has(\"pie\"),x=e._has(\"funnelarea\"),b=e._has(\"gl2d\"),_=e._has(\"ternary\"),w=e._has(\"mapbox\"),T=e._has(\"polar\"),k=e._has(\"sankey\"),M=function(t){for(var e=n.list({_fullLayout:t},null,!0),r=0;r<e.length;r++)if(!e[r].fixedrange)return!1;return!0}(e),A=o(e.hovermode),S=[];function 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e=0;e<t.length;e++)if(!a.traceIs(t[e],\"noHover\"))return!1;return!0}(r)||A)&&(P=[]);!m&&!b||M||(C=[\"zoomIn2d\",\"zoomOut2d\",\"autoScale2d\"],\"resetViews\"!==I[0]&&(I=[\"resetScale2d\"]));g?O=[\"zoom3d\",\"pan3d\",\"orbitRotation\",\"tableRotation\"]:(m||b)&&!M||_?O=[\"zoom2d\",\"pan2d\"]:w||v?O=[\"pan2d\"]:T&&(O=[\"zoom2d\"]);(function(t){for(var e=!1,r=0;r<t.length&&!e;r++){var n=t[r];n._module&&n._module.selectPoints&&(a.traceIs(n,\"scatter-like\")?(i.hasMarkers(n)||i.hasText(n))&&(e=!0):a.traceIs(n,\"box-violin\")&&\"all\"!==n.boxpoints&&\"all\"!==n.points||(e=!0))}return e})(r)&&O.push(\"select2d\",\"lasso2d\");var z=[],D=function(t){-1===z.indexOf(t)&&-1!==P.indexOf(t)&&z.push(t)};if(Array.isArray(p)){for(var R=[],F=0;F<p.length;F++){var B=p[F];\"string\"==typeof B?(B=B.toLowerCase(),-1!==c.indexOf(B)?(e._has(\"mapbox\")||e._has(\"cartesian\"))&&O.push(B):\"togglespikelines\"===B?D(\"toggleSpikelines\"):\"togglehover\"===B?D(\"toggleHover\"):\"hovercompare\"===B?D(\"hoverCompareCartesian\"):\"hoverclosest\"===B?(D(\"hoverClosestCartesian\"),D(\"hoverClosestGeo\"),D(\"hoverClosest3d\"),D(\"hoverClosestGl2d\"),D(\"hoverClosestPie\")):\"v1hovermode\"===B&&(D(\"toggleHover\"),D(\"hoverClosestCartesian\"),D(\"hoverCompareCartesian\"),D(\"hoverClosestGeo\"),D(\"hoverClosest3d\"),D(\"hoverClosestGl2d\"),D(\"hoverClosestPie\"))):R.push(B)}p=R}return E(O),E(C.concat(I)),E(z),function(t,e){if(e.length)if(Array.isArray(e[0]))for(var r=0;r<e.length;r++)t.push(e[r]);else t.push(e);return t}(S,p)}(t),u?u.update(t,f):e._modeBar=s(t,f)}else u&&(u.destroy(),delete 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t.match(n.segmentRE).forEach((function(t){var a=e[t.charAt(0)].drawn;if(void 0!==a){var o=t.substr(1).match(n.paramRE);!o||o.length<a||r.push(i.cleanNumber(o[a]))}})),r},r.getDataToPixel=function(t,e,n,i){var a,o=t._fullLayout._size;if(e)if(\"domain\"===i)a=function(t){return e._length*(n?1-t:t)+e._offset};else{var s=r.shapePositionToRange(e);a=function(t){return e._offset+e.r2p(s(t,!0))},\"date\"===e.type&&(a=r.decodeDate(a))}else a=n?function(t){return o.t+o.h*(1-t)}:function(t){return o.l+o.w*t};return a},r.getPixelToData=function(t,e,n,i){var a,o=t._fullLayout._size;if(e)if(\"domain\"===i)a=function(t){var r=(t-e._offset)/e._length;return n?1-r:r};else{var s=r.rangeToShapePosition(e);a=function(t){return s(e.p2r(t-e._offset))}}else a=n?function(t){return 1-(t-o.t)/o.h}:function(t){return(t-o.l)/o.w};return a},r.roundPositionForSharpStrokeRendering=function(t,e){var r=1===Math.round(e%2),n=Math.round(t);return r?n+.5:n},r.makeOptionsAndPlotinfo=function(t,e){var r=t._fullLayout.shapes[e]||{},n=t._fullLayout._plots[r.xref+r.yref];return!!n?n._hadPlotinfo=!0:(n={},r.xref&&\"paper\"!==r.xref&&(n.xaxis=t._fullLayout[r.xref+\"axis\"]),r.yref&&\"paper\"!==r.yref&&(n.yaxis=t._fullLayout[r.yref+\"axis\"])),n.xsizemode=r.xsizemode,n.ysizemode=r.ysizemode,n.xanchor=r.xanchor,n.yanchor=r.yanchor,{options:r,plotinfo:n}}},{\"../../lib\":799,\"./constants\":744}],754:[function(t,e,r){\"use strict\";var n=t(\"./draw\");e.exports={moduleType:\"component\",name:\"shapes\",layoutAttributes:t(\"./attributes\"),supplyLayoutDefaults:t(\"./defaults\"),supplyDrawNewShapeDefaults:t(\"./draw_newshape/defaults\"),includeBasePlot:t(\"../../plots/cartesian/include_components\")(\"shapes\"),calcAutorange:t(\"./calc_autorange\"),draw:n.draw,drawOne:n.drawOne}},{\"../../plots/cartesian/include_components\":862,\"./attributes\":742,\"./calc_autorange\":743,\"./defaults\":745,\"./draw\":746,\"./draw_newshape/defaults\":749}],755:[function(t,e,r){\"use strict\";var n=t(\"../../plots/font_attributes\"),i=t(\"../../plots/pad_attributes\"),a=t(\"../../lib/extend\").extendDeepAll,o=t(\"../../plot_api/edit_types\").overrideAll,s=t(\"../../plots/animation_attributes\"),l=t(\"../../plot_api/plot_template\").templatedArray,c=t(\"./constants\"),u=l(\"step\",{visible:{valType:\"boolean\",dflt:!0},method:{valType:\"enumerated\",values:[\"restyle\",\"relayout\",\"animate\",\"update\",\"skip\"],dflt:\"restyle\"},args:{valType:\"info_array\",freeLength:!0,items:[{valType:\"any\"},{valType:\"any\"},{valType:\"any\"}]},label:{valType:\"string\"},value:{valType:\"string\"},execute:{valType:\"boolean\",dflt:!0}});e.exports=o(l(\"slider\",{visible:{valType:\"boolean\",dflt:!0},active:{valType:\"number\",min:0,dflt:0},steps:u,lenmode:{valType:\"enumerated\",values:[\"fraction\",\"pixels\"],dflt:\"fraction\"},len:{valType:\"number\",min:0,dflt:1},x:{valType:\"number\",min:-2,max:3,dflt:0},pad:a(i({editType:\"arraydraw\"}),{},{t:{dflt:20}}),xanchor:{valType:\"enumerated\",values:[\"auto\",\"left\",\"center\",\"right\"],dflt:\"left\"},y:{valType:\"number\",min:-2,max:3,dflt:0},yanchor:{valType:\"enumerated\",values:[\"auto\",\"top\",\"middle\",\"bottom\"],dflt:\"top\"},transition:{duration:{valType:\"number\",min:0,dflt:150},easing:{valType:\"enumerated\",values:s.transition.easing.values,dflt:\"cubic-in-out\"}},currentvalue:{visible:{valType:\"boolean\",dflt:!0},xanchor:{valType:\"enumerated\",values:[\"left\",\"center\",\"right\"],dflt:\"left\"},offset:{valType:\"number\",dflt:10},prefix:{valType:\"string\"},suffix:{valType:\"string\"},font:n({})},font:n({}),activebgcolor:{valType:\"color\",dflt:c.gripBgActiveColor},bgcolor:{valType:\"color\",dflt:c.railBgColor},bordercolor:{valType:\"color\",dflt:c.railBorderColor},borderwidth:{valType:\"number\",min:0,dflt:c.railBorderWidth},ticklen:{valType:\"number\",min:0,dflt:c.tickLength},tickcolor:{valType:\"color\",dflt:c.tickColor},tickwidth:{valType:\"number\",min:0,dflt:1},minorticklen:{valType:\"number\",min:0,dflt:c.minorTickLength}}),\"arraydraw\",\"from-root\")},{\"../../lib/extend\":789,\"../../plot_api/edit_types\":831,\"../../plot_api/plot_template\":838,\"../../plots/animation_attributes\":843,\"../../plots/font_attributes\":878,\"../../plots/pad_attributes\":912,\"./constants\":756}],756:[function(t,e,r){\"use 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a=Math.round(n*(r._stepCount-1)),o=r._visibleSteps[a]._index;o!==r.active&&k(t,e,r,o,!0,i)}function k(t,e,r,n,a,o){var s=r.active;r.active=n,u(t.layout,f.name,r).applyUpdate(\"active\",n);var l=r.steps[r.active];e.call(S,r,o),e.call(x,r),t.emit(\"plotly_sliderchange\",{slider:r,step:r.steps[r.active],interaction:a,previousActive:s}),l&&l.method&&a&&(e._nextMethod?(e._nextMethod.step=l,e._nextMethod.doCallback=a,e._nextMethod.doTransition=o):(e._nextMethod={step:l,doCallback:a,doTransition:o},e._nextMethodRaf=window.requestAnimationFrame((function(){var r=e._nextMethod.step;r.method&&(r.execute&&i.executeAPICommand(t,r.method,r.args),e._nextMethod=null,e._nextMethodRaf=null)}))))}function M(t,e,r){var i=r.node(),o=n.select(e);function s(){return r.data()[0]}t.on(\"mousedown\",(function(){var t=s();e.emit(\"plotly_sliderstart\",{slider:t});var l=r.select(\".\"+f.gripRectClass);n.event.stopPropagation(),n.event.preventDefault(),l.call(a.fill,t.activebgcolor);var c=L(t,n.mouse(i)[0]);T(e,r,t,c,!0),t._dragging=!0,o.on(\"mousemove\",(function(){var t=s(),a=L(t,n.mouse(i)[0]);T(e,r,t,a,!1)})),o.on(\"mouseup\",(function(){var t=s();t._dragging=!1,l.call(a.fill,t.bgcolor),o.on(\"mouseup\",null),o.on(\"mousemove\",null),e.emit(\"plotly_sliderend\",{slider:t,step:t.steps[t.active]})}))}))}function A(t,e){var r=t.selectAll(\"rect.\"+f.tickRectClass).data(e._visibleSteps),i=e._dims;r.enter().append(\"rect\").classed(f.tickRectClass,!0),r.exit().remove(),r.attr({width:e.tickwidth+\"px\",\"shape-rendering\":\"crispEdges\"}),r.each((function(t,r){var s=r%i.labelStride==0,l=n.select(this);l.attr({height:s?e.ticklen:e.minorticklen}).call(a.fill,e.tickcolor),o.setTranslate(l,E(e,r/(e._stepCount-1))-.5*e.tickwidth,(s?f.tickOffset:f.minorTickOffset)+i.currentValueTotalHeight)}))}function S(t,e,r){for(var n=t.select(\"rect.\"+f.gripRectClass),i=0,a=0;a<e._stepCount;a++)if(e._visibleSteps[a]._index===e.active){i=a;break}var o=E(e,i/(e._stepCount-1));if(!e._invokingCommand){var s=n;r&&e.transition.duration>0&&(s=s.transition().duration(e.transition.duration).ease(e.transition.easing)),s.attr(\"transform\",l(o-.5*f.gripWidth,e._dims.currentValueTotalHeight))}}function E(t,e){var r=t._dims;return r.inputAreaStart+f.stepInset+(r.inputAreaLength-2*f.stepInset)*Math.min(1,Math.max(0,e))}function L(t,e){var r=t._dims;return Math.min(1,Math.max(0,(e-f.stepInset-r.inputAreaStart)/(r.inputAreaLength-2*f.stepInset-2*r.inputAreaStart)))}function C(t,e,r){var n=r._dims,i=s.ensureSingle(t,\"rect\",f.railTouchRectClass,(function(n){n.call(M,e,t,r).style(\"pointer-events\",\"all\")}));i.attr({width:n.inputAreaLength,height:Math.max(n.inputAreaWidth,f.tickOffset+r.ticklen+n.labelHeight)}).call(a.fill,r.bgcolor).attr(\"opacity\",0),o.setTranslate(i,0,n.currentValueTotalHeight)}function P(t,e){var 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o=e.length===r.length;if(!i&&!o)return{fullReplot:!0,calc:!0};var s,l,c=T.traceFlags();c.arrays={},c.nChanges=0,c.nChangesAnim=0;var u={getValObject:function(t){var e=f.getTraceValObject(l,t);return!l._module.animatable&&e.anim&&(e.anim=!1),e},flags:c,immutable:n,transition:i,newDataRevision:a,gd:t},p={};for(s=0;s<e.length;s++)if(r[s]){if(l=r[s]._fullInput,h.hasMakesDataTransform(l)&&(l=r[s]),p[l.uid])continue;p[l.uid]=1,it(e[s]._fullInput,l,[],u)}(c.calc||c.plot)&&(c.fullReplot=!0);i&&c.nChanges&&c.nChangesAnim&&(c.anim=c.nChanges===c.nChangesAnim&&o?\"all\":\"some\");return c}(t,c,v,x,b,M);if(X(t)&&(k.layoutReplot=!0),S.calc||k.calc){t.calcdata=void 0;for(var E=Object.getOwnPropertyNames(y),C=0;C<E.length;C++){var P=E[C],I=P.substring(0,5);if(\"xaxis\"===I||\"yaxis\"===I){var O=y[P]._emptyCategories;O&&O()}}}else h.supplyDefaultsUpdateCalc(t.calcdata,v);var z=[];if(a&&(t._transitionData={},h.createTransitionData(t),z.push((function(){return 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t.dtick=e>.3?\"D2\":\"D1\"}else\"category\"===t.type||\"multicategory\"===t.type?(t.tick0=0,t.dtick=Math.ceil(Math.max(e,1))):Mt(t)?(t.tick0=0,r=1,t.dtick=ot(e,r,at)):(t.tick0=0,r=n(10),t.dtick=ot(e,r,$));if(0===t.dtick&&(t.dtick=1),!i(t.dtick)&&\"string\"!=typeof t.dtick){var f=t.dtick;throw t.dtick=1,\"ax.dtick error: \"+String(f)}},V.tickIncrement=function(t,e,r,a){var o=r?-1:1;if(i(e))return s.increment(t,o*e);var l=e.charAt(0),c=o*Number(e.substr(1));if(\"M\"===l)return s.incrementMonth(t,c,a);if(\"L\"===l)return Math.log(Math.pow(10,t)+c)/Math.LN10;if(\"D\"===l){var u=\"D2\"===e?it:nt,f=t+.01*o,h=s.roundUp(s.mod(f,1),u,r);return Math.floor(f)+Math.log(n.round(Math.pow(10,h),1))/Math.LN10}throw\"unrecognized dtick \"+String(e)},V.tickFirst=function(t,e){var r=t.r2l||Number,a=s.simpleMap(t.range,r,void 0,void 0,e),o=a[1]<a[0],l=o?Math.floor:Math.ceil,c=X(a)[0],u=t.dtick,f=r(t.tick0);if(i(u)){var 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h=s.simpleMap(t.range,t.r2l),p=(Math.abs(h[1]-h[0])-(t._lBreaks||0))/1e4;for(a=0;a<t.ticktext.length&&!(Math.abs(e-f(t.tickvals[a]))<p);a++);if(a<t.ticktext.length)return o.text=String(t.ticktext[a]),o}function d(n){if(void 0===n)return!0;if(r)return\"none\"===n;var i={first:t._tmin,last:t._tmax}[n];return\"all\"!==n&&e!==i}var m=r?\"never\":\"none\"!==t.exponentformat&&d(t.showexponent)?\"hide\":\"\";if(\"date\"===u?function(t,e,r,n){var a=t._tickround,o=r&&t.hoverformat||V.getTickFormat(t);n&&(a=i(a)?4:{y:\"m\",m:\"d\",d:\"M\",M:\"S\",S:4}[a]);var l,c=s.formatDate(e.x,o,a,t._dateFormat,t.calendar,t._extraFormat),u=c.indexOf(\"\\n\");-1!==u&&(l=c.substr(u+1),c=c.substr(0,u));n&&(\"00:00:00\"===c||\"00:00\"===c?(c=l,l=\"\"):8===c.length&&(c=c.replace(/:00$/,\"\")));if(l)if(r)\"d\"===a?c+=\", \"+l:c=l+(c?\", \"+c:\"\");else if(t._inCalcTicks&&t._prevDateHead===l){var f=St(t),h=t._realSide||t.side;(!f&&\"top\"===h||f&&\"bottom\"===h)&&(c+=\"<br> \")}else t._prevDateHead=l,c+=\"<br>\"+l;e.text=c}(t,o,r,c):\"log\"===u?function(t,e,r,n,a){var o=t.dtick,l=e.x,c=t.tickformat,u=\"string\"==typeof o&&o.charAt(0);\"never\"===a&&(a=\"\");n&&\"L\"!==u&&(o=\"L3\",u=\"L\");if(c||\"L\"===u)e.text=ht(Math.pow(10,l),t,a,n);else if(i(o)||\"D\"===u&&s.mod(l+.01,1)<.1){var f=Math.round(l),h=Math.abs(f),p=t.exponentformat;\"power\"===p||ut(p)&&ft(f)?(e.text=0===f?1:1===f?\"10\":\"10<sup>\"+(f>1?\"\":P)+h+\"</sup>\",e.fontSize*=1.25):(\"e\"===p||\"E\"===p)&&h>2?e.text=\"1\"+p+(f>0?\"+\":P)+h:(e.text=ht(Math.pow(10,l),t,\"\",\"fakehover\"),\"D1\"===o&&\"y\"===t._id.charAt(0)&&(e.dy-=e.fontSize/6))}else{if(\"D\"!==u)throw\"unrecognized dtick \"+String(o);e.text=String(Math.round(Math.pow(10,s.mod(l,1)))),e.fontSize*=.75}if(\"D1\"===t.dtick){var 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a=r[i],o=a._id,s=rt.xLinks[o]||rt.yLinks[o],l=n[0]||U[s]||X[s];l&&(e?(e[a._name+\".range[0]\"]=e[l._name+\".range[0]\"],e[a._name+\".range[1]\"]=e[l._name+\".range[1]\"]):a.range=l.range.slice())}}function Vt(){var e,r=[];function n(t){for(e=0;e<t.length;e++)t[e].fixedrange||r.push(t[e]._id)}for(ot&&(n(Z),n(et.xaxes),n(rt.xaxes)),st&&(n(J),n(et.yaxes),n(rt.yaxes)),ft={},e=0;e<r.length;e++){var i=r[e],a=T(t,i);p.drawOne(t,a,{skipTitle:!0}),ft[a._name+\".range[0]\"]=a.range[0],ft[a._name+\".range[1]\"]=a.range[1]}p.redrawComponents(t,r)}function qt(){if(!t._transitioningWithDuration){var e=t._context.doubleClick,r=[];nt&&(r=r.concat(Z)),it&&(r=r.concat(J)),rt.xaxes&&(r=r.concat(rt.xaxes)),rt.yaxes&&(r=r.concat(rt.yaxes));var n,i,a,s={};if(\"reset+autosize\"===e)for(e=\"autosize\",i=0;i<r.length;i++)if((n=r[i])._rangeInitial&&(n.range[0]!==n._rangeInitial[0]||n.range[1]!==n._rangeInitial[1])||!n._rangeInitial&&!n.autorange){e=\"reset\";break}if(\"autosize\"===e)for(i=0;i<r.length;i++)(n=r[i]).fixedrange||(s[n._name+\".autorange\"]=!0);else if(\"reset\"===e)for((nt||et.isSubplotConstrained)&&(r=r.concat(et.xaxes)),it&&!et.isSubplotConstrained&&(r=r.concat(et.yaxes)),et.isSubplotConstrained&&(nt?it||(r=r.concat(J)):r=r.concat(Z)),i=0;i<r.length;i++)(n=r[i]).fixedrange||(n._rangeInitial?(a=n._rangeInitial,s[n._name+\".range[0]\"]=a[0],s[n._name+\".range[1]\"]=a[1]):s[n._name+\".autorange\"]=!0);t.emit(\"plotly_doubleclick\",null),o.call(\"_guiRelayout\",t,s)}}function Ht(){Gt([0,0,$,tt]),s.syncOrAsync([w.previousPromises,function(){t._fullLayout._replotting=!1,o.call(\"_guiRelayout\",t,ft)}],t)}function Gt(e){var r,n,i,a,l=t._fullLayout,c=l._plots,u=l._subplots.cartesian;if(ct&&o.subplotsRegistry.splom.drag(t),lt)for(r=0;r<u.length;r++)if(i=(n=c[u[r]]).xaxis,a=n.yaxis,n._scene){var h=s.simpleMap(i.range,i.r2l),p=s.simpleMap(a.range,a.r2l);n._scene.update({range:[h[0],p[0],h[1],p[1]]})}if((ct||lt)&&(b(t),_(t)),ut){var m=e[2]/P._length,v=e[3]/I._length;for(r=0;r<u.length;r++){i=(n=c[u[r]]).xaxis,a=n.yaxis;var y,x,w,T,k=(ot||rt.isSubplotConstrained)&&!i.fixedrange&&U[i._id],M=(st||rt.isSubplotConstrained)&&!a.fixedrange&&X[a._id];if(k?(y=m,w=g||rt.isSubplotConstrained?e[0]:Xt(i,y)):rt.xaHash[i._id]?(y=m,w=e[0]*i._length/P._length):rt.yaHash[i._id]?(y=v,w=\"ns\"===it?-e[1]*i._length/I._length:Xt(i,y,{n:\"top\",s:\"bottom\"}[it])):w=Wt(i,y=Yt(i,m,v)),M?(x=v,T=d||rt.isSubplotConstrained?e[1]:Xt(a,x)):rt.yaHash[a._id]?(x=v,T=e[1]*a._length/I._length):rt.xaHash[a._id]?(x=m,T=\"ew\"===nt?-e[0]*a._length/P._length:Xt(a,x,{e:\"right\",w:\"left\"}[nt])):T=Wt(a,x=Yt(a,m,v)),y||x){y||(y=1),x||(x=1);var A=i._offset-w/y,S=a._offset-T/x;n.clipRect.call(f.setTranslate,w,T).call(f.setScale,y,x),n.plot.call(f.setTranslate,A,S).call(f.setScale,1/y,1/x),y===n.xScaleFactor&&x===n.yScaleFactor||(f.setPointGroupScale(n.zoomScalePts,y,x),f.setTextPointsScale(n.zoomScaleTxt,y,x)),f.hideOutsideRangePoints(n.clipOnAxisFalseTraces,n),n.xScaleFactor=y,n.yScaleFactor=x}}}}function Yt(t,e,r){return t.fixedrange?0:ot&&et.xaHash[t._id]?e:st&&(et.isSubplotConstrained?et.xaHash:et.yaHash)[t._id]?r:0}function Wt(t,e){return e?(t.range=t._r.slice(),S(t,e),Xt(t,e)):0}function Xt(t,e,r){return t._length*(1-e)*x[r||t.constraintoward||\"middle\"]}return d.length*g.length!=1&&Y(xt,(function(e){if(t._context._scrollZoom.cartesian||t._fullLayout._enablescrollzoom){if(Pt(),t._transitioningWithDuration)return e.preventDefault(),void e.stopPropagation();vt(),clearTimeout(Ft);var r=-e.deltaY;if(isFinite(r)||(r=e.wheelDelta/10),isFinite(r)){var 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c&&(Z.addPt([it,at]),Q=Z.filtered);i.selectionDefs&&i.selectionDefs.length?(K=function(t,e,r){if(r)return n.difference({regions:t,inverted:!1},{regions:[e],inverted:!1}).regions;return n.union({regions:t,inverted:!1},{regions:[e],inverted:!1}).regions}(i.mergedPolygons,Q,ut),Q.subtract=ut,J=R(i.selectionDefs.concat([Q]))):(K=[Q],J=S(Q)),m(q(K,x),ft,i),w&&b.throttle(mt,k.SELECTDELAY,(function(){var t;gt=[];var e,r=[];for($=0;$<vt.length;$++)if(e=(tt=vt[$])._module.selectPoints(tt,J),r.push(e),t=V(e,tt),gt.length)for(var n=0;n<t.length;n++)gt.push(t[n]);else gt=t;U(L,vt,et={points:gt}),pt(et,Q,Z),i.gd.emit(\"plotly_selecting\",et)}))},i.clickFn=function(t,e){if(dt.remove(),L._fullLayout._activeShapeIndex>=0)L._fullLayout._deactivateShape(L);else if(!_){var r=z.clickmode;b.done(mt).then((function(){if(b.clear(mt),2===t){for(ft.remove(),$=0;$<vt.length;$++)(tt=vt[$])._module.selectPoints(tt,!1);U(L,vt),B(i),L.emit(\"plotly_deselect\",null)}else 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e=this.scene,r=e.fullLayout._subplots.gl2d;return 0!==n.findSubplotsWithAxis(r,t).indexOf(e.id)},o.hasAxisInDfltPos=function(t,e){var r=e.side;return\"xaxis\"===t?\"bottom\"===r:\"yaxis\"===t?\"left\"===r:void 0},o.hasAxisInAltrPos=function(t,e){var r=e.side;return\"xaxis\"===t?\"top\"===r:\"yaxis\"===t?\"right\"===r:void 0},o.getLabelPad=function(t,e){var r=e.title.font.size,n=e.showticklabels;return\"xaxis\"===t?\"top\"===e.side?r*(1.5+(n?1:0))-10:r*(1.5+(n?.5:0))-10:\"yaxis\"===t?\"right\"===e.side?10+r*(1.5+(n?1:.5)):10+r*(1.5+(n?.5:0)):void 0},o.getTickPad=function(t){return\"outside\"===t.ticks?10+t.ticklen:15},o.getTickMarkLength=function(t){if(!t.ticks)return 0;var e=t.ticklen;return\"inside\"===t.ticks?-e:e},e.exports=function(t){return new a(t)}},{\"../../lib/str2rgbarray\":823,\"../cartesian/axes\":849}],890:[function(t,e,r){\"use strict\";var n=t(\"../../plot_api/edit_types\").overrideAll,i=t(\"./scene2d\"),a=t(\"../layout_attributes\"),o=t(\"../../constants/xmlns_namespaces\"),s=t(\"../cartesian/constants\"),l=t(\"../cartesian\"),c=t(\"../../components/fx/layout_attributes\"),u=t(\"../get_data\").getSubplotData;r.name=\"gl2d\",r.attr=[\"xaxis\",\"yaxis\"],r.idRoot=[\"x\",\"y\"],r.idRegex=s.idRegex,r.attrRegex=s.attrRegex,r.attributes=t(\"../cartesian/attributes\"),r.supplyLayoutDefaults=function(t,e,r){e._has(\"cartesian\")||l.supplyLayoutDefaults(t,e,r)},r.layoutAttrOverrides=n(l.layoutAttributes,\"plot\",\"from-root\"),r.baseLayoutAttrOverrides=n({plot_bgcolor:a.plot_bgcolor,hoverlabel:c.hoverlabel},\"plot\",\"nested\"),r.plot=function(t){for(var e=t._fullLayout,r=t._fullData,n=e._subplots.gl2d,a=0;a<n.length;a++){var o=n[a],s=e._plots[o],l=u(r,\"gl2d\",o),c=s._scene2d;void 0===c&&(c=new i({id:o,graphDiv:t,container:t.querySelector(\".gl-container\"),staticPlot:t._context.staticPlot,plotGlPixelRatio:t._context.plotGlPixelRatio},e),s._scene2d=c),c.plot(l,t.calcdata,e,t.layout)}},r.clean=function(t,e,r,n){for(var i=n._subplots.gl2d||[],a=0;a<i.length;a++){var o=i[a],s=n._plots[o];if(s._scene2d){var c=u(t,\"gl2d\",o);0===c.length&&(s._scene2d.destroy(),delete n._plots[o])}}l.clean.apply(this,arguments)},r.drawFramework=function(t){t._context.staticPlot||l.drawFramework(t)},r.toSVG=function(t){for(var e=t._fullLayout,r=e._subplots.gl2d,n=0;n<r.length;n++){var i=e._plots[r[n]]._scene2d,a=i.toImage(\"png\");e._glimages.append(\"svg:image\").attr({xmlns:o.svg,\"xlink:href\":a,x:0,y:0,width:\"100%\",height:\"100%\",preserveAspectRatio:\"none\"}),i.destroy()}},r.updateFx=function(t){for(var e=t._fullLayout,r=e._subplots.gl2d,n=0;n<r.length;n++){e._plots[r[n]]._scene2d.updateFx(e.dragmode)}}},{\"../../components/fx/layout_attributes\":703,\"../../constants/xmlns_namespaces\":776,\"../../plot_api/edit_types\":831,\"../cartesian\":863,\"../cartesian/attributes\":847,\"../cartesian/constants\":856,\"../get_data\":887,\"../layout_attributes\":904,\"./scene2d\":891}],891:[function(t,e,r){\"use strict\";var n,i,a=t(\"../../registry\"),o=t(\"../../plots/cartesian/axes\"),s=t(\"../../components/fx\"),l=t(\"gl-plot2d\"),c=t(\"gl-spikes2d\"),u=t(\"gl-select-box\"),f=t(\"webgl-context\"),h=t(\"./convert\"),p=t(\"./camera\"),d=t(\"../../lib/show_no_webgl_msg\"),m=t(\"../cartesian/constraints\"),g=m.enforce,v=m.clean,y=t(\"../cartesian/autorange\").doAutoRange,x=t(\"../../components/dragelement/helpers\"),b=x.drawMode,_=x.selectMode,w=[\"xaxis\",\"yaxis\"],T=t(\"../cartesian/constants\").SUBPLOT_PATTERN;function k(t,e){this.container=t.container,this.graphDiv=t.graphDiv,this.pixelRatio=t.plotGlPixelRatio||window.devicePixelRatio,this.id=t.id,this.staticPlot=!!t.staticPlot,this.scrollZoom=this.graphDiv._context._scrollZoom.cartesian,this.fullData=null,this.updateRefs(e),this.makeFramework(),this.stopped||(this.glplotOptions=h(this),this.glplotOptions.merge(e),this.glplot=l(this.glplotOptions),this.camera=p(this),this.traces={},this.spikes=c(this.glplot),this.selectBox=u(this.glplot,{innerFill:!1,outerFill:!0}),this.lastButtonState=0,this.pickResult=null,this.isMouseOver=!0,this.stopped=!1,this.redraw=this.draw.bind(this),this.redraw())}e.exports=k;var M=k.prototype;M.makeFramework=function(){if(this.staticPlot){if(!(i||(n=document.createElement(\"canvas\"),i=f({canvas:n,preserveDrawingBuffer:!1,premultipliedAlpha:!0,antialias:!0}))))throw new Error(\"Error creating static canvas/context for image server\");this.canvas=n,this.gl=i}else{var t=this.container.querySelector(\".gl-canvas-focus\"),e=f({canvas:t,preserveDrawingBuffer:!0,premultipliedAlpha:!0});if(!e)return d(this),void(this.stopped=!0);this.canvas=t,this.gl=e}var r=this.canvas;r.style.width=\"100%\",r.style.height=\"100%\",r.style.position=\"absolute\",r.style.top=\"0px\",r.style.left=\"0px\",r.style[\"pointer-events\"]=\"none\",this.updateSize(r);var a=this.svgContainer=document.createElementNS(\"http://www.w3.org/2000/svg\",\"svg\");a.style.position=\"absolute\",a.style.top=a.style.left=\"0px\",a.style.width=a.style.height=\"100%\",a.style[\"z-index\"]=20,a.style[\"pointer-events\"]=\"none\";var o=this.mouseContainer=document.createElement(\"div\");o.style.position=\"absolute\",o.style[\"pointer-events\"]=\"auto\",this.pickCanvas=this.container.querySelector(\".gl-canvas-pick\");var s=this.container;s.appendChild(a),s.appendChild(o);var l=this;o.addEventListener(\"mouseout\",(function(){l.isMouseOver=!1,l.unhover()})),o.addEventListener(\"mouseover\",(function(){l.isMouseOver=!0}))},M.toImage=function(t){t||(t=\"png\"),this.stopped=!0,this.staticPlot&&this.container.appendChild(n),this.updateSize(this.canvas);var e=this.glplot.gl,r=e.drawingBufferWidth,i=e.drawingBufferHeight;e.clearColor(1,1,1,0),e.clear(e.COLOR_BUFFER_BIT|e.DEPTH_BUFFER_BIT),this.glplot.setDirty(),this.glplot.draw(),e.bindFramebuffer(e.FRAMEBUFFER,null);var a=new Uint8Array(r*i*4);e.readPixels(0,0,r,i,e.RGBA,e.UNSIGNED_BYTE,a);for(var o=0,s=i-1;o<s;++o,--s)for(var l=0;l<r;++l)for(var c=0;c<4;++c){var u=a[4*(r*o+l)+c];a[4*(r*o+l)+c]=a[4*(r*s+l)+c],a[4*(r*s+l)+c]=u}var f=document.createElement(\"canvas\");f.width=r,f.height=i;var h,p=f.getContext(\"2d\"),d=p.createImageData(r,i);switch(d.data.set(a),p.putImageData(d,0,0),t){case\"jpeg\":h=f.toDataURL(\"image/jpeg\");break;case\"webp\":h=f.toDataURL(\"image/webp\");break;default:h=f.toDataURL(\"image/png\")}return this.staticPlot&&this.container.removeChild(n),h},M.updateSize=function(t){t||(t=this.canvas);var e=this.pixelRatio,r=this.fullLayout,n=r.width,i=r.height,a=0|Math.ceil(e*n),o=0|Math.ceil(e*i);return t.width===a&&t.height===o||(t.width=a,t.height=o),t},M.computeTickMarks=function(){this.xaxis.setScale(),this.yaxis.setScale();for(var t=[o.calcTicks(this.xaxis),o.calcTicks(this.yaxis)],e=0;e<2;++e)for(var r=0;r<t[e].length;++r)t[e][r].text=t[e][r].text+\"\";return t},M.updateRefs=function(t){this.fullLayout=t;var e=this.id.match(T),r=\"xaxis\"+e[1],n=\"yaxis\"+e[2];this.xaxis=this.fullLayout[r],this.yaxis=this.fullLayout[n]},M.relayoutCallback=function(){var t=this.graphDiv,e=this.xaxis,r=this.yaxis,n=t.layout,i={},o=i[e._name+\".range\"]=e.range.slice(),s=i[r._name+\".range\"]=r.range.slice();i[e._name+\".autorange\"]=e.autorange,i[r._name+\".autorange\"]=r.autorange,a.call(\"_storeDirectGUIEdit\",t.layout,t._fullLayout._preGUI,i);var l=n[e._name];l.range=o,l.autorange=e.autorange;var c=n[r._name];c.range=s,c.autorange=r.autorange,i.lastInputTime=this.camera.lastInputTime,t.emit(\"plotly_relayout\",i)},M.cameraChanged=function(){var t=this.camera;this.glplot.setDataBox(this.calcDataBox());var e=this.computeTickMarks();(function(t,e){for(var r=0;r<2;++r){var n=t[r],i=e[r];if(n.length!==i.length)return!0;for(var a=0;a<n.length;++a)if(n[a].x!==i[a].x)return!0}return!1})(e,this.glplotOptions.ticks)&&(this.glplotOptions.ticks=e,this.glplotOptions.dataBox=t.dataBox,this.glplot.update(this.glplotOptions),this.handleAnnotations())},M.handleAnnotations=function(){for(var t=this.graphDiv,e=this.fullLayout.annotations,r=0;r<e.length;r++){var n=e[r];n.xref===this.xaxis._id&&n.yref===this.yaxis._id&&a.getComponentMethod(\"annotations\",\"drawOne\")(t,r)}},M.destroy=function(){if(this.glplot){var t=this.traces;t&&Object.keys(t).map((function(e){t[e].dispose(),delete t[e]})),this.glplot.dispose(),this.container.removeChild(this.svgContainer),this.container.removeChild(this.mouseContainer),this.fullData=null,this.glplot=null,this.stopped=!0,this.camera.mouseListener.enabled=!1,this.mouseContainer.removeEventListener(\"wheel\",this.camera.wheelListener),this.camera=null}},M.plot=function(t,e,r){var n=this.glplot;this.updateRefs(r),this.xaxis.clearCalc(),this.yaxis.clearCalc(),this.updateTraces(t,e),this.updateFx(r.dragmode);var i=r.width,a=r.height;this.updateSize(this.canvas);var o=this.glplotOptions;o.merge(r),o.screenBox=[0,0,i,a];var s={_fullLayout:{_axisConstraintGroups:r._axisConstraintGroups,xaxis:this.xaxis,yaxis:this.yaxis,_size:r._size}};v(s,this.xaxis),v(s,this.yaxis);var l,c,u=r._size,f=this.xaxis.domain,h=this.yaxis.domain;for(o.viewBox=[u.l+f[0]*u.w,u.b+h[0]*u.h,i-u.r-(1-f[1])*u.w,a-u.t-(1-h[1])*u.h],this.mouseContainer.style.width=u.w*(f[1]-f[0])+\"px\",this.mouseContainer.style.height=u.h*(h[1]-h[0])+\"px\",this.mouseContainer.height=u.h*(h[1]-h[0]),this.mouseContainer.style.left=u.l+f[0]*u.w+\"px\",this.mouseContainer.style.top=u.t+(1-h[1])*u.h+\"px\",c=0;c<2;++c)(l=this[w[c]])._length=o.viewBox[c+2]-o.viewBox[c],y(this.graphDiv,l),l.setScale();g(s),o.ticks=this.computeTickMarks(),o.dataBox=this.calcDataBox(),o.merge(r),n.update(o),this.glplot.draw()},M.calcDataBox=function(){var t=this.xaxis,e=this.yaxis,r=t.range,n=e.range,i=t.r2l,a=e.r2l;return[i(r[0]),a(n[0]),i(r[1]),a(n[1])]},M.setRanges=function(t){var e=this.xaxis,r=this.yaxis,n=e.l2r,i=r.l2r;e.range=[n(t[0]),n(t[2])],r.range=[i(t[1]),i(t[3])]},M.updateTraces=function(t,e){var r,n,i,a=Object.keys(this.traces);this.fullData=t;t:for(r=0;r<a.length;r++){var o=a[r],s=this.traces[o];for(n=0;n<t.length;n++)if((i=t[n]).uid===o&&i.type===s.type)continue t;s.dispose(),delete this.traces[o]}for(r=0;r<t.length;r++){i=t[r];var l=e[r],c=this.traces[i.uid];c?c.update(i,l):(c=i._module.plot(this,i,l),this.traces[i.uid]=c)}this.glplot.objects.sort((function(t,e){return t._trace.index-e._trace.index}))},M.updateFx=function(t){_(t)||b(t)?(this.pickCanvas.style[\"pointer-events\"]=\"none\",this.mouseContainer.style[\"pointer-events\"]=\"none\"):(this.pickCanvas.style[\"pointer-events\"]=\"auto\",this.mouseContainer.style[\"pointer-events\"]=\"auto\"),this.mouseContainer.style.cursor=\"pan\"===t?\"move\":\"zoom\"===t?\"crosshair\":null},M.emitPointAction=function(t,e){for(var r,n=t.trace.uid,i=t.pointIndex,a=0;a<this.fullData.length;a++)this.fullData[a].uid===n&&(r=this.fullData[a]);var o={x:t.traceCoord[0],y:t.traceCoord[1],curveNumber:r.index,pointNumber:i,data:r._input,fullData:this.fullData,xaxis:this.xaxis,yaxis:this.yaxis};s.appendArrayPointValue(o,r,i),this.graphDiv.emit(e,{points:[o]})},M.draw=function(){if(!this.stopped){requestAnimationFrame(this.redraw);var t=this.glplot,e=this.camera,r=e.mouseListener,n=1===this.lastButtonState&&0===r.buttons,i=this.fullLayout;this.lastButtonState=r.buttons,this.cameraChanged();var a,o=r.x*t.pixelRatio,l=this.canvas.height-t.pixelRatio*r.y;if(e.boxEnabled&&\"zoom\"===i.dragmode){this.selectBox.enabled=!0;for(var c=this.selectBox.selectBox=[Math.min(e.boxStart[0],e.boxEnd[0]),Math.min(e.boxStart[1],e.boxEnd[1]),Math.max(e.boxStart[0],e.boxEnd[0]),Math.max(e.boxStart[1],e.boxEnd[1])],u=0;u<2;u++)e.boxStart[u]===e.boxEnd[u]&&(c[u]=t.dataBox[u],c[u+2]=t.dataBox[u+2]);t.setDirty()}else if(!e.panning&&this.isMouseOver){this.selectBox.enabled=!1;var f=i._size,h=this.xaxis.domain,p=this.yaxis.domain,d=(a=t.pick(o/t.pixelRatio+f.l+h[0]*f.w,l/t.pixelRatio-(f.t+(1-p[1])*f.h)))&&a.object._trace.handlePick(a);if(d&&n&&this.emitPointAction(d,\"plotly_click\"),a&&\"skip\"!==a.object._trace.hoverinfo&&i.hovermode&&d&&(!this.lastPickResult||this.lastPickResult.traceUid!==d.trace.uid||this.lastPickResult.dataCoord[0]!==d.dataCoord[0]||this.lastPickResult.dataCoord[1]!==d.dataCoord[1])){var m=d;this.lastPickResult={traceUid:d.trace?d.trace.uid:null,dataCoord:d.dataCoord.slice()},this.spikes.update({center:a.dataCoord}),m.screenCoord=[((t.viewBox[2]-t.viewBox[0])*(a.dataCoord[0]-t.dataBox[0])/(t.dataBox[2]-t.dataBox[0])+t.viewBox[0])/t.pixelRatio,(this.canvas.height-(t.viewBox[3]-t.viewBox[1])*(a.dataCoord[1]-t.dataBox[1])/(t.dataBox[3]-t.dataBox[1])-t.viewBox[1])/t.pixelRatio],this.emitPointAction(d,\"plotly_hover\");var g=this.fullData[m.trace.index]||{},v=m.pointIndex,y=s.castHoverinfo(g,i,v);if(y&&\"all\"!==y){var x=y.split(\"+\");-1===x.indexOf(\"x\")&&(m.traceCoord[0]=void 0),-1===x.indexOf(\"y\")&&(m.traceCoord[1]=void 0),-1===x.indexOf(\"z\")&&(m.traceCoord[2]=void 0),-1===x.indexOf(\"text\")&&(m.textLabel=void 0),-1===x.indexOf(\"name\")&&(m.name=void 0)}s.loneHover({x:m.screenCoord[0],y:m.screenCoord[1],xLabel:this.hoverFormatter(\"xaxis\",m.traceCoord[0]),yLabel:this.hoverFormatter(\"yaxis\",m.traceCoord[1]),zLabel:m.traceCoord[2],text:m.textLabel,name:m.name,color:s.castHoverOption(g,v,\"bgcolor\")||m.color,borderColor:s.castHoverOption(g,v,\"bordercolor\"),fontFamily:s.castHoverOption(g,v,\"font.family\"),fontSize:s.castHoverOption(g,v,\"font.size\"),fontColor:s.castHoverOption(g,v,\"font.color\"),nameLength:s.castHoverOption(g,v,\"namelength\"),textAlign:s.castHoverOption(g,v,\"align\")},{container:this.svgContainer,gd:this.graphDiv})}}a||this.unhover(),t.draw()}},M.unhover=function(){this.lastPickResult&&(this.spikes.update({}),this.lastPickResult=null,this.graphDiv.emit(\"plotly_unhover\"),s.loneUnhover(this.svgContainer))},M.hoverFormatter=function(t,e){if(void 0!==e){var r=this[t];return o.tickText(r,r.c2l(e),\"hover\").text}}},{\"../../components/dragelement/helpers\":680,\"../../components/fx\":702,\"../../lib/show_no_webgl_msg\":821,\"../../plots/cartesian/axes\":849,\"../../registry\":927,\"../cartesian/autorange\":848,\"../cartesian/constants\":856,\"../cartesian/constraints\":857,\"./camera\":888,\"./convert\":889,\"gl-plot2d\":318,\"gl-select-box\":333,\"gl-spikes2d\":342,\"webgl-context\":625}],892:[function(t,e,r){\"use strict\";var n=t(\"../../plot_api/edit_types\").overrideAll,i=t(\"../../components/fx/layout_attributes\"),a=t(\"./scene\"),o=t(\"../get_data\").getSubplotData,s=t(\"../../lib\"),l=t(\"../../constants/xmlns_namespaces\");r.name=\"gl3d\",r.attr=\"scene\",r.idRoot=\"scene\",r.idRegex=r.attrRegex=s.counterRegex(\"scene\"),r.attributes=t(\"./layout/attributes\"),r.layoutAttributes=t(\"./layout/layout_attributes\"),r.baseLayoutAttrOverrides=n({hoverlabel:i.hoverlabel},\"plot\",\"nested\"),r.supplyLayoutDefaults=t(\"./layout/defaults\"),r.plot=function(t){for(var e=t._fullLayout,r=t._fullData,n=e._subplots.gl3d,i=0;i<n.length;i++){var s=n[i],l=o(r,\"gl3d\",s),c=e[s],u=c.camera,f=c._scene;f||(f=new a({id:s,graphDiv:t,container:t.querySelector(\".gl-container\"),staticPlot:t._context.staticPlot,plotGlPixelRatio:t._context.plotGlPixelRatio,camera:u},e),c._scene=f),f.viewInitial||(f.viewInitial={up:{x:u.up.x,y:u.up.y,z:u.up.z},eye:{x:u.eye.x,y:u.eye.y,z:u.eye.z},center:{x:u.center.x,y:u.center.y,z:u.center.z}}),f.plot(l,e,t.layout)}},r.clean=function(t,e,r,n){for(var i=n._subplots.gl3d||[],a=0;a<i.length;a++){var o=i[a];!e[o]&&n[o]._scene&&(n[o]._scene.destroy(),n._infolayer&&n._infolayer.selectAll(\".annotation-\"+o).remove())}},r.toSVG=function(t){for(var e=t._fullLayout,r=e._subplots.gl3d,n=e._size,i=0;i<r.length;i++){var a=e[r[i]],o=a.domain,s=a._scene,c=s.toImage(\"png\");e._glimages.append(\"svg:image\").attr({xmlns:l.svg,\"xlink:href\":c,x:n.l+n.w*o.x[0],y:n.t+n.h*(1-o.y[1]),width:n.w*(o.x[1]-o.x[0]),height:n.h*(o.y[1]-o.y[0]),preserveAspectRatio:\"none\"}),s.destroy()}},r.cleanId=function(t){if(t.match(/^scene[0-9]*$/)){var e=t.substr(5);return\"1\"===e&&(e=\"\"),\"scene\"+e}},r.updateFx=function(t){for(var e=t._fullLayout,r=e._subplots.gl3d,n=0;n<r.length;n++){e[r[n]]._scene.updateFx(e.dragmode,e.hovermode)}}},{\"../../components/fx/layout_attributes\":703,\"../../constants/xmlns_namespaces\":776,\"../../lib\":799,\"../../plot_api/edit_types\":831,\"../get_data\":887,\"./layout/attributes\":893,\"./layout/defaults\":897,\"./layout/layout_attributes\":898,\"./scene\":902}],893:[function(t,e,r){\"use strict\";e.exports={scene:{valType:\"subplotid\",dflt:\"scene\",editType:\"calc+clearAxisTypes\"}}},{}],894:[function(t,e,r){\"use strict\";var n=t(\"../../../components/color\"),i=t(\"../../cartesian/layout_attributes\"),a=t(\"../../../lib/extend\").extendFlat,o=t(\"../../../plot_api/edit_types\").overrideAll;e.exports=o({visible:i.visible,showspikes:{valType:\"boolean\",dflt:!0},spikesides:{valType:\"boolean\",dflt:!0},spikethickness:{valType:\"number\",min:0,dflt:2},spikecolor:{valType:\"color\",dflt:n.defaultLine},showbackground:{valType:\"boolean\",dflt:!1},backgroundcolor:{valType:\"color\",dflt:\"rgba(204, 204, 204, 0.5)\"},showaxeslabels:{valType:\"boolean\",dflt:!0},color:i.color,categoryorder:i.categoryorder,categoryarray:i.categoryarray,title:{text:i.title.text,font:i.title.font},type:a({},i.type,{values:[\"-\",\"linear\",\"log\",\"date\",\"category\"]}),autotypenumbers:i.autotypenumbers,autorange:i.autorange,rangemode:i.rangemode,range:a({},i.range,{items:[{valType:\"any\",editType:\"plot\",impliedEdits:{\"^autorange\":!1}},{valType:\"any\",editType:\"plot\",impliedEdits:{\"^autorange\":!1}}],anim:!1}),tickmode:i.tickmode,nticks:i.nticks,tick0:i.tick0,dtick:i.dtick,tickvals:i.tickvals,ticktext:i.ticktext,ticks:i.ticks,mirror:i.mirror,ticklen:i.ticklen,tickwidth:i.tickwidth,tickcolor:i.tickcolor,showticklabels:i.showticklabels,tickfont:i.tickfont,tickangle:i.tickangle,tickprefix:i.tickprefix,showtickprefix:i.showtickprefix,ticksuffix:i.ticksuffix,showticksuffix:i.showticksuffix,showexponent:i.showexponent,exponentformat:i.exponentformat,minexponent:i.minexponent,separatethousands:i.separatethousands,tickformat:i.tickformat,tickformatstops:i.tickformatstops,hoverformat:i.hoverformat,showline:i.showline,linecolor:i.linecolor,linewidth:i.linewidth,showgrid:i.showgrid,gridcolor:a({},i.gridcolor,{dflt:\"rgb(204, 204, 204)\"}),gridwidth:i.gridwidth,zeroline:i.zeroline,zerolinecolor:i.zerolinecolor,zerolinewidth:i.zerolinewidth,_deprecated:{title:i._deprecated.title,titlefont:i._deprecated.titlefont}},\"plot\",\"from-root\")},{\"../../../components/color\":662,\"../../../lib/extend\":789,\"../../../plot_api/edit_types\":831,\"../../cartesian/layout_attributes\":864}],895:[function(t,e,r){\"use strict\";var n=t(\"tinycolor2\").mix,i=t(\"../../../lib\"),a=t(\"../../../plot_api/plot_template\"),o=t(\"./axis_attributes\"),s=t(\"../../cartesian/type_defaults\"),l=t(\"../../cartesian/axis_defaults\"),c=[\"xaxis\",\"yaxis\",\"zaxis\"];e.exports=function(t,e,r){var u,f;function h(t,e){return i.coerce(u,f,o,t,e)}for(var p=0;p<c.length;p++){var d=c[p];u=t[d]||{},(f=a.newContainer(e,d))._id=d[0]+r.scene,f._name=d,s(u,f,h,r),l(u,f,h,{font:r.font,letter:d[0],data:r.data,showGrid:!0,noTickson:!0,noTicklabelmode:!0,noTicklabelposition:!0,noTicklabeloverflow:!0,bgColor:r.bgColor,calendar:r.calendar},r.fullLayout),h(\"gridcolor\",n(f.color,r.bgColor,13600/187).toRgbString()),h(\"title.text\",d[0]),f.setScale=i.noop,h(\"showspikes\")&&(h(\"spikesides\"),h(\"spikethickness\"),h(\"spikecolor\",f.color)),h(\"showaxeslabels\"),h(\"showbackground\")&&h(\"backgroundcolor\")}}},{\"../../../lib\":799,\"../../../plot_api/plot_template\":838,\"../../cartesian/axis_defaults\":851,\"../../cartesian/type_defaults\":875,\"./axis_attributes\":894,tinycolor2:594}],896:[function(t,e,r){\"use strict\";var n=t(\"../../../lib/str2rgbarray\"),i=t(\"../../../lib\"),a=[\"xaxis\",\"yaxis\",\"zaxis\"];function o(){this.bounds=[[-10,-10,-10],[10,10,10]],this.ticks=[[],[],[]],this.tickEnable=[!0,!0,!0],this.tickFont=[\"sans-serif\",\"sans-serif\",\"sans-serif\"],this.tickSize=[12,12,12],this.tickAngle=[0,0,0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[18,18,18],this.labels=[\"x\",\"y\",\"z\"],this.labelEnable=[!0,!0,!0],this.labelFont=[\"Open Sans\",\"Open Sans\",\"Open Sans\"],this.labelSize=[20,20,20],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[30,30,30],this.lineEnable=[!0,!0,!0],this.lineMirror=[!1,!1,!1],this.lineWidth=[1,1,1],this.lineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.lineTickEnable=[!0,!0,!0],this.lineTickMirror=[!1,!1,!1],this.lineTickLength=[10,10,10],this.lineTickWidth=[1,1,1],this.lineTickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.gridEnable=[!0,!0,!0],this.gridWidth=[1,1,1],this.gridColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroEnable=[!0,!0,!0],this.zeroLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroLineWidth=[2,2,2],this.backgroundEnable=[!0,!0,!0],this.backgroundColor=[[.8,.8,.8,.5],[.8,.8,.8,.5],[.8,.8,.8,.5]],this._defaultTickPad=this.tickPad.slice(),this._defaultLabelPad=this.labelPad.slice(),this._defaultLineTickLength=this.lineTickLength.slice()}o.prototype.merge=function(t,e){for(var r=0;r<3;++r){var o=e[a[r]];o.visible?(this.labels[r]=t._meta?i.templateString(o.title.text,t._meta):o.title.text,\"font\"in o.title&&(o.title.font.color&&(this.labelColor[r]=n(o.title.font.color)),o.title.font.family&&(this.labelFont[r]=o.title.font.family),o.title.font.size&&(this.labelSize[r]=o.title.font.size)),\"showline\"in o&&(this.lineEnable[r]=o.showline),\"linecolor\"in o&&(this.lineColor[r]=n(o.linecolor)),\"linewidth\"in o&&(this.lineWidth[r]=o.linewidth),\"showgrid\"in o&&(this.gridEnable[r]=o.showgrid),\"gridcolor\"in o&&(this.gridColor[r]=n(o.gridcolor)),\"gridwidth\"in o&&(this.gridWidth[r]=o.gridwidth),\"log\"===o.type?this.zeroEnable[r]=!1:\"zeroline\"in o&&(this.zeroEnable[r]=o.zeroline),\"zerolinecolor\"in o&&(this.zeroLineColor[r]=n(o.zerolinecolor)),\"zerolinewidth\"in o&&(this.zeroLineWidth[r]=o.zerolinewidth),\"ticks\"in o&&o.ticks?this.lineTickEnable[r]=!0:this.lineTickEnable[r]=!1,\"ticklen\"in o&&(this.lineTickLength[r]=this._defaultLineTickLength[r]=o.ticklen),\"tickcolor\"in o&&(this.lineTickColor[r]=n(o.tickcolor)),\"tickwidth\"in o&&(this.lineTickWidth[r]=o.tickwidth),\"tickangle\"in o&&(this.tickAngle[r]=\"auto\"===o.tickangle?-3600:Math.PI*-o.tickangle/180),\"showticklabels\"in o&&(this.tickEnable[r]=o.showticklabels),\"tickfont\"in o&&(o.tickfont.color&&(this.tickColor[r]=n(o.tickfont.color)),o.tickfont.family&&(this.tickFont[r]=o.tickfont.family),o.tickfont.size&&(this.tickSize[r]=o.tickfont.size)),\"mirror\"in o?-1!==[\"ticks\",\"all\",\"allticks\"].indexOf(o.mirror)?(this.lineTickMirror[r]=!0,this.lineMirror[r]=!0):!0===o.mirror?(this.lineTickMirror[r]=!1,this.lineMirror[r]=!0):(this.lineTickMirror[r]=!1,this.lineMirror[r]=!1):this.lineMirror[r]=!1,\"showbackground\"in o&&!1!==o.showbackground?(this.backgroundEnable[r]=!0,this.backgroundColor[r]=n(o.backgroundcolor)):this.backgroundEnable[r]=!1):(this.tickEnable[r]=!1,this.labelEnable[r]=!1,this.lineEnable[r]=!1,this.lineTickEnable[r]=!1,this.gridEnable[r]=!1,this.zeroEnable[r]=!1,this.backgroundEnable[r]=!1)}},e.exports=function(t,e){var r=new o;return r.merge(t,e),r}},{\"../../../lib\":799,\"../../../lib/str2rgbarray\":823}],897:[function(t,e,r){\"use strict\";var n=t(\"../../../lib\"),i=t(\"../../../components/color\"),a=t(\"../../../registry\"),o=t(\"../../subplot_defaults\"),s=t(\"./axis_defaults\"),l=t(\"./layout_attributes\"),c=t(\"../../get_data\").getSubplotData;function u(t,e,r,n){for(var o=r(\"bgcolor\"),l=i.combine(o,n.paper_bgcolor),u=[\"up\",\"center\",\"eye\"],f=0;f<u.length;f++)r(\"camera.\"+u[f]+\".x\"),r(\"camera.\"+u[f]+\".y\"),r(\"camera.\"+u[f]+\".z\");r(\"camera.projection.type\");var h=!!r(\"aspectratio.x\")&&!!r(\"aspectratio.y\")&&!!r(\"aspectratio.z\"),p=r(\"aspectmode\",h?\"manual\":\"auto\");h||(t.aspectratio=e.aspectratio={x:1,y:1,z:1},\"manual\"===p&&(e.aspectmode=\"auto\"),t.aspectmode=e.aspectmode);var d=c(n.fullData,\"gl3d\",n.id);s(t,e,{font:n.font,scene:n.id,data:d,bgColor:l,calendar:n.calendar,autotypenumbersDflt:n.autotypenumbersDflt,fullLayout:n.fullLayout}),a.getComponentMethod(\"annotations3d\",\"handleDefaults\")(t,e,n);var m=n.getDfltFromLayout(\"dragmode\");if(!1!==m&&!m)if(m=\"orbit\",t.camera&&t.camera.up){var g=t.camera.up.x,v=t.camera.up.y,y=t.camera.up.z;0!==y&&(g&&v&&y?y/Math.sqrt(g*g+v*v+y*y)>.999&&(m=\"turntable\"):m=\"turntable\")}else m=\"turntable\";r(\"dragmode\",m),r(\"hovermode\",n.getDfltFromLayout(\"hovermode\"))}e.exports=function(t,e,r){var i=e._basePlotModules.length>1;o(t,e,r,{type:\"gl3d\",attributes:l,handleDefaults:u,fullLayout:e,font:e.font,fullData:r,getDfltFromLayout:function(e){if(!i)return n.validate(t[e],l[e])?t[e]:void 0},autotypenumbersDflt:e.autotypenumbers,paper_bgcolor:e.paper_bgcolor,calendar:e.calendar})}},{\"../../../components/color\":662,\"../../../lib\":799,\"../../../registry\":927,\"../../get_data\":887,\"../../subplot_defaults\":921,\"./axis_defaults\":895,\"./layout_attributes\":898}],898:[function(t,e,r){\"use strict\";var n=t(\"./axis_attributes\"),i=t(\"../../domain\").attributes,a=t(\"../../../lib/extend\").extendFlat,o=t(\"../../../lib\").counterRegex;function s(t,e,r){return{x:{valType:\"number\",dflt:t,editType:\"camera\"},y:{valType:\"number\",dflt:e,editType:\"camera\"},z:{valType:\"number\",dflt:r,editType:\"camera\"},editType:\"camera\"}}e.exports={_arrayAttrRegexps:[o(\"scene\",\".annotations\",!0)],bgcolor:{valType:\"color\",dflt:\"rgba(0,0,0,0)\",editType:\"plot\"},camera:{up:a(s(0,0,1),{}),center:a(s(0,0,0),{}),eye:a(s(1.25,1.25,1.25),{}),projection:{type:{valType:\"enumerated\",values:[\"perspective\",\"orthographic\"],dflt:\"perspective\",editType:\"calc\"},editType:\"calc\"},editType:\"camera\"},domain:i({name:\"scene\",editType:\"plot\"}),aspectmode:{valType:\"enumerated\",values:[\"auto\",\"cube\",\"data\",\"manual\"],dflt:\"auto\",editType:\"plot\",impliedEdits:{\"aspectratio.x\":void 0,\"aspectratio.y\":void 0,\"aspectratio.z\":void 0}},aspectratio:{x:{valType:\"number\",min:0,editType:\"plot\",impliedEdits:{\"^aspectmode\":\"manual\"}},y:{valType:\"number\",min:0,editType:\"plot\",impliedEdits:{\"^aspectmode\":\"manual\"}},z:{valType:\"number\",min:0,editType:\"plot\",impliedEdits:{\"^aspectmode\":\"manual\"}},editType:\"plot\",impliedEdits:{aspectmode:\"manual\"}},xaxis:n,yaxis:n,zaxis:n,dragmode:{valType:\"enumerated\",values:[\"orbit\",\"turntable\",\"zoom\",\"pan\",!1],editType:\"plot\"},hovermode:{valType:\"enumerated\",values:[\"closest\",!1],dflt:\"closest\",editType:\"modebar\"},uirevision:{valType:\"any\",editType:\"none\"},editType:\"plot\",_deprecated:{cameraposition:{valType:\"info_array\",editType:\"camera\"}}}},{\"../../../lib\":799,\"../../../lib/extend\":789,\"../../domain\":877,\"./axis_attributes\":894}],899:[function(t,e,r){\"use strict\";var n=t(\"../../../lib/str2rgbarray\"),i=[\"xaxis\",\"yaxis\",\"zaxis\"];function a(){this.enabled=[!0,!0,!0],this.colors=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.drawSides=[!0,!0,!0],this.lineWidth=[1,1,1]}a.prototype.merge=function(t){for(var e=0;e<3;++e){var r=t[i[e]];r.visible?(this.enabled[e]=r.showspikes,this.colors[e]=n(r.spikecolor),this.drawSides[e]=r.spikesides,this.lineWidth[e]=r.spikethickness):(this.enabled[e]=!1,this.drawSides[e]=!1)}},e.exports=function(t){var e=new a;return e.merge(t),e}},{\"../../../lib/str2rgbarray\":823}],900:[function(t,e,r){\"use strict\";e.exports=function(t){for(var e=t.axesOptions,r=t.glplot.axesPixels,s=t.fullSceneLayout,l=[[],[],[]],c=0;c<3;++c){var u=s[a[c]];if(u._length=(r[c].hi-r[c].lo)*r[c].pixelsPerDataUnit/t.dataScale[c],Math.abs(u._length)===1/0||isNaN(u._length))l[c]=[];else{u._input_range=u.range.slice(),u.range[0]=r[c].lo/t.dataScale[c],u.range[1]=r[c].hi/t.dataScale[c],u._m=1/(t.dataScale[c]*r[c].pixelsPerDataUnit),u.range[0]===u.range[1]&&(u.range[0]-=1,u.range[1]+=1);var f=u.tickmode;if(\"auto\"===u.tickmode){u.tickmode=\"linear\";var h=u.nticks||i.constrain(u._length/40,4,9);n.autoTicks(u,Math.abs(u.range[1]-u.range[0])/h)}for(var p=n.calcTicks(u,{msUTC:!0}),d=0;d<p.length;++d)p[d].x=p[d].x*t.dataScale[c],\"date\"===u.type&&(p[d].text=p[d].text.replace(/\\<br\\>/g,\" \"));l[c]=p,u.tickmode=f}}e.ticks=l;for(c=0;c<3;++c){o[c]=.5*(t.glplot.bounds[0][c]+t.glplot.bounds[1][c]);for(d=0;d<2;++d)e.bounds[d][c]=t.glplot.bounds[d][c]}t.contourLevels=function(t){for(var e=new Array(3),r=0;r<3;++r){for(var n=t[r],i=new Array(n.length),a=0;a<n.length;++a)i[a]=n[a].x;e[r]=i}return e}(l)};var n=t(\"../../cartesian/axes\"),i=t(\"../../../lib\"),a=[\"xaxis\",\"yaxis\",\"zaxis\"],o=[0,0,0]},{\"../../../lib\":799,\"../../cartesian/axes\":849}],901:[function(t,e,r){\"use strict\";function n(t,e){var r,n,i=[0,0,0,0];for(r=0;r<4;++r)for(n=0;n<4;++n)i[n]+=t[4*r+n]*e[r];return i}e.exports=function(t,e){return n(t.projection,n(t.view,n(t.model,[e[0],e[1],e[2],1])))}},{}],902:[function(t,e,r){\"use strict\";var n,i,a=t(\"gl-plot3d\"),o=a.createCamera,s=a.createScene,l=t(\"webgl-context\"),c=t(\"has-passive-events\"),u=t(\"../../registry\"),f=t(\"../../lib\"),h=f.preserveDrawingBuffer(),p=t(\"../../plots/cartesian/axes\"),d=t(\"../../components/fx\"),m=t(\"../../lib/str2rgbarray\"),g=t(\"../../lib/show_no_webgl_msg\"),v=t(\"./project\"),y=t(\"./layout/convert\"),x=t(\"./layout/spikes\"),b=t(\"./layout/tick_marks\");function _(t,e){var r=document.createElement(\"div\"),n=t.container;this.graphDiv=t.graphDiv;var i=document.createElementNS(\"http://www.w3.org/2000/svg\",\"svg\");i.style.position=\"absolute\",i.style.top=i.style.left=\"0px\",i.style.width=i.style.height=\"100%\",i.style[\"z-index\"]=20,i.style[\"pointer-events\"]=\"none\",r.appendChild(i),this.svgContainer=i,r.id=t.id,r.style.position=\"absolute\",r.style.top=r.style.left=\"0px\",r.style.width=r.style.height=\"100%\",n.appendChild(r),this.fullLayout=e,this.id=t.id||\"scene\",this.fullSceneLayout=e[this.id],this.plotArgs=[[],{},{}],this.axesOptions=y(e,e[this.id]),this.spikeOptions=x(e[this.id]),this.container=r,this.staticMode=!!t.staticPlot,this.pixelRatio=this.pixelRatio||t.plotGlPixelRatio||2,this.dataScale=[1,1,1],this.contourLevels=[[],[],[]],this.convertAnnotations=u.getComponentMethod(\"annotations3d\",\"convert\"),this.drawAnnotations=u.getComponentMethod(\"annotations3d\",\"draw\"),this.initializeGLPlot()}var w=_.prototype;w.prepareOptions=function(){var t={canvas:this.canvas,gl:this.gl,glOptions:{preserveDrawingBuffer:h,premultipliedAlpha:!0,antialias:!0},container:this.container,axes:this.axesOptions,spikes:this.spikeOptions,pickRadius:10,snapToData:!0,autoScale:!0,autoBounds:!1,cameraObject:this.camera,pixelRatio:this.pixelRatio};if(this.staticMode){if(!(i||(n=document.createElement(\"canvas\"),i=l({canvas:n,preserveDrawingBuffer:!0,premultipliedAlpha:!0,antialias:!0}))))throw new Error(\"error creating static canvas/context for image server\");t.gl=i,t.canvas=n}return t};var T=!0;w.tryCreatePlot=function(){var t=this.prepareOptions(),e=!0;try{this.glplot=s(t)}catch(r){if(this.staticMode||!T||h)e=!1;else{f.warn([\"webgl setup failed possibly due to\",\"false preserveDrawingBuffer config.\",\"The mobile/tablet device may not be detected by is-mobile module.\",\"Enabling preserveDrawingBuffer in second attempt to create webgl scene...\"].join(\" \"));try{h=t.glOptions.preserveDrawingBuffer=!0,this.glplot=s(t)}catch(r){h=t.glOptions.preserveDrawingBuffer=!1,e=!1}}}return T=!1,e},w.initializeGLCamera=function(){var t=this.fullSceneLayout.camera,e=\"orthographic\"===t.projection.type;this.camera=o(this.container,{center:[t.center.x,t.center.y,t.center.z],eye:[t.eye.x,t.eye.y,t.eye.z],up:[t.up.x,t.up.y,t.up.z],_ortho:e,zoomMin:.01,zoomMax:100,mode:\"orbit\"})},w.initializeGLPlot=function(){var t=this;if(t.initializeGLCamera(),!t.tryCreatePlot())return g(t);t.traces={},t.make4thDimension();var e=t.graphDiv,r=e.layout,n=function(){var e={};return t.isCameraChanged(r)&&(e[t.id+\".camera\"]=t.getCamera()),t.isAspectChanged(r)&&(e[t.id+\".aspectratio\"]=t.glplot.getAspectratio(),\"manual\"!==r[t.id].aspectmode&&(t.fullSceneLayout.aspectmode=r[t.id].aspectmode=e[t.id+\".aspectmode\"]=\"manual\")),e},i=function(t){if(!1!==t.fullSceneLayout.dragmode){var e=n();t.saveLayout(r),t.graphDiv.emit(\"plotly_relayout\",e)}};return t.glplot.canvas&&(t.glplot.canvas.addEventListener(\"mouseup\",(function(){i(t)})),t.glplot.canvas.addEventListener(\"wheel\",(function(r){if(e._context._scrollZoom.gl3d){if(t.camera._ortho){var n=r.deltaX>r.deltaY?1.1:1/1.1,a=t.glplot.getAspectratio();t.glplot.setAspectratio({x:n*a.x,y:n*a.y,z:n*a.z})}i(t)}}),!!c&&{passive:!1}),t.glplot.canvas.addEventListener(\"mousemove\",(function(){if(!1!==t.fullSceneLayout.dragmode&&0!==t.camera.mouseListener.buttons){var e=n();t.graphDiv.emit(\"plotly_relayouting\",e)}})),t.staticMode||t.glplot.canvas.addEventListener(\"webglcontextlost\",(function(r){e&&e.emit&&e.emit(\"plotly_webglcontextlost\",{event:r,layer:t.id})}),!1)),t.glplot.oncontextloss=function(){t.recoverContext()},t.glplot.onrender=function(){t.render()},!0},w.render=function(){var t,e=this,r=e.graphDiv,n=e.svgContainer,i=e.container.getBoundingClientRect();r._fullLayout._calcInverseTransform(r);var a=r._fullLayout._invScaleX,o=r._fullLayout._invScaleY,s=i.width*a,l=i.height*o;n.setAttributeNS(null,\"viewBox\",\"0 0 \"+s+\" \"+l),n.setAttributeNS(null,\"width\",s),n.setAttributeNS(null,\"height\",l),b(e),e.glplot.axes.update(e.axesOptions);for(var c,u=Object.keys(e.traces),h=null,m=e.glplot.selection,g=0;g<u.length;++g)\"skip\"!==(t=e.traces[u[g]]).data.hoverinfo&&t.handlePick(m)&&(h=t),t.setContourLevels&&t.setContourLevels();function y(t,r,n){var i=e.fullSceneLayout[t+\"axis\"];return\"log\"!==i.type&&(r=i.d2l(r)),p.hoverLabelText(i,r,n)}if(null!==h){var x=v(e.glplot.cameraParams,m.dataCoordinate);t=h.data;var _,w=r._fullData[t.index],T=m.index,k={xLabel:y(\"x\",m.traceCoordinate[0],t.xhoverformat),yLabel:y(\"y\",m.traceCoordinate[1],t.yhoverformat),zLabel:y(\"z\",m.traceCoordinate[2],t.zhoverformat)},M=d.castHoverinfo(w,e.fullLayout,T),A=(M||\"\").split(\"+\"),S=M&&\"all\"===M;w.hovertemplate||S||(-1===A.indexOf(\"x\")&&(k.xLabel=void 0),-1===A.indexOf(\"y\")&&(k.yLabel=void 0),-1===A.indexOf(\"z\")&&(k.zLabel=void 0),-1===A.indexOf(\"text\")&&(m.textLabel=void 0),-1===A.indexOf(\"name\")&&(h.name=void 0));var E=[];\"cone\"===t.type||\"streamtube\"===t.type?(k.uLabel=y(\"x\",m.traceCoordinate[3],t.uhoverformat),(S||-1!==A.indexOf(\"u\"))&&E.push(\"u: \"+k.uLabel),k.vLabel=y(\"y\",m.traceCoordinate[4],t.vhoverformat),(S||-1!==A.indexOf(\"v\"))&&E.push(\"v: \"+k.vLabel),k.wLabel=y(\"z\",m.traceCoordinate[5],t.whoverformat),(S||-1!==A.indexOf(\"w\"))&&E.push(\"w: \"+k.wLabel),k.normLabel=m.traceCoordinate[6].toPrecision(3),(S||-1!==A.indexOf(\"norm\"))&&E.push(\"norm: \"+k.normLabel),\"streamtube\"===t.type&&(k.divergenceLabel=m.traceCoordinate[7].toPrecision(3),(S||-1!==A.indexOf(\"divergence\"))&&E.push(\"divergence: \"+k.divergenceLabel)),m.textLabel&&E.push(m.textLabel),_=E.join(\"<br>\")):\"isosurface\"===t.type||\"volume\"===t.type?(k.valueLabel=p.hoverLabelText(e._mockAxis,e._mockAxis.d2l(m.traceCoordinate[3]),t.valuehoverformat),E.push(\"value: \"+k.valueLabel),m.textLabel&&E.push(m.textLabel),_=E.join(\"<br>\")):_=m.textLabel;var L={x:m.traceCoordinate[0],y:m.traceCoordinate[1],z:m.traceCoordinate[2],data:w._input,fullData:w,curveNumber:w.index,pointNumber:T};d.appendArrayPointValue(L,w,T),t._module.eventData&&(L=w._module.eventData(L,m,w,{},T));var C={points:[L]};e.fullSceneLayout.hovermode&&d.loneHover({trace:w,x:(.5+.5*x[0]/x[3])*s,y:(.5-.5*x[1]/x[3])*l,xLabel:k.xLabel,yLabel:k.yLabel,zLabel:k.zLabel,text:_,name:h.name,color:d.castHoverOption(w,T,\"bgcolor\")||h.color,borderColor:d.castHoverOption(w,T,\"bordercolor\"),fontFamily:d.castHoverOption(w,T,\"font.family\"),fontSize:d.castHoverOption(w,T,\"font.size\"),fontColor:d.castHoverOption(w,T,\"font.color\"),nameLength:d.castHoverOption(w,T,\"namelength\"),textAlign:d.castHoverOption(w,T,\"align\"),hovertemplate:f.castOption(w,T,\"hovertemplate\"),hovertemplateLabels:f.extendFlat({},L,k),eventData:[L]},{container:n,gd:r}),m.buttons&&m.distance<5?r.emit(\"plotly_click\",C):r.emit(\"plotly_hover\",C),c=C}else d.loneUnhover(n),r.emit(\"plotly_unhover\",c);e.drawAnnotations(e)},w.recoverContext=function(){var t=this;t.glplot.dispose();var e=function(){t.glplot.gl.isContextLost()?requestAnimationFrame(e):t.initializeGLPlot()?t.plot.apply(t,t.plotArgs):f.error(\"Catastrophic and 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e,r=t.camera;this.camera.lookAt.apply(this,[[(e=r).eye.x,e.eye.y,e.eye.z],[e.center.x,e.center.y,e.center.z],[e.up.x,e.up.y,e.up.z]]),this.glplot.setAspectratio(t.aspectratio),\"orthographic\"===r.projection.type!==this.camera._ortho&&(this.glplot.redraw(),this.glplot.clearRGBA(),this.glplot.dispose(),this.initializeGLPlot())},w.isCameraChanged=function(t){var e=this.getCamera(),r=f.nestedProperty(t,this.id+\".camera\").get();function n(t,e,r,n){var i=[\"up\",\"center\",\"eye\"],a=[\"x\",\"y\",\"z\"];return e[i[r]]&&t[i[r]][a[n]]===e[i[r]][a[n]]}var i=!1;if(void 0===r)i=!0;else{for(var a=0;a<3;a++)for(var o=0;o<3;o++)if(!n(e,r,a,o)){i=!0;break}(!r.projection||e.projection&&e.projection.type!==r.projection.type)&&(i=!0)}return i},w.isAspectChanged=function(t){var e=this.glplot.getAspectratio(),r=f.nestedProperty(t,this.id+\".aspectratio\").get();return void 0===r||r.x!==e.x||r.y!==e.y||r.z!==e.z},w.saveLayout=function(t){var e,r,n,i,a,o,s=this.fullLayout,l=this.isCameraChanged(t),c=this.isAspectChanged(t),h=l||c;if(h){var p={};if(l&&(e=this.getCamera(),n=(r=f.nestedProperty(t,this.id+\".camera\")).get(),p[this.id+\".camera\"]=n),c&&(i=this.glplot.getAspectratio(),o=(a=f.nestedProperty(t,this.id+\".aspectratio\")).get(),p[this.id+\".aspectratio\"]=o),u.call(\"_storeDirectGUIEdit\",t,s._preGUI,p),l)r.set(e),f.nestedProperty(s,this.id+\".camera\").set(e);if(c)a.set(i),f.nestedProperty(s,this.id+\".aspectratio\").set(i),this.glplot.redraw()}return h},w.updateFx=function(t,e){var r=this.camera;if(r)if(\"orbit\"===t)r.mode=\"orbit\",r.keyBindingMode=\"rotate\";else if(\"turntable\"===t){r.up=[0,0,1],r.mode=\"turntable\",r.keyBindingMode=\"rotate\";var n=this.graphDiv,i=n._fullLayout,a=this.fullSceneLayout.camera,o=a.up.x,s=a.up.y,l=a.up.z;if(l/Math.sqrt(o*o+s*s+l*l)<.999){var c=this.id+\".camera.up\",h={x:0,y:0,z:1},p={};p[c]=h;var 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s,l=o.getContext(\"2d\"),c=l.createImageData(r,i);switch(c.data.set(a),l.putImageData(c,0,0),t){case\"jpeg\":s=o.toDataURL(\"image/jpeg\");break;case\"webp\":s=o.toDataURL(\"image/webp\");break;default:s=o.toDataURL(\"image/png\")}return this.staticMode&&this.container.removeChild(n),s},w.setConvert=function(){for(var t=0;t<3;t++){var e=this.fullSceneLayout[k[t]];p.setConvert(e,this.fullLayout),e.setScale=f.noop}},w.make4thDimension=function(){var t=this.graphDiv._fullLayout;this._mockAxis={type:\"linear\",showexponent:\"all\",exponentformat:\"B\"},p.setConvert(this._mockAxis,t)},e.exports=_},{\"../../components/fx\":702,\"../../lib\":799,\"../../lib/show_no_webgl_msg\":821,\"../../lib/str2rgbarray\":823,\"../../plots/cartesian/axes\":849,\"../../registry\":927,\"./layout/convert\":896,\"./layout/spikes\":899,\"./layout/tick_marks\":900,\"./project\":901,\"gl-plot3d\":322,\"has-passive-events\":440,\"webgl-context\":625}],903:[function(t,e,r){\"use strict\";e.exports=function(t,e,r,n){n=n||t.length;for(var i=new Array(n),a=0;a<n;a++)i[a]=[t[a],e[a],r[a]];return i}},{}],904:[function(t,e,r){\"use strict\";var n=t(\"./font_attributes\"),i=t(\"./animation_attributes\"),a=t(\"../components/color/attributes\"),o=t(\"../components/shapes/draw_newshape/attributes\"),s=t(\"./pad_attributes\"),l=t(\"../lib/extend\").extendFlat,c=n({editType:\"calc\"});c.family.dflt='\"Open Sans\", verdana, arial, sans-serif',c.size.dflt=12,c.color.dflt=a.defaultLine,e.exports={font:c,title:{text:{valType:\"string\",editType:\"layoutstyle\"},font:n({editType:\"layoutstyle\"}),xref:{valType:\"enumerated\",dflt:\"container\",values:[\"container\",\"paper\"],editType:\"layoutstyle\"},yref:{valType:\"enumerated\",dflt:\"container\",values:[\"container\",\"paper\"],editType:\"layoutstyle\"},x:{valType:\"number\",min:0,max:1,dflt:.5,editType:\"layoutstyle\"},y:{valType:\"number\",min:0,max:1,dflt:\"auto\",editType:\"layoutstyle\"},xanchor:{valType:\"enumerated\",dflt:\"auto\",values:[\"auto\",\"left\",\"center\",\"right\"],editType:\"layoutstyle\"},yanchor:{valType:\"enumerated\",dflt:\"auto\",values:[\"auto\",\"top\",\"middle\",\"bottom\"],editType:\"layoutstyle\"},pad:l(s({editType:\"layoutstyle\"}),{}),editType:\"layoutstyle\"},uniformtext:{mode:{valType:\"enumerated\",values:[!1,\"hide\",\"show\"],dflt:!1,editType:\"plot\"},minsize:{valType:\"number\",min:0,dflt:0,editType:\"plot\"},editType:\"plot\"},autosize:{valType:\"boolean\",dflt:!1,editType:\"none\"},width:{valType:\"number\",min:10,dflt:700,editType:\"plot\"},height:{valType:\"number\",min:10,dflt:450,editType:\"plot\"},margin:{l:{valType:\"number\",min:0,dflt:80,editType:\"plot\"},r:{valType:\"number\",min:0,dflt:80,editType:\"plot\"},t:{valType:\"number\",min:0,dflt:100,editType:\"plot\"},b:{valType:\"number\",min:0,dflt:80,editType:\"plot\"},pad:{valType:\"number\",min:0,dflt:0,editType:\"plot\"},autoexpand:{valType:\"boolean\",dflt:!0,editType:\"plot\"},editType:\"plot\"},computed:{valType:\"any\",editType:\"none\"},paper_bgcolor:{valType:\"color\",dflt:a.background,editType:\"plot\"},plot_bgcolor:{valType:\"color\",dflt:a.background,editType:\"layoutstyle\"},autotypenumbers:{valType:\"enumerated\",values:[\"convert 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n='\\xa9 <a target=\"_blank\" href=\"https://www.openstreetmap.org/copyright\">OpenStreetMap</a> contributors',i=['\\xa9 <a target=\"_blank\" href=\"https://carto.com/\">Carto</a>',n].join(\" \"),a=['Map tiles by <a target=\"_blank\" href=\"https://stamen.com\">Stamen Design</a>','under <a target=\"_blank\" href=\"https://creativecommons.org/licenses/by/3.0\">CC BY 3.0</a>',\"|\",'Data by <a target=\"_blank\" href=\"https://openstreetmap.org\">OpenStreetMap</a> contributors','under <a target=\"_blank\" href=\"https://www.openstreetmap.org/copyright\">ODbL</a>'].join(\" \"),o={\"open-street-map\":{id:\"osm\",version:8,sources:{\"plotly-osm-tiles\":{type:\"raster\",attribution:n,tiles:[\"https://a.tile.openstreetmap.org/{z}/{x}/{y}.png\",\"https://b.tile.openstreetmap.org/{z}/{x}/{y}.png\"],tileSize:256}},layers:[{id:\"plotly-osm-tiles\",type:\"raster\",source:\"plotly-osm-tiles\",minzoom:0,maxzoom:22}]},\"white-bg\":{id:\"white-bg\",version:8,sources:{},layers:[{id:\"white-bg\",type:\"background\",paint:{\"background-color\":\"#FFFFFF\"},minzoom:0,maxzoom:22}]},\"carto-positron\":{id:\"carto-positron\",version:8,sources:{\"plotly-carto-positron\":{type:\"raster\",attribution:i,tiles:[\"https://cartodb-basemaps-c.global.ssl.fastly.net/light_all/{z}/{x}/{y}.png\"],tileSize:256}},layers:[{id:\"plotly-carto-positron\",type:\"raster\",source:\"plotly-carto-positron\",minzoom:0,maxzoom:22}]},\"carto-darkmatter\":{id:\"carto-darkmatter\",version:8,sources:{\"plotly-carto-darkmatter\":{type:\"raster\",attribution:i,tiles:[\"https://cartodb-basemaps-c.global.ssl.fastly.net/dark_all/{z}/{x}/{y}.png\"],tileSize:256}},layers:[{id:\"plotly-carto-darkmatter\",type:\"raster\",source:\"plotly-carto-darkmatter\",minzoom:0,maxzoom:22}]},\"stamen-terrain\":{id:\"stamen-terrain\",version:8,sources:{\"plotly-stamen-terrain\":{type:\"raster\",attribution:a,tiles:[\"https://stamen-tiles.a.ssl.fastly.net/terrain/{z}/{x}/{y}.png\"],tileSize:256}},layers:[{id:\"plotly-stamen-terrain\",type:\"raster\",source:\"plotly-stamen-terrain\",minzoom:0,maxzoom:22}]},\"stamen-toner\":{id:\"stamen-toner\",version:8,sources:{\"plotly-stamen-toner\":{type:\"raster\",attribution:a,tiles:[\"https://stamen-tiles.a.ssl.fastly.net/toner/{z}/{x}/{y}.png\"],tileSize:256}},layers:[{id:\"plotly-stamen-toner\",type:\"raster\",source:\"plotly-stamen-toner\",minzoom:0,maxzoom:22}]},\"stamen-watercolor\":{id:\"stamen-watercolor\",version:8,sources:{\"plotly-stamen-watercolor\":{type:\"raster\",attribution:['Map tiles by <a target=\"_blank\" href=\"https://stamen.com\">Stamen Design</a>','under <a target=\"_blank\" href=\"https://creativecommons.org/licenses/by/3.0\">CC BY 3.0</a>',\"|\",'Data by <a target=\"_blank\" href=\"https://openstreetmap.org\">OpenStreetMap</a> contributors','under <a target=\"_blank\" href=\"https://creativecommons.org/licenses/by-sa/3.0\">CC BY SA</a>'].join(\" \"),tiles:[\"https://stamen-tiles.a.ssl.fastly.net/watercolor/{z}/{x}/{y}.png\"],tileSize:256}},layers:[{id:\"plotly-stamen-watercolor\",type:\"raster\",source:\"plotly-stamen-watercolor\",minzoom:0,maxzoom:22}]}},s=Object.keys(o);e.exports={requiredVersion:\"1.10.1\",styleUrlPrefix:\"mapbox://styles/mapbox/\",styleUrlSuffix:\"v9\",styleValuesMapbox:[\"basic\",\"streets\",\"outdoors\",\"light\",\"dark\",\"satellite\",\"satellite-streets\"],styleValueDflt:\"basic\",stylesNonMapbox:o,styleValuesNonMapbox:s,traceLayerPrefix:\"plotly-trace-layer-\",layoutLayerPrefix:\"plotly-layout-layer-\",wrongVersionErrorMsg:[\"Your custom plotly.js bundle is not using the correct mapbox-gl version\",\"Please install mapbox-gl@1.10.1.\"].join(\"\\n\"),noAccessTokenErrorMsg:[\"Missing Mapbox access token.\",\"Mapbox trace type require a Mapbox access token to be registered.\",\"For example:\",\"  Plotly.newPlot(gd, data, layout, { mapboxAccessToken: 'my-access-token' });\",\"More info here: 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n=t(\"mapbox-gl/dist/mapbox-gl-unminified\"),i=t(\"../../lib\"),a=i.strTranslate,o=i.strScale,s=t(\"../../plots/get_data\").getSubplotCalcData,l=t(\"../../constants/xmlns_namespaces\"),c=t(\"@plotly/d3\"),u=t(\"../../components/drawing\"),f=t(\"../../lib/svg_text_utils\"),h=t(\"./mapbox\"),p=r.constants=t(\"./constants\");function d(t){return\"string\"==typeof t&&(-1!==p.styleValuesMapbox.indexOf(t)||0===t.indexOf(\"mapbox://\"))}r.name=\"mapbox\",r.attr=\"subplot\",r.idRoot=\"mapbox\",r.idRegex=r.attrRegex=i.counterRegex(\"mapbox\"),r.attributes={subplot:{valType:\"subplotid\",dflt:\"mapbox\",editType:\"calc\"}},r.layoutAttributes=t(\"./layout_attributes\"),r.supplyLayoutDefaults=t(\"./layout_defaults\"),r.plot=function(t){var e=t._fullLayout,r=t.calcdata,a=e._subplots.mapbox;if(n.version!==p.requiredVersion)throw new Error(p.wrongVersionErrorMsg);var o=function(t,e){var r=t._fullLayout;if(\"\"===t._context.mapboxAccessToken)return\"\";for(var n=[],a=[],o=!1,s=!1,l=0;l<e.length;l++){var c=r[e[l]],u=c.accesstoken;d(c.style)&&(u?i.pushUnique(n,u):(d(c._input.style)&&(i.error(\"Uses Mapbox map style, but did not set an access token.\"),o=!0),s=!0)),u&&i.pushUnique(a,u)}if(s){var f=o?p.noAccessTokenErrorMsg:p.missingStyleErrorMsg;throw i.error(f),new Error(f)}return n.length?(n.length>1&&i.warn(p.multipleTokensErrorMsg),n[0]):(a.length&&i.log([\"Listed mapbox access token(s)\",a.join(\",\"),\"but did not use a Mapbox map style, ignoring token(s).\"].join(\" \")),\"\")}(t,a);n.accessToken=o;for(var l=0;l<a.length;l++){var c=a[l],u=s(r,\"mapbox\",c),f=e[c],m=f._subplot;m||(m=new h(t,c),e[c]._subplot=m),m.viewInitial||(m.viewInitial={center:i.extendFlat({},f.center),zoom:f.zoom,bearing:f.bearing,pitch:f.pitch}),m.plot(u,e,t._promises)}},r.clean=function(t,e,r,n){for(var i=n._subplots.mapbox||[],a=0;a<i.length;a++){var o=i[a];!e[o]&&n[o]._subplot&&n[o]._subplot.destroy()}},r.toSVG=function(t){for(var e=t._fullLayout,r=e._subplots.mapbox,n=e._size,i=0;i<r.length;i++){var s=e[r[i]],h=s.domain,d=s._subplot.toImage(\"png\");e._glimages.append(\"svg:image\").attr({xmlns:l.svg,\"xlink:href\":d,x:n.l+n.w*h.x[0],y:n.t+n.h*(1-h.y[1]),width:n.w*(h.x[1]-h.x[0]),height:n.h*(h.y[1]-h.y[0]),preserveAspectRatio:\"none\"});var m=c.select(s._subplot.div);if(!(null===m.select(\".mapboxgl-ctrl-logo\").node().offsetParent)){var g=e._glimages.append(\"g\");g.attr(\"transform\",a(n.l+n.w*h.x[0]+10,n.t+n.h*(1-h.y[0])-31)),g.append(\"path\").attr(\"d\",p.mapboxLogo.path0).style({opacity:.9,fill:\"#ffffff\",\"enable-background\":\"new\"}),g.append(\"path\").attr(\"d\",p.mapboxLogo.path1).style(\"opacity\",.35).style(\"enable-background\",\"new\"),g.append(\"path\").attr(\"d\",p.mapboxLogo.path2).style(\"opacity\",.35).style(\"enable-background\",\"new\"),g.append(\"polygon\").attr(\"points\",p.mapboxLogo.polygon).style({opacity:.9,fill:\"#ffffff\",\"enable-background\":\"new\"})}var v=m.select(\".mapboxgl-ctrl-attrib\").text().replace(\"Improve this map\",\"\"),y=e._glimages.append(\"g\"),x=y.append(\"text\");x.text(v).classed(\"static-attribution\",!0).attr({\"font-size\":12,\"font-family\":\"Arial\",color:\"rgba(0, 0, 0, 0.75)\",\"text-anchor\":\"end\",\"data-unformatted\":v});var b=u.bBox(x.node()),_=n.w*(h.x[1]-h.x[0]);if(b.width>_/2){var w=v.split(\"|\").join(\"<br>\");x.text(w).attr(\"data-unformatted\",w).call(f.convertToTspans,t),b=u.bBox(x.node())}x.attr(\"transform\",a(-3,8-b.height)),y.insert(\"rect\",\".static-attribution\").attr({x:-b.width-6,y:-b.height-3,width:b.width+6,height:b.height+3,fill:\"rgba(255, 255, 255, 0.75)\"});var T=1;b.width+6>_&&(T=_/(b.width+6));var k=[n.l+n.w*h.x[1],n.t+n.h*(1-h.y[0])];y.attr(\"transform\",a(k[0],k[1])+o(T))}},r.updateFx=function(t){for(var e=t._fullLayout,r=e._subplots.mapbox,n=0;n<r.length;n++){e[r[n]]._subplot.updateFx(e)}}},{\"../../components/drawing\":684,\"../../constants/xmlns_namespaces\":776,\"../../lib\":799,\"../../lib/svg_text_utils\":824,\"../../plots/get_data\":887,\"./constants\":905,\"./layout_attributes\":909,\"./layout_defaults\":910,\"./mapbox\":911,\"@plotly/d3\":58,\"mapbox-gl/dist/mapbox-gl-unminified\":457}],908:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../../lib/svg_text_utils\").sanitizeHTML,a=t(\"./convert_text_opts\"),o=t(\"./constants\");function s(t,e){this.subplot=t,this.uid=t.uid+\"-\"+e,this.index=e,this.idSource=\"source-\"+this.uid,this.idLayer=o.layoutLayerPrefix+this.uid,this.sourceType=null,this.source=null,this.layerType=null,this.below=null,this.visible=!1}var l=s.prototype;function c(t){if(!t.visible)return!1;var e=t.source;if(Array.isArray(e)&&e.length>0){for(var r=0;r<e.length;r++)if(\"string\"!=typeof e[r]||0===e[r].length)return!1;return!0}return n.isPlainObject(e)||\"string\"==typeof e&&e.length>0}function u(t){var e={},r={};switch(t.type){case\"circle\":n.extendFlat(r,{\"circle-radius\":t.circle.radius,\"circle-color\":t.color,\"circle-opacity\":t.opacity});break;case\"line\":n.extendFlat(r,{\"line-width\":t.line.width,\"line-color\":t.color,\"line-opacity\":t.opacity,\"line-dasharray\":t.line.dash});break;case\"fill\":n.extendFlat(r,{\"fill-color\":t.color,\"fill-outline-color\":t.fill.outlinecolor,\"fill-opacity\":t.opacity});break;case\"symbol\":var i=t.symbol,o=a(i.textposition,i.iconsize);n.extendFlat(e,{\"icon-image\":i.icon+\"-15\",\"icon-size\":i.iconsize/10,\"text-field\":i.text,\"text-size\":i.textfont.size,\"text-anchor\":o.anchor,\"text-offset\":o.offset,\"symbol-placement\":i.placement}),n.extendFlat(r,{\"icon-color\":t.color,\"text-color\":i.textfont.color,\"text-opacity\":t.opacity});break;case\"raster\":n.extendFlat(r,{\"raster-fade-duration\":0,\"raster-opacity\":t.opacity})}return{layout:e,paint:r}}l.update=function(t){this.visible?this.needsNewImage(t)?this.updateImage(t):this.needsNewSource(t)?(this.removeLayer(),this.updateSource(t),this.updateLayer(t)):this.needsNewLayer(t)?this.updateLayer(t):this.updateStyle(t):(this.updateSource(t),this.updateLayer(t)),this.visible=c(t)},l.needsNewImage=function(t){return this.subplot.map.getSource(this.idSource)&&\"image\"===this.sourceType&&\"image\"===t.sourcetype&&(this.source!==t.source||JSON.stringify(this.coordinates)!==JSON.stringify(t.coordinates))},l.needsNewSource=function(t){return this.sourceType!==t.sourcetype||JSON.stringify(this.source)!==JSON.stringify(t.source)||this.layerType!==t.type},l.needsNewLayer=function(t){return this.layerType!==t.type||this.below!==this.subplot.belowLookup[\"layout-\"+this.index]},l.lookupBelow=function(){return this.subplot.belowLookup[\"layout-\"+this.index]},l.updateImage=function(t){this.subplot.map.getSource(this.idSource).updateImage({url:t.source,coordinates:t.coordinates});var e=this.findFollowingMapboxLayerId(this.lookupBelow());null!==e&&this.subplot.map.moveLayer(this.idLayer,e)},l.updateSource=function(t){var e=this.subplot.map;if(e.getSource(this.idSource)&&e.removeSource(this.idSource),this.sourceType=t.sourcetype,this.source=t.source,c(t)){var r=function(t){var e,r=t.sourcetype,n=t.source,a={type:r};\"geojson\"===r?e=\"data\":\"vector\"===r?e=\"string\"==typeof n?\"url\":\"tiles\":\"raster\"===r?(e=\"tiles\",a.tileSize=256):\"image\"===r&&(e=\"url\",a.coordinates=t.coordinates);a[e]=n,t.sourceattribution&&(a.attribution=i(t.sourceattribution));return a}(t);e.addSource(this.idSource,r)}},l.findFollowingMapboxLayerId=function(t){if(\"traces\"===t)for(var e=this.subplot.getMapLayers(),r=0;r<e.length;r++){var n=e[r].id;if(\"string\"==typeof n&&0===n.indexOf(o.traceLayerPrefix)){t=n;break}}return t},l.updateLayer=function(t){var e=this.subplot,r=u(t),n=this.lookupBelow(),i=this.findFollowingMapboxLayerId(n);this.removeLayer(),c(t)&&e.addLayer({id:this.idLayer,source:this.idSource,\"source-layer\":t.sourcelayer||\"\",type:t.type,minzoom:t.minzoom,maxzoom:t.maxzoom,layout:r.layout,paint:r.paint},i),this.layerType=t.type,this.below=n},l.updateStyle=function(t){if(c(t)){var e=u(t);this.subplot.setOptions(this.idLayer,\"setLayoutProperty\",e.layout),this.subplot.setOptions(this.idLayer,\"setPaintProperty\",e.paint)}},l.removeLayer=function(){var t=this.subplot.map;t.getLayer(this.idLayer)&&t.removeLayer(this.idLayer)},l.dispose=function(){var t=this.subplot.map;t.getLayer(this.idLayer)&&t.removeLayer(this.idLayer),t.getSource(this.idSource)&&t.removeSource(this.idSource)},e.exports=function(t,e,r){var n=new s(t,e);return n.update(r),n}},{\"../../lib\":799,\"../../lib/svg_text_utils\":824,\"./constants\":905,\"./convert_text_opts\":906}],909:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../../components/color\").defaultLine,a=t(\"../domain\").attributes,o=t(\"../font_attributes\"),s=t(\"../../traces/scatter/attributes\").textposition,l=t(\"../../plot_api/edit_types\").overrideAll,c=t(\"../../plot_api/plot_template\").templatedArray,u=t(\"./constants\"),f=o({});f.family.dflt=\"Open Sans Regular, Arial Unicode MS 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t=c._fullLayout[s.id];q=i(t.bgcolor).getLuminance(),(H=g.makeZoombox(f,q,x,T,U)).attr(\"fill-rule\",\"evenodd\"),G=g.makeCorners(f,x,T),w(c)}();break;case\"select\":case\"lasso\":b(t,n,a,Y,l)}},R.onmousemove=function(t){y.hover(c,t,s.id),c._fullLayout._lasthover=R,c._fullLayout._hoversubplot=s.id},R.onmouseout=function(t){c._dragging||v.unhover(c,t)},v.init(Y)},z.updateRadialDrag=function(t,e,r){var i=this,c=i.gd,u=i.layers,f=i.radius,h=i.innerRadius,p=i.cx,d=i.cy,m=i.radialAxis,y=S.radialDragBoxSize,x=y/2;if(m.visible){var b,_,T,A=P(i.radialAxisAngle),E=m._rl,L=E[0],C=E[1],O=E[r],z=.75*(E[1]-E[0])/(1-e.hole)/f;r?(b=p+(f+x)*Math.cos(A),_=d-(f+x)*Math.sin(A),T=\"radialdrag\"):(b=p+(h-x)*Math.cos(A),_=d-(h-x)*Math.sin(A),T=\"radialdrag-inner\");var D,B,N,j=g.makeRectDragger(u,T,\"crosshair\",-x,-x,y,y),U={element:j,gd:c};F(n.select(j),m.visible&&h<f,{transform:l(b,_)}),U.prepFn=function(){D=null,B=null,N=null,U.moveFn=V,U.doneFn=q,w(c)},U.clampFn=function(t,e){return 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n=t(\"../../components/color/attributes\"),i=t(\"../domain\").attributes,a=t(\"../cartesian/layout_attributes\"),o=t(\"../../plot_api/edit_types\").overrideAll,s=t(\"../../lib/extend\").extendFlat,l={title:{text:a.title.text,font:a.title.font},color:a.color,tickmode:a.tickmode,nticks:s({},a.nticks,{dflt:6,min:1}),tick0:a.tick0,dtick:a.dtick,tickvals:a.tickvals,ticktext:a.ticktext,ticks:a.ticks,ticklen:a.ticklen,tickwidth:a.tickwidth,tickcolor:a.tickcolor,showticklabels:a.showticklabels,showtickprefix:a.showtickprefix,tickprefix:a.tickprefix,showticksuffix:a.showticksuffix,ticksuffix:a.ticksuffix,showexponent:a.showexponent,exponentformat:a.exponentformat,minexponent:a.minexponent,separatethousands:a.separatethousands,tickfont:a.tickfont,tickangle:a.tickangle,tickformat:a.tickformat,tickformatstops:a.tickformatstops,hoverformat:a.hoverformat,showline:s({},a.showline,{dflt:!0}),linecolor:a.linecolor,linewidth:a.linewidth,showgrid:s({},a.showgrid,{dflt:!0}),gridcolor:a.gridcolor,gridwidth:a.gridwidth,layer:a.layer,min:{valType:\"number\",dflt:0,min:0},_deprecated:{title:a._deprecated.title,titlefont:a._deprecated.titlefont}},c=e.exports=o({domain:i({name:\"ternary\"}),bgcolor:{valType:\"color\",dflt:n.background},sum:{valType:\"number\",dflt:1,min:0},aaxis:l,baxis:l,caxis:l},\"plot\",\"from-root\");c.uirevision={valType:\"any\",editType:\"none\"},c.aaxis.uirevision=c.baxis.uirevision=c.caxis.uirevision={valType:\"any\",editType:\"none\"}},{\"../../components/color/attributes\":661,\"../../lib/extend\":789,\"../../plot_api/edit_types\":831,\"../cartesian/layout_attributes\":864,\"../domain\":877}],925:[function(t,e,r){\"use 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n=t(\"@plotly/d3\"),i=t(\"tinycolor2\"),a=t(\"../../registry\"),o=t(\"../../lib\"),s=o.strTranslate,l=o._,c=t(\"../../components/color\"),u=t(\"../../components/drawing\"),f=t(\"../cartesian/set_convert\"),h=t(\"../../lib/extend\").extendFlat,p=t(\"../plots\"),d=t(\"../cartesian/axes\"),m=t(\"../../components/dragelement\"),g=t(\"../../components/fx\"),v=t(\"../../components/dragelement/helpers\"),y=v.freeMode,x=v.rectMode,b=t(\"../../components/titles\"),_=t(\"../cartesian/select\").prepSelect,w=t(\"../cartesian/select\").selectOnClick,T=t(\"../cartesian/select\").clearSelect,k=t(\"../cartesian/select\").clearSelectionsCache,M=t(\"../cartesian/constants\");function A(t,e){this.id=t.id,this.graphDiv=t.graphDiv,this.init(e),this.makeFramework(e),this.aTickLayout=null,this.bTickLayout=null,this.cTickLayout=null}e.exports=A;var S=A.prototype;S.init=function(t){this.container=t._ternarylayer,this.defs=t._defs,this.layoutId=t._uid,this.traceHash={},this.layers={}},S.plot=function(t,e){var 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E=Math.sqrt(4/3);S.adjustLayout=function(t,e){var r,n,i,a,o,l,p=this,d=t.domain,m=(d.x[0]+d.x[1])/2,g=(d.y[0]+d.y[1])/2,v=d.x[1]-d.x[0],y=d.y[1]-d.y[0],x=v*e.w,b=y*e.h,_=t.sum,w=t.aaxis.min,T=t.baxis.min,k=t.caxis.min;x>E*b?i=(a=b)*E:a=(i=x)/E,o=v*i/x,l=y*a/b,r=e.l+e.w*m-i/2,n=e.t+e.h*(1-g)-a/2,p.x0=r,p.y0=n,p.w=i,p.h=a,p.sum=_,p.xaxis={type:\"linear\",range:[w+2*k-_,_-w-2*T],domain:[m-o/2,m+o/2],_id:\"x\"},f(p.xaxis,p.graphDiv._fullLayout),p.xaxis.setScale(),p.xaxis.isPtWithinRange=function(t){return t.a>=p.aaxis.range[0]&&t.a<=p.aaxis.range[1]&&t.b>=p.baxis.range[1]&&t.b<=p.baxis.range[0]&&t.c>=p.caxis.range[1]&&t.c<=p.caxis.range[0]},p.yaxis={type:\"linear\",range:[w,_-T-k],domain:[g-l/2,g+l/2],_id:\"y\"},f(p.yaxis,p.graphDiv._fullLayout),p.yaxis.setScale(),p.yaxis.isPtWithinRange=function(){return!0};var 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n=t(\"../../components/fx\"),i=t(\"../../registry\"),a=t(\"../../components/color\"),o=t(\"../../lib\").fillText,s=t(\"./helpers\").getLineWidth,l=t(\"../../plots/cartesian/axes\").hoverLabelText,c=t(\"../../constants/numerical\").BADNUM;function u(t,e,r,i,a){var s,u,f,h,p,d,m,g=t.cd,v=g[0].trace,y=g[0].t,x=\"closest\"===i,b=\"waterfall\"===v.type,_=t.maxHoverDistance,w=t.maxSpikeDistance;\"h\"===v.orientation?(s=r,u=e,f=\"y\",h=\"x\",p=I,d=C):(s=e,u=r,f=\"x\",h=\"y\",d=I,p=C);var T=v[f+\"period\"];function k(t){return A(t,-1)}function M(t){return A(t,1)}function A(t,e){return T?t.p+e*Math.abs(t.p-t.orig_p):t[f]+e*t.w/2}var S=x||T?k:function(t){return Math.min(k(t),t.p-y.bardelta/2)},E=x||T?M:function(t){return Math.max(M(t),t.p+y.bardelta/2)};function L(t,e,r){return a.finiteRange&&(r=0),n.inbox(t-s,e-s,r+Math.min(1,Math.abs(e-t)/m)-1)}function C(t){return L(S(t),E(t),_)}function P(t){var e=t[h];if(b){var r=Math.abs(t.rawS)||0;u>0?e+=r:u<0&&(e-=r)}return e}function I(t){var 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n=t(\"../../components/fx\"),i=t(\"../../lib\"),a=t(\"../bar/hover\").getTraceColor,o=i.fillText,s=t(\"../scatterpolar/hover\").makeHoverPointText,l=t(\"../../plots/polar/helpers\").isPtInsidePolygon;e.exports=function(t,e,r){var c=t.cd,u=c[0].trace,f=t.subplot,h=f.radialAxis,p=f.angularAxis,d=f.vangles,m=d?l:i.isPtInsideSector,g=t.maxHoverDistance,v=p._period||2*Math.PI,y=Math.abs(h.g2p(Math.sqrt(e*e+r*r))),x=Math.atan2(r,e);h.range[0]>h.range[1]&&(x+=Math.PI);if(n.getClosest(c,(function(t){return m(y,x,[t.rp0,t.rp1],[t.thetag0,t.thetag1],d)?g+Math.min(1,Math.abs(t.thetag1-t.thetag0)/v)-1+(t.rp1-y)/(t.rp1-t.rp0)-1:1/0}),t),!1!==t.index){var b=c[t.index];t.x0=t.x1=b.ct[0],t.y0=t.y1=b.ct[1];var _=i.extendFlat({},b,{r:b.s,theta:b.p});return o(b,u,t),s(_,u,f,t),t.hovertemplate=u.hovertemplate,t.color=a(u,b),t.xLabelVal=t.yLabelVal=void 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n=t(\"../../components/colorscale/attributes\"),i=t(\"../../plots/cartesian/axis_format_attributes\").axisHoverFormat,a=t(\"../../plots/template_attributes\").hovertemplateAttrs,o=t(\"../mesh3d/attributes\"),s=t(\"../../plots/attributes\"),l=t(\"../../lib/extend\").extendFlat,c={x:{valType:\"data_array\",editType:\"calc+clearAxisTypes\"},y:{valType:\"data_array\",editType:\"calc+clearAxisTypes\"},z:{valType:\"data_array\",editType:\"calc+clearAxisTypes\"},u:{valType:\"data_array\",editType:\"calc\"},v:{valType:\"data_array\",editType:\"calc\"},w:{valType:\"data_array\",editType:\"calc\"},sizemode:{valType:\"enumerated\",values:[\"scaled\",\"absolute\"],editType:\"calc\",dflt:\"scaled\"},sizeref:{valType:\"number\",editType:\"calc\",min:0},anchor:{valType:\"enumerated\",editType:\"calc\",values:[\"tip\",\"tail\",\"cm\",\"center\"],dflt:\"cm\"},text:{valType:\"string\",dflt:\"\",arrayOk:!0,editType:\"calc\"},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0,editType:\"calc\"},hovertemplate:a({editType:\"calc\"},{keys:[\"norm\"]}),uhoverformat:i(\"u\",1),vhoverformat:i(\"v\",1),whoverformat:i(\"w\",1),xhoverformat:i(\"x\"),yhoverformat:i(\"y\"),zhoverformat:i(\"z\"),showlegend:l({},s.showlegend,{dflt:!1})};l(c,n(\"\",{colorAttr:\"u/v/w 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n=t(\"../heatmap/attributes\"),i=t(\"../scatter/attributes\"),a=t(\"../../plots/cartesian/axis_format_attributes\"),o=a.axisHoverFormat,s=a.descriptionOnlyNumbers,l=t(\"../../components/colorscale/attributes\"),c=t(\"../../components/drawing/attributes\").dash,u=t(\"../../plots/font_attributes\"),f=t(\"../../lib/extend\").extendFlat,h=t(\"../../constants/filter_ops\"),p=h.COMPARISON_OPS2,d=h.INTERVAL_OPS,m=i.line;e.exports=f({z:n.z,x:n.x,x0:n.x0,dx:n.dx,y:n.y,y0:n.y0,dy:n.dy,xperiod:n.xperiod,yperiod:n.yperiod,xperiod0:i.xperiod0,yperiod0:i.yperiod0,xperiodalignment:n.xperiodalignment,yperiodalignment:n.yperiodalignment,text:n.text,hovertext:n.hovertext,transpose:n.transpose,xtype:n.xtype,ytype:n.ytype,xhoverformat:o(\"x\"),yhoverformat:o(\"y\"),zhoverformat:o(\"z\",1),hovertemplate:n.hovertemplate,hoverongaps:n.hoverongaps,connectgaps:f({},n.connectgaps,{}),fillcolor:{valType:\"color\",editType:\"calc\"},autocontour:{valType:\"boolean\",dflt:!0,editType:\"calc\",impliedEdits:{\"contours.start\":void 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n=t(\"../pie/attributes\"),i=t(\"../../plots/attributes\"),a=t(\"../../plots/domain\").attributes,o=t(\"../../plots/template_attributes\").hovertemplateAttrs,s=t(\"../../plots/template_attributes\").texttemplateAttrs,l=t(\"../../lib/extend\").extendFlat;e.exports={labels:n.labels,label0:n.label0,dlabel:n.dlabel,values:n.values,marker:{colors:n.marker.colors,line:{color:l({},n.marker.line.color,{dflt:null}),width:l({},n.marker.line.width,{dflt:1}),editType:\"calc\"},editType:\"calc\"},text:n.text,hovertext:n.hovertext,scalegroup:l({},n.scalegroup,{}),textinfo:l({},n.textinfo,{flags:[\"label\",\"text\",\"value\",\"percent\"]}),texttemplate:s({editType:\"plot\"},{keys:[\"label\",\"color\",\"value\",\"text\",\"percent\"]}),hoverinfo:l({},i.hoverinfo,{flags:[\"label\",\"text\",\"value\",\"percent\",\"name\"]}),hovertemplate:o({},{keys:[\"label\",\"color\",\"value\",\"text\",\"percent\"]}),textposition:l({},n.textposition,{values:[\"inside\",\"none\"],dflt:\"inside\"}),textfont:n.textfont,insidetextfont:n.insidetextfont,title:{text:n.title.text,font:n.title.font,position:l({},n.title.position,{values:[\"top 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n=t(\"../../lib\"),i=t(\"./attributes\"),a=t(\"../../plots/domain\").defaults,o=t(\"../bar/defaults\").handleText,s=t(\"../pie/defaults\").handleLabelsAndValues;e.exports=function(t,e,r,l){function c(r,a){return n.coerce(t,e,i,r,a)}var u=c(\"labels\"),f=c(\"values\"),h=s(u,f),p=h.len;if(e._hasLabels=h.hasLabels,e._hasValues=h.hasValues,!e._hasLabels&&e._hasValues&&(c(\"label0\"),c(\"dlabel\")),p){e._length=p,c(\"marker.line.width\")&&c(\"marker.line.color\",l.paper_bgcolor),c(\"marker.colors\"),c(\"scalegroup\");var d,m=c(\"text\"),g=c(\"texttemplate\");if(g||(d=c(\"textinfo\",Array.isArray(m)?\"text+percent\":\"percent\")),c(\"hovertext\"),c(\"hovertemplate\"),g||d&&\"none\"!==d){var v=c(\"textposition\");o(t,e,l,c,v,{moduleHasSelected:!1,moduleHasUnselected:!1,moduleHasConstrain:!1,moduleHasCliponaxis:!1,moduleHasTextangle:!1,moduleHasInsideanchor:!1})}a(e,l,c),c(\"title.text\")&&(c(\"title.position\"),n.coerceFont(c,\"title.font\",l.font)),c(\"aspectratio\"),c(\"baseratio\")}else e.visible=!1}},{\"../../lib\":799,\"../../plots/domain\":877,\"../bar/defaults\":941,\"../pie/defaults\":1191,\"./attributes\":1072}],1076:[function(t,e,r){\"use strict\";e.exports={moduleType:\"trace\",name:\"funnelarea\",basePlotModule:t(\"./base_plot\"),categories:[\"pie-like\",\"funnelarea\",\"showLegend\"],attributes:t(\"./attributes\"),layoutAttributes:t(\"./layout_attributes\"),supplyDefaults:t(\"./defaults\"),supplyLayoutDefaults:t(\"./layout_defaults\"),calc:t(\"./calc\").calc,crossTraceCalc:t(\"./calc\").crossTraceCalc,plot:t(\"./plot\"),style:t(\"./style\"),styleOne:t(\"../pie/style_one\"),meta:{}}},{\"../pie/style_one\":1199,\"./attributes\":1072,\"./base_plot\":1073,\"./calc\":1074,\"./defaults\":1075,\"./layout_attributes\":1077,\"./layout_defaults\":1078,\"./plot\":1079,\"./style\":1080}],1077:[function(t,e,r){\"use strict\";var n=t(\"../pie/layout_attributes\").hiddenlabels;e.exports={hiddenlabels:n,funnelareacolorway:{valType:\"colorlist\",editType:\"calc\"},extendfunnelareacolors:{valType:\"boolean\",dflt:!0,editType:\"calc\"}}},{\"../pie/layout_attributes\":1195}],1078:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"./layout_attributes\");e.exports=function(t,e){function r(r,a){return n.coerce(t,e,i,r,a)}r(\"hiddenlabels\"),r(\"funnelareacolorway\",e.colorway),r(\"extendfunnelareacolors\")}},{\"../../lib\":799,\"./layout_attributes\":1077}],1079:[function(t,e,r){\"use strict\";var 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n=t(\"../../lib\"),i=t(\"./attributes\"),a=t(\"../../plots/domain\").defaults,o=t(\"../../plot_api/plot_template\"),s=t(\"../../plots/array_container_defaults\"),l=t(\"./constants.js\"),c=t(\"../../plots/cartesian/tick_value_defaults\"),u=t(\"../../plots/cartesian/tick_mark_defaults\"),f=t(\"../../plots/cartesian/tick_label_defaults\");function h(t,e){function r(r,a){return n.coerce(t,e,i.gauge.steps,r,a)}r(\"color\"),r(\"line.color\"),r(\"line.width\"),r(\"range\"),r(\"thickness\")}e.exports={supplyDefaults:function(t,e,r,p){function d(r,a){return n.coerce(t,e,i,r,a)}a(e,p,d),d(\"mode\"),e._hasNumber=-1!==e.mode.indexOf(\"number\"),e._hasDelta=-1!==e.mode.indexOf(\"delta\"),e._hasGauge=-1!==e.mode.indexOf(\"gauge\");var m=d(\"value\");e._range=[0,\"number\"==typeof m?1.5*m:1];var g,v,y,x,b,_,w=new Array(2);function T(t,e){return n.coerce(y,x,i.gauge,t,e)}function k(t,e){return n.coerce(b,_,i.gauge.axis,t,e)}if(e._hasNumber&&(d(\"number.valueformat\"),d(\"number.font.color\",p.font.color),d(\"number.font.family\",p.font.family),d(\"number.font.size\"),void 0===e.number.font.size&&(e.number.font.size=l.defaultNumberFontSize,w[0]=!0),d(\"number.prefix\"),d(\"number.suffix\"),g=e.number.font.size),e._hasDelta&&(d(\"delta.font.color\",p.font.color),d(\"delta.font.family\",p.font.family),d(\"delta.font.size\"),void 0===e.delta.font.size&&(e.delta.font.size=(e._hasNumber?.5:1)*(g||l.defaultNumberFontSize),w[1]=!0),d(\"delta.reference\",e.value),d(\"delta.relative\"),d(\"delta.valueformat\",e.delta.relative?\"2%\":\"\"),d(\"delta.increasing.symbol\"),d(\"delta.increasing.color\"),d(\"delta.decreasing.symbol\"),d(\"delta.decreasing.color\"),d(\"delta.position\"),v=e.delta.font.size),e._scaleNumbers=(!e._hasNumber||w[0])&&(!e._hasDelta||w[1])||!1,d(\"title.font.color\",p.font.color),d(\"title.font.family\",p.font.family),d(\"title.font.size\",.25*(g||v||l.defaultNumberFontSize)),d(\"title.text\"),e._hasGauge){(y=t.gauge)||(y={}),x=o.newContainer(e,\"gauge\"),T(\"shape\"),(e._isBullet=\"bullet\"===e.gauge.shape)||d(\"title.align\",\"center\"),(e._isAngular=\"angular\"===e.gauge.shape)||d(\"align\",\"center\"),T(\"bgcolor\",p.paper_bgcolor),T(\"borderwidth\"),T(\"bordercolor\"),T(\"bar.color\"),T(\"bar.line.color\"),T(\"bar.line.width\"),T(\"bar.thickness\",l.valueThickness*(\"bullet\"===e.gauge.shape?.5:1)),s(y,x,{name:\"steps\",handleItemDefaults:h}),T(\"threshold.value\"),T(\"threshold.thickness\"),T(\"threshold.line.width\"),T(\"threshold.line.color\"),b={},y&&(b=y.axis||{}),_=o.newContainer(x,\"axis\"),k(\"visible\"),e._range=k(\"range\",e._range);var M={outerTicks:!0};c(b,_,k,\"linear\"),f(b,_,k,\"linear\",M),u(b,_,k,M)}else d(\"title.align\",\"center\"),d(\"align\",\"center\"),e._isAngular=e._isBullet=!1;e._length=null}}},{\"../../lib\":799,\"../../plot_api/plot_template\":838,\"../../plots/array_container_defaults\":844,\"../../plots/cartesian/tick_label_defaults\":871,\"../../plots/cartesian/tick_mark_defaults\":872,\"../../plots/cartesian/tick_value_defaults\":873,\"../../plots/domain\":877,\"./attributes\":1143,\"./constants.js\":1146}],1148:[function(t,e,r){\"use strict\";e.exports={moduleType:\"trace\",name:\"indicator\",basePlotModule:t(\"./base_plot\"),categories:[\"svg\",\"noOpacity\",\"noHover\"],animatable:!0,attributes:t(\"./attributes\"),supplyDefaults:t(\"./defaults\").supplyDefaults,calc:t(\"./calc\").calc,plot:t(\"./plot\"),meta:{}}},{\"./attributes\":1143,\"./base_plot\":1144,\"./calc\":1145,\"./defaults\":1147,\"./plot\":1149}],1149:[function(t,e,r){\"use strict\";var n=t(\"@plotly/d3\"),i=t(\"../../lib\"),a=i.strScale,o=i.strTranslate,s=i.rad2deg,l=t(\"../../constants/alignment\").MID_SHIFT,c=t(\"../../components/drawing\"),u=t(\"./constants\"),f=t(\"../../lib/svg_text_utils\"),h=t(\"../../plots/cartesian/axes\"),p=t(\"../../plots/cartesian/axis_defaults\"),d=t(\"../../plots/cartesian/position_defaults\"),m=t(\"../../plots/cartesian/layout_attributes\"),g=t(\"../../components/color\"),v={left:\"start\",center:\"middle\",right:\"end\"},y={left:0,center:.5,right:1},x=/[yzafpn\\xb5mkMGTPEZY]/;function b(t){return t&&t.duration>0}function _(t){t.each((function(t){g.stroke(n.select(this),t.line.color)})).each((function(t){g.fill(n.select(this),t.color)})).style(\"stroke-width\",(function(t){return t.line.width}))}function w(t,e,r){var n=t._fullLayout,a=i.extendFlat({type:\"linear\",ticks:\"outside\",range:r,showline:!0},e),o={type:\"linear\",_id:\"x\"+e._id},s={letter:\"x\",font:n.font,noHover:!0,noTickson:!0};function l(t,e){return i.coerce(a,o,m,t,e)}return p(a,o,l,s,n),d(a,o,l,s),o}function T(t,e,r){return[Math.min(e/t.width,r/t.height),t,e+\"x\"+r]}function k(t,e,r,i){var a=document.createElementNS(\"http://www.w3.org/2000/svg\",\"text\"),o=n.select(a);return o.text(t).attr(\"x\",0).attr(\"y\",0).attr(\"text-anchor\",r).attr(\"data-unformatted\",t).call(f.convertToTspans,i).call(c.font,e),c.bBox(o.node())}function M(t,e,r,n,a,o){var s=\"_cache\"+e;t[s]&&t[s].key===a||(t[s]={key:a,value:r});var l=i.aggNums(o,null,[t[s].value,n],2);return t[s].value=l,l}e.exports=function(t,e,r,p){var d,m=t._fullLayout;b(r)&&p&&(d=p()),i.makeTraceGroups(m._indicatorlayer,e,\"trace\").each((function(e){var p,A,S,E,L,C=e[0].trace,P=n.select(this),I=C._hasGauge,O=C._isAngular,z=C._isBullet,D=C.domain,R={w:m._size.w*(D.x[1]-D.x[0]),h:m._size.h*(D.y[1]-D.y[0]),l:m._size.l+m._size.w*D.x[0],r:m._size.r+m._size.w*(1-D.x[1]),t:m._size.t+m._size.h*(1-D.y[1]),b:m._size.b+m._size.h*D.y[0]},F=R.l+R.w/2,B=R.t+R.h/2,N=Math.min(R.w/2,R.h),j=u.innerRadius*N,U=C.align||\"center\";if(A=B,I){if(O&&(p=F,A=B+N/2,S=function(t){return function(t,e){var r=Math.sqrt(t.width/2*(t.width/2)+t.height*t.height);return[e/r,t,e]}(t,.9*j)}),z){var V=u.bulletPadding,q=1-u.bulletNumberDomainSize+V;p=R.l+(q+(1-q)*y[U])*R.w,S=function(t){return T(t,(u.bulletNumberDomainSize-V)*R.w,R.h)}}}else p=R.l+y[U]*R.w,S=function(t){return T(t,R.w,R.h)};!function(t,e,r,s){var l,u,p,d=r[0].trace,m=s.numbersX,_=s.numbersY,T=d.align||\"center\",A=v[T],S=s.transitionOpts,E=s.onComplete,L=i.ensureSingle(e,\"g\",\"numbers\"),C=[];d._hasNumber&&C.push(\"number\");d._hasDelta&&(C.push(\"delta\"),\"left\"===d.delta.position&&C.reverse());var P=L.selectAll(\"text\").data(C);function I(e,r,n,i){if(!e.match(\"s\")||n>=0==i>=0||r(n).slice(-1).match(x)||r(i).slice(-1).match(x))return r;var a=e.slice().replace(\"s\",\"f\").replace(/\\d+/,(function(t){return parseInt(t)-1})),o=w(t,{tickformat:a});return function(t){return Math.abs(t)<1?h.tickText(o,t).text:r(t)}}P.enter().append(\"text\"),P.attr(\"text-anchor\",(function(){return A})).attr(\"class\",(function(t){return t})).attr(\"x\",null).attr(\"y\",null).attr(\"dx\",null).attr(\"dy\",null),P.exit().remove();var O,z=d.mode+d.align;d._hasDelta&&(O=function(){var e=w(t,{tickformat:d.delta.valueformat},d._range);e.setScale(),h.prepTicks(e);var i=function(t){return h.tickText(e,t).text},a=function(t){return d.delta.relative?t.relativeDelta:t.delta},o=function(t,e){return 0===t||\"number\"!=typeof t||isNaN(t)?\"-\":(t>0?d.delta.increasing.symbol:d.delta.decreasing.symbol)+e(t)},s=function(t){return t.delta>=0?d.delta.increasing.color:d.delta.decreasing.color};void 0===d._deltaLastValue&&(d._deltaLastValue=a(r[0]));var l=L.select(\"text.delta\");function p(){l.text(o(a(r[0]),i)).call(g.fill,s(r[0])).call(f.convertToTspans,t)}return l.call(c.font,d.delta.font).call(g.fill,s({delta:d._deltaLastValue})),b(S)?l.transition().duration(S.duration).ease(S.easing).tween(\"text\",(function(){var t=n.select(this),e=a(r[0]),l=d._deltaLastValue,c=I(d.delta.valueformat,i,l,e),u=n.interpolateNumber(l,e);return d._deltaLastValue=e,function(e){t.text(o(u(e),c)),t.call(g.fill,s({delta:u(e)}))}})).each(\"end\",(function(){p(),E&&E()})).each(\"interrupt\",(function(){p(),E&&E()})):p(),u=k(o(a(r[0]),i),d.delta.font,A,t),l}(),z+=d.delta.position+d.delta.font.size+d.delta.font.family+d.delta.valueformat,z+=d.delta.increasing.symbol+d.delta.decreasing.symbol,p=u);d._hasNumber&&(!function(){var e=w(t,{tickformat:d.number.valueformat},d._range);e.setScale(),h.prepTicks(e);var i=function(t){return h.tickText(e,t).text},a=d.number.suffix,o=d.number.prefix,s=L.select(\"text.number\");function u(){var e=\"number\"==typeof r[0].y?o+i(r[0].y)+a:\"-\";s.text(e).call(c.font,d.number.font).call(f.convertToTspans,t)}b(S)?s.transition().duration(S.duration).ease(S.easing).each(\"end\",(function(){u(),E&&E()})).each(\"interrupt\",(function(){u(),E&&E()})).attrTween(\"text\",(function(){var t=n.select(this),e=n.interpolateNumber(r[0].lastY,r[0].y);d._lastValue=r[0].y;var s=I(d.number.valueformat,i,r[0].lastY,r[0].y);return function(r){t.text(o+s(e(r))+a)}})):u(),l=k(o+i(r[0].y)+a,d.number.font,A,t)}(),z+=d.number.font.size+d.number.font.family+d.number.valueformat+d.number.suffix+d.number.prefix,p=l);if(d._hasDelta&&d._hasNumber){var D,R,F=[(l.left+l.right)/2,(l.top+l.bottom)/2],B=[(u.left+u.right)/2,(u.top+u.bottom)/2],N=.75*d.delta.font.size;\"left\"===d.delta.position&&(D=M(d,\"deltaPos\",0,-1*(l.width*y[d.align]+u.width*(1-y[d.align])+N),z,Math.min),R=F[1]-B[1],p={width:l.width+u.width+N,height:Math.max(l.height,u.height),left:u.left+D,right:l.right,top:Math.min(l.top,u.top+R),bottom:Math.max(l.bottom,u.bottom+R)}),\"right\"===d.delta.position&&(D=M(d,\"deltaPos\",0,l.width*(1-y[d.align])+u.width*y[d.align]+N,z,Math.max),R=F[1]-B[1],p={width:l.width+u.width+N,height:Math.max(l.height,u.height),left:l.left,right:u.right+D,top:Math.min(l.top,u.top+R),bottom:Math.max(l.bottom,u.bottom+R)}),\"bottom\"===d.delta.position&&(D=null,R=u.height,p={width:Math.max(l.width,u.width),height:l.height+u.height,left:Math.min(l.left,u.left),right:Math.max(l.right,u.right),top:l.bottom-l.height,bottom:l.bottom+u.height}),\"top\"===d.delta.position&&(D=null,R=l.top,p={width:Math.max(l.width,u.width),height:l.height+u.height,left:Math.min(l.left,u.left),right:Math.max(l.right,u.right),top:l.bottom-l.height-u.height,bottom:l.bottom}),O.attr({dx:D,dy:R})}(d._hasNumber||d._hasDelta)&&L.attr(\"transform\",(function(){var t=s.numbersScaler(p);z+=t[2];var e,r=M(d,\"numbersScale\",1,t[0],z,Math.min);d._scaleNumbers||(r=1),e=d._isAngular?_-r*p.bottom:_-r*(p.top+p.bottom)/2,d._numbersTop=r*p.top+e;var n=p[T];\"center\"===T&&(n=(p.left+p.right)/2);var i=m-r*n;return i=M(d,\"numbersTranslate\",0,i,z,Math.max),o(i,e)+a(r)}))}(t,P,e,{numbersX:p,numbersY:A,numbersScaler:S,transitionOpts:r,onComplete:d}),I&&(E={range:C.gauge.axis.range,color:C.gauge.bgcolor,line:{color:C.gauge.bordercolor,width:0},thickness:1},L={range:C.gauge.axis.range,color:\"rgba(0, 0, 0, 0)\",line:{color:C.gauge.bordercolor,width:C.gauge.borderwidth},thickness:1});var H=P.selectAll(\"g.angular\").data(O?e:[]);H.exit().remove();var G=P.selectAll(\"g.angularaxis\").data(O?e:[]);G.exit().remove(),O&&function(t,e,r,i){var a,c,u,f,p=r[0].trace,d=i.size,m=i.radius,g=i.innerRadius,v=i.gaugeBg,y=i.gaugeOutline,x=[d.l+d.w/2,d.t+d.h/2+m/2],T=i.gauge,k=i.layer,M=i.transitionOpts,A=i.onComplete,S=Math.PI/2;function E(t){var e=p.gauge.axis.range[0],r=(t-e)/(p.gauge.axis.range[1]-e)*Math.PI-S;return r<-S?-S:r>S?S:r}function L(t){return n.svg.arc().innerRadius((g+m)/2-t/2*(m-g)).outerRadius((g+m)/2+t/2*(m-g)).startAngle(-S)}function C(t){t.attr(\"d\",(function(t){return L(t.thickness).startAngle(E(t.range[0])).endAngle(E(t.range[1]))()}))}T.enter().append(\"g\").classed(\"angular\",!0),T.attr(\"transform\",o(x[0],x[1])),k.enter().append(\"g\").classed(\"angularaxis\",!0).classed(\"crisp\",!0),k.selectAll(\"g.xangularaxistick,path,text\").remove(),(a=w(t,p.gauge.axis)).type=\"linear\",a.range=p.gauge.axis.range,a._id=\"xangularaxis\",a.ticklabeloverflow=\"allow\",a.setScale();var P=function(t){return(a.range[0]-t.x)/(a.range[1]-a.range[0])*Math.PI+Math.PI},I={},O=h.makeLabelFns(a,0).labelStandoff;I.xFn=function(t){var e=P(t);return Math.cos(e)*O},I.yFn=function(t){var e=P(t),r=Math.sin(e)>0?.2:1;return-Math.sin(e)*(O+t.fontSize*r)+Math.abs(Math.cos(e))*(t.fontSize*l)},I.anchorFn=function(t){var e=P(t),r=Math.cos(e);return Math.abs(r)<.1?\"middle\":r>0?\"start\":\"end\"},I.heightFn=function(t,e,r){var n=P(t);return-.5*(1+Math.sin(n))*r};var z=function(t){return o(x[0]+m*Math.cos(t),x[1]-m*Math.sin(t))};u=function(t){return z(P(t))};if(c=h.calcTicks(a),f=h.getTickSigns(a)[2],a.visible){f=\"inside\"===a.ticks?-1:1;var D=(a.linewidth||1)/2;h.drawTicks(t,a,{vals:c,layer:k,path:\"M\"+f*D+\",0h\"+f*a.ticklen,transFn:function(t){var e=P(t);return z(e)+\"rotate(\"+-s(e)+\")\"}}),h.drawLabels(t,a,{vals:c,layer:k,transFn:u,labelFns:I})}var R=[v].concat(p.gauge.steps),F=T.selectAll(\"g.bg-arc\").data(R);F.enter().append(\"g\").classed(\"bg-arc\",!0).append(\"path\"),F.select(\"path\").call(C).call(_),F.exit().remove();var B=L(p.gauge.bar.thickness),N=T.selectAll(\"g.value-arc\").data([p.gauge.bar]);N.enter().append(\"g\").classed(\"value-arc\",!0).append(\"path\");var j=N.select(\"path\");b(M)?(j.transition().duration(M.duration).ease(M.easing).each(\"end\",(function(){A&&A()})).each(\"interrupt\",(function(){A&&A()})).attrTween(\"d\",(U=B,V=E(r[0].lastY),q=E(r[0].y),function(){var t=n.interpolate(V,q);return function(e){return U.endAngle(t(e))()}})),p._lastValue=r[0].y):j.attr(\"d\",\"number\"==typeof r[0].y?B.endAngle(E(r[0].y)):\"M0,0Z\");var U,V,q;j.call(_),N.exit().remove(),R=[];var H=p.gauge.threshold.value;(H||0===H)&&R.push({range:[H,H],color:p.gauge.threshold.color,line:{color:p.gauge.threshold.line.color,width:p.gauge.threshold.line.width},thickness:p.gauge.threshold.thickness});var G=T.selectAll(\"g.threshold-arc\").data(R);G.enter().append(\"g\").classed(\"threshold-arc\",!0).append(\"path\"),G.select(\"path\").call(C).call(_),G.exit().remove();var Y=T.selectAll(\"g.gauge-outline\").data([y]);Y.enter().append(\"g\").classed(\"gauge-outline\",!0).append(\"path\"),Y.select(\"path\").call(C).call(_),Y.exit().remove()}(t,0,e,{radius:N,innerRadius:j,gauge:H,layer:G,size:R,gaugeBg:E,gaugeOutline:L,transitionOpts:r,onComplete:d});var Y=P.selectAll(\"g.bullet\").data(z?e:[]);Y.exit().remove();var W=P.selectAll(\"g.bulletaxis\").data(z?e:[]);W.exit().remove(),z&&function(t,e,r,n){var i,a,s,l,c,f=r[0].trace,p=n.gauge,d=n.layer,m=n.gaugeBg,v=n.gaugeOutline,y=n.size,x=f.domain,T=n.transitionOpts,k=n.onComplete;p.enter().append(\"g\").classed(\"bullet\",!0),p.attr(\"transform\",o(y.l,y.t)),d.enter().append(\"g\").classed(\"bulletaxis\",!0).classed(\"crisp\",!0),d.selectAll(\"g.xbulletaxistick,path,text\").remove();var M=y.h,A=f.gauge.bar.thickness*M,S=x.x[0],E=x.x[0]+(x.x[1]-x.x[0])*(f._hasNumber||f._hasDelta?1-u.bulletNumberDomainSize:1);(i=w(t,f.gauge.axis))._id=\"xbulletaxis\",i.domain=[S,E],i.setScale(),a=h.calcTicks(i),s=h.makeTransTickFn(i),l=h.getTickSigns(i)[2],c=y.t+y.h,i.visible&&(h.drawTicks(t,i,{vals:\"inside\"===i.ticks?h.clipEnds(i,a):a,layer:d,path:h.makeTickPath(i,c,l),transFn:s}),h.drawLabels(t,i,{vals:a,layer:d,transFn:s,labelFns:h.makeLabelFns(i,c)}));function L(t){t.attr(\"width\",(function(t){return Math.max(0,i.c2p(t.range[1])-i.c2p(t.range[0]))})).attr(\"x\",(function(t){return i.c2p(t.range[0])})).attr(\"y\",(function(t){return.5*(1-t.thickness)*M})).attr(\"height\",(function(t){return t.thickness*M}))}var C=[m].concat(f.gauge.steps),P=p.selectAll(\"g.bg-bullet\").data(C);P.enter().append(\"g\").classed(\"bg-bullet\",!0).append(\"rect\"),P.select(\"rect\").call(L).call(_),P.exit().remove();var I=p.selectAll(\"g.value-bullet\").data([f.gauge.bar]);I.enter().append(\"g\").classed(\"value-bullet\",!0).append(\"rect\"),I.select(\"rect\").attr(\"height\",A).attr(\"y\",(M-A)/2).call(_),b(T)?I.select(\"rect\").transition().duration(T.duration).ease(T.easing).each(\"end\",(function(){k&&k()})).each(\"interrupt\",(function(){k&&k()})).attr(\"width\",Math.max(0,i.c2p(Math.min(f.gauge.axis.range[1],r[0].y)))):I.select(\"rect\").attr(\"width\",\"number\"==typeof r[0].y?Math.max(0,i.c2p(Math.min(f.gauge.axis.range[1],r[0].y))):0);I.exit().remove();var O=r.filter((function(){return f.gauge.threshold.value||0===f.gauge.threshold.value})),z=p.selectAll(\"g.threshold-bullet\").data(O);z.enter().append(\"g\").classed(\"threshold-bullet\",!0).append(\"line\"),z.select(\"line\").attr(\"x1\",i.c2p(f.gauge.threshold.value)).attr(\"x2\",i.c2p(f.gauge.threshold.value)).attr(\"y1\",(1-f.gauge.threshold.thickness)/2*M).attr(\"y2\",(1-(1-f.gauge.threshold.thickness)/2)*M).call(g.stroke,f.gauge.threshold.line.color).style(\"stroke-width\",f.gauge.threshold.line.width),z.exit().remove();var D=p.selectAll(\"g.gauge-outline\").data([v]);D.enter().append(\"g\").classed(\"gauge-outline\",!0).append(\"rect\"),D.select(\"rect\").call(L).call(_),D.exit().remove()}(t,0,e,{gauge:Y,layer:W,size:R,gaugeBg:E,gaugeOutline:L,transitionOpts:r,onComplete:d});var X=P.selectAll(\"text.title\").data(e);X.exit().remove(),X.enter().append(\"text\").classed(\"title\",!0),X.attr(\"text-anchor\",(function(){return z?v.right:v[C.title.align]})).text(C.title.text).call(c.font,C.title.font).call(f.convertToTspans,t),X.attr(\"transform\",(function(){var t,e=R.l+R.w*y[C.title.align],r=u.titlePadding,n=c.bBox(X.node());if(I){if(O)if(C.gauge.axis.visible)t=c.bBox(G.node()).top-r-n.bottom;else t=R.t+R.h/2-N/2-n.bottom-r;z&&(t=A-(n.top+n.bottom)/2,e=R.l-u.bulletPadding*R.w)}else t=C._numbersTop-r-n.bottom;return o(e,t)}))}))}},{\"../../components/color\":662,\"../../components/drawing\":684,\"../../constants/alignment\":767,\"../../lib\":799,\"../../lib/svg_text_utils\":824,\"../../plots/cartesian/axes\":849,\"../../plots/cartesian/axis_defaults\":851,\"../../plots/cartesian/layout_attributes\":864,\"../../plots/cartesian/position_defaults\":867,\"./constants\":1146,\"@plotly/d3\":58}],1150:[function(t,e,r){\"use strict\";var n=t(\"../../components/colorscale/attributes\"),i=t(\"../../plots/cartesian/axis_format_attributes\").axisHoverFormat,a=t(\"../../plots/template_attributes\").hovertemplateAttrs,o=t(\"../mesh3d/attributes\"),s=t(\"../../plots/attributes\"),l=t(\"../../lib/extend\").extendFlat,c=t(\"../../plot_api/edit_types\").overrideAll;var u=e.exports=c(l({x:{valType:\"data_array\"},y:{valType:\"data_array\"},z:{valType:\"data_array\"},value:{valType:\"data_array\"},isomin:{valType:\"number\"},isomax:{valType:\"number\"},surface:{show:{valType:\"boolean\",dflt:!0},count:{valType:\"integer\",dflt:2,min:1},fill:{valType:\"number\",min:0,max:1,dflt:1},pattern:{valType:\"flaglist\",flags:[\"A\",\"B\",\"C\",\"D\",\"E\"],extras:[\"all\",\"odd\",\"even\"],dflt:\"all\"}},spaceframe:{show:{valType:\"boolean\",dflt:!1},fill:{valType:\"number\",min:0,max:1,dflt:.15}},slices:{x:{show:{valType:\"boolean\",dflt:!1},locations:{valType:\"data_array\",dflt:[]},fill:{valType:\"number\",min:0,max:1,dflt:1}},y:{show:{valType:\"boolean\",dflt:!1},locations:{valType:\"data_array\",dflt:[]},fill:{valType:\"number\",min:0,max:1,dflt:1}},z:{show:{valType:\"boolean\",dflt:!1},locations:{valType:\"data_array\",dflt:[]},fill:{valType:\"number\",min:0,max:1,dflt:1}}},caps:{x:{show:{valType:\"boolean\",dflt:!0},fill:{valType:\"number\",min:0,max:1,dflt:1}},y:{show:{valType:\"boolean\",dflt:!0},fill:{valType:\"number\",min:0,max:1,dflt:1}},z:{show:{valType:\"boolean\",dflt:!0},fill:{valType:\"number\",min:0,max:1,dflt:1}}},text:{valType:\"string\",dflt:\"\",arrayOk:!0},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0},hovertemplate:a(),xhoverformat:i(\"x\"),yhoverformat:i(\"y\"),zhoverformat:i(\"z\"),valuehoverformat:i(\"value\",1),showlegend:l({},s.showlegend,{dflt:!1})},n(\"\",{colorAttr:\"`value`\",showScaleDflt:!0,editTypeOverride:\"calc\"}),{opacity:o.opacity,lightposition:o.lightposition,lighting:o.lighting,flatshading:o.flatshading,contour:o.contour,hoverinfo:l({},s.hoverinfo)}),\"calc\",\"nested\");u.flatshading.dflt=!0,u.lighting.facenormalsepsilon.dflt=0,u.x.editType=u.y.editType=u.z.editType=u.value.editType=\"calc+clearAxisTypes\",u.transforms=void 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0===e.isomax||null===e.isomin?s:e.isomax,n(t,e,{vals:[e._vMin,e._vMax],containerStr:\"\",cLetter:\"c\"})}},{\"../../components/colorscale/calc\":670,\"../streamtube/calc\":1322}],1152:[function(t,e,r){\"use strict\";var n=t(\"gl-mesh3d\"),i=t(\"../../lib/gl_format_color\").parseColorScale,a=t(\"../../lib/str2rgbarray\"),o=t(\"../../components/colorscale\").extractOpts,s=t(\"../../plots/gl3d/zip3\"),l=function(t,e){for(var r=e.length-1;r>0;r--){var n=Math.min(e[r],e[r-1]),i=Math.max(e[r],e[r-1]);if(i>n&&n<t&&t<=i)return{id:r,distRatio:(i-t)/(i-n)}}return{id:0,distRatio:0}};function c(t,e,r){this.scene=t,this.uid=r,this.mesh=e,this.name=\"\",this.data=null,this.showContour=!1}var u=c.prototype;u.handlePick=function(t){if(t.object===this.mesh){var e=t.data.index,r=this.data._meshX[e],n=this.data._meshY[e],i=this.data._meshZ[e],a=this.data._Ys.length,o=this.data._Zs.length,s=l(r,this.data._Xs).id,c=l(n,this.data._Ys).id,u=l(i,this.data._Zs).id,f=t.index=u+o*c+o*a*s;t.traceCoordinate=[this.data._meshX[f],this.data._meshY[f],this.data._meshZ[f],this.data._value[f]];var h=this.data.hovertext||this.data.text;return Array.isArray(h)&&void 0!==h[f]?t.textLabel=h[f]:h&&(t.textLabel=h),!0}},u.update=function(t){var e=this.scene,r=e.fullSceneLayout;function n(t,e,r,n){return e.map((function(e){return t.d2l(e,0,n)*r}))}this.data=h(t);var l={positions:s(n(r.xaxis,t._meshX,e.dataScale[0],t.xcalendar),n(r.yaxis,t._meshY,e.dataScale[1],t.ycalendar),n(r.zaxis,t._meshZ,e.dataScale[2],t.zcalendar)),cells:s(t._meshI,t._meshJ,t._meshK),lightPosition:[t.lightposition.x,t.lightposition.y,t.lightposition.z],ambient:t.lighting.ambient,diffuse:t.lighting.diffuse,specular:t.lighting.specular,roughness:t.lighting.roughness,fresnel:t.lighting.fresnel,vertexNormalsEpsilon:t.lighting.vertexnormalsepsilon,faceNormalsEpsilon:t.lighting.facenormalsepsilon,opacity:t.opacity,contourEnable:t.contour.show,contourColor:a(t.contour.color).slice(0,3),contourWidth:t.contour.width,useFacetNormals:t.flatshading},c=o(t);l.vertexIntensity=t._meshIntensity,l.vertexIntensityBounds=[c.min,c.max],l.colormap=i(t),this.mesh.update(l)},u.dispose=function(){this.scene.glplot.remove(this.mesh),this.mesh.dispose()};var f=[\"xyz\",\"xzy\",\"yxz\",\"yzx\",\"zxy\",\"zyx\"];function h(t){t._meshI=[],t._meshJ=[],t._meshK=[];var 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v={positions:h,cells:n,lightPosition:[t.lightposition.x,t.lightposition.y,t.lightposition.z],ambient:t.lighting.ambient,diffuse:t.lighting.diffuse,specular:t.lighting.specular,roughness:t.lighting.roughness,fresnel:t.lighting.fresnel,vertexNormalsEpsilon:t.lighting.vertexnormalsepsilon,faceNormalsEpsilon:t.lighting.facenormalsepsilon,opacity:t.opacity,contourEnable:t.contour.show,contourColor:l(t.contour.color).slice(0,3),contourWidth:t.contour.width,useFacetNormals:t.flatshading};if(t.intensity){var y=c(t);this.color=\"#fff\";var x=t.intensitymode;v[x+\"Intensity\"]=t.intensity,v[x+\"IntensityBounds\"]=[y.min,y.max],v.colormap=s(t)}else t.vertexcolor?(this.color=t.vertexcolor[0],v.vertexColors=p(t.vertexcolor)):t.facecolor?(this.color=t.facecolor[0],v.cellColors=p(t.facecolor)):(this.color=t.color,v.meshColor=l(t.color));this.mesh.update(v)},h.dispose=function(){this.scene.glplot.remove(this.mesh),this.mesh.dispose()},e.exports=function(t,e){var r=t.glplot.gl,i=n({gl:r}),a=new 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i=r(\"values\"),a=r(\"visible\");if(i&&i.length||(a=e.visible=!1),a){r(\"label\"),r(\"displayindex\",e._index);var o,s=t.categoryarray,c=Array.isArray(s)&&s.length>0;c&&(o=\"array\");var u=r(\"categoryorder\",o);\"array\"===u?(r(\"categoryarray\"),r(\"ticktext\")):(delete t.categoryarray,delete t.ticktext),c||\"array\"!==u||(e.categoryorder=\"trace\")}}e.exports=function(t,e,r,f){function h(r,i){return n.coerce(t,e,l,r,i)}var p=s(t,e,{name:\"dimensions\",handleItemDefaults:u}),d=function(t,e,r,o,s){s(\"line.shape\"),s(\"line.hovertemplate\");var l=s(\"line.color\",o.colorway[0]);if(i(t,\"line\")&&n.isArrayOrTypedArray(l)){if(l.length)return s(\"line.colorscale\"),a(t,e,o,s,{prefix:\"line.\",cLetter:\"c\"}),l.length;e.line.color=r}return 1/0}(t,e,r,f,h);o(e,f,h),Array.isArray(p)&&p.length||(e.visible=!1),c(e,p,\"values\",d),h(\"hoveron\"),h(\"hovertemplate\"),h(\"arrangement\"),h(\"bundlecolors\"),h(\"sortpaths\"),h(\"counts\");var 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n=t(\"@plotly/d3\"),i=t(\"../../plot_api/plot_api\"),a=t(\"../../components/fx\"),o=t(\"../../lib\"),s=o.strTranslate,l=t(\"../../components/drawing\"),c=t(\"tinycolor2\"),u=t(\"../../lib/svg_text_utils\");function f(t,e,r,i){var a=t.map(R.bind(0,e,r)),c=i.selectAll(\"g.parcatslayer\").data([null]);c.enter().append(\"g\").attr(\"class\",\"parcatslayer\").style(\"pointer-events\",\"all\");var f=c.selectAll(\"g.trace.parcats\").data(a,h),v=f.enter().append(\"g\").attr(\"class\",\"trace parcats\");f.attr(\"transform\",(function(t){return s(t.x,t.y)})),v.append(\"g\").attr(\"class\",\"paths\");var y=f.select(\"g.paths\").selectAll(\"path.path\").data((function(t){return t.paths}),h);y.attr(\"fill\",(function(t){return t.model.color}));var _=y.enter().append(\"path\").attr(\"class\",\"path\").attr(\"stroke-opacity\",0).attr(\"fill\",(function(t){return t.model.color})).attr(\"fill-opacity\",0);b(_),y.attr(\"d\",(function(t){return t.svgD})),_.empty()||y.sort(d),y.exit().remove(),y.on(\"mouseover\",m).on(\"mouseout\",g).on(\"click\",x),v.append(\"g\").attr(\"class\",\"dimensions\");var k=f.select(\"g.dimensions\").selectAll(\"g.dimension\").data((function(t){return t.dimensions}),h);k.enter().append(\"g\").attr(\"class\",\"dimension\"),k.attr(\"transform\",(function(t){return s(t.x,0)})),k.exit().remove();var M=k.selectAll(\"g.category\").data((function(t){return t.categories}),h),A=M.enter().append(\"g\").attr(\"class\",\"category\");M.attr(\"transform\",(function(t){return s(0,t.y)})),A.append(\"rect\").attr(\"class\",\"catrect\").attr(\"pointer-events\",\"none\"),M.select(\"rect.catrect\").attr(\"fill\",\"none\").attr(\"width\",(function(t){return t.width})).attr(\"height\",(function(t){return t.height})),w(A);var S=M.selectAll(\"rect.bandrect\").data((function(t){return t.bands}),h);S.each((function(){o.raiseToTop(this)})),S.attr(\"fill\",(function(t){return t.color}));var O=S.enter().append(\"rect\").attr(\"class\",\"bandrect\").attr(\"stroke-opacity\",0).attr(\"fill\",(function(t){return t.color})).attr(\"fill-opacity\",0);S.attr(\"fill\",(function(t){return t.color})).attr(\"width\",(function(t){return t.width})).attr(\"height\",(function(t){return t.height})).attr(\"y\",(function(t){return t.y})).attr(\"cursor\",(function(t){return\"fixed\"===t.parcatsViewModel.arrangement?\"default\":\"perpendicular\"===t.parcatsViewModel.arrangement?\"ns-resize\":\"move\"})),T(O),S.exit().remove(),A.append(\"text\").attr(\"class\",\"catlabel\").attr(\"pointer-events\",\"none\");var z=e._fullLayout.paper_bgcolor;M.select(\"text.catlabel\").attr(\"text-anchor\",(function(t){return p(t)?\"start\":\"end\"})).attr(\"alignment-baseline\",\"middle\").style(\"text-shadow\",u.makeTextShadow(z)).style(\"fill\",\"rgb(0, 0, 0)\").attr(\"x\",(function(t){return p(t)?t.width+5:-5})).attr(\"y\",(function(t){return t.height/2})).text((function(t){return t.model.categoryLabel})).each((function(t){l.font(n.select(this),t.parcatsViewModel.categorylabelfont),u.convertToTspans(n.select(this),e)})),A.append(\"text\").attr(\"class\",\"dimlabel\"),M.select(\"text.dimlabel\").attr(\"text-anchor\",\"middle\").attr(\"alignment-baseline\",\"baseline\").attr(\"cursor\",(function(t){return\"fixed\"===t.parcatsViewModel.arrangement?\"default\":\"ew-resize\"})).attr(\"x\",(function(t){return t.width/2})).attr(\"y\",-5).text((function(t,e){return 0===e?t.parcatsViewModel.model.dimensions[t.model.dimensionInd].dimensionLabel:null})).each((function(t){l.font(n.select(this),t.parcatsViewModel.labelfont)})),M.selectAll(\"rect.bandrect\").on(\"mouseover\",E).on(\"mouseout\",L),M.exit().remove(),k.call(n.behavior.drag().origin((function(t){return{x:t.x,y:0}})).on(\"dragstart\",C).on(\"drag\",P).on(\"dragend\",I)),f.each((function(t){t.traceSelection=n.select(this),t.pathSelection=n.select(this).selectAll(\"g.paths\").selectAll(\"path.path\"),t.dimensionSelection=n.select(this).selectAll(\"g.dimensions\").selectAll(\"g.dimension\")})),f.exit().remove()}function h(t){return t.key}function p(t){var e=t.parcatsViewModel.dimensions.length,r=t.parcatsViewModel.dimensions[e-1].model.dimensionInd;return t.model.dimensionInd===r}function d(t,e){return t.model.rawColor>e.model.rawColor?1:t.model.rawColor<e.model.rawColor?-1:0}function m(t){if(!t.parcatsViewModel.dragDimension&&-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\")){o.raiseToTop(this),_(n.select(this));var e=v(t),r=y(t);if(t.parcatsViewModel.graphDiv.emit(\"plotly_hover\",{points:e,event:n.event,constraints:r}),-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"none\")){var i,s,l,u=n.mouse(this)[0],f=t.parcatsViewModel.graphDiv,h=t.parcatsViewModel.trace,p=f._fullLayout,d=p._paperdiv.node().getBoundingClientRect(),m=t.parcatsViewModel.graphDiv.getBoundingClientRect();for(l=0;l<t.leftXs.length-1;l++)if(t.leftXs[l]+t.dimWidths[l]-2<=u&&u<=t.leftXs[l+1]+2){var g=t.parcatsViewModel.dimensions[l],x=t.parcatsViewModel.dimensions[l+1];i=(g.x+g.width+x.x)/2,s=(t.topYs[l]+t.topYs[l+1]+t.height)/2;break}var b=t.parcatsViewModel.x+i,w=t.parcatsViewModel.y+s,T=c.mostReadable(t.model.color,[\"black\",\"white\"]),k=t.model.count,M=k/t.parcatsViewModel.model.count,A={countLabel:k,probabilityLabel:M.toFixed(3)},S=[];-1!==t.parcatsViewModel.hoverinfoItems.indexOf(\"count\")&&S.push([\"Count:\",A.countLabel].join(\" \")),-1!==t.parcatsViewModel.hoverinfoItems.indexOf(\"probability\")&&S.push([\"P:\",A.probabilityLabel].join(\" \"));var E=S.join(\"<br>\"),L=n.mouse(f)[0];a.loneHover({trace:h,x:b-d.left+m.left,y:w-d.top+m.top,text:E,color:t.model.color,borderColor:\"black\",fontFamily:'Monaco, \"Courier New\", monospace',fontSize:10,fontColor:T,idealAlign:L<b?\"right\":\"left\",hovertemplate:(h.line||{}).hovertemplate,hovertemplateLabels:A,eventData:[{data:h._input,fullData:h,count:k,probability:M}]},{container:p._hoverlayer.node(),outerContainer:p._paper.node(),gd:f})}}}function g(t){if(!t.parcatsViewModel.dragDimension&&(b(n.select(this)),a.loneUnhover(t.parcatsViewModel.graphDiv._fullLayout._hoverlayer.node()),t.parcatsViewModel.pathSelection.sort(d),-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\"))){var e=v(t),r=y(t);t.parcatsViewModel.graphDiv.emit(\"plotly_unhover\",{points:e,event:n.event,constraints:r})}}function v(t){for(var e=[],r=O(t.parcatsViewModel),n=0;n<t.model.valueInds.length;n++){var i=t.model.valueInds[n];e.push({curveNumber:r,pointNumber:i})}return e}function y(t){for(var e={},r=t.parcatsViewModel.model.dimensions,n=0;n<r.length;n++){var i=r[n],a=i.categories[t.model.categoryInds[n]];e[i.containerInd]=a.categoryValue}return void 0!==t.model.rawColor&&(e.color=t.model.rawColor),e}function x(t){if(-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\")){var e=v(t),r=y(t);t.parcatsViewModel.graphDiv.emit(\"plotly_click\",{points:e,event:n.event,constraints:r})}}function b(t){t.attr(\"fill\",(function(t){return t.model.color})).attr(\"fill-opacity\",.6).attr(\"stroke\",\"lightgray\").attr(\"stroke-width\",.2).attr(\"stroke-opacity\",1)}function _(t){t.attr(\"fill-opacity\",.8).attr(\"stroke\",(function(t){return c.mostReadable(t.model.color,[\"black\",\"white\"])})).attr(\"stroke-width\",.3)}function w(t){t.select(\"rect.catrect\").attr(\"stroke\",\"black\").attr(\"stroke-width\",1).attr(\"stroke-opacity\",1)}function T(t){t.attr(\"stroke\",\"black\").attr(\"stroke-width\",.2).attr(\"stroke-opacity\",1).attr(\"fill-opacity\",1)}function k(t){var e=t.parcatsViewModel.pathSelection,r=t.categoryViewModel.model.dimensionInd,n=t.categoryViewModel.model.categoryInd;return e.filter((function(e){return e.model.categoryInds[r]===n&&e.model.color===t.color}))}function M(t,e,r){var i=n.select(t).datum(),a=i.categoryViewModel.model,o=i.parcatsViewModel.graphDiv,s=n.select(t.parentNode).selectAll(\"rect.bandrect\"),l=[];s.each((function(t){k(t).each((function(t){Array.prototype.push.apply(l,v(t))}))}));var c={};c[a.dimensionInd]=a.categoryValue,o.emit(e,{points:l,event:r,constraints:c})}function A(t,e,r){var i=n.select(t).datum(),a=i.categoryViewModel.model,o=i.parcatsViewModel.graphDiv,s=k(i),l=[];s.each((function(t){Array.prototype.push.apply(l,v(t))}));var c={};c[a.dimensionInd]=a.categoryValue,void 0!==i.rawColor&&(c.color=i.rawColor),o.emit(e,{points:l,event:r,constraints:c})}function S(t,e,r){t._fullLayout._calcInverseTransform(t);var i,a,o=t._fullLayout._invScaleX,s=t._fullLayout._invScaleY,l=n.select(r.parentNode).select(\"rect.catrect\"),c=l.node().getBoundingClientRect(),u=l.datum(),f=u.parcatsViewModel,h=f.model.dimensions[u.model.dimensionInd],p=f.trace,d=c.top+c.height/2;f.dimensions.length>1&&h.displayInd===f.dimensions.length-1?(i=c.left,a=\"left\"):(i=c.left+c.width,a=\"right\");var m=u.model.count,g=u.model.categoryLabel,v=m/u.parcatsViewModel.model.count,y={countLabel:m,categoryLabel:g,probabilityLabel:v.toFixed(3)},x=[];-1!==u.parcatsViewModel.hoverinfoItems.indexOf(\"count\")&&x.push([\"Count:\",y.countLabel].join(\" \")),-1!==u.parcatsViewModel.hoverinfoItems.indexOf(\"probability\")&&x.push([\"P(\"+y.categoryLabel+\"):\",y.probabilityLabel].join(\" \"));var b=x.join(\"<br>\");return{trace:p,x:o*(i-e.left),y:s*(d-e.top),text:b,color:\"lightgray\",borderColor:\"black\",fontFamily:'Monaco, \"Courier New\", monospace',fontSize:12,fontColor:\"black\",idealAlign:a,hovertemplate:p.hovertemplate,hovertemplateLabels:y,eventData:[{data:p._input,fullData:p,count:m,category:g,probability:v}]}}function E(t){if(!t.parcatsViewModel.dragDimension&&-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\")){if(n.mouse(this)[1]<-1)return;var e,r=t.parcatsViewModel.graphDiv,i=r._fullLayout,s=i._paperdiv.node().getBoundingClientRect(),l=t.parcatsViewModel.hoveron;if(\"color\"===l?(!function(t){var e=n.select(t).datum(),r=k(e);_(r),r.each((function(){o.raiseToTop(this)})),n.select(t.parentNode).selectAll(\"rect.bandrect\").filter((function(t){return t.color===e.color})).each((function(){o.raiseToTop(this),n.select(this).attr(\"stroke\",\"black\").attr(\"stroke-width\",1.5)}))}(this),A(this,\"plotly_hover\",n.event)):(!function(t){n.select(t.parentNode).selectAll(\"rect.bandrect\").each((function(t){var e=k(t);_(e),e.each((function(){o.raiseToTop(this)}))})),n.select(t.parentNode).select(\"rect.catrect\").attr(\"stroke\",\"black\").attr(\"stroke-width\",2.5)}(this),M(this,\"plotly_hover\",n.event)),-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"none\"))\"category\"===l?e=S(r,s,this):\"color\"===l?e=function(t,e,r){t._fullLayout._calcInverseTransform(t);var i,a,o=t._fullLayout._invScaleX,s=t._fullLayout._invScaleY,l=r.getBoundingClientRect(),u=n.select(r).datum(),f=u.categoryViewModel,h=f.parcatsViewModel,p=h.model.dimensions[f.model.dimensionInd],d=h.trace,m=l.y+l.height/2;h.dimensions.length>1&&p.displayInd===h.dimensions.length-1?(i=l.left,a=\"left\"):(i=l.left+l.width,a=\"right\");var g=f.model.categoryLabel,v=u.parcatsViewModel.model.count,y=0;u.categoryViewModel.bands.forEach((function(t){t.color===u.color&&(y+=t.count)}));var x=f.model.count,b=0;h.pathSelection.each((function(t){t.model.color===u.color&&(b+=t.model.count)}));var _=y/v,w=y/b,T=y/x,k={countLabel:v,categoryLabel:g,probabilityLabel:_.toFixed(3)},M=[];-1!==f.parcatsViewModel.hoverinfoItems.indexOf(\"count\")&&M.push([\"Count:\",k.countLabel].join(\" \")),-1!==f.parcatsViewModel.hoverinfoItems.indexOf(\"probability\")&&(M.push(\"P(color \\u2229 \"+g+\"): \"+k.probabilityLabel),M.push(\"P(\"+g+\" | color): \"+w.toFixed(3)),M.push(\"P(color | \"+g+\"): \"+T.toFixed(3)));var A=M.join(\"<br>\"),S=c.mostReadable(u.color,[\"black\",\"white\"]);return{trace:d,x:o*(i-e.left),y:s*(m-e.top),text:A,color:u.color,borderColor:\"black\",fontFamily:'Monaco, \"Courier New\", monospace',fontColor:S,fontSize:10,idealAlign:a,hovertemplate:d.hovertemplate,hovertemplateLabels:k,eventData:[{data:d._input,fullData:d,category:g,count:v,probability:_,categorycount:x,colorcount:b,bandcolorcount:y}]}}(r,s,this):\"dimension\"===l&&(e=function(t,e,r){var i=[];return n.select(r.parentNode.parentNode).selectAll(\"g.category\").select(\"rect.catrect\").each((function(){i.push(S(t,e,this))})),i}(r,s,this)),e&&a.loneHover(e,{container:i._hoverlayer.node(),outerContainer:i._paper.node(),gd:r})}}function L(t){var e=t.parcatsViewModel;if(!e.dragDimension&&(b(e.pathSelection),w(e.dimensionSelection.selectAll(\"g.category\")),T(e.dimensionSelection.selectAll(\"g.category\").selectAll(\"rect.bandrect\")),a.loneUnhover(e.graphDiv._fullLayout._hoverlayer.node()),e.pathSelection.sort(d),-1===e.hoverinfoItems.indexOf(\"skip\"))){\"color\"===t.parcatsViewModel.hoveron?A(this,\"plotly_unhover\",n.event):M(this,\"plotly_unhover\",n.event)}}function C(t){\"fixed\"!==t.parcatsViewModel.arrangement&&(t.dragDimensionDisplayInd=t.model.displayInd,t.initialDragDimensionDisplayInds=t.parcatsViewModel.model.dimensions.map((function(t){return t.displayInd})),t.dragHasMoved=!1,t.dragCategoryDisplayInd=null,n.select(this).selectAll(\"g.category\").select(\"rect.catrect\").each((function(e){var r=n.mouse(this)[0],i=n.mouse(this)[1];-2<=r&&r<=e.width+2&&-2<=i&&i<=e.height+2&&(t.dragCategoryDisplayInd=e.model.displayInd,t.initialDragCategoryDisplayInds=t.model.categories.map((function(t){return t.displayInd})),e.model.dragY=e.y,o.raiseToTop(this.parentNode),n.select(this.parentNode).selectAll(\"rect.bandrect\").each((function(e){e.y<i&&i<=e.y+e.height&&(t.potentialClickBand=this)})))})),t.parcatsViewModel.dragDimension=t,a.loneUnhover(t.parcatsViewModel.graphDiv._fullLayout._hoverlayer.node()))}function P(t){if(\"fixed\"!==t.parcatsViewModel.arrangement&&(t.dragHasMoved=!0,null!==t.dragDimensionDisplayInd)){var e=t.dragDimensionDisplayInd,r=e-1,i=e+1,a=t.parcatsViewModel.dimensions[e];if(null!==t.dragCategoryDisplayInd){var o=a.categories[t.dragCategoryDisplayInd];o.model.dragY+=n.event.dy;var s=o.model.dragY,l=o.model.displayInd,c=a.categories,u=c[l-1],f=c[l+1];void 0!==u&&s<u.y+u.height/2&&(o.model.displayInd=u.model.displayInd,u.model.displayInd=l),void 0!==f&&s+o.height>f.y+f.height/2&&(o.model.displayInd=f.model.displayInd,f.model.displayInd=l),t.dragCategoryDisplayInd=o.model.displayInd}if(null===t.dragCategoryDisplayInd||\"freeform\"===t.parcatsViewModel.arrangement){a.model.dragX=n.event.x;var h=t.parcatsViewModel.dimensions[r],p=t.parcatsViewModel.dimensions[i];void 0!==h&&a.model.dragX<h.x+h.width&&(a.model.displayInd=h.model.displayInd,h.model.displayInd=e),void 0!==p&&a.model.dragX+a.width>p.x&&(a.model.displayInd=p.model.displayInd,p.model.displayInd=t.dragDimensionDisplayInd),t.dragDimensionDisplayInd=a.model.displayInd}N(t.parcatsViewModel),B(t.parcatsViewModel),D(t.parcatsViewModel),z(t.parcatsViewModel)}}function I(t){if(\"fixed\"!==t.parcatsViewModel.arrangement&&null!==t.dragDimensionDisplayInd){n.select(this).selectAll(\"text\").attr(\"font-weight\",\"normal\");var e={},r=O(t.parcatsViewModel),a=t.parcatsViewModel.model.dimensions.map((function(t){return t.displayInd})),o=t.initialDragDimensionDisplayInds.some((function(t,e){return t!==a[e]}));o&&a.forEach((function(r,n){var i=t.parcatsViewModel.model.dimensions[n].containerInd;e[\"dimensions[\"+i+\"].displayindex\"]=r}));var s=!1;if(null!==t.dragCategoryDisplayInd){var l=t.model.categories.map((function(t){return t.displayInd}));if(s=t.initialDragCategoryDisplayInds.some((function(t,e){return t!==l[e]}))){var c=t.model.categories.slice().sort((function(t,e){return t.displayInd-e.displayInd})),u=c.map((function(t){return t.categoryValue})),f=c.map((function(t){return t.categoryLabel}));e[\"dimensions[\"+t.model.containerInd+\"].categoryarray\"]=[u],e[\"dimensions[\"+t.model.containerInd+\"].ticktext\"]=[f],e[\"dimensions[\"+t.model.containerInd+\"].categoryorder\"]=\"array\"}}if(-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\")&&!t.dragHasMoved&&t.potentialClickBand&&(\"color\"===t.parcatsViewModel.hoveron?A(t.potentialClickBand,\"plotly_click\",n.event.sourceEvent):M(t.potentialClickBand,\"plotly_click\",n.event.sourceEvent)),t.model.dragX=null,null!==t.dragCategoryDisplayInd)t.parcatsViewModel.dimensions[t.dragDimensionDisplayInd].categories[t.dragCategoryDisplayInd].model.dragY=null,t.dragCategoryDisplayInd=null;t.dragDimensionDisplayInd=null,t.parcatsViewModel.dragDimension=null,t.dragHasMoved=null,t.potentialClickBand=null,N(t.parcatsViewModel),B(t.parcatsViewModel),n.transition().duration(300).ease(\"cubic-in-out\").each((function(){D(t.parcatsViewModel,!0),z(t.parcatsViewModel,!0)})).each(\"end\",(function(){(o||s)&&i.restyle(t.parcatsViewModel.graphDiv,e,[r])}))}}function O(t){for(var e,r=t.graphDiv._fullData,n=0;n<r.length;n++)if(t.key===r[n].uid){e=n;break}return e}function z(t,e){var r;void 0===e&&(e=!1),t.pathSelection.data((function(t){return t.paths}),h),(r=t.pathSelection,e?r.transition():r).attr(\"d\",(function(t){return t.svgD}))}function D(t,e){function r(t){return e?t.transition():t}void 0===e&&(e=!1),t.dimensionSelection.data((function(t){return t.dimensions}),h);var i=t.dimensionSelection.selectAll(\"g.category\").data((function(t){return t.categories}),h);r(t.dimensionSelection).attr(\"transform\",(function(t){return s(t.x,0)})),r(i).attr(\"transform\",(function(t){return s(0,t.y)})),i.select(\".dimlabel\").text((function(t,e){return 0===e?t.parcatsViewModel.model.dimensions[t.model.dimensionInd].dimensionLabel:null})),i.select(\".catlabel\").attr(\"text-anchor\",(function(t){return p(t)?\"start\":\"end\"})).attr(\"x\",(function(t){return p(t)?t.width+5:-5})).each((function(t){var e,r;p(t)?(e=t.width+5,r=\"start\"):(e=-5,r=\"end\"),n.select(this).selectAll(\"tspan\").attr(\"x\",e).attr(\"text-anchor\",r)}));var a=i.selectAll(\"rect.bandrect\").data((function(t){return t.bands}),h),l=a.enter().append(\"rect\").attr(\"class\",\"bandrect\").attr(\"cursor\",\"move\").attr(\"stroke-opacity\",0).attr(\"fill\",(function(t){return t.color})).attr(\"fill-opacity\",0);a.attr(\"fill\",(function(t){return t.color})).attr(\"width\",(function(t){return t.width})).attr(\"height\",(function(t){return t.height})).attr(\"y\",(function(t){return t.y})),T(l),a.each((function(){o.raiseToTop(this)})),a.exit().remove()}function R(t,e,r){var n,i=r[0],a=e.margin||{l:80,r:80,t:100,b:80},o=i.trace,s=o.domain,l=e.width,c=e.height,u=Math.floor(l*(s.x[1]-s.x[0])),f=Math.floor(c*(s.y[1]-s.y[0])),h=s.x[0]*l+a.l,p=e.height-s.y[1]*e.height+a.t,d=o.line.shape;n=\"all\"===o.hoverinfo?[\"count\",\"probability\"]:(o.hoverinfo||\"\").split(\"+\");var 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n=t(\"./parcats\");e.exports=function(t,e,r,i){var a=t._fullLayout,o=a._paper,s=a._size;n(t,o,e,{width:s.w,height:s.h,margin:{t:s.t,r:s.r,b:s.b,l:s.l}},r,i)}},{\"./parcats\":1174}],1176:[function(t,e,r){\"use strict\";var n=t(\"../../components/colorscale/attributes\"),i=t(\"../../plots/cartesian/layout_attributes\"),a=t(\"../../plots/font_attributes\"),o=t(\"../../plots/domain\").attributes,s=t(\"../../lib/extend\").extendFlat,l=t(\"../../plot_api/plot_template\").templatedArray;e.exports={domain:o({name:\"parcoords\",trace:!0,editType:\"plot\"}),labelangle:{valType:\"angle\",dflt:0,editType:\"plot\"},labelside:{valType:\"enumerated\",values:[\"top\",\"bottom\"],dflt:\"top\",editType:\"plot\"},labelfont:a({editType:\"plot\"}),tickfont:a({editType:\"plot\"}),rangefont:a({editType:\"plot\"}),dimensions:l(\"dimension\",{label:{valType:\"string\",editType:\"plot\"},tickvals:s({},i.tickvals,{editType:\"plot\"}),ticktext:s({},i.ticktext,{editType:\"plot\"}),tickformat:s({},i.tickformat,{editType:\"plot\"}),visible:{valType:\"boolean\",dflt:!0,editType:\"plot\"},range:{valType:\"info_array\",items:[{valType:\"number\",editType:\"plot\"},{valType:\"number\",editType:\"plot\"}],editType:\"plot\"},constraintrange:{valType:\"info_array\",freeLength:!0,dimensions:\"1-2\",items:[{valType:\"number\",editType:\"plot\"},{valType:\"number\",editType:\"plot\"}],editType:\"plot\"},multiselect:{valType:\"boolean\",dflt:!0,editType:\"plot\"},values:{valType:\"data_array\",editType:\"calc\"},editType:\"calc\"}),line:s({editType:\"calc\"},n(\"line\",{colorscaleDflt:\"Viridis\",autoColorDflt:!1,editTypeOverride:\"calc\"}))}},{\"../../components/colorscale/attributes\":669,\"../../lib/extend\":789,\"../../plot_api/plot_template\":838,\"../../plots/cartesian/layout_attributes\":864,\"../../plots/domain\":877,\"../../plots/font_attributes\":878}],1177:[function(t,e,r){\"use 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r=e.brush,n=r.filter,a=r.svgBrush;a._dragging||(T(t,e),w(t,e),e.brush.svgBrush.wasDragged=!1),a._dragging=!1,i.event.sourceEvent.stopPropagation();var o=a.grabbingBar;if(a.grabbingBar=!1,a.grabLocation=void 0,e.parent.inBrushDrag=!1,y(),!a.wasDragged)return a.wasDragged=void 0,a.clickableOrdinalRange?r.filterSpecified&&e.multiselect?a.extent.push(a.clickableOrdinalRange):(a.extent=[a.clickableOrdinalRange],r.filterSpecified=!0):o?(a.extent=a.stayingIntervals,0===a.extent.length&&A(r)):A(r),a.brushCallback(e),b(t.parentNode),void a.brushEndCallback(r.filterSpecified?n.getConsolidated():[]);var s=function(){n.set(n.getConsolidated())};if(e.ordinal){var l=e.unitTickvals;l[l.length-1]<l[0]&&l.reverse(),a.newExtent=[p(0,l,a.newExtent[0],a.stayingIntervals),p(1,l,a.newExtent[1],a.stayingIntervals)];var c=a.newExtent[1]>a.newExtent[0];a.extent=a.stayingIntervals.concat(c?[a.newExtent]:[]),a.extent.length||A(r),a.brushCallback(e),c?b(t.parentNode,s):(s(),b(t.parentNode))}else 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a=t.selectAll(\".highlight\").data(o);a.enter().append(\"line\").classed(\"highlight\",!0).attr(\"x\",-n.bar.width/2).attr(\"stroke-width\",n.bar.width-n.bar.strokeWidth).attr(\"stroke\",n.bar.fillColor).attr(\"opacity\",n.bar.fillOpacity).attr(\"stroke-linecap\",\"butt\"),a.attr(\"y1\",(function(t){return t.height})).call(x)}(r,e)},cleanRanges:function(t,e){if(Array.isArray(t[0])?(t=t.map((function(t){return t.sort(s)})),t=e.multiselect?S(t.sort(M)):[t[0]]):t=[t.sort(s)],e.tickvals){var r=e.tickvals.slice().sort(s);if(!(t=t.map((function(t){var e=[p(0,r,t[0],[]),p(1,r,t[1],[])];if(e[1]>e[0])return e})).filter((function(t){return t}))).length)return}return t.length>1?t:t[0]}}},{\"../../lib\":799,\"../../lib/gup\":796,\"./constants\":1180,\"@plotly/d3\":58}],1178:[function(t,e,r){\"use strict\";var n=t(\"@plotly/d3\"),i=t(\"../../plots/get_data\").getModuleCalcData,a=t(\"./plot\"),o=t(\"../../constants/xmlns_namespaces\");r.name=\"parcoords\",r.plot=function(t){var e=i(t.calcdata,\"parcoords\")[0];e.length&&a(t,e)},r.clean=function(t,e,r,n){var i=n._has&&n._has(\"parcoords\"),a=e._has&&e._has(\"parcoords\");i&&!a&&(n._paperdiv.selectAll(\".parcoords\").remove(),n._glimages.selectAll(\"*\").remove())},r.toSVG=function(t){var e=t._fullLayout._glimages,r=n.select(t).selectAll(\".svg-container\");r.filter((function(t,e){return e===r.size()-1})).selectAll(\".gl-canvas-context, .gl-canvas-focus\").each((function(){var t=this.toDataURL(\"image/png\");e.append(\"svg:image\").attr({xmlns:o.svg,\"xlink:href\":t,preserveAspectRatio:\"none\",x:0,y:0,width:this.width,height:this.height})})),window.setTimeout((function(){n.selectAll(\"#filterBarPattern\").attr(\"id\",\"filterBarPattern\")}),60)}},{\"../../constants/xmlns_namespaces\":776,\"../../plots/get_data\":887,\"./plot\":1187,\"@plotly/d3\":58}],1179:[function(t,e,r){\"use strict\";var n=t(\"../../lib\").isArrayOrTypedArray,i=t(\"../../components/colorscale\"),a=t(\"../../lib/gup\").wrap;e.exports=function(t,e){var r,o;return i.hasColorscale(e,\"line\")&&n(e.line.color)?(r=e.line.color,o=i.extractOpts(e.line).colorscale,i.calc(t,e,{vals:r,containerStr:\"line\",cLetter:\"c\"})):(r=function(t){for(var e=new Array(t),r=0;r<t;r++)e[r]=.5;return e}(e._length),o=[[0,e.line.color],[1,e.line.color]]),a({lineColor:r,cscale:o})}},{\"../../components/colorscale\":674,\"../../lib\":799,\"../../lib/gup\":796}],1180:[function(t,e,r){\"use 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n=t(\"../../lib\").isTypedArray;r.convertTypedArray=function(t){return n(t)?Array.prototype.slice.call(t):t},r.isOrdinal=function(t){return!!t.tickvals},r.isVisible=function(t){return t.visible||!(\"visible\"in t)}},{\"../../lib\":799}],1183:[function(t,e,r){\"use strict\";e.exports={attributes:t(\"./attributes\"),supplyDefaults:t(\"./defaults\"),calc:t(\"./calc\"),plot:t(\"./plot\"),colorbar:{container:\"line\",min:\"cmin\",max:\"cmax\"},moduleType:\"trace\",name:\"parcoords\",basePlotModule:t(\"./base_plot\"),categories:[\"gl\",\"regl\",\"noOpacity\",\"noHover\"],meta:{}}},{\"./attributes\":1176,\"./base_plot\":1178,\"./calc\":1179,\"./defaults\":1181,\"./plot\":1187}],1184:[function(t,e,r){\"use strict\";var n=t(\"glslify\"),i=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragColor;\\n\\nattribute vec4 p01_04, p05_08, p09_12, p13_16,\\n               p17_20, p21_24, p25_28, p29_32,\\n               p33_36, p37_40, p41_44, p45_48,\\n               p49_52, p53_56, 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hi) {\\n    return (lo < hi) && (lo > p || p > hi);\\n}\\n\\nbool vOutside(vec4 p, vec4 lo, vec4 hi) {\\n    return (\\n        fOutside(p[0], lo[0], hi[0]) ||\\n        fOutside(p[1], lo[1], hi[1]) ||\\n        fOutside(p[2], lo[2], hi[2]) ||\\n        fOutside(p[3], lo[3], hi[3])\\n    );\\n}\\n\\nbool mOutside(mat4 p, mat4 lo, mat4 hi) {\\n    return (\\n        vOutside(p[0], lo[0], hi[0]) ||\\n        vOutside(p[1], lo[1], hi[1]) ||\\n        vOutside(p[2], lo[2], hi[2]) ||\\n        vOutside(p[3], lo[3], hi[3])\\n    );\\n}\\n\\nbool outsideBoundingBox(mat4 A, mat4 B, mat4 C, mat4 D) {\\n    return mOutside(A, loA, hiA) ||\\n           mOutside(B, loB, hiB) ||\\n           mOutside(C, loC, hiC) ||\\n           mOutside(D, loD, hiD);\\n}\\n\\nbool outsideRasterMask(mat4 A, mat4 B, mat4 C, mat4 D) {\\n    mat4 pnts[4];\\n    pnts[0] = A;\\n    pnts[1] = B;\\n    pnts[2] = C;\\n    pnts[3] = D;\\n\\n    for(int i = 0; i < 4; ++i) {\\n        for(int j = 0; j < 4; ++j) {\\n            for(int k = 0; k < 4; ++k) {\\n                if(0 == iMod(\\n                    int(255.0 * texture2D(mask,\\n                        vec2(\\n                            (float(i * 2 + j / 2) + 0.5) / 8.0,\\n                            (pnts[i][j][k] * (maskHeight - 1.0) + 1.0) / maskHeight\\n                        ))[3]\\n                    ) / int(pow(2.0, float(iMod(j * 4 + k, 8)))),\\n                    2\\n                )) return true;\\n            }\\n        }\\n    }\\n    return false;\\n}\\n\\nvec4 position(bool isContext, float v, mat4 A, mat4 B, mat4 C, mat4 D) {\\n    float x = 0.5 * sign(v) + 0.5;\\n    float y = axisY(x, A, B, C, D);\\n    float z = 1.0 - abs(v);\\n\\n    z += isContext ? 0.0 : 2.0 * float(\\n        outsideBoundingBox(A, B, C, D) ||\\n        outsideRasterMask(A, B, C, D)\\n    );\\n\\n    return vec4(\\n        2.0 * (vec2(x, y) * viewBoxSize + viewBoxPos) / resolution - 1.0,\\n        z,\\n        1.0\\n    );\\n}\\n\\nvoid main() {\\n    mat4 A = mat4(p01_04, p05_08, p09_12, p13_16);\\n    mat4 B = mat4(p17_20, p21_24, p25_28, p29_32);\\n    mat4 C = mat4(p33_36, p37_40, p41_44, p45_48);\\n    mat4 D = mat4(p49_52, p53_56, p57_60, ZEROS);\\n\\n    float v = colors[3];\\n\\n    gl_Position = position(isContext, v, A, B, C, D);\\n\\n    fragColor =\\n        isContext ? vec4(contextColor) :\\n        isPick ? vec4(colors.rgb, 1.0) : texture2D(palette, vec2(abs(v), 0.5));\\n}\\n\"]),a=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n    gl_FragColor = fragColor;\\n}\\n\"]),o=t(\"./constants\").maxDimensionCount,s=t(\"../../lib\"),l=new Uint8Array(4),c=new Uint8Array(4),u={shape:[256,1],format:\"rgba\",type:\"uint8\",mag:\"nearest\",min:\"nearest\"};function f(t,e,r,n,i){var a=t._gl;a.enable(a.SCISSOR_TEST),a.scissor(e,r,n,i),t.clear({color:[0,0,0,0],depth:1})}function h(t,e,r,n,i,a){var o=a.key;r.drawCompleted||(!function(t){t.read({x:0,y:0,width:1,height:1,data:l})}(t),r.drawCompleted=!0),function s(l){var c=Math.min(n,i-l*n);0===l&&(window.cancelAnimationFrame(r.currentRafs[o]),delete r.currentRafs[o],f(t,a.scissorX,a.scissorY,a.scissorWidth,a.viewBoxSize[1])),r.clearOnly||(a.count=2*c,a.offset=2*l*n,e(a),l*n+c<i&&(r.currentRafs[o]=window.requestAnimationFrame((function(){s(l+1)}))),r.drawCompleted=!1)}(0)}function p(t,e){for(var r=new Array(256),n=0;n<256;n++)r[n]=t(n/255).concat(e);return r}function d(t,e){return(t>>>8*e)%256/255}function m(t,e,r){for(var n=new Array(8*e),i=0,a=0;a<e;a++)for(var o=0;o<2;o++)for(var s=0;s<4;s++){var l=4*t+s,c=r[64*a+l];63===l&&0===o&&(c*=-1),n[i++]=c}return n}function g(t){var e=\"0\"+t;return e.substr(e.length-2)}function v(t){return t<o?\"p\"+g(t+1)+\"_\"+g(t+4):\"colors\"}function y(t,e,r,n,i,a,o,l,c,u,f,h,p){for(var d=[[],[]],m=0;m<64;m++)d[0][m]=m===i?1:0,d[1][m]=m===a?1:0;var 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r,n,l,g,b,_=e.context,w=e.pick,T=e.regl,k={currentRafs:{},drawCompleted:!0,clearOnly:!1},M=function(t){for(var e={},r=0;r<=o;r+=4)e[v(r)]=t.buffer({usage:\"dynamic\",type:\"float\",data:new Uint8Array(0)});return e}(T),A=T.texture(u),S=[];L(e);var E=T({profile:!1,blend:{enable:_,func:{srcRGB:\"src alpha\",dstRGB:\"one minus src 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L(t){r=t.model,n=t.viewModel,l=n.dimensions.slice(),g=l[0]?l[0].values.length:0;var e=r.lines,i=w?e.color.map((function(t,r){return r/e.color.length})):e.color,a=function(t,e,r){for(var n,i=new Array(t*(o+4)),a=0,s=0;s<t;s++){for(var l=0;l<o;l++)i[a++]=l<e.length?e[l].paddedUnitValues[s]:.5;i[a++]=d(s,2),i[a++]=d(s,1),i[a++]=d(s,0),i[a++]=(n=r[s],Math.max(1e-6,Math.min(.999999,n)))}return i}(g,l,i);!function(t,e,r){for(var n=0;n<=o;n+=4)t[v(n)](m(n/4,e,r))}(M,g,a),_||w||(A=T.texture(s.extendFlat({data:p(r.unitToColor,255)},u)))}return{render:function(t,e,n){var i,a,o,s=t.length,c=1/0,u=-1/0;for(i=0;i<s;i++)t[i].dim0.canvasX<c&&(c=t[i].dim0.canvasX,a=i),t[i].dim1.canvasX>u&&(u=t[i].dim1.canvasX,o=i);0===s&&f(T,0,0,r.canvasWidth,r.canvasHeight);var p=function(t){var e,r,n,i=[[],[]];for(n=0;n<64;n++){var a=!t&&n<l.length?l[n].brush.filter.getBounds():[-1/0,1/0];i[0][n]=a[0],i[1][n]=a[1]}var o=new Array(16384);for(e=0;e<16384;e++)o[e]=255;if(!t)for(e=0;e<l.length;e++){var s=e%8,c=(e-s)/8,u=Math.pow(2,s),f=l[e].brush.filter.get();if(!(f.length<2)){var h=x(f[0])[1];for(r=1;r<f.length;r++){var p=x(f[r]);for(n=h+1;n<p[0];n++)o[8*n+c]&=~u;h=Math.max(h,p[1])}}}var d={shape:[8,2048],format:\"alpha\",type:\"uint8\",mag:\"nearest\",min:\"nearest\",data:o};return b?b(d):b=T.texture(d),{maskTexture:b,maskHeight:2048,loA:i[0].slice(0,16),loB:i[0].slice(16,32),loC:i[0].slice(32,48),loD:i[0].slice(48,64),hiA:i[1].slice(0,16),hiB:i[1].slice(16,32),hiC:i[1].slice(32,48),hiD:i[1].slice(48,64)}}(_);for(i=0;i<s;i++){var d=t[i],m=d.dim0.crossfilterDimensionIndex,v=d.dim1.crossfilterDimensionIndex,M=d.canvasX,A=d.canvasY,L=M+d.panelSizeX;if(e||!S[m]||S[m][0]!==M||S[m][1]!==L){S[m]=[M,L];var C=y(r,a,o,i,m,v,M,A,d.panelSizeX,d.panelSizeY,d.dim0.crossfilterDimensionIndex,_?0:w?2:1,p);k.clearOnly=n;var P=e?r.lines.blockLineCount:g;h(T,E,k,P,g,C)}}},readPixel:function(t,e){return T.read({x:t,y:e,width:1,height:1,data:c}),c},readPixels:function(t,e,r,n){var i=new Uint8Array(4*r*n);return T.read({x:t,y:e,width:r,height:n,data:i}),i},destroy:function(){for(var e in t.style[\"pointer-events\"]=\"none\",A.destroy(),b&&b.destroy(),M)M[e].destroy()},update:L}}},{\"../../lib\":799,\"./constants\":1180,glslify:438}],1185:[function(t,e,r){\"use strict\";e.exports=function(t,e,r,n){var i,a;for(n||(n=1/0),i=0;i<e.length;i++)(a=e[i]).visible&&(n=Math.min(n,a[r].length));for(n===1/0&&(n=0),t._length=n,i=0;i<e.length;i++)(a=e[i]).visible&&(a._length=n);return n}},{}],1186:[function(t,e,r){\"use strict\";var n=t(\"@plotly/d3\"),i=t(\"color-rgba\"),a=t(\"../../plots/cartesian/axes\"),o=t(\"../../lib\"),s=o.strRotate,l=o.strTranslate,c=t(\"../../lib/svg_text_utils\"),u=t(\"../../components/drawing\"),f=t(\"../../components/colorscale\"),h=t(\"../../lib/gup\"),p=h.keyFun,d=h.repeat,m=h.unwrap,g=t(\"./helpers\"),v=t(\"./constants\"),y=t(\"./axisbrush\"),x=t(\"./lines\");function b(t,e,r){return o.aggNums(t,null,e,r)}function _(t,e){return T(b(Math.min,t,e),b(Math.max,t,e))}function w(t){var e=t.range;return e?T(e[0],e[1]):_(t.values,t._length)}function T(t,e){return!isNaN(t)&&isFinite(t)||(t=0),!isNaN(e)&&isFinite(e)||(e=0),t===e&&(0===t?(t-=1,e+=1):(t*=.9,e*=1.1)),[t,e]}function k(t,e,r,i,a){var o,s,l=w(r);return i?n.scale.ordinal().domain(i.map((o=n.format(r.tickformat),s=a,s?function(t,e){var r=s[e];return null==r?o(t):r}:o))).range(i.map((function(r){var n=(r-l[0])/(l[1]-l[0]);return t-e+n*(2*e-t)}))):n.scale.linear().domain(l).range([t-e,e])}function M(t){if(t.tickvals){var e=w(t);return n.scale.ordinal().domain(t.tickvals).range(t.tickvals.map((function(t){return(t-e[0])/(e[1]-e[0])})))}}function A(t){var e=t.map((function(t){return t[0]})),r=t.map((function(t){var e=i(t[1]);return n.rgb(\"rgb(\"+e[0]+\",\"+e[1]+\",\"+e[2]+\")\")})),a=\"rgb\".split(\"\").map((function(t){return n.scale.linear().clamp(!0).domain(e).range(r.map((i=t,function(t){return t[i]})));var i}));return function(t){return a.map((function(e){return e(t)}))}}function S(t){return t.dimensions.some((function(t){return t.brush.filterSpecified}))}function E(t,e,r){var 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L(t,e,r){var i=r.width,a=r.height,s=r.dimensions,l=r.canvasPixelRatio,c=function(t){return i*t/Math.max(1,r.colCount-1)},u=v.verticalPadding/a,f=function(t,e){return n.scale.linear().range([e,t-e])}(a,v.verticalPadding),h={key:r.key,xScale:c,model:r,inBrushDrag:!1},p={};return h.dimensions=s.filter(g.isVisible).map((function(i,s){var d=function(t,e){return n.scale.linear().domain(w(t)).range([e,1-e])}(i,u),m=p[i.label];p[i.label]=(m||0)+1;var x=i.label+(m?\"__\"+m:\"\"),b=i.constraintrange,_=b&&b.length;_&&!Array.isArray(b[0])&&(b=[b]);var T=_?b.map((function(t){return t.map(d)})):[[-1/0,1/0]],A=i.values;A.length>i._length&&(A=A.slice(0,i._length));var E,L=i.tickvals;function C(t,e){return{val:t,text:E[e]}}function P(t,e){return t.val-e.val}if(Array.isArray(L)&&L.length){E=i.ticktext,Array.isArray(E)&&E.length?E.length>L.length?E=E.slice(0,L.length):L.length>E.length&&(L=L.slice(0,E.length)):E=L.map(n.format(i.tickformat));for(var I=1;I<L.length;I++)if(L[I]<L[I-1]){for(var 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X=G.selectAll(\".\"+v.cn.axisExtentBottom).data(d,p);X.enter().append(\"g\").classed(v.cn.axisExtentBottom,!0),X.attr(\"transform\",(function(t){return l(0,t.model.height+v.axisExtentOffset)}));var Z=X.selectAll(\".\"+v.cn.axisExtentBottomText).data(d,p);Z.enter().append(\"text\").classed(v.cn.axisExtentBottomText,!0).attr(\"dy\",\"0.75em\").call(C),Z.text((function(t){return z(t,!1)})).each((function(t){u.font(n.select(this),t.model.rangeFont)})),y.ensureAxisBrush(U,w)}},{\"../../components/colorscale\":674,\"../../components/drawing\":684,\"../../lib\":799,\"../../lib/gup\":796,\"../../lib/svg_text_utils\":824,\"../../plots/cartesian/axes\":849,\"./axisbrush\":1177,\"./constants\":1180,\"./helpers\":1182,\"./lines\":1184,\"@plotly/d3\":58,\"color-rgba\":133}],1187:[function(t,e,r){\"use strict\";var n=t(\"./parcoords\"),i=t(\"../../lib/prepare_regl\"),a=t(\"./helpers\").isVisible;function o(t,e,r){var n=e.indexOf(r),i=t.indexOf(n);return-1===i&&(i+=e.length),i}e.exports=function(t,e){var r=t._fullLayout;if(i(t)){var s={},l={},c={},u={},f=r._size;e.forEach((function(e,r){var n=e[0].trace;c[r]=n.index;var i=u[r]=n._fullInput.index;s[r]=t.data[i].dimensions,l[r]=t.data[i].dimensions.slice()}));n(t,e,{width:f.w,height:f.h,margin:{t:f.t,r:f.r,b:f.b,l:f.l}},{filterChanged:function(e,n,i){var a=l[e][n],o=i.map((function(t){return t.slice()})),s=\"dimensions[\"+n+\"].constraintrange\",f=r._tracePreGUI[t._fullData[c[e]]._fullInput.uid];if(void 0===f[s]){var h=a.constraintrange;f[s]=h||null}var p=t._fullData[c[e]].dimensions[n];o.length?(1===o.length&&(o=o[0]),a.constraintrange=o,p.constraintrange=o.slice(),o=[o]):(delete a.constraintrange,delete p.constraintrange,o=null);var d={};d[s]=o,t.emit(\"plotly_restyle\",[d,[u[e]]])},hover:function(e){t.emit(\"plotly_hover\",e)},unhover:function(e){t.emit(\"plotly_unhover\",e)},axesMoved:function(e,r){var n=function(t,e){return function(r,n){return o(t,e,r)-o(t,e,n)}}(r,l[e].filter(a));s[e].sort(n),l[e].filter((function(t){return!a(t)})).sort((function(t){return l[e].indexOf(t)})).forEach((function(t){s[e].splice(s[e].indexOf(t),1),s[e].splice(l[e].indexOf(t),0,t)})),t.emit(\"plotly_restyle\",[{dimensions:[s[e]]},[u[e]]])}})}}},{\"../../lib/prepare_regl\":812,\"./helpers\":1182,\"./parcoords\":1186}],1188:[function(t,e,r){\"use strict\";var n=t(\"../../plots/attributes\"),i=t(\"../../plots/domain\").attributes,a=t(\"../../plots/font_attributes\"),o=t(\"../../components/color/attributes\"),s=t(\"../../plots/template_attributes\").hovertemplateAttrs,l=t(\"../../plots/template_attributes\").texttemplateAttrs,c=t(\"../../lib/extend\").extendFlat,u=a({editType:\"plot\",arrayOk:!0,colorEditType:\"plot\"});e.exports={labels:{valType:\"data_array\",editType:\"calc\"},label0:{valType:\"number\",dflt:0,editType:\"calc\"},dlabel:{valType:\"number\",dflt:1,editType:\"calc\"},values:{valType:\"data_array\",editType:\"calc\"},marker:{colors:{valType:\"data_array\",editType:\"calc\"},line:{color:{valType:\"color\",dflt:o.defaultLine,arrayOk:!0,editType:\"style\"},width:{valType:\"number\",min:0,dflt:0,arrayOk:!0,editType:\"style\"},editType:\"calc\"},editType:\"calc\"},text:{valType:\"data_array\",editType:\"plot\"},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0,editType:\"style\"},scalegroup:{valType:\"string\",dflt:\"\",editType:\"calc\"},textinfo:{valType:\"flaglist\",flags:[\"label\",\"text\",\"value\",\"percent\"],extras:[\"none\"],editType:\"calc\"},hoverinfo:c({},n.hoverinfo,{flags:[\"label\",\"text\",\"value\",\"percent\",\"name\"]}),hovertemplate:s({},{keys:[\"label\",\"color\",\"value\",\"percent\",\"text\"]}),texttemplate:l({editType:\"plot\"},{keys:[\"label\",\"color\",\"value\",\"percent\",\"text\"]}),textposition:{valType:\"enumerated\",values:[\"inside\",\"outside\",\"auto\",\"none\"],dflt:\"auto\",arrayOk:!0,editType:\"plot\"},textfont:c({},u,{}),insidetextorientation:{valType:\"enumerated\",values:[\"horizontal\",\"radial\",\"tangential\",\"auto\"],dflt:\"auto\",editType:\"plot\"},insidetextfont:c({},u,{}),outsidetextfont:c({},u,{}),automargin:{valType:\"boolean\",dflt:!1,editType:\"plot\"},title:{text:{valType:\"string\",dflt:\"\",editType:\"plot\"},font:c({},u,{}),position:{valType:\"enumerated\",values:[\"top 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right\"],editType:\"calc\"}}}},{\"../../components/color/attributes\":661,\"../../lib/extend\":789,\"../../plots/attributes\":845,\"../../plots/domain\":877,\"../../plots/font_attributes\":878,\"../../plots/template_attributes\":922}],1189:[function(t,e,r){\"use strict\";var n=t(\"../../plots/plots\");r.name=\"pie\",r.plot=function(t,e,i,a){n.plotBasePlot(r.name,t,e,i,a)},r.clean=function(t,e,i,a){n.cleanBasePlot(r.name,t,e,i,a)}},{\"../../plots/plots\":913}],1190:[function(t,e,r){\"use strict\";var n=t(\"fast-isnumeric\"),i=t(\"tinycolor2\"),a=t(\"../../components/color\"),o={};function s(t){return function(e,r){return!!e&&(!!(e=i(e)).isValid()&&(e=a.addOpacity(e,e.getAlpha()),t[r]||(t[r]=e),e))}}function l(t,e){var r,n=JSON.stringify(t),a=e[n];if(!a){for(a=t.slice(),r=0;r<t.length;r++)a.push(i(t[r]).lighten(20).toHexString());for(r=0;r<t.length;r++)a.push(i(t[r]).darken(20).toHexString());e[n]=a}return a}e.exports={calc:function(t,e){var r,i,a=[],o=t._fullLayout,l=o.hiddenlabels||[],c=e.labels,u=e.marker.colors||[],f=e.values,h=e._length,p=e._hasValues&&h;if(e.dlabel)for(c=new Array(h),r=0;r<h;r++)c[r]=String(e.label0+r*e.dlabel);var d={},m=s(o[\"_\"+e.type+\"colormap\"]),g=0,v=!1;for(r=0;r<h;r++){var y,x,b;if(p){if(y=f[r],!n(y))continue;if((y=+y)<0)continue}else y=1;void 0!==(x=c[r])&&\"\"!==x||(x=r);var _=d[x=String(x)];void 0===_?(d[x]=a.length,(b=-1!==l.indexOf(x))||(g+=y),a.push({v:y,label:x,color:m(u[r],x),i:r,pts:[r],hidden:b})):(v=!0,(i=a[_]).v+=y,i.pts.push(r),i.hidden||(g+=y),!1===i.color&&u[r]&&(i.color=m(u[r],x)))}return(\"funnelarea\"===e.type?v:e.sort)&&a.sort((function(t,e){return e.v-t.v})),a[0]&&(a[0].vTotal=g),a},crossTraceCalc:function(t,e){var r=(e||{}).type;r||(r=\"pie\");var n=t._fullLayout,i=t.calcdata,a=n[r+\"colorway\"],s=n[\"_\"+r+\"colormap\"];n[\"extend\"+r+\"colors\"]&&(a=l(a,o));for(var c=0,u=0;u<i.length;u++){var f=i[u];if(f[0].trace.type===r)for(var h=0;h<f.length;h++){var p=f[h];!1===p.color&&(s[p.label]?p.color=s[p.label]:(s[p.label]=p.color=a[c%a.length],c++))}}},makePullColorFn:s,generateExtendedColors:l}},{\"../../components/color\":662,\"fast-isnumeric\":245,tinycolor2:594}],1191:[function(t,e,r){\"use strict\";var n=t(\"fast-isnumeric\"),i=t(\"../../lib\"),a=t(\"./attributes\"),o=t(\"../../plots/domain\").defaults,s=t(\"../bar/defaults\").handleText;function l(t,e){var r=Array.isArray(t),a=i.isArrayOrTypedArray(e),o=Math.min(r?t.length:1/0,a?e.length:1/0);if(isFinite(o)||(o=0),o&&a){for(var s,l=0;l<o;l++){var c=e[l];if(n(c)&&c>0){s=!0;break}}s||(o=0)}return{hasLabels:r,hasValues:a,len:o}}e.exports={handleLabelsAndValues:l,supplyDefaults:function(t,e,r,n){function c(r,n){return i.coerce(t,e,a,r,n)}var u=l(c(\"labels\"),c(\"values\")),f=u.len;if(e._hasLabels=u.hasLabels,e._hasValues=u.hasValues,!e._hasLabels&&e._hasValues&&(c(\"label0\"),c(\"dlabel\")),f){e._length=f,c(\"marker.line.width\")&&c(\"marker.line.color\"),c(\"marker.colors\"),c(\"scalegroup\");var h,p=c(\"text\"),d=c(\"texttemplate\");if(d||(h=c(\"textinfo\",Array.isArray(p)?\"text+percent\":\"percent\")),c(\"hovertext\"),c(\"hovertemplate\"),d||h&&\"none\"!==h){var m=c(\"textposition\");s(t,e,n,c,m,{moduleHasSelected:!1,moduleHasUnselected:!1,moduleHasConstrain:!1,moduleHasCliponaxis:!1,moduleHasTextangle:!1,moduleHasInsideanchor:!1}),(Array.isArray(m)||\"auto\"===m||\"outside\"===m)&&c(\"automargin\"),(\"inside\"===m||\"auto\"===m||Array.isArray(m))&&c(\"insidetextorientation\")}o(e,n,c);var g=c(\"hole\");if(c(\"title.text\")){var v=c(\"title.position\",g?\"middle center\":\"top center\");g||\"middle center\"!==v||(e.title.position=\"top center\"),i.coerceFont(c,\"title.font\",n.font)}c(\"sort\"),c(\"direction\"),c(\"rotation\"),c(\"pull\")}else e.visible=!1}}},{\"../../lib\":799,\"../../plots/domain\":877,\"../bar/defaults\":941,\"./attributes\":1188,\"fast-isnumeric\":245}],1192:[function(t,e,r){\"use strict\";var n=t(\"../../components/fx/helpers\").appendArrayMultiPointValues;e.exports=function(t,e){var r={curveNumber:e.index,pointNumbers:t.pts,data:e._input,fullData:e,label:t.label,color:t.color,value:t.v,percent:t.percent,text:t.text,v:t.v};return 1===t.pts.length&&(r.pointNumber=r.i=t.pts[0]),n(r,e,t.pts),\"funnelarea\"===e.type&&(delete r.v,delete r.i),r}},{\"../../components/fx/helpers\":698}],1193:[function(t,e,r){\"use strict\";var n=t(\"../../lib\");function i(t){return-1!==t.indexOf(\"e\")?t.replace(/[.]?0+e/,\"e\"):-1!==t.indexOf(\".\")?t.replace(/[.]?0+$/,\"\"):t}r.formatPiePercent=function(t,e){var r=i((100*t).toPrecision(3));return n.numSeparate(r,e)+\"%\"},r.formatPieValue=function(t,e){var r=i(t.toPrecision(10));return n.numSeparate(r,e)},r.getFirstFilled=function(t,e){if(Array.isArray(t))for(var r=0;r<e.length;r++){var n=t[e[r]];if(n||0===n||\"\"===n)return n}},r.castOption=function(t,e){return Array.isArray(t)?r.getFirstFilled(t,e):t||void 0},r.getRotationAngle=function(t){return(\"auto\"===t?0:t)*Math.PI/180}},{\"../../lib\":799}],1194:[function(t,e,r){\"use strict\";e.exports={attributes:t(\"./attributes\"),supplyDefaults:t(\"./defaults\").supplyDefaults,supplyLayoutDefaults:t(\"./layout_defaults\"),layoutAttributes:t(\"./layout_attributes\"),calc:t(\"./calc\").calc,crossTraceCalc:t(\"./calc\").crossTraceCalc,plot:t(\"./plot\").plot,style:t(\"./style\"),styleOne:t(\"./style_one\"),moduleType:\"trace\",name:\"pie\",basePlotModule:t(\"./base_plot\"),categories:[\"pie-like\",\"pie\",\"showLegend\"],meta:{}}},{\"./attributes\":1188,\"./base_plot\":1189,\"./calc\":1190,\"./defaults\":1191,\"./layout_attributes\":1195,\"./layout_defaults\":1196,\"./plot\":1197,\"./style\":1198,\"./style_one\":1199}],1195:[function(t,e,r){\"use strict\";e.exports={hiddenlabels:{valType:\"data_array\",editType:\"calc\"},piecolorway:{valType:\"colorlist\",editType:\"calc\"},extendpiecolors:{valType:\"boolean\",dflt:!0,editType:\"calc\"}}},{}],1196:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"./layout_attributes\");e.exports=function(t,e){function r(r,a){return n.coerce(t,e,i,r,a)}r(\"hiddenlabels\"),r(\"piecolorway\",e.colorway),r(\"extendpiecolors\")}},{\"../../lib\":799,\"./layout_attributes\":1195}],1197:[function(t,e,r){\"use strict\";var n=t(\"@plotly/d3\"),i=t(\"../../plots/plots\"),a=t(\"../../components/fx\"),o=t(\"../../components/color\"),s=t(\"../../components/drawing\"),l=t(\"../../lib\"),c=l.strScale,u=l.strTranslate,f=t(\"../../lib/svg_text_utils\"),h=t(\"../bar/uniform_text\"),p=h.recordMinTextSize,d=h.clearMinTextSize,m=t(\"../bar/constants\").TEXTPAD,g=t(\"./helpers\"),v=t(\"./event_data\"),y=t(\"../../lib\").isValidTextValue;function x(t,e,r){var i=r[0],o=i.trace,s=i.cx,c=i.cy;\"_hasHoverLabel\"in o||(o._hasHoverLabel=!1),\"_hasHoverEvent\"in o||(o._hasHoverEvent=!1),t.on(\"mouseover\",(function(t){var r=e._fullLayout,u=e._fullData[o.index];if(!e._dragging&&!1!==r.hovermode){var f=u.hoverinfo;if(Array.isArray(f)&&(f=a.castHoverinfo({hoverinfo:[g.castOption(f,t.pts)],_module:o._module},r,0)),\"all\"===f&&(f=\"label+text+value+percent+name\"),u.hovertemplate||\"none\"!==f&&\"skip\"!==f&&f){var h=t.rInscribed||0,p=s+t.pxmid[0]*(1-h),d=c+t.pxmid[1]*(1-h),m=r.separators,y=[];if(f&&-1!==f.indexOf(\"label\")&&y.push(t.label),t.text=g.castOption(u.hovertext||u.text,t.pts),f&&-1!==f.indexOf(\"text\")){var x=t.text;l.isValidTextValue(x)&&y.push(x)}t.value=t.v,t.valueLabel=g.formatPieValue(t.v,m),f&&-1!==f.indexOf(\"value\")&&y.push(t.valueLabel),t.percent=t.v/i.vTotal,t.percentLabel=g.formatPiePercent(t.percent,m),f&&-1!==f.indexOf(\"percent\")&&y.push(t.percentLabel);var b=u.hoverlabel,_=b.font;a.loneHover({trace:o,x0:p-h*i.r,x1:p+h*i.r,y:d,text:y.join(\"<br>\"),name:u.hovertemplate||-1!==f.indexOf(\"name\")?u.name:void 0,idealAlign:t.pxmid[0]<0?\"left\":\"right\",color:g.castOption(b.bgcolor,t.pts)||t.color,borderColor:g.castOption(b.bordercolor,t.pts),fontFamily:g.castOption(_.family,t.pts),fontSize:g.castOption(_.size,t.pts),fontColor:g.castOption(_.color,t.pts),nameLength:g.castOption(b.namelength,t.pts),textAlign:g.castOption(b.align,t.pts),hovertemplate:g.castOption(u.hovertemplate,t.pts),hovertemplateLabels:t,eventData:[v(t,u)]},{container:r._hoverlayer.node(),outerContainer:r._paper.node(),gd:e}),o._hasHoverLabel=!0}o._hasHoverEvent=!0,e.emit(\"plotly_hover\",{points:[v(t,u)],event:n.event})}})),t.on(\"mouseout\",(function(t){var r=e._fullLayout,i=e._fullData[o.index],s=n.select(this).datum();o._hasHoverEvent&&(t.originalEvent=n.event,e.emit(\"plotly_unhover\",{points:[v(s,i)],event:n.event}),o._hasHoverEvent=!1),o._hasHoverLabel&&(a.loneUnhover(r._hoverlayer.node()),o._hasHoverLabel=!1)})),t.on(\"click\",(function(t){var r=e._fullLayout,i=e._fullData[o.index];e._dragging||!1===r.hovermode||(e._hoverdata=[v(t,i)],a.click(e,n.event))}))}function b(t,e,r){var n=g.castOption(t.insidetextfont.color,e.pts);!n&&t._input.textfont&&(n=g.castOption(t._input.textfont.color,e.pts));var i=g.castOption(t.insidetextfont.family,e.pts)||g.castOption(t.textfont.family,e.pts)||r.family,a=g.castOption(t.insidetextfont.size,e.pts)||g.castOption(t.textfont.size,e.pts)||r.size;return{color:n||o.contrast(e.color),family:i,size:a}}function _(t,e){for(var r,n,i=0;i<t.length;i++)if((n=(r=t[i][0]).trace).title.text){var a=n.title.text;n._meta&&(a=l.templateString(a,n._meta));var o=s.tester.append(\"text\").attr(\"data-notex\",1).text(a).call(s.font,n.title.font).call(f.convertToTspans,e),c=s.bBox(o.node(),!0);r.titleBox={width:c.width,height:c.height},o.remove()}}function w(t,e,r){var n=r.r||e.rpx1,i=e.rInscribed;if(e.startangle===e.stopangle)return{rCenter:1-i,scale:0,rotate:0,textPosAngle:0};var a,o=e.ring,s=1===o&&Math.abs(e.startangle-e.stopangle)===2*Math.PI,l=e.halfangle,c=e.midangle,u=r.trace.insidetextorientation,f=\"horizontal\"===u,h=\"tangential\"===u,p=\"radial\"===u,d=\"auto\"===u,m=[];if(!d){var g,v=function(r,i){if(function(t,e){var r=t.startangle,n=t.stopangle;return r>e&&e>n||r<e&&e<n}(e,r)){var s=Math.abs(r-e.startangle),l=Math.abs(r-e.stopangle),c=s<l?s:l;(a=\"tan\"===i?k(t,n,o,c,0):T(t,n,o,c,Math.PI/2)).textPosAngle=r,m.push(a)}};if(f||h){for(g=4;g>=-4;g-=2)v(Math.PI*g,\"tan\");for(g=4;g>=-4;g-=2)v(Math.PI*(g+1),\"tan\")}if(f||p){for(g=4;g>=-4;g-=2)v(Math.PI*(g+1.5),\"rad\");for(g=4;g>=-4;g-=2)v(Math.PI*(g+.5),\"rad\")}}if(s||d||f){var y=Math.sqrt(t.width*t.width+t.height*t.height);if((a={scale:i*n*2/y,rCenter:1-i,rotate:0}).textPosAngle=(e.startangle+e.stopangle)/2,a.scale>=1)return a;m.push(a)}(d||p)&&((a=T(t,n,o,l,c)).textPosAngle=(e.startangle+e.stopangle)/2,m.push(a)),(d||h)&&((a=k(t,n,o,l,c)).textPosAngle=(e.startangle+e.stopangle)/2,m.push(a));for(var x=0,b=0,_=0;_<m.length;_++){var w=m[_].scale;if(b<w&&(b=w,x=_),!d&&b>=1)break}return m[x]}function T(t,e,r,n,i){e=Math.max(0,e-2*m);var a=t.width/t.height,o=S(a,n,e,r);return{scale:2*o/t.height,rCenter:M(a,o/e),rotate:A(i)}}function k(t,e,r,n,i){e=Math.max(0,e-2*m);var a=t.height/t.width,o=S(a,n,e,r);return{scale:2*o/t.width,rCenter:M(a,o/e),rotate:A(i+Math.PI/2)}}function M(t,e){return Math.cos(e)-t*e}function A(t){return(180/Math.PI*t+720)%180-90}function S(t,e,r,n){var i=t+1/(2*Math.tan(e));return r*Math.min(1/(Math.sqrt(i*i+.5)+i),n/(Math.sqrt(t*t+n/2)+t))}function E(t,e){return t.v!==e.vTotal||e.trace.hole?Math.min(1/(1+1/Math.sin(t.halfangle)),t.ring/2):1}function L(t,e){var r=e.pxmid[0],n=e.pxmid[1],i=t.width/2,a=t.height/2;return r<0&&(i*=-1),n<0&&(a*=-1),{scale:1,rCenter:1,rotate:0,x:i+Math.abs(a)*(i>0?1:-1)/2,y:a/(1+r*r/(n*n)),outside:!0}}function C(t,e){var r,n,i,a=t.trace,o={x:t.cx,y:t.cy},s={tx:0,ty:0};s.ty+=a.title.font.size,i=I(a),-1!==a.title.position.indexOf(\"top\")?(o.y-=(1+i)*t.r,s.ty-=t.titleBox.height):-1!==a.title.position.indexOf(\"bottom\")&&(o.y+=(1+i)*t.r);var l,c,u=(l=t.r,c=t.trace.aspectratio,l/(void 0===c?1:c)),f=e.w*(a.domain.x[1]-a.domain.x[0])/2;return-1!==a.title.position.indexOf(\"left\")?(f+=u,o.x-=(1+i)*u,s.tx+=t.titleBox.width/2):-1!==a.title.position.indexOf(\"center\")?f*=2:-1!==a.title.position.indexOf(\"right\")&&(f+=u,o.x+=(1+i)*u,s.tx-=t.titleBox.width/2),r=f/t.titleBox.width,n=P(t,e)/t.titleBox.height,{x:o.x,y:o.y,scale:Math.min(r,n),tx:s.tx,ty:s.ty}}function P(t,e){var r=t.trace,n=e.h*(r.domain.y[1]-r.domain.y[0]);return Math.min(t.titleBox.height,n/2)}function I(t){var e,r=t.pull;if(!r)return 0;if(Array.isArray(r))for(r=0,e=0;e<t.pull.length;e++)t.pull[e]>r&&(r=t.pull[e]);return r}function O(t,e){for(var r=[],n=0;n<t.length;n++){var i=t[n][0],a=i.trace,o=a.domain,s=e.w*(o.x[1]-o.x[0]),l=e.h*(o.y[1]-o.y[0]);a.title.text&&\"middle center\"!==a.title.position&&(l-=P(i,e));var c=s/2,u=l/2;\"funnelarea\"!==a.type||a.scalegroup||(u/=a.aspectratio),i.r=Math.min(c,u)/(1+I(a)),i.cx=e.l+e.w*(a.domain.x[1]+a.domain.x[0])/2,i.cy=e.t+e.h*(1-a.domain.y[0])-l/2,a.title.text&&-1!==a.title.position.indexOf(\"bottom\")&&(i.cy-=P(i,e)),a.scalegroup&&-1===r.indexOf(a.scalegroup)&&r.push(a.scalegroup)}!function(t,e){for(var r,n,i,a=0;a<e.length;a++){var o=1/0,s=e[a];for(n=0;n<t.length;n++)if(r=t[n][0],(i=r.trace).scalegroup===s){var l;if(\"pie\"===i.type)l=r.r*r.r;else if(\"funnelarea\"===i.type){var c,u;i.aspectratio>1?(c=r.r,u=c/i.aspectratio):(u=r.r,c=u*i.aspectratio),c*=(1+i.baseratio)/2,l=c*u}o=Math.min(o,l/r.vTotal)}for(n=0;n<t.length;n++)if(r=t[n][0],(i=r.trace).scalegroup===s){var f=o*r.vTotal;\"funnelarea\"===i.type&&(f/=(1+i.baseratio)/2,f/=i.aspectratio),r.r=Math.sqrt(f)}}}(t,r)}function z(t,e){return[t*Math.sin(e),-t*Math.cos(e)]}function D(t,e,r){var n=t._fullLayout,i=r.trace,a=i.texttemplate,o=i.textinfo;if(!a&&o&&\"none\"!==o){var s,c=o.split(\"+\"),u=function(t){return-1!==c.indexOf(t)},f=u(\"label\"),h=u(\"text\"),p=u(\"value\"),d=u(\"percent\"),m=n.separators;if(s=f?[e.label]:[],h){var v=g.getFirstFilled(i.text,e.pts);y(v)&&s.push(v)}p&&s.push(g.formatPieValue(e.v,m)),d&&s.push(g.formatPiePercent(e.v/r.vTotal,m)),e.text=s.join(\"<br>\")}if(a){var x=l.castOption(i,e.i,\"texttemplate\");if(x){var b=function(t){return{label:t.label,value:t.v,valueLabel:g.formatPieValue(t.v,n.separators),percent:t.v/r.vTotal,percentLabel:g.formatPiePercent(t.v/r.vTotal,n.separators),color:t.color,text:t.text,customdata:l.castOption(i,t.i,\"customdata\")}}(e),_=g.getFirstFilled(i.text,e.pts);(y(_)||\"\"===_)&&(b.text=_),e.text=l.texttemplateString(x,b,t._fullLayout._d3locale,b,i._meta||{})}else e.text=\"\"}}function R(t,e){var r=t.rotate*Math.PI/180,n=Math.cos(r),i=Math.sin(r),a=(e.left+e.right)/2,o=(e.top+e.bottom)/2;t.textX=a*n-o*i,t.textY=a*i+o*n,t.noCenter=!0}e.exports={plot:function(t,e){var r=t._fullLayout,a=r._size;d(\"pie\",r),_(e,t),O(e,a);var h=l.makeTraceGroups(r._pielayer,e,\"trace\").each((function(e){var h=n.select(this),d=e[0],m=d.trace;!function(t){var e,r,n,i=t[0],a=i.r,o=i.trace,s=g.getRotationAngle(o.rotation),l=2*Math.PI/i.vTotal,c=\"px0\",u=\"px1\";if(\"counterclockwise\"===o.direction){for(e=0;e<t.length&&t[e].hidden;e++);if(e===t.length)return;s+=l*t[e].v,l*=-1,c=\"px1\",u=\"px0\"}for(n=z(a,s),e=0;e<t.length;e++)(r=t[e]).hidden||(r[c]=n,r.startangle=s,s+=l*r.v/2,r.pxmid=z(a,s),r.midangle=s,s+=l*r.v/2,n=z(a,s),r.stopangle=s,r[u]=n,r.largeArc=r.v>i.vTotal/2?1:0,r.halfangle=Math.PI*Math.min(r.v/i.vTotal,.5),r.ring=1-o.hole,r.rInscribed=E(r,i))}(e),h.attr(\"stroke-linejoin\",\"round\"),h.each((function(){var v=n.select(this).selectAll(\"g.slice\").data(e);v.enter().append(\"g\").classed(\"slice\",!0),v.exit().remove();var y=[[[],[]],[[],[]]],_=!1;v.each((function(i,a){if(i.hidden)n.select(this).selectAll(\"path,g\").remove();else{i.pointNumber=i.i,i.curveNumber=m.index,y[i.pxmid[1]<0?0:1][i.pxmid[0]<0?0:1].push(i);var o=d.cx,c=d.cy,u=n.select(this),h=u.selectAll(\"path.surface\").data([i]);if(h.enter().append(\"path\").classed(\"surface\",!0).style({\"pointer-events\":\"all\"}),u.call(x,t,e),m.pull){var v=+g.castOption(m.pull,i.pts)||0;v>0&&(o+=v*i.pxmid[0],c+=v*i.pxmid[1])}i.cxFinal=o,i.cyFinal=c;var T=m.hole;if(i.v===d.vTotal){var k=\"M\"+(o+i.px0[0])+\",\"+(c+i.px0[1])+C(i.px0,i.pxmid,!0,1)+C(i.pxmid,i.px0,!0,1)+\"Z\";T?h.attr(\"d\",\"M\"+(o+T*i.px0[0])+\",\"+(c+T*i.px0[1])+C(i.px0,i.pxmid,!1,T)+C(i.pxmid,i.px0,!1,T)+\"Z\"+k):h.attr(\"d\",k)}else{var M=C(i.px0,i.px1,!0,1);if(T){var A=1-T;h.attr(\"d\",\"M\"+(o+T*i.px1[0])+\",\"+(c+T*i.px1[1])+C(i.px1,i.px0,!1,T)+\"l\"+A*i.px0[0]+\",\"+A*i.px0[1]+M+\"Z\")}else h.attr(\"d\",\"M\"+o+\",\"+c+\"l\"+i.px0[0]+\",\"+i.px0[1]+M+\"Z\")}D(t,i,d);var S=g.castOption(m.textposition,i.pts),E=u.selectAll(\"g.slicetext\").data(i.text&&\"none\"!==S?[0]:[]);E.enter().append(\"g\").classed(\"slicetext\",!0),E.exit().remove(),E.each((function(){var u=l.ensureSingle(n.select(this),\"text\",\"\",(function(t){t.attr(\"data-notex\",1)})),h=l.ensureUniformFontSize(t,\"outside\"===S?function(t,e,r){var n=g.castOption(t.outsidetextfont.color,e.pts)||g.castOption(t.textfont.color,e.pts)||r.color,i=g.castOption(t.outsidetextfont.family,e.pts)||g.castOption(t.textfont.family,e.pts)||r.family,a=g.castOption(t.outsidetextfont.size,e.pts)||g.castOption(t.textfont.size,e.pts)||r.size;return{color:n,family:i,size:a}}(m,i,r.font):b(m,i,r.font));u.text(i.text).attr({class:\"slicetext\",transform:\"\",\"text-anchor\":\"middle\"}).call(s.font,h).call(f.convertToTspans,t);var v,y=s.bBox(u.node());if(\"outside\"===S)v=L(y,i);else if(v=w(y,i,d),\"auto\"===S&&v.scale<1){var x=l.ensureUniformFontSize(t,m.outsidetextfont);u.call(s.font,x),v=L(y=s.bBox(u.node()),i)}var T=v.textPosAngle,k=void 0===T?i.pxmid:z(d.r,T);if(v.targetX=o+k[0]*v.rCenter+(v.x||0),v.targetY=c+k[1]*v.rCenter+(v.y||0),R(v,y),v.outside){var M=v.targetY;i.yLabelMin=M-y.height/2,i.yLabelMid=M,i.yLabelMax=M+y.height/2,i.labelExtraX=0,i.labelExtraY=0,_=!0}v.fontSize=h.size,p(m.type,v,r),e[a].transform=v,u.attr(\"transform\",l.getTextTransform(v))}))}function C(t,e,r,n){var a=n*(e[0]-t[0]),o=n*(e[1]-t[1]);return\"a\"+n*d.r+\",\"+n*d.r+\" 0 \"+i.largeArc+(r?\" 1 \":\" 0 \")+a+\",\"+o}}));var T=n.select(this).selectAll(\"g.titletext\").data(m.title.text?[0]:[]);if(T.enter().append(\"g\").classed(\"titletext\",!0),T.exit().remove(),T.each((function(){var e,r=l.ensureSingle(n.select(this),\"text\",\"\",(function(t){t.attr(\"data-notex\",1)})),i=m.title.text;m._meta&&(i=l.templateString(i,m._meta)),r.text(i).attr({class:\"titletext\",transform:\"\",\"text-anchor\":\"middle\"}).call(s.font,m.title.font).call(f.convertToTspans,t),e=\"middle center\"===m.title.position?function(t){var e=Math.sqrt(t.titleBox.width*t.titleBox.width+t.titleBox.height*t.titleBox.height);return{x:t.cx,y:t.cy,scale:t.trace.hole*t.r*2/e,tx:0,ty:-t.titleBox.height/2+t.trace.title.font.size}}(d):C(d,a),r.attr(\"transform\",u(e.x,e.y)+c(Math.min(1,e.scale))+u(e.tx,e.ty))})),_&&function(t,e){var r,n,i,a,o,s,l,c,u,f,h,p,d;function m(t,e){return t.pxmid[1]-e.pxmid[1]}function v(t,e){return e.pxmid[1]-t.pxmid[1]}function y(t,r){r||(r={});var 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n=t(\"../scatter/attributes\");e.exports={x:n.x,y:n.y,xy:{valType:\"data_array\",editType:\"calc\"},indices:{valType:\"data_array\",editType:\"calc\"},xbounds:{valType:\"data_array\",editType:\"calc\"},ybounds:{valType:\"data_array\",editType:\"calc\"},text:n.text,marker:{color:{valType:\"color\",arrayOk:!1,editType:\"calc\"},opacity:{valType:\"number\",min:0,max:1,dflt:1,arrayOk:!1,editType:\"calc\"},blend:{valType:\"boolean\",dflt:null,editType:\"calc\"},sizemin:{valType:\"number\",min:.1,max:2,dflt:.5,editType:\"calc\"},sizemax:{valType:\"number\",min:.1,dflt:20,editType:\"calc\"},border:{color:{valType:\"color\",arrayOk:!1,editType:\"calc\"},arearatio:{valType:\"number\",min:0,max:1,dflt:0,editType:\"calc\"},editType:\"calc\"},editType:\"calc\"},transforms:void 0}},{\"../scatter/attributes\":1214}],1201:[function(t,e,r){\"use strict\";var n=t(\"gl-pointcloud2d\"),i=t(\"../../lib/str2rgbarray\"),a=t(\"../../plots/cartesian/autorange\").findExtremes,o=t(\"../scatter/get_trace_color\");function s(t,e){this.scene=t,this.uid=e,this.type=\"pointcloud\",this.pickXData=[],this.pickYData=[],this.xData=[],this.yData=[],this.textLabels=[],this.color=\"rgb(0, 0, 0)\",this.name=\"\",this.hoverinfo=\"all\",this.idToIndex=new Int32Array(0),this.bounds=[0,0,0,0],this.pointcloudOptions={positions:new Float32Array(0),idToIndex:this.idToIndex,sizemin:.5,sizemax:12,color:[0,0,0,1],areaRatio:1,borderColor:[0,0,0,1]},this.pointcloud=n(t.glplot,this.pointcloudOptions),this.pointcloud._trace=this}var l=s.prototype;l.handlePick=function(t){var e=this.idToIndex[t.pointId];return{trace:this,dataCoord:t.dataCoord,traceCoord:this.pickXYData?[this.pickXYData[2*e],this.pickXYData[2*e+1]]:[this.pickXData[e],this.pickYData[e]],textLabel:Array.isArray(this.textLabels)?this.textLabels[e]:this.textLabels,color:this.color,name:this.name,pointIndex:e,hoverinfo:this.hoverinfo}},l.update=function(t){this.index=t.index,this.textLabels=t.text,this.name=t.name,this.hoverinfo=t.hoverinfo,this.bounds=[1/0,1/0,-1/0,-1/0],this.updateFast(t),this.color=o(t,{})},l.updateFast=function(t){var e,r,n,o,s,l,c=this.xData=this.pickXData=t.x,u=this.yData=this.pickYData=t.y,f=this.pickXYData=t.xy,h=t.xbounds&&t.ybounds,p=t.indices,d=this.bounds;if(f){if(n=f,e=f.length>>>1,h)d[0]=t.xbounds[0],d[2]=t.xbounds[1],d[1]=t.ybounds[0],d[3]=t.ybounds[1];else for(l=0;l<e;l++)o=n[2*l],s=n[2*l+1],o<d[0]&&(d[0]=o),o>d[2]&&(d[2]=o),s<d[1]&&(d[1]=s),s>d[3]&&(d[3]=s);if(p)r=p;else for(r=new Int32Array(e),l=0;l<e;l++)r[l]=l}else for(e=c.length,n=new Float32Array(2*e),r=new 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r.update(e),r}},{\"../../lib/str2rgbarray\":823,\"../../plots/cartesian/autorange\":848,\"../scatter/get_trace_color\":1224,\"gl-pointcloud2d\":324}],1202:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"./attributes\");e.exports=function(t,e,r){function a(r,a){return n.coerce(t,e,i,r,a)}a(\"x\"),a(\"y\"),a(\"xbounds\"),a(\"ybounds\"),t.xy&&t.xy instanceof Float32Array&&(e.xy=t.xy),t.indices&&t.indices instanceof Int32Array&&(e.indices=t.indices),a(\"text\"),a(\"marker.color\",r),a(\"marker.opacity\"),a(\"marker.blend\"),a(\"marker.sizemin\"),a(\"marker.sizemax\"),a(\"marker.border.color\",r),a(\"marker.border.arearatio\"),e._length=null}},{\"../../lib\":799,\"./attributes\":1200}],1203:[function(t,e,r){\"use strict\";[\"*pointcloud* trace is deprecated!\",\"Please consider switching to the *scattergl* trace type.\"].join(\" 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n=t(\"../../plots/font_attributes\"),i=t(\"../../plots/attributes\"),a=t(\"../../components/color/attributes\"),o=t(\"../../components/fx/attributes\"),s=t(\"../../plots/domain\").attributes,l=t(\"../../plots/template_attributes\").hovertemplateAttrs,c=t(\"../../components/colorscale/attributes\"),u=t(\"../../plot_api/plot_template\").templatedArray,f=t(\"../../plots/cartesian/axis_format_attributes\").descriptionOnlyNumbers,h=t(\"../../lib/extend\").extendFlat,p=t(\"../../plot_api/edit_types\").overrideAll;(e.exports=p({hoverinfo:h({},i.hoverinfo,{flags:[],arrayOk:!1}),hoverlabel:o.hoverlabel,domain:s({name:\"sankey\",trace:!0}),orientation:{valType:\"enumerated\",values:[\"v\",\"h\"],dflt:\"h\"},valueformat:{valType:\"string\",dflt:\".3s\",description:f(\"value\")},valuesuffix:{valType:\"string\",dflt:\"\"},arrangement:{valType:\"enumerated\",values:[\"snap\",\"perpendicular\",\"freeform\",\"fixed\"],dflt:\"snap\"},textfont:n({}),customdata:void 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h={_id:\"x\",c2p:u.identity,_offset:r._sankey.translateX,_length:r._sankey.width},p={_id:\"y\",c2p:u.identity,_offset:r._sankey.translateY,_length:r._sankey.height},d={gd:t,element:o.node(),plotinfo:{id:e,xaxis:h,yaxis:p,fillRangeItems:u.noop},subplot:e,xaxes:[h],yaxes:[p],doneFnCompleted:function(r){var n,i=t._fullData[e],a=i.node.groups.slice(),o=[];function s(t){for(var e=i._sankey.graph.nodes,r=0;r<e.length;r++)if(e[r].pointNumber===t)return e[r]}for(var l=0;l<r.length;l++){var c=s(r[l].pointNumber);if(c)if(c.group){for(var u=0;u<c.childrenNodes.length;u++)o.push(c.childrenNodes[u].pointNumber);a[c.pointNumber-i.node._count]=!1}else o.push(c.pointNumber)}n=a.filter(Boolean).concat([o]),f.call(\"_guiRestyle\",t,{\"node.groups\":[n]},e)},prepFn:function(t,e,r){c(t,e,r,d,i)}};l.init(d)}}r.name=\"sankey\",r.baseLayoutAttrOverrides=n({hoverlabel:o.hoverlabel},\"plot\",\"nested\"),r.plot=function(t){var e=i(t.calcdata,\"sankey\")[0];a(t,e),r.updateFx(t)},r.clean=function(t,e,r,n){var 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m=t._fullData[d].node;t._fullData[d]._viewInitial={node:{groups:m.groups.slice(),x:m.x.slice(),y:m.y.slice()}}}var _=c(t,\"source:\")+\" \",w=c(t,\"target:\")+\" \",T=c(t,\"concentration:\")+\" \",k=c(t,\"incoming flow count:\")+\" \",M=c(t,\"outgoing flow count:\")+\" \";i(t,s,e,{width:f.w,height:f.h,margin:{t:f.t,r:f.r,b:f.b,l:f.l}},{linkEvents:{hover:function(e,r,i){!1!==t._fullLayout.hovermode&&(n.select(e).call(y.bind(0,r,i,!0)),\"skip\"!==r.link.trace.link.hoverinfo&&(r.link.fullData=r.link.trace,t.emit(\"plotly_hover\",{event:n.event,points:[r.link]})))},follow:function(e,i){if(!1!==t._fullLayout.hovermode){var s=i.link.trace.link;if(\"none\"!==s.hoverinfo&&\"skip\"!==s.hoverinfo){for(var l=[],c=0,f=0;f<i.flow.links.length;f++){var d=i.flow.links[f];if(\"closest\"!==t._fullLayout.hovermode||i.link.pointNumber===d.pointNumber){i.link.pointNumber===d.pointNumber&&(c=f),d.fullData=d.trace,s=i.link.trace.link;var m=v(d),g={valueLabel:n.format(i.valueFormat)(d.value)+i.valueSuffix};l.push({x:m[0],y:m[1],name:g.valueLabel,text:[d.label||\"\",_+d.source.label,w+d.target.label,d.concentrationscale?T+n.format(\"%0.2f\")(d.flow.labelConcentration):\"\"].filter(u).join(\"<br>\"),color:b(s,\"bgcolor\")||o.addOpacity(d.color,1),borderColor:b(s,\"bordercolor\"),fontFamily:b(s,\"font.family\"),fontSize:b(s,\"font.size\"),fontColor:b(s,\"font.color\"),nameLength:b(s,\"namelength\"),textAlign:b(s,\"align\"),idealAlign:n.event.x<m[0]?\"right\":\"left\",hovertemplate:s.hovertemplate,hovertemplateLabels:g,eventData:[d]})}}a.loneHover(l,{container:r._hoverlayer.node(),outerContainer:r._paper.node(),gd:t,anchorIndex:c}).each((function(){i.link.concentrationscale||h(this,.65),p(this)}))}}function v(t){var e,r;t.circular?(e=(t.circularPathData.leftInnerExtent+t.circularPathData.rightInnerExtent)/2,r=t.circularPathData.verticalFullExtent):(e=(t.source.x1+t.target.x0)/2,r=(t.y0+t.y1)/2);var n=[e,r];return\"v\"===t.trace.orientation&&n.reverse(),n[0]+=i.parent.translateX,n[1]+=i.parent.translateY,n}},unhover:function(e,i,o){!1!==t._fullLayout.hovermode&&(n.select(e).call(x.bind(0,i,o,!0)),\"skip\"!==i.link.trace.link.hoverinfo&&(i.link.fullData=i.link.trace,t.emit(\"plotly_unhover\",{event:n.event,points:[i.link]})),a.loneUnhover(r._hoverlayer.node()))},select:function(e,r){var i=r.link;i.originalEvent=n.event,t._hoverdata=[i],a.click(t,{target:!0})}},nodeEvents:{hover:function(e,r,i){!1!==t._fullLayout.hovermode&&(n.select(e).call(g,r,i),\"skip\"!==r.node.trace.node.hoverinfo&&(r.node.fullData=r.node.trace,t.emit(\"plotly_hover\",{event:n.event,points:[r.node]})))},follow:function(e,i){if(!1!==t._fullLayout.hovermode){var o=i.node.trace.node;if(\"none\"!==o.hoverinfo&&\"skip\"!==o.hoverinfo){var s=n.select(e).select(\".\"+l.nodeRect),c=t._fullLayout._paperdiv.node().getBoundingClientRect(),f=s.node().getBoundingClientRect(),d=f.left-2-c.left,m=f.right+2-c.left,g=f.top+f.height/4-c.top,v={valueLabel:n.format(i.valueFormat)(i.node.value)+i.valueSuffix};i.node.fullData=i.node.trace,t._fullLayout._calcInverseTransform(t);var y=t._fullLayout._invScaleX,x=t._fullLayout._invScaleY,_=a.loneHover({x0:y*d,x1:y*m,y:x*g,name:n.format(i.valueFormat)(i.node.value)+i.valueSuffix,text:[i.node.label,k+i.node.targetLinks.length,M+i.node.sourceLinks.length].filter(u).join(\"<br>\"),color:b(o,\"bgcolor\")||i.tinyColorHue,borderColor:b(o,\"bordercolor\"),fontFamily:b(o,\"font.family\"),fontSize:b(o,\"font.size\"),fontColor:b(o,\"font.color\"),nameLength:b(o,\"namelength\"),textAlign:b(o,\"align\"),idealAlign:\"left\",hovertemplate:o.hovertemplate,hovertemplateLabels:v,eventData:[i.node]},{container:r._hoverlayer.node(),outerContainer:r._paper.node(),gd:t});h(_,.85),p(_)}}},unhover:function(e,i,o){!1!==t._fullLayout.hovermode&&(n.select(e).call(v,i,o),\"skip\"!==i.node.trace.node.hoverinfo&&(i.node.fullData=i.node.trace,t.emit(\"plotly_unhover\",{event:n.event,points:[i.node]})),a.loneUnhover(r._hoverlayer.node()))},select:function(e,r,i){var 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n,i=y(e),a=i.trace,u=a.domain,f=\"h\"===a.orientation,p=a.node.pad,d=a.node.thickness,m=t.width*(u.x[1]-u.x[0]),g=t.height*(u.y[1]-u.y[0]),v=i._nodes,x=i._links,b=i.circular;(n=b?s.sankeyCircular().circularLinkGap(0):o.sankey()).iterations(l.sankeyIterations).size(f?[m,g]:[g,m]).nodeWidth(d).nodePadding(p).nodeId((function(t){return t.pointNumber})).nodes(v).links(x);var _,w,T,k=n();for(var M in n.nodePadding()<p&&h.warn(\"node.pad was reduced to \",n.nodePadding(),\" to fit within the figure.\"),i._groupLookup){var A,S=parseInt(i._groupLookup[M]);for(_=0;_<k.nodes.length;_++)if(k.nodes[_].pointNumber===S){A=k.nodes[_];break}if(A){var E={pointNumber:parseInt(M),x0:A.x0,x1:A.x1,y0:A.y0,y1:A.y1,partOfGroup:!0,sourceLinks:[],targetLinks:[]};k.nodes.unshift(E),A.childrenNodes.unshift(E)}}if(function(){for(_=0;_<k.nodes.length;_++){var t,e,r=k.nodes[_],n={};for(w=0;w<r.targetLinks.length;w++)t=(e=r.targetLinks[w]).source.pointNumber+\":\"+e.target.pointNumber,n.hasOwnProperty(t)||(n[t]=[]),n[t].push(e);var i=Object.keys(n);for(w=0;w<i.length;w++){var a=n[t=i[w]],o=0,s={};for(T=0;T<a.length;T++)s[(e=a[T]).label]||(s[e.label]=0),s[e.label]+=e.value,o+=e.value;for(T=0;T<a.length;T++)(e=a[T]).flow={value:o,labelConcentration:s[e.label]/o,concentration:e.value/o,links:a},e.concentrationscale&&(e.color=c(e.concentrationscale(e.flow.labelConcentration)))}var l=0;for(w=0;w<r.sourceLinks.length;w++)l+=r.sourceLinks[w].value;for(w=0;w<r.sourceLinks.length;w++)(e=r.sourceLinks[w]).concentrationOut=e.value/l;var u=0;for(w=0;w<r.targetLinks.length;w++)u+=r.targetLinks[w].value;for(w=0;w<r.targetLinks.length;w++)(e=r.targetLinks[w]).concenrationIn=e.value/u}}(),a.node.x.length&&a.node.y.length){for(_=0;_<Math.min(a.node.x.length,a.node.y.length,k.nodes.length);_++)if(a.node.x[_]&&a.node.y[_]){var L=[a.node.x[_]*m,a.node.y[_]*g];k.nodes[_].x0=L[0]-d/2,k.nodes[_].x1=L[0]+d/2;var C=k.nodes[_].y1-k.nodes[_].y0;k.nodes[_].y0=L[1]-C/2,k.nodes[_].y1=L[1]+C/2}if(\"snap\"===a.arrangement)!function(t){t.forEach((function(t){var e,r,n,i=0,a=t.length;for(t.sort((function(t,e){return t.y0-e.y0})),n=0;n<a;++n)(e=t[n]).y0>=i||(r=i-e.y0)>1e-6&&(e.y0+=r,e.y1+=r),i=e.y1+p}))}(function(t){var e,r,n=t.map((function(t,e){return{x0:t.x0,index:e}})).sort((function(t,e){return t.x0-e.x0})),i=[],a=-1,o=-1/0;for(_=0;_<n.length;_++){var s=t[n[_].index];s.x0>o+d&&(a+=1,e=s.x0),o=s.x0,i[a]||(i[a]=[]),i[a].push(s),r=e-s.x0,s.x0+=r,s.x1+=r}return 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n=t(\"@plotly/d3\"),i=t(\"../../components/drawing\"),a=t(\"../../registry\");function o(t,e,r){i.pointStyle(t.selectAll(\"path.point\"),e,r)}function s(t,e,r){i.textPointStyle(t.selectAll(\"text\"),e,r)}e.exports={style:function(t){var e=n.select(t).selectAll(\"g.trace.scatter\");e.style(\"opacity\",(function(t){return t[0].trace.opacity})),e.selectAll(\"g.points\").each((function(e){o(n.select(this),e.trace||e[0].trace,t)})),e.selectAll(\"g.text\").each((function(e){s(n.select(this),e.trace||e[0].trace,t)})),e.selectAll(\"g.trace path.js-line\").call(i.lineGroupStyle),e.selectAll(\"g.trace path.js-fill\").call(i.fillGroupStyle),a.getComponentMethod(\"errorbars\",\"style\")(e)},stylePoints:o,styleText:s,styleOnSelect:function(t,e,r){var 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n=t(\"../../lib\"),i=t(\"../../registry\");e.exports=function(t,e,r,a){var o,s=a(\"x\"),l=a(\"y\");if(i.getComponentMethod(\"calendars\",\"handleTraceDefaults\")(t,e,[\"x\",\"y\"],r),s){var c=n.minRowLength(s);l?o=Math.min(c,n.minRowLength(l)):(o=c,a(\"y0\"),a(\"dy\"))}else{if(!l)return 0;o=n.minRowLength(l),a(\"x0\"),a(\"dx\")}return e._length=o,o}},{\"../../lib\":799,\"../../registry\":927}],1242:[function(t,e,r){\"use strict\";var n=t(\"../scatter/attributes\"),i=t(\"../../components/colorscale/attributes\"),a=t(\"../../plots/cartesian/axis_format_attributes\").axisHoverFormat,o=t(\"../../plots/template_attributes\").hovertemplateAttrs,s=t(\"../../plots/template_attributes\").texttemplateAttrs,l=t(\"../../plots/attributes\"),c=t(\"../../constants/gl3d_dashes\"),u=t(\"../../constants/gl3d_markers\"),f=t(\"../../lib/extend\").extendFlat,h=t(\"../../plot_api/edit_types\").overrideAll,p=n.line,d=n.marker,m=d.line,g=f({width:p.width,dash:{valType:\"enumerated\",values:Object.keys(c),dflt:\"solid\"}},i(\"line\"));var 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a=n.getComponentMethod(\"errorbars\",\"makeComputeError\")(e),o=new Array(t.length),s=0;s<t.length;s++){var l=a(+t[s],s);if(\"log\"===i.type){var c=i.c2l(t[s]),u=t[s]-l[0],f=t[s]+l[1];if(o[s]=[(i.c2l(u,!0)-c)*r,(i.c2l(f,!0)-c)*r],u>0){var h=i.c2l(u);i._lowerLogErrorBound||(i._lowerLogErrorBound=h),i._lowerErrorBound=Math.min(i._lowerLogErrorBound,h)}}else o[s]=[-l[0]*r,l[1]*r]}return o}e.exports=function(t,e,r){var n=[i(t.x,t.error_x,e[0],r.xaxis),i(t.y,t.error_y,e[1],r.yaxis),i(t.z,t.error_z,e[2],r.zaxis)],a=function(t){for(var e=0;e<t.length;e++)if(t[e])return t[e].length;return 0}(n);if(0===a)return null;for(var o=new Array(a),s=0;s<a;s++){for(var l=[[0,0,0],[0,0,0]],c=0;c<3;c++)if(n[c])for(var u=0;u<2;u++)l[u][c]=n[c][s][u];o[s]=l}return o}},{\"../../registry\":927}],1245:[function(t,e,r){\"use strict\";var n=t(\"gl-line3d\"),i=t(\"gl-scatter3d\"),a=t(\"gl-error3d\"),o=t(\"gl-mesh3d\"),s=t(\"delaunay-triangulate\"),l=t(\"../../lib\"),c=t(\"../../lib/str2rgbarray\"),u=t(\"../../lib/gl_format_color\").formatColor,f=t(\"../scatter/make_bubble_size_func\"),h=t(\"../../constants/gl3d_dashes\"),p=t(\"../../constants/gl3d_markers\"),d=t(\"../../plots/cartesian/axes\"),m=t(\"../../components/fx/helpers\").appendArrayPointValue,g=t(\"./calc_errors\");function v(t,e){this.scene=t,this.uid=e,this.linePlot=null,this.scatterPlot=null,this.errorBars=null,this.textMarkers=null,this.delaunayMesh=null,this.color=null,this.mode=\"\",this.dataPoints=[],this.axesBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.textLabels=null,this.data=null}var y=v.prototype;function x(t){return null==t?0:t.indexOf(\"left\")>-1?-1:t.indexOf(\"right\")>-1?1:0}function b(t){return null==t?0:t.indexOf(\"top\")>-1?-1:t.indexOf(\"bottom\")>-1?1:0}function _(t,e){return e(4*t)}function w(t){return p[t]}function T(t,e,r,n,i){var 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e=[0,0,0],r=[[0,0,0],[0,0,0],[0,0,0]],n=[1,1,1],i=0;i<3;i++){var a=t[i];a&&!1!==a.copy_zstyle&&!1!==t[2].visible&&(a=t[2]),a&&a.visible&&(e[i]=a.width/2,r[i]=c(a.color),n[i]=a.thickness)}return{capSize:e,color:r,lineWidth:n}}([e.error_x,e.error_y,e.error_z]);return a.errorColor=X.color,a.errorLineWidth=X.lineWidth,a.errorCapSize=X.capSize,a.delaunayAxis=e.surfaceaxis,a.delaunayColor=c(e.surfacecolor),a}function M(t){if(l.isArrayOrTypedArray(t)){var e=t[0];return l.isArrayOrTypedArray(e)&&(t=e),\"rgb(\"+t.slice(0,3).map((function(t){return Math.round(255*t)}))+\")\"}return null}function A(t){return l.isArrayOrTypedArray(t)?4===t.length&&\"number\"==typeof t[0]?M(t):t.map(M):null}y.handlePick=function(t){if(t.object&&(t.object===this.linePlot||t.object===this.delaunayMesh||t.object===this.textMarkers||t.object===this.scatterPlot)){var e=t.index=t.data.index;return t.object.highlight&&t.object.highlight(null),this.scatterPlot&&(t.object=this.scatterPlot,this.scatterPlot.highlight(t.data)),t.textLabel=\"\",this.textLabels&&(Array.isArray(this.textLabels)?(this.textLabels[e]||0===this.textLabels[e])&&(t.textLabel=this.textLabels[e]):t.textLabel=this.textLabels),t.traceCoordinate=[this.data.x[e],this.data.y[e],this.data.z[e]],!0}},y.update=function(t){var e,r,l,c,u=this.scene.glplot.gl,f=h.solid;this.data=t;var p=k(this.scene,t);\"mode\"in p&&(this.mode=p.mode),\"lineDashes\"in p&&p.lineDashes in 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d=t.opacity;if(t.marker&&t.marker.opacity&&(d*=t.marker.opacity),r={gl:this.scene.glplot.gl,position:p.position,color:p.scatterColor,size:p.scatterSize,glyph:p.scatterMarker,opacity:d,orthographic:!0,lineWidth:p.scatterLineWidth,lineColor:p.scatterLineColor,project:p.project,projectScale:p.projectScale,projectOpacity:p.projectOpacity},-1!==this.mode.indexOf(\"markers\")?this.scatterPlot?this.scatterPlot.update(r):(this.scatterPlot=i(r),this.scatterPlot._trace=this,this.scatterPlot.highlightScale=1,this.scene.glplot.add(this.scatterPlot)):this.scatterPlot&&(this.scene.glplot.remove(this.scatterPlot),this.scatterPlot.dispose(),this.scatterPlot=null),c={gl:this.scene.glplot.gl,position:p.position,glyph:p.text,color:p.textColor,size:p.textSize,angle:p.textAngle,alignment:p.textOffset,font:p.textFont,orthographic:!0,lineWidth:0,project:!1,opacity:t.opacity},this.textLabels=t.hovertext||t.text,-1!==this.mode.indexOf(\"text\")?this.textMarkers?this.textMarkers.update(c):(this.textMarkers=i(c),this.textMarkers._trace=this,this.textMarkers.highlightScale=1,this.scene.glplot.add(this.textMarkers)):this.textMarkers&&(this.scene.glplot.remove(this.textMarkers),this.textMarkers.dispose(),this.textMarkers=null),l={gl:this.scene.glplot.gl,position:p.position,color:p.errorColor,error:p.errorBounds,lineWidth:p.errorLineWidth,capSize:p.errorCapSize,opacity:t.opacity},this.errorBars?p.errorBounds?this.errorBars.update(l):(this.scene.glplot.remove(this.errorBars),this.errorBars.dispose(),this.errorBars=null):p.errorBounds&&(this.errorBars=a(l),this.errorBars._trace=this,this.scene.glplot.add(this.errorBars)),p.delaunayAxis>=0){var m=function(t,e,r){var n,i=(r+1)%3,a=(r+2)%3,o=[],l=[];for(n=0;n<t.length;++n){var c=t[n];!isNaN(c[i])&&isFinite(c[i])&&!isNaN(c[a])&&isFinite(c[a])&&(o.push([c[i],c[a]]),l.push(n))}var u=s(o);for(n=0;n<u.length;++n)for(var f=u[n],h=0;h<f.length;++h)f[h]=l[f[h]];return{positions:t,cells:u,meshColor:e}}(p.position,p.delaunayColor,p.delaunayAxis);m.opacity=t.opacity,this.delaunayMesh?this.delaunayMesh.update(m):(m.gl=u,this.delaunayMesh=o(m),this.delaunayMesh._trace=this,this.scene.glplot.add(this.delaunayMesh))}else this.delaunayMesh&&(this.scene.glplot.remove(this.delaunayMesh),this.delaunayMesh.dispose(),this.delaunayMesh=null)},y.dispose=function(){this.linePlot&&(this.scene.glplot.remove(this.linePlot),this.linePlot.dispose()),this.scatterPlot&&(this.scene.glplot.remove(this.scatterPlot),this.scatterPlot.dispose()),this.errorBars&&(this.scene.glplot.remove(this.errorBars),this.errorBars.dispose()),this.textMarkers&&(this.scene.glplot.remove(this.textMarkers),this.textMarkers.dispose()),this.delaunayMesh&&(this.scene.glplot.remove(this.delaunayMesh),this.delaunayMesh.dispose())},e.exports=function(t,e){var r=new v(t,e.uid);return r.update(e),r}},{\"../../components/fx/helpers\":698,\"../../constants/gl3d_dashes\":772,\"../../constants/gl3d_markers\":773,\"../../lib\":799,\"../../lib/gl_format_color\":795,\"../../lib/str2rgbarray\":823,\"../../plots/cartesian/axes\":849,\"../scatter/make_bubble_size_func\":1231,\"./calc_errors\":1244,\"delaunay-triangulate\":176,\"gl-error3d\":269,\"gl-line3d\":277,\"gl-mesh3d\":311,\"gl-scatter3d\":330}],1246:[function(t,e,r){\"use strict\";var n=t(\"../../registry\"),i=t(\"../../lib\"),a=t(\"../scatter/subtypes\"),o=t(\"../scatter/marker_defaults\"),s=t(\"../scatter/line_defaults\"),l=t(\"../scatter/text_defaults\"),c=t(\"./attributes\");e.exports=function(t,e,r,u){function f(r,n){return i.coerce(t,e,c,r,n)}if(function(t,e,r,i){var a=0,o=r(\"x\"),s=r(\"y\"),l=r(\"z\");n.getComponentMethod(\"calendars\",\"handleTraceDefaults\")(t,e,[\"x\",\"y\",\"z\"],i),o&&s&&l&&(a=Math.min(o.length,s.length,l.length),e._length=e._xlength=e._ylength=e._zlength=a);return 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n=t(\"../../lib\"),i=t(\"../scatter/constants\"),a=t(\"../scatter/subtypes\"),o=t(\"../scatter/marker_defaults\"),s=t(\"../scatter/line_defaults\"),l=t(\"../scatter/line_shape_defaults\"),c=t(\"../scatter/text_defaults\"),u=t(\"../scatter/fillcolor_defaults\"),f=t(\"./attributes\");e.exports=function(t,e,r,h){function p(r,i){return n.coerce(t,e,f,r,i)}p(\"carpet\"),e.xaxis=\"x\",e.yaxis=\"y\";var d=p(\"a\"),m=p(\"b\"),g=Math.min(d.length,m.length);if(g){e._length=g,p(\"text\"),p(\"texttemplate\"),p(\"hovertext\"),p(\"mode\",g<i.PTS_LINESONLY?\"lines+markers\":\"lines\"),a.hasLines(e)&&(s(t,e,r,h,p),l(t,e,p),p(\"connectgaps\")),a.hasMarkers(e)&&o(t,e,r,h,p,{gradient:!0}),a.hasText(e)&&c(t,e,h,p);var v=[];(a.hasMarkers(e)||a.hasText(e))&&(p(\"marker.maxdisplayed\"),v.push(\"points\")),p(\"fill\"),\"none\"!==e.fill&&(u(t,e,r,p),a.hasLines(e)||l(t,e,p)),\"tonext\"!==e.fill&&\"toself\"!==e.fill||v.push(\"fills\"),\"fills\"!==p(\"hoveron\",v.join(\"+\")||\"points\")&&p(\"hovertemplate\"),n.coerceSelectionMarkerOpacity(e,p)}else e.visible=!1}},{\"../../lib\":799,\"../scatter/constants\":1218,\"../scatter/fillcolor_defaults\":1222,\"../scatter/line_defaults\":1227,\"../scatter/line_shape_defaults\":1229,\"../scatter/marker_defaults\":1233,\"../scatter/subtypes\":1239,\"../scatter/text_defaults\":1240,\"./attributes\":1248}],1251:[function(t,e,r){\"use strict\";e.exports=function(t,e,r,n,i){var a=n[i];return t.a=a.a,t.b=a.b,t.y=a.y,t}},{}],1252:[function(t,e,r){\"use strict\";e.exports=function(t,e){var r={},n=e._carpet,i=n.ab2ij([t.a,t.b]),a=Math.floor(i[0]),o=i[0]-a,s=Math.floor(i[1]),l=i[1]-s,c=n.evalxy([],a,s,o,l);return r.yLabel=c[1].toFixed(3),r}},{}],1253:[function(t,e,r){\"use 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n=t(\"../scatter/plot\"),i=t(\"../../plots/cartesian/axes\"),a=t(\"../../components/drawing\");e.exports=function(t,e,r,o){var s,l,c,u=r[0][0].carpet,f={xaxis:i.getFromId(t,u.xaxis||\"x\"),yaxis:i.getFromId(t,u.yaxis||\"y\"),plot:e.plot};for(n(t,f,r,o),s=0;s<r.length;s++)l=r[s][0].trace,c=o.selectAll(\"g.trace\"+l.uid+\" .js-line\"),a.setClipUrl(c,r[s][0].carpet._clipPathId,t)}},{\"../../components/drawing\":684,\"../../plots/cartesian/axes\":849,\"../scatter/plot\":1235}],1256:[function(t,e,r){\"use strict\";var n=t(\"../../plots/template_attributes\").hovertemplateAttrs,i=t(\"../../plots/template_attributes\").texttemplateAttrs,a=t(\"../scatter/attributes\"),o=t(\"../../plots/attributes\"),s=t(\"../../components/colorscale/attributes\"),l=t(\"../../components/drawing/attributes\").dash,c=t(\"../../lib/extend\").extendFlat,u=t(\"../../plot_api/edit_types\").overrideAll,f=a.marker,h=a.line,p=f.line;e.exports=u({lon:{valType:\"data_array\"},lat:{valType:\"data_array\"},locations:{valType:\"data_array\"},locationmode:{valType:\"enumerated\",values:[\"ISO-3\",\"USA-states\",\"country 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n=t(\"../../lib\"),i=t(\"../scatter/subtypes\"),a=t(\"../scatter/marker_defaults\"),o=t(\"../scatter/line_defaults\"),s=t(\"../scatter/text_defaults\"),l=t(\"../scatter/fillcolor_defaults\"),c=t(\"./attributes\");e.exports=function(t,e,r,u){function f(r,i){return n.coerce(t,e,c,r,i)}var h,p=f(\"locations\");if(p&&p.length){var d,m=f(\"geojson\");(\"string\"==typeof m&&\"\"!==m||n.isPlainObject(m))&&(d=\"geojson-id\"),\"geojson-id\"===f(\"locationmode\",d)&&f(\"featureidkey\"),h=p.length}else{var g=f(\"lon\")||[],v=f(\"lat\")||[];h=Math.min(g.length,v.length)}h?(e._length=h,f(\"text\"),f(\"hovertext\"),f(\"hovertemplate\"),f(\"mode\"),i.hasLines(e)&&(o(t,e,r,u,f),f(\"connectgaps\")),i.hasMarkers(e)&&a(t,e,r,u,f,{gradient:!0}),i.hasText(e)&&(f(\"texttemplate\"),s(t,e,u,f)),f(\"fill\"),\"none\"!==e.fill&&l(t,e,r,f),n.coerceSelectionMarkerOpacity(e,f)):e.visible=!1}},{\"../../lib\":799,\"../scatter/fillcolor_defaults\":1222,\"../scatter/line_defaults\":1227,\"../scatter/marker_defaults\":1233,\"../scatter/subtypes\":1239,\"../scatter/text_defaults\":1240,\"./attributes\":1256}],1259:[function(t,e,r){\"use strict\";e.exports=function(t,e,r,n,i){t.lon=e.lon,t.lat=e.lat,t.location=e.loc?e.loc:null;var a=n[i];return a.fIn&&a.fIn.properties&&(t.properties=a.fIn.properties),t}},{}],1260:[function(t,e,r){\"use strict\";var n=t(\"../../plots/cartesian/axes\");e.exports=function(t,e,r){var i={},a=r[e.geo]._subplot.mockAxis,o=t.lonlat;return i.lonLabel=n.tickText(a,a.c2l(o[0]),!0).text,i.latLabel=n.tickText(a,a.c2l(o[1]),!0).text,i}},{\"../../plots/cartesian/axes\":849}],1261:[function(t,e,r){\"use strict\";var n=t(\"../../components/fx\"),i=t(\"../../constants/numerical\").BADNUM,a=t(\"../scatter/get_trace_color\"),o=t(\"../../lib\").fillText,s=t(\"./attributes\");e.exports=function(t,e,r){var l=t.cd,c=l[0].trace,u=t.xa,f=t.ya,h=t.subplot,p=h.projection.isLonLatOverEdges,d=h.project;if(n.getClosest(l,(function(t){var n=t.lonlat;if(n[0]===i)return 1/0;if(p(n))return 1/0;var a=d(n),o=d([e,r]),s=Math.abs(a[0]-o[0]),l=Math.abs(a[1]-o[1]),c=Math.max(3,t.mrc||0);return Math.max(Math.sqrt(s*s+l*l)-c,1-3/c)}),t),!1!==t.index){var m=l[t.index],g=m.lonlat,v=[u.c2p(g),f.c2p(g)],y=m.mrc||1;t.x0=v[0]-y,t.x1=v[0]+y,t.y0=v[1]-y,t.y1=v[1]+y,t.loc=m.loc,t.lon=g[0],t.lat=g[1];var x={};x[c.geo]={_subplot:h};var b=c._module.formatLabels(m,c,x);return t.lonLabel=b.lonLabel,t.latLabel=b.latLabel,t.color=a(c,m),t.extraText=function(t,e,r,n){if(t.hovertemplate)return;var i=e.hi||t.hoverinfo,a=\"all\"===i?s.hoverinfo.flags:i.split(\"+\"),l=-1!==a.indexOf(\"location\")&&Array.isArray(t.locations),c=-1!==a.indexOf(\"lon\"),u=-1!==a.indexOf(\"lat\"),f=-1!==a.indexOf(\"text\"),h=[];function p(t){return t+\"\\xb0\"}l?h.push(e.loc):c&&u?h.push(\"(\"+p(r.latLabel)+\", \"+p(r.lonLabel)+\")\"):c?h.push(n.lon+p(r.lonLabel)):u&&h.push(n.lat+p(r.latLabel));f&&o(e,t,h);return h.join(\"<br>\")}(c,m,t,l[0].t.labels),t.hovertemplate=c.hovertemplate,[t]}}},{\"../../components/fx\":702,\"../../constants/numerical\":775,\"../../lib\":799,\"../scatter/get_trace_color\":1224,\"./attributes\":1256}],1262:[function(t,e,r){\"use 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n=t(\"@plotly/d3\"),i=t(\"../../lib\"),a=t(\"../../lib/topojson_utils\").getTopojsonFeatures,o=t(\"../../lib/geojson_utils\"),s=t(\"../../lib/geo_location_utils\"),l=t(\"../../plots/cartesian/autorange\").findExtremes,c=t(\"../../constants/numerical\").BADNUM,u=t(\"../scatter/calc\").calcMarkerSize,f=t(\"../scatter/subtypes\"),h=t(\"./style\");e.exports={calcGeoJSON:function(t,e){var r,n,i=t[0].trace,o=e[i.geo],f=o._subplot,h=i._length;if(Array.isArray(i.locations)){var p=i.locationmode,d=\"geojson-id\"===p?s.extractTraceFeature(t):a(i,f.topojson);for(r=0;r<h;r++){n=t[r];var m=\"geojson-id\"===p?n.fOut:s.locationToFeature(p,n.loc,d);n.lonlat=m?m.properties.ct:[c,c]}}var g,v,y={padded:!0};if(\"geojson\"===o.fitbounds&&\"geojson-id\"===i.locationmode){var x=s.computeBbox(s.getTraceGeojson(i));g=[x[0],x[2]],v=[x[1],x[3]]}else{for(g=new Array(h),v=new Array(h),r=0;r<h;r++)n=t[r],g[r]=n.lonlat[0],v[r]=n.lonlat[1];y.ppad=u(i,h)}i._extremes.lon=l(o.lonaxis._ax,g,y),i._extremes.lat=l(o.lataxis._ax,v,y)},plot:function(t,e,r){var a=e.layers.frontplot.select(\".scatterlayer\"),s=i.makeTraceGroups(a,r,\"trace scattergeo\");function l(t,e){t.lonlat[0]===c&&n.select(e).remove()}s.selectAll(\"*\").remove(),s.each((function(e){var r=n.select(this),a=e[0].trace;if(f.hasLines(a)||\"none\"!==a.fill){var s=o.calcTraceToLineCoords(e),c=\"none\"!==a.fill?o.makePolygon(s):o.makeLine(s);r.selectAll(\"path.js-line\").data([{geojson:c,trace:a}]).enter().append(\"path\").classed(\"js-line\",!0).style(\"stroke-miterlimit\",2)}f.hasMarkers(a)&&r.selectAll(\"path.point\").data(i.identity).enter().append(\"path\").classed(\"point\",!0).each((function(t){l(t,this)})),f.hasText(a)&&r.selectAll(\"g\").data(i.identity).enter().append(\"g\").append(\"text\").each((function(t){l(t,this)})),h(t,e)}))}}},{\"../../constants/numerical\":775,\"../../lib\":799,\"../../lib/geo_location_utils\":792,\"../../lib/geojson_utils\":793,\"../../lib/topojson_utils\":827,\"../../plots/cartesian/autorange\":848,\"../scatter/calc\":1215,\"../scatter/subtypes\":1239,\"./style\":1265,\"@plotly/d3\":58}],1264:[function(t,e,r){\"use strict\";var n=t(\"../scatter/subtypes\"),i=t(\"../../constants/numerical\").BADNUM;e.exports=function(t,e){var 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n=t(\"../../lib\"),i=t(\"../../registry\"),a=t(\"./helpers\"),o=t(\"./attributes\"),s=t(\"../scatter/constants\"),l=t(\"../scatter/subtypes\"),c=t(\"../scatter/xy_defaults\"),u=t(\"../scatter/period_defaults\"),f=t(\"../scatter/marker_defaults\"),h=t(\"../scatter/line_defaults\"),p=t(\"../scatter/fillcolor_defaults\"),d=t(\"../scatter/text_defaults\");e.exports=function(t,e,r,m){function g(r,i){return n.coerce(t,e,o,r,i)}var v=!!t.marker&&a.isOpenSymbol(t.marker.symbol),y=l.isBubble(t),x=c(t,e,m,g);if(x){u(t,e,m,g),g(\"xhoverformat\"),g(\"yhoverformat\");var b=x<s.PTS_LINESONLY?\"lines+markers\":\"lines\";g(\"text\"),g(\"hovertext\"),g(\"hovertemplate\"),g(\"mode\",b),l.hasLines(e)&&(g(\"connectgaps\"),h(t,e,r,m,g),g(\"line.shape\")),l.hasMarkers(e)&&(f(t,e,r,m,g),g(\"marker.line.width\",v||y?1:0)),l.hasText(e)&&(g(\"texttemplate\"),d(t,e,m,g));var _=(e.line||{}).color,w=(e.marker||{}).color;g(\"fill\"),\"none\"!==e.fill&&p(t,e,r,g);var T=i.getComponentMethod(\"errorbars\",\"supplyDefaults\");T(t,e,_||w||r,{axis:\"y\"}),T(t,e,_||w||r,{axis:\"x\",inherit:\"y\"}),n.coerceSelectionMarkerOpacity(e,g)}else e.visible=!1}},{\"../../lib\":799,\"../../registry\":927,\"../scatter/constants\":1218,\"../scatter/fillcolor_defaults\":1222,\"../scatter/line_defaults\":1227,\"../scatter/marker_defaults\":1233,\"../scatter/period_defaults\":1234,\"../scatter/subtypes\":1239,\"../scatter/text_defaults\":1240,\"../scatter/xy_defaults\":1241,\"./attributes\":1266,\"./helpers\":1273}],1271:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../../components/color\"),a=t(\"../../constants/interactions\").DESELECTDIM;e.exports={styleTextSelection:function(t){var e,r,o=t[0],s=o.trace,l=o.t,c=l._scene,u=l.index,f=c.selectBatch[u],h=c.unselectBatch[u],p=c.textOptions[u],d=c.textSelectedOptions[u]||{},m=c.textUnselectedOptions[u]||{},g=n.extendFlat({},p);if(f.length||h.length){var v=d.color,y=m.color,x=p.color,b=Array.isArray(x);for(g.color=new Array(s._length),e=0;e<f.length;e++)r=f[e],g.color[r]=v||(b?x[r]:x);for(e=0;e<h.length;e++){r=h[e];var _=b?x[r]:x;g.color[r]=y||(v?_:i.addOpacity(_,a))}}c.glText[u].update(g)}}},{\"../../components/color\":662,\"../../constants/interactions\":774,\"../../lib\":799}],1272:[function(t,e,r){\"use strict\";var n=t(\"../scatter/format_labels\");e.exports=function(t,e,r){var i=t.i;return\"x\"in t||(t.x=e._x[i]),\"y\"in t||(t.y=e._y[i]),n(t,e,r)}},{\"../scatter/format_labels\":1223}],1273:[function(t,e,r){\"use strict\";var n=t(\"./constants\");r.isOpenSymbol=function(t){return\"string\"==typeof t?n.OPEN_RE.test(t):t%200>100},r.isDotSymbol=function(t){return\"string\"==typeof t?n.DOT_RE.test(t):t>200}},{\"./constants\":1268}],1274:[function(t,e,r){\"use strict\";var n=t(\"../../registry\"),i=t(\"../../lib\"),a=t(\"../scatter/get_trace_color\");function o(t,e,r,o){var s=t.xa,l=t.ya,c=t.distance,u=t.dxy,f=t.index,h={pointNumber:f,x:e[f],y:r[f]};h.tx=Array.isArray(o.text)?o.text[f]:o.text,h.htx=Array.isArray(o.hovertext)?o.hovertext[f]:o.hovertext,h.data=Array.isArray(o.customdata)?o.customdata[f]:o.customdata,h.tp=Array.isArray(o.textposition)?o.textposition[f]:o.textposition;var p=o.textfont;p&&(h.ts=i.isArrayOrTypedArray(p.size)?p.size[f]:p.size,h.tc=Array.isArray(p.color)?p.color[f]:p.color,h.tf=Array.isArray(p.family)?p.family[f]:p.family);var d=o.marker;d&&(h.ms=i.isArrayOrTypedArray(d.size)?d.size[f]:d.size,h.mo=i.isArrayOrTypedArray(d.opacity)?d.opacity[f]:d.opacity,h.mx=i.isArrayOrTypedArray(d.symbol)?d.symbol[f]:d.symbol,h.mc=i.isArrayOrTypedArray(d.color)?d.color[f]:d.color);var m=d&&d.line;m&&(h.mlc=Array.isArray(m.color)?m.color[f]:m.color,h.mlw=i.isArrayOrTypedArray(m.width)?m.width[f]:m.width);var g=d&&d.gradient;g&&\"none\"!==g.type&&(h.mgt=Array.isArray(g.type)?g.type[f]:g.type,h.mgc=Array.isArray(g.color)?g.color[f]:g.color);var v=s.c2p(h.x,!0),y=l.c2p(h.y,!0),x=h.mrc||1,b=o.hoverlabel;b&&(h.hbg=Array.isArray(b.bgcolor)?b.bgcolor[f]:b.bgcolor,h.hbc=Array.isArray(b.bordercolor)?b.bordercolor[f]:b.bordercolor,h.hts=i.isArrayOrTypedArray(b.font.size)?b.font.size[f]:b.font.size,h.htc=Array.isArray(b.font.color)?b.font.color[f]:b.font.color,h.htf=Array.isArray(b.font.family)?b.font.family[f]:b.font.family,h.hnl=i.isArrayOrTypedArray(b.namelength)?b.namelength[f]:b.namelength);var _=o.hoverinfo;_&&(h.hi=Array.isArray(_)?_[f]:_);var w=o.hovertemplate;w&&(h.ht=Array.isArray(w)?w[f]:w);var T={};T[t.index]=h;var k=o._origX,M=o._origY,A=i.extendFlat({},t,{color:a(o,h),x0:v-x,x1:v+x,xLabelVal:k?k[f]:h.x,y0:y-x,y1:y+x,yLabelVal:M?M[f]:h.y,cd:T,distance:c,spikeDistance:u,hovertemplate:h.ht});return h.htx?A.text=h.htx:h.tx?A.text=h.tx:o.text&&(A.text=o.text),i.fillText(h,o,A),n.getComponentMethod(\"errorbars\",\"hoverInfo\")(h,o,A),A}e.exports={hoverPoints:function(t,e,r,n){var i,a,s,l,c,u,f,h,p,d=t.cd,m=d[0].t,g=d[0].trace,v=t.xa,y=t.ya,x=m.x,b=m.y,_=v.c2p(e),w=y.c2p(r),T=t.distance;if(m.tree){var k=v.p2c(_-T),M=v.p2c(_+T),A=y.p2c(w-T),S=y.p2c(w+T);i=\"x\"===n?m.tree.range(Math.min(k,M),Math.min(y._rl[0],y._rl[1]),Math.max(k,M),Math.max(y._rl[0],y._rl[1])):m.tree.range(Math.min(k,M),Math.min(A,S),Math.max(k,M),Math.max(A,S))}else i=m.ids;var E=T;if(\"x\"===n)for(c=0;c<i.length;c++)s=x[i[c]],u=Math.abs(v.c2p(s)-_),g._origX&&void 0!==g._origX[c]&&(u+=v.c2p(g._origX[c])-v.c2p(s)),u<E&&(E=u,f=y.c2p(b[i[c]])-w,g._origY&&void 0!==g._origY[c]&&(f+=y.c2p(g._origY[c])-y.c2p(l)),p=Math.sqrt(u*u+f*f),a=i[c]);else for(c=i.length-1;c>-1;c--)s=x[i[c]],l=b[i[c]],u=v.c2p(s)-_,f=y.c2p(l)-w,(h=Math.sqrt(u*u+f*f))<E&&(E=p=h,a=i[c]);return t.index=a,t.distance=E,t.dxy=p,void 0===a?[t]:[o(t,x,b,g)]},calcHover:o}},{\"../../lib\":799,\"../../registry\":927,\"../scatter/get_trace_color\":1224}],1275:[function(t,e,r){\"use strict\";var n=t(\"./hover\");e.exports={moduleType:\"trace\",name:\"scattergl\",basePlotModule:t(\"../../plots/cartesian\"),categories:[\"gl\",\"regl\",\"cartesian\",\"symbols\",\"errorBarsOK\",\"showLegend\",\"scatter-like\"],attributes:t(\"./attributes\"),supplyDefaults:t(\"./defaults\"),crossTraceDefaults:t(\"../scatter/cross_trace_defaults\"),colorbar:t(\"../scatter/marker_colorbar\"),formatLabels:t(\"./format_labels\"),calc:t(\"./calc\"),plot:t(\"./plot\"),hoverPoints:n.hoverPoints,selectPoints:t(\"./select\"),meta:{}}},{\"../../plots/cartesian\":863,\"../scatter/cross_trace_defaults\":1220,\"../scatter/marker_colorbar\":1232,\"./attributes\":1266,\"./calc\":1267,\"./defaults\":1270,\"./format_labels\":1272,\"./hover\":1274,\"./plot\":1276,\"./select\":1278}],1276:[function(t,e,r){\"use strict\";var n=t(\"regl-scatter2d\"),i=t(\"regl-line2d\"),a=t(\"regl-error2d\"),o=t(\"gl-text\"),s=t(\"../../lib\"),l=t(\"../../components/dragelement/helpers\").selectMode,c=t(\"../../lib/prepare_regl\"),u=t(\"../scatter/subtypes\"),f=t(\"../scatter/link_traces\"),h=t(\"./edit_style\").styleTextSelection;function p(t,e,r){var n=t._size,i=t.width,a=t.height;return[n.l+e.domain[0]*n.w,n.b+r.domain[0]*n.h,i-n.r-(1-e.domain[1])*n.w,a-n.t-(1-r.domain[1])*n.h]}e.exports=function(t,e,r){if(r.length){var d,m,g=t._fullLayout,v=e._scene,y=e.xaxis,x=e.yaxis;if(v)if(c(t,[\"ANGLE_instanced_arrays\",\"OES_element_index_uint\"])){var b=v.count,_=g._glcanvas.data()[0].regl;if(f(t,e,r),v.dirty){if(!0===v.error2d&&(v.error2d=a(_)),!0===v.line2d&&(v.line2d=i(_)),!0===v.scatter2d&&(v.scatter2d=n(_,{constPointSize:!0})),!0===v.fill2d&&(v.fill2d=i(_)),!0===v.glText)for(v.glText=new Array(b),d=0;d<b;d++)v.glText[d]=new o(_);if(v.glText){if(b>v.glText.length){var w=b-v.glText.length;for(d=0;d<w;d++)v.glText.push(new o(_))}else if(b<v.glText.length){var T=v.glText.length-b;v.glText.splice(b,T).forEach((function(t){t.destroy()}))}for(d=0;d<b;d++)v.glText[d].update(v.textOptions[d])}if(v.line2d&&(v.line2d.update(v.lineOptions),v.lineOptions=v.lineOptions.map((function(t){if(t&&t.positions){for(var e=t.positions,r=0;r<e.length&&(isNaN(e[r])||isNaN(e[r+1]));)r+=2;for(var n=e.length-2;n>r&&(isNaN(e[n])||isNaN(e[n+1]));)n-=2;t.positions=e.slice(r,n+2)}return t})),v.line2d.update(v.lineOptions)),v.error2d){var k=(v.errorXOptions||[]).concat(v.errorYOptions||[]);v.error2d.update(k)}v.scatter2d&&v.scatter2d.update(v.markerOptions),v.fillOrder=s.repeat(null,b),v.fill2d&&(v.fillOptions=v.fillOptions.map((function(t,e){var n=r[e];if(t&&n&&n[0]&&n[0].trace){var i,a,o=n[0],s=o.trace,l=o.t,c=v.lineOptions[e],u=[];s._ownfill&&u.push(e),s._nexttrace&&u.push(e+1),u.length&&(v.fillOrder[e]=u);var f,h,p=[],d=c&&c.positions||l.positions;if(\"tozeroy\"===s.fill){for(f=0;f<d.length&&isNaN(d[f+1]);)f+=2;for(h=d.length-2;h>f&&isNaN(d[h+1]);)h-=2;0!==d[f+1]&&(p=[d[f],0]),p=p.concat(d.slice(f,h+2)),0!==d[h+1]&&(p=p.concat([d[h],0]))}else if(\"tozerox\"===s.fill){for(f=0;f<d.length&&isNaN(d[f]);)f+=2;for(h=d.length-2;h>f&&isNaN(d[h]);)h-=2;0!==d[f]&&(p=[0,d[f+1]]),p=p.concat(d.slice(f,h+2)),0!==d[h]&&(p=p.concat([0,d[h+1]]))}else if(\"toself\"===s.fill||\"tonext\"===s.fill){for(p=[],i=0,t.splitNull=!0,a=0;a<d.length;a+=2)(isNaN(d[a])||isNaN(d[a+1]))&&((p=p.concat(d.slice(i,a))).push(d[i],d[i+1]),p.push(null,null),i=a+2);p=p.concat(d.slice(i)),i&&p.push(d[i],d[i+1])}else{var m=s._nexttrace;if(m){var g=v.lineOptions[e+1];if(g){var y=g.positions;if(\"tonexty\"===s.fill){for(p=d.slice(),e=Math.floor(y.length/2);e--;){var x=y[2*e],b=y[2*e+1];isNaN(x)||isNaN(b)||p.push(x,b)}t.fill=m.fillcolor}}}}if(s._prevtrace&&\"tonext\"===s._prevtrace.fill){var _=v.lineOptions[e-1].positions,w=p.length/2,T=[i=w];for(a=0;a<_.length;a+=2)(isNaN(_[a])||isNaN(_[a+1]))&&(T.push(a/2+w+1),i=a+2);p=p.concat(_),t.hole=T}return t.fillmode=s.fill,t.opacity=s.opacity,t.positions=p,t}})),v.fill2d.update(v.fillOptions))}var M=g.dragmode,A=l(M),S=g.clickmode.indexOf(\"select\")>-1;for(d=0;d<b;d++){var E=r[d][0],L=E.trace,C=E.t,P=C.index,I=L._length,O=C.x,z=C.y;if(L.selectedpoints||A||S){if(A||(A=!0),L.selectedpoints){var D=v.selectBatch[P]=s.selIndices2selPoints(L),R={};for(m=0;m<D.length;m++)R[D[m]]=1;var F=[];for(m=0;m<I;m++)R[m]||F.push(m);v.unselectBatch[P]=F}var B=C.xpx=new Array(I),N=C.ypx=new Array(I);for(m=0;m<I;m++)B[m]=y.c2p(O[m]),N[m]=x.c2p(z[m])}else C.xpx=C.ypx=null}if(A){if(v.select2d||(v.select2d=n(g._glcanvas.data()[1].regl)),v.scatter2d){var j=new Array(b);for(d=0;d<b;d++)j[d]=v.selectBatch[d].length||v.unselectBatch[d].length?v.markerUnselectedOptions[d]:{};v.scatter2d.update(j)}v.select2d&&(v.select2d.update(v.markerOptions),v.select2d.update(v.markerSelectedOptions)),v.glText&&r.forEach((function(t){var e=((t||[])[0]||{}).trace||{};u.hasText(e)&&h(t)}))}else v.scatter2d&&v.scatter2d.update(v.markerOptions);var U={viewport:p(g,y,x),range:[(y._rl||y.range)[0],(x._rl||x.range)[0],(y._rl||y.range)[1],(x._rl||x.range)[1]]},V=s.repeat(U,v.count);v.fill2d&&v.fill2d.update(V),v.line2d&&v.line2d.update(V),v.error2d&&v.error2d.update(V.concat(V)),v.scatter2d&&v.scatter2d.update(V),v.select2d&&v.select2d.update(V),v.glText&&v.glText.forEach((function(t){t.update(U)}))}else v.init()}}},{\"../../components/dragelement/helpers\":680,\"../../lib\":799,\"../../lib/prepare_regl\":812,\"../scatter/link_traces\":1230,\"../scatter/subtypes\":1239,\"./edit_style\":1271,\"gl-text\":351,\"regl-error2d\":532,\"regl-line2d\":533,\"regl-scatter2d\":534}],1277:[function(t,e,r){\"use strict\";var n=t(\"../../lib\");e.exports=function(t,e){var r=e._scene,i={count:0,dirty:!0,lineOptions:[],fillOptions:[],markerOptions:[],markerSelectedOptions:[],markerUnselectedOptions:[],errorXOptions:[],errorYOptions:[],textOptions:[],textSelectedOptions:[],textUnselectedOptions:[],selectBatch:[],unselectBatch:[]},a={fill2d:!1,scatter2d:!1,error2d:!1,line2d:!1,glText:!1,select2d:!1};return e._scene||((r=e._scene={}).init=function(){n.extendFlat(r,a,i)},r.init(),r.update=function(t){var e=n.repeat(t,r.count);if(r.fill2d&&r.fill2d.update(e),r.scatter2d&&r.scatter2d.update(e),r.line2d&&r.line2d.update(e),r.error2d&&r.error2d.update(e.concat(e)),r.select2d&&r.select2d.update(e),r.glText)for(var i=0;i<r.count;i++)r.glText[i].update(t)},r.draw=function(){for(var t=r.count,e=r.fill2d,i=r.error2d,a=r.line2d,o=r.scatter2d,s=r.glText,l=r.select2d,c=r.selectBatch,u=r.unselectBatch,f=0;f<t;f++){if(e&&r.fillOrder[f]&&e.draw(r.fillOrder[f]),a&&r.lineOptions[f]&&a.draw(f),i&&(r.errorXOptions[f]&&i.draw(f),r.errorYOptions[f]&&i.draw(f+t)),o&&r.markerOptions[f])if(u[f].length){var h=n.repeat([],r.count);h[f]=u[f],o.draw(h)}else c[f].length||o.draw(f);s[f]&&r.textOptions[f]&&s[f].render()}l&&l.draw(c),r.dirty=!1},r.destroy=function(){r.fill2d&&r.fill2d.destroy&&r.fill2d.destroy(),r.scatter2d&&r.scatter2d.destroy&&r.scatter2d.destroy(),r.error2d&&r.error2d.destroy&&r.error2d.destroy(),r.line2d&&r.line2d.destroy&&r.line2d.destroy(),r.select2d&&r.select2d.destroy&&r.select2d.destroy(),r.glText&&r.glText.forEach((function(t){t.destroy&&t.destroy()})),r.lineOptions=null,r.fillOptions=null,r.markerOptions=null,r.markerSelectedOptions=null,r.markerUnselectedOptions=null,r.errorXOptions=null,r.errorYOptions=null,r.textOptions=null,r.textSelectedOptions=null,r.textUnselectedOptions=null,r.selectBatch=null,r.unselectBatch=null,e._scene=null}),r.dirty||n.extendFlat(r,i),r}},{\"../../lib\":799}],1278:[function(t,e,r){\"use strict\";var n=t(\"../scatter/subtypes\"),i=t(\"./edit_style\").styleTextSelection;e.exports=function(t,e){var r=t.cd,a=t.xaxis,o=t.yaxis,s=[],l=r[0].trace,c=r[0].t,u=l._length,f=c.x,h=c.y,p=c._scene,d=c.index;if(!p)return s;var m=n.hasText(l),g=n.hasMarkers(l),v=!g&&!m;if(!0!==l.visible||v)return s;var y=[],x=[];if(!1!==e&&!e.degenerate)for(var b=0;b<u;b++)e.contains([c.xpx[b],c.ypx[b]],!1,b,t)?(y.push(b),s.push({pointNumber:b,x:a.c2d(f[b]),y:o.c2d(h[b])})):x.push(b);if(g){var _=p.scatter2d;if(y.length||x.length){if(!p.selectBatch[d].length&&!p.unselectBatch[d].length){var w=new Array(p.count);w[d]=p.markerUnselectedOptions[d],_.update.apply(_,w)}}else{var T=new Array(p.count);T[d]=p.markerOptions[d],_.update.apply(_,T)}}return p.selectBatch[d]=y,p.unselectBatch[d]=x,m&&i(r),s}},{\"../scatter/subtypes\":1239,\"./edit_style\":1271}],1279:[function(t,e,r){\"use strict\";var n=t(\"../../plots/template_attributes\").hovertemplateAttrs,i=t(\"../../plots/template_attributes\").texttemplateAttrs,a=t(\"../scattergeo/attributes\"),o=t(\"../scatter/attributes\"),s=t(\"../../plots/mapbox/layout_attributes\"),l=t(\"../../plots/attributes\"),c=t(\"../../components/colorscale/attributes\"),u=t(\"../../lib/extend\").extendFlat,f=t(\"../../plot_api/edit_types\").overrideAll,h=a.line,p=a.marker;e.exports=f({lon:a.lon,lat:a.lat,mode:u({},o.mode,{dflt:\"markers\"}),text:u({},o.text,{}),texttemplate:i({editType:\"plot\"},{keys:[\"lat\",\"lon\",\"text\"]}),hovertext:u({},o.hovertext,{}),line:{color:h.color,width:h.width},connectgaps:o.connectgaps,marker:u({symbol:{valType:\"string\",dflt:\"circle\",arrayOk:!0},angle:{valType:\"number\",dflt:\"auto\",arrayOk:!0},allowoverlap:{valType:\"boolean\",dflt:!1},opacity:p.opacity,size:p.size,sizeref:p.sizeref,sizemin:p.sizemin,sizemode:p.sizemode},c(\"marker\")),fill:a.fill,fillcolor:o.fillcolor,textfont:s.layers.symbol.textfont,textposition:s.layers.symbol.textposition,below:{valType:\"string\"},selected:{marker:o.selected.marker},unselected:{marker:o.unselected.marker},hoverinfo:u({},l.hoverinfo,{flags:[\"lon\",\"lat\",\"text\",\"name\"]}),hovertemplate:n()},\"calc\",\"nested\")},{\"../../components/colorscale/attributes\":669,\"../../lib/extend\":789,\"../../plot_api/edit_types\":831,\"../../plots/attributes\":845,\"../../plots/mapbox/layout_attributes\":909,\"../../plots/template_attributes\":922,\"../scatter/attributes\":1214,\"../scattergeo/attributes\":1256}],1280:[function(t,e,r){\"use strict\";var n=t(\"fast-isnumeric\"),i=t(\"../../lib\"),a=t(\"../../constants/numerical\").BADNUM,o=t(\"../../lib/geojson_utils\"),s=t(\"../../components/colorscale\"),l=t(\"../../components/drawing\"),c=t(\"../scatter/make_bubble_size_func\"),u=t(\"../scatter/subtypes\"),f=t(\"../../plots/mapbox/convert_text_opts\"),h=t(\"../../components/fx/helpers\").appendArrayPointValue,p=t(\"../../lib/svg_text_utils\").NEWLINES,d=t(\"../../lib/svg_text_utils\").BR_TAG_ALL;function m(){return{geojson:o.makeBlank(),layout:{visibility:\"none\"},paint:{}}}function g(t,e){return i.isArrayOrTypedArray(t)?e?function(e){return n(t[e])?+t[e]:0}:function(e){return t[e]}:t?function(){return t}:v}function v(){return\"\"}function y(t){return t[0]===a}e.exports=function(t,e){var 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n=t(\"../../lib\"),i=t(\"../scatter/subtypes\"),a=t(\"../scatter/marker_defaults\"),o=t(\"../scatter/line_defaults\"),s=t(\"../scatter/line_shape_defaults\"),l=t(\"../scatter/text_defaults\"),c=t(\"../scatter/fillcolor_defaults\"),u=t(\"../scatter/constants\").PTS_LINESONLY,f=t(\"./attributes\");function h(t,e,r,n){var i,a=n(\"r\"),o=n(\"theta\");if(a)o?i=Math.min(a.length,o.length):(i=a.length,n(\"theta0\"),n(\"dtheta\"));else{if(!o)return 0;i=e.theta.length,n(\"r0\"),n(\"dr\")}return e._length=i,i}e.exports={handleRThetaDefaults:h,supplyDefaults:function(t,e,r,p){function d(r,i){return n.coerce(t,e,f,r,i)}var m=h(t,e,p,d);if(m){d(\"thetaunit\"),d(\"mode\",m<u?\"lines+markers\":\"lines\"),d(\"text\"),d(\"hovertext\"),\"fills\"!==e.hoveron&&d(\"hovertemplate\"),i.hasLines(e)&&(o(t,e,r,p,d),s(t,e,d),d(\"connectgaps\")),i.hasMarkers(e)&&a(t,e,r,p,d,{gradient:!0}),i.hasText(e)&&(d(\"texttemplate\"),l(t,e,p,d));var g=[];(i.hasMarkers(e)||i.hasText(e))&&(d(\"cliponaxis\"),d(\"marker.maxdisplayed\"),g.push(\"points\")),d(\"fill\"),\"none\"!==e.fill&&(c(t,e,r,d),i.hasLines(e)||s(t,e,d)),\"tonext\"!==e.fill&&\"toself\"!==e.fill||g.push(\"fills\"),d(\"hoveron\",g.join(\"+\")||\"points\"),n.coerceSelectionMarkerOpacity(e,d)}else e.visible=!1}}},{\"../../lib\":799,\"../scatter/constants\":1218,\"../scatter/fillcolor_defaults\":1222,\"../scatter/line_defaults\":1227,\"../scatter/line_shape_defaults\":1229,\"../scatter/marker_defaults\":1233,\"../scatter/subtypes\":1239,\"../scatter/text_defaults\":1240,\"./attributes\":1288}],1291:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../../plots/cartesian/axes\");e.exports=function(t,e,r){var a,o,s={},l=r[e.subplot]._subplot;l?(a=l.radialAxis,o=l.angularAxis):(a=(l=r[e.subplot]).radialaxis,o=l.angularaxis);var c=a.c2l(t.r);s.rLabel=i.tickText(a,c,!0).text;var u=\"degrees\"===o.thetaunit?n.rad2deg(t.theta):t.theta;return 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i=r._fullLayout,a=r._fullData[d.index],s=n.select(this).datum();if(d._hasHoverEvent&&(e.originalEvent=n.event,r.emit(\"plotly_unhover\",{points:[f(s,a,h.eventDataKeys)],event:n.event}),d._hasHoverEvent=!1),d._hasHoverLabel&&(o.loneUnhover(i._hoverlayer.node()),d._hasHoverLabel=!1),v){var l=t.select(\"path.surface\");h.styleOne(l,s,a,{hovered:!1})}})),t.on(\"click\",(function(t){var e=r._fullLayout,a=r._fullData[d.index],s=g&&(c.isHierarchyRoot(t)||c.isLeaf(t)),u=c.getPtId(t),p=c.isEntry(t)?c.findEntryWithChild(m,u):c.findEntryWithLevel(m,u),v=c.getPtId(p),y={points:[f(t,a,h.eventDataKeys)],event:n.event};s||(y.nextLevel=v);var x=l.triggerHandler(r,\"plotly_\"+d.type+\"click\",y);if(!1!==x&&e.hovermode&&(r._hoverdata=[f(t,a,h.eventDataKeys)],o.click(r,n.event)),!s&&!1!==x&&!r._dragging&&!r._transitioning){i.call(\"_storeDirectGUIEdit\",a,e._tracePreGUI[a.uid],{level:a.level});var b={data:[{level:v}],traces:[d.index]},_={frame:{redraw:!1,duration:h.transitionTime},transition:{duration:h.transitionTime,easing:h.transitionEasing},mode:\"immediate\",fromcurrent:!0};o.loneUnhover(e._hoverlayer.node()),i.call(\"animate\",r,b,_)}}))}},{\"../../components/fx\":702,\"../../components/fx/helpers\":698,\"../../lib\":799,\"../../lib/events\":788,\"../../registry\":927,\"../pie/helpers\":1193,\"./helpers\":1332,\"@plotly/d3\":58}],1332:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../../components/color\"),a=t(\"../../lib/setcursor\"),o=t(\"../pie/helpers\");function s(t){return t.data.data.pid}r.findEntryWithLevel=function(t,e){var n;return e&&t.eachAfter((function(t){if(r.getPtId(t)===e)return n=t.copy()})),n||t},r.findEntryWithChild=function(t,e){var n;return t.eachAfter((function(t){for(var i=t.children||[],a=0;a<i.length;a++){var o=i[a];if(r.getPtId(o)===e)return 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r.isOutsideText(t,e)?function(t,e,n){return{color:r.getOutsideTextFontKey(\"color\",t,e,n),family:r.getOutsideTextFontKey(\"family\",t,e,n),size:r.getOutsideTextFontKey(\"size\",t,e,n)}}(t,e,a):function(t,e,a,o){var s=(o||{}).onPathbar,l=e.data.data,c=l.i,u=n.castOption(t,c,(s?\"pathbar.textfont\":\"insidetextfont\")+\".color\");return!u&&t._input.textfont&&(u=n.castOption(t._input,c,\"textfont.color\")),{color:u||i.contrast(l.color),family:r.getInsideTextFontKey(\"family\",t,e,a,o),size:r.getInsideTextFontKey(\"size\",t,e,a,o)}}(t,e,a,o)},r.hasTransition=function(t){return!!(t&&t.duration>0)},r.getMaxDepth=function(t){return t.maxdepth>=0?t.maxdepth:1/0},r.isHeader=function(t,e){return!(r.isLeaf(t)||t.depth===e._maxDepth-1)},r.getParent=function(t,e){return r.findEntryWithLevel(t,s(e))},r.listPath=function(t,e){var n=t.parent;if(!n)return[];var i=e?[n.data[e]]:[n];return r.listPath(n,e).concat(i)},r.getPath=function(t){return r.listPath(t,\"label\").join(\"/\")+\"/\"},r.formatValue=o.formatPieValue,r.formatPercent=function(t,e){var r=n.formatPercent(t,0);return\"0%\"===r&&(r=o.formatPiePercent(t,e)),r}},{\"../../components/color\":662,\"../../lib\":799,\"../../lib/setcursor\":820,\"../pie/helpers\":1193}],1333:[function(t,e,r){\"use strict\";e.exports={moduleType:\"trace\",name:\"sunburst\",basePlotModule:t(\"./base_plot\"),categories:[],animatable:!0,attributes:t(\"./attributes\"),layoutAttributes:t(\"./layout_attributes\"),supplyDefaults:t(\"./defaults\"),supplyLayoutDefaults:t(\"./layout_defaults\"),calc:t(\"./calc\").calc,crossTraceCalc:t(\"./calc\").crossTraceCalc,plot:t(\"./plot\").plot,style:t(\"./style\").style,colorbar:t(\"../scatter/marker_colorbar\"),meta:{}}},{\"../scatter/marker_colorbar\":1232,\"./attributes\":1326,\"./base_plot\":1327,\"./calc\":1328,\"./defaults\":1330,\"./layout_attributes\":1334,\"./layout_defaults\":1335,\"./plot\":1336,\"./style\":1337}],1334:[function(t,e,r){\"use 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M={};u.label&&(M.label=u.label),u.hasOwnProperty(\"v\")&&(M.value=u.v,M.valueLabel=x.formatValue(u.v,l)),M.currentPath=x.getPath(t.data),h||(M.percentParent=d/x.getValue(p),M.percentParentLabel=x.formatPercent(M.percentParent,l),M.parent=x.getPtLabel(p)),M.percentEntry=d/x.getValue(e),M.percentEntryLabel=x.formatPercent(M.percentEntry,l),M.entry=x.getPtLabel(e),M.percentRoot=d/x.getValue(f),M.percentRootLabel=x.formatPercent(M.percentRoot,l),M.root=x.getPtLabel(f),u.hasOwnProperty(\"color\")&&(M.color=u.color);var A=o.castOption(r,u.i,\"text\");return(o.isValidTextValue(A)||\"\"===A)&&(M.text=A),M.customdata=o.castOption(r,u.i,\"customdata\"),o.texttemplateString(k,M,i._d3locale,M,r._meta||{})}},{\"../../components/drawing\":684,\"../../lib\":799,\"../../lib/svg_text_utils\":824,\"../bar/style\":951,\"../bar/uniform_text\":953,\"../pie/helpers\":1193,\"../pie/plot\":1197,\"./constants\":1329,\"./fx\":1331,\"./helpers\":1332,\"./style\":1337,\"@plotly/d3\":58,\"d3-hierarchy\":167}],1337:[function(t,e,r){\"use strict\";var n=t(\"@plotly/d3\"),i=t(\"../../components/color\"),a=t(\"../../lib\"),o=t(\"../bar/uniform_text\").resizeText;function s(t,e,r){var n=e.data.data,o=!e.children,s=n.i,l=a.castOption(r,s,\"marker.line.color\")||i.defaultLine,c=a.castOption(r,s,\"marker.line.width\")||0;t.style(\"stroke-width\",c).call(i.fill,n.color).call(i.stroke,l).style(\"opacity\",o?r.leaf.opacity:null)}e.exports={style:function(t){var e=t._fullLayout._sunburstlayer.selectAll(\".trace\");o(t,e,\"sunburst\"),e.each((function(t){var e=n.select(this),r=t[0].trace;e.style(\"opacity\",r.opacity),e.selectAll(\"path.surface\").each((function(t){n.select(this).call(s,t,r)}))}))},styleOne:s}},{\"../../components/color\":662,\"../../lib\":799,\"../bar/uniform_text\":953,\"@plotly/d3\":58}],1338:[function(t,e,r){\"use strict\";var n=t(\"../../components/color\"),i=t(\"../../components/colorscale/attributes\"),a=t(\"../../plots/cartesian/axis_format_attributes\").axisHoverFormat,o=t(\"../../plots/template_attributes\").hovertemplateAttrs,s=t(\"../../plots/attributes\"),l=t(\"../../lib/extend\").extendFlat,c=t(\"../../plot_api/edit_types\").overrideAll;function u(t){return{show:{valType:\"boolean\",dflt:!1},start:{valType:\"number\",dflt:null,editType:\"plot\"},end:{valType:\"number\",dflt:null,editType:\"plot\"},size:{valType:\"number\",dflt:null,min:0,editType:\"plot\"},project:{x:{valType:\"boolean\",dflt:!1},y:{valType:\"boolean\",dflt:!1},z:{valType:\"boolean\",dflt:!1}},color:{valType:\"color\",dflt:n.defaultLine},usecolormap:{valType:\"boolean\",dflt:!1},width:{valType:\"number\",min:1,max:16,dflt:2},highlight:{valType:\"boolean\",dflt:!0},highlightcolor:{valType:\"color\",dflt:n.defaultLine},highlightwidth:{valType:\"number\",min:1,max:16,dflt:2}}}var f=e.exports=c(l({z:{valType:\"data_array\"},x:{valType:\"data_array\"},y:{valType:\"data_array\"},text:{valType:\"string\",dflt:\"\",arrayOk:!0},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0},hovertemplate:o(),xhoverformat:a(\"x\"),yhoverformat:a(\"y\"),zhoverformat:a(\"z\"),connectgaps:{valType:\"boolean\",dflt:!1,editType:\"calc\"},surfacecolor:{valType:\"data_array\"}},i(\"\",{colorAttr:\"z or surfacecolor\",showScaleDflt:!0,autoColorDflt:!1,editTypeOverride:\"calc\"}),{contours:{x:u(),y:u(),z:u()},hidesurface:{valType:\"boolean\",dflt:!1},lightposition:{x:{valType:\"number\",min:-1e5,max:1e5,dflt:10},y:{valType:\"number\",min:-1e5,max:1e5,dflt:1e4},z:{valType:\"number\",min:-1e5,max:1e5,dflt:0}},lighting:{ambient:{valType:\"number\",min:0,max:1,dflt:.8},diffuse:{valType:\"number\",min:0,max:1,dflt:.8},specular:{valType:\"number\",min:0,max:2,dflt:.05},roughness:{valType:\"number\",min:0,max:1,dflt:.5},fresnel:{valType:\"number\",min:0,max:5,dflt:.2}},opacity:{valType:\"number\",min:0,max:1,dflt:1},opacityscale:{valType:\"any\",editType:\"calc\"},_deprecated:{zauto:l({},i.zauto,{}),zmin:l({},i.zmin,{}),zmax:l({},i.zmax,{})},hoverinfo:l({},s.hoverinfo),showlegend:l({},s.showlegend,{dflt:!1})}),\"calc\",\"nested\");f.x.editType=f.y.editType=f.z.editType=\"calc+clearAxisTypes\",f.transforms=void 0},{\"../../components/color\":662,\"../../components/colorscale/attributes\":669,\"../../lib/extend\":789,\"../../plot_api/edit_types\":831,\"../../plots/attributes\":845,\"../../plots/cartesian/axis_format_attributes\":852,\"../../plots/template_attributes\":922}],1339:[function(t,e,r){\"use strict\";var n=t(\"../../components/colorscale/calc\");e.exports=function(t,e){e.surfacecolor?n(t,e,{vals:e.surfacecolor,containerStr:\"\",cLetter:\"c\"}):n(t,e,{vals:e.z,containerStr:\"\",cLetter:\"c\"})}},{\"../../components/colorscale/calc\":670}],1340:[function(t,e,r){\"use strict\";var n=t(\"gl-surface3d\"),i=t(\"ndarray\"),a=t(\"ndarray-linear-interpolate\").d2,o=t(\"../heatmap/interp2d\"),s=t(\"../heatmap/find_empties\"),l=t(\"../../lib\").isArrayOrTypedArray,c=t(\"../../lib/gl_format_color\").parseColorScale,u=t(\"../../lib/str2rgbarray\"),f=t(\"../../components/colorscale\").extractOpts;function h(t,e,r){this.scene=t,this.uid=r,this.surface=e,this.data=null,this.showContour=[!1,!1,!1],this.contourStart=[null,null,null],this.contourEnd=[null,null,null],this.contourSize=[0,0,0],this.minValues=[1/0,1/0,1/0],this.maxValues=[-1/0,-1/0,-1/0],this.dataScaleX=1,this.dataScaleY=1,this.refineData=!0,this.objectOffset=[0,0,0]}var p=h.prototype;p.getXat=function(t,e,r,n){var i=l(this.data.x)?l(this.data.x[0])?this.data.x[e][t]:this.data.x[t]:t;return void 0===r?i:n.d2l(i,0,r)},p.getYat=function(t,e,r,n){var i=l(this.data.y)?l(this.data.y[0])?this.data.y[e][t]:this.data.y[e]:e;return void 0===r?i:n.d2l(i,0,r)},p.getZat=function(t,e,r,n){var i=this.data.z[e][t];return null===i&&this.data.connectgaps&&this.data._interpolatedZ&&(i=this.data._interpolatedZ[e][t]),void 0===r?i:n.d2l(i,0,r)},p.handlePick=function(t){if(t.object===this.surface){var e=(t.data.index[0]-1)/this.dataScaleX-1,r=(t.data.index[1]-1)/this.dataScaleY-1,n=Math.max(Math.min(Math.round(e),this.data.z[0].length-1),0),i=Math.max(Math.min(Math.round(r),this.data._ylength-1),0);t.index=[n,i],t.traceCoordinate=[this.getXat(n,i),this.getYat(n,i),this.getZat(n,i)],t.dataCoordinate=[this.getXat(n,i,this.data.xcalendar,this.scene.fullSceneLayout.xaxis),this.getYat(n,i,this.data.ycalendar,this.scene.fullSceneLayout.yaxis),this.getZat(n,i,this.data.zcalendar,this.scene.fullSceneLayout.zaxis)];for(var a=0;a<3;a++){var o=t.dataCoordinate[a];null!=o&&(t.dataCoordinate[a]*=this.scene.dataScale[a])}var s=this.data.hovertext||this.data.text;return Array.isArray(s)&&s[i]&&void 0!==s[i][n]?t.textLabel=s[i][n]:t.textLabel=s||\"\",t.data.dataCoordinate=t.dataCoordinate.slice(),this.surface.highlight(t.data),this.scene.glplot.spikes.position=t.dataCoordinate,!0}};var d=[2,3,5,7,11,13,17,19,23,29,31,37,41,43,47,53,59,61,67,71,73,79,83,89,97,101,103,107,109,113,127,131,137,139,149,151,157,163,167,173,179,181,191,193,197,199,211,223,227,229,233,239,241,251,257,263,269,271,277,281,283,293,307,311,313,317,331,337,347,349,353,359,367,373,379,383,389,397,401,409,419,421,431,433,439,443,449,457,461,463,467,479,487,491,499,503,509,521,523,541,547,557,563,569,571,577,587,593,599,601,607,613,617,619,631,641,643,647,653,659,661,673,677,683,691,701,709,719,727,733,739,743,751,757,761,769,773,787,797,809,811,821,823,827,829,839,853,857,859,863,877,881,883,887,907,911,919,929,937,941,947,953,967,971,977,983,991,997,1009,1013,1019,1021,1031,1033,1039,1049,1051,1061,1063,1069,1087,1091,1093,1097,1103,1109,1117,1123,1129,1151,1153,1163,1171,1181,1187,1193,1201,1213,1217,1223,1229,1231,1237,1249,1259,1277,1279,1283,1289,1291,1297,1301,1303,1307,1319,1321,1327,1361,1367,1373,1381,1399,1409,1423,1427,1429,1433,1439,1447,1451,1453,1459,1471,1481,1483,1487,1489,1493,1499,1511,1523,1531,1543,1549,1553,1559,1567,1571,1579,1583,1597,1601,1607,1609,1613,1619,1621,1627,1637,1657,1663,1667,1669,1693,1697,1699,1709,1721,1723,1733,1741,1747,1753,1759,1777,1783,1787,1789,1801,1811,1823,1831,1847,1861,1867,1871,1873,1877,1879,1889,1901,1907,1913,1931,1933,1949,1951,1973,1979,1987,1993,1997,1999,2003,2011,2017,2027,2029,2039,2053,2063,2069,2081,2083,2087,2089,2099,2111,2113,2129,2131,2137,2141,2143,2153,2161,2179,2203,2207,2213,2221,2237,2239,2243,2251,2267,2269,2273,2281,2287,2293,2297,2309,2311,2333,2339,2341,2347,2351,2357,2371,2377,2381,2383,2389,2393,2399,2411,2417,2423,2437,2441,2447,2459,2467,2473,2477,2503,2521,2531,2539,2543,2549,2551,2557,2579,2591,2593,2609,2617,2621,2633,2647,2657,2659,2663,2671,2677,2683,2687,2689,2693,2699,2707,2711,2713,2719,2729,2731,2741,2749,2753,2767,2777,2789,2791,2797,2801,2803,2819,2833,2837,2843,2851,2857,2861,2879,2887,2897,2903,2909,2917,2927,2939,2953,2957,2963,2969,2971,2999];function m(t,e){if(t<e)return 0;for(var r=0;0===Math.floor(t%e);)t/=e,r++;return r}function g(t){for(var e=[],r=0;r<d.length;r++){var n=d[r];e.push(m(t,n))}return e}function v(t){for(var e=g(t),r=t,n=0;n<d.length;n++)if(e[n]>0){r=d[n];break}return r}function y(t,e){if(!(t<1||e<1)){for(var r=g(t),n=g(e),i=1,a=0;a<d.length;a++)i*=Math.pow(d[a],Math.max(r[a],n[a]));return i}}p.calcXnums=function(t){var e,r=[];for(e=1;e<t;e++){var n=this.getXat(e-1,0),i=this.getXat(e,0);r[e-1]=i!==n&&null!=n&&null!=i?Math.abs(i-n):0}var a=0;for(e=1;e<t;e++)a+=r[e-1];for(e=1;e<t;e++)0===r[e-1]?r[e-1]=1:r[e-1]=Math.round(a/r[e-1]);return r},p.calcYnums=function(t){var e,r=[];for(e=1;e<t;e++){var n=this.getYat(0,e-1),i=this.getYat(0,e);r[e-1]=i!==n&&null!=n&&null!=i?Math.abs(i-n):0}var a=0;for(e=1;e<t;e++)a+=r[e-1];for(e=1;e<t;e++)0===r[e-1]?r[e-1]=1:r[e-1]=Math.round(a/r[e-1]);return r};var x=[1,2,4,6,12,24,36,48,60,120,180,240,360,720,840,1260],b=x[9],_=x[13];function w(t,e,r){var n=r[8]+r[2]*e[0]+r[5]*e[1];return t[0]=(r[6]+r[0]*e[0]+r[3]*e[1])/n,t[1]=(r[7]+r[1]*e[0]+r[4]*e[1])/n,t}function T(t,e,r){return function(t,e,r,n){for(var i=[0,0],o=t.shape[0],s=t.shape[1],l=0;l<o;l++)for(var c=0;c<s;c++)r(i,[l,c],n),t.set(l,c,a(e,i[0],i[1]))}(t,e,w,r),t}function k(t,e){for(var r=!1,n=0;n<t.length;n++)if(e===t[n]){r=!0;break}!1===r&&t.push(e)}p.estimateScale=function(t,e){for(var r=1+function(t){if(0!==t.length){for(var e=1,r=0;r<t.length;r++)e=y(e,t[r]);return e}}(0===e?this.calcXnums(t):this.calcYnums(t));r<b;)r*=2;for(;r>_;)r--,r/=v(r),++r<b&&(r=_);var n=Math.round(r/t);return n>1?n:1},p.refineCoords=function(t){for(var e=this.dataScaleX,r=this.dataScaleY,n=t[0].shape[0],a=t[0].shape[1],o=0|Math.floor(t[0].shape[0]*e+1),s=0|Math.floor(t[0].shape[1]*r+1),l=1+n+1,c=1+a+1,u=i(new Float32Array(l*c),[l,c]),f=[1/e,0,0,0,1/r,0,0,0,1],h=0;h<t.length;++h){this.surface.padField(u,t[h]);var p=i(new Float32Array(o*s),[o,s]);T(p,u,f),t[h]=p}},p.setContourLevels=function(){var t,e,r,n=[[],[],[]],i=[!1,!1,!1],a=!1;for(t=0;t<3;++t)if(this.showContour[t]&&(a=!0,this.contourSize[t]>0&&null!==this.contourStart[t]&&null!==this.contourEnd[t]&&this.contourEnd[t]>this.contourStart[t]))for(i[t]=!0,e=this.contourStart[t];e<this.contourEnd[t];e+=this.contourSize[t])r=e*this.scene.dataScale[t],k(n[t],r);if(a){var o=[[],[],[]];for(t=0;t<3;++t)this.showContour[t]&&(o[t]=i[t]?n[t]:this.scene.contourLevels[t]);this.surface.update({levels:o})}},p.update=function(t){var e,r,n,a,l=this.scene,h=l.fullSceneLayout,p=this.surface,d=c(t),m=l.dataScale,g=t.z[0].length,v=t._ylength,y=l.contourLevels;this.data=t;var x=[];for(e=0;e<3;e++)for(x[e]=[],r=0;r<g;r++)x[e][r]=[];for(r=0;r<g;r++)for(n=0;n<v;n++)x[0][r][n]=this.getXat(r,n,t.xcalendar,h.xaxis),x[1][r][n]=this.getYat(r,n,t.ycalendar,h.yaxis),x[2][r][n]=this.getZat(r,n,t.zcalendar,h.zaxis);if(t.connectgaps)for(t._emptypoints=s(x[2]),o(x[2],t._emptypoints),t._interpolatedZ=[],r=0;r<g;r++)for(t._interpolatedZ[r]=[],n=0;n<v;n++)t._interpolatedZ[r][n]=x[2][r][n];for(e=0;e<3;e++)for(r=0;r<g;r++)for(n=0;n<v;n++)null==(a=x[e][r][n])?x[e][r][n]=NaN:a=x[e][r][n]*=m[e];for(e=0;e<3;e++)for(r=0;r<g;r++)for(n=0;n<v;n++)null!=(a=x[e][r][n])&&(this.minValues[e]>a&&(this.minValues[e]=a),this.maxValues[e]<a&&(this.maxValues[e]=a));for(e=0;e<3;e++)this.objectOffset[e]=.5*(this.minValues[e]+this.maxValues[e]);for(e=0;e<3;e++)for(r=0;r<g;r++)for(n=0;n<v;n++)null!=(a=x[e][r][n])&&(x[e][r][n]-=this.objectOffset[e]);var b=[i(new Float32Array(g*v),[g,v]),i(new Float32Array(g*v),[g,v]),i(new Float32Array(g*v),[g,v])];for(e=0;e<3;e++)for(r=0;r<g;r++)for(n=0;n<v;n++)b[e].set(r,n,x[e][r][n]);x=[];var w={colormap:d,levels:[[],[],[]],showContour:[!0,!0,!0],showSurface:!t.hidesurface,contourProject:[[!1,!1,!1],[!1,!1,!1],[!1,!1,!1]],contourWidth:[1,1,1],contourColor:[[1,1,1,1],[1,1,1,1],[1,1,1,1]],contourTint:[1,1,1],dynamicColor:[[1,1,1,1],[1,1,1,1],[1,1,1,1]],dynamicWidth:[1,1,1],dynamicTint:[1,1,1],opacityscale:t.opacityscale,opacity:t.opacity},T=f(t);if(w.intensityBounds=[T.min,T.max],t.surfacecolor){var k=i(new Float32Array(g*v),[g,v]);for(r=0;r<g;r++)for(n=0;n<v;n++)k.set(r,n,t.surfacecolor[n][r]);b.push(k)}else w.intensityBounds[0]*=m[2],w.intensityBounds[1]*=m[2];(_<b[0].shape[0]||_<b[0].shape[1])&&(this.refineData=!1),!0===this.refineData&&(this.dataScaleX=this.estimateScale(b[0].shape[0],0),this.dataScaleY=this.estimateScale(b[0].shape[1],1),1===this.dataScaleX&&1===this.dataScaleY||this.refineCoords(b)),t.surfacecolor&&(w.intensity=b.pop());var M=[!0,!0,!0],A=[\"x\",\"y\",\"z\"];for(e=0;e<3;++e){var S=t.contours[A[e]];M[e]=S.highlight,w.showContour[e]=S.show||S.highlight,w.showContour[e]&&(w.contourProject[e]=[S.project.x,S.project.y,S.project.z],S.show?(this.showContour[e]=!0,w.levels[e]=y[e],p.highlightColor[e]=w.contourColor[e]=u(S.color),S.usecolormap?p.highlightTint[e]=w.contourTint[e]=0:p.highlightTint[e]=w.contourTint[e]=1,w.contourWidth[e]=S.width,this.contourStart[e]=S.start,this.contourEnd[e]=S.end,this.contourSize[e]=S.size):(this.showContour[e]=!1,this.contourStart[e]=null,this.contourEnd[e]=null,this.contourSize[e]=0),S.highlight&&(w.dynamicColor[e]=u(S.highlightcolor),w.dynamicWidth[e]=S.highlightwidth))}(function(t){var e=t[0].rgb,r=t[t.length-1].rgb;return e[0]===r[0]&&e[1]===r[1]&&e[2]===r[2]&&e[3]===r[3]})(d)&&(w.vertexColor=!0),w.objectOffset=this.objectOffset,w.coords=b,p.update(w),p.visible=t.visible,p.enableDynamic=M,p.enableHighlight=M,p.snapToData=!0,\"lighting\"in t&&(p.ambientLight=t.lighting.ambient,p.diffuseLight=t.lighting.diffuse,p.specularLight=t.lighting.specular,p.roughness=t.lighting.roughness,p.fresnel=t.lighting.fresnel),\"lightposition\"in t&&(p.lightPosition=[t.lightposition.x,t.lightposition.y,t.lightposition.z])},p.dispose=function(){this.scene.glplot.remove(this.surface),this.surface.dispose()},e.exports=function(t,e){var r=t.glplot.gl,i=n({gl:r}),a=new h(t,i,e.uid);return i._trace=a,a.update(e),t.glplot.add(i),a}},{\"../../components/colorscale\":674,\"../../lib\":799,\"../../lib/gl_format_color\":795,\"../../lib/str2rgbarray\":823,\"../heatmap/find_empties\":1087,\"../heatmap/interp2d\":1090,\"gl-surface3d\":350,ndarray:479,\"ndarray-linear-interpolate\":473}],1341:[function(t,e,r){\"use strict\";var n=t(\"../../registry\"),i=t(\"../../lib\"),a=t(\"../../components/colorscale/defaults\"),o=t(\"./attributes\");function s(t,e,r,n){var i=n(\"opacityscale\");\"max\"===i?e.opacityscale=[[0,.1],[1,1]]:\"min\"===i?e.opacityscale=[[0,1],[1,.1]]:\"extremes\"===i?e.opacityscale=function(t,e){for(var r=[],n=0;n<32;n++){var i=n/31,a=e+(1-e)*(1-Math.pow(Math.sin(t*i*Math.PI),2));r.push([i,Math.max(0,Math.min(1,a))])}return r}(1,.1):function(t){var e=0;if(!Array.isArray(t)||t.length<2)return!1;if(!t[0]||!t[t.length-1])return!1;if(0!=+t[0][0]||1!=+t[t.length-1][0])return!1;for(var r=0;r<t.length;r++){var n=t[r];if(2!==n.length||+n[0]<e)return!1;e=+n[0]}return!0}(i)||(e.opacityscale=void 0)}function l(t,e,r){e in t&&!(r in t)&&(t[r]=t[e])}e.exports={supplyDefaults:function(t,e,r,c){var u,f;function h(r,n){return i.coerce(t,e,o,r,n)}var p=h(\"x\"),d=h(\"y\"),m=h(\"z\");if(!m||!m.length||p&&p.length<1||d&&d.length<1)e.visible=!1;else{e._xlength=Array.isArray(p)&&i.isArrayOrTypedArray(p[0])?m.length:m[0].length,e._ylength=m.length,n.getComponentMethod(\"calendars\",\"handleTraceDefaults\")(t,e,[\"x\",\"y\",\"z\"],c),h(\"text\"),h(\"hovertext\"),h(\"hovertemplate\"),h(\"xhoverformat\"),h(\"yhoverformat\"),h(\"zhoverformat\"),[\"lighting.ambient\",\"lighting.diffuse\",\"lighting.specular\",\"lighting.roughness\",\"lighting.fresnel\",\"lightposition.x\",\"lightposition.y\",\"lightposition.z\",\"hidesurface\",\"connectgaps\",\"opacity\"].forEach((function(t){h(t)}));var g=h(\"surfacecolor\"),v=[\"x\",\"y\",\"z\"];for(u=0;u<3;++u){var 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n=t(\"../../plots/template_attributes\").hovertemplateAttrs,i=t(\"../../plots/template_attributes\").texttemplateAttrs,a=t(\"../../components/colorscale/attributes\"),o=t(\"../../plots/domain\").attributes,s=t(\"../pie/attributes\"),l=t(\"../sunburst/attributes\"),c=t(\"./constants\"),u=t(\"../../lib/extend\").extendFlat;e.exports={labels:l.labels,parents:l.parents,values:l.values,branchvalues:l.branchvalues,count:l.count,level:l.level,maxdepth:l.maxdepth,tiling:{packing:{valType:\"enumerated\",values:[\"squarify\",\"binary\",\"dice\",\"slice\",\"slice-dice\",\"dice-slice\"],dflt:\"squarify\",editType:\"plot\"},squarifyratio:{valType:\"number\",min:1,dflt:1,editType:\"plot\"},flip:{valType:\"flaglist\",flags:[\"x\",\"y\"],dflt:\"\",editType:\"plot\"},pad:{valType:\"number\",min:0,dflt:3,editType:\"plot\"},editType:\"calc\"},marker:u({pad:{t:{valType:\"number\",min:0,editType:\"plot\"},l:{valType:\"number\",min:0,editType:\"plot\"},r:{valType:\"number\",min:0,editType:\"plot\"},b:{valType:\"number\",min:0,editType:\"plot\"},editType:\"calc\"},colors:l.marker.colors,depthfade:{valType:\"enumerated\",values:[!0,!1,\"reversed\"],editType:\"style\"},line:l.marker.line,editType:\"calc\"},a(\"marker\",{colorAttr:\"colors\",anim:!1})),pathbar:{visible:{valType:\"boolean\",dflt:!0,editType:\"plot\"},side:{valType:\"enumerated\",values:[\"top\",\"bottom\"],dflt:\"top\",editType:\"plot\"},edgeshape:{valType:\"enumerated\",values:[\">\",\"<\",\"|\",\"/\",\"\\\\\"],dflt:\">\",editType:\"plot\"},thickness:{valType:\"number\",min:12,editType:\"plot\"},textfont:u({},s.textfont,{}),editType:\"calc\"},text:s.text,textinfo:l.textinfo,texttemplate:i({editType:\"plot\"},{keys:c.eventDataKeys.concat([\"label\",\"value\"])}),hovertext:s.hovertext,hoverinfo:l.hoverinfo,hovertemplate:n({},{keys:c.eventDataKeys}),textfont:s.textfont,insidetextfont:s.insidetextfont,outsidetextfont:u({},s.outsidetextfont,{}),textposition:{valType:\"enumerated\",values:[\"top 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v=h(\"pathbar.visible\");s(t,e,c,h,\"auto\",{hasPathbar:v,moduleHasSelected:!1,moduleHasUnselected:!1,moduleHasConstrain:!1,moduleHasCliponaxis:!1,moduleHasTextangle:!1,moduleHasInsideanchor:!1}),h(\"textposition\");var y=-1!==e.textposition.indexOf(\"bottom\");h(\"marker.line.width\")&&h(\"marker.line.color\",c.paper_bgcolor);var x=h(\"marker.colors\");(e._hasColorscale=u(t,\"marker\",\"colors\")||(t.marker||{}).coloraxis)?f(t,e,c,h,{prefix:\"marker.\",cLetter:\"c\"}):h(\"marker.depthfade\",!(x||[]).length);var b=2*e.textfont.size;h(\"marker.pad.t\",y?b/4:b),h(\"marker.pad.l\",b/4),h(\"marker.pad.r\",b/4),h(\"marker.pad.b\",y?b:b/4),e._hovered={marker:{line:{width:2,color:a.contrast(c.paper_bgcolor)}}},v&&(h(\"pathbar.thickness\",e.pathbar.textfont.size+2*l),h(\"pathbar.side\"),h(\"pathbar.edgeshape\")),h(\"sort\"),h(\"root.color\"),o(e,c,h),e._length=null}else 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d=p.barDifY,m=p.width,g=p.height,v=p.viewX,y=p.viewY,x=p.pathSlice,b=p.toMoveInsideSlice,_=p.strTransform,w=p.hasTransition,T=p.handleSlicesExit,k=p.makeUpdateSliceInterpolator,M=p.makeUpdateTextInterpolator,A={},S=t._fullLayout,E=e[0],L=E.trace,C=E.hierarchy,P=m/L._entryDepth,I=u.listPath(r.data,\"id\"),O=s(C.copy(),[m,g],{packing:\"dice\",pad:{inner:0,top:0,left:0,right:0,bottom:0}}).descendants();(O=O.filter((function(t){var e=I.indexOf(t.data.id);return-1!==e&&(t.x0=P*e,t.x1=P*(e+1),t.y0=d,t.y1=d+g,t.onPathbar=!0,!0)}))).reverse(),(h=h.data(O,u.getPtId)).enter().append(\"g\").classed(\"pathbar\",!0),T(h,!0,A,[m,g],x),h.order();var z=h;w&&(z=z.transition().each(\"end\",(function(){var e=n.select(this);u.setSliceCursor(e,t,{hideOnRoot:!1,hideOnLeaves:!1,isTransitioning:!1})}))),z.each((function(s){s._hoverX=v(s.x1-Math.min(m,g)/2),s._hoverY=y(s.y1-g/2);var h=n.select(this),p=i.ensureSingle(h,\"path\",\"surface\",(function(t){t.style(\"pointer-events\",\"all\")}));w?p.transition().attrTween(\"d\",(function(t){var e=k(t,!0,A,[m,g]);return function(t){return x(e(t))}})):p.attr(\"d\",x),h.call(f,r,t,e,{styleOne:l,eventDataKeys:c.eventDataKeys,transitionTime:c.CLICK_TRANSITION_TIME,transitionEasing:c.CLICK_TRANSITION_EASING}).call(u.setSliceCursor,t,{hideOnRoot:!1,hideOnLeaves:!1,isTransitioning:t._transitioning}),p.call(l,s,L,{hovered:!1}),s._text=(u.getPtLabel(s)||\"\").split(\"<br>\").join(\" \")||\"\";var d=i.ensureSingle(h,\"g\",\"slicetext\"),T=i.ensureSingle(d,\"text\",\"\",(function(t){t.attr(\"data-notex\",1)})),E=i.ensureUniformFontSize(t,u.determineTextFont(L,s,S.font,{onPathbar:!0}));T.text(s._text||\" 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n=t(\"../../components/colorscale/attributes\"),i=t(\"../isosurface/attributes\"),a=t(\"../surface/attributes\"),o=t(\"../../plots/attributes\"),s=t(\"../../lib/extend\").extendFlat,l=t(\"../../plot_api/edit_types\").overrideAll,c=e.exports=l(s({x:i.x,y:i.y,z:i.z,value:i.value,isomin:i.isomin,isomax:i.isomax,surface:i.surface,spaceframe:{show:{valType:\"boolean\",dflt:!1},fill:{valType:\"number\",min:0,max:1,dflt:1}},slices:i.slices,caps:i.caps,text:i.text,hovertext:i.hovertext,xhoverformat:i.xhoverformat,yhoverformat:i.yhoverformat,zhoverformat:i.zhoverformat,valuehoverformat:i.valuehoverformat,hovertemplate:i.hovertemplate},n(\"\",{colorAttr:\"`value`\",showScaleDflt:!0,editTypeOverride:\"calc\"}),{colorbar:i.colorbar,opacity:i.opacity,opacityscale:a.opacityscale,lightposition:i.lightposition,lighting:i.lighting,flatshading:i.flatshading,contour:i.contour,hoverinfo:s({},o.hoverinfo),showlegend:s({},o.showlegend,{dflt:!1})}),\"calc\",\"nested\");c.x.editType=c.y.editType=c.z.editType=c.value.editType=\"calc+clearAxisTypes\",c.transforms=void 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+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -1094,41897 +129,44 @@
    ],
    "source": [
     "trace = [results._trace[i][1][2] for i in range(1,len(results._trace))]\n",
-    "fig = go.Figure(data=go.Scatter(x=list(range(2,len(trace)+2)), y=trace,mode='lines+markers',\n",
-    "                               line=dict(color='black', width=2)))\n",
-    "fig.update_layout(xaxis_title='Number of clusters',\n",
-    "                   yaxis_title='Total sum of squared residuals')\n",
-    "fig.show()"
+    "fig, ax = plt.subplots()\n",
+    "ax.plot(list(range(2,len(trace)+2)), trace, '-o', color='black', linewidth=2)\n",
+    "\n",
+    "ax.set(xlabel='Number of clusters', ylabel='Total sum of squared residuals')\n",
+    "ax.grid()\n",
+    "\n",
+    "plt.show()"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "a3c6cf08",
    "metadata": {},
    "source": [
-    "### We can also plot the cluster allocations from the final step using the attribute current_labels_"
+    "Let's say we choose 12 clusters. We can plot the results using geopandas and matplotlib."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
+   "id": "ff616fb3",
    "metadata": {},
    "outputs": [
     {
      "data": {
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+      "text/plain": [
+       "(-127.6195011138916, -64.0817699432373, 23.735178565979005, 50.59252300262451)"
       ]
      },
+     "execution_count": 5,
      "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig = px.choropleth(pd.concat([data,pd.Series(results.current_labels_.astype(str), name='cluster', index=data.index)], axis=1),\n",
-    "                   geojson=data.geometry,\n",
-    "                   locations=data.index,\n",
-    "                   color=\"cluster\")\n",
-    "fig.update_geos(fitbounds=\"locations\", visible=False)\n",
-    "fig.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### The trace can also be used to recall the cluster allocations from any intermediate step"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -42992,25 +174,22 @@
     }
    ],
    "source": [
-    "# _trace[k-1][0] contains the cluster allocation for k clusters.\n",
-    "fig = px.choropleth(pd.concat([data,pd.Series(results._trace[2][0].astype(str), name='cluster', index=data.index)], axis=1),\n",
-    "                   geojson=data.geometry,\n",
-    "                   locations=data.index,\n",
-    "                   color=\"cluster\")\n",
-    "fig.update_geos(fitbounds=\"locations\", visible=False)\n",
-    "fig.show()"
+    "data[\"cl_regions\"] = results._trace[11][0]\n",
+    "data.plot(column=\"cl_regions\", categorical=True, legend=True, cmap='Paired').axis(\"off\")"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "9bef8f61",
    "metadata": {},
    "source": [
-    "### With the cluster allocations, we can call the Regimes methods in spreg to get the full regression results."
+    "With the cluster allocations and selected number of clusters, we can call the Regimes methods in Spreg to get the full regression results and Chow tests on the stability of the coefficients accross the different clusters."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 6,
+   "id": "b13e7942",
    "metadata": {},
    "outputs": [
     {
@@ -43024,182 +203,414 @@
       "---------------------------------------------------------------\n",
       "Data set            :     unknown\n",
       "Weights matrix      :     unknown\n",
-      "Dependent Variable  : 0_['HOVAL']                Number of Observations:          19\n",
-      "Mean dependent var  :     33.0149                Number of Variables   :           3\n",
-      "S.D. dependent var  :     18.0570                Degrees of Freedom    :          16\n",
-      "R-squared           :      0.5949\n",
-      "Adjusted R-squared  :      0.5442\n",
-      "Sum squared residual:    2377.791                F-statistic           :     11.7460\n",
-      "Sigma-square        :     148.612                Prob(F-statistic)     :   0.0007259\n",
-      "S.E. of regression  :      12.191                Log likelihood        :     -72.840\n",
-      "Sigma-square ML     :     125.147                Akaike info criterion :     151.680\n",
-      "S.E of regression ML:     11.1869                Schwarz criterion     :     154.513\n",
+      "Dependent Variable  :  0_['HR90']                Number of Observations:         604\n",
+      "Mean dependent var  :      2.4577                Number of Variables   :           4\n",
+      "S.D. dependent var  :      3.9266                Degrees of Freedom    :         600\n",
+      "R-squared           :      0.3305\n",
+      "Adjusted R-squared  :      0.3271\n",
+      "Sum squared residual:    6224.952                F-statistic           :     98.7116\n",
+      "Sigma-square        :      10.375                Prob(F-statistic)     :   6.109e-52\n",
+      "S.E. of regression  :       3.221                Log likelihood        :   -1561.528\n",
+      "Sigma-square ML     :      10.306                Akaike info criterion :    3131.057\n",
+      "S.E of regression ML:      3.2103                Schwarz criterion     :    3148.671\n",
       "\n",
       "------------------------------------------------------------------------------------\n",
       "            Variable     Coefficient       Std.Error     t-Statistic     Probability\n",
       "------------------------------------------------------------------------------------\n",
-      "          0_CONSTANT      58.9541294      13.3800487       4.4061222       0.0004416\n",
-      "               0_INC       0.4907925       0.7498945       0.6544820       0.5220989\n",
-      "             0_CRIME      -0.7151327       0.1709048      -4.1843910       0.0007010\n",
+      "          0_CONSTANT       4.0851876       0.4756999       8.5877407       0.0000000\n",
+      "              0_RD90       3.1288181       0.2856649      10.9527571       0.0000000\n",
+      "              0_PS90       1.4553321       0.1760078       8.2685645       0.0000000\n",
+      "              0_UE90       0.0530250       0.0612926       0.8651115       0.3873233\n",
       "------------------------------------------------------------------------------------\n",
       "Regimes variable: skater_reg\n",
       "\n",
       "REGRESSION DIAGNOSTICS\n",
-      "MULTICOLLINEARITY CONDITION NUMBER            9.907\n",
+      "MULTICOLLINEARITY CONDITION NUMBER            7.255\n",
       "\n",
       "TEST ON NORMALITY OF ERRORS\n",
       "TEST                             DF        VALUE           PROB\n",
-      "Jarque-Bera                       2           1.054           0.5903\n",
+      "Jarque-Bera                       2       20184.603           0.0000\n",
       "\n",
       "DIAGNOSTICS FOR HETEROSKEDASTICITY\n",
       "RANDOM COEFFICIENTS\n",
       "TEST                             DF        VALUE           PROB\n",
-      "Breusch-Pagan test                2           3.788           0.1505\n",
-      "Koenker-Bassett test              2           5.554           0.0622\n",
+      "Breusch-Pagan test                3          69.424           0.0000\n",
+      "Koenker-Bassett test              3           4.695           0.1956\n",
       "----------\n",
       "\n",
       "SUMMARY OF OUTPUT: ORDINARY LEAST SQUARES ESTIMATION - REGIME 1\n",
       "---------------------------------------------------------------\n",
       "Data set            :     unknown\n",
       "Weights matrix      :     unknown\n",
-      "Dependent Variable  : 1_['HOVAL']                Number of Observations:          10\n",
-      "Mean dependent var  :     53.5350                Number of Variables   :           3\n",
-      "S.D. dependent var  :     20.7972                Degrees of Freedom    :           7\n",
-      "R-squared           :      0.0802\n",
-      "Adjusted R-squared  :     -0.1825\n",
-      "Sum squared residual:    3580.319                F-statistic           :      0.3054\n",
-      "Sigma-square        :     511.474                Prob(F-statistic)     :      0.7462\n",
-      "S.E. of regression  :      22.616                Log likelihood        :     -43.592\n",
-      "Sigma-square ML     :     358.032                Akaike info criterion :      93.185\n",
-      "S.E of regression ML:     18.9217                Schwarz criterion     :      94.093\n",
+      "Dependent Variable  :  1_['HR90']                Number of Observations:         442\n",
+      "Mean dependent var  :      4.2336                Number of Variables   :           4\n",
+      "S.D. dependent var  :      5.8415                Degrees of Freedom    :         438\n",
+      "R-squared           :      0.1285\n",
+      "Adjusted R-squared  :      0.1226\n",
+      "Sum squared residual:   13113.889                F-statistic           :     21.5360\n",
+      "Sigma-square        :      29.940                Prob(F-statistic)     :    4.99e-13\n",
+      "S.E. of regression  :       5.472                Log likelihood        :   -1376.387\n",
+      "Sigma-square ML     :      29.669                Akaike info criterion :    2760.773\n",
+      "S.E of regression ML:      5.4470                Schwarz criterion     :    2777.139\n",
       "\n",
       "------------------------------------------------------------------------------------\n",
       "            Variable     Coefficient       Std.Error     t-Statistic     Probability\n",
       "------------------------------------------------------------------------------------\n",
-      "          1_CONSTANT      12.8763423      93.0707480       0.1383500       0.8938596\n",
-      "               1_INC       1.5088923       2.7661286       0.5454889       0.6023577\n",
-      "             1_CRIME       0.4614679       1.7305497       0.2666597       0.7974158\n",
+      "          1_CONSTANT       2.9419808       0.8336035       3.5292327       0.0004609\n",
+      "              1_RD90       1.4503115       0.5061202       2.8655477       0.0043633\n",
+      "              1_PS90       0.7620325       0.2229427       3.4180638       0.0006897\n",
+      "              1_UE90       0.3611520       0.0977243       3.6956233       0.0002471\n",
       "------------------------------------------------------------------------------------\n",
       "Regimes variable: skater_reg\n",
       "\n",
       "REGRESSION DIAGNOSTICS\n",
-      "MULTICOLLINEARITY CONDITION NUMBER           27.133\n",
+      "MULTICOLLINEARITY CONDITION NUMBER            6.511\n",
       "\n",
       "TEST ON NORMALITY OF ERRORS\n",
       "TEST                             DF        VALUE           PROB\n",
-      "Jarque-Bera                       2           4.445           0.1083\n",
+      "Jarque-Bera                       2       76308.673           0.0000\n",
       "\n",
       "DIAGNOSTICS FOR HETEROSKEDASTICITY\n",
       "RANDOM COEFFICIENTS\n",
       "TEST                             DF        VALUE           PROB\n",
-      "Breusch-Pagan test                2           1.865           0.3935\n",
-      "Koenker-Bassett test              2           1.146           0.5637\n",
+      "Breusch-Pagan test                3         555.665           0.0000\n",
+      "Koenker-Bassett test              3          17.034           0.0007\n",
       "----------\n",
       "\n",
       "SUMMARY OF OUTPUT: ORDINARY LEAST SQUARES ESTIMATION - REGIME 2\n",
       "---------------------------------------------------------------\n",
       "Data set            :     unknown\n",
       "Weights matrix      :     unknown\n",
-      "Dependent Variable  : 2_['HOVAL']                Number of Observations:          10\n",
-      "Mean dependent var  :     42.0358                Number of Variables   :           3\n",
-      "S.D. dependent var  :     16.8610                Degrees of Freedom    :           7\n",
-      "R-squared           :      0.0849\n",
-      "Adjusted R-squared  :     -0.1766\n",
-      "Sum squared residual:    2341.457                F-statistic           :      0.3246\n",
-      "Sigma-square        :     334.494                Prob(F-statistic)     :      0.7331\n",
-      "S.E. of regression  :      18.289                Log likelihood        :     -41.469\n",
-      "Sigma-square ML     :     234.146                Akaike info criterion :      88.938\n",
-      "S.E of regression ML:     15.3018                Schwarz criterion     :      89.846\n",
+      "Dependent Variable  :  2_['HR90']                Number of Observations:         157\n",
+      "Mean dependent var  :      3.2521                Number of Variables   :           4\n",
+      "S.D. dependent var  :      3.4925                Degrees of Freedom    :         153\n",
+      "R-squared           :      0.3735\n",
+      "Adjusted R-squared  :      0.3612\n",
+      "Sum squared residual:    1192.118                F-statistic           :     30.4049\n",
+      "Sigma-square        :       7.792                Prob(F-statistic)     :   1.785e-15\n",
+      "S.E. of regression  :       2.791                Log likelihood        :    -381.912\n",
+      "Sigma-square ML     :       7.593                Akaike info criterion :     771.824\n",
+      "S.E of regression ML:      2.7556                Schwarz criterion     :     784.049\n",
       "\n",
       "------------------------------------------------------------------------------------\n",
       "            Variable     Coefficient       Std.Error     t-Statistic     Probability\n",
       "------------------------------------------------------------------------------------\n",
-      "          2_CONSTANT      11.1682754      53.4235094       0.2090517       0.8403613\n",
-      "               2_INC       1.5447243       2.2293290       0.6929100       0.5106934\n",
-      "             2_CRIME       0.1996688       0.6863689       0.2909059       0.7795537\n",
+      "          2_CONSTANT       1.6648327       1.6499048       1.0090477       0.3145449\n",
+      "              2_RD90       2.5911850       0.5446873       4.7571975       0.0000045\n",
+      "              2_PS90       1.7951113       0.2645028       6.7867381       0.0000000\n",
+      "              2_UE90       0.2831519       0.1896309       1.4931734       0.1374511\n",
       "------------------------------------------------------------------------------------\n",
       "Regimes variable: skater_reg\n",
       "\n",
       "REGRESSION DIAGNOSTICS\n",
-      "MULTICOLLINEARITY CONDITION NUMBER           19.463\n",
+      "MULTICOLLINEARITY CONDITION NUMBER           17.037\n",
       "\n",
       "TEST ON NORMALITY OF ERRORS\n",
       "TEST                             DF        VALUE           PROB\n",
-      "Jarque-Bera                       2           1.957           0.3760\n",
+      "Jarque-Bera                       2         700.804           0.0000\n",
       "\n",
       "DIAGNOSTICS FOR HETEROSKEDASTICITY\n",
       "RANDOM COEFFICIENTS\n",
       "TEST                             DF        VALUE           PROB\n",
-      "Breusch-Pagan test                2           0.228           0.8921\n",
-      "Koenker-Bassett test              2           0.261           0.8776\n",
+      "Breusch-Pagan test                3         128.095           0.0000\n",
+      "Koenker-Bassett test              3          23.069           0.0000\n",
       "----------\n",
       "\n",
       "SUMMARY OF OUTPUT: ORDINARY LEAST SQUARES ESTIMATION - REGIME 3\n",
       "---------------------------------------------------------------\n",
       "Data set            :     unknown\n",
       "Weights matrix      :     unknown\n",
-      "Dependent Variable  : 3_['HOVAL']                Number of Observations:          10\n",
-      "Mean dependent var  :     30.0383                Number of Variables   :           3\n",
-      "S.D. dependent var  :      6.4509                Degrees of Freedom    :           7\n",
-      "R-squared           :      0.8050\n",
-      "Adjusted R-squared  :      0.7493\n",
-      "Sum squared residual:      73.037                F-statistic           :     14.4476\n",
-      "Sigma-square        :      10.434                Prob(F-statistic)     :    0.003275\n",
-      "S.E. of regression  :       3.230                Log likelihood        :     -24.131\n",
-      "Sigma-square ML     :       7.304                Akaike info criterion :      54.263\n",
-      "S.E of regression ML:      2.7025                Schwarz criterion     :      55.170\n",
+      "Dependent Variable  :  3_['HR90']                Number of Observations:         416\n",
+      "Mean dependent var  :      3.5350                Number of Variables   :           4\n",
+      "S.D. dependent var  :      3.5289                Degrees of Freedom    :         412\n",
+      "R-squared           :      0.2384\n",
+      "Adjusted R-squared  :      0.2328\n",
+      "Sum squared residual:    3936.025                F-statistic           :     42.9870\n",
+      "Sigma-square        :       9.553                Prob(F-statistic)     :   3.458e-24\n",
+      "S.E. of regression  :       3.091                Log likelihood        :   -1057.705\n",
+      "Sigma-square ML     :       9.462                Akaike info criterion :    2123.409\n",
+      "S.E of regression ML:      3.0760                Schwarz criterion     :    2139.532\n",
       "\n",
       "------------------------------------------------------------------------------------\n",
       "            Variable     Coefficient       Std.Error     t-Statistic     Probability\n",
       "------------------------------------------------------------------------------------\n",
-      "          3_CONSTANT     -16.2929212      10.1167637      -1.6104875       0.1513267\n",
-      "               3_INC       2.3390074       0.4430624       5.2791824       0.0011491\n",
-      "             3_CRIME       0.4036596       0.1324784       3.0469845       0.0186623\n",
+      "          3_CONSTANT       3.6580644       0.7883611       4.6400874       0.0000047\n",
+      "              3_RD90       2.1705064       0.3732128       5.8157339       0.0000000\n",
+      "              3_PS90       1.6485127       0.2143249       7.6916535       0.0000000\n",
+      "              3_UE90      -0.0049898       0.0843801      -0.0591343       0.9528738\n",
       "------------------------------------------------------------------------------------\n",
       "Regimes variable: skater_reg\n",
       "\n",
       "REGRESSION DIAGNOSTICS\n",
-      "MULTICOLLINEARITY CONDITION NUMBER           20.794\n",
+      "MULTICOLLINEARITY CONDITION NUMBER           11.124\n",
       "\n",
       "TEST ON NORMALITY OF ERRORS\n",
       "TEST                             DF        VALUE           PROB\n",
-      "Jarque-Bera                       2           0.655           0.7207\n",
+      "Jarque-Bera                       2        2163.820           0.0000\n",
       "\n",
       "DIAGNOSTICS FOR HETEROSKEDASTICITY\n",
       "RANDOM COEFFICIENTS\n",
       "TEST                             DF        VALUE           PROB\n",
-      "Breusch-Pagan test                2           0.708           0.7020\n",
-      "Koenker-Bassett test              2           1.358           0.5070\n",
+      "Breusch-Pagan test                3         295.778           0.0000\n",
+      "Koenker-Bassett test              3          48.598           0.0000\n",
+      "----------\n",
+      "\n",
+      "SUMMARY OF OUTPUT: ORDINARY LEAST SQUARES ESTIMATION - REGIME 4\n",
+      "---------------------------------------------------------------\n",
+      "Data set            :     unknown\n",
+      "Weights matrix      :     unknown\n",
+      "Dependent Variable  :  4_['HR90']                Number of Observations:         105\n",
+      "Mean dependent var  :      6.9902                Number of Variables   :           4\n",
+      "S.D. dependent var  :      7.7137                Degrees of Freedom    :         101\n",
+      "R-squared           :      0.5133\n",
+      "Adjusted R-squared  :      0.4989\n",
+      "Sum squared residual:    3011.570                F-statistic           :     35.5107\n",
+      "Sigma-square        :      29.818                Prob(F-statistic)     :   9.373e-16\n",
+      "S.E. of regression  :       5.461                Log likelihood        :    -325.192\n",
+      "Sigma-square ML     :      28.682                Akaike info criterion :     658.384\n",
+      "S.E of regression ML:      5.3555                Schwarz criterion     :     669.000\n",
+      "\n",
+      "------------------------------------------------------------------------------------\n",
+      "            Variable     Coefficient       Std.Error     t-Statistic     Probability\n",
+      "------------------------------------------------------------------------------------\n",
+      "          4_CONSTANT      12.6762323       2.3973782       5.2875396       0.0000007\n",
+      "              4_RD90       7.2357749       0.8959392       8.0761894       0.0000000\n",
+      "              4_PS90       3.0087836       0.5687185       5.2904618       0.0000007\n",
+      "              4_UE90      -0.9087647       0.4285148      -2.1207310       0.0363931\n",
+      "------------------------------------------------------------------------------------\n",
+      "Regimes variable: skater_reg\n",
+      "\n",
+      "REGRESSION DIAGNOSTICS\n",
+      "MULTICOLLINEARITY CONDITION NUMBER           10.336\n",
+      "\n",
+      "TEST ON NORMALITY OF ERRORS\n",
+      "TEST                             DF        VALUE           PROB\n",
+      "Jarque-Bera                       2        1082.543           0.0000\n",
+      "\n",
+      "DIAGNOSTICS FOR HETEROSKEDASTICITY\n",
+      "RANDOM COEFFICIENTS\n",
+      "TEST                             DF        VALUE           PROB\n",
+      "Breusch-Pagan test                3          69.868           0.0000\n",
+      "Koenker-Bassett test              3           8.330           0.0397\n",
+      "----------\n",
+      "\n",
+      "SUMMARY OF OUTPUT: ORDINARY LEAST SQUARES ESTIMATION - REGIME 5\n",
+      "---------------------------------------------------------------\n",
+      "Data set            :     unknown\n",
+      "Weights matrix      :     unknown\n",
+      "Dependent Variable  :  5_['HR90']                Number of Observations:         212\n",
+      "Mean dependent var  :      6.7045                Number of Variables   :           4\n",
+      "S.D. dependent var  :      7.5062                Degrees of Freedom    :         208\n",
+      "R-squared           :      0.6513\n",
+      "Adjusted R-squared  :      0.6463\n",
+      "Sum squared residual:    4145.470                F-statistic           :    129.5007\n",
+      "Sigma-square        :      19.930                Prob(F-statistic)     :   2.429e-47\n",
+      "S.E. of regression  :       4.464                Log likelihood        :    -615.973\n",
+      "Sigma-square ML     :      19.554                Akaike info criterion :    1239.945\n",
+      "S.E of regression ML:      4.4220                Schwarz criterion     :    1253.372\n",
+      "\n",
+      "------------------------------------------------------------------------------------\n",
+      "            Variable     Coefficient       Std.Error     t-Statistic     Probability\n",
+      "------------------------------------------------------------------------------------\n",
+      "          5_CONSTANT       6.8749278       1.4815715       4.6402943       0.0000062\n",
+      "              5_RD90       5.0735729       0.4720012      10.7490683       0.0000000\n",
+      "              5_PS90       4.3257745       0.4290443      10.0823502       0.0000000\n",
+      "              5_UE90      -0.1901823       0.2136345      -0.8902227       0.3743748\n",
+      "------------------------------------------------------------------------------------\n",
+      "Regimes variable: skater_reg\n",
+      "\n",
+      "REGRESSION DIAGNOSTICS\n",
+      "MULTICOLLINEARITY CONDITION NUMBER           10.840\n",
+      "\n",
+      "TEST ON NORMALITY OF ERRORS\n",
+      "TEST                             DF        VALUE           PROB\n",
+      "Jarque-Bera                       2          23.086           0.0000\n",
+      "\n",
+      "DIAGNOSTICS FOR HETEROSKEDASTICITY\n",
+      "RANDOM COEFFICIENTS\n",
+      "TEST                             DF        VALUE           PROB\n",
+      "Breusch-Pagan test                3          68.023           0.0000\n",
+      "Koenker-Bassett test              3          50.360           0.0000\n",
+      "----------\n",
+      "\n",
+      "SUMMARY OF OUTPUT: ORDINARY LEAST SQUARES ESTIMATION - REGIME 6\n",
+      "---------------------------------------------------------------\n",
+      "Data set            :     unknown\n",
+      "Weights matrix      :     unknown\n",
+      "Dependent Variable  :  6_['HR90']                Number of Observations:         142\n",
+      "Mean dependent var  :      6.9674                Number of Variables   :           4\n",
+      "S.D. dependent var  :      7.7639                Degrees of Freedom    :         138\n",
+      "R-squared           :      0.0816\n",
+      "Adjusted R-squared  :      0.0616\n",
+      "Sum squared residual:    7805.895                F-statistic           :      4.0858\n",
+      "Sigma-square        :      56.564                Prob(F-statistic)     :    0.008156\n",
+      "S.E. of regression  :       7.521                Log likelihood        :    -485.973\n",
+      "Sigma-square ML     :      54.971                Akaike info criterion :     979.945\n",
+      "S.E of regression ML:      7.4142                Schwarz criterion     :     991.769\n",
+      "\n",
+      "------------------------------------------------------------------------------------\n",
+      "            Variable     Coefficient       Std.Error     t-Statistic     Probability\n",
+      "------------------------------------------------------------------------------------\n",
+      "          6_CONSTANT       8.9134518       2.2205952       4.0139922       0.0000975\n",
+      "              6_RD90       3.1669956       1.0249618       3.0898670       0.0024224\n",
+      "              6_PS90       0.9219418       0.7333370       1.2571872       0.2108094\n",
+      "              6_UE90      -0.2902235       0.3083686      -0.9411576       0.3482687\n",
+      "------------------------------------------------------------------------------------\n",
+      "Regimes variable: skater_reg\n",
+      "\n",
+      "REGRESSION DIAGNOSTICS\n",
+      "MULTICOLLINEARITY CONDITION NUMBER            7.818\n",
+      "\n",
+      "TEST ON NORMALITY OF ERRORS\n",
+      "TEST                             DF        VALUE           PROB\n",
+      "Jarque-Bera                       2         211.413           0.0000\n",
+      "\n",
+      "DIAGNOSTICS FOR HETEROSKEDASTICITY\n",
+      "RANDOM COEFFICIENTS\n",
+      "TEST                             DF        VALUE           PROB\n",
+      "Breusch-Pagan test                3          16.332           0.0010\n",
+      "Koenker-Bassett test              3           4.771           0.1893\n",
+      "----------\n",
+      "\n",
+      "SUMMARY OF OUTPUT: ORDINARY LEAST SQUARES ESTIMATION - REGIME 7\n",
+      "---------------------------------------------------------------\n",
+      "Data set            :     unknown\n",
+      "Weights matrix      :     unknown\n",
+      "Dependent Variable  :  7_['HR90']                Number of Observations:         494\n",
+      "Mean dependent var  :      9.4357                Number of Variables   :           4\n",
+      "S.D. dependent var  :      6.1868                Degrees of Freedom    :         490\n",
+      "R-squared           :      0.3161\n",
+      "Adjusted R-squared  :      0.3120\n",
+      "Sum squared residual:   12904.325                F-statistic           :     75.5098\n",
+      "Sigma-square        :      26.335                Prob(F-statistic)     :   3.675e-40\n",
+      "S.E. of regression  :       5.132                Log likelihood        :   -1506.863\n",
+      "Sigma-square ML     :      26.122                Akaike info criterion :    3021.726\n",
+      "S.E of regression ML:      5.1110                Schwarz criterion     :    3038.536\n",
+      "\n",
+      "------------------------------------------------------------------------------------\n",
+      "            Variable     Coefficient       Std.Error     t-Statistic     Probability\n",
+      "------------------------------------------------------------------------------------\n",
+      "          7_CONSTANT      10.2257744       0.7060239      14.4836089       0.0000000\n",
+      "              7_RD90       4.9173048       0.3601681      13.6528046       0.0000000\n",
+      "              7_PS90       2.7435413       0.3773269       7.2709932       0.0000000\n",
+      "              7_UE90      -0.5158999       0.1027098      -5.0228862       0.0000007\n",
+      "------------------------------------------------------------------------------------\n",
+      "Regimes variable: skater_reg\n",
+      "\n",
+      "REGRESSION DIAGNOSTICS\n",
+      "MULTICOLLINEARITY CONDITION NUMBER            7.112\n",
+      "\n",
+      "TEST ON NORMALITY OF ERRORS\n",
+      "TEST                             DF        VALUE           PROB\n",
+      "Jarque-Bera                       2         164.804           0.0000\n",
+      "\n",
+      "DIAGNOSTICS FOR HETEROSKEDASTICITY\n",
+      "RANDOM COEFFICIENTS\n",
+      "TEST                             DF        VALUE           PROB\n",
+      "Breusch-Pagan test                3          93.891           0.0000\n",
+      "Koenker-Bassett test              3          45.305           0.0000\n",
+      "----------\n",
+      "\n",
+      "SUMMARY OF OUTPUT: ORDINARY LEAST SQUARES ESTIMATION - REGIME 8\n",
+      "---------------------------------------------------------------\n",
+      "Data set            :     unknown\n",
+      "Weights matrix      :     unknown\n",
+      "Dependent Variable  :  8_['HR90']                Number of Observations:         322\n",
+      "Mean dependent var  :     10.9368                Number of Variables   :           4\n",
+      "S.D. dependent var  :      7.1069                Degrees of Freedom    :         318\n",
+      "R-squared           :      0.1980\n",
+      "Adjusted R-squared  :      0.1904\n",
+      "Sum squared residual:   13003.178                F-statistic           :     26.1676\n",
+      "Sigma-square        :      40.890                Prob(F-statistic)     :   3.738e-15\n",
+      "S.E. of regression  :       6.395                Log likelihood        :   -1052.340\n",
+      "Sigma-square ML     :      40.383                Akaike info criterion :    2112.680\n",
+      "S.E of regression ML:      6.3547                Schwarz criterion     :    2127.779\n",
+      "\n",
+      "------------------------------------------------------------------------------------\n",
+      "            Variable     Coefficient       Std.Error     t-Statistic     Probability\n",
+      "------------------------------------------------------------------------------------\n",
+      "          8_CONSTANT       8.9613419       1.1548606       7.7596741       0.0000000\n",
+      "              8_RD90       3.1036861       0.4676112       6.6373221       0.0000000\n",
+      "              8_PS90       2.0054035       0.4775095       4.1997143       0.0000347\n",
+      "              8_UE90      -0.1659300       0.1618652      -1.0251118       0.3060896\n",
+      "------------------------------------------------------------------------------------\n",
+      "Regimes variable: skater_reg\n",
+      "\n",
+      "REGRESSION DIAGNOSTICS\n",
+      "MULTICOLLINEARITY CONDITION NUMBER            8.728\n",
+      "\n",
+      "TEST ON NORMALITY OF ERRORS\n",
+      "TEST                             DF        VALUE           PROB\n",
+      "Jarque-Bera                       2         805.242           0.0000\n",
+      "\n",
+      "DIAGNOSTICS FOR HETEROSKEDASTICITY\n",
+      "RANDOM COEFFICIENTS\n",
+      "TEST                             DF        VALUE           PROB\n",
+      "Breusch-Pagan test                3         271.848           0.0000\n",
+      "Koenker-Bassett test              3          60.502           0.0000\n",
+      "----------\n",
+      "\n",
+      "SUMMARY OF OUTPUT: ORDINARY LEAST SQUARES ESTIMATION - REGIME 9\n",
+      "---------------------------------------------------------------\n",
+      "Data set            :     unknown\n",
+      "Weights matrix      :     unknown\n",
+      "Dependent Variable  :  9_['HR90']                Number of Observations:         191\n",
+      "Mean dependent var  :     12.6163                Number of Variables   :           4\n",
+      "S.D. dependent var  :      6.4910                Degrees of Freedom    :         187\n",
+      "R-squared           :      0.2010\n",
+      "Adjusted R-squared  :      0.1882\n",
+      "Sum squared residual:    6396.303                F-statistic           :     15.6792\n",
+      "Sigma-square        :      34.205                Prob(F-statistic)     :   3.882e-09\n",
+      "S.E. of regression  :       5.848                Log likelihood        :    -606.337\n",
+      "Sigma-square ML     :      33.488                Akaike info criterion :    1220.674\n",
+      "S.E of regression ML:      5.7869                Schwarz criterion     :    1233.683\n",
+      "\n",
+      "------------------------------------------------------------------------------------\n",
+      "            Variable     Coefficient       Std.Error     t-Statistic     Probability\n",
+      "------------------------------------------------------------------------------------\n",
+      "          9_CONSTANT      10.6523809       1.8667675       5.7063244       0.0000000\n",
+      "              9_RD90       2.8884391       0.5972275       4.8364133       0.0000028\n",
+      "              9_PS90       0.2205890       0.5966193       0.3697316       0.7120008\n",
+      "              9_UE90      -0.0296635       0.3154386      -0.0940388       0.9251790\n",
+      "------------------------------------------------------------------------------------\n",
+      "Regimes variable: skater_reg\n",
+      "\n",
+      "REGRESSION DIAGNOSTICS\n",
+      "MULTICOLLINEARITY CONDITION NUMBER           10.444\n",
+      "\n",
+      "TEST ON NORMALITY OF ERRORS\n",
+      "TEST                             DF        VALUE           PROB\n",
+      "Jarque-Bera                       2          16.443           0.0003\n",
+      "\n",
+      "DIAGNOSTICS FOR HETEROSKEDASTICITY\n",
+      "RANDOM COEFFICIENTS\n",
+      "TEST                             DF        VALUE           PROB\n",
+      "Breusch-Pagan test                3          18.499           0.0003\n",
+      "Koenker-Bassett test              3          11.542           0.0091\n",
       "\n",
       "REGIMES DIAGNOSTICS - CHOW TEST\n",
       "                 VARIABLE        DF        VALUE           PROB\n",
-      "                 CONSTANT         3          20.123           0.0002\n",
-      "                    CRIME         3          26.940           0.0000\n",
-      "                      INC         3           4.537           0.2090\n",
-      "              Global test         9          36.811           0.0000\n",
+      "                 CONSTANT         9         102.431           0.0000\n",
+      "                     PS90         9          77.668           0.0000\n",
+      "                     RD90         9          78.587           0.0000\n",
+      "                     UE90         9          49.001           0.0000\n",
+      "              Global test        36         498.898           0.0000\n",
       "================================ END OF REPORT =====================================\n"
      ]
     }
    ],
    "source": [
     "reg = spreg.OLS_Regimes(y,x,\n",
-    "      regimes=results.current_labels_, w=w, name_y=['HOVAL'], name_x=['INC','CRIME'], name_regimes='skater_reg')\n",
+    "      regimes=results._trace[9][0], w=w, name_y=['HR90'], name_x=['RD90','PS90','UE90'], name_regimes='skater_reg')\n",
     "print(reg.summary)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Analysis",
+   "display_name": "Python [conda env:myenv] *",
    "language": "python",
-   "name": "analysis"
+   "name": "conda-env-myenv-py"
   },
   "language_info": {
    "codemirror_mode": {
@@ -43211,7 +622,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.4"
+   "version": "3.8.15"
   }
  },
  "nbformat": 4,

From f9a2d07ef0fe74c5be602f086025bcff2c389121 Mon Sep 17 00:00:00 2001
From: Pedro Amaral <pedrovma@gmail.com>
Date: Mon, 27 Mar 2023 18:12:50 -0500
Subject: [PATCH 03/28] Changing number of clusters in regimes regression in
 the example notebook.

---
 notebooks/skater_reg.ipynb | 238 +++++++++++++++++++++++++------------
 1 file changed, 162 insertions(+), 76 deletions(-)

diff --git a/notebooks/skater_reg.ipynb b/notebooks/skater_reg.ipynb
index 38f05ac6..bfbcf91c 100644
--- a/notebooks/skater_reg.ipynb
+++ b/notebooks/skater_reg.ipynb
@@ -88,8 +88,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 1min 33s, sys: 31.3 s, total: 2min 4s\n",
-      "Wall time: 34.7 s\n"
+      "CPU times: user 1min 16s, sys: 31.6 s, total: 1min 48s\n",
+      "Wall time: 25.3 s\n"
      ]
     }
    ],
@@ -183,7 +183,7 @@
    "id": "9bef8f61",
    "metadata": {},
    "source": [
-    "With the cluster allocations and selected number of clusters, we can call the Regimes methods in Spreg to get the full regression results and Chow tests on the stability of the coefficients accross the different clusters."
+    "With the cluster allocations and selected number of clusters, we can call the Regimes methods in Spreg to get the full regression results and Chow tests on the stability of the coefficients accross the 12 different clusters."
    ]
   },
   {
@@ -242,46 +242,85 @@
       "---------------------------------------------------------------\n",
       "Data set            :     unknown\n",
       "Weights matrix      :     unknown\n",
-      "Dependent Variable  :  1_['HR90']                Number of Observations:         442\n",
-      "Mean dependent var  :      4.2336                Number of Variables   :           4\n",
-      "S.D. dependent var  :      5.8415                Degrees of Freedom    :         438\n",
-      "R-squared           :      0.1285\n",
-      "Adjusted R-squared  :      0.1226\n",
-      "Sum squared residual:   13113.889                F-statistic           :     21.5360\n",
-      "Sigma-square        :      29.940                Prob(F-statistic)     :    4.99e-13\n",
-      "S.E. of regression  :       5.472                Log likelihood        :   -1376.387\n",
-      "Sigma-square ML     :      29.669                Akaike info criterion :    2760.773\n",
-      "S.E of regression ML:      5.4470                Schwarz criterion     :    2777.139\n",
+      "Dependent Variable  :  1_['HR90']                Number of Observations:         180\n",
+      "Mean dependent var  :      2.6269                Number of Variables   :           4\n",
+      "S.D. dependent var  :      4.5592                Degrees of Freedom    :         176\n",
+      "R-squared           :      0.1473\n",
+      "Adjusted R-squared  :      0.1328\n",
+      "Sum squared residual:    3172.661                F-statistic           :     10.1339\n",
+      "Sigma-square        :      18.026                Prob(F-statistic)     :   3.424e-06\n",
+      "S.E. of regression  :       4.246                Log likelihood        :    -513.652\n",
+      "Sigma-square ML     :      17.626                Akaike info criterion :    1035.304\n",
+      "S.E of regression ML:      4.1983                Schwarz criterion     :    1048.076\n",
       "\n",
       "------------------------------------------------------------------------------------\n",
       "            Variable     Coefficient       Std.Error     t-Statistic     Probability\n",
       "------------------------------------------------------------------------------------\n",
-      "          1_CONSTANT       2.9419808       0.8336035       3.5292327       0.0004609\n",
-      "              1_RD90       1.4503115       0.5061202       2.8655477       0.0043633\n",
-      "              1_PS90       0.7620325       0.2229427       3.4180638       0.0006897\n",
-      "              1_UE90       0.3611520       0.0977243       3.6956233       0.0002471\n",
+      "          1_CONSTANT       1.6802968       1.0041720       1.6733157       0.0960416\n",
+      "              1_RD90       0.9214816       0.7386502       1.2475210       0.2138639\n",
+      "              1_PS90       0.5520464       0.3817853       1.4459601       0.1499668\n",
+      "              1_UE90       0.3793060       0.1012117       3.7476508       0.0002418\n",
       "------------------------------------------------------------------------------------\n",
       "Regimes variable: skater_reg\n",
       "\n",
       "REGRESSION DIAGNOSTICS\n",
-      "MULTICOLLINEARITY CONDITION NUMBER            6.511\n",
+      "MULTICOLLINEARITY CONDITION NUMBER            6.525\n",
       "\n",
       "TEST ON NORMALITY OF ERRORS\n",
       "TEST                             DF        VALUE           PROB\n",
-      "Jarque-Bera                       2       76308.673           0.0000\n",
+      "Jarque-Bera                       2        4249.029           0.0000\n",
       "\n",
       "DIAGNOSTICS FOR HETEROSKEDASTICITY\n",
       "RANDOM COEFFICIENTS\n",
       "TEST                             DF        VALUE           PROB\n",
-      "Breusch-Pagan test                3         555.665           0.0000\n",
-      "Koenker-Bassett test              3          17.034           0.0007\n",
+      "Breusch-Pagan test                3           6.927           0.0743\n",
+      "Koenker-Bassett test              3           0.565           0.9044\n",
       "----------\n",
       "\n",
       "SUMMARY OF OUTPUT: ORDINARY LEAST SQUARES ESTIMATION - REGIME 2\n",
       "---------------------------------------------------------------\n",
       "Data set            :     unknown\n",
       "Weights matrix      :     unknown\n",
-      "Dependent Variable  :  2_['HR90']                Number of Observations:         157\n",
+      "Dependent Variable  :  2_['HR90']                Number of Observations:         105\n",
+      "Mean dependent var  :      5.4586                Number of Variables   :           4\n",
+      "S.D. dependent var  :      3.9328                Degrees of Freedom    :         101\n",
+      "R-squared           :      0.6004\n",
+      "Adjusted R-squared  :      0.5885\n",
+      "Sum squared residual:     642.756                F-statistic           :     50.5868\n",
+      "Sigma-square        :       6.364                Prob(F-statistic)     :   4.794e-20\n",
+      "S.E. of regression  :       2.523                Log likelihood        :    -244.108\n",
+      "Sigma-square ML     :       6.121                Akaike info criterion :     496.217\n",
+      "S.E of regression ML:      2.4742                Schwarz criterion     :     506.832\n",
+      "\n",
+      "------------------------------------------------------------------------------------\n",
+      "            Variable     Coefficient       Std.Error     t-Statistic     Probability\n",
+      "------------------------------------------------------------------------------------\n",
+      "          2_CONSTANT       2.9279569       1.6500241       1.7744934       0.0789946\n",
+      "              2_RD90       3.8978472       0.8119511       4.8005936       0.0000055\n",
+      "              2_PS90       2.5952604       0.2458875      10.5546660       0.0000000\n",
+      "              2_UE90       0.3236171       0.1945793       1.6631633       0.0993799\n",
+      "------------------------------------------------------------------------------------\n",
+      "Regimes variable: skater_reg\n",
+      "\n",
+      "REGRESSION DIAGNOSTICS\n",
+      "MULTICOLLINEARITY CONDITION NUMBER           15.160\n",
+      "\n",
+      "TEST ON NORMALITY OF ERRORS\n",
+      "TEST                             DF        VALUE           PROB\n",
+      "Jarque-Bera                       2           6.807           0.0333\n",
+      "\n",
+      "DIAGNOSTICS FOR HETEROSKEDASTICITY\n",
+      "RANDOM COEFFICIENTS\n",
+      "TEST                             DF        VALUE           PROB\n",
+      "Breusch-Pagan test                3          15.329           0.0016\n",
+      "Koenker-Bassett test              3          14.809           0.0020\n",
+      "----------\n",
+      "\n",
+      "SUMMARY OF OUTPUT: ORDINARY LEAST SQUARES ESTIMATION - REGIME 3\n",
+      "---------------------------------------------------------------\n",
+      "Data set            :     unknown\n",
+      "Weights matrix      :     unknown\n",
+      "Dependent Variable  :  3_['HR90']                Number of Observations:         157\n",
       "Mean dependent var  :      3.2521                Number of Variables   :           4\n",
       "S.D. dependent var  :      3.4925                Degrees of Freedom    :         153\n",
       "R-squared           :      0.3735\n",
@@ -295,10 +334,10 @@
       "------------------------------------------------------------------------------------\n",
       "            Variable     Coefficient       Std.Error     t-Statistic     Probability\n",
       "------------------------------------------------------------------------------------\n",
-      "          2_CONSTANT       1.6648327       1.6499048       1.0090477       0.3145449\n",
-      "              2_RD90       2.5911850       0.5446873       4.7571975       0.0000045\n",
-      "              2_PS90       1.7951113       0.2645028       6.7867381       0.0000000\n",
-      "              2_UE90       0.2831519       0.1896309       1.4931734       0.1374511\n",
+      "          3_CONSTANT       1.6648327       1.6499048       1.0090477       0.3145449\n",
+      "              3_RD90       2.5911850       0.5446873       4.7571975       0.0000045\n",
+      "              3_PS90       1.7951113       0.2645028       6.7867381       0.0000000\n",
+      "              3_UE90       0.2831519       0.1896309       1.4931734       0.1374511\n",
       "------------------------------------------------------------------------------------\n",
       "Regimes variable: skater_reg\n",
       "\n",
@@ -316,11 +355,50 @@
       "Koenker-Bassett test              3          23.069           0.0000\n",
       "----------\n",
       "\n",
-      "SUMMARY OF OUTPUT: ORDINARY LEAST SQUARES ESTIMATION - REGIME 3\n",
+      "SUMMARY OF OUTPUT: ORDINARY LEAST SQUARES ESTIMATION - REGIME 4\n",
       "---------------------------------------------------------------\n",
       "Data set            :     unknown\n",
       "Weights matrix      :     unknown\n",
-      "Dependent Variable  :  3_['HR90']                Number of Observations:         416\n",
+      "Dependent Variable  :  4_['HR90']                Number of Observations:         157\n",
+      "Mean dependent var  :      5.2565                Number of Variables   :           4\n",
+      "S.D. dependent var  :      7.5670                Degrees of Freedom    :         153\n",
+      "R-squared           :      0.0718\n",
+      "Adjusted R-squared  :      0.0536\n",
+      "Sum squared residual:    8291.273                F-statistic           :      3.9445\n",
+      "Sigma-square        :      54.191                Prob(F-statistic)     :    0.009592\n",
+      "S.E. of regression  :       7.361                Log likelihood        :    -534.160\n",
+      "Sigma-square ML     :      52.811                Akaike info criterion :    1076.321\n",
+      "S.E of regression ML:      7.2671                Schwarz criterion     :    1088.546\n",
+      "\n",
+      "------------------------------------------------------------------------------------\n",
+      "            Variable     Coefficient       Std.Error     t-Statistic     Probability\n",
+      "------------------------------------------------------------------------------------\n",
+      "          4_CONSTANT       6.5333323       2.4673414       2.6479239       0.0089474\n",
+      "              4_RD90       2.7602351       1.0586310       2.6073627       0.0100275\n",
+      "              4_PS90      -0.6252142       0.6065058      -1.0308463       0.3042397\n",
+      "              4_UE90      -0.0983422       0.2825469      -0.3480561       0.7282764\n",
+      "------------------------------------------------------------------------------------\n",
+      "Regimes variable: skater_reg\n",
+      "\n",
+      "REGRESSION DIAGNOSTICS\n",
+      "MULTICOLLINEARITY CONDITION NUMBER            9.215\n",
+      "\n",
+      "TEST ON NORMALITY OF ERRORS\n",
+      "TEST                             DF        VALUE           PROB\n",
+      "Jarque-Bera                       2       10522.321           0.0000\n",
+      "\n",
+      "DIAGNOSTICS FOR HETEROSKEDASTICITY\n",
+      "RANDOM COEFFICIENTS\n",
+      "TEST                             DF        VALUE           PROB\n",
+      "Breusch-Pagan test                3         397.444           0.0000\n",
+      "Koenker-Bassett test              3          19.450           0.0002\n",
+      "----------\n",
+      "\n",
+      "SUMMARY OF OUTPUT: ORDINARY LEAST SQUARES ESTIMATION - REGIME 5\n",
+      "---------------------------------------------------------------\n",
+      "Data set            :     unknown\n",
+      "Weights matrix      :     unknown\n",
+      "Dependent Variable  :  5_['HR90']                Number of Observations:         416\n",
       "Mean dependent var  :      3.5350                Number of Variables   :           4\n",
       "S.D. dependent var  :      3.5289                Degrees of Freedom    :         412\n",
       "R-squared           :      0.2384\n",
@@ -334,10 +412,10 @@
       "------------------------------------------------------------------------------------\n",
       "            Variable     Coefficient       Std.Error     t-Statistic     Probability\n",
       "------------------------------------------------------------------------------------\n",
-      "          3_CONSTANT       3.6580644       0.7883611       4.6400874       0.0000047\n",
-      "              3_RD90       2.1705064       0.3732128       5.8157339       0.0000000\n",
-      "              3_PS90       1.6485127       0.2143249       7.6916535       0.0000000\n",
-      "              3_UE90      -0.0049898       0.0843801      -0.0591343       0.9528738\n",
+      "          5_CONSTANT       3.6580644       0.7883611       4.6400874       0.0000047\n",
+      "              5_RD90       2.1705064       0.3732128       5.8157339       0.0000000\n",
+      "              5_PS90       1.6485127       0.2143249       7.6916535       0.0000000\n",
+      "              5_UE90      -0.0049898       0.0843801      -0.0591343       0.9528738\n",
       "------------------------------------------------------------------------------------\n",
       "Regimes variable: skater_reg\n",
       "\n",
@@ -355,11 +433,11 @@
       "Koenker-Bassett test              3          48.598           0.0000\n",
       "----------\n",
       "\n",
-      "SUMMARY OF OUTPUT: ORDINARY LEAST SQUARES ESTIMATION - REGIME 4\n",
+      "SUMMARY OF OUTPUT: ORDINARY LEAST SQUARES ESTIMATION - REGIME 6\n",
       "---------------------------------------------------------------\n",
       "Data set            :     unknown\n",
       "Weights matrix      :     unknown\n",
-      "Dependent Variable  :  4_['HR90']                Number of Observations:         105\n",
+      "Dependent Variable  :  6_['HR90']                Number of Observations:         105\n",
       "Mean dependent var  :      6.9902                Number of Variables   :           4\n",
       "S.D. dependent var  :      7.7137                Degrees of Freedom    :         101\n",
       "R-squared           :      0.5133\n",
@@ -373,10 +451,10 @@
       "------------------------------------------------------------------------------------\n",
       "            Variable     Coefficient       Std.Error     t-Statistic     Probability\n",
       "------------------------------------------------------------------------------------\n",
-      "          4_CONSTANT      12.6762323       2.3973782       5.2875396       0.0000007\n",
-      "              4_RD90       7.2357749       0.8959392       8.0761894       0.0000000\n",
-      "              4_PS90       3.0087836       0.5687185       5.2904618       0.0000007\n",
-      "              4_UE90      -0.9087647       0.4285148      -2.1207310       0.0363931\n",
+      "          6_CONSTANT      12.6762323       2.3973782       5.2875396       0.0000007\n",
+      "              6_RD90       7.2357749       0.8959392       8.0761894       0.0000000\n",
+      "              6_PS90       3.0087836       0.5687185       5.2904618       0.0000007\n",
+      "              6_UE90      -0.9087647       0.4285148      -2.1207310       0.0363931\n",
       "------------------------------------------------------------------------------------\n",
       "Regimes variable: skater_reg\n",
       "\n",
@@ -394,11 +472,11 @@
       "Koenker-Bassett test              3           8.330           0.0397\n",
       "----------\n",
       "\n",
-      "SUMMARY OF OUTPUT: ORDINARY LEAST SQUARES ESTIMATION - REGIME 5\n",
+      "SUMMARY OF OUTPUT: ORDINARY LEAST SQUARES ESTIMATION - REGIME 7\n",
       "---------------------------------------------------------------\n",
       "Data set            :     unknown\n",
       "Weights matrix      :     unknown\n",
-      "Dependent Variable  :  5_['HR90']                Number of Observations:         212\n",
+      "Dependent Variable  :  7_['HR90']                Number of Observations:         212\n",
       "Mean dependent var  :      6.7045                Number of Variables   :           4\n",
       "S.D. dependent var  :      7.5062                Degrees of Freedom    :         208\n",
       "R-squared           :      0.6513\n",
@@ -412,10 +490,10 @@
       "------------------------------------------------------------------------------------\n",
       "            Variable     Coefficient       Std.Error     t-Statistic     Probability\n",
       "------------------------------------------------------------------------------------\n",
-      "          5_CONSTANT       6.8749278       1.4815715       4.6402943       0.0000062\n",
-      "              5_RD90       5.0735729       0.4720012      10.7490683       0.0000000\n",
-      "              5_PS90       4.3257745       0.4290443      10.0823502       0.0000000\n",
-      "              5_UE90      -0.1901823       0.2136345      -0.8902227       0.3743748\n",
+      "          7_CONSTANT       6.8749278       1.4815715       4.6402943       0.0000062\n",
+      "              7_RD90       5.0735729       0.4720012      10.7490683       0.0000000\n",
+      "              7_PS90       4.3257745       0.4290443      10.0823502       0.0000000\n",
+      "              7_UE90      -0.1901823       0.2136345      -0.8902227       0.3743748\n",
       "------------------------------------------------------------------------------------\n",
       "Regimes variable: skater_reg\n",
       "\n",
@@ -433,11 +511,11 @@
       "Koenker-Bassett test              3          50.360           0.0000\n",
       "----------\n",
       "\n",
-      "SUMMARY OF OUTPUT: ORDINARY LEAST SQUARES ESTIMATION - REGIME 6\n",
+      "SUMMARY OF OUTPUT: ORDINARY LEAST SQUARES ESTIMATION - REGIME 8\n",
       "---------------------------------------------------------------\n",
       "Data set            :     unknown\n",
       "Weights matrix      :     unknown\n",
-      "Dependent Variable  :  6_['HR90']                Number of Observations:         142\n",
+      "Dependent Variable  :  8_['HR90']                Number of Observations:         142\n",
       "Mean dependent var  :      6.9674                Number of Variables   :           4\n",
       "S.D. dependent var  :      7.7639                Degrees of Freedom    :         138\n",
       "R-squared           :      0.0816\n",
@@ -451,10 +529,10 @@
       "------------------------------------------------------------------------------------\n",
       "            Variable     Coefficient       Std.Error     t-Statistic     Probability\n",
       "------------------------------------------------------------------------------------\n",
-      "          6_CONSTANT       8.9134518       2.2205952       4.0139922       0.0000975\n",
-      "              6_RD90       3.1669956       1.0249618       3.0898670       0.0024224\n",
-      "              6_PS90       0.9219418       0.7333370       1.2571872       0.2108094\n",
-      "              6_UE90      -0.2902235       0.3083686      -0.9411576       0.3482687\n",
+      "          8_CONSTANT       8.9134518       2.2205952       4.0139922       0.0000975\n",
+      "              8_RD90       3.1669956       1.0249618       3.0898670       0.0024224\n",
+      "              8_PS90       0.9219418       0.7333370       1.2571872       0.2108094\n",
+      "              8_UE90      -0.2902235       0.3083686      -0.9411576       0.3482687\n",
       "------------------------------------------------------------------------------------\n",
       "Regimes variable: skater_reg\n",
       "\n",
@@ -472,11 +550,11 @@
       "Koenker-Bassett test              3           4.771           0.1893\n",
       "----------\n",
       "\n",
-      "SUMMARY OF OUTPUT: ORDINARY LEAST SQUARES ESTIMATION - REGIME 7\n",
+      "SUMMARY OF OUTPUT: ORDINARY LEAST SQUARES ESTIMATION - REGIME 9\n",
       "---------------------------------------------------------------\n",
       "Data set            :     unknown\n",
       "Weights matrix      :     unknown\n",
-      "Dependent Variable  :  7_['HR90']                Number of Observations:         494\n",
+      "Dependent Variable  :  9_['HR90']                Number of Observations:         494\n",
       "Mean dependent var  :      9.4357                Number of Variables   :           4\n",
       "S.D. dependent var  :      6.1868                Degrees of Freedom    :         490\n",
       "R-squared           :      0.3161\n",
@@ -490,10 +568,10 @@
       "------------------------------------------------------------------------------------\n",
       "            Variable     Coefficient       Std.Error     t-Statistic     Probability\n",
       "------------------------------------------------------------------------------------\n",
-      "          7_CONSTANT      10.2257744       0.7060239      14.4836089       0.0000000\n",
-      "              7_RD90       4.9173048       0.3601681      13.6528046       0.0000000\n",
-      "              7_PS90       2.7435413       0.3773269       7.2709932       0.0000000\n",
-      "              7_UE90      -0.5158999       0.1027098      -5.0228862       0.0000007\n",
+      "          9_CONSTANT      10.2257744       0.7060239      14.4836089       0.0000000\n",
+      "              9_RD90       4.9173048       0.3601681      13.6528046       0.0000000\n",
+      "              9_PS90       2.7435413       0.3773269       7.2709932       0.0000000\n",
+      "              9_UE90      -0.5158999       0.1027098      -5.0228862       0.0000007\n",
       "------------------------------------------------------------------------------------\n",
       "Regimes variable: skater_reg\n",
       "\n",
@@ -511,11 +589,11 @@
       "Koenker-Bassett test              3          45.305           0.0000\n",
       "----------\n",
       "\n",
-      "SUMMARY OF OUTPUT: ORDINARY LEAST SQUARES ESTIMATION - REGIME 8\n",
-      "---------------------------------------------------------------\n",
+      "SUMMARY OF OUTPUT: ORDINARY LEAST SQUARES ESTIMATION - REGIME 10\n",
+      "----------------------------------------------------------------\n",
       "Data set            :     unknown\n",
       "Weights matrix      :     unknown\n",
-      "Dependent Variable  :  8_['HR90']                Number of Observations:         322\n",
+      "Dependent Variable  : 10_['HR90']                Number of Observations:         322\n",
       "Mean dependent var  :     10.9368                Number of Variables   :           4\n",
       "S.D. dependent var  :      7.1069                Degrees of Freedom    :         318\n",
       "R-squared           :      0.1980\n",
@@ -529,10 +607,10 @@
       "------------------------------------------------------------------------------------\n",
       "            Variable     Coefficient       Std.Error     t-Statistic     Probability\n",
       "------------------------------------------------------------------------------------\n",
-      "          8_CONSTANT       8.9613419       1.1548606       7.7596741       0.0000000\n",
-      "              8_RD90       3.1036861       0.4676112       6.6373221       0.0000000\n",
-      "              8_PS90       2.0054035       0.4775095       4.1997143       0.0000347\n",
-      "              8_UE90      -0.1659300       0.1618652      -1.0251118       0.3060896\n",
+      "         10_CONSTANT       8.9613419       1.1548606       7.7596741       0.0000000\n",
+      "             10_RD90       3.1036861       0.4676112       6.6373221       0.0000000\n",
+      "             10_PS90       2.0054035       0.4775095       4.1997143       0.0000347\n",
+      "             10_UE90      -0.1659300       0.1618652      -1.0251118       0.3060896\n",
       "------------------------------------------------------------------------------------\n",
       "Regimes variable: skater_reg\n",
       "\n",
@@ -550,11 +628,11 @@
       "Koenker-Bassett test              3          60.502           0.0000\n",
       "----------\n",
       "\n",
-      "SUMMARY OF OUTPUT: ORDINARY LEAST SQUARES ESTIMATION - REGIME 9\n",
-      "---------------------------------------------------------------\n",
+      "SUMMARY OF OUTPUT: ORDINARY LEAST SQUARES ESTIMATION - REGIME 11\n",
+      "----------------------------------------------------------------\n",
       "Data set            :     unknown\n",
       "Weights matrix      :     unknown\n",
-      "Dependent Variable  :  9_['HR90']                Number of Observations:         191\n",
+      "Dependent Variable  : 11_['HR90']                Number of Observations:         191\n",
       "Mean dependent var  :     12.6163                Number of Variables   :           4\n",
       "S.D. dependent var  :      6.4910                Degrees of Freedom    :         187\n",
       "R-squared           :      0.2010\n",
@@ -568,10 +646,10 @@
       "------------------------------------------------------------------------------------\n",
       "            Variable     Coefficient       Std.Error     t-Statistic     Probability\n",
       "------------------------------------------------------------------------------------\n",
-      "          9_CONSTANT      10.6523809       1.8667675       5.7063244       0.0000000\n",
-      "              9_RD90       2.8884391       0.5972275       4.8364133       0.0000028\n",
-      "              9_PS90       0.2205890       0.5966193       0.3697316       0.7120008\n",
-      "              9_UE90      -0.0296635       0.3154386      -0.0940388       0.9251790\n",
+      "         11_CONSTANT      10.6523809       1.8667675       5.7063244       0.0000000\n",
+      "             11_RD90       2.8884391       0.5972275       4.8364133       0.0000028\n",
+      "             11_PS90       0.2205890       0.5966193       0.3697316       0.7120008\n",
+      "             11_UE90      -0.0296635       0.3154386      -0.0940388       0.9251790\n",
       "------------------------------------------------------------------------------------\n",
       "Regimes variable: skater_reg\n",
       "\n",
@@ -590,20 +668,28 @@
       "\n",
       "REGIMES DIAGNOSTICS - CHOW TEST\n",
       "                 VARIABLE        DF        VALUE           PROB\n",
-      "                 CONSTANT         9         102.431           0.0000\n",
-      "                     PS90         9          77.668           0.0000\n",
-      "                     RD90         9          78.587           0.0000\n",
-      "                     UE90         9          49.001           0.0000\n",
-      "              Global test        36         498.898           0.0000\n",
+      "                 CONSTANT        11         110.297           0.0000\n",
+      "                     PS90        11          95.473           0.0000\n",
+      "                     RD90        11          75.733           0.0000\n",
+      "                     UE90        11          52.373           0.0000\n",
+      "              Global test        44         532.110           0.0000\n",
       "================================ END OF REPORT =====================================\n"
      ]
     }
    ],
    "source": [
     "reg = spreg.OLS_Regimes(y,x,\n",
-    "      regimes=results._trace[9][0], w=w, name_y=['HR90'], name_x=['RD90','PS90','UE90'], name_regimes='skater_reg')\n",
+    "      regimes=results._trace[11][0], w=w, name_y=['HR90'], name_x=['RD90','PS90','UE90'], name_regimes='skater_reg')\n",
     "print(reg.summary)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "887b4d0d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

From 72320cf9eb0a8387ec675feceb2c499cecfa88bf Mon Sep 17 00:00:00 2001
From: Pedro Amaral <pedrovma@gmail.com>
Date: Wed, 29 Mar 2023 20:52:57 -0500
Subject: [PATCH 04/28] Fixing a bug in the printing of results of Chow Test in
 ML_Lag_Regimes

---
 spreg/ml_lag_regimes.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/spreg/ml_lag_regimes.py b/spreg/ml_lag_regimes.py
index ec9bfe5e..4e603d09 100644
--- a/spreg/ml_lag_regimes.py
+++ b/spreg/ml_lag_regimes.py
@@ -318,7 +318,7 @@ def __init__(
         set_warn(self, warn)
         name_x = USER.set_name_x(name_x, x_constant, constant=True)
 
-        self.name_x_r = name_x + [USER.set_name_yend_sp(name_y)]
+        self.name_x_r = USER.set_name_x(name_x, x_constant) + [USER.set_name_yend_sp(name_y)]
         self.method = method
         self.epsilon = epsilon
         self.name_regimes = USER.set_name_ds(name_regimes)

From 6921d00152159ed6656a0b66d0bd51649186d1ae Mon Sep 17 00:00:00 2001
From: Pedro Amaral <pedrovma@gmail.com>
Date: Thu, 30 Mar 2023 08:33:07 -0500
Subject: [PATCH 05/28] Fixing a bug in the printing of results of Chow Test in
 ML_Lag_Regimes (#112)

---
 spreg/ml_lag_regimes.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/spreg/ml_lag_regimes.py b/spreg/ml_lag_regimes.py
index ec9bfe5e..4e603d09 100644
--- a/spreg/ml_lag_regimes.py
+++ b/spreg/ml_lag_regimes.py
@@ -318,7 +318,7 @@ def __init__(
         set_warn(self, warn)
         name_x = USER.set_name_x(name_x, x_constant, constant=True)
 
-        self.name_x_r = name_x + [USER.set_name_yend_sp(name_y)]
+        self.name_x_r = USER.set_name_x(name_x, x_constant) + [USER.set_name_yend_sp(name_y)]
         self.method = method
         self.epsilon = epsilon
         self.name_regimes = USER.set_name_ds(name_regimes)

From 197be6237422ea0b3ef6a80a58e09a07e6ecbe63 Mon Sep 17 00:00:00 2001
From: Pedro Amaral <pedrovma@gmail.com>
Date: Mon, 3 Apr 2023 11:34:55 -0500
Subject: [PATCH 06/28] Fixing minor bugs in the way results were printed for
 Combo and GM_Error regimes models.

---
 spreg/error_sp_het_regimes.py |  2 +-
 spreg/summary_output.py       | 14 +++++++-------
 2 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/spreg/error_sp_het_regimes.py b/spreg/error_sp_het_regimes.py
index a5e6aafc..9c5a4586 100644
--- a/spreg/error_sp_het_regimes.py
+++ b/spreg/error_sp_het_regimes.py
@@ -329,7 +329,7 @@ def __init__(
         x_constant, name_x, warn = USER.check_constant(x, name_x, just_rem=True)
         set_warn(self, warn)
         name_x = USER.set_name_x(name_x, x_constant, constant=True)
-        self.name_x_r = name_x
+        self.name_x_r = USER.set_name_x(name_x, x_constant)
 
         cols2regi = REGI.check_cols2regi(constant_regi, cols2regi, x_constant)
         self.regimes_set = REGI._get_regimes_set(regimes)
diff --git a/spreg/summary_output.py b/spreg/summary_output.py
index 6be95ec1..0e3d2fbb 100755
--- a/spreg/summary_output.py
+++ b/spreg/summary_output.py
@@ -459,7 +459,7 @@ def GM_Error_Hom(reg, vm, w, regimes=False):
     # build coefficients table body
     beta_position = summary_coefs_allx(reg, reg.z_stat, lambd=True)
     if regimes:
-        summary_coefs_lambda(reg, reg.z_stat)
+        #summary_coefs_lambda(reg, reg.z_stat)
         summary_regimes(reg)
     summary_warning(reg)
     summary(reg=reg, vm=vm, instruments=False, nonspat_diag=False, spat_diag=False)
@@ -501,7 +501,7 @@ def GM_Endog_Error_Hom(reg, vm, w, regimes=False):
     summary_coefs_allx(reg, reg.z_stat, lambd=True)
     summary_coefs_instruments(reg)
     if regimes:
-        summary_coefs_lambda(reg, reg.z_stat)
+        #summary_coefs_lambda(reg, reg.z_stat)
         summary_regimes(reg)
     summary_warning(reg)
     summary(reg=reg, vm=vm, instruments=True, nonspat_diag=False, spat_diag=False)
@@ -543,7 +543,7 @@ def GM_Error_Het(reg, vm, w, regimes=False):
     # build coefficients table body
     beta_position = summary_coefs_allx(reg, reg.z_stat, lambd=True)
     if regimes:
-        summary_coefs_lambda(reg, reg.z_stat)
+        #summary_coefs_lambda(reg, reg.z_stat)
         summary_regimes(reg)
     summary_warning(reg)
     summary(reg=reg, vm=vm, instruments=False, nonspat_diag=False, spat_diag=False)
@@ -585,7 +585,7 @@ def GM_Endog_Error_Het(reg, vm, w, regimes=False):
     summary_coefs_allx(reg, reg.z_stat, lambd=True)
     summary_coefs_instruments(reg)
     if regimes:
-        summary_coefs_lambda(reg, reg.z_stat)
+        #summary_coefs_lambda(reg, reg.z_stat)
         summary_regimes(reg)
     summary_warning(reg)
     summary(reg=reg, vm=vm, instruments=True, nonspat_diag=False, spat_diag=False)
@@ -671,7 +671,7 @@ def GM_Combo_Hom(reg, vm, w, regimes=False):
     summary_coefs_allx(reg, reg.z_stat, lambd=True)
     summary_coefs_instruments(reg)
     if regimes:
-        summary_coefs_lambda(reg, reg.z_stat)
+        #summary_coefs_lambda(reg, reg.z_stat)
         summary_regimes(reg)
     summary_warning(reg)
     summary(reg=reg, vm=vm, instruments=True, nonspat_diag=False, spat_diag=False)
@@ -715,7 +715,7 @@ def GM_Combo_Het(reg, vm, w, regimes=False):
     summary_coefs_allx(reg, reg.z_stat, lambd=True)
     summary_coefs_instruments(reg)
     if regimes:
-        summary_coefs_lambda(reg, reg.z_stat)
+        #summary_coefs_lambda(reg, reg.z_stat)
         summary_regimes(reg)
     summary_warning(reg)
     summary(reg=reg, vm=vm, instruments=True, nonspat_diag=False, spat_diag=False)
@@ -1182,7 +1182,7 @@ def _get_var_indices(reg, zt_stat, lambd=False):
     try:
         kf = reg.kf
         if lambd:
-            kf += -len(zt_stat) - len(reg.betas)
+            kf += len(zt_stat) - len(reg.betas)
         krex = reg.kr - reg.kryd
         try:
             kfyd = reg.yend.shape[1] - reg.nr * reg.kryd

From 01badd695d8d396ba27197aba8c3ed1a9e5151e4 Mon Sep 17 00:00:00 2001
From: Pedro Amaral <pedrovma@gmail.com>
Date: Mon, 3 Apr 2023 11:42:30 -0500
Subject: [PATCH 07/28] Adding TSLS_Regimes to the api.

---
 docs/api.rst | 1 +
 1 file changed, 1 insertion(+)

diff --git a/docs/api.rst b/docs/api.rst
index 7ca22b87..792be6e8 100644
--- a/docs/api.rst
+++ b/docs/api.rst
@@ -47,6 +47,7 @@ Regimes models are variants of spatial regression models which allow for structu
     :toctree: generated/
 
     spreg.OLS_Regimes
+    spreg.TSLS_Regimes
     spreg.ML_Lag_Regimes
     spreg.ML_Error_Regimes
     spreg.GM_Lag_Regimes

From 79b67586377d0e02f475cc350d45d4643eea513e Mon Sep 17 00:00:00 2001
From: Pedro Amaral <pedrovma@gmail.com>
Date: Sat, 8 Apr 2023 07:07:42 -0500
Subject: [PATCH 08/28] Fixing minor bug in the results report for
 GM_Endog_Error_Regimes methods.

---
 spreg/summary_output.py | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/spreg/summary_output.py b/spreg/summary_output.py
index 0e3d2fbb..989aaf52 100755
--- a/spreg/summary_output.py
+++ b/spreg/summary_output.py
@@ -1194,6 +1194,8 @@ def _get_var_indices(reg, zt_stat, lambd=False):
         for i in range(reg.nr):
             j = i * krex
             jyd = krex * reg.nr + i * reg.kryd + kf - kfyd
+            if len(zt_stat) == len(reg.betas) and lambd:
+                jyd += -1
             name_reg = var_names[j + j_con : j + krex] + var_names[jyd : jyd + reg.kryd]
             # name_reg.sort()
             if reg.constant_regi == "many":
@@ -1203,10 +1205,14 @@ def _get_var_indices(reg, zt_stat, lambd=False):
         if reg.constant_regi == "one":
             indices += [krex * reg.nr]
         if len(indices) < last_v:
-            name_reg = (
+            if len(indices) - last_v == -1 and lambd:
+                name_reg = ['lambda']
+            else:
+                name_reg = (
                 var_names[krex * reg.nr + 1 - j_con : krex * reg.nr + kf - kfyd]
                 + var_names[reg.kr * reg.nr + kf - kfyd : reg.kr * reg.nr + kf]
             )
+
             # name_reg.sort()
             indices += [var_names.index(ind) for ind in name_reg]
     except:

From a8e9e95cd415ee057e6d11ea094580f0fe246602 Mon Sep 17 00:00:00 2001
From: Pedro Amaral <pedrovma@gmail.com>
Date: Sat, 8 Apr 2023 07:10:35 -0500
Subject: [PATCH 09/28] Documenting fix.

---
 spreg/summary_output.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/spreg/summary_output.py b/spreg/summary_output.py
index 989aaf52..ae117a49 100755
--- a/spreg/summary_output.py
+++ b/spreg/summary_output.py
@@ -1195,7 +1195,7 @@ def _get_var_indices(reg, zt_stat, lambd=False):
             j = i * krex
             jyd = krex * reg.nr + i * reg.kryd + kf - kfyd
             if len(zt_stat) == len(reg.betas) and lambd:
-                jyd += -1
+                jyd += -1 #discount lambda as fixed coefficient in this counter
             name_reg = var_names[j + j_con : j + krex] + var_names[jyd : jyd + reg.kryd]
             # name_reg.sort()
             if reg.constant_regi == "many":

From 2be303ac543c38547038f0e05c0037e20712854e Mon Sep 17 00:00:00 2001
From: Pedro Amaral <pedrovma@gmail.com>
Date: Mon, 10 Apr 2023 14:06:11 -0500
Subject: [PATCH 10/28] More Regimes results printing fixes.

---
 spreg/summary_output.py | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/spreg/summary_output.py b/spreg/summary_output.py
index ae117a49..627b3f73 100755
--- a/spreg/summary_output.py
+++ b/spreg/summary_output.py
@@ -1205,13 +1205,12 @@ def _get_var_indices(reg, zt_stat, lambd=False):
         if reg.constant_regi == "one":
             indices += [krex * reg.nr]
         if len(indices) < last_v:
-            if len(indices) - last_v == -1 and lambd:
+            if len(indices) - last_v == -1 and reg.kryd>0 and lambd:
                 name_reg = ['lambda']
             else:
                 name_reg = (
                 var_names[krex * reg.nr + 1 - j_con : krex * reg.nr + kf - kfyd]
-                + var_names[reg.kr * reg.nr + kf - kfyd : reg.kr * reg.nr + kf]
-            )
+                + var_names[reg.kr * reg.nr + kf - kfyd : reg.kr * reg.nr + kf])
 
             # name_reg.sort()
             indices += [var_names.index(ind) for ind in name_reg]

From 43e291262677d9291d95da0b79060dec59d15cd0 Mon Sep 17 00:00:00 2001
From: Pedro Amaral <pedrovma@gmail.com>
Date: Thu, 27 Apr 2023 11:58:56 -0500
Subject: [PATCH 11/28] Fixing API annotations.

---
 docs/api.rst       | 15 ++++-----------
 spreg/sur_error.py |  4 ++--
 2 files changed, 6 insertions(+), 13 deletions(-)

diff --git a/docs/api.rst b/docs/api.rst
index 792be6e8..76f36332 100644
--- a/docs/api.rst
+++ b/docs/api.rst
@@ -76,17 +76,6 @@ Seemingly-unrelated regression models are a generalization of linear regression.
     spreg.SURlagIV
     spreg.ThreeSLS
 
-Panel Models
-------------
-
-.. autosummary::
-    :toctree: generated/
-
-    spreg.Panel_FE_Lag
-    spreg.Panel_FE_Error
-    spreg.Panel_RE_Lag
-    spreg.Panel_RE_Error
-
 Spatial Panel Models
 --------------------
 
@@ -95,6 +84,10 @@ Spatial panel models allow for evaluating correlation in both spatial and time d
 .. autosummary::
    :toctree: generated/
     
+    spreg.Panel_FE_Lag
+    spreg.Panel_FE_Error
+    spreg.Panel_RE_Lag
+    spreg.Panel_RE_Error
     spreg.GM_KKP
 
 Diagnostics
diff --git a/spreg/sur_error.py b/spreg/sur_error.py
index 37f2d6d1..b88e1220 100644
--- a/spreg/sur_error.py
+++ b/spreg/sur_error.py
@@ -38,7 +38,7 @@
 
 
 class BaseSURerrorGM:
-    """Base class for SUR Error estimation by Generalized Moment Estimation
+    """Base class for SUR Error estimation by Generalized Moments
 
     Parameters
     ----------
@@ -162,7 +162,7 @@ def _momentsGM_sur_Error(w, u):
 
 class SURerrorGM(BaseSURerrorGM, REGI.Regimes_Frame):
     """
-    User class for SUR Error estimation by Maximum Likelihood
+    User class for SUR Error estimation by Generalized Moments
 
     Parameters
     ----------

From 1b78d18a89f32c2e9f094f8ee2e7951af2de02e7 Mon Sep 17 00:00:00 2001
From: Pedro Amaral <pedrovma@gmail.com>
Date: Thu, 27 Apr 2023 12:18:25 -0500
Subject: [PATCH 12/28] Fixing API annotations.

---
 spreg/sp_panels.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/spreg/sp_panels.py b/spreg/sp_panels.py
index ab88ea94..68d170a2 100644
--- a/spreg/sp_panels.py
+++ b/spreg/sp_panels.py
@@ -25,6 +25,7 @@ class BaseGM_KKP(RegressionPropsY):
     '''
     Base GMM method for a spatial random effects panel model based on
     Kapoor, Kelejian and Prucha (2007) :cite:`KKP2007`.
+
     Parameters
     ----------
     y          : array
@@ -120,6 +121,7 @@ class GM_KKP(BaseGM_KKP, REGI.Regimes_Frame):
     '''
     GMM method for a spatial random effects panel model based on
     Kapoor, Kelejian and Prucha (2007) :cite:`KKP2007`.
+
     Parameters
     ----------
     y          : array
@@ -356,6 +358,7 @@ def _moments_kkp(ws, u, i, trace_w2=None):
     """
     Compute G and g matrices for the KKP model.
     ...
+
     Parameters
     ----------
     ws          : Sparse matrix

From f4282739e78259446a2677879b9667bb15ca4832 Mon Sep 17 00:00:00 2001
From: eli knaap <ek@knaaptime.com>
Date: Wed, 9 Aug 2023 15:22:48 -0700
Subject: [PATCH 13/28] add panel diagnostics

---
 docs/api.rst | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/docs/api.rst b/docs/api.rst
index 76f36332..abd4e60c 100644
--- a/docs/api.rst
+++ b/docs/api.rst
@@ -123,3 +123,8 @@ Diagnostic tests are useful for identifying model fit, sufficiency, and specific
     spreg.surLMe
     spreg.surLMlag
     spreg.constant_check
+    spreg.panel_LMlag
+    spreg.panel_LMerror
+    spreg.panel_rLMlag
+    spreg.panel_rLMerror
+    spreg.panel_Hausman

From bc552108507e3e7867d8f66ed0e95a37c4eca54d Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Mon, 4 Sep 2023 22:03:12 -0400
Subject: [PATCH 14/28] Bump actions/checkout from 3 to 4 (#117)

Bumps [actions/checkout](https://github.com/actions/checkout) from 3 to 4.
- [Release notes](https://github.com/actions/checkout/releases)
- [Changelog](https://github.com/actions/checkout/blob/main/CHANGELOG.md)
- [Commits](https://github.com/actions/checkout/compare/v3...v4)

---
updated-dependencies:
- dependency-name: actions/checkout
  dependency-type: direct:production
  update-type: version-update:semver-major
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 .github/workflows/build_docs.yml          | 2 +-
 .github/workflows/release_and_publish.yml | 2 +-
 .github/workflows/unittests.yml           | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/build_docs.yml b/.github/workflows/build_docs.yml
index 64d1b201..dda4d0b0 100644
--- a/.github/workflows/build_docs.yml
+++ b/.github/workflows/build_docs.yml
@@ -27,7 +27,7 @@
      
      steps:
        - name: checkout repo
-         uses: actions/checkout@v3
+         uses: actions/checkout@v4
        
        - name: setup micromamba
          uses: mamba-org/provision-with-micromamba@main
diff --git a/.github/workflows/release_and_publish.yml b/.github/workflows/release_and_publish.yml
index 319ad8ac..8d4da65f 100644
--- a/.github/workflows/release_and_publish.yml
+++ b/.github/workflows/release_and_publish.yml
@@ -21,7 +21,7 @@
      runs-on: ubuntu-latest
      steps:
        - name: Checkout repo
-         uses: actions/checkout@v3
+         uses: actions/checkout@v4
  
        - name: Set up python
          uses: actions/setup-python@v4
diff --git a/.github/workflows/unittests.yml b/.github/workflows/unittests.yml
index 07f7f311..5e023225 100644
--- a/.github/workflows/unittests.yml
+++ b/.github/workflows/unittests.yml
@@ -37,7 +37,7 @@
      
      steps:
        - name: checkout repo
-         uses: actions/checkout@v3
+         uses: actions/checkout@v4
        
        - name: setup micromamba
          uses: mamba-org/provision-with-micromamba@main

From 8a82e2a592a3637e65081cb772f0b7e4d23d7032 Mon Sep 17 00:00:00 2001
From: Pedro Amaral <pedrovma@gmail.com>
Date: Wed, 13 Sep 2023 16:12:19 -0300
Subject: [PATCH 15/28] Several changes

This commit incorporates several developments into spreg.

Main changes:
- Refactoring of the output. The way the results from the models are printed to the users has been completely refactored. The new function is simpler and generic. It is based on pandas to create an 'output' attribute to the regression object that contains the most important information on the estimates from the model.
- Addition of slx_lags to all cross-section models. An attribute slx_lags was added to all cross-section models to allow for the direct estimation of SLX models.
- Creation of wrapper functions for the error models. Both error_sp and error_sp_regimes now have wrapper functions that allow for easy estimation of error models based on the kp98, hom or het estimators. The wrapper functions also allow specifications with extra endogenous variables or spatial lag of the dependent variable.
- Several minor updates of fixes were also developed in this commit.

Despite the major changes, this commit does not break any previous code. This update was developed in a private branch by Luc Anselin and Pedro Amaral.
---
 spreg/diagnostics_sp.py       |  44 +-
 spreg/error_sp.py             | 562 ++++++++++++++++--------
 spreg/error_sp_het.py         | 106 ++++-
 spreg/error_sp_het_regimes.py | 255 ++++++++---
 spreg/error_sp_hom.py         | 115 ++++-
 spreg/error_sp_hom_regimes.py | 253 ++++++++---
 spreg/error_sp_regimes.py     | 779 +++++++++++++++++++++++++++-------
 spreg/ml_error.py             |  56 ++-
 spreg/ml_error_regimes.py     | 102 +++--
 spreg/ml_lag.py               |  84 +++-
 spreg/ml_lag_regimes.py       |  81 ++--
 spreg/ols.py                  |  72 +++-
 spreg/ols_regimes.py          | 313 +++++++++-----
 spreg/output.py               | 476 +++++++++++++++++++++
 spreg/regimes.py              |  42 +-
 spreg/twosls.py               |  60 ++-
 spreg/twosls_regimes.py       | 183 ++++++--
 spreg/twosls_sp.py            |  63 ++-
 spreg/twosls_sp_regimes.py    | 120 ++++--
 spreg/user_output.py          |  25 +-
 spreg/utils.py                |  66 ++-
 21 files changed, 3063 insertions(+), 794 deletions(-)
 mode change 100644 => 100755 spreg/ml_lag.py
 create mode 100644 spreg/output.py

diff --git a/spreg/diagnostics_sp.py b/spreg/diagnostics_sp.py
index a3aaeaac..bf0dbfe7 100644
--- a/spreg/diagnostics_sp.py
+++ b/spreg/diagnostics_sp.py
@@ -18,7 +18,6 @@
 
 
 class LMtests:
-
     """
     Lagrange Multiplier tests. Implemented as presented in :cite:`Anselin1996a`
 
@@ -142,7 +141,6 @@ def __init__(self, ols, w, tests=["all"]):
 
 
 class MoranRes:
-
     """
     Moran's I for spatial autocorrelation in residuals from OLS regression
 
@@ -396,7 +394,6 @@ def __init__(self, iv, w, case="nosp"):
 
 
 class spDcache:
-
     """
     Helper class to compute reusable pieces in the spatial diagnostics module
     ...
@@ -669,7 +666,7 @@ def get_vI(ols, w, ei, spDcache):
     B = spDcache.AB[1]
     trB = np.sum(B.diagonal()) * 4.0
     vi = (w.n ** 2 / (w.s0 ** 2 * (w.n - ols.k) * (w.n - ols.k + 2.0))) * (
-        w.s1 + 2.0 * trA2 - trB - ((2.0 * (spDcache.trA ** 2)) / (w.n - ols.k))
+            w.s1 + 2.0 * trA2 - trB - ((2.0 * (spDcache.trA ** 2)) / (w.n - ols.k))
     )
     return vi
 
@@ -716,9 +713,6 @@ def akTest(iv, w, spDcache):
     p           : float
                   P-value of the test
 
-    ToDo:
-        * Code in as Nancy
-        * Compare both
     """
     mi = get_mI(iv, w, spDcache)
     # Phi2
@@ -731,6 +725,42 @@ def akTest(iv, w, spDcache):
     return (mi, ak[0][0], pval[0][0])
 
 
+def comfac_test(lambd, beta, gamma, vm):
+    """
+    Computes the Spatial Common Factor Hypothesis test as shown in Anselin (1988, p. 226-229)
+
+    Parameters
+    ----------
+
+    lambd       : float
+                  Spatial autoregressive coefficient (as in lambd*Wy)
+    beta        : array
+                  Coefficients of the exogenous (not spatially lagged) variables, without the constant (as in X*beta)
+    gamma       : array
+                  coefficients of the spatially lagged exogenous variables (as in WX*gamma)
+    vm          : array
+                  Variance-covariance matrix of the coefficients
+                  Obs. Needs to match the order of theta' = [beta', gamma', lambda]
+
+    Returns
+    -------
+    W       : float
+              Wald statistic
+    pvalue  : float
+              P value for Wald statistic calculated as a Chi sq. distribution
+              with k-1 degrees of freedom
+
+    """
+    g = lambd * beta + gamma
+    G = np.vstack((lambd * np.eye(beta.shape[0]), np.eye(beta.shape[0]), beta.T))
+
+    GVGi = la.inv(np.dot(G.T, np.dot(vm, G)))
+    W = np.dot(g.T, np.dot(GVGi, g))[0][0]
+    df = G.shape[1]
+    pvalue = chisqprob(W, df)
+    return W, pvalue
+
+
 def _test():
     import doctest
 
diff --git a/spreg/error_sp.py b/spreg/error_sp.py
index 5fe47e00..765fddc8 100644
--- a/spreg/error_sp.py
+++ b/spreg/error_sp.py
@@ -9,23 +9,16 @@
 import numpy as np
 from numpy import linalg as la
 from . import ols as OLS
-from libpysal.weights.spatial_lag import lag_spatial
-from .utils import power_expansion, set_endog, iter_msg, sp_att
-from .utils import (
-    get_A1_hom,
-    get_A2_hom,
-    get_A1_het,
-    optim_moments,
-    get_spFilter,
-    get_lags,
-    _moments2eqs,
-)
-from .utils import spdot, RegressionPropsY, set_warn
+from .utils import set_endog, sp_att, optim_moments, get_spFilter, get_lags, spdot, RegressionPropsY, set_warn
 from . import twosls as TSLS
 from . import user_output as USER
-from . import summary_output as SUMMARY
+import pandas as pd
+from .output import output, _spat_pseudo_r2
+from .error_sp_het import GM_Error_Het, GM_Endog_Error_Het, GM_Combo_Het
+from .error_sp_hom import GM_Error_Hom, GM_Endog_Error_Hom, GM_Combo_Hom
 
-__all__ = ["GM_Error", "GM_Endog_Error", "GM_Combo"]
+
+__all__ = ["GMM_Error", "GM_Error", "GM_Endog_Error", "GM_Combo"]
 
 
 class BaseGM_Error(RegressionPropsY):
@@ -138,6 +131,9 @@ class GM_Error(BaseGM_Error):
                    independent (exogenous) variable, excluding the constant
     w            : pysal W object
                    Spatial weights object (always needed)
+    slx_lags     : integer
+                   Number of spatial lags of X to include in the model specification.
+                   If slx_lags>0, the specification becomes of the SDEM type.
     vm           : boolean
                    If True, include variance-covariance matrix in summary
                    results
@@ -149,9 +145,13 @@ class GM_Error(BaseGM_Error):
                    Name of weights matrix for use in output
     name_ds      : string
                    Name of dataset for use in output
+    latex        : boolean
+                   Specifies if summary is to be printed in latex format
 
     Attributes
     ----------
+    output       : dataframe
+                   regression results pandas dataframe
     summary      : string
                    Summary of regression results and diagnostics (note: use in
                    conjunction with the print command)
@@ -287,7 +287,7 @@ class GM_Error(BaseGM_Error):
     """
 
     def __init__(
-        self, y, x, w, vm=False, name_y=None, name_x=None, name_w=None, name_ds=None
+        self, y, x, w, slx_lags=0, vm=False, name_y=None, name_x=None, name_w=None, name_ds=None, latex=False
     ):
 
         n = USER.check_arrays(y, x)
@@ -295,14 +295,24 @@ def __init__(
         USER.check_weights(w, y, w_required=True)
         x_constant, name_x, warn = USER.check_constant(x, name_x)
         set_warn(self, warn)
+        
+        self.title = "GM SPATIALLY WEIGHTED LEAST SQUARES"
+        if slx_lags >0:
+            lag_x = get_lags(w, x_constant[:, 1:], slx_lags)
+            x_constant = np.hstack((x_constant, lag_x))
+            name_x += USER.set_name_spatial_lags(name_x, slx_lags)
+            self.title += " WITH SLX (SDEM)"
+        
         BaseGM_Error.__init__(self, y=y, x=x_constant, w=w.sparse)
-        self.title = "SPATIALLY WEIGHTED LEAST SQUARES"
         self.name_ds = USER.set_name_ds(name_ds)
         self.name_y = USER.set_name_y(name_y)
         self.name_x = USER.set_name_x(name_x, x_constant)
         self.name_x.append("lambda")
         self.name_w = USER.set_name_w(name_w, w)
-        SUMMARY.GM_Error(reg=self, w=w, vm=vm)
+        self.output = pd.DataFrame(self.name_x, columns=['var_names'])
+        self.output['var_type'] = ['x'] * (len(self.name_x) - 1) + ['lambda']
+        self.output['regime'], self.output['equation'] = (0, 0)
+        output(reg=self, vm=vm, robust=False, other_end=False, latex=latex)
 
 
 class BaseGM_Endog_Error(RegressionPropsY):
@@ -379,7 +389,7 @@ class BaseGM_Endog_Error(RegressionPropsY):
     >>> w.transform='r'
     >>> model = spreg.error_sp.BaseGM_Endog_Error(y, x, yend, q, w=w.sparse)
     >>> np.around(model.betas, decimals=4)
-    array([[82.573 ],
+    array([[82.5723],
            [ 0.581 ],
            [-1.4481],
            [ 0.3499]])
@@ -438,6 +448,9 @@ class GM_Endog_Error(BaseGM_Endog_Error):
                    this should not contain any variables from x)
     w            : pysal W object
                    Spatial weights object (always needed)
+    slx_lags     : integer
+                   Number of spatial lags of X to include in the model specification.
+                   If slx_lags>0, the specification becomes of the SDEM type.
     vm           : boolean
                    If True, include variance-covariance matrix in summary
                    results
@@ -453,9 +466,13 @@ class GM_Endog_Error(BaseGM_Endog_Error):
                    Name of weights matrix for use in output
     name_ds      : string
                    Name of dataset for use in output
+    latex        : boolean
+                   Specifies if summary is to be printed in latex format
 
     Attributes
     ----------
+    output       : dataframe
+                   regression results pandas dataframe
     summary      : string
                    Summary of regression results and diagnostics (note: use in
                    conjunction with the print command)
@@ -603,12 +620,12 @@ class GM_Endog_Error(BaseGM_Endog_Error):
     >>> print(model.name_z)
     ['CONSTANT', 'inc', 'hoval', 'lambda']
     >>> np.around(model.betas, decimals=4)
-    array([[82.573 ],
+    array([[82.5723],
            [ 0.581 ],
            [-1.4481],
            [ 0.3499]])
     >>> np.around(model.std_err, decimals=4)
-    array([16.1381,  1.3545,  0.7862])
+    array([16.1382,  1.3545,  0.7862])
 
     """
 
@@ -619,6 +636,7 @@ def __init__(
         yend,
         q,
         w,
+        slx_lags=0,
         vm=False,
         name_y=None,
         name_x=None,
@@ -626,6 +644,7 @@ def __init__(
         name_q=None,
         name_w=None,
         name_ds=None,
+        latex=False,
     ):
 
         n = USER.check_arrays(y, x, yend, q)
@@ -633,8 +652,13 @@ def __init__(
         USER.check_weights(w, y, w_required=True)
         x_constant, name_x, warn = USER.check_constant(x, name_x)
         set_warn(self, warn)
+        self.title = "GM SPATIALLY WEIGHTED TWO STAGE LEAST SQUARES"
+        if slx_lags >0:
+            lag_x = get_lags(w, x_constant[:, 1:], slx_lags)
+            x_constant = np.hstack((x_constant, lag_x))
+            name_x += USER.set_name_spatial_lags(name_x, slx_lags)
+            self.title += " WITH SLX (SDEM)"        
         BaseGM_Endog_Error.__init__(self, y=y, x=x_constant, w=w.sparse, yend=yend, q=q)
-        self.title = "SPATIALLY WEIGHTED TWO STAGE LEAST SQUARES"
         self.name_ds = USER.set_name_ds(name_ds)
         self.name_y = USER.set_name_y(name_y)
         self.name_x = USER.set_name_x(name_x, x_constant)
@@ -644,137 +668,14 @@ def __init__(
         self.name_q = USER.set_name_q(name_q, q)
         self.name_h = USER.set_name_h(self.name_x, self.name_q)
         self.name_w = USER.set_name_w(name_w, w)
-        SUMMARY.GM_Endog_Error(reg=self, w=w, vm=vm)
-
-
-class BaseGM_Combo(BaseGM_Endog_Error):
-
-    """
-    GMM method for a spatial lag and error model, with endogenous variables
-    (note: no consistency checks, diagnostics or constant added); based on
-    Kelejian and Prucha (1998, 1999) :cite:`Kelejian1998` :cite:`Kelejian1999`.
-
-    Parameters
-    ----------
-    y            : array
-                   nx1 array for dependent variable
-    x            : array
-                   Two dimensional array with n rows and one column for each
-                   independent (exogenous) variable, excluding the constant
-    yend         : array
-                   Two dimensional array with n rows and one column for each
-                   endogenous variable
-    q            : array
-                   Two dimensional array with n rows and one column for each
-                   external exogenous variable to use as instruments (note:
-                   this should not contain any variables from x)
-    w            : Sparse matrix
-                   Spatial weights sparse matrix
-    w_lags       : integer
-                   Orders of W to include as instruments for the spatially
-                   lagged dependent variable. For example, w_lags=1, then
-                   instruments are WX; if w_lags=2, then WX, WWX; and so on.
-    lag_q        : boolean
-                   If True, then include spatial lags of the additional
-                   instruments (q).
-
-    Attributes
-    ----------
-    betas        : array
-                   kx1 array of estimated coefficients
-    u            : array
-                   nx1 array of residuals
-    e_filtered   : array
-                   nx1 array of spatially filtered residuals
-    predy        : array
-                   nx1 array of predicted y values
-    n            : integer
-                   Number of observations
-    k            : integer
-                   Number of variables for which coefficients are estimated
-                   (including the constant)
-    y            : array
-                   nx1 array for dependent variable
-    x            : array
-                   Two dimensional array with n rows and one column for each
-                   independent (exogenous) variable, including the constant
-    yend         : array
-                   Two dimensional array with n rows and one column for each
-                   endogenous variable
-    z            : array
-                   nxk array of variables (combination of x and yend)
-    mean_y       : float
-                   Mean of dependent variable
-    std_y        : float
-                   Standard deviation of dependent variable
-    vm           : array
-                   Variance covariance matrix (kxk)
-    sig2         : float
-                   Sigma squared used in computations
+        self.output = pd.DataFrame(self.name_z,
+                                   columns=['var_names'])
+        self.output['var_type'] = ['x'] * len(self.name_x) + ['yend'] * len(self.name_yend) + ['lambda']
+        self.output['regime'], self.output['equation'] = (0, 0)
+        output(reg=self, vm=vm, robust=False, other_end=False, latex=latex)
 
-    Examples
-    --------
-
-    >>> import numpy as np
-    >>> import libpysal
-    >>> import spreg
-    >>> db = libpysal.io.open(libpysal.examples.get_path('columbus.dbf'),'r')
-    >>> y = np.array(db.by_col("CRIME"))
-    >>> y = np.reshape(y, (49,1))
-    >>> X = []
-    >>> X.append(db.by_col("INC"))
-    >>> X = np.array(X).T
-    >>> w = libpysal.weights.Rook.from_shapefile(libpysal.examples.get_path("columbus.shp"))
-    >>> w.transform = 'r'
-    >>> w_lags = 1
-    >>> yd2, q2 = spreg.set_endog(y, X, w, None, None, w_lags, True)
-    >>> X = np.hstack((np.ones(y.shape),X))
-
-    Example only with spatial lag
-
-    >>> reg = spreg.error_sp.BaseGM_Combo(y, X, yend=yd2, q=q2, w=w.sparse)
-
-    Print the betas
-
-    >>> print(np.around(np.hstack((reg.betas[:-1],np.sqrt(reg.vm.diagonal()).reshape(3,1))),3))
-    [[39.059 11.86 ]
-     [-1.404  0.391]
-     [ 0.467  0.2  ]]
-
-    And lambda
-
-    >>> print('Lamda: ', np.around(reg.betas[-1], 3))
-    Lamda:  [-0.048]
-
-    Example with both spatial lag and other endogenous variables
-
-    >>> X = []
-    >>> X.append(db.by_col("INC"))
-    >>> X = np.array(X).T
-    >>> yd = []
-    >>> yd.append(db.by_col("HOVAL"))
-    >>> yd = np.array(yd).T
-    >>> q = []
-    >>> q.append(db.by_col("DISCBD"))
-    >>> q = np.array(q).T
-    >>> yd2, q2 = spreg.set_endog(y, X, w, yd, q, w_lags, True)
-    >>> X = np.hstack((np.ones(y.shape),X))
-    >>> reg = spreg.error_sp.BaseGM_Combo(y, X, yd2, q2, w=w.sparse)
-    >>> betas = np.array([['CONSTANT'],['INC'],['HOVAL'],['W_CRIME']])
-    >>> print(np.hstack((betas, np.around(np.hstack((reg.betas[:-1], np.sqrt(reg.vm.diagonal()).reshape(4,1))),4))))
-    [['CONSTANT' '50.0944' '14.3593']
-     ['INC' '-0.2552' '0.5667']
-     ['HOVAL' '-0.6885' '0.3029']
-     ['W_CRIME' '0.4375' '0.2314']]
-
-    """
-
-    def __init__(self, y, x, yend=None, q=None, w=None, w_lags=1, lag_q=True):
-
-        BaseGM_Endog_Error.__init__(self, y=y, x=x, w=w, yend=yend, q=q)
 
-
-class GM_Combo(BaseGM_Combo):
+class GM_Combo(BaseGM_Endog_Error):
 
     """
     GMM method for a spatial lag and error model with endogenous variables,
@@ -801,9 +702,13 @@ class GM_Combo(BaseGM_Combo):
                    Orders of W to include as instruments for the spatially
                    lagged dependent variable. For example, w_lags=1, then
                    instruments are WX; if w_lags=2, then WX, WWX; and so on.
+    slx_lags     : integer
+                   Number of spatial lags of X to include in the model specification.
+                   If slx_lags>0, the specification becomes of the General Nesting
+                   Spatial Model (GNSM) type.
     lag_q        : boolean
                    If True, then include spatial lags of the additional
-                   instruments (q).
+                   instruments (q)
     vm           : boolean
                    If True, include variance-covariance matrix in summary
                    results
@@ -819,9 +724,13 @@ class GM_Combo(BaseGM_Combo):
                    Name of weights matrix for use in output
     name_ds      : string
                    Name of dataset for use in output
+    latex        : boolean
+                   Specifies if summary is to be printed in latex format
 
     Attributes
     ----------
+    output       : dataframe
+                   regression results pandas dataframe
     summary      : string
                    Summary of regression results and diagnostics (note: use in
                    conjunction with the print command)
@@ -1015,6 +924,7 @@ def __init__(
         q=None,
         w=None,
         w_lags=1,
+        slx_lags=0,
         lag_q=True,
         vm=False,
         name_y=None,
@@ -1023,6 +933,7 @@ def __init__(
         name_q=None,
         name_w=None,
         name_ds=None,
+        latex=False,
     ):
 
         n = USER.check_arrays(y, x, yend, q)
@@ -1030,23 +941,23 @@ def __init__(
         USER.check_weights(w, y, w_required=True)
         x_constant, name_x, warn = USER.check_constant(x, name_x)
         set_warn(self, warn)
-        yend2, q2 = set_endog(y, x_constant[:, 1:], w, yend, q, w_lags, lag_q)
-        BaseGM_Combo.__init__(
-            self,
-            y=y,
-            x=x_constant,
-            w=w.sparse,
-            yend=yend2,
-            q=q2,
-            w_lags=w_lags,
-            lag_q=lag_q,
-        )
+        if slx_lags == 0:
+            yend2, q2 = set_endog(y, x_constant[:, 1:], w, yend, q, w_lags, lag_q)
+        else:
+            yend2, q2, wx = set_endog(y, x_constant[:, 1:], w, yend, q, w_lags, lag_q, slx_lags)
+            x_constant = np.hstack((x_constant, wx))
+
+        BaseGM_Endog_Error.__init__(self, y=y, x=x_constant, w=w.sparse, yend=yend2, q=q2)
+
         self.rho = self.betas[-2]
         self.predy_e, self.e_pred, warn = sp_att(
             w, self.y, self.predy, yend2[:, -1].reshape(self.n, 1), self.rho
         )
         set_warn(self, warn)
-        self.title = "SPATIALLY WEIGHTED TWO STAGE LEAST SQUARES"
+        self.title = "SPATIALLY WEIGHTED 2SLS - GM-COMBO MODEL"
+        if slx_lags > 0:
+            name_x += USER.set_name_spatial_lags(name_x, slx_lags)
+            self.title += " WITH SLX (GNSM)"
         self.name_ds = USER.set_name_ds(name_ds)
         self.name_y = USER.set_name_y(name_y)
         self.name_x = USER.set_name_x(name_x, x_constant)
@@ -1058,7 +969,294 @@ def __init__(
         self.name_q.extend(USER.set_name_q_sp(self.name_x, w_lags, self.name_q, lag_q))
         self.name_h = USER.set_name_h(self.name_x, self.name_q)
         self.name_w = USER.set_name_w(name_w, w)
-        SUMMARY.GM_Combo(reg=self, w=w, vm=vm)
+        self.output = pd.DataFrame(self.name_z,
+                                   columns=['var_names'])
+        self.output['var_type'] = ['x'] * len(self.name_x) + ['yend'] * (len(self.name_yend) - 1) + ['rho', 'lambda']
+        self.output['regime'], self.output['equation'] = (0, 0)
+        self.other_top = _spat_pseudo_r2(self)
+        output(reg=self, vm=vm, robust=False, other_end=False, latex=latex)
+
+
+class GMM_Error(GM_Error, GM_Endog_Error, GM_Combo, GM_Error_Het, GM_Endog_Error_Het, 
+               GM_Combo_Het, GM_Error_Hom, GM_Endog_Error_Hom, GM_Combo_Hom):
+
+    """
+    Wrapper function to call any of the GMM methods for a spatial error model available in spreg
+
+    Parameters
+    ----------
+    y            : array
+                   nx1 array for dependent variable
+    x            : array
+                   Two dimensional array with n rows and one column for each
+                   independent (exogenous) variable, excluding the constant
+    w            : pysal W object
+                   Spatial weights object (always needed)
+    yend         : array
+                   Two dimensional array with n rows and one column for each
+                   endogenous variable (if any)
+    q            : array
+                   Two dimensional array with n rows and one column for each
+                   external exogenous variable to use as instruments (if any)
+                   (note: this should not contain any variables from x)
+    estimator    : string
+                   Choice of estimator to be used. Options are: 'het', which
+                   is robust to heteroskedasticity, 'hom', which assumes
+                   homoskedasticity, and 'kp98', which does not provide
+                   inference on the spatial parameter for the error term.
+    add_wy       : boolean
+                   If True, then a spatial lag of the dependent variable is included.           
+    slx_lags     : integer
+                   Number of spatial lags of X to include in the model specification.
+                   If slx_lags>0, the specification becomes of the SDEM or GNSM type.             
+    vm           : boolean
+                   If True, include variance-covariance matrix in summary
+                   results
+    name_y       : string
+                   Name of dependent variable for use in output
+    name_x       : list of strings
+                   Names of independent variables for use in output
+    name_w       : string
+                   Name of weights matrix for use in output
+    name_yend    : list of strings
+                   Names of endogenous variables for use in output
+    name_q       : list of strings
+                   Names of instruments for use in output                   
+    name_ds      : string
+                   Name of dataset for use in output
+    latex        : boolean
+                   Specifies if summary is to be printed in latex format
+    **kwargs     : keywords
+                   Additional arguments to pass on to the estimators. 
+                   See the specific functions for details on what can be used.
+
+    Attributes
+    ----------
+    output       : dataframe
+                   regression results pandas dataframe
+    summary      : string
+                   Summary of regression results and diagnostics (note: use in
+                   conjunction with the print command)
+    betas        : array
+                   kx1 array of estimated coefficients
+    u            : array
+                   nx1 array of residuals
+    e_filtered   : array
+                   nx1 array of spatially filtered residuals
+    predy        : array
+                   nx1 array of predicted y values
+    n            : integer
+                   Number of observations
+    k            : integer
+                   Number of variables for which coefficients are estimated
+                   (including the constant)
+    y            : array
+                   nx1 array for dependent variable
+    x            : array
+                   Two dimensional array with n rows and one column for each
+                   independent (exogenous) variable, including the constant
+    mean_y       : float
+                   Mean of dependent variable
+    std_y        : float
+                   Standard deviation of dependent variable
+    pr2          : float
+                   Pseudo R squared (squared correlation between y and ypred)
+    vm           : array
+                   Variance covariance matrix (kxk)
+    sig2         : float
+                   Sigma squared used in computations
+    std_err      : array
+                   1xk array of standard errors of the betas
+    z_stat       : list of tuples
+                   z statistic; each tuple contains the pair (statistic,
+                   p-value), where each is a float
+    name_y       : string
+                   Name of dependent variable for use in output
+    name_x       : list of strings
+                   Names of independent variables for use in output
+    name_w       : string
+                   Name of weights matrix for use in output
+    name_ds      : string
+                   Name of dataset for use in output
+    title        : string
+                   Name of the regression method used
+    name_yend    : list of strings (optional)
+                    Names of endogenous variables for use in output
+    name_z       : list of strings (optional)
+                    Names of exogenous and endogenous variables for use in
+                    output
+    name_q       : list of strings (optional)
+                    Names of external instruments
+    name_h       : list of strings (optional)
+                    Names of all instruments used in ouput                   
+    
+
+    Examples
+    --------
+
+    We first need to import the needed modules, namely numpy to convert the
+    data we read into arrays that ``spreg`` understands and ``libpysal`` to
+    handle the weights and file management.
+
+    >>> import numpy as np
+    >>> import libpysal
+    >>> from libpysal.examples import load_example
+
+    Open data on NCOVR US County Homicides (3085 areas) using libpysal.io.open().
+    This is the DBF associated with the NAT shapefile.  Note that
+    libpysal.io.open() also reads data in CSV format; since the actual class
+    requires data to be passed in as numpy arrays, the user can read their
+    data in using any method.
+
+    >>> nat = load_example('Natregimes')
+    >>> db = libpysal.io.open(nat.get_path("natregimes.dbf"),'r')
+
+    Extract the HR90 column (homicide rates in 1990) from the DBF file and make it the
+    dependent variable for the regression. Note that PySAL requires this to be
+    an numpy array of shape (n, 1) as opposed to the also common shape of (n, )
+    that other packages accept.
+
+    >>> y_var = 'HR90'
+    >>> y = np.array([db.by_col(y_var)]).reshape(3085,1)
+
+    Extract UE90 (unemployment rate) and PS90 (population structure) vectors from
+    the DBF to be used as independent variables in the regression. Other variables
+    can be inserted by adding their names to x_var, such as x_var = ['Var1','Var2','...]
+    Note that PySAL requires this to be an nxj numpy array, where j is the
+    number of independent variables (not including a constant). By default
+    this model adds a vector of ones to the independent variables passed in.
+
+    >>> x_var = ['PS90','UE90']
+    >>> x = np.array([db.by_col(name) for name in x_var]).T
+
+     Since we want to run a spatial error model, we need to specify
+    the spatial weights matrix that includes the spatial configuration of the
+    observations. To do that, we can open an already existing gal file or
+    create a new one. In this case, we will create one from ``NAT.shp``.
+
+    >>> w = libpysal.weights.Rook.from_shapefile(nat.get_path("natregimes.shp"))
+
+    Unless there is a good reason not to do it, the weights have to be
+    row-standardized so every row of the matrix sums to one. Among other
+    things, this allows to interpret the spatial lag of a variable as the
+    average value of the neighboring observations. In PySAL, this can be
+    easily performed in the following way:
+
+    >>> w.transform = 'r'
+
+    The GMM_Error class can run error models and SARAR models, that is a spatial lag+error model.
+    In this example we will run a simple version of the latter, where we have the
+    spatial effects as well as exogenous variables. Since it is a spatial
+    model, we have to pass in the weights matrix. If we want to
+    have the names of the variables printed in the output summary, we will
+    have to pass them in as well, although this is optional.
+
+    >>> from spreg import GMM_Error
+    >>> model = GMM_Error(y, x, w=w, add_wy=True, name_y=y_var, name_x=x_var, name_ds='NAT')
+
+    Once we have run the model, we can explore a little bit the output. The
+    regression object we have created has many attributes so take your time to
+    discover them.
+
+    >>> print(model.output)
+      var_names coefficients   std_err    zt_stat      prob
+    0  CONSTANT     2.176007  1.115807   1.950165  0.051156
+    1      PS90     1.108054  0.207964   5.328096       0.0
+    2      UE90     0.664362  0.061294   10.83893       0.0
+    3    W_HR90    -0.066539  0.154395  -0.430964  0.666494
+    4    lambda     0.765087   0.04268  17.926245       0.0
+
+    This class also allows the user to run a spatial lag+error model with the
+    extra feature of including non-spatial endogenous regressors. This means
+    that, in addition to the spatial lag and error, we consider some of the
+    variables on the right-hand side of the equation as endogenous and we
+    instrument for this. In this case we consider RD90 (resource deprivation)
+    as an endogenous regressor.  We use FP89 (families below poverty)
+    for this and hence put it in the instruments parameter, 'q'.
+
+    >>> yd_var = ['RD90']
+    >>> yd = np.array([db.by_col(name) for name in yd_var]).T
+    >>> q_var = ['FP89']
+    >>> q = np.array([db.by_col(name) for name in q_var]).T
+
+    And then we can run and explore the model analogously to the previous combo:
+
+    >>> model = GMM_Error(y, x, yend=yd, q=q, w=w, add_wy=True, name_y=y_var, name_x=x_var, name_yend=yd_var, name_q=q_var, name_ds='NAT')
+    >>> print(model.output)
+      var_names coefficients   std_err    zt_stat      prob
+    0  CONSTANT      5.44035  0.560476   9.706652       0.0
+    1      PS90     1.427042    0.1821   7.836572       0.0
+    2      UE90    -0.075224  0.050031  -1.503544  0.132699
+    3      RD90     3.316266  0.261269  12.692924       0.0
+    4    W_HR90     0.200314  0.057433   3.487777  0.000487
+    5    lambda     0.136933  0.070098   1.953457  0.050765
+
+    The class also allows for estimating a GNS model by adding spatial lags of the exogenous variables, using the argument slx_lags:
+
+    >>> model = GMM_Error(y, x, w=w, add_wy=True, slx_lags=1, name_y=y_var, name_x=x_var, name_ds='NAT')
+    >>> print(model.output)
+      var_names coefficients   std_err   zt_stat      prob
+    0  CONSTANT    -0.554756  0.551765  -1.00542  0.314695
+    1      PS90      1.09369  0.225895  4.841583  0.000001
+    2      UE90     0.697393  0.082744  8.428291       0.0
+    3    W_PS90    -1.533378  0.396651 -3.865811  0.000111
+    4    W_UE90    -1.107944   0.33523 -3.305028   0.00095
+    5    W_HR90     1.529277  0.389354  3.927728  0.000086
+    6    lambda    -0.917928  0.079569 -11.53625       0.0
+
+
+    """
+
+    def __init__(
+        self, y, x, w, yend=None, q=None, estimator='het', add_wy=False, slx_lags=0, vm=False, name_y=None, name_x=None, name_w=None, name_yend=None,
+        name_q=None, name_ds=None, latex=False, **kwargs):
+
+        if estimator == 'het':
+            if yend is None and not add_wy:
+                GM_Error_Het.__init__(self, y=y, x=x, w=w, slx_lags=slx_lags, vm=vm, name_y=name_y, name_x=name_x, 
+                                      name_w=name_w, name_ds=name_ds, latex=latex, **kwargs)
+            elif yend is not None and not add_wy:
+                GM_Endog_Error_Het.__init__(self, y=y, x=x, yend=yend, q=q, w=w, slx_lags=slx_lags, vm=vm, name_y=name_y, name_x=name_x,
+                                            name_yend=name_yend, name_q=name_q, name_w=name_w, name_ds=name_ds, latex=latex, **kwargs)
+            elif add_wy:
+                GM_Combo_Het.__init__(self, y=y, x=x, yend=yend, q=q, w=w, slx_lags=slx_lags, vm=vm, name_y=name_y, name_x=name_x,
+                                            name_yend=name_yend, name_q=name_q, name_w=name_w, name_ds=name_ds, latex=latex, **kwargs)
+            else:
+                set_warn(self, 'Combination of arguments passed to GMM_Error not allowed. Using default arguments instead.')
+                GM_Error_Het.__init__(self, y=y, x=x, w=w, slx_lags=slx_lags, vm=vm, name_y=name_y, name_x=name_x, 
+                                      name_w=name_w, name_ds=name_ds, latex=latex)
+        elif estimator == 'hom':
+            if yend is None and not add_wy:
+                GM_Error_Hom.__init__(self, y=y, x=x, w=w, slx_lags=slx_lags, vm=vm, name_y=name_y, name_x=name_x, 
+                                      name_w=name_w, name_ds=name_ds, latex=latex, **kwargs)
+            elif yend is not None and not add_wy:
+                GM_Endog_Error_Hom.__init__(self, y=y, x=x, yend=yend, q=q, w=w, slx_lags=slx_lags, vm=vm, name_y=name_y, name_x=name_x,
+                                            name_yend=name_yend, name_q=name_q, name_w=name_w, name_ds=name_ds, latex=latex, **kwargs)
+            elif add_wy:
+                GM_Combo_Hom.__init__(self, y=y, x=x, yend=yend, q=q, w=w, slx_lags=slx_lags, vm=vm, name_y=name_y, name_x=name_x,
+                                            name_yend=name_yend, name_q=name_q, name_w=name_w, name_ds=name_ds, latex=latex, **kwargs)
+            else:
+                set_warn(self, 'Combination of arguments passed to GMM_Error not allowed. Using default arguments instead.')
+                GM_Error_Hom.__init__(self, y=y, x=x, w=w, slx_lags=slx_lags, vm=vm, name_y=name_y, name_x=name_x, 
+                                      name_w=name_w, name_ds=name_ds, latex=latex)
+        elif estimator == 'kp98':
+            if yend is None and not add_wy:
+                GM_Error.__init__(self, y=y, x=x, w=w, slx_lags=slx_lags, vm=vm, name_y=name_y, name_x=name_x, 
+                                      name_w=name_w, name_ds=name_ds, latex=latex, **kwargs)
+            elif yend is not None and not add_wy:
+                GM_Endog_Error.__init__(self, y=y, x=x, yend=yend, q=q, w=w, slx_lags=slx_lags, vm=vm, name_y=name_y, name_x=name_x,
+                                            name_yend=name_yend, name_q=name_q, name_w=name_w, name_ds=name_ds, latex=latex, **kwargs)
+            elif add_wy:
+                GM_Combo.__init__(self, y=y, x=x, yend=yend, q=q, w=w, slx_lags=slx_lags, vm=vm, name_y=name_y, name_x=name_x,
+                                            name_yend=name_yend, name_q=name_q, name_w=name_w, name_ds=name_ds, latex=latex, **kwargs)
+            else:
+                set_warn(self, 'Combination of arguments passed to GMM_Error not allowed. Using default arguments instead.')
+                GM_Error.__init__(self, y=y, x=x, w=w, slx_lags=slx_lags, vm=vm, name_y=name_y, name_x=name_x, 
+                                      name_w=name_w, name_ds=name_ds, latex=latex)
+        else:
+            set_warn(self, 'Combination of arguments passed to GMM_Error not allowed. Using default arguments instead.')
+            GM_Error_Het.__init__(self, y=y, x=x, w=w, slx_lags=slx_lags, vm=vm, name_y=name_y, name_x=name_x, 
+                                    name_w=name_w, name_ds=name_ds, latex=latex)
 
 
 def _momentsGM_Error(w, u):
@@ -1103,16 +1301,30 @@ def _test():
 if __name__ == "__main__":
 
     _test()
-    import libpysal
+
     import numpy as np
+    import libpysal
 
-    dbf = libpysal.io.open(libpysal.examples.get_path("columbus.dbf"), "r")
-    y = np.array([dbf.by_col("HOVAL")]).T
-    names_to_extract = ["INC", "CRIME"]
-    x = np.array([dbf.by_col(name) for name in names_to_extract]).T
-    w = libpysal.io.open(libpysal.examples.get_path("columbus.gal"), "r").read()
-    w.transform = "r"
-    model = GM_Error(
-        y, x, w, name_y="hoval", name_x=["income", "crime"], name_ds="columbus"
-    )
-    print(model.summary)
+    db = libpysal.io.open(libpysal.examples.get_path('columbus.dbf'),'r')
+    y = np.array(db.by_col("HOVAL"))
+    y = np.reshape(y, (49,1))
+    X = []
+    X.append(db.by_col("INC"))
+    X = np.array(X).T
+    yd = []
+    yd.append(db.by_col("CRIME"))
+    yd = np.array(yd).T
+    q = []
+    q.append(db.by_col("DISCBD"))
+    q = np.array(q).T
+
+    w = libpysal.weights.Rook.from_shapefile(libpysal.examples.get_path("columbus.shp"))
+    w.transform = 'r'
+    #reg = GM_Error(y, X, w=w, name_x=['inc'], name_y='hoval', name_ds='columbus', vm=True)
+    #reg = GM_Endog_Error(y, X, yd, q, w=w, name_x=['inc'], name_y='hoval', name_yend=['crime'],
+    #                         name_q=['discbd'], name_ds='columbus',vm=True)
+    reg = GM_Combo(y, X, yd, q, w=w, name_x=['inc'], name_y='hoval', name_yend=['crime'], name_q=['discbd'],
+                       name_ds='columbus', vm=True)
+
+    print(reg.output)
+    print(reg.summary)
\ No newline at end of file
diff --git a/spreg/error_sp_het.py b/spreg/error_sp_het.py
index f40a7ae9..50b838fb 100755
--- a/spreg/error_sp_het.py
+++ b/spreg/error_sp_het.py
@@ -12,12 +12,13 @@
 import numpy.linalg as la
 from . import ols as OLS
 from . import user_output as USER
-from . import summary_output as SUMMARY
 from . import twosls as TSLS
 from . import utils as UTILS
-from .utils import RegressionPropsY, spdot, set_endog, sphstack, set_warn
+from .utils import RegressionPropsY, spdot, set_endog, sphstack, set_warn, get_lags
 from scipy import sparse as SP
 from libpysal.weights.spatial_lag import lag_spatial
+import pandas as pd
+from .output import output, _summary_iteration, _spat_pseudo_r2
 
 __all__ = ["GM_Error_Het", "GM_Endog_Error_Het", "GM_Combo_Het"]
 
@@ -170,6 +171,9 @@ class GM_Error_Het(BaseGM_Error_Het):
                    independent (exogenous) variable, excluding the constant
     w            : pysal W object
                    Spatial weights object
+    slx_lags     : integer
+                   Number of spatial lags of X to include in the model specification.
+                   If slx_lags>0, the specification becomes of the SDEM type.
     max_iter     : int
                    Maximum number of iterations of steps 2a and 2b from :cite:`Arraiz2010`.
                    Note: epsilon provides an additional
@@ -191,9 +195,13 @@ class GM_Error_Het(BaseGM_Error_Het):
                    Name of weights matrix for use in output
     name_ds      : string
                    Name of dataset for use in output
+    latex        : boolean
+                   Specifies if summary is to be printed in latex format
 
     Attributes
     ----------
+    output       : dataframe
+                   regression results pandas dataframe
     summary      : string
                    Summary of regression results and diagnostics (note: use in
                    conjunction with the print command)
@@ -336,6 +344,7 @@ def __init__(
         y,
         x,
         w,
+        slx_lags=0,
         max_iter=1,
         epsilon=0.00001,
         step1c=False,
@@ -344,6 +353,7 @@ def __init__(
         name_x=None,
         name_w=None,
         name_ds=None,
+        latex=False,
     ):
 
         n = USER.check_arrays(y, x)
@@ -351,6 +361,12 @@ def __init__(
         USER.check_weights(w, y, w_required=True)
         x_constant, name_x, warn = USER.check_constant(x, name_x)
         set_warn(self, warn)
+        self.title = "GM SPATIALLY WEIGHTED LEAST SQUARES (HET)"
+        if slx_lags >0:
+            lag_x = get_lags(w, x_constant[:, 1:], slx_lags)
+            x_constant = np.hstack((x_constant, lag_x))
+            name_x += USER.set_name_spatial_lags(name_x, slx_lags)
+            self.title += " WITH SLX (SDEM)"
         BaseGM_Error_Het.__init__(
             self,
             y,
@@ -360,13 +376,16 @@ def __init__(
             step1c=step1c,
             epsilon=epsilon,
         )
-        self.title = "SPATIALLY WEIGHTED LEAST SQUARES (HET)"
         self.name_ds = USER.set_name_ds(name_ds)
         self.name_y = USER.set_name_y(name_y)
         self.name_x = USER.set_name_x(name_x, x_constant)
         self.name_x.append("lambda")
         self.name_w = USER.set_name_w(name_w, w)
-        SUMMARY.GM_Error_Het(reg=self, w=w, vm=vm)
+        self.output = pd.DataFrame(self.name_x, columns=['var_names'])
+        self.output['var_type'] = ['x'] * (len(self.name_x)-1) + ['lambda']
+        self.output['regime'], self.output['equation'] = (0, 0)
+        self.other_top = _summary_iteration(self)
+        output(reg=self, vm=vm, robust=False, other_end=False, latex=latex)
 
 
 class BaseGM_Endog_Error_Het(RegressionPropsY):
@@ -582,6 +601,9 @@ class GM_Endog_Error_Het(BaseGM_Endog_Error_Het):
                    this should not contain any variables from x)
     w            : pysal W object
                    Spatial weights object
+    slx_lags     : integer
+                   Number of spatial lags of X to include in the model specification.
+                   If slx_lags>0, the specification becomes of the SDEM type.
     max_iter     : int
                    Maximum number of iterations of steps 2a and 2b from
                    :cite:`Arraiz2010`. Note: epsilon provides an additional
@@ -611,9 +633,13 @@ class GM_Endog_Error_Het(BaseGM_Endog_Error_Het):
                    Name of weights matrix for use in output
     name_ds      : string
                    Name of dataset for use in output
+    latex        : boolean
+                   Specifies if summary is to be printed in latex format
 
     Attributes
     ----------
+    output       : dataframe
+                   regression results pandas dataframe
     summary      : string
                    Summary of regression results and diagnostics (note: use in
                    conjunction with the print command)
@@ -788,6 +814,7 @@ def __init__(
         yend,
         q,
         w,
+        slx_lags=0,
         max_iter=1,
         epsilon=0.00001,
         step1c=False,
@@ -799,6 +826,7 @@ def __init__(
         name_q=None,
         name_w=None,
         name_ds=None,
+        latex=False,
     ):
 
         n = USER.check_arrays(y, x, yend, q)
@@ -806,6 +834,12 @@ def __init__(
         USER.check_weights(w, y, w_required=True)
         x_constant, name_x, warn = USER.check_constant(x, name_x)
         set_warn(self, warn)
+        self.title = "GM SPATIALLY WEIGHTED TWO STAGE LEAST SQUARES (HET)"
+        if slx_lags > 0:
+            lag_x = get_lags(w, x_constant[:, 1:], slx_lags)
+            x_constant = np.hstack((x_constant, lag_x))
+            name_x += USER.set_name_spatial_lags(name_x, slx_lags)
+            self.title += " WITH SLX (SDEM)"
         BaseGM_Endog_Error_Het.__init__(
             self,
             y=y,
@@ -818,7 +852,6 @@ def __init__(
             epsilon=epsilon,
             inv_method=inv_method,
         )
-        self.title = "SPATIALLY WEIGHTED TWO STAGE LEAST SQUARES (HET)"
         self.name_ds = USER.set_name_ds(name_ds)
         self.name_y = USER.set_name_y(name_y)
         self.name_x = USER.set_name_x(name_x, x_constant)
@@ -828,7 +861,12 @@ def __init__(
         self.name_q = USER.set_name_q(name_q, q)
         self.name_h = USER.set_name_h(self.name_x, self.name_q)
         self.name_w = USER.set_name_w(name_w, w)
-        SUMMARY.GM_Endog_Error_Het(reg=self, w=w, vm=vm)
+        self.output = pd.DataFrame(self.name_z,
+                                   columns=['var_names'])
+        self.output['var_type'] = ['x'] * len(self.name_x) + ['yend'] * len(self.name_yend) + ['lambda']
+        self.output['regime'], self.output['equation'] = (0, 0)
+        self.other_top = _summary_iteration(self)
+        output(reg=self, vm=vm, robust=False, other_end=False, latex=latex)
 
 
 class BaseGM_Combo_Het(BaseGM_Endog_Error_Het):
@@ -1026,6 +1064,10 @@ class GM_Combo_Het(BaseGM_Combo_Het):
                    Orders of W to include as instruments for the spatially
                    lagged dependent variable. For example, w_lags=1, then
                    instruments are WX; if w_lags=2, then WX, WWX; and so on.
+    slx_lags     : integer
+                   Number of spatial lags of X to include in the model specification.
+                   If slx_lags>0, the specification becomes of the General Nesting
+                   Spatial Model (GNSM) type.
     lag_q        : boolean
                    If True, then include spatial lags of the additional
                    instruments (q).
@@ -1058,9 +1100,13 @@ class GM_Combo_Het(BaseGM_Combo_Het):
                    Name of weights matrix for use in output
     name_ds      : string
                    Name of dataset for use in output
+    latex        : boolean
+                   Specifies if summary is to be printed in latex format
 
     Attributes
     ----------
+    output       : dataframe
+                   regression results pandas dataframe
     summary      : string
                    Summary of regression results and diagnostics (note: use in
                    conjunction with the print command)
@@ -1255,6 +1301,7 @@ def __init__(
         q=None,
         w=None,
         w_lags=1,
+        slx_lags=0,
         lag_q=True,
         max_iter=1,
         epsilon=0.00001,
@@ -1267,6 +1314,7 @@ def __init__(
         name_q=None,
         name_w=None,
         name_ds=None,
+        latex=False,
     ):
 
         n = USER.check_arrays(y, x, yend, q)
@@ -1274,7 +1322,13 @@ def __init__(
         USER.check_weights(w, y, w_required=True)
         x_constant, name_x, warn = USER.check_constant(x, name_x)
         set_warn(self, warn)
-        yend2, q2 = set_endog(y, x_constant[:, 1:], w, yend, q, w_lags, lag_q)
+
+        if slx_lags == 0:
+            yend2, q2 = set_endog(y, x_constant[:, 1:], w, yend, q, w_lags, lag_q)
+        else:
+            yend2, q2, wx = set_endog(y, x_constant[:, 1:], w, yend, q, w_lags, lag_q, slx_lags)
+            x_constant = np.hstack((x_constant, wx))
+
         BaseGM_Combo_Het.__init__(
             self,
             y=y,
@@ -1294,7 +1348,10 @@ def __init__(
             w, self.y, self.predy, yend2[:, -1].reshape(self.n, 1), self.rho
         )
         UTILS.set_warn(self, warn)
-        self.title = "SPATIALLY WEIGHTED TWO STAGE LEAST SQUARES (HET)"
+        self.title = "SPATIALLY WEIGHTED 2SLS- GM-COMBO MODEL (HET)"
+        if slx_lags > 0:
+            name_x += USER.set_name_spatial_lags(name_x, slx_lags)
+            self.title += " WITH SLX (GNSM)"
         self.name_ds = USER.set_name_ds(name_ds)
         self.name_y = USER.set_name_y(name_y)
         self.name_x = USER.set_name_x(name_x, x_constant)
@@ -1306,7 +1363,13 @@ def __init__(
         self.name_q.extend(USER.set_name_q_sp(self.name_x, w_lags, self.name_q, lag_q))
         self.name_h = USER.set_name_h(self.name_x, self.name_q)
         self.name_w = USER.set_name_w(name_w, w)
-        SUMMARY.GM_Combo_Het(reg=self, w=w, vm=vm)
+        self.output = pd.DataFrame(self.name_z,
+                                   columns=['var_names'])
+        self.output['var_type'] = ['x'] * len(self.name_x) + ['yend'] * (len(self.name_yend)-1) + ['rho', 'lambda']
+        self.output['regime'], self.output['equation'] = (0, 0)
+        self.other_top = _spat_pseudo_r2(self)
+        self.other_top += _summary_iteration(self)
+        output(reg=self, vm=vm, robust=False, other_end=False, latex=latex)
 
 
 # Functions
@@ -1551,3 +1614,28 @@ def _test():
 
 if __name__ == "__main__":
     _test()
+    import numpy as np
+    import libpysal
+
+    db = libpysal.io.open(libpysal.examples.get_path('columbus.dbf'),'r')
+    y = np.array(db.by_col("HOVAL"))
+    y = np.reshape(y, (49,1))
+    X = []
+    X.append(db.by_col("INC"))
+    X = np.array(X).T
+    yd = []
+    yd.append(db.by_col("CRIME"))
+    yd = np.array(yd).T
+    q = []
+    q.append(db.by_col("DISCBD"))
+    q = np.array(q).T
+
+    w = libpysal.weights.Rook.from_shapefile(libpysal.examples.get_path("columbus.shp"))
+    w.transform = 'r'
+    # reg = GM_Error_Het(y, X, w=w, name_x=['inc'], name_y='hoval', name_ds='columbus', vm=True)
+    # reg = GM_Endog_Error_Het(y, X, yd, q, w=w, name_x=['inc'], name_y='hoval', name_yend=['crime'],
+    #                         name_q=['discbd'], name_ds='columbus',vm=True)
+    reg = GM_Combo_Het(y, X, yd, q, w=w, step1c=True, name_x=['inc'], name_y='hoval', name_yend=['crime'], name_q=['discbd'], name_ds='columbus',
+                       vm=True)
+    print(reg.output)
+    print(reg.summary)
\ No newline at end of file
diff --git a/spreg/error_sp_het_regimes.py b/spreg/error_sp_het_regimes.py
index 9c5a4586..2f812d74 100644
--- a/spreg/error_sp_het_regimes.py
+++ b/spreg/error_sp_het_regimes.py
@@ -6,7 +6,6 @@
 import numpy as np
 import multiprocessing as mp
 from . import user_output as USER
-from . import summary_output as SUMMARY
 from . import utils as UTILS
 from . import regimes as REGI
 from .ols import BaseOLS
@@ -22,10 +21,12 @@
     get_vc_het_tsls,
     get_Omega_GS2SLS,
 )
-from .utils import RegressionPropsY, spdot, set_endog, sphstack, set_warn, sp_att
+from .utils import RegressionPropsY, spdot, set_endog, sphstack, set_warn, sp_att, get_lags
 from scipy import sparse as SP
 from libpysal.weights.spatial_lag import lag_spatial
 from platform import system
+import pandas as pd
+from .output import output, _summary_iteration, _spat_pseudo_r2
 
 
 class GM_Error_Het_Regimes(RegressionPropsY, REGI.Regimes_Frame):
@@ -64,6 +65,9 @@ class GM_Error_Het_Regimes(RegressionPropsY, REGI.Regimes_Frame):
                     If True, a separate regression is run for each regime.
     regime_lag_sep: boolean
                     Always False, kept for consistency, ignored.
+    slx_lags     : integer
+                   Number of spatial lags of X to include in the model specification.
+                   If slx_lags>0, the specification becomes of the SDEM type.
     max_iter     : int
                    Maximum number of iterations of steps 2a and 2b from Arraiz
                    et al. Note: epsilon provides an additional stop condition.
@@ -90,9 +94,13 @@ class GM_Error_Het_Regimes(RegressionPropsY, REGI.Regimes_Frame):
                    Name of dataset for use in output
     name_regimes : string
                    Name of regime variable for use in the output
+    latex        : boolean
+                   Specifies if summary is to be printed in latex format
 
     Attributes
     ----------
+    output       : dataframe
+                   regression results pandas dataframe
     summary      : string
                    Summary of regression results and diagnostics (note: use in
                    conjunction with the print command)
@@ -304,6 +312,7 @@ def __init__(
         cols2regi="all",
         regime_err_sep=False,
         regime_lag_sep=False,
+        slx_lags=0,
         cores=False,
         vm=False,
         name_y=None,
@@ -311,6 +320,7 @@ def __init__(
         name_w=None,
         name_ds=None,
         name_regimes=None,
+        latex=False,
     ):
 
         n = USER.check_arrays(y, x)
@@ -329,6 +339,12 @@ def __init__(
         x_constant, name_x, warn = USER.check_constant(x, name_x, just_rem=True)
         set_warn(self, warn)
         name_x = USER.set_name_x(name_x, x_constant, constant=True)
+
+        if slx_lags >0:
+            lag_x = get_lags(w, x_constant, slx_lags)
+            x_constant = np.hstack((x_constant, lag_x))
+            name_x += USER.set_name_spatial_lags(name_x, slx_lags)
+
         self.name_x_r = USER.set_name_x(name_x, x_constant)
 
         cols2regi = REGI.check_cols2regi(constant_regi, cols2regi, x_constant)
@@ -339,11 +355,12 @@ def __init__(
 
         if regime_err_sep == True:
             if set(cols2regi) == set([True]):
-                self._error_regimes_multi(
+                self._error_het_regimes_multi(
                     y,
                     x_constant,
                     regimes,
                     w,
+                    slx_lags,
                     cores,
                     max_iter,
                     epsilon,
@@ -351,21 +368,23 @@ def __init__(
                     cols2regi,
                     vm,
                     name_x,
+                    latex,
                 )
             else:
                 raise Exception(
-                    "All coefficients must vary accross regimes if regime_err_sep = True."
+                    "All coefficients must vary across regimes if regime_err_sep = True."
                 )
         else:
             x_constant = sphstack(np.ones((x_constant.shape[0], 1)), x_constant)
             name_x = USER.set_name_x(name_x, x_constant)
-            self.x, self.name_x = REGI.Regimes_Frame.__init__(
+            self.x, self.name_x, x_rlist = REGI.Regimes_Frame.__init__(
                 self,
                 x_constant,
                 regimes,
                 constant_regi=None,
                 cols2regi=cols2regi,
                 names=name_x,
+                rlist=True,
             )
             ols = BaseOLS(y=y, x=self.x)
             self.k = ols.x.shape[1]
@@ -412,16 +431,25 @@ def __init__(
             self.vm = get_vm_het(moments_i[0], lambda3, self, w.sparse, vc3)
             self.betas = np.vstack((ols_s.betas, lambda3))
             self.e_filtered = self.u - lambda3 * lag_spatial(w, self.u)
-            self.title = "SPATIALLY WEIGHTED LEAST SQUARES (HET) - REGIMES"
+            if slx_lags == 0:
+                self.title = "GM SPATIALLY WEIGHTED MODEL (HET) - REGIMES"
+            else:
+                self.title = "GM SPATIALLY WEIGHTED MODEL + SLX (SDEM-HET) - REGIMES"            
+            
             self.name_x.append("lambda")
             self.kf += 1
             self.chow = REGI.Chow(self)
             self._cache = {}
-
-            SUMMARY.GM_Error_Het(reg=self, w=w, vm=vm, regimes=True)
-
-    def _error_regimes_multi(
-        self, y, x, regimes, w, cores, max_iter, epsilon, step1c, cols2regi, vm, name_x
+            self.output = pd.DataFrame(self.name_x,
+                                       columns=['var_names'])
+            self.output['var_type'] = ['x']*(len(self.name_x)-1)+['lambda']
+            self.output['regime'] = x_rlist + ['_Global']
+            self.output['equation'] = 0
+            self.other_top = _summary_iteration(self)
+            output(reg=self, vm=vm, robust=False, other_end=False, latex=latex)
+
+    def _error_het_regimes_multi(
+        self, y, x, regimes, w, slx_lags, cores, max_iter, epsilon, step1c, cols2regi, vm, name_x, latex
     ):
 
         regi_ids = dict(
@@ -459,6 +487,7 @@ def _error_regimes_multi(
                         name_x + ["lambda"],
                         self.name_w,
                         self.name_regimes,
+                        slx_lags,
                     ),
                 )
             else:
@@ -477,6 +506,7 @@ def _error_regimes_multi(
                         name_x + ["lambda"],
                         self.name_w,
                         self.name_regimes,
+                        slx_lags,
                     )
                 )
 
@@ -501,6 +531,7 @@ def _error_regimes_multi(
         results = {}
         self.name_y, self.name_x = [], []
         counter = 0
+        self.output = pd.DataFrame(columns=['var_names', 'var_type', 'regime', 'equation'])
         for r in self.regimes_set:
             """
             if is_win:
@@ -530,11 +561,17 @@ def _error_regimes_multi(
                 regi_ids[r],
             ] = results[r].e_filtered
             self.name_y += results[r].name_y
+            results[r].name_x = [str(r) + '_lambda' if value == 'lambda' else value for value in results[r].name_x]
             self.name_x += results[r].name_x
+            results[r].other_top = _summary_iteration(results[r])
+            self.output = pd.concat([self.output, pd.DataFrame({'var_names': results[r].name_x,
+                                                                'var_type': ['x'] * (len(results[r].name_x)-1) +
+                                                                            ['lambda'],
+                                                                'regime': r, 'equation': r})], ignore_index=True)
             counter += 1
         self.chow = REGI.Chow(self)
         self.multi = results
-        SUMMARY.GM_Error_Het_multi(reg=self, multireg=self.multi, vm=vm, regimes=True)
+        output(reg=self, vm=vm, robust=False, other_end=False, latex=latex)
 
 
 class GM_Endog_Error_Het_Regimes(RegressionPropsY, REGI.Regimes_Frame):
@@ -581,6 +618,9 @@ class GM_Endog_Error_Het_Regimes(RegressionPropsY, REGI.Regimes_Frame):
                      If True, a separate regression is run for each regime.
     regime_lag_sep : boolean
                      Always False, kept for consistency, ignored.
+    slx_lags     : integer
+                   Number of spatial lags of X to include in the model specification.
+                   If slx_lags>0, the specification becomes of the SDEM type.
     max_iter     : int
                    Maximum number of iterations of steps 2a and 2b from
                    :cite:`Arraiz2010`. Note: epsilon provides an additional stop condition.
@@ -615,9 +655,13 @@ class GM_Endog_Error_Het_Regimes(RegressionPropsY, REGI.Regimes_Frame):
                    Name of dataset for use in output
     name_regimes : string
                    Name of regime variable for use in the output
+    latex        : boolean
+                   Specifies if summary is to be printed in latex format
 
     Attributes
     ----------
+    output       : dataframe
+                   regression results pandas dataframe
     summary      : string
                    Summary of regression results and diagnostics (note: use in
                    conjunction with the print command)
@@ -869,6 +913,7 @@ def __init__(
         cols2regi="all",
         regime_err_sep=False,
         regime_lag_sep=False,
+        slx_lags=0,
         inv_method="power_exp",
         cores=False,
         vm=False,
@@ -881,6 +926,7 @@ def __init__(
         name_regimes=None,
         summ=True,
         add_lag=False,
+        latex=False,
     ):
 
         n = USER.check_arrays(y, x, yend, q)
@@ -898,12 +944,16 @@ def __init__(
         set_warn(self, warn)
         name_x = USER.set_name_x(name_x, x_constant, constant=True)
 
+        if slx_lags > 0:
+            lag_x = get_lags(w, x_constant, slx_lags)
+            x_constant = np.hstack((x_constant, lag_x))
+            name_x += USER.set_name_spatial_lags(name_x, slx_lags)
+
         if summ:
             name_yend = USER.set_name_yend(name_yend, yend)
             self.name_y = USER.set_name_y(name_y)
             name_q = USER.set_name_q(name_q, q)
         self.name_x_r = USER.set_name_x(name_x, x_constant) + name_yend
-
         cols2regi = REGI.check_cols2regi(
             constant_regi, cols2regi, x_constant, yend=yend
         )
@@ -914,13 +964,14 @@ def __init__(
 
         if regime_err_sep == True:
             if set(cols2regi) == set([True]):
-                self._endog_error_regimes_multi(
+                self._endog_error_het_regimes_multi(
                     y,
                     x_constant,
                     regimes,
                     w,
                     yend,
                     q,
+                    slx_lags,
                     cores,
                     max_iter,
                     epsilon,
@@ -932,10 +983,11 @@ def __init__(
                     name_yend,
                     name_q,
                     add_lag,
+                    latex,
                 )
             else:
                 raise Exception(
-                    "All coefficients must vary accross regimes if regime_err_sep = True."
+                    "All coefficients must vary across regimes if regime_err_sep = True."
                 )
         else:
             x_constant = sphstack(np.ones((x_constant.shape[0], 1)), x_constant)
@@ -943,15 +995,16 @@ def __init__(
             q, name_q = REGI.Regimes_Frame.__init__(
                 self, q, regimes, constant_regi=None, cols2regi="all", names=name_q
             )
-            x, name_x = REGI.Regimes_Frame.__init__(
+            x, name_x, x_rlist = REGI.Regimes_Frame.__init__(
                 self,
                 x_constant,
                 regimes,
                 constant_regi=None,
                 cols2regi=cols2regi,
                 names=name_x,
+                rlist=True,
             )
-            yend2, name_yend = REGI.Regimes_Frame.__init__(
+            yend2, name_yend, yend_rlist = REGI.Regimes_Frame.__init__(
                 self,
                 yend,
                 regimes,
@@ -959,6 +1012,7 @@ def __init__(
                 cols2regi=cols2regi,
                 yend=True,
                 names=name_yend,
+                rlist=True,
             )
 
             # 1a. S2SLS --> \tilde{\delta}
@@ -1047,13 +1101,20 @@ def __init__(
             self.kf += 1
             self.chow = REGI.Chow(self)
             self._cache = {}
+            self.output = pd.DataFrame(self.name_z,
+                                       columns=['var_names'])
+            self.output['var_type'] = ['x']*len(self.name_x)+['yend']*len(self.name_yend)+['lambda']
+            self.output['regime'] = x_rlist + yend_rlist + ['_Global']
+            self.output['equation'] = 0
             if summ:
-                self.title = (
-                    "SPATIALLY WEIGHTED TWO STAGE LEAST SQUARES (HET) - REGIMES"
-                )
-                SUMMARY.GM_Endog_Error_Het(reg=self, w=w, vm=vm, regimes=True)
-
-    def _endog_error_regimes_multi(
+                self.other_top = _summary_iteration(self)
+                if slx_lags == 0:
+                    self.title = ("GM SPATIALLY WEIGHTED 2SLS (HET) - REGIMES")
+                else:
+                    self.title = ("GM SPATIALLY WEIGHTED 2SLS + SLX (SDEM-HET) - REGIMES")
+                output(reg=self, vm=vm, robust=False, other_end=False, latex=latex)
+
+    def _endog_error_het_regimes_multi(
         self,
         y,
         x,
@@ -1061,6 +1122,7 @@ def _endog_error_regimes_multi(
         w,
         yend,
         q,
+        slx_lags,
         cores,
         max_iter,
         epsilon,
@@ -1072,6 +1134,7 @@ def _endog_error_regimes_multi(
         name_yend,
         name_q,
         add_lag,
+        latex,
     ):
 
         regi_ids = dict(
@@ -1120,6 +1183,7 @@ def _endog_error_regimes_multi(
                         self.name_w,
                         self.name_regimes,
                         add_lag,
+                        slx_lags,
                     ),
                 )
             else:
@@ -1144,6 +1208,7 @@ def _endog_error_regimes_multi(
                         self.name_w,
                         self.name_regimes,
                         add_lag,
+                        slx_lags,
                     )
                 )
 
@@ -1174,6 +1239,7 @@ def _endog_error_regimes_multi(
             self.name_h,
         ) = ([], [], [], [], [], [])
         counter = 0
+        self.output = pd.DataFrame(columns=['var_names', 'var_type', 'regime', 'equation'])
         for r in self.regimes_set:
             """
             if is_win:
@@ -1215,17 +1281,19 @@ def _endog_error_regimes_multi(
                 self.e_pred[
                     regi_ids[r],
                 ] = results[r].e_pred
+                results[r].other_top = _spat_pseudo_r2(results[r])
+                v_type = ['x'] * len(results[r].name_x) + ['yend'] * (len(results[r].name_yend)-1) + ['rho','lambda']
+            else:
+                results[r].other_top = ""
+                v_type = ['x'] * len(results[r].name_x) + ['yend'] * len(results[r].name_yend) + ['lambda']
+            results[r].other_top += _summary_iteration(results[r])
+            self.output = pd.concat([self.output, pd.DataFrame({'var_names': results[r].name_z,
+                                                                'var_type': v_type,
+                                                                'regime': r, 'equation': r})], ignore_index=True)
             counter += 1
         self.chow = REGI.Chow(self)
         self.multi = results
-        if add_lag != False:
-            SUMMARY.GM_Combo_Het_multi(
-                reg=self, multireg=self.multi, vm=vm, regimes=True
-            )
-        else:
-            SUMMARY.GM_Endog_Error_Het_multi(
-                reg=self, multireg=self.multi, vm=vm, regimes=True
-            )
+        output(reg=self, vm=vm, robust=False, other_end=False, latex=latex)
 
 
 class GM_Combo_Het_Regimes(GM_Endog_Error_Het_Regimes):
@@ -1274,6 +1342,9 @@ class GM_Combo_Het_Regimes(GM_Endog_Error_Het_Regimes):
                    If True, the spatial parameter for spatial lag is also
                    computed according to different regimes. If False (default),
                    the spatial parameter is fixed across regimes.
+    slx_lags     : integer
+                   Number of spatial lags of X to include in the model specification.
+                   If slx_lags>0, the specification becomes of the GNSM type.                   
     w_lags       : integer
                    Orders of W to include as instruments for the spatially
                    lagged dependent variable. For example, w_lags=1, then
@@ -1315,9 +1386,13 @@ class GM_Combo_Het_Regimes(GM_Endog_Error_Het_Regimes):
                    Name of dataset for use in output
     name_regimes : string
                    Name of regime variable for use in the output
+    latex        : boolean
+                   Specifies if summary is to be printed in latex format
 
     Attributes
     ----------
+    output       : dataframe
+                   regression results pandas dataframe
     summary      : string
                    Summary of regression results and diagnostics (note: use in
                    conjunction with the print command)
@@ -1595,6 +1670,7 @@ def __init__(
         yend=None,
         q=None,
         w=None,
+        slx_lags=0,
         w_lags=1,
         lag_q=True,
         max_iter=1,
@@ -1614,8 +1690,14 @@ def __init__(
         name_w=None,
         name_ds=None,
         name_regimes=None,
+        latex=False,
     ):
-
+        if regime_lag_sep and not regime_err_sep:
+            set_warn(self, "regime_err_sep set to True when regime_lag_sep=True.")                
+            regime_err_sep = True
+        if regime_err_sep and not regime_lag_sep:
+            set_warn(self, "regime_err_sep set to False when regime_lag_sep=False.")                
+            regime_err_sep = False
         n = USER.check_arrays(y, x)
         self.step1c = step1c
         y = USER.check_y(y, n)
@@ -1623,16 +1705,27 @@ def __init__(
         x_constant, name_x, warn = USER.check_constant(x, name_x, just_rem=True)
         set_warn(self, warn)
         name_x = USER.set_name_x(name_x, x_constant, constant=True)
-        self.name_x_r = USER.set_name_x(name_x, x_constant)
 
         self.name_y = USER.set_name_y(name_y)
         name_yend = USER.set_name_yend(name_yend, yend)
         name_q = USER.set_name_q(name_q, q)
-        name_q.extend(USER.set_name_q_sp(name_x, w_lags, name_q, lag_q, force_all=True))
 
-        cols2regi = REGI.check_cols2regi(
-            constant_regi, cols2regi, x_constant, yend=yend, add_cons=False
-        )
+        if regime_err_sep and any(col != True for col in cols2regi):
+            set_warn(self, "All coefficients must vary across regimes if regime_err_sep = True, so setting cols2regi = 'all'.")
+            cols2regi = "all"
+
+        if slx_lags > 0:
+            yend2, q2, wx = set_endog(y, x_constant, w, yend, q, w_lags, lag_q, slx_lags)
+            x_constant = np.hstack((x_constant, wx))
+            name_slx = USER.set_name_spatial_lags(name_x, slx_lags)
+            name_q.extend(USER.set_name_q_sp(name_slx[-len(name_x):], w_lags, name_q, lag_q, force_all=True))
+            name_x += name_slx   
+            cols2regi = REGI.check_cols2regi(constant_regi, cols2regi, x_constant[:, :-1], yend=yend2, add_cons=False)
+        else:
+            name_q.extend(USER.set_name_q_sp(name_x, w_lags, name_q, lag_q, force_all=True))
+            yend2, q2 = yend, q
+            cols2regi = REGI.check_cols2regi(constant_regi, cols2regi, x_constant, yend=yend2, add_cons=False)
+
         self.regimes_set = REGI._get_regimes_set(regimes)
         self.regimes = regimes
         USER.check_regimes(self.regimes_set, n, x_constant.shape[1])
@@ -1640,27 +1733,25 @@ def __init__(
         self.regime_lag_sep = regime_lag_sep
 
         if regime_lag_sep == True:
-            if regime_err_sep == False:
-                raise Exception(
-                    "For spatial combo models, if spatial lag is set by regimes (regime_lag_sep=True), spatial error must also be set by regimes (regime_err_sep=True)."
-                )
-            add_lag = [w_lags, lag_q]
+            if slx_lags == 0:
+                add_lag = [w_lags, lag_q]
+            else:
+                cols2regi += [True]
+                add_lag = False
         else:
-            cols2regi += [False]
             add_lag = False
-            if regime_err_sep == True:
-                raise Exception(
-                    "For spatial combo models, if spatial error is set by regimes (regime_err_sep=True), all coefficients including lambda (regime_lag_sep=True) must be set by regimes."
-                )
-            yend, q = set_endog(y, x_constant, w, yend, q, w_lags, lag_q)
+            cols2regi += [False]
+            if slx_lags == 0:
+                yend2, q2 = set_endog(y, x_constant, w, yend2, q2, w_lags, lag_q)
+
         name_yend.append(USER.set_name_yend_sp(self.name_y))
 
         GM_Endog_Error_Het_Regimes.__init__(
             self,
             y=y,
             x=x_constant,
-            yend=yend,
-            q=q,
+            yend=yend2,
+            q=q2,
             regimes=regimes,
             w=w,
             constant_regi=constant_regi,
@@ -1681,17 +1772,24 @@ def __init__(
             name_regimes=name_regimes,
             summ=False,
             add_lag=add_lag,
+            latex=latex,
         )
 
         if regime_err_sep != True:
             self.rho = self.betas[-2]
             self.predy_e, self.e_pred, warn = UTILS.sp_att(
-                w, self.y, self.predy, yend[:, -1].reshape(self.n, 1), self.rho
+                w, self.y, self.predy, yend2[:, -1].reshape(self.n, 1), self.rho
             )
             UTILS.set_warn(self, warn)
             self.regime_lag_sep = regime_lag_sep
-            self.title = "SPATIALLY WEIGHTED TWO STAGE LEAST SQUARES (HET) - REGIMES"
-            SUMMARY.GM_Combo_Het(reg=self, w=w, vm=vm, regimes=True)
+            if slx_lags == 0:
+                self.title = "GM SPATIALLY WEIGHTED 2SLS-COMBO MODEL (HET) - REGIMES"
+            else:
+                self.title = "GM SPATIALLY WEIGHTED 2SLS-COMBO + SLX (GNSM-HET) - REGIMES"
+            self.output.iat[-2, self.output.columns.get_loc('var_type')] = 'rho'
+            self.other_top = _spat_pseudo_r2(self)
+            self.other_top += _summary_iteration(self)
+            output(reg=self, vm=vm, robust=False, other_end=False, latex=latex)
 
 
 def _work_error(
@@ -1708,6 +1806,7 @@ def _work_error(
     name_x,
     name_w,
     name_regimes,
+    slx_lags,
 ):
     w_r, warn = REGI.w_regime(w, regi_ids[r], r, transform=True)
     y_r = y[regi_ids[r]]
@@ -1717,7 +1816,10 @@ def _work_error(
     )
     set_warn(model, warn)
     model.w = w_r
-    model.title = "SPATIALLY WEIGHTED LEAST SQUARES ESTIMATION (HET) - REGIME %s" % r
+    if slx_lags == 0:
+        model.title = "GM SPATIALLY WEIGHTED LS (HET) - REGIME %s" % r
+    else:
+        model.title = "GM SPATIALLY WEIGHTED LS + SLX (SDEM-HET) - REGIME %s" % r
     model.name_ds = name_ds
     model.name_y = "%s_%s" % (str(r), name_y)
     model.name_x = ["%s_%s" % (str(r), i) for i in name_x]
@@ -1746,6 +1848,7 @@ def _work_endog_error(
     name_w,
     name_regimes,
     add_lag,
+    slx_lags,
 ):
     w_r, warn = REGI.w_regime(w, regi_ids[r], r, transform=True)
     y_r = y[regi_ids[r]]
@@ -1777,12 +1880,22 @@ def _work_endog_error(
             w_r, model.y, model.predy, model.yend[:, -1].reshape(model.n, 1), model.rho
         )
         set_warn(model, warn)
-    model.title = "SPATIALLY WEIGHTED TWO STAGE LEAST SQUARES (HET) - REGIME %s" % r
+        
+    if slx_lags == 0:
+        if add_lag != False:
+            model.title = "GM SPATIALLY WEIGHTED 2SLS-COMBO MODEL (HET)- REGIME %s" % r            
+        else:
+            model.title = "GM SPATIALLY WEIGHTED 2SLS (HET) - REGIME %s" % r
+    else:
+        if add_lag != False:
+            model.title = "GM SPATIAL COMBO MODEL + SLX (GNSM-HET) - REGIME %s" % r   
+        else:
+            model.title = "GM SPATIALLY WEIGHTED 2SLS + SLX (SDEM-HET) - REGIME %s" % r
     model.name_ds = name_ds
     model.name_y = "%s_%s" % (str(r), name_y)
     model.name_x = ["%s_%s" % (str(r), i) for i in name_x]
     model.name_yend = ["%s_%s" % (str(r), i) for i in name_yend]
-    model.name_z = model.name_x + model.name_yend + ["lambda"]
+    model.name_z = model.name_x + model.name_yend + [str(r)+"lambda"]
     model.name_q = ["%s_%s" % (str(r), i) for i in name_q]
     model.name_h = model.name_x + model.name_q
     model.name_w = name_w
@@ -1801,3 +1914,33 @@ def _test():
 
 if __name__ == "__main__":
     _test()
+
+    import numpy as np
+    import libpysal
+
+    db = libpysal.io.open(libpysal.examples.get_path('columbus.dbf'),'r')
+    y = np.array(db.by_col("HOVAL"))
+    y = np.reshape(y, (49,1))
+    X = []
+    X.append(db.by_col("INC"))
+    X = np.array(X).T
+    yd = []
+    yd.append(db.by_col("CRIME"))
+    yd = np.array(yd).T
+    q = []
+    q.append(db.by_col("DISCBD"))
+    q = np.array(q).T
+
+    r_var = 'NSA'
+    regimes = db.by_col(r_var)
+
+    w = libpysal.weights.Rook.from_shapefile(libpysal.examples.get_path("columbus.shp"))
+    w.transform = 'r'
+    #reg = GM_Error_Het_Regimes(y, X, regimes, w=w, name_x=['inc'], name_y='hoval', name_ds='columbus', vm=True,
+    #                           regime_err_sep=True)
+    #reg = GM_Endog_Error_Het_Regimes(y, X, yd, q, regimes, w=w, name_x=['inc'], name_y='hoval', name_yend=['crime'],
+    #                         name_q=['discbd'], name_ds='columbus',vm=True, regime_err_sep=True)
+    reg = GM_Combo_Het_Regimes(y, X, regimes, yd, q, w=w, step1c=True, name_x=['inc'], name_y='hoval', name_yend=['crime'],
+                       name_q=['discbd'], name_ds='columbus', vm=True, regime_err_sep=False, regime_lag_sep=False)
+    print(reg.output)
+    print(reg.summary)
\ No newline at end of file
diff --git a/spreg/error_sp_hom.py b/spreg/error_sp_hom.py
index 5a4d8042..6208b8af 100644
--- a/spreg/error_sp_hom.py
+++ b/spreg/error_sp_hom.py
@@ -10,14 +10,14 @@
 import numpy as np
 from numpy import linalg as la
 from . import ols as OLS
-from libpysal.weights.spatial_lag import lag_spatial
-from .utils import power_expansion, set_endog, iter_msg, sp_att
+from .utils import set_endog, iter_msg, sp_att
 from .utils import get_A1_hom, get_A2_hom, get_A1_het, optim_moments
-from .utils import get_spFilter, get_lags, _moments2eqs
+from .utils import get_spFilter, get_lags
 from .utils import spdot, RegressionPropsY, set_warn
 from . import twosls as TSLS
 from . import user_output as USER
-from . import summary_output as SUMMARY
+import pandas as pd
+from .output import output, _spat_pseudo_r2, _summary_iteration
 
 __all__ = ["GM_Error_Hom", "GM_Endog_Error_Hom", "GM_Combo_Hom"]
 
@@ -178,6 +178,9 @@ class GM_Error_Hom(BaseGM_Error_Hom):
                    independent (exogenous) variable, excluding the constant
     w            : pysal W object
                    Spatial weights object
+    slx_lags     : integer
+                   Number of spatial lags of X to include in the model specification.
+                   If slx_lags>0, the specification becomes of the SDEM type.
     max_iter     : int
                    Maximum number of iterations of steps 2a and 2b from :cite:`Arraiz2010`.
                    Note: epsilon provides an additional stop condition.
@@ -201,10 +204,13 @@ class GM_Error_Hom(BaseGM_Error_Hom):
                    Name of weights matrix for use in output
     name_ds      : string
                    Name of dataset for use in output
-
+    latex        : boolean
+                   Specifies if summary is to be printed in latex format
 
     Attributes
     ----------
+    output       : dataframe
+                   regression results pandas dataframe
     summary      : string
                    Summary of regression results and diagnostics (note: use in
                    conjunction with the print command)
@@ -341,6 +347,7 @@ def __init__(
         y,
         x,
         w,
+        slx_lags=0,
         max_iter=1,
         epsilon=0.00001,
         A1="hom_sc",
@@ -349,6 +356,7 @@ def __init__(
         name_x=None,
         name_w=None,
         name_ds=None,
+        latex=False,
     ):
 
         n = USER.check_arrays(y, x)
@@ -356,6 +364,12 @@ def __init__(
         USER.check_weights(w, y, w_required=True)
         x_constant, name_x, warn = USER.check_constant(x, name_x)
         set_warn(self, warn)
+        self.title = "GM SPATIALLY WEIGHTED LEAST SQUARES (HOM)"
+        if slx_lags >0:
+            lag_x = get_lags(w, x_constant[:, 1:], slx_lags)
+            x_constant = np.hstack((x_constant, lag_x))
+            name_x += USER.set_name_spatial_lags(name_x, slx_lags)
+            self.title += " WITH SLX (SDEM)"        
         BaseGM_Error_Hom.__init__(
             self,
             y=y,
@@ -365,13 +379,17 @@ def __init__(
             max_iter=max_iter,
             epsilon=epsilon,
         )
-        self.title = "SPATIALLY WEIGHTED LEAST SQUARES (HOM)"
         self.name_ds = USER.set_name_ds(name_ds)
         self.name_y = USER.set_name_y(name_y)
         self.name_x = USER.set_name_x(name_x, x_constant)
         self.name_x.append("lambda")
         self.name_w = USER.set_name_w(name_w, w)
-        SUMMARY.GM_Error_Hom(reg=self, w=w, vm=vm)
+        self.A1 = A1
+        self.output = pd.DataFrame(self.name_x, columns=['var_names'])
+        self.output['var_type'] = ['x'] * (len(self.name_x) - 1) + ['lambda']
+        self.output['regime'], self.output['equation'] = (0, 0)
+        self.other_top = _summary_iteration(self)
+        output(reg=self, vm=vm, robust=False, other_end=False, latex=latex)
 
 
 class BaseGM_Endog_Error_Hom(RegressionPropsY):
@@ -564,6 +582,9 @@ class GM_Endog_Error_Hom(BaseGM_Endog_Error_Hom):
                    this should not contain any variables from x)
     w            : pysal W object
                    Spatial weights object
+    slx_lags     : integer
+                   Number of spatial lags of X to include in the model specification.
+                   If slx_lags>0, the specification becomes of the SDEM type.                   
     max_iter     : int
                    Maximum number of iterations of steps 2a and 2b from
                    :cite:`Arraiz2010`. Note: epsilon provides an additional stop condition.
@@ -591,9 +612,13 @@ class GM_Endog_Error_Hom(BaseGM_Endog_Error_Hom):
                    Name of weights matrix for use in output
     name_ds      : string
                    Name of dataset for use in output
+    latex        : boolean
+                   Specifies if summary is to be printed in latex format
 
     Attributes
     ----------
+    output       : dataframe
+                   regression results pandas dataframe
     summary      : string
                    Summary of regression results and diagnostics (note: use in
                    conjunction with the print command)
@@ -769,6 +794,7 @@ def __init__(
         yend,
         q,
         w,
+        slx_lags=0,
         max_iter=1,
         epsilon=0.00001,
         A1="hom_sc",
@@ -779,6 +805,7 @@ def __init__(
         name_q=None,
         name_w=None,
         name_ds=None,
+        latex=False,
     ):
 
         n = USER.check_arrays(y, x, yend, q)
@@ -786,6 +813,12 @@ def __init__(
         USER.check_weights(w, y, w_required=True)
         x_constant, name_x, warn = USER.check_constant(x, name_x)
         set_warn(self, warn)
+        self.title = "GM SPATIALLY WEIGHTED TWO STAGE LEAST SQUARES (HOM)"
+        if slx_lags > 0:
+            lag_x = get_lags(w, x_constant[:, 1:], slx_lags)
+            x_constant = np.hstack((x_constant, lag_x))
+            name_x += USER.set_name_spatial_lags(name_x, slx_lags)
+            self.title += " WITH SLX (SDEM)"
         BaseGM_Endog_Error_Hom.__init__(
             self,
             y=y,
@@ -797,7 +830,6 @@ def __init__(
             max_iter=max_iter,
             epsilon=epsilon,
         )
-        self.title = "SPATIALLY WEIGHTED TWO STAGE LEAST SQUARES (HOM)"
         self.name_ds = USER.set_name_ds(name_ds)
         self.name_y = USER.set_name_y(name_y)
         self.name_x = USER.set_name_x(name_x, x_constant)
@@ -807,7 +839,13 @@ def __init__(
         self.name_q = USER.set_name_q(name_q, q)
         self.name_h = USER.set_name_h(self.name_x, self.name_q)
         self.name_w = USER.set_name_w(name_w, w)
-        SUMMARY.GM_Endog_Error_Hom(reg=self, w=w, vm=vm)
+        self.A1 = A1
+        self.output = pd.DataFrame(self.name_z,
+                                   columns=['var_names'])
+        self.output['var_type'] = ['x'] * len(self.name_x) + ['yend'] * len(self.name_yend) + ['lambda']
+        self.output['regime'], self.output['equation'] = (0, 0)
+        self.other_top = _summary_iteration(self)
+        output(reg=self, vm=vm, robust=False, other_end=False, latex=latex)
 
 
 class BaseGM_Combo_Hom(BaseGM_Endog_Error_Hom):
@@ -1007,6 +1045,10 @@ class GM_Combo_Hom(BaseGM_Combo_Hom):
                    Orders of W to include as instruments for the spatially
                    lagged dependent variable. For example, w_lags=1, then
                    instruments are WX; if w_lags=2, then WX, WWX; and so on.
+    slx_lags     : integer
+                   Number of spatial lags of X to include in the model specification.
+                   If slx_lags>0, the specification becomes of the General Nesting
+                   Spatial Model (GNSM) type.
     lag_q        : boolean
                    If True, then include spatial lags of the additional 
                    instruments (q).
@@ -1038,9 +1080,13 @@ class GM_Combo_Hom(BaseGM_Combo_Hom):
                    Name of weights matrix for use in output
     name_ds      : string
                    Name of dataset for use in output
+    latex        : boolean
+                   Specifies if summary is to be printed in latex format
 
     Attributes
     ----------
+    output       : dataframe
+                   regression results pandas dataframe
     summary      : string
                    Summary of regression results and diagnostics (note: use in
                    conjunction with the print command)
@@ -1232,6 +1278,7 @@ def __init__(
         q=None,
         w=None,
         w_lags=1,
+        slx_lags=0,
         lag_q=True,
         max_iter=1,
         epsilon=0.00001,
@@ -1243,6 +1290,7 @@ def __init__(
         name_q=None,
         name_w=None,
         name_ds=None,
+        latex=False,
     ):
 
         n = USER.check_arrays(y, x, yend, q)
@@ -1250,7 +1298,11 @@ def __init__(
         USER.check_weights(w, y, w_required=True)
         x_constant, name_x, warn = USER.check_constant(x, name_x)
         set_warn(self, warn)
-        yend2, q2 = set_endog(y, x_constant[:, 1:], w, yend, q, w_lags, lag_q)
+        if slx_lags == 0:
+            yend2, q2 = set_endog(y, x_constant[:, 1:], w, yend, q, w_lags, lag_q)
+        else:
+            yend2, q2, wx = set_endog(y, x_constant[:, 1:], w, yend, q, w_lags, lag_q, slx_lags)
+            x_constant = np.hstack((x_constant, wx))
         BaseGM_Combo_Hom.__init__(
             self,
             y=y,
@@ -1269,7 +1321,10 @@ def __init__(
             w, self.y, self.predy, yend2[:, -1].reshape(self.n, 1), self.rho
         )
         set_warn(self, warn)
-        self.title = "SPATIALLY WEIGHTED TWO STAGE LEAST SQUARES (HOM)"
+        self.title = "SPATIALLY WEIGHTED 2SLS- GM-COMBO MODEL (HOM)"
+        if slx_lags > 0:
+            name_x += USER.set_name_spatial_lags(name_x, slx_lags)
+            self.title += " WITH SLX (GNSM)"
         self.name_ds = USER.set_name_ds(name_ds)
         self.name_y = USER.set_name_y(name_y)
         self.name_x = USER.set_name_x(name_x, x_constant)
@@ -1281,8 +1336,14 @@ def __init__(
         self.name_q.extend(USER.set_name_q_sp(self.name_x, w_lags, self.name_q, lag_q))
         self.name_h = USER.set_name_h(self.name_x, self.name_q)
         self.name_w = USER.set_name_w(name_w, w)
-        SUMMARY.GM_Combo_Hom(reg=self, w=w, vm=vm)
-
+        self.A1 = A1
+        self.output = pd.DataFrame(self.name_z,
+                                   columns=['var_names'])
+        self.output['var_type'] = ['x'] * len(self.name_x) + ['yend'] * (len(self.name_yend) - 1) + ['rho', 'lambda']
+        self.output['regime'], self.output['equation'] = (0, 0)
+        self.other_top = _spat_pseudo_r2(self)
+        self.other_top += _summary_iteration(self)
+        output(reg=self, vm=vm, robust=False, other_end=False, latex=latex)
 
 # Functions
 
@@ -1514,5 +1575,31 @@ def _test():
 
 
 if __name__ == "__main__":
-
     _test()
+
+    import numpy as np
+    import libpysal
+
+    db = libpysal.io.open(libpysal.examples.get_path('columbus.dbf'),'r')
+    y = np.array(db.by_col("HOVAL"))
+    y = np.reshape(y, (49,1))
+    X = []
+    X.append(db.by_col("INC"))
+    X = np.array(X).T
+    yd = []
+    yd.append(db.by_col("CRIME"))
+    yd = np.array(yd).T
+    q = []
+    q.append(db.by_col("DISCBD"))
+    q = np.array(q).T
+
+    w = libpysal.weights.Rook.from_shapefile(libpysal.examples.get_path("columbus.shp"))
+    w.transform = 'r'
+    #reg = GM_Error_Hom(y, X, w=w, name_x=['inc'], name_y='hoval', name_ds='columbus', vm=True)
+    #reg = GM_Endog_Error_Hom(y, X, yd, q, w=w, name_x=['inc'], name_y='hoval', name_yend=['crime'],
+    #                         name_q=['discbd'], name_ds='columbus',vm=True)
+    reg = GM_Combo_Hom(y, X, yd, q, w=w, name_x=['inc'], name_y='hoval', name_yend=['crime'], name_q=['discbd'],
+                       name_ds='columbus', vm=True)
+
+    print(reg.output)
+    print(reg.summary)
\ No newline at end of file
diff --git a/spreg/error_sp_hom_regimes.py b/spreg/error_sp_hom_regimes.py
index 9812584e..f8d7dfae 100644
--- a/spreg/error_sp_hom_regimes.py
+++ b/spreg/error_sp_hom_regimes.py
@@ -26,9 +26,9 @@
 )
 from . import regimes as REGI
 from . import user_output as USER
-from . import summary_output as SUMMARY
 from platform import system
-
+import pandas as pd
+from .output import output, _summary_iteration, _spat_pseudo_r2
 
 class GM_Error_Hom_Regimes(RegressionPropsY, REGI.Regimes_Frame):
 
@@ -67,6 +67,9 @@ class GM_Error_Hom_Regimes(RegressionPropsY, REGI.Regimes_Frame):
                     If True, a separate regression is run for each regime.
     regime_lag_sep: boolean
                     Always False, kept for consistency, ignored.
+    slx_lags     : integer
+                   Number of spatial lags of X to include in the model specification.
+                   If slx_lags>0, the specification becomes of the SDEM type.
     max_iter     : int
                    Maximum number of iterations of steps 2a and 2b from
                    :cite:`Arraiz2010`. Note: epsilon provides an additional
@@ -97,9 +100,13 @@ class GM_Error_Hom_Regimes(RegressionPropsY, REGI.Regimes_Frame):
                    Name of dataset for use in output
     name_regimes : string
                    Name of regime variable for use in the output
+    latex        : boolean
+                   Specifies if summary is to be printed in latex format
 
     Attributes
     ----------
+    output       : dataframe
+                   regression results pandas dataframe
     summary      : string
                    Summary of regression results and diagnostics (note: use in
                    conjunction with the print command)
@@ -317,12 +324,14 @@ def __init__(
         cols2regi="all",
         regime_err_sep=False,
         regime_lag_sep=False,
+        slx_lags=0,
         vm=False,
         name_y=None,
         name_x=None,
         name_w=None,
         name_ds=None,
         name_regimes=None,
+        latex=False,
     ):
 
         n = USER.check_arrays(y, x)
@@ -341,6 +350,12 @@ def __init__(
         x_constant, name_x, warn = USER.check_constant(x, name_x, just_rem=True)
         set_warn(self, warn)
         name_x = USER.set_name_x(name_x, x_constant, constant=True)
+
+        if slx_lags >0:
+            lag_x = get_lags(w, x_constant, slx_lags)
+            x_constant = np.hstack((x_constant, lag_x))
+            name_x += USER.set_name_spatial_lags(name_x, slx_lags)
+
         self.name_x_r = USER.set_name_x(name_x, x_constant)
 
         cols2regi = REGI.check_cols2regi(constant_regi, cols2regi, x_constant)
@@ -351,11 +366,12 @@ def __init__(
 
         if regime_err_sep == True:
             if set(cols2regi) == set([True]):
-                self._error_regimes_multi(
+                self._error_hom_regimes_multi(
                     y,
                     x_constant,
                     regimes,
                     w,
+                    slx_lags,
                     cores,
                     max_iter,
                     epsilon,
@@ -363,10 +379,11 @@ def __init__(
                     cols2regi,
                     vm,
                     name_x,
+                    latex,
                 )
             else:
                 raise Exception(
-                    "All coefficients must vary accross regimes if regime_err_sep = True."
+                    "All coefficients must vary across regimes if regime_err_sep = True."
                 )
         else:
             x_constant = sphstack(np.ones((x_constant.shape[0], 1)), x_constant)
@@ -381,13 +398,14 @@ def __init__(
             wA2 = get_A2_hom(w.sparse)
 
             # 1a. OLS --> \tilde{\delta}
-            self.x, self.name_x = REGI.Regimes_Frame.__init__(
+            self.x, self.name_x, x_rlist = REGI.Regimes_Frame.__init__(
                 self,
                 x_constant,
                 regimes,
                 constant_regi=None,
                 cols2regi=cols2regi,
                 names=name_x,
+                rlist=True,
             )
             ols = BaseOLS(y=y, x=self.x)
             self.k = ols.x.shape[1]
@@ -425,16 +443,25 @@ def __init__(
                 w.sparse, wA1, wA2, self, lambda2, moments[0]
             )
             self.e_filtered = self.u - lambda2 * lag_spatial(w, self.u)
-            self.title = "SPATIALLY WEIGHTED LEAST SQUARES (HOM) - REGIMES"
+            if slx_lags == 0:
+                self.title = "GM SPATIALLY WEIGHTED MODEL (HOM) - REGIMES"
+            else:
+                self.title = "GM SPATIALLY WEIGHTED MODEL + SLX (SDEM-HOM) - REGIMES"          
             self.name_x.append("lambda")
             self.kf += 1
             self.chow = REGI.Chow(self)
             self._cache = {}
-            SUMMARY.GM_Error_Hom(reg=self, w=w, vm=vm, regimes=True)
-
-    def _error_regimes_multi(
-        self, y, x, regimes, w, cores, max_iter, epsilon, A1, cols2regi, vm, name_x
-    ):
+            self.A1 = A1
+            self.output = pd.DataFrame(self.name_x,
+                                       columns=['var_names'])
+            self.output['var_type'] = ['x']*(len(self.name_x)-1)+['lambda']
+            self.output['regime'] = x_rlist + ['_Global']
+            self.output['equation'] = 0
+            self.other_top = _summary_iteration(self)
+            output(reg=self, vm=vm, robust=False, other_end=False, latex=latex)
+
+    def _error_hom_regimes_multi(
+        self, y, x, regimes, w, slx_lags, cores, max_iter, epsilon, A1, cols2regi, vm, name_x, latex):
 
         regi_ids = dict(
             (r, list(np.where(np.array(regimes) == r)[0])) for r in self.regimes_set
@@ -471,6 +498,7 @@ def _error_regimes_multi(
                         name_x + ["lambda"],
                         self.name_w,
                         self.name_regimes,
+                        slx_lags,
                     ),
                 )
             else:
@@ -489,6 +517,7 @@ def _error_regimes_multi(
                         name_x + ["lambda"],
                         self.name_w,
                         self.name_regimes,
+                        slx_lags,
                     )
                 )
 
@@ -513,6 +542,7 @@ def _error_regimes_multi(
 
         results = {}
         counter = 0
+        self.output = pd.DataFrame(columns=['var_names', 'var_type', 'regime', 'equation'])
         for r in self.regimes_set:
             """
             if is_win:
@@ -543,10 +573,16 @@ def _error_regimes_multi(
             ] = results[r].e_filtered
             self.name_y += results[r].name_y
             self.name_x += results[r].name_x
+            results[r].A1 = A1
+            results[r].other_top = _summary_iteration(results[r])
+            self.output = pd.concat([self.output, pd.DataFrame({'var_names': results[r].name_x,
+                                                                'var_type': ['x'] * (len(results[r].name_x) - 1) +
+                                                                            ['lambda'],
+                                                                'regime': r, 'equation': r})], ignore_index=True)
             counter += 1
         self.chow = REGI.Chow(self)
         self.multi = results
-        SUMMARY.GM_Error_Hom_multi(reg=self, multireg=self.multi, vm=vm, regimes=True)
+        output(reg=self, vm=vm, robust=False, other_end=False, latex=latex)
 
 
 class GM_Endog_Error_Hom_Regimes(RegressionPropsY, REGI.Regimes_Frame):
@@ -594,6 +630,9 @@ class GM_Endog_Error_Hom_Regimes(RegressionPropsY, REGI.Regimes_Frame):
                      If True, a separate regression is run for each regime.
     regime_lag_sep : boolean
                      Always False, kept for consistency, ignored.
+    slx_lags     : integer
+                   Number of spatial lags of X to include in the model specification.
+                   If slx_lags>0, the specification becomes of the SDEM type.
     max_iter     : int
                    Maximum number of iterations of steps 2a and 2b from
                    :cite:`Arraiz2010`. Note: epsilon provides an additional stop condition.
@@ -624,9 +663,13 @@ class GM_Endog_Error_Hom_Regimes(RegressionPropsY, REGI.Regimes_Frame):
                    Name of dataset for use in output
     name_regimes : string
                    Name of regime variable for use in the output
+    latex        : boolean
+                   Specifies if summary is to be printed in latex format
 
     Attributes
     ----------
+    output       : dataframe
+                   regression results pandas dataframe
     summary      : string
                    Summary of regression results and diagnostics (note: use in
                    conjunction with the print command)
@@ -884,6 +927,7 @@ def __init__(
         cols2regi="all",
         regime_err_sep=False,
         regime_lag_sep=False,
+        slx_lags=0,
         max_iter=1,
         epsilon=0.00001,
         A1="het",
@@ -898,6 +942,7 @@ def __init__(
         name_regimes=None,
         summ=True,
         add_lag=False,
+        latex=False,
     ):
 
         n = USER.check_arrays(y, x, yend, q)
@@ -914,6 +959,10 @@ def __init__(
         x_constant, name_x, warn = USER.check_constant(x, name_x, just_rem=True)
         set_warn(self, warn)
         name_x = USER.set_name_x(name_x, x_constant, constant=True)
+        if slx_lags > 0:
+            lag_x = get_lags(w, x_constant, slx_lags)
+            x_constant = np.hstack((x_constant, lag_x))
+            name_x += USER.set_name_spatial_lags(name_x, slx_lags)
 
         if summ:
             name_yend = USER.set_name_yend(name_yend, yend)
@@ -931,13 +980,14 @@ def __init__(
 
         if regime_err_sep == True:
             if set(cols2regi) == set([True]):
-                self._endog_error_regimes_multi(
+                self._endog_error_hom_regimes_multi(
                     y,
                     x_constant,
                     regimes,
                     w,
                     yend,
                     q,
+                    slx_lags,
                     cores,
                     max_iter,
                     epsilon,
@@ -948,10 +998,11 @@ def __init__(
                     name_yend,
                     name_q,
                     add_lag,
+                    latex,
                 )
             else:
                 raise Exception(
-                    "All coefficients must vary accross regimes if regime_err_sep = True."
+                    "All coefficients must vary across regimes if regime_err_sep = True."
                 )
         else:
             x_constant = sphstack(np.ones((x_constant.shape[0], 1)), x_constant)
@@ -959,15 +1010,16 @@ def __init__(
             q, name_q = REGI.Regimes_Frame.__init__(
                 self, q, regimes, constant_regi=None, cols2regi="all", names=name_q
             )
-            x, name_x = REGI.Regimes_Frame.__init__(
+            x, name_x, x_rlist = REGI.Regimes_Frame.__init__(
                 self,
                 x_constant,
                 regimes,
                 constant_regi=None,
                 cols2regi=cols2regi,
                 names=name_x,
+                rlist=True,
             )
-            yend2, name_yend = REGI.Regimes_Frame.__init__(
+            yend2, name_yend, yend_rlist = REGI.Regimes_Frame.__init__(
                 self,
                 yend,
                 regimes,
@@ -975,6 +1027,7 @@ def __init__(
                 cols2regi=cols2regi,
                 yend=True,
                 names=name_yend,
+                rlist=True,
             )
             if A1 == "hom":
                 wA1 = get_A1_hom(w.sparse)
@@ -1042,13 +1095,21 @@ def __init__(
             self.kf += 1
             self.chow = REGI.Chow(self)
             self._cache = {}
+            self.A1 = A1
+            self.output = pd.DataFrame(self.name_z,
+                                       columns=['var_names'])
+            self.output['var_type'] = ['x'] * len(self.name_x) + ['yend'] * len(self.name_yend) + ['lambda']
+            self.output['regime'] = x_rlist + yend_rlist + ['_Global']
+            self.output['equation'] = 0
             if summ:
-                self.title = (
-                    "SPATIALLY WEIGHTED TWO STAGE LEAST SQUARES (HOM) - REGIMES"
-                )
-                SUMMARY.GM_Endog_Error_Hom(reg=self, w=w, vm=vm, regimes=True)
-
-    def _endog_error_regimes_multi(
+                self.other_top = _summary_iteration(self)
+                if slx_lags == 0:
+                    self.title = ("GM SPATIALLY WEIGHTED 2SLS (HOM) - REGIMES")
+                else:
+                    self.title = ("GM SPATIALLY WEIGHTED 2SLS WITH SLX (SDEM-HOM) - REGIMES")
+                output(reg=self, vm=vm, robust=False, other_end=False, latex=latex)
+
+    def _endog_error_hom_regimes_multi(
         self,
         y,
         x,
@@ -1056,6 +1117,7 @@ def _endog_error_regimes_multi(
         w,
         yend,
         q,
+        slx_lags,
         cores,
         max_iter,
         epsilon,
@@ -1066,6 +1128,7 @@ def _endog_error_regimes_multi(
         name_yend,
         name_q,
         add_lag,
+        latex,
     ):
 
         regi_ids = dict(
@@ -1113,6 +1176,7 @@ def _endog_error_regimes_multi(
                         self.name_w,
                         self.name_regimes,
                         add_lag,
+                        slx_lags,
                     ),
                 )
             else:
@@ -1136,6 +1200,7 @@ def _endog_error_regimes_multi(
                         self.name_w,
                         self.name_regimes,
                         add_lag,
+                        slx_lags,
                     )
                 )
 
@@ -1166,6 +1231,7 @@ def _endog_error_regimes_multi(
             self.name_h,
         ) = ([], [], [], [], [], [])
         counter = 0
+        self.output = pd.DataFrame(columns=['var_names', 'var_type', 'regime', 'equation'])
         for r in self.regimes_set:
             """
             if is_win:
@@ -1207,17 +1273,20 @@ def _endog_error_regimes_multi(
                 self.e_pred[
                     regi_ids[r],
                 ] = results[r].e_pred
+                results[r].other_top = _spat_pseudo_r2(results[r])
+                v_type = ['x'] * len(results[r].name_x) + ['yend'] * (len(results[r].name_yend)-1) + ['rho','lambda']
+            else:
+                results[r].other_top = ""
+                v_type = ['x'] * len(results[r].name_x) + ['yend'] * len(results[r].name_yend) + ['lambda']
+            results[r].A1 = A1
+            results[r].other_top += _summary_iteration(results[r])
+            self.output = pd.concat([self.output, pd.DataFrame({'var_names': results[r].name_z,
+                                                                'var_type': v_type,
+                                                                'regime': r, 'equation': r})], ignore_index=True)
             counter += 1
         self.chow = REGI.Chow(self)
         self.multi = results
-        if add_lag != False:
-            SUMMARY.GM_Combo_Hom_multi(
-                reg=self, multireg=self.multi, vm=vm, regimes=True
-            )
-        else:
-            SUMMARY.GM_Endog_Error_Hom_multi(
-                reg=self, multireg=self.multi, vm=vm, regimes=True
-            )
+        output(reg=self, vm=vm, robust=False, other_end=False, latex=latex)
 
 
 class GM_Combo_Hom_Regimes(GM_Endog_Error_Hom_Regimes):
@@ -1267,6 +1336,9 @@ class GM_Combo_Hom_Regimes(GM_Endog_Error_Hom_Regimes):
                    If True, the spatial parameter for spatial lag is also
                    computed according to different regimes. If False (default),
                    the spatial parameter is fixed across regimes.
+    slx_lags     : integer
+                   Number of spatial lags of X to include in the model specification.
+                   If slx_lags>0, the specification becomes of the GNSM type.     
     w_lags       : integer
                    Orders of W to include as instruments for the spatially
                    lagged dependent variable. For example, w_lags=1, then
@@ -1307,9 +1379,13 @@ class GM_Combo_Hom_Regimes(GM_Endog_Error_Hom_Regimes):
                    Name of dataset for use in output
     name_regimes : string
                    Name of regime variable for use in the output
+    latex        : boolean
+                   Specifies if summary is to be printed in latex format
 
     Attributes
     ----------
+    output       : dataframe
+                   regression results pandas dataframe
     summary      : string
                    Summary of regression results and diagnostics (note: use in
                    conjunction with the print command)
@@ -1593,6 +1669,7 @@ def __init__(
         yend=None,
         q=None,
         w=None,
+        slx_lags=0,
         w_lags=1,
         lag_q=True,
         cores=False,
@@ -1611,22 +1688,42 @@ def __init__(
         name_w=None,
         name_ds=None,
         name_regimes=None,
+        latex=False,
     ):
 
+        if regime_lag_sep and not regime_err_sep:
+            set_warn(self, "regime_err_sep set to True when regime_lag_sep=True.")                
+            regime_err_sep = True
+        if regime_err_sep and not regime_lag_sep:
+            set_warn(self, "regime_err_sep set to False when regime_lag_sep=False.")                
+            regime_err_sep = False
         n = USER.check_arrays(y, x)
         y = USER.check_y(y, n)
         USER.check_weights(w, y, w_required=True)
         x_constant, name_x, warn = USER.check_constant(x, name_x, just_rem=True)
         set_warn(self, warn)
         name_x = USER.set_name_x(name_x, x_constant, constant=True)
+
         self.name_y = USER.set_name_y(name_y)
         name_yend = USER.set_name_yend(name_yend, yend)
         name_q = USER.set_name_q(name_q, q)
-        name_q.extend(USER.set_name_q_sp(name_x, w_lags, name_q, lag_q, force_all=True))
 
-        cols2regi = REGI.check_cols2regi(
-            constant_regi, cols2regi, x_constant, yend=yend, add_cons=False
-        )
+        if regime_err_sep and any(col != True for col in cols2regi):
+            set_warn(self, "All coefficients must vary across regimes if regime_err_sep = True, so setting cols2regi = 'all'.")
+            cols2regi = "all"
+
+        if slx_lags > 0:
+            yend2, q2, wx = set_endog(y, x_constant, w, yend, q, w_lags, lag_q, slx_lags)
+            x_constant = np.hstack((x_constant, wx))
+            name_slx = USER.set_name_spatial_lags(name_x, slx_lags)
+            name_q.extend(USER.set_name_q_sp(name_slx[-len(name_x):], w_lags, name_q, lag_q, force_all=True))
+            name_x += name_slx   
+            cols2regi = REGI.check_cols2regi(constant_regi, cols2regi, x_constant[:, :-1], yend=yend2, add_cons=False)
+        else:
+            name_q.extend(USER.set_name_q_sp(name_x, w_lags, name_q, lag_q, force_all=True))
+            yend2, q2 = yend, q
+            cols2regi = REGI.check_cols2regi(constant_regi, cols2regi, x_constant, yend=yend2, add_cons=False)
+            
         self.regimes_set = REGI._get_regimes_set(regimes)
         self.regimes = regimes
         USER.check_regimes(self.regimes_set, n, x_constant.shape[1])
@@ -1634,27 +1731,26 @@ def __init__(
         self.regime_lag_sep = regime_lag_sep
 
         if regime_lag_sep == True:
-            if regime_err_sep == False:
-                raise Exception(
-                    "For spatial combo models, if spatial lag is set by regimes (regime_lag_sep=True), spatial error must also be set by regimes (regime_err_sep=True)."
-                )
-            add_lag = [w_lags, lag_q]
+            if slx_lags == 0:
+                add_lag = [w_lags, lag_q]
+            else:
+                add_lag = False
+                cols2regi += [True]
         else:
-            cols2regi += [False]
             add_lag = False
-            if regime_err_sep == True:
-                raise Exception(
-                    "For spatial combo models, if spatial error is set by regimes (regime_err_sep=True), all coefficients including lambda (regime_lag_sep=True) must be set by regimes."
-                )
-            yend, q = set_endog(y, x_constant, w, yend, q, w_lags, lag_q)
+            cols2regi += [False]
+            if slx_lags == 0:
+                yend2, q2 = set_endog(y, x_constant, w, yend2, q2, w_lags, lag_q)
+
+
         name_yend.append(USER.set_name_yend_sp(self.name_y))
 
         GM_Endog_Error_Hom_Regimes.__init__(
             self,
             y=y,
             x=x_constant,
-            yend=yend,
-            q=q,
+            yend=yend2,
+            q=q2,
             regimes=regimes,
             w=w,
             vm=vm,
@@ -1674,17 +1770,24 @@ def __init__(
             name_regimes=name_regimes,
             summ=False,
             add_lag=add_lag,
+            latex=latex,
         )
 
         if regime_err_sep != True:
             self.rho = self.betas[-2]
             self.predy_e, self.e_pred, warn = sp_att(
-                w, self.y, self.predy, yend[:, -1].reshape(self.n, 1), self.rho
+                w, self.y, self.predy, yend2[:, -1].reshape(self.n, 1), self.rho
             )
             set_warn(self, warn)
             self.regime_lag_sep = regime_lag_sep
-            self.title = "SPATIALLY WEIGHTED TWO STAGE LEAST SQUARES (HOM) - REGIMES"
-            SUMMARY.GM_Combo_Hom(reg=self, w=w, vm=vm, regimes=True)
+            if slx_lags == 0:
+                self.title = "GM SPATIALLY WEIGHTED 2SLS-COMBO MODEL (HOM) - REGIMES"
+            else:
+                self.title = "GM SPATIALLY WEIGHTED 2SLS-COMBO WITH SLX (GNSM-HOM) - REGIMES"
+            self.output.iat[-2, self.output.columns.get_loc('var_type')] = 'rho'
+            self.other_top = _spat_pseudo_r2(self)
+            self.other_top += _summary_iteration(self)
+            output(reg=self, vm=vm, robust=False, other_end=False, latex=latex)
 
 
 def _work_error(
@@ -1701,6 +1804,7 @@ def _work_error(
     name_x,
     name_w,
     name_regimes,
+    slx_lags,
 ):
     w_r, warn = REGI.w_regime(w, regi_ids[r], r, transform=True)
     y_r = y[regi_ids[r]]
@@ -1710,7 +1814,10 @@ def _work_error(
     )
     set_warn(model, warn)
     model.w = w_r
-    model.title = "SPATIALLY WEIGHTED LEAST SQUARES ESTIMATION (HOM) - REGIME %s" % r
+    if slx_lags == 0:
+        model.title = "GM SPATIALLY WEIGHTED 2SLS (HOM) - REGIME %s" % r
+    else:
+        model.title = "GM SPATIALLY WEIGHTED 2SLS + SLX (SDEM-HOM) - REGIME %s" % r    
     model.name_ds = name_ds
     model.name_y = "%s_%s" % (str(r), name_y)
     model.name_x = ["%s_%s" % (str(r), i) for i in name_x]
@@ -1738,6 +1845,7 @@ def _work_endog_error(
     name_w,
     name_regimes,
     add_lag,
+    slx_lags,
 ):
     w_r, warn = REGI.w_regime(w, regi_ids[r], r, transform=True)
     y_r = y[regi_ids[r]]
@@ -1762,12 +1870,21 @@ def _work_endog_error(
             w_r, model.y, model.predy, model.yend[:, -1].reshape(model.n, 1), model.rho
         )
         set_warn(model, warn)
-    model.title = "SPATIALLY WEIGHTED TWO STAGE LEAST SQUARES (HOM) - REGIME %s" % r
+    if slx_lags == 0:
+        if add_lag != False:
+            model.title = "GM SPATIALLY WEIGHTED 2SLS-COMBO MODEL (HOM)- REGIME %s" % r            
+        else:
+            model.title = "GM SPATIALLY WEIGHTED 2SLS (HOM) - REGIME %s" % r
+    else:
+        if add_lag != False:
+            model.title = "GM SPATIAL COMBO MODEL + SLX (GNSM-HOM) - REGIME %s" % r   
+        else:
+            model.title = "GM SPATIALLY WEIGHTED 2SLS + SLX (SDEM-HOM) - REGIME %s" % r
     model.name_ds = name_ds
     model.name_y = "%s_%s" % (str(r), name_y)
     model.name_x = ["%s_%s" % (str(r), i) for i in name_x]
     model.name_yend = ["%s_%s" % (str(r), i) for i in name_yend]
-    model.name_z = model.name_x + model.name_yend + ["lambda"]
+    model.name_z = model.name_x + model.name_yend + [str(r)+"_lambda"]
     model.name_q = ["%s_%s" % (str(r), i) for i in name_q]
     model.name_h = model.name_x + model.name_q
     model.name_w = name_w
@@ -1786,3 +1903,33 @@ def _test():
 
 if __name__ == "__main__":
     _test()
+
+    import numpy as np
+    import libpysal
+
+    db = libpysal.io.open(libpysal.examples.get_path('columbus.dbf'),'r')
+    y = np.array(db.by_col("HOVAL"))
+    y = np.reshape(y, (49,1))
+    X = []
+    X.append(db.by_col("INC"))
+    X = np.array(X).T
+    yd = []
+    yd.append(db.by_col("CRIME"))
+    yd = np.array(yd).T
+    q = []
+    q.append(db.by_col("DISCBD"))
+    q = np.array(q).T
+
+    r_var = 'NSA'
+    regimes = db.by_col(r_var)
+
+    w = libpysal.weights.Rook.from_shapefile(libpysal.examples.get_path("columbus.shp"))
+    w.transform = 'r'
+    #reg = GM_Error_Hom_Regimes(y, X, regimes, w=w, name_x=['inc'], name_y='hoval', name_ds='columbus', vm=True,
+    #                           regime_err_sep=True)
+    #reg = GM_Endog_Error_Hom_Regimes(y, X, yd, q, regimes, w=w, name_x=['inc'], name_y='hoval', name_yend=['crime'],
+    #                         name_q=['discbd'], name_ds='columbus',vm=True, regime_err_sep=True)
+    reg = GM_Combo_Hom_Regimes(y, X, regimes, yd, q, w=w, name_x=['inc'], name_y='hoval', name_yend=['crime'],
+                       name_q=['discbd'], name_ds='columbus', vm=True, regime_err_sep=False, regime_lag_sep=False)
+    print(reg.output)
+    print(reg.summary)
\ No newline at end of file
diff --git a/spreg/error_sp_regimes.py b/spreg/error_sp_regimes.py
index b1c1feea..8c62c7d8 100644
--- a/spreg/error_sp_regimes.py
+++ b/spreg/error_sp_regimes.py
@@ -8,7 +8,6 @@
 import multiprocessing as mp
 from . import regimes as REGI
 from . import user_output as USER
-from . import summary_output as SUMMARY
 from libpysal.weights.spatial_lag import lag_spatial
 from .ols import BaseOLS
 from .twosls import BaseTSLS
@@ -17,6 +16,10 @@
 from .utils import optim_moments, get_spFilter, get_lags
 from .utils import spdot, RegressionPropsY
 from .sputils import sphstack
+import pandas as pd
+from .output import output, _spat_pseudo_r2
+from .error_sp_het_regimes import GM_Error_Het_Regimes, GM_Endog_Error_Het_Regimes, GM_Combo_Het_Regimes
+from .error_sp_hom_regimes import GM_Error_Hom_Regimes, GM_Endog_Error_Hom_Regimes, GM_Combo_Hom_Regimes
 
 
 class GM_Error_Regimes(RegressionPropsY, REGI.Regimes_Frame):
@@ -55,6 +58,9 @@ class GM_Error_Regimes(RegressionPropsY, REGI.Regimes_Frame):
                     If True, a separate regression is run for each regime.
     regime_lag_sep: boolean
                     Always False, kept for consistency, ignored.
+    slx_lags     : integer
+                   Number of spatial lags of X to include in the model specification.
+                   If slx_lags>0, the specification becomes of the SDEM type.
     vm           : boolean
                    If True, include variance-covariance matrix in summary
                    results
@@ -72,10 +78,13 @@ class GM_Error_Regimes(RegressionPropsY, REGI.Regimes_Frame):
                    Name of dataset for use in output
     name_regimes : string
                    Name of regime variable for use in the output
-
+    latex        : boolean
+                   Specifies if summary is to be printed in latex format
 
     Attributes
     ----------
+    output       : dataframe
+                   regression results pandas dataframe
     summary      : string
                    Summary of regression results and diagnostics (note: use in
                    conjunction with the print command)
@@ -251,28 +260,15 @@ class GM_Error_Regimes(RegressionPropsY, REGI.Regimes_Frame):
     values in model.se_betas). Alternatively, we can have a summary of the
     output by typing: model.summary
 
-    >>> print(model.name_x)
-    ['0_CONSTANT', '0_PS90', '0_UE90', '1_CONSTANT', '1_PS90', '1_UE90', 'lambda']
-    >>> np.around(model.betas, decimals=6)
-    array([[0.074807],
-           [0.786107],
-           [0.538849],
-           [5.103756],
-           [1.196009],
-           [0.600533],
-           [0.364103]])
-    >>> np.around(model.std_err, decimals=6)
-    array([0.379864, 0.152316, 0.051942, 0.471285, 0.19867 , 0.057252])
-    >>> np.around(model.z_stat, decimals=6)
-    array([[ 0.196932,  0.843881],
-           [ 5.161042,  0.      ],
-           [10.37397 ,  0.      ],
-           [10.829455,  0.      ],
-           [ 6.02007 ,  0.      ],
-           [10.489215,  0.      ]])
-    >>> np.around(model.sig2, decimals=6)
-    28.172732
-
+    >>> print(model.output)
+        var_names coefficients   std_err    zt_stat      prob
+    0  0_CONSTANT     0.074811  0.379864   0.196942  0.843873
+    1      0_PS90     0.786105  0.152315   5.161043       0.0
+    2      0_UE90     0.538848  0.051942  10.373969       0.0
+    3  1_CONSTANT     5.103761  0.471284   10.82949       0.0
+    4      1_PS90     1.196009   0.19867   6.020074       0.0
+    5      1_UE90     0.600532  0.057252  10.489217       0.0
+    6      lambda       0.3641      None       None      None
     """
 
     def __init__(
@@ -289,17 +285,26 @@ def __init__(
         cols2regi="all",
         regime_err_sep=False,
         regime_lag_sep=False,
+        slx_lags=0,
         cores=False,
         name_ds=None,
         name_regimes=None,
+        latex=False,
     ):
 
         n = USER.check_arrays(y, x)
         y = USER.check_y(y, n)
         USER.check_weights(w, y, w_required=True)
-        x_constant, name_x, warn = USER.check_constant(x, name_x, just_rem=True)
+        x_constant, name_x, warn = USER.check_constant(
+            x, name_x, just_rem=True)
         set_warn(self, warn)
         name_x = USER.set_name_x(name_x, x_constant, constant=True)
+
+        if slx_lags >0:
+            lag_x = get_lags(w, x_constant, slx_lags)
+            x_constant = np.hstack((x_constant, lag_x))
+            name_x += USER.set_name_spatial_lags(name_x, slx_lags)
+
         self.name_x_r = USER.set_name_x(name_x, x_constant)
 
         self.constant_regi = constant_regi
@@ -319,22 +324,24 @@ def __init__(
         if regime_err_sep == True:
             if set(cols2regi) == set([True]):
                 self._error_regimes_multi(
-                    y, x_constant, regimes, w, cores, cols2regi, vm, name_x
+                    y, x_constant, regimes, w, slx_lags, cores, cols2regi, vm, name_x, latex
                 )
             else:
                 raise Exception(
-                    "All coefficients must vary accross regimes if regime_err_sep = True."
+                    "All coefficients must vary across regimes if regime_err_sep = True."
                 )
         else:
-            x_constant = sphstack(np.ones((x_constant.shape[0], 1)), x_constant)
+            x_constant = sphstack(
+                np.ones((x_constant.shape[0], 1)), x_constant)
             name_x = USER.set_name_x(name_x, x_constant)
-            self.x, self.name_x = REGI.Regimes_Frame.__init__(
+            self.x, self.name_x, x_rlist = REGI.Regimes_Frame.__init__(
                 self,
                 x_constant,
                 regimes,
                 constant_regi=None,
                 cols2regi=cols2regi,
                 names=name_x,
+                rlist=True,
             )
             ols = BaseOLS(y=y, x=self.x)
             self.k = ols.x.shape[1]
@@ -354,14 +361,22 @@ def __init__(
             self.sig2 = ols2.sig2n
             self.e_filtered = self.u - lambda1 * lag_spatial(w, self.u)
             self.vm = self.sig2 * ols2.xtxi
-            self.title = "SPATIALLY WEIGHTED LEAST SQUARES - REGIMES"
+            if slx_lags == 0:
+                self.title = "GM SPATIALLY WEIGHTED MODEL - REGIMES"
+            else:
+                self.title = "GM SPATIALLY WEIGHTED MODEL + SLX (SDEM) - REGIMES"       
             self.name_x.append("lambda")
             self.kf += 1
             self.chow = REGI.Chow(self)
             self._cache = {}
-            SUMMARY.GM_Error(reg=self, w=w, vm=vm, regimes=True)
-
-    def _error_regimes_multi(self, y, x, regimes, w, cores, cols2regi, vm, name_x):
+            self.output = pd.DataFrame(self.name_x,
+                                       columns=['var_names'])
+            self.output['var_type'] = ['x']*(len(self.name_x)-1)+['lambda']
+            self.output['regime'] = x_rlist + ['_Global']
+            self.output['equation'] = 0
+            output(reg=self, vm=vm, robust=False, other_end=False, latex=latex)
+
+    def _error_regimes_multi(self, y, x, regimes, w, slx_lags, cores, cols2regi, vm, name_x, latex):
         regi_ids = dict(
             (r, list(np.where(np.array(regimes) == r)[0])) for r in self.regimes_set
         )
@@ -394,6 +409,7 @@ def _error_regimes_multi(self, y, x, regimes, w, cores, cols2regi, vm, name_x):
                         name_x + ["lambda"],
                         self.name_w,
                         self.name_regimes,
+                        slx_lags,
                     ),
                 )
             else:
@@ -409,6 +425,7 @@ def _error_regimes_multi(self, y, x, regimes, w, cores, cols2regi, vm, name_x):
                         name_x + ["lambda"],
                         self.name_w,
                         self.name_regimes,
+                        slx_lags,
                     )
                 )
 
@@ -432,6 +449,8 @@ def _error_regimes_multi(self, y, x, regimes, w, cores, cols2regi, vm, name_x):
 
         results = {}
         self.name_y, self.name_x = [], []
+        self.output = pd.DataFrame(
+            columns=['var_names', 'var_type', 'regime', 'equation'])
         counter = 0
         for r in self.regimes_set:
             """
@@ -446,11 +465,11 @@ def _error_regimes_multi(self, y, x, regimes, w, cores, cols2regi, vm, name_x):
                 results[r] = results_p[r].get()
 
             self.vm[
-                (counter * self.kr) : ((counter + 1) * self.kr),
-                (counter * self.kr) : ((counter + 1) * self.kr),
+                (counter * self.kr): ((counter + 1) * self.kr),
+                (counter * self.kr): ((counter + 1) * self.kr),
             ] = results[r].vm
             self.betas[
-                (counter * (self.kr + 1)) : ((counter + 1) * (self.kr + 1)),
+                (counter * (self.kr + 1)): ((counter + 1) * (self.kr + 1)),
             ] = results[r].betas
             self.u[
                 regi_ids[r],
@@ -463,10 +482,14 @@ def _error_regimes_multi(self, y, x, regimes, w, cores, cols2regi, vm, name_x):
             ] = results[r].e_filtered
             self.name_y += results[r].name_y
             self.name_x += results[r].name_x
+            self.output = pd.concat([self.output, pd.DataFrame({'var_names': results[r].name_x,
+                                                                'var_type': ['x'] * (len(results[r].name_x) - 1) +
+                                                                            ['lambda'],
+                                                                'regime': r, 'equation': r})], ignore_index=True)
             counter += 1
         self.chow = REGI.Chow(self)
         self.multi = results
-        SUMMARY.GM_Error_multi(reg=self, multireg=self.multi, vm=vm, regimes=True)
+        output(reg=self, vm=vm, robust=False, other_end=False, latex=latex)
 
 
 class GM_Endog_Error_Regimes(RegressionPropsY, REGI.Regimes_Frame):
@@ -513,6 +536,9 @@ class GM_Endog_Error_Regimes(RegressionPropsY, REGI.Regimes_Frame):
                     If True, a separate regression is run for each regime.
     regime_lag_sep: boolean
                     Always False, kept for consistency, ignored.
+    slx_lags     : integer
+                   Number of spatial lags of X to include in the model specification.
+                   If slx_lags>0, the specification becomes of the SDEM type.
     vm           : boolean
                    If True, include variance-covariance matrix in summary
                    results
@@ -534,9 +560,13 @@ class GM_Endog_Error_Regimes(RegressionPropsY, REGI.Regimes_Frame):
                    Name of dataset for use in output
     name_regimes : string
                    Name of regime variable for use in the output
+    latex        : boolean
+                   Specifies if summary is to be printed in latex format
 
     Attributes
     ----------
+    output       : dataframe
+                   regression results pandas dataframe
     summary      : string
                    Summary of regression results and diagnostics (note: use in
                    conjunction with the print command)
@@ -742,22 +772,17 @@ class GM_Endog_Error_Regimes(RegressionPropsY, REGI.Regimes_Frame):
     endogenous variables included. Alternatively, we can have a summary of the
     output by typing: model.summary
 
-    >>> print(model.name_z)
-    ['0_CONSTANT', '0_PS90', '0_UE90', '1_CONSTANT', '1_PS90', '1_UE90', '0_RD90', '1_RD90', 'lambda']
-    >>> np.around(model.betas, decimals=5)
-    array([[ 3.59718],
-           [ 1.0652 ],
-           [ 0.15822],
-           [ 9.19754],
-           [ 1.88082],
-           [-0.24878],
-           [ 2.46161],
-           [ 3.57943],
-           [ 0.25564]])
-    >>> np.around(model.std_err, decimals=6)
-    array([0.522633, 0.137555, 0.063054, 0.473654, 0.18335 , 0.072786,
-           0.300711, 0.240413])
-
+    >>> print(model.output)
+        var_names coefficients   std_err    zt_stat      prob
+    0  0_CONSTANT     3.597178  0.522633   6.882796       0.0
+    1      0_PS90     1.065203  0.137555   7.743852       0.0
+    2      0_UE90      0.15822  0.063054   2.509282  0.012098
+    6      0_RD90     2.461609  0.300711   8.185967       0.0
+    3  1_CONSTANT     9.197542  0.473654  19.418268       0.0
+    4      1_PS90     1.880815   0.18335  10.258046       0.0
+    5      1_UE90    -0.248777  0.072786  -3.417919  0.000631
+    7      1_RD90     3.579429  0.240413  14.888666       0.0
+    8      lambda     0.255639      None       None      None
     """
 
     def __init__(
@@ -774,6 +799,7 @@ def __init__(
         cols2regi="all",
         regime_err_sep=False,
         regime_lag_sep=False,
+        slx_lags=0,
         name_y=None,
         name_x=None,
         name_yend=None,
@@ -783,14 +809,22 @@ def __init__(
         name_regimes=None,
         summ=True,
         add_lag=False,
+        latex=False,
     ):
 
         n = USER.check_arrays(y, x, yend, q)
         y = USER.check_y(y, n)
         USER.check_weights(w, y, w_required=True)
-        x_constant, name_x, warn = USER.check_constant(x, name_x, just_rem=True)
+        x_constant, name_x, warn = USER.check_constant(
+            x, name_x, just_rem=True)
         set_warn(self, warn)
         name_x = USER.set_name_x(name_x, x_constant, constant=True)
+
+        if slx_lags > 0:
+            lag_x = get_lags(w, x_constant, slx_lags)
+            x_constant = np.hstack((x_constant, lag_x))
+            name_x += USER.set_name_spatial_lags(name_x, slx_lags)
+
         self.constant_regi = constant_regi
         self.cols2regi = cols2regi
         self.name_ds = USER.set_name_ds(name_ds)
@@ -822,6 +856,7 @@ def __init__(
                     w,
                     yend,
                     q,
+                    slx_lags,
                     cores,
                     cols2regi,
                     vm,
@@ -829,26 +864,29 @@ def __init__(
                     name_yend,
                     name_q,
                     add_lag,
+                    latex,
                 )
             else:
                 raise Exception(
-                    "All coefficients must vary accross regimes if regime_err_sep = True."
+                    "All coefficients must vary across regimes if regime_err_sep = True."
                 )
         else:
-            x_constant = sphstack(np.ones((x_constant.shape[0], 1)), x_constant)
+            x_constant = sphstack(
+                np.ones((x_constant.shape[0], 1)), x_constant)
             name_x = USER.set_name_x(name_x, x_constant)
             q, name_q = REGI.Regimes_Frame.__init__(
                 self, q, regimes, constant_regi=None, cols2regi="all", names=name_q
             )
-            x, name_x = REGI.Regimes_Frame.__init__(
+            x, name_x, x_rlist = REGI.Regimes_Frame.__init__(
                 self,
                 x_constant,
                 regimes,
                 constant_regi=None,
                 cols2regi=cols2regi,
                 names=name_x,
+                rlist=True,
             )
-            yend2, name_yend = REGI.Regimes_Frame.__init__(
+            yend2, name_yend, yend_rlist = REGI.Regimes_Frame.__init__(
                 self,
                 yend,
                 regimes,
@@ -856,6 +894,7 @@ def __init__(
                 cols2regi=cols2regi,
                 yend=True,
                 names=name_yend,
+                rlist=True,
             )
 
             tsls = BaseTSLS(y=y, x=x, yend=yend2, q=q)
@@ -896,9 +935,19 @@ def __init__(
             self.kf += 1
             self.chow = REGI.Chow(self)
             self._cache = {}
+            self.output = pd.DataFrame(self.name_z,
+                                       columns=['var_names'])
+            self.output['var_type'] = [
+                'x'] * len(self.name_x) + ['yend'] * len(self.name_yend) + ['lambda']
+            self.output['regime'] = x_rlist + yend_rlist + ['_Global']
+            self.output['equation'] = 0
             if summ:
-                self.title = "SPATIALLY WEIGHTED TWO STAGE LEAST SQUARES - REGIMES"
-                SUMMARY.GM_Endog_Error(reg=self, w=w, vm=vm, regimes=True)
+                if slx_lags == 0:
+                    self.title = ("GM SPATIALLY WEIGHTED 2SLS - REGIMES")
+                else:
+                    self.title = ("GM SPATIALLY WEIGHTED 2SLS WITH SLX (SDEM) - REGIMES")
+                output(reg=self, vm=vm, robust=False,
+                       other_end=False, latex=latex)
 
     def _endog_error_regimes_multi(
         self,
@@ -908,6 +957,7 @@ def _endog_error_regimes_multi(
         w,
         yend,
         q,
+        slx_lags,
         cores,
         cols2regi,
         vm,
@@ -915,6 +965,7 @@ def _endog_error_regimes_multi(
         name_yend,
         name_q,
         add_lag,
+        latex,
     ):
 
         regi_ids = dict(
@@ -959,6 +1010,7 @@ def _endog_error_regimes_multi(
                         self.name_w,
                         self.name_regimes,
                         add_lag,
+                        slx_lags,
                     ),
                 )
             else:
@@ -979,6 +1031,7 @@ def _endog_error_regimes_multi(
                         self.name_w,
                         self.name_regimes,
                         add_lag,
+                        slx_lags,
                     )
                 )
 
@@ -1009,6 +1062,8 @@ def _endog_error_regimes_multi(
             self.name_h,
         ) = ([], [], [], [], [], [])
         counter = 0
+        self.output = pd.DataFrame(
+            columns=['var_names', 'var_type', 'regime', 'equation'])
         for r in self.regimes_set:
             """
             if is_win:
@@ -1022,11 +1077,11 @@ def _endog_error_regimes_multi(
                 results[r] = results_p[r].get()
 
             self.vm[
-                (counter * self.kr) : ((counter + 1) * self.kr),
-                (counter * self.kr) : ((counter + 1) * self.kr),
+                (counter * self.kr): ((counter + 1) * self.kr),
+                (counter * self.kr): ((counter + 1) * self.kr),
             ] = results[r].vm
             self.betas[
-                (counter * (self.kr + 1)) : ((counter + 1) * (self.kr + 1)),
+                (counter * (self.kr + 1)): ((counter + 1) * (self.kr + 1)),
             ] = results[r].betas
             self.u[
                 regi_ids[r],
@@ -1050,15 +1105,20 @@ def _endog_error_regimes_multi(
                 self.e_pred[
                     regi_ids[r],
                 ] = results[r].e_pred
+                results[r].other_top = _spat_pseudo_r2(results[r])
+                v_type = ['x'] * len(results[r].name_x) + ['yend'] * \
+                    (len(results[r].name_yend) - 1) + ['rho', 'lambda']
+            else:
+                results[r].other_top = ""
+                v_type = ['x'] * len(results[r].name_x) + ['yend'] * \
+                    len(results[r].name_yend) + ['lambda']
+            self.output = pd.concat([self.output, pd.DataFrame({'var_names': results[r].name_z,
+                                                                'var_type': v_type,
+                                                                'regime': r, 'equation': r})], ignore_index=True)
             counter += 1
         self.chow = REGI.Chow(self)
         self.multi = results
-        if add_lag != False:
-            SUMMARY.GM_Combo_multi(reg=self, multireg=self.multi, vm=vm, regimes=True)
-        else:
-            SUMMARY.GM_Endog_Error_multi(
-                reg=self, multireg=self.multi, vm=vm, regimes=True
-            )
+        output(reg=self, vm=vm, robust=False, other_end=False, latex=latex)
 
 
 class GM_Combo_Regimes(GM_Endog_Error_Regimes, REGI.Regimes_Frame):
@@ -1107,6 +1167,9 @@ class GM_Combo_Regimes(GM_Endog_Error_Regimes, REGI.Regimes_Frame):
                     If True, the spatial parameter for spatial lag is also
                     computed according to different regimes. If False (default),
                     the spatial parameter is fixed accross regimes.
+    slx_lags     : integer
+                   Number of spatial lags of X to include in the model specification.
+                   If slx_lags>0, the specification becomes of the GNSM type.       
     w_lags       : integer
                    Orders of W to include as instruments for the spatially
                    lagged dependent variable. For example, w_lags=1, then
@@ -1135,9 +1198,13 @@ class GM_Combo_Regimes(GM_Endog_Error_Regimes, REGI.Regimes_Frame):
                    Name of dataset for use in output
     name_regimes : string
                    Name of regime variable for use in the output
+    latex        : boolean
+                   Specifies if summary is to be printed in latex format
 
     Attributes
     ----------
+    output       : dataframe
+                   regression results pandas dataframe
     summary      : string
                    Summary of regression results and diagnostics (note: use in
                    conjunction with the print command)
@@ -1350,22 +1417,16 @@ class GM_Combo_Regimes(GM_Endog_Error_Regimes, REGI.Regimes_Frame):
     output by typing: model.summary
     Alternatively, we can check the betas:
 
-    >>> print(model.name_z)
-    ['0_CONSTANT', '0_PS90', '0_UE90', '1_CONSTANT', '1_PS90', '1_UE90', '_Global_W_HR90', 'lambda']
-    >>> print(np.around(model.betas,4))
-    [[ 1.4607]
-     [ 0.958 ]
-     [ 0.5658]
-     [ 9.113 ]
-     [ 1.1338]
-     [ 0.6517]
-     [-0.4583]
-     [ 0.6136]]
-
-    And lambda:
-
-    >>> print('lambda: ', np.around(model.betas[-1], 4))
-    lambda:  [0.6136]
+    >>> print(model.output)
+            var_names coefficients   std_err    zt_stat      prob
+    0      0_CONSTANT     1.460707  0.704174   2.074356  0.038046
+    1          0_PS90      0.95795  0.171485   5.586214       0.0
+    2          0_UE90     0.565805  0.053665  10.543203       0.0
+    3      1_CONSTANT     9.112998  1.525875   5.972311       0.0
+    4          1_PS90      1.13382   0.20552    5.51683       0.0
+    5          1_UE90      0.65169  0.061106  10.664938       0.0
+    6  _Global_W_HR90    -0.458326  0.145599  -3.147859  0.001645
+    7          lambda     0.613599      None       None      None
 
     This class also allows the user to run a spatial lag+error model with the
     extra feature of including non-spatial endogenous regressors. This means
@@ -1383,24 +1444,18 @@ class GM_Combo_Regimes(GM_Endog_Error_Regimes, REGI.Regimes_Frame):
     And then we can run and explore the model analogously to the previous combo:
 
     >>> model = GM_Combo_Regimes(y, x, regimes, yd, q, w=w, name_y=y_var, name_x=x_var, name_yend=yd_var, name_q=q_var, name_regimes=r_var, name_ds='NAT')
-    >>> print(model.name_z)
-    ['0_CONSTANT', '0_PS90', '0_UE90', '1_CONSTANT', '1_PS90', '1_UE90', '0_RD90', '1_RD90', '_Global_W_HR90', 'lambda']
-    >>> print(model.betas)
-    [[ 3.41963782]
-     [ 1.04065841]
-     [ 0.16634393]
-     [ 8.86544628]
-     [ 1.85120528]
-     [-0.24908469]
-     [ 2.43014046]
-     [ 3.61645481]
-     [ 0.03308671]
-     [ 0.18684992]]
-    >>> print(np.sqrt(model.vm.diagonal()))
-    [0.53067577 0.13271426 0.06058025 0.76406411 0.17969783 0.07167421
-     0.28943121 0.25308326 0.06126529]
-    >>> print('lambda: ', np.around(model.betas[-1], 4))
-    lambda:  [0.1868]
+    >>> print(model.output)
+            var_names coefficients   std_err    zt_stat      prob
+    0      0_CONSTANT     3.419638  0.530676   6.443931       0.0
+    1          0_PS90     1.040658  0.132714   7.841346       0.0
+    2          0_UE90     0.166344   0.06058   2.745844  0.006036
+    6          0_RD90      2.43014  0.289431   8.396263       0.0
+    3      1_CONSTANT     8.865446  0.764064  11.603014       0.0
+    4          1_PS90     1.851205  0.179698  10.301769       0.0
+    5          1_UE90    -0.249085  0.071674  -3.475235   0.00051
+    7          1_RD90     3.616455  0.253083  14.289586       0.0
+    8  _Global_W_HR90     0.033087  0.061265   0.540057  0.589158
+    9          lambda      0.18685      None       None      None
     """
 
     def __init__(
@@ -1411,6 +1466,7 @@ def __init__(
         yend=None,
         q=None,
         w=None,
+        slx_lags=0,
         w_lags=1,
         lag_q=True,
         cores=False,
@@ -1426,22 +1482,41 @@ def __init__(
         name_w=None,
         name_ds=None,
         name_regimes=None,
+        latex=False,
     ):
-
+        if regime_lag_sep and not regime_err_sep:
+            set_warn(self, "regime_err_sep set to True when regime_lag_sep=True.")                
+            regime_err_sep = True
+        if regime_err_sep and not regime_lag_sep:
+            set_warn(self, "regime_err_sep set to False when regime_lag_sep=False.")                
+            regime_err_sep = False
         n = USER.check_arrays(y, x)
         y = USER.check_y(y, n)
         USER.check_weights(w, y, w_required=True)
-        x_constant, name_x, warn = USER.check_constant(x, name_x, just_rem=True)
+        x_constant, name_x, warn = USER.check_constant(
+            x, name_x, just_rem=True)
         set_warn(self, warn)
         name_x = USER.set_name_x(name_x, x_constant, constant=True)
         self.name_y = USER.set_name_y(name_y)
         name_yend = USER.set_name_yend(name_yend, yend)
         name_q = USER.set_name_q(name_q, q)
-        name_q.extend(USER.set_name_q_sp(name_x, w_lags, name_q, lag_q, force_all=True))
 
-        cols2regi = REGI.check_cols2regi(
-            constant_regi, cols2regi, x_constant, yend=yend, add_cons=False
-        )
+        if regime_err_sep and any(col != True for col in cols2regi):
+            set_warn(self, "All coefficients must vary across regimes if regime_err_sep = True, so setting cols2regi = 'all'.")
+            cols2regi = "all"
+            
+        if slx_lags > 0:
+            yend2, q2, wx = set_endog(y, x_constant, w, yend, q, w_lags, lag_q, slx_lags)
+            x_constant = np.hstack((x_constant, wx))
+            name_slx = USER.set_name_spatial_lags(name_x, slx_lags)
+            name_q.extend(USER.set_name_q_sp(name_slx[-len(name_x):], w_lags, name_q, lag_q, force_all=True))
+            name_x += name_slx   
+            cols2regi = REGI.check_cols2regi(constant_regi, cols2regi, x_constant[:, :-1], yend=yend2, add_cons=False)
+        else:
+            name_q.extend(USER.set_name_q_sp(name_x, w_lags, name_q, lag_q, force_all=True))
+            yend2, q2 = yend, q
+            cols2regi = REGI.check_cols2regi(constant_regi, cols2regi, x_constant, yend=yend2, add_cons=False)
+
         self.regimes_set = REGI._get_regimes_set(regimes)
         self.regimes = regimes
         USER.check_regimes(self.regimes_set, n, x_constant.shape[1])
@@ -1449,27 +1524,27 @@ def __init__(
         self.regime_lag_sep = regime_lag_sep
 
         if regime_lag_sep == True:
-            if regime_err_sep == False:
-                raise Exception(
-                    "For spatial combo models, if spatial lag is set by regimes (regime_lag_sep=True), spatial error must also be set by regimes (regime_err_sep=True)."
-                )
-            add_lag = [w_lags, lag_q]
+            if slx_lags == 0:
+                add_lag = [w_lags, lag_q]
+            else:
+                add_lag = False
+                cols2regi += [True]
+
         else:
-            if regime_err_sep == True:
-                raise Exception(
-                    "For spatial combo models, if spatial error is set by regimes (regime_err_sep=True), all coefficients including lambda (regime_lag_sep=True) must be set by regimes."
-                )
-            cols2regi += [False]
             add_lag = False
-            yend, q = set_endog(y, x_constant, w, yend, q, w_lags, lag_q)
+            cols2regi += [False]
+            if slx_lags == 0:
+                yend2, q2 = set_endog(y, x_constant, w, yend2, q2, w_lags, lag_q)
+            
         name_yend.append(USER.set_name_yend_sp(self.name_y))
 
+        print(cols2regi, x_constant.shape[1], yend2.shape[1], name_x, name_yend, name_q)
         GM_Endog_Error_Regimes.__init__(
             self,
             y=y,
             x=x_constant,
-            yend=yend,
-            q=q,
+            yend=yend2,
+            q=q2,
             regimes=regimes,
             w=w,
             vm=vm,
@@ -1486,17 +1561,399 @@ def __init__(
             name_regimes=name_regimes,
             summ=False,
             add_lag=add_lag,
+            latex=latex,
         )
 
         if regime_err_sep != True:
             self.rho = self.betas[-2]
             self.predy_e, self.e_pred, warn = sp_att(
-                w, self.y, self.predy, yend[:, -1].reshape(self.n, 1), self.rho
+                w, self.y, self.predy, yend2[:, -1].reshape(self.n, 1), self.rho
             )
             set_warn(self, warn)
-            self.title = "SPATIALLY WEIGHTED TWO STAGE LEAST SQUARES - REGIMES"
-            SUMMARY.GM_Combo(reg=self, w=w, vm=vm, regimes=True)
+            if slx_lags == 0:
+                self.title = "SPATIALLY WEIGHTED 2SLS - GM-COMBO MODEL - REGIMES"
+            else:
+                self.title = "SPATIALLY WEIGHTED 2SLS - GM-COMBO WITH SLX (GNSM) - REGIMES"
+            self.output.iat[-2,
+                            self.output.columns.get_loc('var_type')] = 'rho'
+            self.other_top = _spat_pseudo_r2(self)
+            output(reg=self, vm=vm, robust=False, other_end=False, latex=latex)
+
+
+class GMM_Error_Regimes(GM_Error_Regimes, GM_Combo_Regimes, GM_Endog_Error_Regimes,
+                        GM_Error_Het_Regimes, GM_Combo_Het_Regimes, GM_Endog_Error_Het_Regimes, 
+                        GM_Error_Hom_Regimes, GM_Combo_Hom_Regimes, GM_Endog_Error_Hom_Regimes
+                        ):
+
+    """
+    Wrapper function to call any of the GM methods for a spatial error regimes model available in spreg
+
+    Parameters
+    ----------
+    y            : array
+                   nx1 array for dependent variable
+    x            : array
+                   Two dimensional array with n rows and one column for each
+                   independent (exogenous) variable, excluding the constant
+    regimes      : list
+                   List of n values with the mapping of each
+                   observation to a regime. Assumed to be aligned with 'x'.
+    w            : pysal W object
+                   Spatial weights object (always needed)
+    yend         : array
+                   Two dimensional array with n rows and one column for each
+                   endogenous variable (if any)
+    q            : array
+                   Two dimensional array with n rows and one column for each
+                   external exogenous variable to use as instruments (if any)
+                   (note: this should not contain any variables from x)
+    estimator    : string
+                   Choice of estimator to be used. Options are: 'het', which
+                   is robust to heteroskedasticity, 'hom', which assumes
+                   homoskedasticity, and 'kp98', which does not provide
+                   inference on the spatial parameter for the error term.
+    constant_regi: string, optional
+                   Switcher controlling the constant term setup. It may take
+                   the following values:
+
+                   * 'one': a vector of ones is appended to x and held constant across regimes.
+
+                   * 'many': a vector of ones is appended to x and considered different per regime (default).
+    cols2regi    : list, 'all'
+                   Argument indicating whether each
+                   column of x should be considered as different per regime
+                   or held constant across regimes (False).
+                   If a list, k booleans indicating for each variable the
+                   option (True if one per regime, False to be held constant).
+                   If 'all' (default), all the variables vary by regime.
+    regime_err_sep: boolean
+                    If True, a separate regression is run for each regime.
+    regime_lag_sep: boolean
+                    Always False, kept for consistency, ignored.                   
+    add_wy       : boolean
+                   If True, then a spatial lag of the dependent variable is included.           
+    slx_lags     : integer
+                   Number of spatial lags of X to include in the model specification.
+                   If slx_lags>0, the specification becomes of the SDEM or GNSM type.             
+    vm           : boolean
+                   If True, include variance-covariance matrix in summary
+                   results
+    name_y       : string
+                   Name of dependent variable for use in output
+    name_x       : list of strings
+                   Names of independent variables for use in output
+    name_w       : string
+                   Name of weights matrix for use in output
+    name_regimes : string
+                   Name of regime variable for use in the output
+    name_yend    : list of strings
+                   Names of endogenous variables for use in output
+    name_q       : list of strings
+                   Names of instruments for use in output                   
+    name_ds      : string
+                   Name of dataset for use in output
+    latex        : boolean
+                   Specifies if summary is to be printed in latex format
+    **kwargs     : keywords
+                   Additional arguments to pass on to the estimators. 
+                   See the specific functions for details on what can be used.
+
+    Attributes
+    ----------
+    output       : dataframe
+                   regression results pandas dataframe
+    summary      : string
+                   Summary of regression results and diagnostics (note: use in
+                   conjunction with the print command)
+    betas        : array
+                   kx1 array of estimated coefficients
+    u            : array
+                   nx1 array of residuals
+    e_filtered   : array
+                   nx1 array of spatially filtered residuals
+    predy        : array
+                   nx1 array of predicted y values
+    n            : integer
+                   Number of observations
+    k            : integer
+                   Number of variables for which coefficients are estimated
+                   (including the constant)
+    y            : array
+                   nx1 array for dependent variable
+    x            : array
+                   Two dimensional array with n rows and one column for each
+                   independent (exogenous) variable, including the constant
+    mean_y       : float
+                   Mean of dependent variable
+    std_y        : float
+                   Standard deviation of dependent variable
+    pr2          : float
+                   Pseudo R squared (squared correlation between y and ypred)
+    vm           : array
+                   Variance covariance matrix (kxk)
+    sig2         : float
+                   Sigma squared used in computations
+    std_err      : array
+                   1xk array of standard errors of the betas
+    z_stat       : list of tuples
+                   z statistic; each tuple contains the pair (statistic,
+                   p-value), where each is a float
+    name_y       : string
+                   Name of dependent variable for use in output
+    name_x       : list of strings
+                   Names of independent variables for use in output
+    name_w       : string
+                   Name of weights matrix for use in output
+    name_ds      : string
+                   Name of dataset for use in output
+    name_regimes : string
+                   Name of regime variable for use in the output
+    title        : string
+                   Name of the regression method used
+                   Only available in dictionary 'multi' when multiple regressions
+                   (see 'multi' below for details)
+    regimes      : list
+                   List of n values with the mapping of each
+                   observation to a regime. Assumed to be aligned with 'x'.
+    constant_regi: string
+                   Ignored if regimes=False. Constant option for regimes.
+                   Switcher controlling the constant term setup. It may take
+                   the following values:
 
+                   *  'one': a vector of ones is appended to x and held constant across regimes
+
+                   * 'many': a vector of ones is appended to x and considered different per regime
+    cols2regi    : list, 'all'
+                   Ignored if regimes=False. Argument indicating whether each
+                   column of x should be considered as different per regime
+                   or held constant across regimes (False).
+                   If a list, k booleans indicating for each variable the
+                   option (True if one per regime, False to be held constant).
+                   If 'all', all the variables vary by regime.
+    regime_err_sep: boolean
+                    If True, a separate regression is run for each regime.
+    kr           : int
+                   Number of variables/columns to be "regimized" or subject
+                   to change by regime. These will result in one parameter
+                   estimate by regime for each variable (i.e. nr parameters per
+                   variable)
+    kf           : int
+                   Number of variables/columns to be considered fixed or
+                   global across regimes and hence only obtain one parameter
+                   estimate
+    nr           : int
+                   Number of different regimes in the 'regimes' list
+    name_yend    : list of strings (optional)
+                    Names of endogenous variables for use in output
+    name_z       : list of strings (optional)
+                    Names of exogenous and endogenous variables for use in
+                    output
+    name_q       : list of strings (optional)
+                    Names of external instruments
+    name_h       : list of strings (optional)
+                    Names of all instruments used in ouput                   
+    multi        : dictionary
+                   Only available when multiple regressions are estimated,
+                   i.e. when regime_err_sep=True and no variable is fixed
+                   across regimes.
+                   Contains all attributes of each individual regression    
+
+    Examples
+    --------
+
+    We first need to import the needed modules, namely numpy to convert the
+    data we read into arrays that ``spreg`` understands and ``libpysal`` to
+    handle the weights and file management.
+
+    >>> import numpy as np
+    >>> import libpysal
+    >>> from libpysal.examples import load_example
+
+    Open data on NCOVR US County Homicides (3085 areas) using libpysal.io.open().
+    This is the DBF associated with the NAT shapefile.  Note that
+    libpysal.io.open() also reads data in CSV format; since the actual class
+    requires data to be passed in as numpy arrays, the user can read their
+    data in using any method.
+
+    >>> nat = load_example('Natregimes')
+    >>> db = libpysal.io.open(nat.get_path("natregimes.dbf"),'r')
+
+    Extract the HR90 column (homicide rates in 1990) from the DBF file and make it the
+    dependent variable for the regression. Note that PySAL requires this to be
+    an numpy array of shape (n, 1) as opposed to the also common shape of (n, )
+    that other packages accept.
+
+    >>> y_var = 'HR90'
+    >>> y = np.array([db.by_col(y_var)]).reshape(3085,1)
+
+    Extract UE90 (unemployment rate) and PS90 (population structure) vectors from
+    the DBF to be used as independent variables in the regression. Other variables
+    can be inserted by adding their names to x_var, such as x_var = ['Var1','Var2','...]
+    Note that PySAL requires this to be an nxj numpy array, where j is the
+    number of independent variables (not including a constant). By default
+    this model adds a vector of ones to the independent variables passed in.
+
+    >>> x_var = ['PS90','UE90']
+    >>> x = np.array([db.by_col(name) for name in x_var]).T
+
+    The different regimes in this data are given according to the North and
+    South dummy (SOUTH).
+
+    >>> r_var = 'SOUTH'
+    >>> regimes = db.by_col(r_var)
+
+    Since we want to run a spatial error model, we need to specify
+    the spatial weights matrix that includes the spatial configuration of the
+    observations. To do that, we can open an already existing gal file or
+    create a new one. In this case, we will create one from ``NAT.shp``.
+
+    >>> w = libpysal.weights.Rook.from_shapefile(nat.get_path("natregimes.shp"))
+
+    Unless there is a good reason not to do it, the weights have to be
+    row-standardized so every row of the matrix sums to one. Among other
+    things, this allows to interpret the spatial lag of a variable as the
+    average value of the neighboring observations. In PySAL, this can be
+    easily performed in the following way:
+
+    >>> w.transform = 'r'
+
+    The GMM_Error_Regimes class can run error models and SARAR models, that is a spatial lag+error model.
+    In this example we will run a simple version of the latter, where we have the
+    spatial effects as well as exogenous variables. Since it is a spatial
+    model, we have to pass in the weights matrix. If we want to
+    have the names of the variables printed in the output summary, we will
+    have to pass them in as well, although this is optional.
+
+    >>> from spreg import GMM_Error_Regimes
+    >>> model = GMM_Error_Regimes(y, x, regimes, w=w, add_wy=True, name_y=y_var, name_x=x_var, name_regimes=r_var, name_ds='NAT')
+
+    Once we have run the model, we can explore a little bit the output. The
+    regression object we have created has many attributes so take your time to
+    discover them.
+
+    >>> print(model.output)
+            var_names coefficients   std_err    zt_stat      prob
+    0      0_CONSTANT     1.461317  0.848361   1.722517  0.084976
+    1          0_PS90     0.958711  0.239834   3.997388  0.000064
+    2          0_UE90     0.565825  0.063726   8.879088       0.0
+    3      1_CONSTANT     9.115738  1.976874   4.611189  0.000004
+    4          1_PS90     1.132419  0.334107   3.389387    0.0007
+    5          1_UE90     0.651804  0.105518   6.177197       0.0
+    6  _Global_W_HR90    -0.458677  0.180997  -2.534173  0.011271
+    7          lambda     0.734354  0.035255  20.829823       0.0
+
+    This class also allows the user to run a spatial lag+error model with the
+    extra feature of including non-spatial endogenous regressors. This means
+    that, in addition to the spatial lag and error, we consider some of the
+    variables on the right-hand side of the equation as endogenous and we
+    instrument for this. In this case we consider RD90 (resource deprivation)
+    as an endogenous regressor.  We use FP89 (families below poverty)
+    for this and hence put it in the instruments parameter, 'q'.
+
+    >>> yd_var = ['RD90']
+    >>> yd = np.array([db.by_col(name) for name in yd_var]).T
+    >>> q_var = ['FP89']
+    >>> q = np.array([db.by_col(name) for name in q_var]).T
+
+    And then we can run and explore the model analogously to the previous combo:
+
+    >>> model = GMM_Error_Regimes(y, x, regimes, yend=yd, q=q, w=w, add_wy=True, name_y=y_var, name_x=x_var, name_yend=yd_var, name_q=q_var, name_regimes=r_var, name_ds='NAT')
+    >>> print(model.output)
+            var_names coefficients   std_err    zt_stat      prob
+    0      0_CONSTANT     1.461317  0.848361   1.722517  0.084976
+    1          0_PS90     0.958711  0.239834   3.997388  0.000064
+    2          0_UE90     0.565825  0.063726   8.879088       0.0
+    3      1_CONSTANT     9.115738  1.976874   4.611189  0.000004
+    4          1_PS90     1.132419  0.334107   3.389387    0.0007
+    5          1_UE90     0.651804  0.105518   6.177197       0.0
+    6  _Global_W_HR90    -0.458677  0.180997  -2.534173  0.011271
+    7          lambda     0.734354  0.035255  20.829823       0.0
+
+    The class also allows for estimating a GNS model by adding spatial lags of the exogenous variables, using the argument slx_lags:
+
+    >>> model = GMM_Error_Regimes(y, x, regimes, w=w, add_wy=True, slx_lags=1, name_y=y_var, name_x=x_var, name_regimes=r_var, name_ds='NAT')
+    >>> print(model.output)
+             var_names coefficients   std_err   zt_stat      prob
+    0       0_CONSTANT     0.192699  0.256922   0.75003  0.453237
+    1           0_PS90     1.098019  0.232054  4.731743  0.000002
+    2           0_UE90     0.606622   0.07762  7.815325       0.0
+    3         0_W_PS90    -1.068778  0.203911 -5.241381       0.0
+    4         0_W_UE90    -0.657932  0.176073  -3.73671  0.000186
+    5       1_CONSTANT    -0.104299  1.790953 -0.058237   0.95356
+    6           1_PS90     1.219796  0.316425  3.854936  0.000116
+    7           1_UE90     0.678922  0.120491  5.634647       0.0
+    8         1_W_PS90    -1.308599  0.536231 -2.440366  0.014672
+    9         1_W_UE90    -0.708492  0.167057  -4.24102  0.000022
+    10  _Global_W_HR90     1.033956  0.269252  3.840111  0.000123
+    11          lambda    -0.384968  0.192256 -2.002366  0.045245
+    
+
+    """
+
+    def __init__(
+            self, y, x, regimes, w, yend=None, q=None, estimator='het', constant_regi="many", cols2regi="all", regime_err_sep=False,
+            regime_lag_sep=False, add_wy=False, slx_lags=0, vm=False, name_y=None, name_x=None, name_w=None, name_regimes=None, name_yend=None,
+            name_q=None, name_ds=None, latex=False, **kwargs):
+
+        if estimator == 'het':
+            if yend is None and not add_wy:
+                GM_Error_Het_Regimes.__init__(self, y=y, x=x, regimes=regimes, w=w, slx_lags=slx_lags, vm=vm, name_y=name_y, name_x=name_x,
+                                              constant_regi=constant_regi, cols2regi=cols2regi, regime_err_sep=regime_err_sep,
+                                              name_w=name_w, name_regimes=name_regimes, name_ds=name_ds, latex=latex, **kwargs)
+            elif yend is not None and not add_wy:
+                GM_Endog_Error_Het_Regimes.__init__(self, y=y, x=x, regimes=regimes, yend=yend, q=q, w=w, slx_lags=slx_lags, vm=vm, name_y=name_y, name_x=name_x,
+                                                    constant_regi=constant_regi, cols2regi=cols2regi, regime_err_sep=regime_err_sep,
+                                                    name_yend=name_yend, name_q=name_q, name_w=name_w, name_regimes=name_regimes, name_ds=name_ds, latex=latex, **kwargs)
+            elif add_wy:
+                GM_Combo_Het_Regimes.__init__(self, y=y, x=x, regimes=regimes, yend=yend, q=q, w=w, slx_lags=slx_lags, vm=vm, name_y=name_y, name_x=name_x,
+                                              constant_regi=constant_regi, cols2regi=cols2regi, regime_err_sep=regime_err_sep, regime_lag_sep=regime_lag_sep,
+                                              name_yend=name_yend, name_q=name_q, name_w=name_w, name_regimes=name_regimes, name_ds=name_ds, latex=latex, **kwargs)
+            else:
+                set_warn(self, 'Combination of arguments passed to GMM_Error_Regimes not allowed. Using default arguments instead.')
+                GM_Error_Het_Regimes.__init__(self, y=y, x=x, regimes=regimes, w=w, slx_lags=slx_lags, vm=vm, name_y=name_y, name_x=name_x,
+                                              constant_regi=constant_regi, cols2regi=cols2regi, regime_err_sep=regime_err_sep,
+                                              name_w=name_w, name_regimes=name_regimes, name_ds=name_ds, latex=latex)
+        elif estimator == 'hom':
+            if yend is None and not add_wy:
+                GM_Error_Hom_Regimes.__init__(self, y=y, x=x, regimes=regimes, w=w, slx_lags=slx_lags, vm=vm, name_y=name_y, name_x=name_x,
+                                              constant_regi=constant_regi, cols2regi=cols2regi, regime_err_sep=regime_err_sep,
+                                              name_w=name_w, name_regimes=name_regimes, name_ds=name_ds, latex=latex, **kwargs)
+            elif yend is not None and not add_wy:
+                GM_Endog_Error_Hom_Regimes.__init__(self, y=y, x=x, regimes=regimes, yend=yend, q=q, w=w, slx_lags=slx_lags, vm=vm, name_y=name_y, name_x=name_x,
+                                                    constant_regi=constant_regi, cols2regi=cols2regi, regime_err_sep=regime_err_sep,
+                                                    name_yend=name_yend, name_q=name_q, name_w=name_w, name_regimes=name_regimes, name_ds=name_ds, latex=latex, **kwargs)
+            elif add_wy:
+                GM_Combo_Hom_Regimes.__init__(self, y=y, x=x, regimes=regimes, yend=yend, q=q, w=w, slx_lags=slx_lags, vm=vm, name_y=name_y, name_x=name_x,
+                                              constant_regi=constant_regi, cols2regi=cols2regi, regime_err_sep=regime_err_sep, regime_lag_sep=regime_lag_sep,
+                                              name_yend=name_yend, name_q=name_q, name_w=name_w, name_regimes=name_regimes, name_ds=name_ds, latex=latex, **kwargs)
+            else:
+                set_warn(self, 'Combination of arguments passed to GMM_Error_Regimes not allowed. Using default arguments instead.')
+                GM_Error_Hom_Regimes.__init__(self, y=y, x=x, regimes=regimes, w=w, slx_lags=slx_lags, vm=vm, name_y=name_y, name_x=name_x,
+                                              constant_regi=constant_regi, cols2regi=cols2regi, regime_err_sep=regime_err_sep,
+                                              name_w=name_w, name_regimes=name_regimes, name_ds=name_ds, latex=latex)        
+        elif estimator == 'kp98':
+            if yend is None and not add_wy:
+                GM_Error_Regimes.__init__(self, y=y, x=x, regimes=regimes, w=w, slx_lags=slx_lags, vm=vm, name_y=name_y, name_x=name_x,
+                                          constant_regi=constant_regi, cols2regi=cols2regi, regime_err_sep=regime_err_sep,
+                                          name_w=name_w, name_regimes=name_regimes, name_ds=name_ds, latex=latex, **kwargs)
+            elif yend is not None and not add_wy:
+                GM_Endog_Error_Regimes.__init__(self, y=y, x=x, regimes=regimes, yend=yend, q=q, w=w, slx_lags=slx_lags, vm=vm, name_y=name_y, name_x=name_x,
+                                                constant_regi=constant_regi, cols2regi=cols2regi, regime_err_sep=regime_err_sep,
+                                                name_yend=name_yend, name_q=name_q, name_w=name_w, name_regimes=name_regimes, name_ds=name_ds, latex=latex, **kwargs)
+            elif add_wy:
+                GM_Combo_Regimes.__init__(self, y=y, x=x, regimes=regimes, yend=yend, q=q, w=w, slx_lags=slx_lags, vm=vm, name_y=name_y, name_x=name_x,
+                                          constant_regi=constant_regi, cols2regi=cols2regi, regime_err_sep=regime_err_sep, regime_lag_sep=regime_lag_sep,
+                                          name_yend=name_yend, name_q=name_q, name_w=name_w, name_regimes=name_regimes, name_ds=name_ds, latex=latex, **kwargs)
+            else:
+                set_warn(self, 'Combination of arguments passed to GMM_Error_Regimes not allowed. Using default arguments instead.')
+                GM_Error_Regimes.__init__(self, y=y, x=x, regimes=regimes, w=w, slx_lags=slx_lags, vm=vm, name_y=name_y, name_x=name_x,
+                                          constant_regi=constant_regi, cols2regi=cols2regi, regime_err_sep=regime_err_sep,
+                                          name_w=name_w, name_regimes=name_regimes, name_ds=name_ds, latex=latex)     
+        else:
+            set_warn(self, 'Combination of arguments passed to GMM_Error_Regimes not allowed. Using default arguments instead.')
+            GM_Error_Het_Regimes.__init__(self, y=y, x=x, regimes=regimes, w=w, slx_lags=slx_lags, vm=vm, name_y=name_y, name_x=name_x,
+                                            constant_regi=constant_regi, cols2regi=cols2regi, regime_err_sep=regime_err_sep,
+                                            name_w=name_w, name_regimes=name_regimes, name_ds=name_ds, latex=latex)
 
 def _work_error(y, x, regi_ids, r, w, name_ds, name_y, name_x, name_w, name_regimes):
     w_r, warn = REGI.w_regime(w, regi_ids[r], r, transform=True)
@@ -1505,7 +1962,7 @@ def _work_error(y, x, regi_ids, r, w, name_ds, name_y, name_x, name_w, name_regi
     model = BaseGM_Error(y_r, x_r, w_r.sparse)
     set_warn(model, warn)
     model.w = w_r
-    model.title = "SPATIALLY WEIGHTED LEAST SQUARES ESTIMATION - REGIME %s" % r
+    model.title = "GM SPATIALLY WEIGHTED LEAST SQUARES ESTIMATION - REGIME %s" % r
     model.name_ds = name_ds
     model.name_y = "%s_%s" % (str(r), name_y)
     model.name_x = ["%s_%s" % (str(r), i) for i in name_x]
@@ -1530,6 +1987,7 @@ def _work_endog_error(
     name_w,
     name_regimes,
     add_lag,
+    slx_lags,
 ):
     w_r, warn = REGI.w_regime(w, regi_ids[r], r, transform=True)
     y_r = y[regi_ids[r]]
@@ -1548,16 +2006,26 @@ def _work_endog_error(
     if add_lag != False:
         model.rho = model.betas[-2]
         model.predy_e, model.e_pred, warn = sp_att(
-            w_r, model.y, model.predy, model.yend[:, -1].reshape(model.n, 1), model.rho
+            w_r, model.y, model.predy, model.yend[:, -
+                                                  1].reshape(model.n, 1), model.rho
         )
         set_warn(model, warn)
     model.w = w_r
-    model.title = "SPATIALLY WEIGHTED TWO STAGE LEAST SQUARES - REGIME %s" % r
+    if slx_lags == 0:
+        if add_lag != False:
+            model.title = "SPATIALLY WEIGHTED 2SLS - GM-COMBO MODEL - REGIME %s" % r            
+        else:
+            model.title = "SPATIALLY WEIGHTED 2SLS (GM) - REGIME %s" % r
+    else:
+        if add_lag != False:
+            model.title = "GM SPATIAL COMBO MODEL + SLX (GNSM) - REGIME %s" % r            
+        else:
+            model.title = "GM SPATIALLY WEIGHTED 2SLS + SLX (SDEM) - REGIME %s" % r
     model.name_ds = name_ds
     model.name_y = "%s_%s" % (str(r), name_y)
     model.name_x = ["%s_%s" % (str(r), i) for i in name_x]
     model.name_yend = ["%s_%s" % (str(r), i) for i in name_yend]
-    model.name_z = model.name_x + model.name_yend + ["lambda"]
+    model.name_z = model.name_x + model.name_yend + [str(r)+"_lambda"]
     model.name_q = ["%s_%s" % (str(r), i) for i in name_q]
     model.name_h = model.name_x + model.name_q
     model.name_w = name_w
@@ -1575,31 +2043,34 @@ def _test():
 
 
 if __name__ == "__main__":
-
     _test()
-    import libpysal
     import numpy as np
+    import libpysal
 
-    dbf = libpysal.io.open(libpysal.examples.get_path("columbus.dbf"), "r")
-    y = np.array([dbf.by_col("CRIME")]).T
-    names_to_extract = ["INC"]
-    x = np.array([dbf.by_col(name) for name in names_to_extract]).T
-    yd_var = ["HOVAL"]
-    yend = np.array([dbf.by_col(name) for name in yd_var]).T
-    q_var = ["DISCBD"]
-    q = np.array([dbf.by_col(name) for name in q_var]).T
-    regimes = regimes = dbf.by_col("NSA")
-    w = libpysal.io.open(libpysal.examples.get_path("columbus.gal"), "r").read()
-    w.transform = "r"
-    model = GM_Error_Regimes(
-        y,
-        x,
-        regimes=regimes,
-        w=w,
-        name_y="crime",
-        name_x=["income"],
-        name_regimes="nsa",
-        name_ds="columbus",
-        regime_err_sep=True,
-    )
-    print(model.summary)
+    db = libpysal.io.open(libpysal.examples.get_path('columbus.dbf'), 'r')
+    y = np.array(db.by_col("HOVAL"))
+    y = np.reshape(y, (49, 1))
+    X = []
+    X.append(db.by_col("INC"))
+    X = np.array(X).T
+    yd = []
+    yd.append(db.by_col("CRIME"))
+    yd = np.array(yd).T
+    q = []
+    q.append(db.by_col("DISCBD"))
+    q = np.array(q).T
+
+    r_var = 'NSA'
+    regimes = db.by_col(r_var)
+
+    w = libpysal.weights.Rook.from_shapefile(
+        libpysal.examples.get_path("columbus.shp"))
+    w.transform = 'r'
+    # reg = GM_Error_Regimes(y, X, regimes, w=w, name_x=['inc'], name_y='hoval', name_ds='columbus',
+    #                       regime_err_sep=True)
+    # reg = GM_Endog_Error_Regimes(y, X, yd, q, regimes, w=w, name_x=['inc'], name_y='hoval', name_yend=['crime'],
+    #                         name_q=['discbd'], name_ds='columbus', regime_err_sep=True)
+    reg = GM_Combo_Regimes(y, X, regimes, yd, q, w=w, name_x=['inc'], name_y='hoval', name_yend=['crime'],
+                           name_q=['discbd'], name_ds='columbus', regime_err_sep=True, regime_lag_sep=True)
+    print(reg.output)
+    print(reg.summary)
diff --git a/spreg/ml_error.py b/spreg/ml_error.py
index 2d505324..bb338677 100644
--- a/spreg/ml_error.py
+++ b/spreg/ml_error.py
@@ -10,12 +10,13 @@
 import numpy.linalg as la
 from scipy import sparse as sp
 from scipy.sparse.linalg import splu as SuperLU
-from .utils import RegressionPropsY, RegressionPropsVM, set_warn
+from .utils import RegressionPropsY, RegressionPropsVM, set_warn, get_lags
 from . import diagnostics as DIAG
 from . import user_output as USER
-from . import summary_output as SUMMARY
 from . import regimes as REGI
 from .w_utils import symmetrize
+import pandas as pd
+from .output import output, _nonspat_top
 
 try:
     from scipy.optimize import minimize_scalar
@@ -310,6 +311,9 @@ class ML_Error(BaseML_Error):
                    independent (exogenous) variable, excluding the constant
     w            : Sparse matrix
                    Spatial weights sparse matrix
+    slx_lags     : integer
+                   Number of spatial lags of X to include in the model specification.
+                   If slx_lags>0, the specification becomes of the SDEM type.                   
     method       : string
                    if 'full', brute force calculation (full matrix expressions)
                    if 'ord', Ord eigenvalue method
@@ -327,9 +331,13 @@ class ML_Error(BaseML_Error):
                    Name of weights matrix for use in output
     name_ds      : string
                    Name of dataset for use in output
+    latex        : boolean
+                   Specifies if summary is to be printed in latex format
 
     Attributes
     ----------
+    output       : dataframe
+                   regression results pandas dataframe
     betas        : array
                    (k+1)x1 array of estimated coefficients (rho first)
     lam          : float
@@ -463,6 +471,7 @@ def __init__(
         y,
         x,
         w,
+        slx_lags=0,
         method="full",
         epsilon=0.0000001,
         vm=False,
@@ -470,17 +479,26 @@ def __init__(
         name_x=None,
         name_w=None,
         name_ds=None,
+        latex=False,
     ):
         n = USER.check_arrays(y, x)
         y = USER.check_y(y, n)
         USER.check_weights(w, y, w_required=True)
         x_constant, name_x, warn = USER.check_constant(x, name_x)
         set_warn(self, warn)
+
+        self.title = "ML SPATIAL ERROR"
+        if slx_lags >0:
+            lag_x = get_lags(w, x_constant[:, 1:], slx_lags)
+            x_constant = np.hstack((x_constant, lag_x))
+            name_x += USER.set_name_spatial_lags(name_x, slx_lags)
+            self.title += " WITH SLX (SDEM)"
+        self.title += " (METHOD = " + method + ")"
+
         method = method.upper()
         BaseML_Error.__init__(
             self, y=y, x=x_constant, w=w, method=method, epsilon=epsilon
         )
-        self.title = "MAXIMUM LIKELIHOOD SPATIAL ERROR" + " (METHOD = " + method + ")"
         self.name_ds = USER.set_name_ds(name_ds)
         self.name_y = USER.set_name_y(name_y)
         self.name_x = USER.set_name_x(name_x, x)
@@ -488,7 +506,11 @@ def __init__(
         self.name_w = USER.set_name_w(name_w, w)
         self.aic = DIAG.akaike(reg=self)
         self.schwarz = DIAG.schwarz(reg=self)
-        SUMMARY.ML_Error(reg=self, w=w, vm=vm, spat_diag=False)
+        self.output = pd.DataFrame(self.name_x, columns=['var_names'])
+        self.output['var_type'] = ['x'] * (len(self.name_x) - 1) + ['lambda']
+        self.output['regime'], self.output['equation'] = (0, 0)
+        self.other_top = _nonspat_top(self, ml=True)
+        output(reg=self, vm=vm, robust=False, other_end=False, latex=latex)
 
 
 def err_c_loglik(lam, n, y, ylag, x, xlag, W):
@@ -565,3 +587,29 @@ def _test():
     np.set_printoptions(suppress=True)
     doctest.testmod()
     np.set_printoptions(suppress=start_suppress)
+
+if __name__ == "__main__":
+    _test()
+
+    import numpy as np
+    import libpysal
+
+    db = libpysal.io.open(libpysal.examples.get_path("columbus.dbf"), "r")
+    y_var = "CRIME"
+    y = np.array([db.by_col(y_var)]).reshape(49, 1)
+    x_var = ["INC"]
+    x = np.array([db.by_col(name) for name in x_var]).T
+    w = libpysal.weights.Rook.from_shapefile(libpysal.examples.get_path("columbus.shp"))
+    w.transform = "r"
+    model = ML_Error(
+        y,
+        x,
+        w=w,
+        vm=False,
+        name_y=y_var,
+        name_x=x_var,
+        name_ds="columbus",
+        name_w="columbus.gal",
+    )
+    print(model.output)
+    print(model.summary)
diff --git a/spreg/ml_error_regimes.py b/spreg/ml_error_regimes.py
index 03384495..721fd115 100644
--- a/spreg/ml_error_regimes.py
+++ b/spreg/ml_error_regimes.py
@@ -9,12 +9,13 @@
 import multiprocessing as mp
 from . import regimes as REGI
 from . import user_output as USER
-from . import summary_output as SUMMARY
 from . import diagnostics as DIAG
-from .utils import set_warn
+from .utils import set_warn, get_lags
 from .sputils import sphstack
 from .ml_error import BaseML_Error
 from platform import system
+import pandas as pd
+from .output import output, _nonspat_top
 
 __all__ = ["ML_Error_Regimes"]
 
@@ -51,6 +52,9 @@ class ML_Error_Regimes(BaseML_Error, REGI.Regimes_Frame):
                    If 'all' (default), all the variables vary by regime.
     w            : Sparse matrix
                    Spatial weights sparse matrix 
+    slx_lags     : integer
+                   Number of spatial lags of X to include in the model specification.
+                   If slx_lags>0, the specification becomes of the SDEM type.                          
     method       : string
                    if 'full', brute force calculation (full matrix expressions)
                    if 'ord', Ord eigenvalue computation
@@ -78,9 +82,13 @@ class ML_Error_Regimes(BaseML_Error, REGI.Regimes_Frame):
                    Name of dataset for use in output
     name_regimes : string
                    Name of regimes variable for use in output
+    latex        : boolean
+                   Specifies if summary is to be printed in latex format
 
     Attributes
     ----------
+    output       : dataframe
+                   regression results pandas dataframe
     summary      : string
                    Summary of regression results and diagnostics (note: use in
                    conjunction with the print command)
@@ -276,6 +284,7 @@ def __init__(
         x,
         regimes,
         w=None,
+        slx_lags=0,
         constant_regi="many",
         cols2regi="all",
         method="full",
@@ -289,14 +298,13 @@ def __init__(
         name_w=None,
         name_ds=None,
         name_regimes=None,
+        latex=False,
     ):
 
         n = USER.check_arrays(y, x)
         y = USER.check_y(y, n)
         USER.check_weights(w, y, w_required=True)
         self.constant_regi = constant_regi
-        self.cols2regi = cols2regi
-        self.regime_err_sep = regime_err_sep
         self.name_ds = USER.set_name_ds(name_ds)
         self.name_y = USER.set_name_y(name_y)
         self.name_w = USER.set_name_w(name_w, w)
@@ -307,9 +315,16 @@ def __init__(
         x_constant, name_x, warn = USER.check_constant(x, name_x, just_rem=True)
         set_warn(self, warn)
         name_x = USER.set_name_x(name_x, x_constant, constant=True)
+
+        if slx_lags >0:
+            lag_x = get_lags(w, x_constant, slx_lags)
+            x_constant = np.hstack((x_constant, lag_x))
+            name_x += USER.set_name_spatial_lags(name_x, slx_lags)
+
         self.name_x_r = USER.set_name_x(name_x, x_constant)
 
-        cols2regi = REGI.check_cols2regi(constant_regi, cols2regi, x)
+        cols2regi = REGI.check_cols2regi(constant_regi, cols2regi, x_constant)
+        self.cols2regi = cols2regi        
         self.regimes_set = REGI._get_regimes_set(regimes)
         self.regimes = regimes
         USER.check_regimes(self.regimes_set, self.n, x.shape[1])
@@ -322,16 +337,18 @@ def __init__(
                     x_constant,
                     regimes,
                     w,
+                    slx_lags,
                     cores,
                     method,
                     epsilon,
                     cols2regi,
                     vm,
                     name_x,
+                    latex,
                 )
             else:
                 raise Exception(
-                    "All coefficients must vary accross regimes if regime_err_sep = True."
+                    "All coefficients must vary across regimes if regime_err_sep = True."
                 )
         else:
             x_constant = sphstack(np.ones((x_constant.shape[0], 1)), x_constant)
@@ -340,15 +357,15 @@ def __init__(
             regimes_att["x"] = x_constant
             regimes_att["regimes"] = regimes
             regimes_att["cols2regi"] = cols2regi
-            x, name_x = REGI.Regimes_Frame.__init__(
+            x, name_x, x_rlist = REGI.Regimes_Frame.__init__(
                 self,
                 x_constant,
                 regimes,
                 constant_regi=None,
                 cols2regi=cols2regi,
                 names=name_x,
+                rlist=True
             )
-
             BaseML_Error.__init__(
                 self,
                 y=y,
@@ -359,22 +376,26 @@ def __init__(
                 regimes_att=regimes_att,
             )
 
-            self.title = (
-                "MAXIMUM LIKELIHOOD SPATIAL ERROR - REGIMES"
-                + " (METHOD = "
-                + method
-                + ")"
-            )
+            self.title = "ML SPATIAL ERROR"
+            if slx_lags >0:
+                self.title += " WITH SLX (SDEM)"
+            self.title += " - REGIMES (METHOD = " + method + ")"
+
             self.name_x = USER.set_name_x(name_x, x, constant=True)
             self.name_x.append("lambda")
             self.kf += 1  # Adding a fixed k to account for lambda.
             self.chow = REGI.Chow(self)
             self.aic = DIAG.akaike(reg=self)
             self.schwarz = DIAG.schwarz(reg=self)
-            SUMMARY.ML_Error(reg=self, w=w, vm=vm, spat_diag=False, regimes=True)
+            self.output = pd.DataFrame(self.name_x, columns=['var_names'])
+            self.output['var_type'] = ['x'] * (len(self.name_x) - 1) + ['lambda']
+            self.output['regime'] = x_rlist + ['_Global']
+            self.output['equation'] = 0
+            self.other_top = _nonspat_top(self, ml=True)
+            output(reg=self, vm=vm, robust=False, other_end=False, latex=latex)
 
     def _error_regimes_multi(
-        self, y, x, regimes, w, cores, method, epsilon, cols2regi, vm, name_x
+        self, y, x, regimes, w, slx_lags, cores, method, epsilon, cols2regi, vm, name_x, latex
     ):
 
         regi_ids = dict(
@@ -404,6 +425,7 @@ def _error_regimes_multi(
                         regi_ids,
                         r,
                         w,
+                        slx_lags,
                         method,
                         epsilon,
                         self.name_ds,
@@ -421,6 +443,7 @@ def _error_regimes_multi(
                         regi_ids,
                         r,
                         w,
+                        slx_lags,
                         method,
                         epsilon,
                         self.name_ds,
@@ -452,6 +475,7 @@ def _error_regimes_multi(
 
         results = {}
         counter = 0
+        self.output = pd.DataFrame(columns=['var_names', 'var_type', 'regime', 'equation'])
         for r in self.regimes_set:
             """
             if is_win:
@@ -482,16 +506,18 @@ def _error_regimes_multi(
             ] = results[r].e_filtered
             self.name_y += results[r].name_y
             self.name_x += results[r].name_x
+            results[r].other_top = _nonspat_top(results[r], ml=True)
+            self.output = pd.concat([self.output, pd.DataFrame({'var_names': results[r].name_x,
+                                                                'var_type': ['x'] * (len(results[r].name_x) - 1) + ['lambda'],
+                                                                'regime': r, 'equation': r})], ignore_index=True)
             counter += 1
         self.chow = REGI.Chow(self)
         self.multi = results
-        SUMMARY.ML_Error_multi(
-            reg=self, multireg=self.multi, vm=vm, spat_diag=False, regimes=True, w=w
-        )
+        output(reg=self, vm=vm, robust=False, other_end=False, latex=latex)
 
 
 def _work_error(
-    y, x, regi_ids, r, w, method, epsilon, name_ds, name_y, name_x, name_w, name_regimes
+    y, x, regi_ids, r, w, slx_lags, method, epsilon, name_ds, name_y, name_x, name_w, name_regimes
 ):
     w_r, warn = REGI.w_regime(w, regi_ids[r], r, transform=True)
     y_r = y[regi_ids[r]]
@@ -499,13 +525,10 @@ def _work_error(
     model = BaseML_Error(y=y_r, x=x_r, w=w_r, method=method, epsilon=epsilon)
     set_warn(model, warn)
     model.w = w_r
-    model.title = (
-        "MAXIMUM LIKELIHOOD SPATIAL ERROR - REGIME "
-        + str(r)
-        + " (METHOD = "
-        + method
-        + ")"
-    )
+    model.title = "ML SPATIAL ERROR"
+    if slx_lags >0:
+        model.title += " WITH SLX (SDEM)"
+    model.title += " - REGIME " + str(r) + " (METHOD = " + method + ")"
     model.name_ds = name_ds
     model.name_y = "%s_%s" % (str(r), name_y)
     model.name_x = ["%s_%s" % (str(r), i) for i in name_x]
@@ -528,30 +551,23 @@ def _test():
 if __name__ == "__main__":
     _test()
     import numpy as np
-    import libpysal
+    import libpysal as ps
 
-    db = libpysal.io.open(libpysal.examples.get_path("baltim.dbf"), "r")
+    db = ps.io.open(ps.examples.get_path("baltim.dbf"), "r")
     ds_name = "baltim.dbf"
     y_name = "PRICE"
     y = np.array(db.by_col(y_name)).T
     y.shape = (len(y), 1)
     x_names = ["NROOM", "NBATH", "PATIO", "FIREPL", "AC", "GAR", "AGE", "LOTSZ", "SQFT"]
     x = np.array([db.by_col(var) for var in x_names]).T
-    ww = ps.open(ps.examples.get_path("baltim_q.gal"))
+    ww = ps.io.open(ps.examples.get_path("baltim_q.gal"))
     w = ww.read()
     ww.close()
     w_name = "baltim_q.gal"
     w.transform = "r"
+    regimes = db.by_col("CITCOU")
 
-    regimes = []
-    y_coord = np.array(db.by_col("Y"))
-    for i in y_coord:
-        if i > 544.5:
-            regimes.append("North")
-        else:
-            regimes.append("South")
-
-    mlerror = ML_Error_Regimes(
+    model = ML_Error_Regimes(
         y,
         x,
         regimes,
@@ -561,7 +577,9 @@ def _test():
         name_x=x_names,
         name_w=w_name,
         name_ds=ds_name,
-        regime_err_sep=False,
-        name_regimes="North",
+        regime_err_sep=True,
+        constant_regi="many",
+        name_regimes="CITCOU",
     )
-    print(mlerror.summary)
+    print(model.output)
+    print(model.summary)
diff --git a/spreg/ml_lag.py b/spreg/ml_lag.py
old mode 100644
new mode 100755
index 7d7e2e9f..c3ec794c
--- a/spreg/ml_lag.py
+++ b/spreg/ml_lag.py
@@ -10,11 +10,12 @@
 import numpy.linalg as la
 from scipy import sparse as sp
 from scipy.sparse.linalg import splu as SuperLU
-from .utils import RegressionPropsY, RegressionPropsVM, inverse_prod, set_warn
+from .utils import RegressionPropsY, RegressionPropsVM, inverse_prod, set_warn, get_lags
 from .sputils import spdot, spfill_diagonal, spinv, spbroadcast
 from . import diagnostics as DIAG
 from . import user_output as USER
-from . import summary_output as SUMMARY
+import pandas as pd
+from .output import output, _nonspat_top, _spat_pseudo_r2
 from .w_utils import symmetrize
 from libpysal import weights
 
@@ -43,6 +44,9 @@ class BaseML_Lag(RegressionPropsY, RegressionPropsVM):
                    independent (exogenous) variable, excluding the constant
     w            : pysal W object
                    Spatial weights object
+    slx_lags     : integer
+                   Number of spatial lags of X to include in the model specification.
+                   If slx_lags>0, the specification becomes of the Spatial Durbin type.
     method       : string
                    if 'full', brute force calculation (full matrix expressions)
                    if 'ord', Ord eigenvalue method
@@ -179,25 +183,29 @@ class BaseML_Lag(RegressionPropsY, RegressionPropsVM):
 
     """
 
-    def __init__(self, y, x, w, method="full", epsilon=0.0000001):
+    def __init__(self, y, x, w, slx_lags=0, method="full", epsilon=0.0000001):
         # set up main regression variables and spatial filters
         self.y = y
         self.x = x
-        self.n, self.k = self.x.shape
         self.method = method
         self.epsilon = epsilon
         # W = w.full()[0]
         # Wsp = w.sparse
         ylag = weights.lag_spatial(w, y)
         # b0, b1, e0 and e1
+
+        if slx_lags>0:
+            self.x = np.hstack((self.x, get_lags(w, self.x[:, 1:], slx_lags)))
+
+        self.n, self.k = self.x.shape
         xtx = spdot(self.x.T, self.x)
         xtxi = la.inv(xtx)
         xty = spdot(self.x.T, self.y)
         xtyl = spdot(self.x.T, ylag)
         b0 = spdot(xtxi, xty)
         b1 = spdot(xtxi, xtyl)
-        e0 = self.y - spdot(x, b0)
-        e1 = ylag - spdot(x, b1)
+        e0 = self.y - spdot(self.x, b0)
+        e1 = ylag - spdot(self.x, b1)
         methodML = method.upper()
         # call minimizer using concentrated log-likelihood to get rho
         if methodML in ["FULL", "LU", "ORD"]:
@@ -209,19 +217,19 @@ def __init__(self, y, x, w, method="full", epsilon=0.0000001):
                     bounds=(-1.0, 1.0),
                     args=(self.n, e0, e1, W),
                     method="bounded",
-                    tol=epsilon,
+                    options={'xatol': epsilon},
                 )
             elif methodML == "LU":
                 I = sp.identity(w.n)
                 Wsp = w.sparse  # moved here
-                W = Wsp
+                W = Wsp#.tocsc()
                 res = minimize_scalar(
                     lag_c_loglik_sp,
                     0.0,
                     bounds=(-1.0, 1.0),
                     args=(self.n, e0, e1, I, Wsp),
                     method="bounded",
-                    tol=epsilon,
+                    options={'xatol': epsilon},
                 )
             elif methodML == "ORD":
                 # check on symmetry structure
@@ -239,7 +247,7 @@ def __init__(self, y, x, w, method="full", epsilon=0.0000001):
                     bounds=(-1.0, 1.0),
                     args=(self.n, e0, e1, evals),
                     method="bounded",
-                    tol=epsilon,
+                    options={'xatol': epsilon},
                 )
         else:
             # program will crash, need to catch
@@ -261,7 +269,7 @@ def __init__(self, y, x, w, method="full", epsilon=0.0000001):
         self.u = e0 - self.rho * e1
         self.predy = self.y - self.u
 
-        xb = spdot(x, b)
+        xb = spdot(self.x, b)
 
         self.predy_e = inverse_prod(
             w.sparse, xb, self.rho, inv_method="power_exp", threshold=epsilon
@@ -289,7 +297,7 @@ def __init__(self, y, x, w, method="full", epsilon=0.0000001):
 
         wpredy = weights.lag_spatial(w, self.predy_e)
         wpyTwpy = spdot(wpredy.T, wpredy)
-        xTwpy = spdot(x.T, wpredy)
+        xTwpy = spdot(self.x.T, wpredy)
 
         # order of variables is beta, rho, sigma2
 
@@ -321,6 +329,9 @@ class ML_Lag(BaseML_Lag):
                    independent (exogenous) variable, excluding the constant
     w            : pysal W object
                    Spatial weights object
+    slx_lags     : integer
+                   Number of spatial lags of X to include in the model specification.
+                   If slx_lags>0, the specification becomes of the Spatial Durbin type.
     method       : string
                    if 'full', brute force calculation (full matrix expressions)
                    if 'ord', Ord eigenvalue method
@@ -337,9 +348,13 @@ class ML_Lag(BaseML_Lag):
                    Name of weights matrix for use in output
     name_ds      : string
                    Name of dataset for use in output
+    latex        : boolean
+                   Specifies if summary is to be printed in latex format
 
     Attributes
     ----------
+    output       : dataframe
+                   regression results pandas dataframe
     betas        : array
                    (k+1)x1 array of estimated coefficients (rho first)
     rho          : float
@@ -567,6 +582,7 @@ def __init__(
         y,
         x,
         w,
+        slx_lags=0,
         method="full",
         epsilon=0.0000001,
         vm=False,
@@ -574,6 +590,7 @@ def __init__(
         name_x=None,
         name_w=None,
         name_ds=None,
+        latex=False,
     ):
         n = USER.check_arrays(y, x)
         y = USER.check_y(y, n)
@@ -582,11 +599,16 @@ def __init__(
         set_warn(self, warn)
         method = method.upper()
         BaseML_Lag.__init__(
-            self, y=y, x=x_constant, w=w, method=method, epsilon=epsilon
+            self, y=y, x=x_constant, w=w, slx_lags=slx_lags, method=method, epsilon=epsilon
         )
         # increase by 1 to have correct aic and sc, include rho in count
         self.k += 1
-        self.title = "MAXIMUM LIKELIHOOD SPATIAL LAG" + " (METHOD = " + method + ")"
+
+        if slx_lags>0:
+            name_x += USER.set_name_spatial_lags(name_x, slx_lags)
+            self.title = "MAXIMUM LIKELIHOOD SPATIAL LAG WITH SLX - SPATIAL DURBIN MODEL" + " (METHOD = " + method + ")"
+        else:
+            self.title = "MAXIMUM LIKELIHOOD SPATIAL LAG" + " (METHOD = " + method + ")"
         self.name_ds = USER.set_name_ds(name_ds)
         self.name_y = USER.set_name_y(name_y)
         self.name_x = USER.set_name_x(name_x, x)
@@ -595,8 +617,12 @@ def __init__(
         self.name_w = USER.set_name_w(name_w, w)
         self.aic = DIAG.akaike(reg=self)
         self.schwarz = DIAG.schwarz(reg=self)
-        SUMMARY.ML_Lag(reg=self, w=w, vm=vm, spat_diag=False)
-
+        self.output = pd.DataFrame(self.name_x, columns=['var_names'])
+        self.output['var_type'] = ['x'] * (len(self.name_x)-1) + ['rho']
+        self.output['regime'], self.output['equation'] = (0, 0)
+        self.other_top = _spat_pseudo_r2(self)
+        self.other_top += _nonspat_top(self, ml=True)
+        output(reg=self, vm=vm, robust=False, other_end=False, latex=latex)
 
 def lag_c_loglik(rho, n, e0, e1, W):
     # concentrated log-lik for lag model, no constants, brute force
@@ -647,3 +673,29 @@ def _test():
     np.set_printoptions(suppress=True)
     doctest.testmod()
     np.set_printoptions(suppress=start_suppress)
+
+if __name__ == "__main__":
+    _test()
+
+    import numpy as np
+    import libpysal
+
+    db = libpysal.io.open(libpysal.examples.get_path("columbus.dbf"), "r")
+    y_var = "CRIME"
+    y = np.array([db.by_col(y_var)]).reshape(49, 1)
+    x_var = ["INC"]
+    x = np.array([db.by_col(name) for name in x_var]).T
+    w = libpysal.weights.Rook.from_shapefile(libpysal.examples.get_path("columbus.shp"))
+    w.transform = "r"
+    model = ML_Lag(
+        y,
+        x,
+        w=w,
+        vm=False,
+        name_y=y_var,
+        name_x=x_var,
+        name_ds="columbus",
+        name_w="columbus.gal",
+    )
+    print(model.output)
+    print(model.summary)
diff --git a/spreg/ml_lag_regimes.py b/spreg/ml_lag_regimes.py
index 4e603d09..a05c00b2 100644
--- a/spreg/ml_lag_regimes.py
+++ b/spreg/ml_lag_regimes.py
@@ -7,12 +7,14 @@
 import numpy as np
 from . import regimes as REGI
 from . import user_output as USER
-from . import summary_output as SUMMARY
 from . import diagnostics as DIAG
 import multiprocessing as mp
 from .ml_lag import BaseML_Lag
-from .utils import set_warn
+from .utils import set_warn, get_lags
 from platform import system
+import pandas as pd
+from .output import output, _nonspat_top, _spat_pseudo_r2
+
 
 __all__ = ["ML_Lag_Regimes"]
 
@@ -55,6 +57,9 @@ class ML_Lag_Regimes(BaseML_Lag, REGI.Regimes_Frame):
                    if 'LU', LU sparse matrix decomposition
     epsilon      : float
                    tolerance criterion in mimimize_scalar function and inverse_product
+    slx_lags     : integer
+                   Number of spatial lags of X to include in the model specification.
+                   If slx_lags>0, the specification becomes of the Spatial Durbin type.                   
     regime_lag_sep: boolean
                     If True, the spatial parameter for spatial lag is also
                     computed according to different regimes. If False (default),
@@ -76,9 +81,13 @@ class ML_Lag_Regimes(BaseML_Lag, REGI.Regimes_Frame):
                    Name of dataset for use in output
     name_regimes : string
                    Name of regimes variable for use in output
+    latex        : boolean
+                   Specifies if summary is to be printed in latex format
 
     Attributes
     ----------
+    output       : dataframe
+                   regression results pandas dataframe
     summary      : string
                    Summary of regression results and diagnostics (note: use in
                    conjunction with the print command)
@@ -297,8 +306,8 @@ def __init__(
         cols2regi="all",
         method="full",
         epsilon=0.0000001,
+        slx_lags=0,
         regime_lag_sep=False,
-        regime_err_sep=False,
         cores=False,
         vm=False,
         name_y=None,
@@ -306,6 +315,7 @@ def __init__(
         name_w=None,
         name_ds=None,
         name_regimes=None,
+        latex=False,
     ):
 
         n = USER.check_arrays(y, x)
@@ -318,6 +328,11 @@ def __init__(
         set_warn(self, warn)
         name_x = USER.set_name_x(name_x, x_constant, constant=True)
 
+        if slx_lags >0:
+            lag_x = get_lags(w, x_constant, slx_lags)
+            x_constant = np.hstack((x_constant, lag_x))
+            name_x += USER.set_name_spatial_lags(name_x, slx_lags)
+
         self.name_x_r = USER.set_name_x(name_x, x_constant) + [USER.set_name_yend_sp(name_y)]
         self.method = method
         self.epsilon = epsilon
@@ -364,6 +379,7 @@ def __init__(
                 w_i,
                 w,
                 regi_ids,
+                slx_lags=slx_lags,
                 cores=cores,
                 cols2regi=cols2regi,
                 method=method,
@@ -374,17 +390,19 @@ def __init__(
                 name_regimes=self.name_regimes,
                 name_w=name_w,
                 name_ds=name_ds,
+                latex=latex,
             )
         else:
             # if regime_lag_sep == True:
             #    w = REGI.w_regimes_union(w, w_i, self.regimes_set)
-            x, self.name_x = REGI.Regimes_Frame.__init__(
+            x, self.name_x, x_rlist = REGI.Regimes_Frame.__init__(
                 self,
                 x_constant,
                 regimes,
                 constant_regi,
                 cols2regi=cols2regi[:-1],
                 names=name_x,
+                rlist=True
             )
             self.name_x.append("_Global_" + USER.set_name_yend_sp(name_y))
             BaseML_Lag.__init__(self, y=y, x=x, w=w, method=method, epsilon=epsilon)
@@ -395,13 +413,17 @@ def __init__(
             self.aic = DIAG.akaike(reg=self)
             self.schwarz = DIAG.schwarz(reg=self)
             self.regime_lag_sep = regime_lag_sep
-            self.title = (
-                "MAXIMUM LIKELIHOOD SPATIAL LAG - REGIMES"
-                + " (METHOD = "
-                + method
-                + ")"
-            )
-            SUMMARY.ML_Lag(reg=self, w=w, vm=vm, spat_diag=False, regimes=True)
+            self.output = pd.DataFrame(self.name_x, columns=['var_names'])
+            self.output['var_type'] = ['x'] * (len(self.name_x) - 1) + ['rho']
+            self.output['regime'] = x_rlist+['_Global']
+            self.output['equation'] = 0
+            self.other_top = _spat_pseudo_r2(self)
+            self.other_top += _nonspat_top(self, ml=True)
+            if slx_lags == 0:
+                self.title = ("MAXIMUM LIKELIHOOD SPATIAL LAG - REGIMES"+ " (METHOD = "+ method+ ")")
+            else:
+                self.title = ("MAXIMUM LIKELIHOOD SPATIAL DURBIN - REGIMES"+ " (METHOD = "+ method+ ")")
+            output(reg=self, vm=vm, robust=False, other_end=False, latex=latex)
 
     def ML_Lag_Regimes_Multi(
         self,
@@ -410,6 +432,7 @@ def ML_Lag_Regimes_Multi(
         w_i,
         w,
         regi_ids,
+        slx_lags,
         cores,
         cols2regi,
         method,
@@ -420,8 +443,9 @@ def ML_Lag_Regimes_Multi(
         name_regimes,
         name_w,
         name_ds,
+        latex,
     ):
-        #        pool = mp.Pool(cores)
+        #pool = mp.Pool(cores)
         results_p = {}
         """
         for r in self.regimes_set:
@@ -445,6 +469,7 @@ def ML_Lag_Regimes_Multi(
                         regi_ids,
                         r,
                         w_i[r],
+                        slx_lags,
                         method,
                         epsilon,
                         name_ds,
@@ -462,6 +487,7 @@ def ML_Lag_Regimes_Multi(
                         regi_ids,
                         r,
                         w_i[r],
+                        slx_lags,
                         method,
                         epsilon,
                         name_ds,
@@ -496,6 +522,7 @@ def ML_Lag_Regimes_Multi(
         results = {}
         self.name_y, self.name_x = [], []
         counter = 0
+        self.output = pd.DataFrame(columns=['var_names', 'var_type', 'regime', 'equation'])
         for r in self.regimes_set:
             """
             if is_win:
@@ -528,12 +555,16 @@ def ML_Lag_Regimes_Multi(
             ] = results[r].e_pred
             self.name_y += results[r].name_y
             self.name_x += results[r].name_x
+            results[r].other_top = _spat_pseudo_r2(results[r])
+            results[r].other_top += _nonspat_top(results[r], ml=True)
+            results[r].other_mid = ""
+            self.output = pd.concat([self.output, pd.DataFrame({'var_names': results[r].name_x,
+                                                                'var_type': ['x'] * (len(results[r].name_x) - 1) + ['rho'],
+                                                                'regime': r, 'equation': r})], ignore_index=True)
             counter += 1
         self.multi = results
         self.chow = REGI.Chow(self)
-        SUMMARY.ML_Lag_multi(
-            reg=self, multireg=self.multi, vm=vm, spat_diag=False, regimes=True, w=w
-        )
+        output(reg=self, vm=vm, robust=False, other_end=False, latex=latex)
 
 
 def _work(
@@ -542,6 +573,7 @@ def _work(
     regi_ids,
     r,
     w_r,
+    slx_lags,
     method,
     epsilon,
     name_ds,
@@ -553,13 +585,10 @@ def _work(
     y_r = y[regi_ids[r]]
     x_r = x[regi_ids[r]]
     model = BaseML_Lag(y_r, x_r, w_r, method=method, epsilon=epsilon)
-    model.title = (
-        "MAXIMUM LIKELIHOOD SPATIAL LAG - REGIME "
-        + str(r)
-        + " (METHOD = "
-        + method
-        + ")"
-    )
+    if slx_lags == 0:
+        model.title = ("MAXIMUM LIKELIHOOD SPATIAL LAG - REGIME "+ str(r)+ " (METHOD = "+ method+ ")")
+    else:
+        model.title = ("MAXIMUM LIKELIHOOD SPATIAL DURBIN - REGIME "+ str(r)+ " (METHOD = "+ method+ ")")
     model.name_ds = name_ds
     model.name_y = "%s_%s" % (str(r), name_y)
     model.name_x = ["%s_%s" % (str(r), i) for i in name_x]
@@ -583,16 +612,16 @@ def _test():
 if __name__ == "__main__":
     _test()
     import numpy as np
-    import libpysal
+    import libpysal as ps
 
-    db = libpysal.io.open(libpysal.examples.get_path("baltim.dbf"), "r")
+    db = ps.io.open(ps.examples.get_path("baltim.dbf"), "r")
     ds_name = "baltim.dbf"
     y_name = "PRICE"
     y = np.array(db.by_col(y_name)).T
     y.shape = (len(y), 1)
     x_names = ["NROOM", "NBATH", "PATIO", "FIREPL", "AC", "GAR", "AGE", "LOTSZ", "SQFT"]
     x = np.array([db.by_col(var) for var in x_names]).T
-    ww = ps.open(ps.examples.get_path("baltim_q.gal"))
+    ww = ps.io.open(ps.examples.get_path("baltim_q.gal"))
     w = ww.read()
     ww.close()
     w_name = "baltim_q.gal"
@@ -610,7 +639,9 @@ def _test():
         name_w=w_name,
         name_ds=ds_name,
         regime_lag_sep=True,
+        regime_err_sep=False,
         constant_regi="many",
         name_regimes="CITCOU",
     )
+    print(mllag.output)
     print(mllag.summary)
diff --git a/spreg/ols.py b/spreg/ols.py
index eb739230..f22bac19 100644
--- a/spreg/ols.py
+++ b/spreg/ols.py
@@ -1,13 +1,13 @@
 """Ordinary Least Squares regression classes."""
 
-__author__ = "Luc Anselin luc.anselin@asu.edu, David C. Folch david.folch@asu.edu"
+__author__ = "Luc Anselin lanselin@gmail.com, Pedro Amaral pedrovma@gmail.com, David C. Folch david.folch@asu.edu"
 import numpy as np
-import copy as COPY
 import numpy.linalg as la
 from . import user_output as USER
-from . import summary_output as SUMMARY
+from .output import output, _spat_diag_out, _nonspat_mid, _nonspat_top
 from . import robust as ROBUST
-from .utils import spdot, sphstack, RegressionPropsY, RegressionPropsVM, set_warn
+from .utils import spdot, RegressionPropsY, RegressionPropsVM, set_warn, get_lags
+import pandas as pd
 
 __all__ = ["OLS"]
 
@@ -144,6 +144,9 @@ class OLS(BaseOLS):
     gwk          : pysal W object
                    Kernel spatial weights needed for HAC estimation. Note:
                    matrix must have ones along the main diagonal.
+    slx_lags     : integer
+                   Number of spatial lags of X to include in the model specification.
+                   If slx_lags>0, the specification becomes of the SLX type.
     sig2n_k      : boolean
                    If True, then use n-k to estimate sigma^2. If False, use n.
     nonspat_diag : boolean
@@ -171,10 +174,13 @@ class OLS(BaseOLS):
                    Name of kernel weights matrix for use in output
     name_ds      : string
                    Name of dataset for use in output
-
+    latex        : boolean
+                   Specifies if summary is to be printed in latex format
 
     Attributes
     ----------
+    output       : dataframe
+                   regression results pandas dataframe
     summary      : string
                    Summary of regression results and diagnostics (note: use in
                    conjunction with the print command)
@@ -349,8 +355,9 @@ class OLS(BaseOLS):
     ready to be printed:
 
     >>> print(ols.summary)
-    REGRESSION
-    ----------
+    REGRESSION RESULTS
+    ------------------
+    <BLANKLINE>
     SUMMARY OF OUTPUT: ORDINARY LEAST SQUARES
     -----------------------------------------
     Data set            :    columbus
@@ -360,7 +367,7 @@ class OLS(BaseOLS):
     S.D. dependent var  :     18.4661                Degrees of Freedom    :          46
     R-squared           :      0.3495
     Adjusted R-squared  :      0.3212
-    Sum squared residual:   10647.015                F-statistic           :     12.3582
+    Sum squared residual:       10647                F-statistic           :     12.3582
     Sigma-square        :     231.457                Prob(F-statistic)     :   5.064e-05
     S.E. of regression  :      15.214                Log likelihood        :    -201.368
     Sigma-square ML     :     217.286                Akaike info criterion :     408.735
@@ -426,6 +433,7 @@ def __init__(
         w=None,
         robust=None,
         gwk=None,
+        slx_lags = 0,
         sig2n_k=True,
         nonspat_diag=True,
         spat_diag=False,
@@ -437,34 +445,57 @@ def __init__(
         name_w=None,
         name_gwk=None,
         name_ds=None,
+        latex=False,
     ):
 
         n = USER.check_arrays(y, x)
         y = USER.check_y(y, n)
-        USER.check_weights(w, y)
         USER.check_robust(robust, gwk)
+        if robust == "hac" and spat_diag:
+                set_warn(
+                    self,
+                    "Spatial diagnostics are not available for HAC estimation. Hence, spatial diagnostics have been disabled for this model.",
+                )
+                spat_diag = False
+        if robust in ["hac", "white"] and white_test:
+                set_warn(
+                    self,
+                    "White test not available when standard errors are estimated by HAC or White correction.",
+                )
+                white_test = False
         USER.check_spat_diag(spat_diag, w)
         x_constant, name_x, warn = USER.check_constant(x, name_x)
+        self.name_x = USER.set_name_x(name_x, x_constant)
+        if slx_lags >0:
+            USER.check_weights(w, y, w_required=True)
+            lag_x = get_lags(w, x_constant[:, 1:], slx_lags)
+            x_constant = np.hstack((x_constant, lag_x))
+            self.name_x += USER.set_name_spatial_lags(self.name_x[1:], slx_lags)
+        else:
+            USER.check_weights(w, y, w_required=False)
         set_warn(self, warn)
         BaseOLS.__init__(
             self, y=y, x=x_constant, robust=robust, gwk=gwk, sig2n_k=sig2n_k
         )
-        self.title = "ORDINARY LEAST SQUARES"
         self.name_ds = USER.set_name_ds(name_ds)
         self.name_y = USER.set_name_y(name_y)
-        self.name_x = USER.set_name_x(name_x, x_constant)
+        self.title = "ORDINARY LEAST SQUARES"
+        if slx_lags > 0:
+            self.title += " WITH SPATIALLY LAGGED X (SLX)"
         self.robust = USER.set_robust(robust)
         self.name_w = USER.set_name_w(name_w, w)
         self.name_gwk = USER.set_name_w(name_gwk, gwk)
-        SUMMARY.OLS(
-            reg=self,
-            vm=vm,
-            w=w,
-            nonspat_diag=nonspat_diag,
-            spat_diag=spat_diag,
-            moran=moran,
-            white_test=white_test,
-        )
+        self.output = pd.DataFrame(self.name_x, columns=['var_names'])
+        self.output['var_type'] = ['x'] * len(self.name_x)
+        self.output['regime'], self.output['equation'] = (0, 0)
+        self.other_top, self.other_mid, other_end = ("", "", "")  # strings where function-specific diag. are stored
+        if nonspat_diag:
+            self.other_mid += _nonspat_mid(self, white_test=white_test)
+            self.other_top += _nonspat_top(self)
+        if spat_diag:
+            other_end += _spat_diag_out(self, w, 'ols', moran=moran)
+        output(reg=self, vm=vm, robust=robust, other_end=other_end, latex=latex)
+
 
 
 def _test():
@@ -505,4 +536,5 @@ def _test():
         sig2n_k=True,
         moran=True,
     )
+    print(ols.output)
     print(ols.summary)
diff --git a/spreg/ols_regimes.py b/spreg/ols_regimes.py
index bf06f1f2..20b2acea 100755
--- a/spreg/ols_regimes.py
+++ b/spreg/ols_regimes.py
@@ -2,23 +2,20 @@
 Ordinary Least Squares regression with regimes.
 """
 
-__author__ = "Luc Anselin luc.anselin@asu.edu, Pedro V. Amaral pedro.amaral@asu.edu, Daniel Arribas-Bel darribas@asu.edu"
+__author__ = "Luc Anselin, Pedro V. Amaral, Daniel Arribas-Bel"
 
+import numpy as np
+import multiprocessing as mp
+import pandas as pd
 from . import regimes as REGI
 from . import user_output as USER
+from .utils import set_warn, RegressionProps_basic, spdot, RegressionPropsY, get_lags
 from .ols import BaseOLS
-from .utils import set_warn, spbroadcast, RegressionProps_basic, RegressionPropsY, spdot
 from .robust import hac_multi
-from . import summary_output as SUMMARY
-import numpy as np
-import multiprocessing as mp
-from platform import system
-import scipy.sparse as SP
-import copy as COPY
+from .output import output, _spat_diag_out, _nonspat_mid, _nonspat_top
 
 
 class OLS_Regimes(BaseOLS, REGI.Regimes_Frame, RegressionPropsY):
-
     """
     Ordinary least squares with results and diagnostics.
 
@@ -43,6 +40,10 @@ class OLS_Regimes(BaseOLS, REGI.Regimes_Frame, RegressionPropsY):
     gwk          : pysal W object
                    Kernel spatial weights needed for HAC estimation. Note:
                    matrix must have ones along the main diagonal.
+    slx_lags       : integer
+                   Number of spatial lags of X to include in the model specification.
+                   If slx_lags>0, the specification becomes of the SLX type.
+                   Note: WX is computed using the complete weights matrix
     sig2n_k      : boolean
                    If True, then use n-k to estimate sigma^2. If False, use n.
     nonspat_diag : boolean
@@ -92,10 +93,13 @@ class OLS_Regimes(BaseOLS, REGI.Regimes_Frame, RegressionPropsY):
                    Name of dataset for use in output
     name_regimes : string
                    Name of regime variable for use in the output
-
+    latex        : boolean
+                   Specifies if summary is to be printed in latex format
 
     Attributes
     ----------
+    output       : dataframe
+                   regression results pandas dataframe
     summary      : string
                    Summary of regression results and diagnostics (note: use in
                    conjunction with the print command)
@@ -307,7 +311,7 @@ class OLS_Regimes(BaseOLS, REGI.Regimes_Frame, RegressionPropsY):
 
     >>> y_var = 'HR90'
     >>> y = db.by_col(y_var)
-    >>> y = np.array(y).reshape(len(y), 1)
+    >>> y = np.array(y)
 
     Extract UE90 (unemployment rate) and PS90 (population structure) vectors from
     the DBF to be used as independent variables in the regression. Other variables
@@ -327,58 +331,122 @@ class OLS_Regimes(BaseOLS, REGI.Regimes_Frame, RegressionPropsY):
 
     We can now run the regression and then have a summary of the output
     by typing: olsr.summary
-    Alternatively, we can just check the betas and standard errors of the
-    parameters:
 
     >>> olsr = OLS_Regimes(y, x, regimes, nonspat_diag=False, name_y=y_var, name_x=['PS90','UE90'], name_regimes=r_var, name_ds='NAT')
-    >>> olsr.betas
-    array([[0.39642899],
-           [0.65583299],
-           [0.48703937],
-           [5.59835   ],
-           [1.16210453],
-           [0.53163886]])
-    >>> np.sqrt(olsr.vm.diagonal())
-    array([0.24816345, 0.09662678, 0.03628629, 0.46894564, 0.21667395,
-           0.05945651])
-    >>> olsr.cols2regi
-    'all'
+    >>> print(olsr.summary)
+    REGRESSION RESULTS
+    ------------------
+    <BLANKLINE>
+    SUMMARY OF OUTPUT: ORDINARY LEAST SQUARES ESTIMATION - REGIME 0
+    ---------------------------------------------------------------
+    Data set            :         NAT
+    Weights matrix      :        None
+    Dependent Variable  :      0_HR90                Number of Observations:        1673
+    Mean dependent var  :      3.3416                Number of Variables   :           3
+    S.D. dependent var  :      4.6795                Degrees of Freedom    :        1670
+    R-squared           :      0.1271
+    Adjusted R-squared  :      0.1260
+    <BLANKLINE>
+    ------------------------------------------------------------------------------------
+                Variable     Coefficient       Std.Error     t-Statistic     Probability
+    ------------------------------------------------------------------------------------
+              0_CONSTANT       0.3964290       0.2481634       1.5974512       0.1103544
+                  0_PS90       0.6558330       0.0966268       6.7872800       0.0000000
+                  0_UE90       0.4870394       0.0362863      13.4221336       0.0000000
+    ------------------------------------------------------------------------------------
+    Regimes variable: SOUTH
+    <BLANKLINE>
+    SUMMARY OF OUTPUT: ORDINARY LEAST SQUARES ESTIMATION - REGIME 1
+    ---------------------------------------------------------------
+    Data set            :         NAT
+    Weights matrix      :        None
+    Dependent Variable  :      1_HR90                Number of Observations:        1412
+    Mean dependent var  :      9.5493                Number of Variables   :           3
+    S.D. dependent var  :      7.0389                Degrees of Freedom    :        1409
+    R-squared           :      0.0661
+    Adjusted R-squared  :      0.0647
+    <BLANKLINE>
+    ------------------------------------------------------------------------------------
+                Variable     Coefficient       Std.Error     t-Statistic     Probability
+    ------------------------------------------------------------------------------------
+              1_CONSTANT       5.5983500       0.4689456      11.9381640       0.0000000
+                  1_PS90       1.1621045       0.2166740       5.3633790       0.0000001
+                  1_UE90       0.5316389       0.0594565       8.9416422       0.0000000
+    ------------------------------------------------------------------------------------
+    Regimes variable: SOUTH
+    ------------------------------------------------------------------------------------
+    GLOBAL DIAGNOSTICS
+    <BLANKLINE>
+    REGIMES DIAGNOSTICS - CHOW TEST
+                     VARIABLE        DF        VALUE           PROB
+                     CONSTANT         1          96.129           0.0000
+                         PS90         1           4.554           0.0328
+                         UE90         1           0.410           0.5220
+                  Global test         3         680.960           0.0000
+    ================================ END OF REPORT =====================================
     """
 
     def __init__(
-        self,
-        y,
-        x,
-        regimes,
-        w=None,
-        robust=None,
-        gwk=None,
-        sig2n_k=True,
-        nonspat_diag=True,
-        spat_diag=False,
-        moran=False,
-        white_test=False,
-        vm=False,
-        constant_regi="many",
-        cols2regi="all",
-        regime_err_sep=True,
-        cores=False,
-        name_y=None,
-        name_x=None,
-        name_regimes=None,
-        name_w=None,
-        name_gwk=None,
-        name_ds=None,
+            self,
+            y,
+            x,
+            regimes,
+            w=None,
+            robust=None,
+            gwk=None,
+            slx_lags=0,
+            sig2n_k=True,
+            nonspat_diag=True,
+            spat_diag=False,
+            moran=False,
+            white_test=False,
+            vm=False,
+            constant_regi="many",
+            cols2regi="all",
+            regime_err_sep=True,
+            cores=False,
+            name_y=None,
+            name_x=None,
+            name_regimes=None,
+            name_w=None,
+            name_gwk=None,
+            name_ds=None,
+            latex=False
     ):
 
         n = USER.check_arrays(y, x)
         y = USER.check_y(y, n)
-        USER.check_weights(w, y)
         USER.check_robust(robust, gwk)
+        if robust == "hac":
+            if regime_err_sep:
+                set_warn(
+                    self,
+                    "Error by regimes is not available for HAC estimation. The error by regimes has been disabled for this model.",
+                )
+                regime_err_sep = False
+            if spat_diag:
+                set_warn(
+                    self,
+                    "Spatial diagnostics are not available for HAC estimation. The spatial diagnostics have been disabled for this model.",
+                )
+                spat_diag = False
+        if robust in ["hac", "white"] and white_test:
+            set_warn(
+                self,
+                "White test not available when standard errors are estimated by HAC or White correction.",
+            )
+            white_test = False
         USER.check_spat_diag(spat_diag, w)
         x_constant, name_x, warn = USER.check_constant(x, name_x, just_rem=True)
-        set_warn(self, warn)
         name_x = USER.set_name_x(name_x, x_constant, constant=True)
+        if slx_lags > 0:
+            USER.check_weights(w, y, w_required=True)
+            lag_x = get_lags(w, x_constant, slx_lags)
+            x_constant = np.hstack((x_constant, lag_x))
+            name_x += USER.set_name_spatial_lags(name_x, slx_lags)
+        else:
+            USER.check_weights(w, y, w_required=False)
+        set_warn(self, warn)
         self.name_x_r = USER.set_name_x(name_x, x_constant)
         self.constant_regi = constant_regi
         self.cols2regi = cols2regi
@@ -394,17 +462,11 @@ def __init__(
         self.regimes_set = REGI._get_regimes_set(regimes)
         self.regimes = regimes
         USER.check_regimes(self.regimes_set, self.n, x_constant.shape[1])
-        if regime_err_sep == True and robust == "hac":
-            set_warn(
-                self,
-                "Error by regimes is incompatible with HAC estimation. Hence, error by regimes has been disabled for this model.",
-            )
-            regime_err_sep = False
         self.regime_err_sep = regime_err_sep
         if (
-            regime_err_sep == True
-            and set(cols2regi) == set([True])
-            and constant_regi == "many"
+                regime_err_sep == True
+                and set(cols2regi) == set([True])
+                and constant_regi == "many"
         ):
             self.y = y
             regi_ids = dict(
@@ -416,6 +478,7 @@ def __init__(
                 regi_ids,
                 cores,
                 gwk,
+                slx_lags,
                 sig2n_k,
                 robust,
                 nonspat_diag,
@@ -424,11 +487,19 @@ def __init__(
                 name_x,
                 moran,
                 white_test,
+                latex
             )
         else:
-            x, self.name_x = REGI.Regimes_Frame.__init__(
-                self, x_constant, regimes, constant_regi, cols2regi, name_x
+            x, self.name_x, x_rlist = REGI.Regimes_Frame.__init__(
+                self, x_constant, regimes, constant_regi, cols2regi, name_x, rlist=True
             )
+
+            self.output = pd.DataFrame(self.name_x,
+                                       columns=['var_names'])
+            self.output['var_type'] = ['x'] * len(self.name_x)
+            self.output['regime'] = x_rlist
+            self.output['equation'] = 0
+
             BaseOLS.__init__(self, y=y, x=x, robust=robust, gwk=gwk, sig2n_k=sig2n_k)
             if regime_err_sep == True and robust == None:
                 y2, x2 = REGI._get_weighted_var(
@@ -439,41 +510,45 @@ def __init__(
                 self.title = (
                     "ORDINARY LEAST SQUARES - REGIMES (Group-wise heteroskedasticity)"
                 )
+                if slx_lags > 0:
+                    self.title = "ORDINARY LEAST SQUARES WITH SLX - REGIMES (Group-wise heteroskedasticity)"
                 nonspat_diag = None
                 set_warn(
                     self,
                     "Residuals treated as homoskedastic for the purpose of diagnostics.",
                 )
             else:
-                self.title = "ORDINARY LEAST SQUARES - REGIMES"
+                if slx_lags == 0:
+                    self.title = "ORDINARY LEAST SQUARES - REGIMES"
+                else:
+                    self.title = "ORDINARY LEAST SQUARES WITH SLX - REGIMES"
             self.robust = USER.set_robust(robust)
             self.chow = REGI.Chow(self)
-            SUMMARY.OLS(
-                reg=self,
-                vm=vm,
-                w=w,
-                nonspat_diag=nonspat_diag,
-                spat_diag=spat_diag,
-                moran=moran,
-                white_test=white_test,
-                regimes=True,
-            )
+            self.other_top, self.other_mid, other_end = ("", "", "")  # strings where function-specific diag. are stored
+            if nonspat_diag:
+                self.other_mid += _nonspat_mid(self, white_test=white_test)
+                self.other_top += _nonspat_top(self)
+            if spat_diag:
+                other_end += _spat_diag_out(self, w, 'ols', moran=moran)
+            output(reg=self, vm=vm, robust=robust, other_end=other_end, latex=latex)
 
     def _ols_regimes_multi(
-        self,
-        x,
-        w,
-        regi_ids,
-        cores,
-        gwk,
-        sig2n_k,
-        robust,
-        nonspat_diag,
-        spat_diag,
-        vm,
-        name_x,
-        moran,
-        white_test,
+            self,
+            x,
+            w,
+            regi_ids,
+            cores,
+            gwk,
+            slx_lags,
+            sig2n_k,
+            robust,
+            nonspat_diag,
+            spat_diag,
+            vm,
+            name_x,
+            moran,
+            white_test,
+            latex
     ):
         results_p = {}
         """
@@ -506,6 +581,7 @@ def _ols_regimes_multi(
                         name_x,
                         self.name_w,
                         self.name_regimes,
+                        slx_lags
                     ),
                 )
             else:
@@ -523,6 +599,7 @@ def _ols_regimes_multi(
                         name_x,
                         self.name_w,
                         self.name_regimes,
+                        slx_lags
                     )
                 )
         self.kryd = 0
@@ -545,6 +622,7 @@ def _ols_regimes_multi(
         results = {}
         self.name_y, self.name_x = [], []
         counter = 0
+        self.output = pd.DataFrame(columns=['var_names', 'var_type', 'regime', 'equation'])
         for r in self.regimes_set:
             """
             if is_win:
@@ -558,11 +636,11 @@ def _ols_regimes_multi(
                 results[r] = results_p[r].get()
 
             self.vm[
-                (counter * self.kr) : ((counter + 1) * self.kr),
-                (counter * self.kr) : ((counter + 1) * self.kr),
+            (counter * self.kr): ((counter + 1) * self.kr),
+            (counter * self.kr): ((counter + 1) * self.kr),
             ] = results[r].vm
             self.betas[
-                (counter * self.kr) : ((counter + 1) * self.kr),
+            (counter * self.kr): ((counter + 1) * self.kr),
             ] = results[r].betas
             self.u[
                 regi_ids[r],
@@ -572,25 +650,24 @@ def _ols_regimes_multi(
             ] = results[r].predy
             self.name_y += results[r].name_y
             self.name_x += results[r].name_x
+            self.output = pd.concat([self.output, pd.DataFrame({'var_names': results[r].name_x,
+                                                       'var_type': ['x']*len(results[r].name_x),
+                                                       'regime': r, 'equation': r})], ignore_index=True)
+            results[r].other_top, results[r].other_mid = ("", "")
+            if nonspat_diag:
+                results[r].other_mid += _nonspat_mid(results[r], white_test=white_test)
+                results[r].other_top += _nonspat_top(results[r])
             counter += 1
         self.multi = results
         self.hac_var = x_constant[:, 1:]
         if robust == "hac":
             hac_multi(self, gwk)
         self.chow = REGI.Chow(self)
+        other_end = ""
         if spat_diag:
             self._get_spat_diag_props(x_constant, sig2n_k)
-        SUMMARY.OLS_multi(
-            reg=self,
-            multireg=self.multi,
-            vm=vm,
-            nonspat_diag=nonspat_diag,
-            spat_diag=spat_diag,
-            moran=moran,
-            white_test=white_test,
-            regimes=True,
-            w=w,
-        )
+            other_end += _spat_diag_out(self, w, 'ols', moran=moran)
+        output(reg=self, vm=vm, robust=robust, other_end=other_end, latex=latex)
 
     def _get_spat_diag_props(self, x, sig2n_k):
         self.k = self.kr
@@ -603,7 +680,7 @@ def _get_spat_diag_props(self, x, sig2n_k):
 
 
 def _work(
-    y, x, w, regi_ids, r, robust, sig2n_k, name_ds, name_y, name_x, name_w, name_regimes
+        y, x, w, regi_ids, r, robust, sig2n_k, name_ds, name_y, name_x, name_w, name_regimes, slx_lags
 ):
     y_r = y[regi_ids[r]]
     x_r = x[regi_ids[r]]
@@ -612,7 +689,10 @@ def _work(
     if robust == "hac":
         robust = None
     model = BaseOLS(y_r, x_r, robust=robust, sig2n_k=sig2n_k)
-    model.title = "ORDINARY LEAST SQUARES ESTIMATION - REGIME %s" % r
+    if slx_lags == 0:
+        model.title = "ORDINARY LEAST SQUARES ESTIMATION - REGIME %s" % r
+    else:
+        model.title = "ORDINARY LEAST SQUARES ESTIMATION WITH SLX - REGIME %s" % r
     model.robust = USER.set_robust(robust)
     model.name_ds = name_ds
     model.name_y = "%s_%s" % (str(r), name_y)
@@ -639,32 +719,35 @@ def _test():
     _test()
     import numpy as np
     import libpysal
+    import pysal
 
     db = libpysal.io.open(libpysal.examples.get_path("NAT.dbf"), "r")
-    y_var = "CRIME"
-    y = np.array([db.by_col(y_var)]).reshape(49, 1)
-    x_var = ["INC", "HOVAL"]
+    y_var = "HR90"
+    y = np.array(db.by_col(y_var)).reshape(-1,1)
+    x_var = ['PS90','UE90']
     x = np.array([db.by_col(name) for name in x_var]).T
-    r_var = "NSA"
+    r_var = "SOUTH"
     regimes = db.by_col(r_var)
-    w = libpysal.weights.Rook.from_shapefile(libpysal.examples.get_path("columbus.shp"))
+    w = libpysal.weights.Rook.from_shapefile(libpysal.examples.get_path("NAT.shp"))
     w.transform = "r"
+    #olsr = pysal.model.spreg.OLS_Regimes(
     olsr = OLS_Regimes(
-        y,
+            y,
         x,
         regimes,
         w=w,
         constant_regi="many",
-        nonspat_diag=False,
-        spat_diag=False,
+        nonspat_diag=True,
+        spat_diag=True,
         name_y=y_var,
-        name_x=["INC", "HOVAL"],
-        name_ds="columbus",
+        name_x=x_var,
+        name_ds="NAT",
         name_regimes=r_var,
-        name_w="columbus.gal",
         regime_err_sep=True,
         cols2regi=[True, True],
-        sig2n_k=True,
-        robust="white",
+        sig2n_k=False,
+        white_test=True,
+        #robust="white"
     )
+    print(olsr.output)
     print(olsr.summary)
diff --git a/spreg/output.py b/spreg/output.py
new file mode 100644
index 00000000..c871c842
--- /dev/null
+++ b/spreg/output.py
@@ -0,0 +1,476 @@
+"""Internal helper files for user output."""
+
+__author__ = "Luc Anselin, Pedro V. Amaral"
+
+import textwrap as TW
+import numpy as np
+import pandas as pd
+from . import diagnostics as diagnostics
+from . import diagnostics_tsls as diagnostics_tsls
+from . import diagnostics_sp as diagnostics_sp
+
+__all__ = []
+
+###############################################################################
+############### Primary functions for running summary diagnostics #############
+###############################################################################
+
+def output(reg, vm, other_end=False, robust=False, latex=False):
+    strSummary = output_start(reg)
+    for eq in reg.output['equation'].unique():
+        try:
+            reg.multi[eq].__summary = {}
+            strSummary, reg.multi[eq] = out_part_top(strSummary, reg, eq)
+        except:
+            eq = None
+            strSummary, reg = out_part_top(strSummary, reg, eq)
+        strSummary, reg = out_part_middle(strSummary, reg, robust, m=eq)
+    strSummary, reg = out_part_end(strSummary, reg, vm, other_end, m=eq)
+    reg.summary = strSummary
+    reg.output.sort_values(by=['equation', 'regime'], inplace=True)
+    reg.output.drop(['var_type', 'regime', 'equation'], axis=1, inplace=True)
+
+def output_start(reg):
+    reg.__summary = {}
+    strSummary = "REGRESSION RESULTS\n"
+    strSummary += "------------------\n"
+    reg.output = reg.output.assign(coefficients=[None] * len(reg.output), std_err=[None] * len(reg.output),
+                                   zt_stat=[None] * len(reg.output), prob=[None] * len(reg.output))
+    return strSummary
+
+def out_part_top(strSummary, reg, m):
+    # Top part of summary output.
+    # m = None for single models, m = 1,2,3... for multiple equation models
+    if m == None:
+        _reg = reg  # _reg = local object with regression results
+    else:
+        _reg = reg.multi[m]  # _reg = local object with equation specific regression results
+    title = "\nSUMMARY OF OUTPUT: " + _reg.title + "\n"
+    strSummary += title
+    strSummary += "-" * (len(title) - 2) + "\n"
+    strSummary += "%-20s:%12s\n" % ("Data set", _reg.name_ds)
+    try:
+        strSummary += "%-20s:%12s\n" % ("Weights matrix", _reg.name_w)
+    except:
+        pass
+
+    strSummary += "%-20s:%12s                %-22s:%12d\n" % (
+        "Dependent Variable",
+        _reg.name_y,
+        "Number of Observations",
+        _reg.n,
+    )
+
+    strSummary += "%-20s:%12.4f                %-22s:%12d\n" % (
+        "Mean dependent var",
+        _reg.mean_y,
+        "Number of Variables",
+        _reg.k,
+    )
+    strSummary += "%-20s:%12.4f                %-22s:%12d\n" % (
+        "S.D. dependent var",
+        _reg.std_y,
+        "Degrees of Freedom",
+        _reg.n - _reg.k,
+    )
+
+    _reg.std_err = diagnostics.se_betas(_reg)
+    if 'OLS' in reg.__class__.__name__:
+        _reg.t_stat = diagnostics.t_stat(_reg)
+        _reg.r2 = diagnostics.r2(_reg)
+        _reg.ar2 = diagnostics.ar2(_reg)
+        strSummary += "%-20s:%12.4f\n%-20s:%12.4f\n" % (
+            "R-squared",
+            _reg.r2,
+            "Adjusted R-squared",
+            _reg.ar2,
+        )
+        _reg.__summary["summary_zt"] = "t"
+
+    else:
+        _reg.z_stat = diagnostics.t_stat(_reg, z_stat=True)
+        _reg.pr2 = diagnostics_tsls.pr2_aspatial(_reg)
+        strSummary += "%-20s:%12.4f\n" % ("Pseudo R-squared", _reg.pr2)
+        _reg.__summary["summary_zt"] = "z"
+
+    try:  # Adding additional top part if there is one
+        strSummary += _reg.other_top
+    except:
+        pass
+
+    return (strSummary, _reg)
+
+def out_part_middle(strSummary, reg, robust, m=None):
+    # Middle part of summary output.
+    # m = None for single models, m = 1,2,3... for multiple equation models
+    if m==None:
+        _reg = reg #_reg = local object with regression results
+        m = reg.output['equation'].unique()[0]
+    else:
+        _reg = reg.multi[m] #_reg = local object with equation specific regression results
+    coefs = pd.DataFrame(_reg.betas, columns=['coefficients'])
+    coefs['std_err'] = pd.DataFrame(_reg.std_err)
+    try:
+        coefs = pd.concat([coefs, pd.DataFrame(_reg.z_stat, columns=['zt_stat', 'prob'])], axis=1)
+    except AttributeError:
+        coefs = pd.concat([coefs, pd.DataFrame(_reg.t_stat, columns=['zt_stat', 'prob'])], axis=1)
+    coefs.index = reg.output[reg.output['equation'] == m].index
+    reg.output.update(coefs)
+    strSummary += "\n"
+    if robust:
+        if robust == "white":
+            strSummary += "White Standard Errors\n"
+        elif robust == "hac":
+            strSummary += "HAC Standard Errors; Kernel Weights: " + _reg.name_gwk + "\n"
+        elif robust == "ogmm":
+            strSummary += "Optimal GMM used to estimate the coefficients and the variance-covariance matrix\n"            
+    strSummary += "------------------------------------------------------------------------------------\n"
+    strSummary += (
+            "            Variable     Coefficient       Std.Error     %1s-Statistic     Probability\n"
+            % (_reg.__summary["summary_zt"])
+    )
+    strSummary += "------------------------------------------------------------------------------------\n"
+    m_output = reg.output[reg.output['equation'] == m]
+    for row in m_output.iloc[np.lexsort((m_output.index, m_output['regime']))].itertuples():
+        try:
+            strSummary += "%20s    %12.5f    %12.5f    %12.5f    %12.5f\n" % (
+                row.var_names,
+                row.coefficients,
+                row.std_err,
+                row.zt_stat,
+                row.prob
+            )
+        except TypeError:  # special case for models that do not have inference on the lambda term
+            strSummary += "%20s    %12.5f    \n" % (
+                row.var_names,
+                row.coefficients
+            )
+    strSummary += "------------------------------------------------------------------------------------\n"
+
+    try:  # Adding info on instruments if they are present
+        name_q = _reg.name_q
+        name_yend = _reg.name_yend
+        insts = "Instruments: "
+        for name in sorted(name_q):
+            insts += name + ", "
+        text_wrapper = TW.TextWrapper(width=76, subsequent_indent="             ")
+        insts = text_wrapper.fill(insts[:-2])
+        insts += "\n"
+        inst2 = "Instrumented: "
+        for name in sorted(name_yend):
+            inst2 += name + ", "
+        text_wrapper = TW.TextWrapper(width=76, subsequent_indent="             ")
+        inst2 = text_wrapper.fill(inst2[:-2])
+        inst2 += "\n"
+        inst2 += insts
+        strSummary += inst2
+    except:
+        pass
+
+    try:  # Adding info on regimes if they are present
+        strSummary += ("Regimes variable: %s\n" % _reg.name_regimes)
+        strSummary += _summary_chow(_reg)  # If local regimes present, compute Chow test.
+    except:
+        pass
+
+    try:  # Adding local warning if there is one
+        if _reg.warning:
+            strSummary += _reg.warning
+    except:
+        pass
+
+    try:  # Adding other middle part if there are any
+        strSummary += _reg.other_mid
+    except:
+        pass
+
+    return (strSummary, reg)
+
+def out_part_end(strSummary, reg, vm, other_end, m=None):
+    if m is not None:
+        strSummary += "------------------------------------------------------------------------------------\n"
+        try:  # Adding global warning if there is one
+            strSummary += reg.warning
+        except:
+            pass
+        strSummary += "\nGLOBAL DIAGNOSTICS\n"
+        try:  # Adding global Chow test if there is one
+            strSummary += _summary_chow(reg)
+        except:
+            pass
+    if other_end:
+        strSummary += other_end
+    if vm:
+        strSummary += _summary_vm(reg)
+    strSummary += "================================ END OF REPORT ====================================="
+    return (strSummary, reg)
+
+def _summary_chow(reg):
+    sum_text = "\nREGIMES DIAGNOSTICS - CHOW TEST"
+    name_x_r = reg.name_x_r
+    joint, regi = reg.chow.joint, reg.chow.regi
+    sum_text += "\n                 VARIABLE        DF        VALUE           PROB\n"
+    if reg.cols2regi == "all":
+        names_chow = name_x_r[1:]
+    else:
+        names_chow = [name_x_r[1:][i] for i in np.where(reg.cols2regi)[0]]
+    if reg.constant_regi == "many":
+        names_chow = ["CONSTANT"] + names_chow
+
+    if 'lambda' in reg.output.var_type.values:
+        if reg.output.var_type.value_counts()['lambda'] > 1:
+            names_chow += ["lambda"]
+
+    reg.output_chow = pd.DataFrame()
+    reg.output_chow['var_names'] = names_chow
+    reg.output_chow['df'] = reg.nr - 1
+    reg.output_chow = pd.concat([reg.output_chow, pd.DataFrame(regi, columns=['value', 'prob'])], axis=1)
+    reg.output_chow = pd.concat([reg.output_chow, pd.DataFrame([{'var_names': 'Global test',
+                                              'df':  reg.kr * (reg.nr - 1),
+                                              'value': joint[0], 'prob': joint[1]}])], ignore_index=True)
+    for row in reg.output_chow.itertuples():
+        sum_text += "%25s        %2d    %12.3f        %9.4f\n" % (
+            row.var_names,
+            row.df,
+            row.value,
+            row.prob
+        )
+
+    return sum_text
+
+
+def _spat_diag_out(reg, w, type, moran=False):
+    strSummary = "\nDIAGNOSTICS FOR SPATIAL DEPENDENCE\n"
+    if not moran:
+        strSummary += (
+            "TEST                              DF       VALUE           PROB\n"
+        )
+    else:
+        strSummary += (
+            "TEST                           MI/DF       VALUE           PROB\n"
+        )
+
+    cache = diagnostics_sp.spDcache(reg, w)
+    if type == "yend":
+        mi, ak, ak_p = diagnostics_sp.akTest(reg, w, cache)
+        reg.ak_test = ak, ak_p
+        strSummary += "%-27s      %2d    %12.3f        %9.4f\n" % (
+            "Anselin-Kelejian Test",
+            1,
+            reg.ak_test[0],
+            reg.ak_test[1],
+        )
+    elif type == "ols":
+        lm_tests = diagnostics_sp.LMtests(reg, w)
+        reg.lm_error = lm_tests.lme
+        reg.lm_lag = lm_tests.lml
+        reg.rlm_error = lm_tests.rlme
+        reg.rlm_lag = lm_tests.rlml
+        reg.lm_sarma = lm_tests.sarma
+        if moran:
+            moran_res = diagnostics_sp.MoranRes(reg, w, z=True)
+            reg.moran_res = moran_res.I, moran_res.zI, moran_res.p_norm
+            strSummary += "%-27s  %8.4f     %9.3f        %9.4f\n" % (
+                "Moran's I (error)",
+                reg.moran_res[0],
+                reg.moran_res[1],
+                reg.moran_res[2],
+            )
+        strSummary += "%-27s      %2d    %12.3f        %9.4f\n" % (
+            "Lagrange Multiplier (lag)",
+            1,
+            reg.lm_lag[0],
+            reg.lm_lag[1],
+        )
+        strSummary += "%-27s      %2d    %12.3f        %9.4f\n" % (
+            "Robust LM (lag)",
+            1,
+            reg.rlm_lag[0],
+            reg.rlm_lag[1],
+        )
+        strSummary += "%-27s      %2d    %12.3f        %9.4f\n" % (
+            "Lagrange Multiplier (error)",
+            1,
+            reg.lm_error[0],
+            reg.lm_error[1],
+        )
+        strSummary += "%-27s      %2d    %12.3f        %9.4f\n" % (
+            "Robust LM (error)",
+            1,
+            reg.rlm_error[0],
+            reg.rlm_error[1],
+        )
+        strSummary += "%-27s      %2d    %12.3f        %9.4f\n\n" % (
+            "Lagrange Multiplier (SARMA)",
+            2,
+            reg.lm_sarma[0],
+            reg.lm_sarma[1],
+        )
+    return strSummary
+
+def _nonspat_top(reg, ml=False):
+    if not ml:
+        reg.sig2ML = reg.sig2n
+        reg.f_stat = diagnostics.f_stat(reg)
+        reg.logll = diagnostics.log_likelihood(reg)
+        reg.aic = diagnostics.akaike(reg)
+        reg.schwarz = diagnostics.schwarz(reg)
+
+        strSummary = "%-20s:%12.6g                %-22s:%12.4f\n" % (
+            "Sum squared residual", reg.utu, "F-statistic", reg.f_stat[0],)
+        strSummary += "%-20s:%12.3f                %-22s:%12.4g\n" % (
+            "Sigma-square",
+            reg.sig2,
+            "Prob(F-statistic)",
+            reg.f_stat[1],
+        )
+        strSummary += "%-20s:%12.3f                %-22s:%12.3f\n" % (
+            "S.E. of regression",
+            np.sqrt(reg.sig2),
+            "Log likelihood",
+            reg.logll,
+        )
+        strSummary += "%-20s:%12.3f                %-22s:%12.3f\n" % (
+            "Sigma-square ML",
+            reg.sig2ML,
+            "Akaike info criterion",
+            reg.aic,
+        )
+        strSummary += "%-20s:%12.4f                %-22s:%12.3f\n" % (
+            "S.E of regression ML",
+            np.sqrt(reg.sig2ML),
+            "Schwarz criterion",
+            reg.schwarz,
+        )
+    else:
+        strSummary = "%-20s:%12.4f\n" % (
+            "Log likelihood",
+            reg.logll,
+        )
+        strSummary += "%-20s:%12.4f                %-22s:%12.3f\n" % (
+            "Sigma-square ML",
+            reg.sig2,
+            "Akaike info criterion",
+            reg.aic,
+        )
+        strSummary += "%-20s:%12.4f                %-22s:%12.3f\n" % (
+            "S.E of regression",
+            np.sqrt(reg.sig2),
+            "Schwarz criterion",
+            reg.schwarz,
+        )
+
+    return strSummary
+
+def _nonspat_mid(reg, white_test=False):
+    # compute diagnostics
+    reg.mulColli = diagnostics.condition_index(reg)
+    reg.jarque_bera = diagnostics.jarque_bera(reg)
+    reg.breusch_pagan = diagnostics.breusch_pagan(reg)
+    reg.koenker_bassett = diagnostics.koenker_bassett(reg)
+
+    if white_test:
+        reg.white = diagnostics.white(reg)
+
+    strSummary = "\nREGRESSION DIAGNOSTICS\n"
+    if reg.mulColli:
+        strSummary += "MULTICOLLINEARITY CONDITION NUMBER %16.3f\n\n" % (reg.mulColli)
+    strSummary += "TEST ON NORMALITY OF ERRORS\n"
+    strSummary += "TEST                             DF        VALUE           PROB\n"
+    strSummary += "%-27s      %2d  %14.3f        %9.4f\n\n" % (
+        "Jarque-Bera",
+        reg.jarque_bera["df"],
+        reg.jarque_bera["jb"],
+        reg.jarque_bera["pvalue"],
+    )
+    strSummary += "DIAGNOSTICS FOR HETEROSKEDASTICITY\n"
+    strSummary += "RANDOM COEFFICIENTS\n"
+    strSummary += "TEST                             DF        VALUE           PROB\n"
+    strSummary += "%-27s      %2d    %12.3f        %9.4f\n" % (
+        "Breusch-Pagan test",
+        reg.breusch_pagan["df"],
+        reg.breusch_pagan["bp"],
+        reg.breusch_pagan["pvalue"],
+    )
+    strSummary += "%-27s      %2d    %12.3f        %9.4f\n" % (
+        "Koenker-Bassett test",
+        reg.koenker_bassett["df"],
+        reg.koenker_bassett["kb"],
+        reg.koenker_bassett["pvalue"],
+    )
+    try:
+        if reg.white:
+            strSummary += "\nSPECIFICATION ROBUST TEST\n"
+            if len(reg.white) > 3:
+                strSummary += reg.white + "\n"
+            else:
+                strSummary += (
+                    "TEST                             DF        VALUE           PROB\n"
+                )
+                strSummary += "%-27s      %2d    %12.3f        %9.4f\n" % (
+                    "White",
+                    reg.white["df"],
+                    reg.white["wh"],
+                    reg.white["pvalue"],
+                )
+    except:
+        pass
+    return strSummary
+
+def _spat_pseudo_r2(reg):
+    if np.abs(reg.rho) < 1:
+        reg.pr2_e = diagnostics_tsls.pr2_spatial(reg)
+        strSummary = "%-20s:  %5.4f\n" % ("Spatial Pseudo R-squared", reg.pr2_e)
+    else:
+        strSummary =  "Spatial Pseudo R-squared: omitted due to rho outside the boundary (-1, 1).\n"
+    return strSummary
+
+def _summary_vm(reg):
+    strVM = "\n"
+    strVM += "COEFFICIENTS VARIANCE MATRIX\n"
+    strVM += "----------------------------\n"
+    try:
+        for name in reg.name_z:
+            strVM += "%12s" % (name)
+    except:
+        for name in reg.name_x:
+            strVM += "%12s" % (name)
+    strVM += "\n"
+    nrow = reg.vm.shape[0]
+    ncol = reg.vm.shape[1]
+    for i in range(nrow):
+        for j in range(ncol):
+            strVM += "%12.6f" % (reg.vm[i][j])
+        strVM += "\n"
+    return strVM
+
+def _summary_iteration(reg):
+    """Reports the number of iterations computed and the type of estimator used for hom and het models."""
+    try:
+        niter = reg.niter
+    except:
+        niter = reg.iteration
+
+    txt = "%-20s:%12s\n" % ("N. of iterations", niter)
+
+    try:
+        if reg.step1c:
+            step1c = "Yes"
+        else:
+            step1c = "No"
+        txt = txt[:-1] + "                %-22s:%12s\n" % (
+            "Step1c computed",
+            step1c,
+        )
+    except:
+       pass
+
+    try:
+        txt = txt[:-1] + "                %-22s:%12s" % (
+            "A1 type: ",
+            reg.A1,
+        )
+    except:
+        pass
+
+    return txt
diff --git a/spreg/regimes.py b/spreg/regimes.py
index fad91d71..d0c4db06 100755
--- a/spreg/regimes.py
+++ b/spreg/regimes.py
@@ -3,8 +3,6 @@
 import scipy.sparse as SP
 import itertools as iter
 from scipy.stats import f, chi2
-
-chisqprob = chi2.sf
 import itertools as iter
 import numpy.linalg as la
 from .utils import spbroadcast, set_warn
@@ -186,6 +184,10 @@ class Regimes_Frame:
                    If 'all' (default), all the variables vary by regime.
     names         : None, list of strings
                    Names of independent variables for use in output
+    yend         : boolean
+                   If True, the last columns of cols2regi are considered for endogenous variables
+    rlist        : boolean
+                   If True, returns a list with the regimes indicators for each variable
 
     Returns
     -------
@@ -212,7 +214,7 @@ class Regimes_Frame:
 
     """
 
-    def __init__(self, x, regimes, constant_regi, cols2regi, names=None, yend=False):
+    def __init__(self, x, regimes, constant_regi, cols2regi, names=None, yend=False, rlist=False):
         if cols2regi == "all":
             cols2regi = [True] * x.shape[1]
         else:
@@ -228,8 +230,8 @@ def __init__(self, x, regimes, constant_regi, cols2regi, names=None, yend=False)
                 cols2regi.insert(0, True)
             else:
                 raise Exception(
-                    "Invalid argument (%s) passed for 'constant_regi'. Please secify a valid term."
-                    % str(constant)
+                    "Invalid argument (%s) passed for 'constant_regi'. Please specify a valid term."
+                    % str(constant_regi)
                 )
         try:
             x = regimeX_setup(
@@ -252,12 +254,18 @@ def __init__(self, x, regimes, constant_regi, cols2regi, names=None, yend=False)
             self.kryd = 0
         self.nr = len(set(regimes))
 
-        if names:
-            names = set_name_x_regimes(
-                names, regimes, constant_regi, cols2regi, self.regimes_set
-            )
-
-        return (x, names)
+        if rlist:
+            if names:
+                names, r_list = set_name_x_regimes(
+                    names, regimes, constant_regi, cols2regi, self.regimes_set, rlist
+                )
+            return (x, names, r_list)
+        else:
+            if names:
+                names = set_name_x_regimes(
+                    names, regimes, constant_regi, cols2regi, self.regimes_set
+                )
+            return (x, names)
 
 
 def wald_test(betas, r, q, vm):
@@ -290,7 +298,7 @@ def wald_test(betas, r, q, vm):
     rvri = la.inv(np.dot(r, np.dot(vm, r.T)))
     w = np.dot(rbq.T, np.dot(rvri, rbq))[0][0]
     df = r.shape[0]
-    pvalue = chisqprob(w, df)
+    pvalue = chi2.sf(w, df)
     return w, pvalue
 
 
@@ -425,7 +433,7 @@ def regimeX_setup(x, regimes, cols2regi, regimes_set, constant=False):
     return xsp
 
 
-def set_name_x_regimes(name_x, regimes, constant_regi, cols2regi, regimes_set):
+def set_name_x_regimes(name_x, regimes, constant_regi, cols2regi, regimes_set, rlist=False):
     """
     Generate the set of variable names in a regimes setup, according to the
     order of the betas
@@ -475,11 +483,17 @@ def set_name_x_regimes(name_x, regimes, constant_regi, cols2regi, regimes_set):
     vars_regi = nxa[np.where(c2ra == True)]
     vars_glob = nxa[np.where(c2ra == False)]
     name_x_regi = []
+    r_list = []
     for r in regimes_set:
         rl = ["%s_%s" % (str(r), i) for i in vars_regi]
         name_x_regi.extend(rl)
+        r_list.extend([str(r)] * len(rl))
     name_x_regi.extend(["_Global_%s" % i for i in vars_glob])
-    return name_x_regi
+    r_list.extend(["_Global"] * len(vars_glob))
+    if rlist:
+        return (name_x_regi, r_list)
+    else:
+        return name_x_regi
 
 
 def w_regime(w, regi_ids, regi_i, transform=True, min_n=None):
diff --git a/spreg/twosls.py b/spreg/twosls.py
index 6a6bd68f..0fe355b1 100644
--- a/spreg/twosls.py
+++ b/spreg/twosls.py
@@ -1,11 +1,12 @@
 import numpy as np
 import numpy.linalg as la
-from . import summary_output as SUMMARY
 from . import robust as ROBUST
 from . import user_output as USER
-from .utils import spdot, sphstack, RegressionPropsY, RegressionPropsVM, set_warn
+from .utils import spdot, sphstack, RegressionPropsY, RegressionPropsVM, set_warn, get_lags
+import pandas as pd
+from .output import output, _spat_diag_out
 
-__author__ = "Luc Anselin luc.anselin@asu.edu, David C. Folch david.folch@asu.edu, Jing Yao jingyao@asu.edu"
+__author__ = "Luc Anselin lanselin@gmail.com, Pedro Amaral pedrovma@gmail.com, David C. Folch david.folch@asu.edu, Jing Yao jingyao@asu.edu"
 __all__ = ["TSLS"]
 
 
@@ -123,10 +124,8 @@ class BaseTSLS(RegressionPropsY, RegressionPropsVM):
     >>> q.append(db.by_col("DISCBD"))
     >>> q = np.array(q).T
     >>> reg = spreg.twosls.BaseTSLS(y, X, yd, q=q)
-    >>> print(reg.betas)
-     [[88.46579584]
-     [ 0.5200379 ]
-     [-1.58216593]]
+    >>> print(reg.betas.T)
+    [[88.46579584  0.5200379  -1.58216593]]
     >>> reg = spreg.twosls.BaseTSLS(y, X, yd, q=q, robust="white")
 
     """
@@ -248,6 +247,9 @@ class TSLS(BaseTSLS):
     gwk          : pysal W object
                    Kernel spatial weights needed for HAC estimation. Note:
                    matrix must have ones along the main diagonal.
+    slx_lags     : integer
+                   Number of spatial lags of X to include in the model specification.
+                   If slx_lags>0, the specification becomes of the SLX type.
     sig2n_k      : boolean
                    If True, then use n-k to estimate sigma^2. If False, use n.
     spat_diag    : boolean
@@ -269,10 +271,13 @@ class TSLS(BaseTSLS):
                    Name of kernel weights matrix for use in output
     name_ds      : string
                    Name of dataset for use in output
-
+    latex        : boolean
+                   Specifies if summary is to be printed in latex format
 
     Attributes
     ----------
+    output       : dataframe
+                   regression results pandas dataframe
     summary      : string
                    Summary of regression results and diagnostics (note: use in
                    conjunction with the print command)
@@ -423,11 +428,8 @@ class TSLS(BaseTSLS):
 
     >>> from spreg import TSLS
     >>> reg = TSLS(y, X, yd, q, name_x=['inc'], name_y='crime', name_yend=['hoval'], name_q=['discbd'], name_ds='columbus')
-    >>> print(reg.betas)
-    [[88.46579584]
-     [ 0.5200379 ]
-     [-1.58216593]]
-
+    >>> print(reg.betas.T)
+    [[88.46579584  0.5200379  -1.58216593]]
     """
 
     def __init__(
@@ -439,6 +441,7 @@ def __init__(
         w=None,
         robust=None,
         gwk=None,
+        slx_lags=0,
         sig2n_k=False,
         spat_diag=False,
         vm=False,
@@ -449,14 +452,28 @@ def __init__(
         name_w=None,
         name_gwk=None,
         name_ds=None,
+        latex=False,
     ):
 
         n = USER.check_arrays(y, x, yend, q)
         y = USER.check_y(y, n)
-        USER.check_weights(w, y)
         USER.check_robust(robust, gwk)
+        if robust == "hac" and spat_diag:
+                set_warn(
+                    self,
+                    "Spatial diagnostics are not available for HAC estimation. The spatial diagnostics have been disabled for this model.",
+                )
+                spat_diag = False
         USER.check_spat_diag(spat_diag, w)
         x_constant, name_x, warn = USER.check_constant(x, name_x)
+        self.name_x = USER.set_name_x(name_x, x_constant)
+        if slx_lags>0:
+            USER.check_weights(w, y, w_required=True)
+            lag_x = get_lags(w, x_constant[:, 1:], slx_lags)
+            x_constant = np.hstack((x_constant, lag_x))
+            self.name_x += USER.set_name_spatial_lags(self.name_x[1:], slx_lags)
+        else:
+            USER.check_weights(w, y, w_required=False)
         set_warn(self, warn)
         BaseTSLS.__init__(
             self,
@@ -469,9 +486,10 @@ def __init__(
             sig2n_k=sig2n_k,
         )
         self.title = "TWO STAGE LEAST SQUARES"
+        if slx_lags > 0:
+            self.title += " WITH SPATIALLY LAGGED X (2SLS-SLX)"
         self.name_ds = USER.set_name_ds(name_ds)
         self.name_y = USER.set_name_y(name_y)
-        self.name_x = USER.set_name_x(name_x, x_constant)
         self.name_yend = USER.set_name_yend(name_yend, yend)
         self.name_z = self.name_x + self.name_yend
         self.name_q = USER.set_name_q(name_q, q)
@@ -479,8 +497,15 @@ def __init__(
         self.robust = USER.set_robust(robust)
         self.name_w = USER.set_name_w(name_w, w)
         self.name_gwk = USER.set_name_w(name_gwk, gwk)
-        SUMMARY.TSLS(reg=self, vm=vm, w=w, spat_diag=spat_diag)
-
+        self.output = pd.DataFrame(self.name_x + self.name_yend,
+                                   columns=['var_names'])
+        self.output['var_type'] = ['x'] * len(self.name_x) + ['yend'] * len(self.name_yend)
+        self.output['regime'], self.output['equation'] = (0, 0)
+        if spat_diag:
+            diag_out = _spat_diag_out(self, w, 'yend')
+        else:
+            diag_out = None
+        output(reg=self, vm=vm, robust=robust, other_end=diag_out, latex=latex)
 
 def _test():
     import doctest
@@ -521,4 +546,5 @@ def _test():
         name_ds="columbus",
         name_w="columbus.gal",
     )
+    print(tsls.output)
     print(tsls.summary)
diff --git a/spreg/twosls_regimes.py b/spreg/twosls_regimes.py
index 8c4ddfcb..50a36853 100644
--- a/spreg/twosls_regimes.py
+++ b/spreg/twosls_regimes.py
@@ -1,19 +1,18 @@
 import numpy as np
+import multiprocessing as mp
+import pandas as pd
 from . import regimes as REGI
 from . import user_output as USER
-import multiprocessing as mp
-import scipy.sparse as SP
-from .utils import sphstack, set_warn, RegressionProps_basic, spdot, sphstack
+from .utils import set_warn, RegressionProps_basic, spdot, sphstack, get_lags
 from .twosls import BaseTSLS
 from .robust import hac_multi
-from . import summary_output as SUMMARY
-from platform import system
+from .output import output, _spat_diag_out
 
 """
 Two-stage Least Squares estimation with regimes.
 """
 
-__author__ = "Luc Anselin luc.anselin@asu.edu, Pedro V. Amaral pedro.amaral@asu.edu, David C. Folch david.folch@asu.edu"
+__author__ = "Luc Anselin, Pedro V. Amaral, David C. Folch"
 
 
 class TSLS_Regimes(BaseTSLS, REGI.Regimes_Frame):
@@ -65,6 +64,10 @@ class TSLS_Regimes(BaseTSLS, REGI.Regimes_Frame):
     gwk          : pysal W object
                    Kernel spatial weights needed for HAC estimation. Note:
                    matrix must have ones along the main diagonal.
+    slx_lags       : integer
+                   Number of spatial lags of X to include in the model specification.
+                   If slx_lags>0, the specification becomes of the SLX type.
+                   Note: WX is computed using the complete weights matrix
     sig2n_k      : boolean
                    If True, then use n-k to estimate sigma^2. If False, use n.
     vm           : boolean
@@ -89,9 +92,16 @@ class TSLS_Regimes(BaseTSLS, REGI.Regimes_Frame):
                    Name of kernel weights matrix for use in output
     name_ds      : string
                    Name of dataset for use in output
+    latex        : boolean
+                   Specifies if summary is to be printed in latex format
 
     Attributes
     ----------
+    output       : dataframe
+                   regression results pandas dataframe
+    summary      : string
+                   Summary of regression results and diagnostics (note: use in
+                   conjunction with the print command)
     betas        : array
                    kx1 array of estimated coefficients
     u            : array
@@ -269,6 +279,62 @@ class TSLS_Regimes(BaseTSLS, REGI.Regimes_Frame):
     array([0.38389901, 0.09963973, 0.04672091, 0.22725012, 0.49181223,
            0.19630774, 0.07784587, 0.25529011])
 
+    >>> print(tslsr.summary)
+    REGRESSION RESULTS
+    ------------------
+    <BLANKLINE>
+    SUMMARY OF OUTPUT: TWO STAGE LEAST SQUARES ESTIMATION - REGIME 0
+    ----------------------------------------------------------------
+    Data set            :         NAT
+    Weights matrix      :     NAT.shp
+    Dependent Variable  :      0_HR90                Number of Observations:        1673
+    Mean dependent var  :      3.3416                Number of Variables   :           4
+    S.D. dependent var  :      4.6795                Degrees of Freedom    :        1669
+    Pseudo R-squared    :      0.2092
+    <BLANKLINE>
+    ------------------------------------------------------------------------------------
+                Variable     Coefficient       Std.Error     z-Statistic     Probability
+    ------------------------------------------------------------------------------------
+              0_CONSTANT       3.6697356       0.3838990       9.5591172       0.0000000
+                  0_PS90       1.0695047       0.0996397      10.7337170       0.0000000
+                  0_UE90       0.1468095       0.0467209       3.1422643       0.0016765
+                  0_RD90       2.4586420       0.2272501      10.8191009       0.0000000
+    ------------------------------------------------------------------------------------
+    Instrumented: 0_RD90
+    Instruments: 0_FP89
+    Regimes variable: SOUTH
+    <BLANKLINE>
+    SUMMARY OF OUTPUT: TWO STAGE LEAST SQUARES ESTIMATION - REGIME 1
+    ----------------------------------------------------------------
+    Data set            :         NAT
+    Weights matrix      :     NAT.shp
+    Dependent Variable  :      1_HR90                Number of Observations:        1412
+    Mean dependent var  :      9.5493                Number of Variables   :           4
+    S.D. dependent var  :      7.0389                Degrees of Freedom    :        1408
+    Pseudo R-squared    :      0.2987
+    <BLANKLINE>
+    ------------------------------------------------------------------------------------
+                Variable     Coefficient       Std.Error     z-Statistic     Probability
+    ------------------------------------------------------------------------------------
+              1_CONSTANT       9.5587324       0.4918122      19.4357356       0.0000000
+                  1_PS90       1.9466635       0.1963077       9.9163867       0.0000000
+                  1_UE90      -0.3081021       0.0778459      -3.9578483       0.0000756
+                  1_RD90       3.6871812       0.2552901      14.4431026       0.0000000
+    ------------------------------------------------------------------------------------
+    Instrumented: 1_RD90
+    Instruments: 1_FP89
+    Regimes variable: SOUTH
+    ------------------------------------------------------------------------------------
+    GLOBAL DIAGNOSTICS
+    <BLANKLINE>
+    REGIMES DIAGNOSTICS - CHOW TEST
+                     VARIABLE        DF        VALUE           PROB
+                     CONSTANT         1          89.093           0.0000
+                         PS90         1          15.876           0.0001
+                         UE90         1          25.106           0.0000
+                         RD90         1          12.920           0.0003
+                  Global test         4         201.237           0.0000
+    ================================ END OF REPORT =====================================
     """
 
     def __init__(
@@ -281,6 +347,7 @@ def __init__(
         w=None,
         robust=None,
         gwk=None,
+        slx_lags=0,
         sig2n_k=True,
         spat_diag=False,
         vm=False,
@@ -297,16 +364,36 @@ def __init__(
         name_gwk=None,
         name_ds=None,
         summ=True,
+        latex=False,
     ):
 
         n = USER.check_arrays(y, x)
         y = USER.check_y(y, n)
-        USER.check_weights(w, y)
         USER.check_robust(robust, gwk)
+        if robust == "hac":
+            if regime_err_sep:
+                set_warn(
+                    self,
+                    "Error by regimes is not available for HAC estimation. The error by regimes has been disabled for this model.",
+                )
+                regime_err_sep = False
+            if spat_diag:
+                set_warn(
+                    self,
+                    "Spatial diagnostics are not available for HAC estimation. The spatial diagnostics have been disabled for this model.",
+                )
+                spat_diag = False
         USER.check_spat_diag(spat_diag, w)
         x_constant, name_x, warn = USER.check_constant(x, name_x, just_rem=True)
         set_warn(self, warn)
         name_x = USER.set_name_x(name_x, x_constant, constant=True)
+        if slx_lags > 0:
+            USER.check_weights(w, y, w_required=True)
+            lag_x = get_lags(w, x_constant, slx_lags)
+            x_constant = np.hstack((x_constant, lag_x))
+            name_x += USER.set_name_spatial_lags(name_x, slx_lags)
+        else:
+            USER.check_weights(w, y, w_required=False)
         self.constant_regi = constant_regi
         self.cols2regi = cols2regi
         self.name_ds = USER.set_name_ds(name_ds)
@@ -324,12 +411,6 @@ def __init__(
         self.regimes_set = REGI._get_regimes_set(regimes)
         self.regimes = regimes
         USER.check_regimes(self.regimes_set, self.n, x_constant.shape[1])
-        if regime_err_sep == True and robust == "hac":
-            set_warn(
-                self,
-                "Error by regimes is incompatible with HAC estimation for 2SLS models. Hence, the error by regimes has been disabled for this model.",
-            )
-            regime_err_sep = False
         self.regime_err_sep = regime_err_sep
         if (
             regime_err_sep == True
@@ -348,6 +429,7 @@ def __init__(
                 regi_ids,
                 cores,
                 gwk,
+                slx_lags,
                 sig2n_k,
                 robust,
                 spat_diag,
@@ -355,20 +437,23 @@ def __init__(
                 name_x,
                 name_yend,
                 name_q,
+                summ,
+                latex
             )
         else:
             q, self.name_q = REGI.Regimes_Frame.__init__(
                 self, q, regimes, constant_regi=None, cols2regi="all", names=name_q
             )
-            x, self.name_x = REGI.Regimes_Frame.__init__(
+            x, self.name_x, x_rlist = REGI.Regimes_Frame.__init__(
                 self,
                 x_constant,
                 regimes,
                 constant_regi,
                 cols2regi=cols2regi,
                 names=name_x,
+                rlist=True
             )
-            yend, self.name_yend = REGI.Regimes_Frame.__init__(
+            yend, self.name_yend, yend_rlist = REGI.Regimes_Frame.__init__(
                 self,
                 yend,
                 regimes,
@@ -376,13 +461,23 @@ def __init__(
                 cols2regi=cols2regi,
                 yend=True,
                 names=name_yend,
+                rlist=True
             )
-            if regime_err_sep == True and robust == None:
-                robust = "white"
+            self.output = pd.DataFrame(self.name_x+self.name_yend,
+                                       columns=['var_names'])
+            self.output['var_type'] = ['x']*len(self.name_x)+['yend']*len(self.name_yend)
+            self.output['regime'] = x_rlist+yend_rlist
+            self.output['equation'] = 0
+
             BaseTSLS.__init__(
                 self, y=y, x=x, yend=yend, q=q, robust=robust, gwk=gwk, sig2n_k=sig2n_k
             )
-            self.title = "TWO STAGE LEAST SQUARES - REGIMES"
+
+            if slx_lags == 0:
+                self.title = "TWO STAGE LEAST SQUARES - REGIMES"
+            else:
+                self.title = "TWO STAGE LEAST SQUARES WITH SPATIALLY LAGGED X (2SLS-SLX) - REGIMES"
+
             if robust == "ogmm":
                 _optimal_weight(self, sig2n_k)
             self.name_z = self.name_x + self.name_yend
@@ -390,7 +485,12 @@ def __init__(
             self.chow = REGI.Chow(self)
             self.robust = USER.set_robust(robust)
             if summ:
-                SUMMARY.TSLS(reg=self, vm=vm, w=w, spat_diag=spat_diag, regimes=True)
+                if spat_diag:
+                    diag_out = _spat_diag_out(self, w, 'yend')
+                else:
+                    diag_out = None
+                output(reg=self, vm=vm, robust=robust, other_end=diag_out, latex=latex)
+
 
     def _tsls_regimes_multi(
         self,
@@ -401,6 +501,7 @@ def _tsls_regimes_multi(
         regi_ids,
         cores,
         gwk,
+        slx_lags,
         sig2n_k,
         robust,
         spat_diag,
@@ -408,6 +509,8 @@ def _tsls_regimes_multi(
         name_x,
         name_yend,
         name_q,
+        summ,
+        latex
     ):
         results_p = {}
         """
@@ -421,7 +524,7 @@ def _tsls_regimes_multi(
                 is_win = False
         """
         x_constant, name_x = REGI.check_const_regi(self, x, name_x, regi_ids)
-        self.name_x_r = name_x
+        self.name_x_r = name_x + name_yend
         for r in self.regimes_set:
             if cores:
                 pool = mp.Pool(None)
@@ -444,6 +547,7 @@ def _tsls_regimes_multi(
                         name_q,
                         self.name_w,
                         self.name_regimes,
+                        slx_lags
                     ),
                 )
             else:
@@ -465,6 +569,7 @@ def _tsls_regimes_multi(
                         name_q,
                         self.name_w,
                         self.name_regimes,
+                        slx_lags
                     )
                 )
 
@@ -495,6 +600,7 @@ def _tsls_regimes_multi(
             self.name_h,
         ) = ([], [], [], [], [], [])
         counter = 0
+        self.output = pd.DataFrame(columns=['var_names', 'var_type', 'regime', 'equation'])
         for r in self.regimes_set:
             """
             if is_win:
@@ -526,8 +632,13 @@ def _tsls_regimes_multi(
             self.name_q += results[r].name_q
             self.name_z += results[r].name_z
             self.name_h += results[r].name_h
+            self.output = pd.concat([self.output, pd.DataFrame({'var_names': results[r].name_x+results[r].name_yend,
+                                                       'var_type': ['x']*len(results[r].name_x)+['yend']*len(results[r].name_yend),
+                                                       'regime': r, 'equation': r})], ignore_index=True)
+
             counter += 1
         self.multi = results
+
         self.hac_var = sphstack(x_constant[:, 1:], q)
         if robust == "hac":
             hac_multi(self, gwk)
@@ -539,9 +650,12 @@ def _tsls_regimes_multi(
         self.chow = REGI.Chow(self)
         if spat_diag:
             self._get_spat_diag_props(results, regi_ids, x_constant, yend, q)
-        SUMMARY.TSLS_multi(
-            reg=self, multireg=self.multi, vm=vm, spat_diag=spat_diag, regimes=True, w=w
-        )
+            diag_out = _spat_diag_out(self, w, 'yend')
+        else:
+            diag_out = None
+        if summ:
+            self.output.sort_values(by='regime', inplace=True)
+            output(reg=self, vm=vm, robust=robust, other_end=diag_out, latex=latex)
 
     def _get_spat_diag_props(self, results, regi_ids, x, yend, q):
         self._cache = {}
@@ -578,6 +692,7 @@ def _work(
     name_q,
     name_w,
     name_regimes,
+    slx_lags,
 ):
     y_r = y[regi_ids[r]]
     x_r = x[regi_ids[r]]
@@ -588,7 +703,10 @@ def _work(
     else:
         robust2 = robust
     model = BaseTSLS(y_r, x_r, yend_r, q_r, robust=robust2, sig2n_k=sig2n_k)
-    model.title = "TWO STAGE LEAST SQUARES ESTIMATION - REGIME %s" % r
+    if slx_lags == 0:
+        model.title = "TWO STAGE LEAST SQUARES ESTIMATION - REGIME %s" % r
+    else:
+        model.title = "TWO STAGE LEAST SQUARES ESTIMATION WITH SLX - REGIME %s" % r
     if robust == "ogmm":
         _optimal_weight(model, sig2n_k, warn=False)
     model.robust = USER.set_robust(robust)
@@ -628,7 +746,7 @@ def _optimal_weight(reg, sig2n_k, warn=True):
     else:
         vm = fac2 * reg.n
     RegressionProps_basic(reg, betas=betas, vm=vm, sig2=False)
-    reg.title += " (Optimal-Weighted GMM)"
+    #reg.title += " (Optimal-Weighted GMM)"
     if warn:
         set_warn(
             reg, "Residuals treated as homoskedastic for the purpose of diagnostics."
@@ -663,20 +781,31 @@ def _test():
     q = np.array([db.by_col(name) for name in q_var]).T
     r_var = "SOUTH"
     regimes = db.by_col(r_var)
+    w = libpysal.weights.Rook.from_shapefile(nat.get_path("natregimes.shp"))
+    w.transform = "r"
     tslsr = TSLS_Regimes(
         y,
         x,
         yd,
         q,
         regimes,
+        w = w,
         constant_regi="many",
-        spat_diag=False,
+        spat_diag=True,
         name_y=y_var,
         name_x=x_var,
         name_yend=yd_var,
         name_q=q_var,
         name_regimes=r_var,
-        cols2regi=[False, True, True, True],
+        #cols2regi=[False, True, True, False],
         sig2n_k=False,
+        regime_err_sep = True,
+        #robust = 'hac',
+        vm = False
     )
+    print(tslsr.output)
     print(tslsr.summary)
+
+
+
+
diff --git a/spreg/twosls_sp.py b/spreg/twosls_sp.py
index 47803d69..e5ff8552 100755
--- a/spreg/twosls_sp.py
+++ b/spreg/twosls_sp.py
@@ -7,8 +7,9 @@
 import numpy as np
 from . import twosls as TSLS
 from . import user_output as USER
-from . import summary_output as SUMMARY
 from .utils import set_endog, sp_att, set_warn
+import pandas as pd
+from .output import output, _spat_diag_out, _spat_pseudo_r2
 
 __all__ = ["GM_Lag"]
 
@@ -44,6 +45,9 @@ class BaseGM_Lag(TSLS.BaseTSLS):
     lag_q        : boolean
                    If True, then include spatial lags of the additional
                    instruments (q).
+    slx_lags     : integer
+                   Number of spatial lags of X to include in the model specification.
+                   If slx_lags>0, the specification becomes of the Spatial Durbin type.
     robust       : string
                    If 'white', then a White consistent estimator of the
                    variance-covariance matrix is given.  If 'hac', then a
@@ -172,15 +176,19 @@ def __init__(
         q=None,
         w=None,
         w_lags=1,
+        slx_lags=0,
         lag_q=True,
         robust=None,
         gwk=None,
         sig2n_k=False,
     ):
 
-        yend2, q2 = set_endog(
-            y, x[:, 1:], w, yend, q, w_lags, lag_q
-        )  # assumes constant in first column
+        if slx_lags > 0:
+            yend2, q2, wx = set_endog(y, x[:, 1:], w, yend, q, w_lags, lag_q, slx_lags)
+            x = np.hstack((x, wx))
+        else:
+            yend2, q2 = set_endog(y, x[:, 1:], w, yend, q, w_lags, lag_q)
+
         TSLS.BaseTSLS.__init__(
             self, y=y, x=x, yend=yend2, q=q2, robust=robust, gwk=gwk, sig2n_k=sig2n_k
         )
@@ -216,6 +224,9 @@ class GM_Lag(BaseGM_Lag):
     lag_q        : boolean
                    If True, then include spatial lags of the additional
                    instruments (q).
+    slx_lags     : integer
+                   Number of spatial lags of X to include in the model specification.
+                   If slx_lags>0, the specification becomes of the Spatial Durbin type.
     robust       : string
                    If 'white', then a White consistent estimator of the
                    variance-covariance matrix is given.  If 'hac', then a
@@ -245,9 +256,13 @@ class GM_Lag(BaseGM_Lag):
                    Name of kernel weights matrix for use in output
     name_ds      : string
                    Name of dataset for use in output
+    latex        : boolean
+                   Specifies if summary is to be printed in latex format
 
     Attributes
     ----------
+    output       : dataframe
+                   regression results pandas dataframe
     summary      : string
                    Summary of regression results and diagnostics (note: use in
                    conjunction with the print command)
@@ -478,6 +493,7 @@ def __init__(
         w=None,
         w_lags=1,
         lag_q=True,
+        slx_lags=0,
         robust=None,
         gwk=None,
         sig2n_k=False,
@@ -490,13 +506,24 @@ def __init__(
         name_w=None,
         name_gwk=None,
         name_ds=None,
+        latex=False,
     ):
 
         n = USER.check_arrays(x, yend, q)
         y = USER.check_y(y, n)
         USER.check_weights(w, y, w_required=True)
         USER.check_robust(robust, gwk)
+        if robust == "hac" and spat_diag:
+                set_warn(
+                    self,
+                    "Spatial diagnostics are not available for HAC estimation. The spatial diagnostics have been disabled for this model.",
+                )
+                spat_diag = False
         x_constant, name_x, warn = USER.check_constant(x, name_x)
+
+        if slx_lags > 0:
+            name_x += USER.set_name_spatial_lags(name_x, slx_lags)
+
         set_warn(self, warn)
         BaseGM_Lag.__init__(
             self,
@@ -506,6 +533,7 @@ def __init__(
             yend=yend,
             q=q,
             w_lags=w_lags,
+            slx_lags=slx_lags,
             robust=robust,
             gwk=gwk,
             lag_q=lag_q,
@@ -517,20 +545,38 @@ def __init__(
         )
         set_warn(self, warn)
         self.title = "SPATIAL TWO STAGE LEAST SQUARES"
+        if slx_lags > 0:
+            self.title += " WITH SLX (SPATIAL DURBIN MODEL)"
         self.name_ds = USER.set_name_ds(name_ds)
         self.name_y = USER.set_name_y(name_y)
-        self.name_x = USER.set_name_x(name_x, x_constant)
+        self.name_x = USER.set_name_x(name_x, x_constant)   # name_x contains SLX terms for slx_lags > 0
         self.name_yend = USER.set_name_yend(name_yend, yend)
         self.name_yend.append(USER.set_name_yend_sp(self.name_y))
         self.name_z = self.name_x + self.name_yend
         self.name_q = USER.set_name_q(name_q, q)
-        self.name_q.extend(USER.set_name_q_sp(self.name_x, w_lags, self.name_q, lag_q))
+        if slx_lags > 0:  # need to remove all but last SLX variables from name_x
+            self.name_x0 = []
+            self.name_x0.append(self.name_x[0])   # constant
+#            print(f"x0 first {self.name_x0}")
+            kx = int((self.k -self.kstar -1)/(slx_lags +1) )   # number of original exogenous vars
+            self.name_x0.extend(self.name_x[-kx:])
+#            print(f"in here {self.name_x0}")
+            self.name_q.extend(USER.set_name_q_sp(self.name_x0, w_lags, self.name_q, lag_q))
+        else:
+            self.name_q.extend(USER.set_name_q_sp(self.name_x, w_lags, self.name_q, lag_q))
         self.name_h = USER.set_name_h(self.name_x, self.name_q)
         self.robust = USER.set_robust(robust)
         self.name_w = USER.set_name_w(name_w, w)
         self.name_gwk = USER.set_name_w(name_gwk, gwk)
-        SUMMARY.GM_Lag(reg=self, w=w, vm=vm, spat_diag=spat_diag)
-
+        self.output = pd.DataFrame(self.name_x + self.name_yend, columns=['var_names'])
+        self.output['var_type'] = ['x'] * len(self.name_x) + ['yend'] * (len(self.name_yend)-1) + ['rho']
+        self.output['regime'], self.output['equation'] = (0, 0)
+        self.other_top = _spat_pseudo_r2(self)
+        if spat_diag:
+            diag_out = _spat_diag_out(self, w, 'yend')
+        else:
+            diag_out = None
+        output(reg=self, vm=vm, robust=robust, other_end=diag_out, latex=latex)
 
 def _test():
     import doctest
@@ -572,4 +618,5 @@ def _test():
         name_ds="columbus",
         name_w="columbus.gal",
     )
+    print(model.output)
     print(model.summary)
diff --git a/spreg/twosls_sp_regimes.py b/spreg/twosls_sp_regimes.py
index cfc0bda7..db0b23c3 100644
--- a/spreg/twosls_sp_regimes.py
+++ b/spreg/twosls_sp_regimes.py
@@ -7,12 +7,13 @@
 import numpy as np
 from . import regimes as REGI
 from . import user_output as USER
-from . import summary_output as SUMMARY
 import multiprocessing as mp
 from .twosls_regimes import TSLS_Regimes, _optimal_weight
 from .twosls import BaseTSLS
 from .utils import set_endog, set_endog_sparse, sp_att, set_warn, sphstack, spdot
 from .robust import hac_multi
+import pandas as pd
+from .output import output, _spat_diag_out, _spat_pseudo_r2
 
 
 class GM_Lag_Regimes(TSLS_Regimes, REGI.Regimes_Frame):
@@ -62,6 +63,9 @@ class GM_Lag_Regimes(TSLS_Regimes, REGI.Regimes_Frame):
     lag_q        : boolean
                    If True, then include spatial lags of the additional
                    instruments (q).
+    slx_lags     : integer
+                   Number of spatial lags of X to include in the model specification.
+                   If slx_lags>0, the specification becomes of the Spatial Durbin type.
     regime_lag_sep: boolean
                     If True (default), the spatial parameter for spatial lag is also
                     computed according to different regimes. If False,
@@ -107,9 +111,13 @@ class GM_Lag_Regimes(TSLS_Regimes, REGI.Regimes_Frame):
                    Name of dataset for use in output
     name_regimes : string
                    Name of regimes variable for use in output
+    latex        : boolean
+                   Specifies if summary is to be printed in latex format
 
     Attributes
     ----------
+    output       : dataframe
+                   regression results pandas dataframe
     summary      : string
                    Summary of regression results and diagnostics (note: use in
                    conjunction with the print command)
@@ -378,15 +386,16 @@ class GM_Lag_Regimes(TSLS_Regimes, REGI.Regimes_Frame):
     models, the argument regime_lag_sep must be set to True:
 
     >>> model=GM_Lag_Regimes(y, x, regimes, w=w, regime_lag_sep=True, name_y=y_var, name_x=x_var, name_regimes=r_var, name_ds='NAT', name_w='NAT.shp')
-    >>> print(np.hstack((np.array(model.name_z).reshape(8,1),model.betas,np.sqrt(model.vm.diagonal().reshape(8,1)))))
-    [['0_CONSTANT' '1.3658476998618099' '0.3985472089832652']
-     ['0_PS90' '0.8087573074246643' '0.11324884794883601']
-     ['0_UE90' '0.5694681319188577' '0.04625087717092595']
-     ['0_W_HR90' '-0.43424389464634316' '0.13350159258670305']
-     ['1_CONSTANT' '7.90731073341874' '1.6360187416950998']
-     ['1_PS90' '1.2746570332609135' '0.2470987049452741']
-     ['1_UE90' '0.6016769336173784' '0.07993322102145078']
-     ['1_W_HR90' '-0.2960338343846942' '0.19934459782427025']]
+    >>> print(model.output)
+        var_names coefficients   std_err    zt_stat      prob
+    0  0_CONSTANT     1.365848  0.398547   3.427066   0.00061
+    1      0_PS90     0.808757  0.113249   7.141418       0.0
+    2      0_UE90     0.569468  0.046251  12.312591       0.0
+    3    0_W_HR90    -0.434244  0.133502  -3.252724  0.001143
+    4  1_CONSTANT     7.907311  1.636019   4.833264  0.000001
+    5      1_PS90     1.274657  0.247099   5.158493       0.0
+    6      1_UE90     0.601677  0.079933   7.527245       0.0
+    7    1_W_HR90    -0.296034  0.199345  -1.485036  0.137534
 
     Alternatively, we can type: 'model.summary' to see the organized results output.
     The class is flexible enough to accomodate a spatial lag model that,
@@ -432,6 +441,7 @@ def __init__(
         q=None,
         w=None,
         w_lags=1,
+        slx_lags=0,
         lag_q=True,
         robust=None,
         gwk=None,
@@ -439,7 +449,7 @@ def __init__(
         spat_diag=False,
         constant_regi="many",
         cols2regi="all",
-        regime_lag_sep=False,
+        regime_lag_sep=True,
         regime_err_sep=True,
         cores=False,
         vm=False,
@@ -451,12 +461,32 @@ def __init__(
         name_w=None,
         name_gwk=None,
         name_ds=None,
+        latex=False,
     ):
 
         n = USER.check_arrays(y, x)
         y = USER.check_y(y, n)
         USER.check_weights(w, y, w_required=True)
         USER.check_robust(robust, gwk)
+        if regime_lag_sep and not regime_err_sep:
+            set_warn(self, "regime_err_sep set to True when regime_lag_sep=True.")                
+            regime_err_sep = True
+        if regime_err_sep and not regime_lag_sep:
+            set_warn(self, "Groupwise heteroskedasticity is not currently available for this method,\n so regime_err_sep has been set to False.")                
+            regime_err_sep = False
+        if robust == "hac":
+            if regime_err_sep:
+                set_warn(
+                    self,
+                    "Error by regimes is not available for HAC estimation. The error by regimes has been disabled for this model.",
+                )
+                regime_err_sep = False
+            if spat_diag:
+                set_warn(
+                    self,
+                    "Spatial diagnostics are not available for HAC estimation. The spatial diagnostics have been disabled for this model.",
+                )
+                spat_diag = False
         USER.check_spat_diag(spat_diag, w)
         x_constant, name_x, warn = USER.check_constant(x, name_x, just_rem=True)
         set_warn(self, warn)
@@ -464,12 +494,20 @@ def __init__(
         name_y = USER.set_name_y(name_y)
         name_yend = USER.set_name_yend(name_yend, yend)
         name_q = USER.set_name_q(name_q, q)
-        name_q.extend(USER.set_name_q_sp(name_x, w_lags, name_q, lag_q, force_all=True))
         self.name_regimes = USER.set_name_ds(name_regimes)
         self.constant_regi = constant_regi
-        self.n = n
+        if slx_lags > 0:
+            yend2, q2, wx = set_endog(y, x_constant, w, yend, q, w_lags, lag_q, slx_lags)
+            x_constant = np.hstack((x_constant, wx))
+            name_slx = USER.set_name_spatial_lags(name_x, slx_lags)
+            name_q.extend(USER.set_name_q_sp(name_slx[-len(name_x):], w_lags, name_q, lag_q, force_all=True))
+            name_x += name_slx   
+        else:
+            name_q.extend(USER.set_name_q_sp(name_x, w_lags, name_q, lag_q, force_all=True))
+            yend2, q2 = yend, q
+        self.n = x_constant.shape[0]
         cols2regi = REGI.check_cols2regi(
-            constant_regi, cols2regi, x_constant, yend=yend, add_cons=False
+            constant_regi, cols2regi, x_constant, yend=yend2, add_cons=False
         )
         self.cols2regi = cols2regi
         self.regimes_set = REGI._get_regimes_set(regimes)
@@ -485,8 +523,6 @@ def __init__(
         self.regime_err_sep = regime_err_sep
         self.regime_lag_sep = regime_lag_sep
         if regime_lag_sep == True:
-            if not regime_err_sep:
-                raise Exception("regime_err_sep must be True when regime_lag_sep=True.")
             cols2regi += [True]
             w_i, regi_ids, warn = REGI.w_regimes(
                 w,
@@ -513,9 +549,10 @@ def __init__(
                 w_i,
                 w,
                 regi_ids,
-                yend=yend,
-                q=q,
+                yend=yend2,
+                q=q2,
                 w_lags=w_lags,
+                slx_lags=slx_lags,
                 lag_q=lag_q,
                 cores=cores,
                 robust=robust,
@@ -532,11 +569,13 @@ def __init__(
                 name_w=name_w,
                 name_gwk=name_gwk,
                 name_ds=name_ds,
+                latex=latex,
             )
         else:
             if regime_lag_sep == True:
                 w = REGI.w_regimes_union(w, w_i, self.regimes_set)
-            yend2, q2 = set_endog(y, x_constant, w, yend, q, w_lags, lag_q)
+            if slx_lags == 0:
+                yend2, q2 = set_endog(y, x_constant, w, yend2, q2, w_lags, lag_q)
             name_yend.append(USER.set_name_yend_sp(name_y))
             TSLS_Regimes.__init__(
                 self,
@@ -564,17 +603,26 @@ def __init__(
                 name_ds=name_ds,
                 summ=False,
             )
-            if regime_lag_sep:
+
+            if regime_lag_sep:  # not currently available.
                 self.sp_att_reg(w_i, regi_ids, yend2[:, -1].reshape(self.n, 1))
             else:
                 self.rho = self.betas[-1]
+                self.output.iat[-1, self.output.columns.get_loc('var_type')] = 'rho'
                 self.predy_e, self.e_pred, warn = sp_att(
                     w, self.y, self.predy, yend2[:, -1].reshape(self.n, 1), self.rho
                 )
                 set_warn(self, warn)
             self.regime_lag_sep = regime_lag_sep
             self.title = "SPATIAL " + self.title
-            SUMMARY.GM_Lag(reg=self, w=w, vm=vm, spat_diag=spat_diag, regimes=True)
+            if slx_lags > 0:
+                self.title = " SPATIAL 2SLS WITH SLX (SPATIAL DURBIN MODEL) - REGIMES"
+            self.other_top = _spat_pseudo_r2(self)
+            if spat_diag:
+                diag_out = _spat_diag_out(self, w, 'yend')
+            else:
+                diag_out = None
+            output(reg=self, vm=vm, robust=robust, other_end=diag_out, latex=latex)
 
     def GM_Lag_Regimes_Multi(
         self,
@@ -587,6 +635,7 @@ def GM_Lag_Regimes_Multi(
         yend=None,
         q=None,
         w_lags=1,
+        slx_lags=0,
         lag_q=True,
         robust=None,
         gwk=None,
@@ -602,6 +651,7 @@ def GM_Lag_Regimes_Multi(
         name_w=None,
         name_gwk=None,
         name_ds=None,
+        latex=False,
     ):
         #        pool = mp.Pool(cores)
         self.name_ds = USER.set_name_ds(name_ds)
@@ -636,6 +686,7 @@ def GM_Lag_Regimes_Multi(
                         q,
                         w_r,
                         w_lags,
+                        slx_lags,
                         lag_q,
                         robust,
                         sig2n_k,
@@ -659,6 +710,7 @@ def GM_Lag_Regimes_Multi(
                         q,
                         w_r,
                         w_lags,
+                        slx_lags,
                         lag_q,
                         robust,
                         sig2n_k,
@@ -702,6 +754,7 @@ def GM_Lag_Regimes_Multi(
             self.name_h,
         ) = ([], [], [], [], [], [])
         counter = 0
+        self.output = pd.DataFrame(columns=['var_names', 'var_type', 'regime', 'equation'])
         for r in self.regimes_set:
             """
             if is_win:
@@ -752,6 +805,14 @@ def GM_Lag_Regimes_Multi(
             self.hac_var[
                 regi_ids[r],
             ] = results[r].h
+            results[r].other_top = _spat_pseudo_r2(results[r])
+            results[r].other_mid = ""
+            if spat_diag:
+                results[r].other_mid += _spat_diag_out(results[r], results[r].w, 'yend')
+            self.output = pd.concat([self.output, pd.DataFrame({'var_names': results[r].name_x + results[r].name_yend,
+                                                                'var_type': ['x'] * len(results[r].name_x) + [
+                                                                    'yend'] * (len(results[r].name_yend)-1) + ['rho'],
+                                                                'regime': r, 'equation': r})], ignore_index=True)
             counter += 1
         self.multi = results
         if robust == "hac":
@@ -763,11 +824,9 @@ def GM_Lag_Regimes_Multi(
             )
         self.chow = REGI.Chow(self)
         if spat_diag:
-            pass
             # self._get_spat_diag_props(y, x, w, yend, q, w_lags, lag_q)
-        SUMMARY.GM_Lag_multi(
-            reg=self, multireg=self.multi, vm=vm, spat_diag=spat_diag, regimes=True, w=w
-        )
+            pass
+        output(reg=self, vm=vm, robust=robust, other_end=False, latex=latex)
 
     def sp_att_reg(self, w_i, regi_ids, wy):
         predy_e_r, e_pred_r = {}, {}
@@ -823,6 +882,7 @@ def _work(
     q,
     w_r,
     w_lags,
+    slx_lags,
     lag_q,
     robust,
     sig2n_k,
@@ -844,14 +904,18 @@ def _work(
         q_r = q[regi_ids[r]]
     else:
         q_r = q
-    yend_r, q_r = set_endog_sparse(y_r, x_r[:, 1:], w_r, yend_r, q_r, w_lags, lag_q)
+    if slx_lags == 0:
+        yend_r, q_r = set_endog_sparse(y_r, x_r[:, 1:], w_r, yend_r, q_r, w_lags, lag_q)
+        title = "SPATIAL TWO STAGE LEAST SQUARES ESTIMATION - REGIME %s" % r
+    else:
+        title = "SPATIAL 2SLS WITH SLX (SPATIAL DURBIN MODEL) - REGIME %s" % r
     # x_constant = USER.check_constant(x_r)
     if robust == "hac" or robust == "ogmm":
         robust2 = None
     else:
         robust2 = robust
     model = BaseTSLS(y_r, x_r, yend_r, q_r, robust=robust2, sig2n_k=sig2n_k)
-    model.title = "SPATIAL TWO STAGE LEAST SQUARES ESTIMATION - REGIME %s" % r
+    model.title = title
     if robust == "ogmm":
         _optimal_weight(model, sig2n_k, warn=False)
     model.rho = model.betas[-1]
@@ -917,6 +981,8 @@ def _test():
         name_ds="columbus",
         name_w="columbus.gal",
         regime_err_sep=True,
+        regime_lag_sep = True,
         robust="white",
     )
+    print(model.output)
     print(model.summary)
diff --git a/spreg/user_output.py b/spreg/user_output.py
index ffdb2324..cf7f9e68 100755
--- a/spreg/user_output.py
+++ b/spreg/user_output.py
@@ -1,7 +1,8 @@
 """Internal helper files for user output."""
 
 __author__ = (
-    "Luc Anselin luc.anselin@asu.edu, "
+    "Luc Anselin lanselin@gmail.com, "
+    "Pedro Amaral pedrovma@gmail.com"
     "David C. Folch david.folch@asu.edu, "
     "Levi John Wolf levi.john.wolf@gmail.com, "
     "Jing Yao jingyao@asu.edu"
@@ -147,6 +148,23 @@ def set_name_yend_sp(name_y):
     """
     return "W_" + name_y
 
+def set_name_spatial_lags(names, w_lags):
+    """Set the spatial lag names for multiple variables and lag orders"
+
+    Parameters
+    ----------
+    names      : string
+                 Original variables' names.
+
+    Returns
+    -------
+    lag_names : string
+
+    """
+    lag_names = ["W_" + s for s in names]
+    for i in range(w_lags-1):
+        lag_names += ["W" + str(i+2) + "_" + s for s in names]
+    return lag_names
 
 def set_name_q_sp(name_x, w_lags, name_q, lag_q, force_all=False):
     """Set the spatial instrument names in regression; return generic name if user
@@ -544,8 +562,9 @@ def check_robust(robust, wk):
                     # NOTE: we are not checking for the case of exactly 1.0 ###
                     raise Exception("Off-diagonal entries must be less than 1.")
         elif robust.lower() == "white" or robust.lower() == "ogmm":
-            if wk:
-                raise Exception("White requires that wk be set to None")
+  #          if wk:
+  #              raise Exception("White requires that wk be set to None")
+            pass      # these options are not affected by wk
         else:
             raise Exception(
                 "invalid value passed to robust, see docs for valid options"
diff --git a/spreg/utils.py b/spreg/utils.py
index ff4f6d2c..c369c74d 100755
--- a/spreg/utils.py
+++ b/spreg/utils.py
@@ -479,7 +479,7 @@ def get_lags(w, x, w_lags):
     Returns
     --------
     rs      : array
-              nxk*(w_lags+1) array with original and spatially lagged variables
+              nxk*(w_lags) array with spatially lagged variables
 
     """
     lag = lag_spatial(w, x)
@@ -489,6 +489,43 @@ def get_lags(w, x, w_lags):
         spat_lags = sphstack(spat_lags, lag)
     return spat_lags
 
+def get_lags_split(w, x, max_lags, split_at):
+    """
+    Calculates a given order of spatial lags and all the smaller orders,
+    separated into two groups (up to split_at and above)
+
+    Parameters
+    ----------
+    w       : weight
+              PySAL weights instance
+    x       : array
+              nxk arrays with the variables to be lagged
+    max_lags  : integer
+              Maximum order of spatial lag
+    split_at: integer
+              Separates the resulting lags into two groups: up to split_at and above
+
+    Returns
+    --------
+    rs_l,rs_h: tuple of arrays
+               rs_l: nxk*(split_at) array with spatially lagged variables up to split_at
+               rs_h: nxk*(w_lags-split_at) array with spatially lagged variables above split_at
+
+    """
+    rs_l = lag = lag_spatial(w, x)
+    rs_h = None
+    if 0 < split_at < max_lags:
+        for _ in range(split_at-1):
+            lag = lag_spatial(w, lag)
+            rs_l = sphstack(rs_l, lag)
+
+        for i in range(max_lags - split_at):
+            lag = lag_spatial(w, lag)
+            rs_h = sphstack(rs_h, lag) if i > 0 else lag
+    else:
+        raise ValueError("max_lags must be greater than split_at and split_at must be greater than 0")
+
+    return rs_l, rs_h
 
 def inverse_prod(
     w,
@@ -571,9 +608,11 @@ def inverse_prod(
         except:
             matrix = la.inv(np.eye(w.shape[0]) - (scalar * w))
         if post_multiply:
-            inv_prod = spdot(data.T, matrix)
+#            inv_prod = spdot(data.T, matrix)
+            inv_prod = np.matmul(data.T,matrix)   # inverse matrix is dense, wrong type in spdot
         else:
-            inv_prod = spdot(matrix, data)
+#            inv_prod = spdot(matrix, data)
+            inv_prod = np.matmul(matrix,data)
     else:
         raise Exception("Invalid method selected for inversion.")
     return inv_prod
@@ -623,24 +662,35 @@ def power_expansion(
     return running_total
 
 
-def set_endog(y, x, w, yend, q, w_lags, lag_q):
+def set_endog(y, x, w, yend, q, w_lags, lag_q, slx_lags=0):
     # Create spatial lag of y
     yl = lag_spatial(w, y)
     # spatial and non-spatial instruments
     if issubclass(type(yend), np.ndarray):
+        if slx_lags > 0:
+            lag_x, lag_xq = get_lags_split(w, x, slx_lags+1, slx_lags)
+        else:
+            lag_xq = x
         if lag_q:
-            lag_vars = sphstack(x, q)
+            lag_vars = sphstack(lag_xq, q)
         else:
-            lag_vars = x
+            lag_vars = lag_xq
         spatial_inst = get_lags(w, lag_vars, w_lags)
         q = sphstack(q, spatial_inst)
         yend = sphstack(yend, yl)
     elif yend == None:  # spatial instruments only
-        q = get_lags(w, x, w_lags)
+        if slx_lags > 0:
+            lag_x, lag_xq = get_lags_split(w, x, slx_lags+w_lags, slx_lags)
+        else:
+            lag_xq = get_lags(w, x, w_lags)
+        q = lag_xq
         yend = yl
     else:
         raise Exception("invalid value passed to yend")
-    return yend, q
+    if slx_lags == 0:
+        return yend, q
+    else:
+        return yend, q, lag_x
 
     lag = lag_spatial(w, x)
     spat_lags = lag

From 0fb086f325f1197100c2bb6a88436712e10bad04 Mon Sep 17 00:00:00 2001
From: Pedro Amaral <pedrovma@gmail.com>
Date: Wed, 13 Sep 2023 17:13:00 -0300
Subject: [PATCH 16/28] Fixing non-SUR test failures

---
 spreg/ml_error.py                     |   2 +-
 spreg/tests/test_error_sp.py          | 431 ++++++--------
 spreg/tests/test_error_sp_sparse.py   | 438 ++++++---------
 spreg/tests/test_twosls_sp_regimes.py | 774 ++++++++++----------------
 4 files changed, 627 insertions(+), 1018 deletions(-)

diff --git a/spreg/ml_error.py b/spreg/ml_error.py
index bb338677..b6913d25 100644
--- a/spreg/ml_error.py
+++ b/spreg/ml_error.py
@@ -501,7 +501,7 @@ def __init__(
         )
         self.name_ds = USER.set_name_ds(name_ds)
         self.name_y = USER.set_name_y(name_y)
-        self.name_x = USER.set_name_x(name_x, x)
+        self.name_x = USER.set_name_x(name_x, x_constant)
         self.name_x.append("lambda")
         self.name_w = USER.set_name_w(name_w, w)
         self.aic = DIAG.akaike(reg=self)
diff --git a/spreg/tests/test_error_sp.py b/spreg/tests/test_error_sp.py
index 610d25bf..c87999af 100644
--- a/spreg/tests/test_error_sp.py
+++ b/spreg/tests/test_error_sp.py
@@ -6,129 +6,104 @@
 from spreg import utils
 from libpysal.common import RTOL
 
-
 class TestBaseGMError(unittest.TestCase):
     def setUp(self):
-        db = libpysal.io.open(libpysal.examples.get_path("columbus.dbf"), "r")
+        db=libpysal.io.open(libpysal.examples.get_path("columbus.dbf"),"r")
         y = np.array(db.by_col("HOVAL"))
-        self.y = np.reshape(y, (49, 1))
+        self.y = np.reshape(y, (49,1))
         X = []
         X.append(db.by_col("INC"))
         X.append(db.by_col("CRIME"))
         self.X = np.array(X).T
-        self.X = np.hstack((np.ones(self.y.shape), self.X))
-        self.w = libpysal.weights.Rook.from_shapefile(
-            libpysal.examples.get_path("columbus.shp")
-        )
-        self.w.transform = "r"
+        self.X = np.hstack((np.ones(self.y.shape),self.X))
+        self.w = libpysal.weights.Rook.from_shapefile(libpysal.examples.get_path("columbus.shp"))
+        self.w.transform = 'r'
 
     def test_model(self):
         reg = SP.BaseGM_Error(self.y, self.X, self.w.sparse)
-        betas = np.array([[47.94371455], [0.70598088], [-0.55571746], [0.37230161]])
-        np.testing.assert_allclose(reg.betas, betas, RTOL)
-        u = np.array([27.4739775])
-        np.testing.assert_allclose(reg.u[0], u, RTOL)
-        predy = np.array([52.9930255])
-        np.testing.assert_allclose(reg.predy[0], predy, RTOL)
+        betas = np.array([[ 47.94371455], [  0.70598088], [ -0.55571746], [  0.37230161]])
+        np.testing.assert_allclose(reg.betas,betas,RTOL)
+        u = np.array([ 27.4739775])
+        np.testing.assert_allclose(reg.u[0],u,RTOL)
+        predy = np.array([ 52.9930255])
+        np.testing.assert_allclose(reg.predy[0],predy,RTOL)
         n = 49
-        np.testing.assert_allclose(reg.n, n, RTOL)
+        np.testing.assert_allclose(reg.n,n,RTOL)
         k = 3
-        np.testing.assert_allclose(reg.k, k, RTOL)
-        y = np.array([80.467003])
-        np.testing.assert_allclose(reg.y[0], y, RTOL)
-        x = np.array([1.0, 19.531, 15.72598])
-        np.testing.assert_allclose(reg.x[0], x, RTOL)
-        e = np.array([31.89620319])
-        np.testing.assert_allclose(reg.e_filtered[0], e, RTOL)
-        predy = np.array([52.9930255])
-        np.testing.assert_allclose(reg.predy[0], predy, RTOL)
+        np.testing.assert_allclose(reg.k,k,RTOL)
+        y = np.array([ 80.467003])
+        np.testing.assert_allclose(reg.y[0],y,RTOL)
+        x = np.array([  1.     ,  19.531  ,  15.72598])
+        np.testing.assert_allclose(reg.x[0],x,RTOL)
+        e = np.array([ 31.89620319])
+        np.testing.assert_allclose(reg.e_filtered[0],e,RTOL)
+        predy = np.array([ 52.9930255])
+        np.testing.assert_allclose(reg.predy[0],predy,RTOL)
         my = 38.43622446938776
-        np.testing.assert_allclose(reg.mean_y, my, RTOL)
+        np.testing.assert_allclose(reg.mean_y,my,RTOL)
         sy = 18.466069465206047
-        np.testing.assert_allclose(reg.std_y, sy, RTOL)
-        vm = np.array(
-            [
-                [1.51884943e02, -5.37622793e00, -1.86970286e00],
-                [-5.37622793e00, 2.48972661e-01, 5.26564244e-02],
-                [-1.86970286e00, 5.26564244e-02, 3.18930650e-02],
-            ]
-        )
-        np.testing.assert_allclose(reg.vm, vm, RTOL)
+        np.testing.assert_allclose(reg.std_y,sy,RTOL)
+        vm = np.array([[  1.51884943e+02,  -5.37622793e+00,  -1.86970286e+00], [ -5.37622793e+00,   2.48972661e-01,   5.26564244e-02], [ -1.86970286e+00,   5.26564244e-02, 3.18930650e-02]])
+        np.testing.assert_allclose(reg.vm,vm,RTOL)
         sig2 = 191.73716465732355
-        np.testing.assert_allclose(reg.sig2, sig2, RTOL)
-
+        np.testing.assert_allclose(reg.sig2,sig2,RTOL)
 
 class TestGMError(unittest.TestCase):
     def setUp(self):
-        db = libpysal.io.open(libpysal.examples.get_path("columbus.dbf"), "r")
+        db=libpysal.io.open(libpysal.examples.get_path("columbus.dbf"),"r")
         y = np.array(db.by_col("HOVAL"))
-        self.y = np.reshape(y, (49, 1))
+        self.y = np.reshape(y, (49,1))
         X = []
         X.append(db.by_col("INC"))
         X.append(db.by_col("CRIME"))
         self.X = np.array(X).T
-        self.w = libpysal.weights.Rook.from_shapefile(
-            libpysal.examples.get_path("columbus.shp")
-        )
-        self.w.transform = "r"
+        self.w = libpysal.weights.Rook.from_shapefile(libpysal.examples.get_path("columbus.shp"))
+        self.w.transform = 'r'
 
     def test_model(self):
         reg = SP.GM_Error(self.y, self.X, self.w)
-        betas = np.array([[47.94371455], [0.70598088], [-0.55571746], [0.37230161]])
-        np.testing.assert_allclose(reg.betas, betas, RTOL)
-        u = np.array([27.4739775])
-        np.testing.assert_allclose(reg.u[0], u, RTOL)
-        predy = np.array([52.9930255])
-        np.testing.assert_allclose(reg.predy[0], predy, RTOL)
+        betas = np.array([[ 47.94371455], [  0.70598088], [ -0.55571746], [  0.37230161]])
+        np.testing.assert_allclose(reg.betas,betas,RTOL)
+        u = np.array([ 27.4739775])
+        np.testing.assert_allclose(reg.u[0],u,RTOL)
+        predy = np.array([ 52.9930255])
+        np.testing.assert_allclose(reg.predy[0],predy,RTOL)
         n = 49
-        np.testing.assert_allclose(reg.n, n, RTOL)
+        np.testing.assert_allclose(reg.n,n,RTOL)
         k = 3
-        np.testing.assert_allclose(reg.k, k, RTOL)
-        y = np.array([80.467003])
-        np.testing.assert_allclose(reg.y[0], y, RTOL)
-        x = np.array([1.0, 19.531, 15.72598])
-        np.testing.assert_allclose(reg.x[0], x, RTOL)
-        e = np.array([31.89620319])
-        np.testing.assert_allclose(reg.e_filtered[0], e, RTOL)
-        predy = np.array([52.9930255])
-        np.testing.assert_allclose(reg.predy[0], predy, RTOL)
+        np.testing.assert_allclose(reg.k,k,RTOL)
+        y = np.array([ 80.467003])
+        np.testing.assert_allclose(reg.y[0],y,RTOL)
+        x = np.array([  1.     ,  19.531  ,  15.72598])
+        np.testing.assert_allclose(reg.x[0],x,RTOL)
+        e = np.array([ 31.89620319])
+        np.testing.assert_allclose(reg.e_filtered[0],e,RTOL)
+        predy = np.array([ 52.9930255])
+        np.testing.assert_allclose(reg.predy[0],predy,RTOL)
         my = 38.43622446938776
-        np.testing.assert_allclose(reg.mean_y, my, RTOL)
+        np.testing.assert_allclose(reg.mean_y,my,RTOL)
         sy = 18.466069465206047
-        np.testing.assert_allclose(reg.std_y, sy, RTOL)
-        vm = np.array(
-            [
-                [1.51884943e02, -5.37622793e00, -1.86970286e00],
-                [-5.37622793e00, 2.48972661e-01, 5.26564244e-02],
-                [-1.86970286e00, 5.26564244e-02, 3.18930650e-02],
-            ]
-        )
-        np.testing.assert_allclose(reg.vm, vm, RTOL)
+        np.testing.assert_allclose(reg.std_y,sy,RTOL)
+        vm = np.array([[  1.51884943e+02,  -5.37622793e+00,  -1.86970286e+00], [ -5.37622793e+00,   2.48972661e-01,   5.26564244e-02], [ -1.86970286e+00,   5.26564244e-02, 3.18930650e-02]])
+        np.testing.assert_allclose(reg.vm,vm,RTOL)
         sig2 = 191.73716465732355
-        np.testing.assert_allclose(reg.sig2, sig2, RTOL)
+        np.testing.assert_allclose(reg.sig2,sig2,RTOL)
         pr2 = 0.3495097406012179
-        np.testing.assert_allclose(reg.pr2, pr2)
-        std_err = np.array([12.32416094, 0.4989716, 0.1785863])
-        np.testing.assert_allclose(reg.std_err, std_err, RTOL)
-        z_stat = np.array(
-            [
-                [3.89022140e00, 1.00152805e-04],
-                [1.41487186e00, 1.57106070e-01],
-                [-3.11175868e00, 1.85976455e-03],
-            ]
-        )
-        np.testing.assert_allclose(reg.z_stat, z_stat, RTOL)
-
+        np.testing.assert_allclose(reg.pr2,pr2)
+        std_err = np.array([ 12.32416094,   0.4989716 ,   0.1785863 ])
+        np.testing.assert_allclose(reg.std_err,std_err,RTOL)
+        z_stat = np.array([[  3.89022140e+00,   1.00152805e-04], [  1.41487186e+00,   1.57106070e-01], [ -3.11175868e+00,   1.85976455e-03]])
+        np.testing.assert_allclose(reg.z_stat,z_stat,RTOL)
 
 class TestBaseGMEndogError(unittest.TestCase):
     def setUp(self):
-        db = libpysal.io.open(libpysal.examples.get_path("columbus.dbf"), "r")
+        db=libpysal.io.open(libpysal.examples.get_path("columbus.dbf"),"r")
         y = np.array(db.by_col("HOVAL"))
-        self.y = np.reshape(y, (49, 1))
+        self.y = np.reshape(y, (49,1))
         X = []
         X.append(db.by_col("INC"))
         self.X = np.array(X).T
-        self.X = np.hstack((np.ones(self.y.shape), self.X))
+        self.X = np.hstack((np.ones(self.y.shape),self.X))
         yd = []
         yd.append(db.by_col("CRIME"))
         self.yd = np.array(yd).T
@@ -136,55 +111,50 @@ def setUp(self):
         q.append(db.by_col("DISCBD"))
         self.q = np.array(q).T
         self.w = weights.Rook.from_shapefile(libpysal.examples.get_path("columbus.shp"))
-        self.w.transform = "r"
+        self.w.transform = 'r'
 
     def test_model(self):
         reg = SP.BaseGM_Endog_Error(self.y, self.X, self.yd, self.q, self.w.sparse)
-        betas = np.array([[55.36095292], [0.46411479], [-0.66883535], [0.38989939]])
-        # raising a warning causes a failure...
-        print("Running reduced precision test in L120 of test_error_sp.py")
-        np.testing.assert_allclose(reg.betas, betas, RTOL + 0.0001)
-        u = np.array([26.55951566])
-        np.testing.assert_allclose(reg.u[0], u, RTOL)
-        e = np.array([31.23925425])
-        np.testing.assert_allclose(reg.e_filtered[0], e, RTOL)
-        predy = np.array([53.9074875])
-        np.testing.assert_allclose(reg.predy[0], predy, RTOL)
+        betas = np.array([[ 55.36095292], [  0.46411479], [ -0.66883535], [  0.38989939]])
+        #raising a warning causes a failure...
+        print('Running reduced precision test in L120 of test_error_sp.py')
+        np.testing.assert_allclose(reg.betas,betas,RTOL+.0001)
+        u = np.array([ 26.55951566])
+        np.testing.assert_allclose(reg.u[0],u,RTOL)
+        e = np.array([ 31.23925425])
+        np.testing.assert_allclose(reg.e_filtered[0],e,RTOL)
+        predy = np.array([ 53.9074875])
+        np.testing.assert_allclose(reg.predy[0],predy,RTOL)
         n = 49
-        np.testing.assert_allclose(reg.n, n, RTOL)
+        np.testing.assert_allclose(reg.n,n,RTOL)
         k = 3
-        np.testing.assert_allclose(reg.k, k, RTOL)
-        y = np.array([80.467003])
-        np.testing.assert_allclose(reg.y[0], y, RTOL)
-        x = np.array([1.0, 19.531])
-        np.testing.assert_allclose(reg.x[0], x, RTOL)
-        yend = np.array([15.72598])
-        np.testing.assert_allclose(reg.yend[0], yend, RTOL)
-        z = np.array([1.0, 19.531, 15.72598])
-        np.testing.assert_allclose(reg.z[0], z, RTOL)
+        np.testing.assert_allclose(reg.k,k,RTOL)
+        y = np.array([ 80.467003])
+        np.testing.assert_allclose(reg.y[0],y,RTOL)
+        x = np.array([  1.   ,  19.531])
+        np.testing.assert_allclose(reg.x[0],x,RTOL)
+        yend = np.array([  15.72598])
+        np.testing.assert_allclose(reg.yend[0],yend,RTOL)
+        z = np.array([  1.     ,  19.531  ,  15.72598])
+        np.testing.assert_allclose(reg.z[0],z,RTOL)
         my = 38.43622446938776
-        np.testing.assert_allclose(reg.mean_y, my, RTOL)
-        # std_y
+        np.testing.assert_allclose(reg.mean_y,my,RTOL)
+        #std_y
         sy = 18.466069465206047
-        np.testing.assert_allclose(reg.std_y, sy, RTOL)
-        # vm
-        vm = np.array(
-            [
-                [5.29158422e02, -1.57833675e01, -8.38021080e00],
-                [-1.57833675e01, 5.40235041e-01, 2.31120327e-01],
-                [-8.38021080e00, 2.31120327e-01, 1.44977385e-01],
-            ]
-        )
-        np.testing.assert_allclose(reg.vm, vm, RTOL)
+        np.testing.assert_allclose(reg.std_y,sy,RTOL)
+        #vm
+        vm = np.array([[  5.29158422e+02,  -1.57833675e+01,  -8.38021080e+00],
+       [ -1.57833675e+01,   5.40235041e-01,   2.31120327e-01],
+       [ -8.38021080e+00,   2.31120327e-01,   1.44977385e-01]])
+        np.testing.assert_allclose(reg.vm,vm,RTOL)
         sig2 = 192.50022721929574
-        np.testing.assert_allclose(reg.sig2, sig2, RTOL)
-
+        np.testing.assert_allclose(reg.sig2,sig2,RTOL)
 
 class TestGMEndogError(unittest.TestCase):
     def setUp(self):
-        db = libpysal.io.open(libpysal.examples.get_path("columbus.dbf"), "r")
+        db=libpysal.io.open(libpysal.examples.get_path("columbus.dbf"),"r")
         y = np.array(db.by_col("HOVAL"))
-        self.y = np.reshape(y, (49, 1))
+        self.y = np.reshape(y, (49,1))
         X = []
         X.append(db.by_col("INC"))
         self.X = np.array(X).T
@@ -194,180 +164,105 @@ def setUp(self):
         q = []
         q.append(db.by_col("DISCBD"))
         self.q = np.array(q).T
-        self.w = libpysal.weights.Rook.from_shapefile(
-            libpysal.examples.get_path("columbus.shp")
-        )
-        self.w.transform = "r"
+        self.w = libpysal.weights.Rook.from_shapefile(libpysal.examples.get_path("columbus.shp"))
+        self.w.transform = 'r'
 
     def test_model(self):
         reg = SP.GM_Endog_Error(self.y, self.X, self.yd, self.q, self.w)
-        betas = np.array([[55.36095292], [0.46411479], [-0.66883535], [0.38989939]])
-        print("Running reduced precision test in L175 of test_error_sp.py")
-        np.testing.assert_allclose(reg.betas, betas, RTOL + 0.0001)
-        u = np.array([26.55951566])
-        np.testing.assert_allclose(reg.u[0], u, RTOL)
-        e = np.array([31.23925425])
-        np.testing.assert_allclose(reg.e_filtered[0], e, RTOL)
-        predy = np.array([53.9074875])
-        np.testing.assert_allclose(reg.predy[0], predy, RTOL)
+        betas = np.array([[ 55.36095292], [  0.46411479], [ -0.66883535], [  0.38989939]])
+        print('Running reduced precision test in L175 of test_error_sp.py')
+        np.testing.assert_allclose(reg.betas,betas,RTOL +.0001)
+        u = np.array([ 26.55951566])
+        np.testing.assert_allclose(reg.u[0],u,RTOL)
+        e = np.array([ 31.23925425])
+        np.testing.assert_allclose(reg.e_filtered[0],e,RTOL)
+        predy = np.array([ 53.9074875])
+        np.testing.assert_allclose(reg.predy[0],predy,RTOL)
         n = 49
-        np.testing.assert_allclose(reg.n, n, RTOL)
+        np.testing.assert_allclose(reg.n,n,RTOL)
         k = 3
-        np.testing.assert_allclose(reg.k, k, RTOL)
-        y = np.array([80.467003])
-        np.testing.assert_allclose(reg.y[0], y, RTOL)
-        x = np.array([1.0, 19.531])
-        np.testing.assert_allclose(reg.x[0], x, RTOL)
-        yend = np.array([15.72598])
-        np.testing.assert_allclose(reg.yend[0], yend, RTOL)
-        z = np.array([1.0, 19.531, 15.72598])
-        np.testing.assert_allclose(reg.z[0], z, RTOL)
+        np.testing.assert_allclose(reg.k,k,RTOL)
+        y = np.array([ 80.467003])
+        np.testing.assert_allclose(reg.y[0],y,RTOL)
+        x = np.array([  1.   ,  19.531])
+        np.testing.assert_allclose(reg.x[0],x,RTOL)
+        yend = np.array([  15.72598])
+        np.testing.assert_allclose(reg.yend[0],yend,RTOL)
+        z = np.array([  1.     ,  19.531  ,  15.72598])
+        np.testing.assert_allclose(reg.z[0],z,RTOL)
         my = 38.43622446938776
-        np.testing.assert_allclose(reg.mean_y, my, RTOL)
+        np.testing.assert_allclose(reg.mean_y,my,RTOL)
         sy = 18.466069465206047
-        np.testing.assert_allclose(reg.std_y, sy, RTOL)
-        vm = np.array(
-            [
-                [5.29158422e02, -1.57833675e01, -8.38021080e00],
-                [-1.57833675e01, 5.40235041e-01, 2.31120327e-01],
-                [-8.38021080e00, 2.31120327e-01, 1.44977385e-01],
-            ]
-        )
-        np.testing.assert_allclose(reg.vm, vm, RTOL)
+        np.testing.assert_allclose(reg.std_y,sy,RTOL)
+        vm = np.array([[  5.29158422e+02,  -1.57833675e+01,  -8.38021080e+00],
+       [ -1.57833675e+01,   5.40235041e-01,   2.31120327e-01],
+       [ -8.38021080e+00,   2.31120327e-01,   1.44977385e-01]])
+        np.testing.assert_allclose(reg.vm,vm,RTOL)
         pr2 = 0.346472557570858
-        np.testing.assert_allclose(reg.pr2, pr2, RTOL)
+        np.testing.assert_allclose(reg.pr2,pr2,RTOL)
         sig2 = 192.50022721929574
-        np.testing.assert_allclose(reg.sig2, sig2, RTOL)
-        std_err = np.array([23.003401, 0.73500657, 0.38075777])
-        np.testing.assert_allclose(reg.std_err, std_err, RTOL)
-        z_stat = np.array(
-            [
-                [2.40664208, 0.01609994],
-                [0.63144305, 0.52775088],
-                [-1.75659016, 0.07898769],
-            ]
-        )
-        np.testing.assert_allclose(reg.z_stat, z_stat, RTOL)
-
-
-class TestBaseGMCombo(unittest.TestCase):
-    def setUp(self):
-        db = libpysal.io.open(libpysal.examples.get_path("columbus.dbf"), "r")
-        y = np.array(db.by_col("HOVAL"))
-        self.y = np.reshape(y, (49, 1))
-        X = []
-        X.append(db.by_col("INC"))
-        X.append(db.by_col("CRIME"))
-        self.X = np.array(X).T
-        self.w = libpysal.weights.Rook.from_shapefile(
-            libpysal.examples.get_path("columbus.shp")
-        )
-        self.w.transform = "r"
-
-    def test_model(self):
-        # Only spatial lag
-        yd2, q2 = utils.set_endog(self.y, self.X, self.w, None, None, 1, True)
-        self.X = np.hstack((np.ones(self.y.shape), self.X))
-        reg = SP.BaseGM_Combo(self.y, self.X, yend=yd2, q=q2, w=self.w.sparse)
-        betas = np.array(
-            [[57.61123461], [0.73441314], [-0.59459416], [-0.21762921], [0.54732051]]
-        )
-        np.testing.assert_allclose(reg.betas, betas, RTOL)
-        u = np.array([25.57932637])
-        np.testing.assert_allclose(reg.u[0], u, RTOL)
-        e_filtered = np.array([31.65374945])
-        np.testing.assert_allclose(reg.e_filtered[0], e_filtered, RTOL)
-        predy = np.array([54.88767663])
-        np.testing.assert_allclose(reg.predy[0], predy, RTOL)
-        n = 49
-        np.testing.assert_allclose(reg.n, n, RTOL)
-        k = 4
-        np.testing.assert_allclose(reg.k, k, RTOL)
-        y = np.array([80.467003])
-        np.testing.assert_allclose(reg.y[0], y, RTOL)
-        x = np.array([1.0, 19.531, 15.72598])
-        np.testing.assert_allclose(reg.x[0], x, RTOL)
-        yend = np.array([35.4585005])
-        np.testing.assert_allclose(reg.yend[0], yend, RTOL)
-        z = np.array([1.0, 19.531, 15.72598, 35.4585005])
-        np.testing.assert_allclose(reg.z[0], z, RTOL)
-        my = 38.43622446938776
-        np.testing.assert_allclose(reg.mean_y, my, RTOL)
-        sy = 18.466069465206047
-        np.testing.assert_allclose(reg.std_y, sy, RTOL)
-        vm = np.array([5.22438365e02, 2.38012873e-01, 3.20924172e-02, 2.15753599e-01])
-        np.testing.assert_allclose(np.diag(reg.vm), vm, RTOL)
-        sig2 = 181.78650186468832
-        np.testing.assert_allclose(reg.sig2, sig2, RTOL)
+        np.testing.assert_allclose(reg.sig2,sig2,RTOL)
+        std_err = np.array([ 23.003401  ,   0.73500657,   0.38075777])
+        np.testing.assert_allclose(reg.std_err,std_err,RTOL)
+        z_stat = np.array([[ 2.40664208,  0.01609994], [ 0.63144305,  0.52775088], [-1.75659016,  0.07898769]])
+        np.testing.assert_allclose(reg.z_stat,z_stat,RTOL)
 
 
 class TestGMCombo(unittest.TestCase):
     def setUp(self):
-        db = libpysal.io.open(libpysal.examples.get_path("columbus.dbf"), "r")
+        db=libpysal.io.open(libpysal.examples.get_path("columbus.dbf"),"r")
         y = np.array(db.by_col("HOVAL"))
-        self.y = np.reshape(y, (49, 1))
+        self.y = np.reshape(y, (49,1))
         X = []
         X.append(db.by_col("INC"))
         X.append(db.by_col("CRIME"))
         self.X = np.array(X).T
-        self.w = libpysal.weights.Rook.from_shapefile(
-            libpysal.examples.get_path("columbus.shp")
-        )
-        self.w.transform = "r"
-
+        self.w = libpysal.weights.Rook.from_shapefile(libpysal.examples.get_path("columbus.shp"))
+        self.w.transform = 'r'
     def test_model(self):
         # Only spatial lag
         reg = SP.GM_Combo(self.y, self.X, w=self.w)
-        e_reduced = np.array([28.18617481])
-        np.testing.assert_allclose(reg.e_pred[0], e_reduced, RTOL)
-        predy_e = np.array([52.28082782])
-        np.testing.assert_allclose(reg.predy_e[0], predy_e, RTOL)
-        betas = np.array(
-            [[57.61123515], [0.73441313], [-0.59459416], [-0.21762921], [0.54732051]]
-        )
-        np.testing.assert_allclose(reg.betas, betas, RTOL)
-        u = np.array([25.57932637])
-        np.testing.assert_allclose(reg.u[0], u, RTOL)
-        e_filtered = np.array([31.65374945])
-        np.testing.assert_allclose(reg.e_filtered[0], e_filtered, RTOL)
-        predy = np.array([54.88767685])
-        np.testing.assert_allclose(reg.predy[0], predy, RTOL)
+        e_reduced = np.array([ 28.18617481])
+        np.testing.assert_allclose(reg.e_pred[0],e_reduced,RTOL)
+        predy_e = np.array([ 52.28082782])
+        np.testing.assert_allclose(reg.predy_e[0],predy_e,RTOL)
+        betas = np.array([[ 57.61123515],[  0.73441313], [ -0.59459416], [ -0.21762921], [  0.54732051]])
+        np.testing.assert_allclose(reg.betas,betas,RTOL)
+        u = np.array([ 25.57932637])
+        np.testing.assert_allclose(reg.u[0],u,RTOL)
+        e_filtered = np.array([ 31.65374945])
+        np.testing.assert_allclose(reg.e_filtered[0],e_filtered,RTOL)
+        predy = np.array([ 54.88767685])
+        np.testing.assert_allclose(reg.predy[0],predy,RTOL)
         n = 49
-        np.testing.assert_allclose(reg.n, n, RTOL)
+        np.testing.assert_allclose(reg.n,n,RTOL)
         k = 4
-        np.testing.assert_allclose(reg.k, k, RTOL)
-        y = np.array([80.467003])
-        np.testing.assert_allclose(reg.y[0], y, RTOL)
-        x = np.array([1.0, 19.531, 15.72598])
-        np.testing.assert_allclose(reg.x[0], x, RTOL)
-        yend = np.array([35.4585005])
-        np.testing.assert_allclose(reg.yend[0], yend, RTOL)
-        z = np.array([1.0, 19.531, 15.72598, 35.4585005])
-        np.testing.assert_allclose(reg.z[0], z, RTOL)
+        np.testing.assert_allclose(reg.k,k,RTOL)
+        y = np.array([ 80.467003])
+        np.testing.assert_allclose(reg.y[0],y,RTOL)
+        x = np.array([  1.     ,  19.531  ,  15.72598])
+        np.testing.assert_allclose(reg.x[0],x,RTOL)
+        yend = np.array([  35.4585005])
+        np.testing.assert_allclose(reg.yend[0],yend,RTOL)
+        z = np.array([  1.       ,  19.531    ,  15.72598  ,  35.4585005])
+        np.testing.assert_allclose(reg.z[0],z,RTOL)
         my = 38.43622446938776
-        np.testing.assert_allclose(reg.mean_y, my)
+        np.testing.assert_allclose(reg.mean_y,my)
         sy = 18.466069465206047
-        np.testing.assert_allclose(reg.std_y, sy)
-        vm = np.array([5.22438333e02, 2.38012875e-01, 3.20924173e-02, 2.15753579e-01])
-        np.testing.assert_allclose(np.diag(reg.vm), vm, RTOL)
+        np.testing.assert_allclose(reg.std_y,sy)
+        vm = np.array([  5.22438333e+02,   2.38012875e-01,   3.20924173e-02,
+         2.15753579e-01])
+        np.testing.assert_allclose(np.diag(reg.vm),vm,RTOL)
         sig2 = 181.78650186468832
-        np.testing.assert_allclose(reg.sig2, sig2, RTOL)
+        np.testing.assert_allclose(reg.sig2,sig2,RTOL)
         pr2 = 0.3018280166937799
-        np.testing.assert_allclose(reg.pr2, pr2, RTOL)
+        np.testing.assert_allclose(reg.pr2,pr2,RTOL)
         pr2_e = 0.3561355586759414
-        np.testing.assert_allclose(reg.pr2_e, pr2_e, RTOL)
-        std_err = np.array([22.85692222, 0.48786559, 0.17914356, 0.46449318])
-        np.testing.assert_allclose(reg.std_err, std_err, RTOL)
-        z_stat = np.array(
-            [
-                [2.52051597e00, 1.17182922e-02],
-                [1.50535954e00, 1.32231664e-01],
-                [-3.31909311e00, 9.03103123e-04],
-                [-4.68530506e-01, 6.39405261e-01],
-            ]
-        )
-        np.testing.assert_allclose(reg.z_stat, z_stat, RTOL)
-
+        np.testing.assert_allclose(reg.pr2_e,pr2_e,RTOL)
+        std_err = np.array([ 22.85692222,  0.48786559,  0.17914356,  0.46449318])
+        np.testing.assert_allclose(reg.std_err,std_err,RTOL)
+        z_stat = np.array([[  2.52051597e+00,   1.17182922e-02], [  1.50535954e+00,   1.32231664e-01], [ -3.31909311e+00,   9.03103123e-04], [ -4.68530506e-01,   6.39405261e-01]])
+        np.testing.assert_allclose(reg.z_stat,z_stat,RTOL)
 
-if __name__ == "__main__":
+if __name__ == '__main__':
     unittest.main()
diff --git a/spreg/tests/test_error_sp_sparse.py b/spreg/tests/test_error_sp_sparse.py
index 07fd942a..4fad34cc 100755
--- a/spreg/tests/test_error_sp_sparse.py
+++ b/spreg/tests/test_error_sp_sparse.py
@@ -5,133 +5,108 @@
 from scipy import sparse
 from spreg import utils
 from spreg import error_sp as SP
-from libpysal.common import RTOL
-
+from libpysal.common import RTOL 
 
 class TestBaseGMError(unittest.TestCase):
     def setUp(self):
-        db = libpysal.io.open(libpysal.examples.get_path("columbus.dbf"), "r")
+        db=libpysal.io.open(libpysal.examples.get_path("columbus.dbf"),"r")
         y = np.array(db.by_col("HOVAL"))
-        self.y = np.reshape(y, (49, 1))
+        self.y = np.reshape(y, (49,1))
         X = []
         X.append(db.by_col("INC"))
         X.append(db.by_col("CRIME"))
         self.X = np.array(X).T
-        self.X = np.hstack((np.ones(self.y.shape), self.X))
+        self.X = np.hstack((np.ones(self.y.shape),self.X))
         self.X = sparse.csr_matrix(self.X)
-        self.w = libpysal.weights.Rook.from_shapefile(
-            libpysal.examples.get_path("columbus.shp")
-        )
-        self.w.transform = "r"
+        self.w = libpysal.weights.Rook.from_shapefile(libpysal.examples.get_path("columbus.shp"))
+        self.w.transform = 'r'
 
     def test_model(self):
         reg = SP.BaseGM_Error(self.y, self.X, self.w.sparse)
-        betas = np.array([[47.94371455], [0.70598088], [-0.55571746], [0.37230161]])
-        np.testing.assert_allclose(reg.betas, betas, RTOL)
-        u = np.array([27.4739775])
-        np.testing.assert_allclose(reg.u[0], u, RTOL)
-        predy = np.array([52.9930255])
-        np.testing.assert_allclose(reg.predy[0], predy, RTOL)
+        betas = np.array([[ 47.94371455], [  0.70598088], [ -0.55571746], [  0.37230161]])
+        np.testing.assert_allclose(reg.betas,betas,RTOL)
+        u = np.array([ 27.4739775])
+        np.testing.assert_allclose(reg.u[0],u,RTOL)
+        predy = np.array([ 52.9930255])
+        np.testing.assert_allclose(reg.predy[0],predy,RTOL)
         n = 49
-        np.testing.assert_allclose(reg.n, n, RTOL)
+        np.testing.assert_allclose(reg.n,n,RTOL)
         k = 3
-        np.testing.assert_allclose(reg.k, k, RTOL)
-        y = np.array([80.467003])
-        np.testing.assert_allclose(reg.y[0], y, RTOL)
-        x = np.array([1.0, 19.531, 15.72598])
-        np.testing.assert_allclose(reg.x.toarray()[0], x, RTOL)
-        e = np.array([31.89620319])
-        np.testing.assert_allclose(reg.e_filtered[0], e, RTOL)
-        predy = np.array([52.9930255])
-        np.testing.assert_allclose(reg.predy[0], predy, RTOL)
+        np.testing.assert_allclose(reg.k,k,RTOL)
+        y = np.array([ 80.467003])
+        np.testing.assert_allclose(reg.y[0],y,RTOL)
+        x = np.array([  1.     ,  19.531  ,  15.72598])
+        np.testing.assert_allclose(reg.x.toarray()[0],x,RTOL)
+        e = np.array([ 31.89620319])
+        np.testing.assert_allclose(reg.e_filtered[0],e,RTOL)
+        predy = np.array([ 52.9930255])
+        np.testing.assert_allclose(reg.predy[0],predy,RTOL)
         my = 38.43622446938776
-        np.testing.assert_allclose(reg.mean_y, my, RTOL)
+        np.testing.assert_allclose(reg.mean_y,my,RTOL)
         sy = 18.466069465206047
-        np.testing.assert_allclose(reg.std_y, sy, RTOL)
-        vm = np.array(
-            [
-                [1.51884943e02, -5.37622793e00, -1.86970286e00],
-                [-5.37622793e00, 2.48972661e-01, 5.26564244e-02],
-                [-1.86970286e00, 5.26564244e-02, 3.18930650e-02],
-            ]
-        )
-        np.testing.assert_allclose(reg.vm, vm, RTOL)
+        np.testing.assert_allclose(reg.std_y,sy,RTOL)
+        vm = np.array([[  1.51884943e+02,  -5.37622793e+00,  -1.86970286e+00], [ -5.37622793e+00,   2.48972661e-01,   5.26564244e-02], [ -1.86970286e+00,   5.26564244e-02, 3.18930650e-02]])
+        np.testing.assert_allclose(reg.vm,vm,RTOL)
         sig2 = 191.73716465732355
-        np.testing.assert_allclose(reg.sig2, sig2, RTOL)
-
+        np.testing.assert_allclose(reg.sig2,sig2,RTOL)
 
 class TestGMError(unittest.TestCase):
     def setUp(self):
-        db = libpysal.io.open(libpysal.examples.get_path("columbus.dbf"), "r")
+        db=libpysal.io.open(libpysal.examples.get_path("columbus.dbf"),"r")
         y = np.array(db.by_col("HOVAL"))
-        self.y = np.reshape(y, (49, 1))
+        self.y = np.reshape(y, (49,1))
         X = []
         X.append(db.by_col("INC"))
         X.append(db.by_col("CRIME"))
         self.X = np.array(X).T
         self.X = sparse.csr_matrix(self.X)
-        self.w = libpysal.weights.Rook.from_shapefile(
-            libpysal.examples.get_path("columbus.shp")
-        )
-        self.w.transform = "r"
+        self.w = libpysal.weights.Rook.from_shapefile(libpysal.examples.get_path("columbus.shp"))
+        self.w.transform = 'r'
 
     def test_model(self):
         reg = SP.GM_Error(self.y, self.X, self.w)
-        betas = np.array([[47.94371455], [0.70598088], [-0.55571746], [0.37230161]])
-        np.testing.assert_allclose(reg.betas, betas, RTOL)
-        u = np.array([27.4739775])
-        np.testing.assert_allclose(reg.u[0], u, RTOL)
-        predy = np.array([52.9930255])
-        np.testing.assert_allclose(reg.predy[0], predy, RTOL)
+        betas = np.array([[ 47.94371455], [  0.70598088], [ -0.55571746], [  0.37230161]])
+        np.testing.assert_allclose(reg.betas,betas,RTOL)
+        u = np.array([ 27.4739775])
+        np.testing.assert_allclose(reg.u[0],u,RTOL)
+        predy = np.array([ 52.9930255])
+        np.testing.assert_allclose(reg.predy[0],predy,RTOL)
         n = 49
-        np.testing.assert_allclose(reg.n, n, RTOL)
+        np.testing.assert_allclose(reg.n,n,RTOL)
         k = 3
-        np.testing.assert_allclose(reg.k, k, RTOL)
-        y = np.array([80.467003])
-        np.testing.assert_allclose(reg.y[0], y, RTOL)
-        x = np.array([1.0, 19.531, 15.72598])
-        np.testing.assert_allclose(reg.x.toarray()[0], x, RTOL)
-        e = np.array([31.89620319])
-        np.testing.assert_allclose(reg.e_filtered[0], e, RTOL)
-        predy = np.array([52.9930255])
-        np.testing.assert_allclose(reg.predy[0], predy, RTOL)
+        np.testing.assert_allclose(reg.k,k,RTOL)
+        y = np.array([ 80.467003])
+        np.testing.assert_allclose(reg.y[0],y,RTOL)
+        x = np.array([  1.     ,  19.531  ,  15.72598])
+        np.testing.assert_allclose(reg.x.toarray()[0],x,RTOL)
+        e = np.array([ 31.89620319])
+        np.testing.assert_allclose(reg.e_filtered[0],e,RTOL)
+        predy = np.array([ 52.9930255])
+        np.testing.assert_allclose(reg.predy[0],predy,RTOL)
         my = 38.43622446938776
-        np.testing.assert_allclose(reg.mean_y, my, RTOL)
+        np.testing.assert_allclose(reg.mean_y,my,RTOL)
         sy = 18.466069465206047
-        np.testing.assert_allclose(reg.std_y, sy, RTOL)
-        vm = np.array(
-            [
-                [1.51884943e02, -5.37622793e00, -1.86970286e00],
-                [-5.37622793e00, 2.48972661e-01, 5.26564244e-02],
-                [-1.86970286e00, 5.26564244e-02, 3.18930650e-02],
-            ]
-        )
-        np.testing.assert_allclose(reg.vm, vm, RTOL)
+        np.testing.assert_allclose(reg.std_y,sy,RTOL)
+        vm = np.array([[  1.51884943e+02,  -5.37622793e+00,  -1.86970286e+00], [ -5.37622793e+00,   2.48972661e-01,   5.26564244e-02], [ -1.86970286e+00,   5.26564244e-02, 3.18930650e-02]])
+        np.testing.assert_allclose(reg.vm,vm,RTOL)
         sig2 = 191.73716465732355
-        np.testing.assert_allclose(reg.sig2, sig2, RTOL)
+        np.testing.assert_allclose(reg.sig2,sig2,RTOL)
         pr2 = 0.3495097406012179
-        np.testing.assert_allclose(reg.pr2, pr2, RTOL)
-        std_err = np.array([12.32416094, 0.4989716, 0.1785863])
-        np.testing.assert_allclose(reg.std_err, std_err, RTOL)
-        z_stat = np.array(
-            [
-                [3.89022140e00, 1.00152805e-04],
-                [1.41487186e00, 1.57106070e-01],
-                [-3.11175868e00, 1.85976455e-03],
-            ]
-        )
-        np.testing.assert_allclose(reg.z_stat, z_stat, RTOL)
-
+        np.testing.assert_allclose(reg.pr2,pr2,RTOL)
+        std_err = np.array([ 12.32416094,   0.4989716 ,   0.1785863 ])
+        np.testing.assert_allclose(reg.std_err,std_err,RTOL)
+        z_stat = np.array([[  3.89022140e+00,   1.00152805e-04], [  1.41487186e+00,   1.57106070e-01], [ -3.11175868e+00,   1.85976455e-03]])
+        np.testing.assert_allclose(reg.z_stat,z_stat,RTOL)
 
 class TestBaseGMEndogError(unittest.TestCase):
     def setUp(self):
-        db = libpysal.io.open(libpysal.examples.get_path("columbus.dbf"), "r")
+        db=libpysal.io.open(libpysal.examples.get_path("columbus.dbf"),"r")
         y = np.array(db.by_col("HOVAL"))
-        self.y = np.reshape(y, (49, 1))
+        self.y = np.reshape(y, (49,1))
         X = []
         X.append(db.by_col("INC"))
         self.X = np.array(X).T
-        self.X = np.hstack((np.ones(self.y.shape), self.X))
+        self.X = np.hstack((np.ones(self.y.shape),self.X))
         self.X = sparse.csr_matrix(self.X)
         yd = []
         yd.append(db.by_col("CRIME"))
@@ -139,57 +114,50 @@ def setUp(self):
         q = []
         q.append(db.by_col("DISCBD"))
         self.q = np.array(q).T
-        self.w = libpysal.weights.Rook.from_shapefile(
-            libpysal.examples.get_path("columbus.shp")
-        )
-        self.w.transform = "r"
+        self.w = libpysal.weights.Rook.from_shapefile(libpysal.examples.get_path("columbus.shp"))
+        self.w.transform = 'r'
 
     def test_model(self):
         reg = SP.BaseGM_Endog_Error(self.y, self.X, self.yd, self.q, self.w.sparse)
-        betas = np.array([[55.36095292], [0.46411479], [-0.66883535], [0.38989939]])
-        print("Running reduced-precision test in L125 of test_error_sp_sparse.py")
-        np.testing.assert_allclose(reg.betas, betas, RTOL + 0.0001)
-        u = np.array([26.55951566])
-        np.testing.assert_allclose(reg.u[0], u, RTOL)
-        e = np.array([31.23925425])
-        np.testing.assert_allclose(reg.e_filtered[0], e, RTOL)
-        predy = np.array([53.9074875])
-        np.testing.assert_allclose(reg.predy[0], predy, RTOL)
+        betas = np.array([[ 55.36095292], [  0.46411479], [ -0.66883535], [  0.38989939]])
+        print('Running reduced-precision test in L125 of test_error_sp_sparse.py')
+        np.testing.assert_allclose(reg.betas,betas,RTOL + .0001)
+        u = np.array([ 26.55951566])
+        np.testing.assert_allclose(reg.u[0],u,RTOL)
+        e = np.array([ 31.23925425])
+        np.testing.assert_allclose(reg.e_filtered[0],e,RTOL)
+        predy = np.array([ 53.9074875])
+        np.testing.assert_allclose(reg.predy[0],predy,RTOL)
         n = 49
-        np.testing.assert_allclose(reg.n, n, RTOL)
+        np.testing.assert_allclose(reg.n,n,RTOL)
         k = 3
-        np.testing.assert_allclose(reg.k, k, RTOL)
-        y = np.array([80.467003])
-        np.testing.assert_allclose(reg.y[0], y, RTOL)
-        x = np.array([1.0, 19.531])
-        np.testing.assert_allclose(reg.x.toarray()[0], x, RTOL)
-        yend = np.array([15.72598])
-        np.testing.assert_allclose(reg.yend[0], yend, RTOL)
-        z = np.array([1.0, 19.531, 15.72598])
-        np.testing.assert_allclose(reg.z.toarray()[0], z, RTOL)
+        np.testing.assert_allclose(reg.k,k,RTOL)
+        y = np.array([ 80.467003])
+        np.testing.assert_allclose(reg.y[0],y,RTOL)
+        x = np.array([  1.   ,  19.531])
+        np.testing.assert_allclose(reg.x.toarray()[0],x,RTOL)
+        yend = np.array([  15.72598])
+        np.testing.assert_allclose(reg.yend[0],yend,RTOL)
+        z = np.array([  1.     ,  19.531  ,  15.72598])
+        np.testing.assert_allclose(reg.z.toarray()[0],z,RTOL)
         my = 38.43622446938776
-        np.testing.assert_allclose(reg.mean_y, my, RTOL)
-        # std_y
+        np.testing.assert_allclose(reg.mean_y,my,RTOL)
+        #std_y
         sy = 18.466069465206047
-        np.testing.assert_allclose(reg.std_y, sy, RTOL)
-        # vm
-        vm = np.array(
-            [
-                [529.15840986, -15.78336736, -8.38021053],
-                [-15.78336736, 0.54023504, 0.23112032],
-                [-8.38021053, 0.23112032, 0.14497738],
-            ]
-        )
-        np.testing.assert_allclose(reg.vm, vm, RTOL)
+        np.testing.assert_allclose(reg.std_y,sy,RTOL)
+        #vm
+        vm = np.array([[ 529.15840986,  -15.78336736,   -8.38021053],
+       [ -15.78336736,    0.54023504,    0.23112032],
+       [  -8.38021053,    0.23112032,    0.14497738]])
+        np.testing.assert_allclose(reg.vm,vm,RTOL)
         sig2 = 192.5002
-        np.testing.assert_allclose(reg.sig2, sig2, RTOL)
-
+        np.testing.assert_allclose(reg.sig2,sig2,RTOL)
 
 class TestGMEndogError(unittest.TestCase):
     def setUp(self):
-        db = libpysal.io.open(libpysal.examples.get_path("columbus.dbf"), "r")
+        db=libpysal.io.open(libpysal.examples.get_path("columbus.dbf"),"r")
         y = np.array(db.by_col("HOVAL"))
-        self.y = np.reshape(y, (49, 1))
+        self.y = np.reshape(y, (49,1))
         X = []
         X.append(db.by_col("INC"))
         self.X = np.array(X).T
@@ -200,185 +168,109 @@ def setUp(self):
         q = []
         q.append(db.by_col("DISCBD"))
         self.q = np.array(q).T
-        self.w = libpysal.weights.Rook.from_shapefile(
-            libpysal.examples.get_path("columbus.shp")
-        )
-        self.w.transform = "r"
+        self.w = libpysal.weights.Rook.from_shapefile(libpysal.examples.get_path("columbus.shp"))
+        self.w.transform = 'r'
 
     def test_model(self):
         reg = SP.GM_Endog_Error(self.y, self.X, self.yd, self.q, self.w)
-        betas = np.array([[55.36095292], [0.46411479], [-0.66883535], [0.38989939]])
-        print("Running reduced-tolernace test in L181 of test_error_sp_sparse.py")
-        np.testing.assert_allclose(reg.betas, betas, RTOL + 0.0001)
-        u = np.array([26.55951566])
-        np.testing.assert_allclose(reg.u[0], u, RTOL)
-        e = np.array([31.23925425])
-        np.testing.assert_allclose(reg.e_filtered[0], e, RTOL)
-        predy = np.array([53.9074875])
-        np.testing.assert_allclose(reg.predy[0], predy, RTOL)
+        betas = np.array([[ 55.36095292], [  0.46411479], [ -0.66883535], [  0.38989939]])
+        print('Running reduced-tolernace test in L181 of test_error_sp_sparse.py')
+        np.testing.assert_allclose(reg.betas,betas,RTOL + .0001)
+        u = np.array([ 26.55951566])
+        np.testing.assert_allclose(reg.u[0],u,RTOL)
+        e = np.array([ 31.23925425])
+        np.testing.assert_allclose(reg.e_filtered[0],e,RTOL)
+        predy = np.array([ 53.9074875])
+        np.testing.assert_allclose(reg.predy[0],predy,RTOL)
         n = 49
-        np.testing.assert_allclose(reg.n, n, RTOL)
+        np.testing.assert_allclose(reg.n,n,RTOL)
         k = 3
-        np.testing.assert_allclose(reg.k, k, RTOL)
-        y = np.array([80.467003])
-        np.testing.assert_allclose(reg.y[0], y, RTOL)
-        x = np.array([1.0, 19.531])
-        np.testing.assert_allclose(reg.x.toarray()[0], x, RTOL)
-        yend = np.array([15.72598])
-        np.testing.assert_allclose(reg.yend[0], yend, RTOL)
-        z = np.array([1.0, 19.531, 15.72598])
-        np.testing.assert_allclose(reg.z.toarray()[0], z, RTOL)
+        np.testing.assert_allclose(reg.k,k,RTOL)
+        y = np.array([ 80.467003])
+        np.testing.assert_allclose(reg.y[0],y,RTOL)
+        x = np.array([  1.   ,  19.531])
+        np.testing.assert_allclose(reg.x.toarray()[0],x,RTOL)
+        yend = np.array([  15.72598])
+        np.testing.assert_allclose(reg.yend[0],yend,RTOL)
+        z = np.array([  1.     ,  19.531  ,  15.72598])
+        np.testing.assert_allclose(reg.z.toarray()[0],z,RTOL)
         my = 38.43622446938776
-        np.testing.assert_allclose(reg.mean_y, my, RTOL)
+        np.testing.assert_allclose(reg.mean_y,my,RTOL)
         sy = 18.466069465206047
-        np.testing.assert_allclose(reg.std_y, sy, RTOL)
-        vm = np.array(
-            [
-                [529.15840986, -15.78336736, -8.38021053],
-                [-15.78336736, 0.54023504, 0.23112032],
-                [-8.38021053, 0.23112032, 0.14497738],
-            ]
-        )
-        np.testing.assert_allclose(reg.vm, vm, RTOL)
+        np.testing.assert_allclose(reg.std_y,sy,RTOL)
+        vm = np.array([[ 529.15840986,  -15.78336736,   -8.38021053],
+       [ -15.78336736,    0.54023504,    0.23112032],
+       [  -8.38021053,    0.23112032,    0.14497738]])
+        np.testing.assert_allclose(reg.vm,vm,RTOL)
         pr2 = 0.346472557570858
-        np.testing.assert_allclose(reg.pr2, pr2, RTOL)
+        np.testing.assert_allclose(reg.pr2,pr2,RTOL)
         sig2 = 192.5002
-        np.testing.assert_allclose(reg.sig2, sig2, RTOL)
-        std_err = np.array([23.003401, 0.73500657, 0.38075777])
-        np.testing.assert_allclose(reg.std_err, std_err, RTOL)
-        z_stat = np.array(
-            [
-                [2.40664208, 0.01609994],
-                [0.63144305, 0.52775088],
-                [-1.75659016, 0.07898769],
-            ]
-        )
-        np.testing.assert_allclose(reg.z_stat, z_stat, RTOL)
-
-
-class TestBaseGMCombo(unittest.TestCase):
-    def setUp(self):
-        db = libpysal.io.open(libpysal.examples.get_path("columbus.dbf"), "r")
-        y = np.array(db.by_col("HOVAL"))
-        self.y = np.reshape(y, (49, 1))
-        X = []
-        X.append(db.by_col("INC"))
-        X.append(db.by_col("CRIME"))
-        self.X = np.array(X).T
-        self.w = libpysal.weights.Rook.from_shapefile(
-            libpysal.examples.get_path("columbus.shp")
-        )
-        self.w.transform = "r"
-
-    def test_model(self):
-        # Only spatial lag
-        yd2, q2 = utils.set_endog(self.y, self.X, self.w, None, None, 1, True)
-        self.X = np.hstack((np.ones(self.y.shape), self.X))
-        self.X = sparse.csr_matrix(self.X)
-        reg = SP.BaseGM_Combo(self.y, self.X, yend=yd2, q=q2, w=self.w.sparse)
-        betas = np.array(
-            [[57.61123461], [0.73441314], [-0.59459416], [-0.21762921], [0.54732051]]
-        )
-        np.testing.assert_allclose(reg.betas, betas, RTOL)
-        u = np.array([25.57932637])
-        np.testing.assert_allclose(reg.u[0], u, RTOL)
-        e_filtered = np.array([31.65374945])
-        np.testing.assert_allclose(reg.e_filtered[0], e_filtered, RTOL)
-        predy = np.array([54.88767663])
-        np.testing.assert_allclose(reg.predy[0], predy, RTOL)
-        n = 49
-        np.testing.assert_allclose(reg.n, n, RTOL)
-        k = 4
-        np.testing.assert_allclose(reg.k, k, RTOL)
-        y = np.array([80.467003])
-        np.testing.assert_allclose(reg.y[0], y, RTOL)
-        x = np.array([1.0, 19.531, 15.72598])
-        np.testing.assert_allclose(reg.x.toarray()[0], x, RTOL)
-        yend = np.array([35.4585005])
-        np.testing.assert_allclose(reg.yend[0], yend, RTOL)
-        z = np.array([1.0, 19.531, 15.72598, 35.4585005])
-        np.testing.assert_allclose(reg.z.toarray()[0], z, RTOL)
-        my = 38.43622446938776
-        np.testing.assert_allclose(reg.mean_y, my, RTOL)
-        sy = 18.466069465206047
-        np.testing.assert_allclose(reg.std_y, sy, RTOL)
-        vm = np.array([22.85691168, 0.48786563, 0.17914357, 0.46449287])
-        np.testing.assert_allclose(np.sqrt(reg.vm.diagonal()), vm, RTOL)
-        sig2 = 181.78650186468832
-        np.testing.assert_allclose(reg.sig2, sig2, RTOL)
+        np.testing.assert_allclose(reg.sig2,sig2,RTOL)
+        std_err = np.array([ 23.003401  ,   0.73500657,   0.38075777])
+        np.testing.assert_allclose(reg.std_err,std_err,RTOL)
+        z_stat = np.array([[ 2.40664208,  0.01609994], [ 0.63144305,  0.52775088], [-1.75659016,  0.07898769]])
+        np.testing.assert_allclose(reg.z_stat,z_stat,RTOL)
 
 
 class TestGMCombo(unittest.TestCase):
     def setUp(self):
-        db = libpysal.io.open(libpysal.examples.get_path("columbus.dbf"), "r")
+        db=libpysal.io.open(libpysal.examples.get_path("columbus.dbf"),"r")
         y = np.array(db.by_col("HOVAL"))
-        self.y = np.reshape(y, (49, 1))
+        self.y = np.reshape(y, (49,1))
         X = []
         X.append(db.by_col("INC"))
         X.append(db.by_col("CRIME"))
         self.X = np.array(X).T
         self.X = sparse.csr_matrix(self.X)
-        self.w = libpysal.weights.Rook.from_shapefile(
-            libpysal.examples.get_path("columbus.shp")
-        )
-        self.w.transform = "r"
-
+        self.w = libpysal.weights.Rook.from_shapefile(libpysal.examples.get_path("columbus.shp"))
+        self.w.transform = 'r'
     def test_model(self):
         # Only spatial lag
         reg = SP.GM_Combo(self.y, self.X, w=self.w)
-        e_reduced = np.array([28.18617481])
-        np.testing.assert_allclose(reg.e_pred[0], e_reduced, RTOL)
-        predy_e = np.array([52.28082782])
-        np.testing.assert_allclose(reg.predy_e[0], predy_e, RTOL)
-        betas = np.array(
-            [[57.61123515], [0.73441313], [-0.59459416], [-0.21762921], [0.54732051]]
-        )
-        np.testing.assert_allclose(reg.betas, betas, RTOL)
-        u = np.array([25.57932637])
-        np.testing.assert_allclose(reg.u[0], u, RTOL)
-        e_filtered = np.array([31.65374945])
-        np.testing.assert_allclose(reg.e_filtered[0], e_filtered, RTOL)
-        predy = np.array([54.88767685])
-        np.testing.assert_allclose(reg.predy[0], predy, RTOL)
+        e_reduced = np.array([ 28.18617481])
+        np.testing.assert_allclose(reg.e_pred[0],e_reduced,RTOL)
+        predy_e = np.array([ 52.28082782])
+        np.testing.assert_allclose(reg.predy_e[0],predy_e,RTOL)
+        betas = np.array([[ 57.61123515],[  0.73441313], [ -0.59459416], [ -0.21762921], [  0.54732051]])
+        np.testing.assert_allclose(reg.betas,betas,RTOL)
+        u = np.array([ 25.57932637])
+        np.testing.assert_allclose(reg.u[0],u,RTOL)
+        e_filtered = np.array([ 31.65374945])
+        np.testing.assert_allclose(reg.e_filtered[0],e_filtered,RTOL)
+        predy = np.array([ 54.88767685])
+        np.testing.assert_allclose(reg.predy[0],predy,RTOL)
         n = 49
-        np.testing.assert_allclose(reg.n, n, RTOL)
+        np.testing.assert_allclose(reg.n,n,RTOL)
         k = 4
-        np.testing.assert_allclose(reg.k, k, RTOL)
-        y = np.array([80.467003])
-        np.testing.assert_allclose(reg.y[0], y, RTOL)
-        x = np.array([1.0, 19.531, 15.72598])
-        np.testing.assert_allclose(reg.x.toarray()[0], x, RTOL)
-        yend = np.array([35.4585005])
-        np.testing.assert_allclose(reg.yend[0], yend, RTOL)
-        z = np.array([1.0, 19.531, 15.72598, 35.4585005])
-        np.testing.assert_allclose(reg.z.toarray()[0], z, RTOL)
+        np.testing.assert_allclose(reg.k,k,RTOL)
+        y = np.array([ 80.467003])
+        np.testing.assert_allclose(reg.y[0],y,RTOL)
+        x = np.array([  1.     ,  19.531  ,  15.72598])
+        np.testing.assert_allclose(reg.x.toarray()[0],x,RTOL)
+        yend = np.array([  35.4585005])
+        np.testing.assert_allclose(reg.yend[0],yend,RTOL)
+        z = np.array([  1.       ,  19.531    ,  15.72598  ,  35.4585005])
+        np.testing.assert_allclose(reg.z.toarray()[0],z,RTOL)
         my = 38.43622446938776
-        np.testing.assert_allclose(reg.mean_y, my, RTOL)
+        np.testing.assert_allclose(reg.mean_y,my,RTOL)
         sy = 18.466069465206047
-        np.testing.assert_allclose(reg.std_y, sy, RTOL)
-        vm = np.array([22.85691168, 0.48786563, 0.17914357, 0.46449287])
+        np.testing.assert_allclose(reg.std_y,sy,RTOL)
+        vm = np.array([22.85691168,  0.48786563,  0.17914357,  0.46449287])
         np.testing.assert_allclose(np.sqrt(reg.vm.diagonal()), vm, RTOL)
         sig2 = 181.78650186468832
-        np.testing.assert_allclose(reg.sig2, sig2, RTOL)
+        np.testing.assert_allclose(reg.sig2,sig2,RTOL)
         pr2 = 0.3018280166937799
-        np.testing.assert_allclose(reg.pr2, pr2, RTOL)
+        np.testing.assert_allclose(reg.pr2,pr2,RTOL)
         pr2_e = 0.3561355587000738
-        np.testing.assert_allclose(reg.pr2_e, pr2_e, RTOL)
-        std_err = np.array([22.85692222, 0.48786559, 0.17914356, 0.46449318])
-        np.testing.assert_allclose(reg.std_err, std_err, RTOL)
-        z_stat = np.array(
-            [
-                [2.52051597e00, 1.17182922e-02],
-                [1.50535954e00, 1.32231664e-01],
-                [-3.31909311e00, 9.03103123e-04],
-                [-4.68530506e-01, 6.39405261e-01],
-            ]
-        )
-        np.testing.assert_allclose(reg.z_stat, z_stat, RTOL)
-
+        np.testing.assert_allclose(reg.pr2_e,pr2_e,RTOL)
+        std_err = np.array([ 22.85692222,  0.48786559,  0.17914356,  0.46449318])
+        np.testing.assert_allclose(reg.std_err,std_err,RTOL)
+        z_stat = np.array([[  2.52051597e+00,   1.17182922e-02], [  1.50535954e+00,   1.32231664e-01], [ -3.31909311e+00,   9.03103123e-04], [ -4.68530506e-01,   6.39405261e-01]])
+        np.testing.assert_allclose(reg.z_stat,z_stat,RTOL)
 
-if __name__ == "__main__":
-    start_suppress = np.get_printoptions()["suppress"]
-    np.set_printoptions(suppress=True)
+if __name__ == '__main__':
+    start_suppress = np.get_printoptions()['suppress']
+    np.set_printoptions(suppress=True) 
     unittest.main()
     np.set_printoptions(suppress=start_suppress)
+
diff --git a/spreg/tests/test_twosls_sp_regimes.py b/spreg/tests/test_twosls_sp_regimes.py
index a264a973..f763ac74 100644
--- a/spreg/tests/test_twosls_sp_regimes.py
+++ b/spreg/tests/test_twosls_sp_regimes.py
@@ -6,527 +6,349 @@
 from spreg.twosls_sp import GM_Lag
 from libpysal.common import RTOL
 
-
 class TestGMLag_Regimes(unittest.TestCase):
     def setUp(self):
-        self.w = libpysal.weights.Queen.from_shapefile(
-            libpysal.examples.get_path("columbus.shp")
-        )
-        self.w.transform = "r"
-        self.db = libpysal.io.open(libpysal.examples.get_path("columbus.dbf"), "r")
+        self.w = libpysal.weights.Queen.from_shapefile(libpysal.examples.get_path("columbus.shp"))
+        self.w.transform = 'r'
+        self.db = libpysal.io.open(libpysal.examples.get_path("columbus.dbf"), 'r')
         y = np.array(self.db.by_col("CRIME"))
-        self.y = np.reshape(y, (49, 1))
-        self.r_var = "NSA"
+        self.y = np.reshape(y, (49,1))
+        self.r_var = 'NSA'
         self.regimes = self.db.by_col(self.r_var)
 
     def test___init__(self):
-        # Matches SpaceStat
+        #Matches SpaceStat
         X = []
         X.append(self.db.by_col("INC"))
         X.append(self.db.by_col("HOVAL"))
         self.X = np.array(X).T
-        reg = GM_Lag_Regimes(
-            self.y,
-            self.X,
-            self.regimes,
-            w=self.w,
-            sig2n_k=True,
-            regime_lag_sep=False,
-            regime_err_sep=False,
-        )
-        betas = np.array(
-            [
-                [45.14892906],
-                [-1.42593383],
-                [-0.11501037],
-                [40.99023016],
-                [-0.81498302],
-                [-0.28391409],
-                [0.4736163],
-            ]
-        )
-        np.testing.assert_allclose(reg.betas, betas, RTOL)
-        e_5 = np.array(
-            [[-1.47960519], [-7.93748769], [-5.88561835], [-13.37941105], [5.2524303]]
-        )
-        np.testing.assert_allclose(reg.e_pred[0:5], e_5, RTOL)
-        h_0 = np.array(
-            [[0.0, 0.0, 0.0, 1.0, 19.531, 80.467003, 0.0, 0.0, 18.594, 35.4585005]]
-        )
-        np.testing.assert_allclose(reg.h[0] * np.eye(10), h_0)
+        reg = GM_Lag_Regimes(self.y, self.X, self.regimes, w=self.w, sig2n_k=True, regime_lag_sep=False, regime_err_sep=False) 
+        betas = np.array([[ 45.14892906],
+       [ -1.42593383],
+       [ -0.11501037],
+       [ 40.99023016],
+       [ -0.81498302],
+       [ -0.28391409],
+       [  0.4736163 ]])
+        np.testing.assert_allclose(reg.betas, betas,RTOL)
+        e_5 = np.array([[ -1.47960519],
+       [ -7.93748769],
+       [ -5.88561835],
+       [-13.37941105],
+       [  5.2524303 ]])
+        np.testing.assert_allclose(reg.e_pred[0:5], e_5,RTOL)
+        h_0 = np.array([[  0.       ,   0.       ,   0.       ,   1.       ,  19.531    ,
+         80.467003 ,   0.       ,   0.       ,  18.594    ,  35.4585005]])
+        np.testing.assert_allclose(reg.h[0]*np.eye(10), h_0)
         self.assertEqual(reg.k, 7)
         self.assertEqual(reg.kstar, 1)
-        np.testing.assert_allclose(reg.mean_y, 35.128823897959187, RTOL)
+        np.testing.assert_allclose(reg.mean_y, 35.128823897959187,RTOL)
         self.assertEqual(reg.n, 49)
-        np.testing.assert_allclose(reg.pr2, 0.6572182131915739, RTOL)
-        np.testing.assert_allclose(reg.pr2_e, 0.5779687278635434, RTOL)
-        pfora1a2 = np.array(
-            [
-                -2.15017629,
-                -0.30169328,
-                -0.07603704,
-                -22.06541809,
-                0.45738058,
-                0.02805828,
-                0.39073923,
-            ]
-        )
-        np.testing.assert_allclose(reg.pfora1a2[0], pfora1a2, RTOL)
-        predy_5 = np.array(
-            [[13.93216104], [23.46424269], [34.43510955], [44.32473878], [44.39117516]]
-        )
-        np.testing.assert_allclose(reg.predy[0:5], predy_5, RTOL)
-        predy_e_5 = np.array(
-            [[17.20558519], [26.73924169], [36.51239935], [45.76717105], [45.4790797]]
-        )
-        np.testing.assert_allclose(reg.predy_e[0:5], predy_e_5, RTOL)
-        q_5 = np.array([[0.0, 0.0, 18.594, 35.4585005]])
-        np.testing.assert_allclose(reg.q[0] * np.eye(4), q_5, RTOL)
-        self.assertEqual(reg.robust, "unadjusted")
-        np.testing.assert_allclose(reg.sig2n_k, 109.76462904625834, RTOL)
-        np.testing.assert_allclose(reg.sig2n, 94.08396775393571, RTOL)
-        np.testing.assert_allclose(reg.sig2, 109.76462904625834, RTOL)
-        np.testing.assert_allclose(reg.std_y, 16.732092091229699, RTOL)
-        u_5 = np.array(
-            [[1.79381896], [-4.66248869], [-3.80832855], [-11.93697878], [6.34033484]]
-        )
-        np.testing.assert_allclose(reg.u[0:5], u_5, RTOL)
-        np.testing.assert_allclose(reg.utu, 4610.11441994285, RTOL)
-        varb = np.array(
-            [
-                1.23841820e00,
-                -3.65620114e-02,
-                -1.21919663e-03,
-                1.00057547e00,
-                -2.07403182e-02,
-                -1.27232693e-03,
-                -1.77184084e-02,
-            ]
-        )
-        np.testing.assert_allclose(reg.varb[0], varb, RTOL)
-        vm = np.array(
-            [
-                1.35934514e02,
-                -4.01321561e00,
-                -1.33824666e-01,
-                1.09827796e02,
-                -2.27655334e00,
-                -1.39656494e-01,
-                -1.94485452e00,
-            ]
-        )
-        np.testing.assert_allclose(reg.vm[0], vm, RTOL)
-        x_0 = np.array([[0.0, 0.0, 0.0, 1.0, 19.531, 80.467003]])
-        np.testing.assert_allclose(reg.x[0] * np.eye(6), x_0, RTOL)
-        y_5 = np.array([[15.72598], [18.801754], [30.626781], [32.38776], [50.73151]])
-        np.testing.assert_allclose(reg.y[0:5], y_5, RTOL)
-        yend_5 = np.array(
-            [[24.7142675], [26.24684033], [29.411751], [34.64647575], [40.4653275]]
-        )
-        np.testing.assert_allclose(reg.yend[0:5] * np.array([[1]]), yend_5, RTOL)
-        z_0 = np.array([[0.0, 0.0, 0.0, 1.0, 19.531, 80.467003, 24.7142675]])
-        np.testing.assert_allclose(reg.z[0] * np.eye(7), z_0, RTOL)
-        zthhthi = np.array(
-            [
-                1.00000000e00,
-                -2.35922393e-16,
-                5.55111512e-17,
-                0.00000000e00,
-                0.00000000e00,
-                0.00000000e00,
-                -4.44089210e-16,
-                2.22044605e-16,
-                0.00000000e00,
-                0.00000000e00,
-            ]
-        )
+        np.testing.assert_allclose(reg.pr2, 0.6572182131915739,RTOL)
+        np.testing.assert_allclose(reg.pr2_e, 0.5779687278635434,RTOL)
+        pfora1a2 = np.array([ -2.15017629,  -0.30169328,  -0.07603704, -22.06541809,
+         0.45738058,   0.02805828,   0.39073923]) 
+        np.testing.assert_allclose(reg.pfora1a2[0], pfora1a2,RTOL)
+        predy_5 = np.array([[ 13.93216104],
+       [ 23.46424269],
+       [ 34.43510955],
+       [ 44.32473878],
+       [ 44.39117516]])
+        np.testing.assert_allclose(reg.predy[0:5], predy_5,RTOL)
+        predy_e_5 = np.array([[ 17.20558519],
+       [ 26.73924169],
+       [ 36.51239935],
+       [ 45.76717105],
+       [ 45.4790797 ]])
+        np.testing.assert_allclose(reg.predy_e[0:5], predy_e_5,RTOL)
+        q_5 = np.array([[  0.       ,   0.       ,  18.594    ,  35.4585005]])
+        np.testing.assert_allclose(reg.q[0]*np.eye(4), q_5, RTOL)
+        self.assertEqual(reg.robust, 'unadjusted')
+        np.testing.assert_allclose(reg.sig2n_k, 109.76462904625834,RTOL)
+        np.testing.assert_allclose(reg.sig2n, 94.08396775393571,RTOL)
+        np.testing.assert_allclose(reg.sig2, 109.76462904625834,RTOL)
+        np.testing.assert_allclose(reg.std_y, 16.732092091229699,RTOL)
+        u_5 = np.array([[  1.79381896],
+       [ -4.66248869],
+       [ -3.80832855],
+       [-11.93697878],
+       [  6.34033484]])
+        np.testing.assert_allclose(reg.u[0:5], u_5,RTOL)
+        np.testing.assert_allclose(reg.utu, 4610.11441994285,RTOL)
+        varb = np.array([  1.23841820e+00,  -3.65620114e-02,  -1.21919663e-03,
+         1.00057547e+00,  -2.07403182e-02,  -1.27232693e-03,
+        -1.77184084e-02])
+        np.testing.assert_allclose(reg.varb[0], varb,RTOL)
+        vm = np.array([  1.35934514e+02,  -4.01321561e+00,  -1.33824666e-01,
+         1.09827796e+02,  -2.27655334e+00,  -1.39656494e-01,
+        -1.94485452e+00])
+        np.testing.assert_allclose(reg.vm[0], vm,RTOL)
+        x_0 = np.array([[  0.      ,   0.      ,   0.      ,   1.      ,  19.531   ,
+         80.467003]])
+        np.testing.assert_allclose(reg.x[0]*np.eye(6), x_0,RTOL)
+        y_5 = np.array([[ 15.72598 ],
+       [ 18.801754],
+       [ 30.626781],
+       [ 32.38776 ],
+       [ 50.73151 ]])
+        np.testing.assert_allclose(reg.y[0:5], y_5,RTOL)
+        yend_5 = np.array([[ 24.7142675 ],
+       [ 26.24684033],
+       [ 29.411751  ],
+       [ 34.64647575],
+       [ 40.4653275 ]])
+        np.testing.assert_allclose(reg.yend[0:5]*np.array([[1]]), yend_5,RTOL)
+        z_0 = np.array([[  0.       ,   0.       ,   0.       ,   1.       ,  19.531    ,
+         80.467003 ,  24.7142675]]) 
+        np.testing.assert_allclose(reg.z[0]*np.eye(7), z_0,RTOL)
+        zthhthi = np.array([  1.00000000e+00,  -2.35922393e-16,   5.55111512e-17,
+         0.00000000e+00,   0.00000000e+00,   0.00000000e+00,
+        -4.44089210e-16,   2.22044605e-16,   0.00000000e+00,
+         0.00000000e+00])
         # np.testing.assert_allclose(reg.zthhthi[0], zthhthi, RTOL)
         np.testing.assert_array_almost_equal(reg.zthhthi[0], zthhthi)
-        chow_regi = np.array(
-            [[0.19692667, 0.65721307], [0.5666492, 0.45159351], [0.45282066, 0.5009985]]
-        )
-        np.testing.assert_allclose(reg.chow.regi, chow_regi, RTOL)
-        np.testing.assert_allclose(reg.chow.joint[0], 0.82409867601863462, RTOL)
-
+        chow_regi = np.array([[ 0.19692667,  0.65721307],
+       [ 0.5666492 ,  0.45159351],
+       [ 0.45282066,  0.5009985 ]])
+        np.testing.assert_allclose(reg.chow.regi, chow_regi,RTOL)
+        np.testing.assert_allclose(reg.chow.joint[0], 0.82409867601863462,RTOL)
+    
     def test_init_discbd(self):
-        # Matches SpaceStat.
+        #Matches SpaceStat.
         X = np.array(self.db.by_col("INC"))
-        X = np.reshape(X, (49, 1))
+        X = np.reshape(X, (49,1))
         yd = np.array(self.db.by_col("HOVAL"))
-        yd = np.reshape(yd, (49, 1))
+        yd = np.reshape(yd, (49,1))
         q = np.array(self.db.by_col("DISCBD"))
-        q = np.reshape(q, (49, 1))
-        reg = GM_Lag_Regimes(
-            self.y,
-            X,
-            self.regimes,
-            yend=yd,
-            q=q,
-            lag_q=False,
-            w=self.w,
-            sig2n_k=True,
-            regime_lag_sep=False,
-            regime_err_sep=False,
-        )
-        tbetas = np.array(
-            [
-                [42.7266306],
-                [-0.15552345],
-                [37.70545276],
-                [-0.5341577],
-                [-0.68305796],
-                [-0.37106077],
-                [0.55809516],
-            ]
-        )
-        np.testing.assert_allclose(tbetas, reg.betas, RTOL)
-        vm = np.array(
-            [
-                270.62979422,
-                3.62539081,
-                327.89638627,
-                6.24949355,
-                -5.25333106,
-                -6.01743515,
-                -4.19290074,
-            ]
-        )
-        np.testing.assert_allclose(reg.vm[0], vm, RTOL)
-        e_3 = np.array([[-0.33142796], [-9.51719607], [-7.86272153]])
-        np.testing.assert_allclose(reg.e_pred[0:3], e_3, RTOL)
-        u_3 = np.array([[4.51839601], [-5.67363147], [-5.1927562]])
-        np.testing.assert_allclose(reg.u[0:3], u_3, RTOL)
-        predy_3 = np.array([[11.20758399], [24.47538547], [35.8195372]])
-        np.testing.assert_allclose(reg.predy[0:3], predy_3, RTOL)
-        predy_e_3 = np.array([[16.05740796], [28.31895007], [38.48950253]])
-        np.testing.assert_allclose(reg.predy_e[0:3], predy_e_3, RTOL)
-        chow_regi = np.array(
-            [
-                [0.13130991, 0.71707772],
-                [0.04740966, 0.82763357],
-                [0.15474413, 0.6940423],
-            ]
-        )
-        np.testing.assert_allclose(reg.chow.regi, chow_regi, RTOL)
-        np.testing.assert_allclose(reg.chow.joint[0], 0.31248100032096549, RTOL)
-
+        q = np.reshape(q, (49,1))
+        reg = GM_Lag_Regimes(self.y, X, self.regimes, yend=yd, q=q, lag_q=False, w=self.w, sig2n_k=True, regime_lag_sep=False, regime_err_sep=False) 
+        tbetas = np.array([[ 42.7266306 ],
+       [ -0.15552345],
+       [ 37.70545276],
+       [ -0.5341577 ],
+       [ -0.68305796],
+       [ -0.37106077],
+       [  0.55809516]])
+        np.testing.assert_allclose(tbetas, reg.betas,RTOL)
+        vm = np.array([ 270.62979422,    3.62539081,  327.89638627,    6.24949355,
+         -5.25333106,   -6.01743515,   -4.19290074])
+        np.testing.assert_allclose(reg.vm[0], vm,RTOL)
+        e_3 = np.array([[-0.33142796],
+       [-9.51719607],
+       [-7.86272153]])
+        np.testing.assert_allclose(reg.e_pred[0:3], e_3,RTOL)
+        u_3 = np.array([[ 4.51839601],
+       [-5.67363147],
+       [-5.1927562 ]])
+        np.testing.assert_allclose(reg.u[0:3], u_3,RTOL)
+        predy_3 = np.array([[ 11.20758399],
+       [ 24.47538547],
+       [ 35.8195372 ]])
+        np.testing.assert_allclose(reg.predy[0:3], predy_3,RTOL)
+        predy_e_3 = np.array([[ 16.05740796],
+       [ 28.31895007],
+       [ 38.48950253]])
+        np.testing.assert_allclose(reg.predy_e[0:3], predy_e_3,RTOL)
+        chow_regi = np.array([[ 0.13130991,  0.71707772],
+       [ 0.04740966,  0.82763357],
+       [ 0.15474413,  0.6940423 ]])
+        np.testing.assert_allclose(reg.chow.regi, chow_regi,RTOL)
+        np.testing.assert_allclose(reg.chow.joint[0], 0.31248100032096549,RTOL)
+    
     def test_lag_q(self):
         X = np.array(self.db.by_col("INC"))
-        X = np.reshape(X, (49, 1))
+        X = np.reshape(X, (49,1))
         yd = np.array(self.db.by_col("HOVAL"))
-        yd = np.reshape(yd, (49, 1))
+        yd = np.reshape(yd, (49,1))
         q = np.array(self.db.by_col("DISCBD"))
-        q = np.reshape(q, (49, 1))
-        reg = GM_Lag_Regimes(
-            self.y,
-            X,
-            self.regimes,
-            yend=yd,
-            q=q,
-            w=self.w,
-            sig2n_k=True,
-            regime_lag_sep=False,
-            regime_err_sep=False,
-        )
-        tbetas = np.array(
-            [
-                [37.87698329],
-                [-0.89426982],
-                [31.4714777],
-                [-0.71640525],
-                [-0.28494432],
-                [-0.2294271],
-                [0.62996544],
-            ]
-        )
-        np.testing.assert_allclose(tbetas, reg.betas, RTOL)
-        vm = np.array(
-            [
-                128.25714554,
-                -0.38975354,
-                95.7271044,
-                -1.8429218,
-                -1.75331978,
-                -0.18240338,
-                -1.67767464,
-            ]
-        )
-        np.testing.assert_allclose(reg.vm[0], vm, RTOL)
-        chow_regi = np.array(
-            [
-                [0.43494049, 0.50957463],
-                [0.02089281, 0.88507135],
-                [0.01180501, 0.91347943],
-            ]
-        )
-        np.testing.assert_allclose(reg.chow.regi, chow_regi, RTOL)
-        np.testing.assert_allclose(reg.chow.joint[0], 0.54288190938307757, RTOL)
-
+        q = np.reshape(q, (49,1))
+        reg = GM_Lag_Regimes(self.y, X, self.regimes, yend=yd, q=q, w=self.w, sig2n_k=True, regime_lag_sep=False, regime_err_sep=False) 
+        tbetas = np.array([[ 37.87698329],
+       [ -0.89426982],
+       [ 31.4714777 ],
+       [ -0.71640525],
+       [ -0.28494432],
+       [ -0.2294271 ],
+       [  0.62996544]])
+        np.testing.assert_allclose(tbetas, reg.betas,RTOL)
+        vm = np.array([ 128.25714554,   -0.38975354,   95.7271044 ,   -1.8429218 ,
+         -1.75331978,   -0.18240338,   -1.67767464])
+        np.testing.assert_allclose(reg.vm[0], vm,RTOL)
+        chow_regi = np.array([[ 0.43494049,  0.50957463],
+       [ 0.02089281,  0.88507135],
+       [ 0.01180501,  0.91347943]])
+        np.testing.assert_allclose(reg.chow.regi, chow_regi,RTOL)
+        np.testing.assert_allclose(reg.chow.joint[0], 0.54288190938307757,RTOL)
+    
     def test_all_regi(self):
         X = np.array(self.db.by_col("INC"))
-        X = np.reshape(X, (49, 1))
+        X = np.reshape(X, (49,1))
         yd = np.array(self.db.by_col("HOVAL"))
-        yd = np.reshape(yd, (49, 1))
+        yd = np.reshape(yd, (49,1))
         q = np.array(self.db.by_col("DISCBD"))
-        q = np.reshape(q, (49, 1))
-        reg = GM_Lag_Regimes(
-            self.y,
-            X,
-            self.regimes,
-            yend=yd,
-            q=q,
-            w=self.w,
-            regime_lag_sep=False,
-            regime_err_sep=True,
-        )
-        tbetas = np.array(
-            [
-                [
-                    37.87698329,
-                    -0.89426982,
-                    31.4714777,
-                    -0.71640525,
-                    -0.28494432,
-                    -0.2294271,
-                    0.62996544,
-                ]
-            ]
-        )
-        np.testing.assert_allclose(tbetas, reg.betas.T, RTOL)
-        vm = np.array(
-            [
-                70.38291551,
-                -0.64868787,
-                49.25453215,
-                -0.62851534,
-                -0.75413453,
-                -0.12674433,
-                -0.97179236,
-            ]
-        )
-        np.testing.assert_allclose(reg.vm[0], vm, RTOL)
-        e_3 = np.array([[-2.66997799], [-7.69786264], [-4.39412782]])
-        np.testing.assert_allclose(reg.e_pred[0:3], e_3, RTOL)
-        u_3 = np.array([[1.13879007], [-3.76873198], [-1.89671717]])
-        np.testing.assert_allclose(reg.u[0:3], u_3, RTOL)
-        predy_3 = np.array([[14.58718993], [22.57048598], [32.52349817]])
-        np.testing.assert_allclose(reg.predy[0:3], predy_3, RTOL)
-        predy_e_3 = np.array([[18.39595799], [26.49961664], [35.02090882]])
-        np.testing.assert_allclose(reg.predy_e[0:3], predy_e_3, RTOL)
-        chow_regi = np.array(
-            [
-                [0.60091096, 0.43823066],
-                [0.03006744, 0.8623373],
-                [0.01943727, 0.88912016],
-            ]
-        )
-        np.testing.assert_allclose(reg.chow.regi, chow_regi, RTOL)
-        np.testing.assert_allclose(reg.chow.joint[0], 0.88634854058300516, RTOL)
-
+        q = np.reshape(q, (49,1))
+        reg = GM_Lag_Regimes(self.y, X, self.regimes, yend=yd, q=q, w=self.w, regime_lag_sep=False, regime_err_sep=False) 
+        tbetas = np.array([[ 37.87698329,  -0.89426982,  31.4714777 ,  -0.71640525,
+         -0.28494432,  -0.2294271 ,   0.62996544]])
+        np.testing.assert_allclose(tbetas, reg.betas.T,RTOL)
+        vm = np.array([109.934696,  -0.334074,  82.051804,  -1.579647,  -1.502846,
+        -0.156346,  -1.438007])
+        np.testing.assert_allclose(reg.vm[0], vm,RTOL)
+        e_3 = np.array([[-2.66997799],
+       [-7.69786264],
+       [-4.39412782]])
+        np.testing.assert_allclose(reg.e_pred[0:3], e_3,RTOL)
+        u_3 = np.array([[ 1.13879007],
+       [-3.76873198],
+       [-1.89671717]])
+        np.testing.assert_allclose(reg.u[0:3], u_3,RTOL)
+        predy_3 = np.array([[ 14.58718993],
+       [ 22.57048598],
+       [ 32.52349817]])
+        np.testing.assert_allclose(reg.predy[0:3], predy_3,RTOL)
+        predy_e_3 = np.array([[ 18.39595799],
+       [ 26.49961664],
+       [ 35.02090882]])
+        np.testing.assert_allclose(reg.predy_e[0:3], predy_e_3,RTOL)
+        chow_regi = np.array([[0.507431, 0.476253],
+       [0.024375, 0.875935],
+       [0.013773, 0.906578]])
+        np.testing.assert_allclose(reg.chow.regi, chow_regi,1e-04)
+        np.testing.assert_allclose(reg.chow.joint[0], 0.633362,RTOL)
+    
     def test_all_regi_sig2(self):
-        # Artficial:
+        #Artficial:
         n = 256
-        x1 = np.random.uniform(-10, 10, (n, 1))
-        x2 = np.random.uniform(1, 5, (n, 1))
-        q = x2 + np.random.normal(0, 1, (n, 1))
-        x = np.hstack((x1, x2))
-        y = np.dot(
-            np.hstack((np.ones((n, 1)), x)), np.array([[1], [0.5], [2]])
-        ) + np.random.normal(0, 1, (n, 1))
+        x1 = np.random.uniform(-10,10,(n,1))
+        x2 = np.random.uniform(1,5,(n,1))
+        q = x2 + np.random.normal(0,1,(n,1))
+        x = np.hstack((x1,x2))
+        y = np.dot(np.hstack((np.ones((n,1)),x)),np.array([[1],[0.5],[2]])) + np.random.normal(0,1,(n,1))
         latt = int(np.sqrt(n))
-        w = libpysal.weights.util.lat2W(latt, latt)
-        w.transform = "r"
-        regi = [0] * (n // 2) + [1] * (n // 2)
-        model = GM_Lag_Regimes(
-            y, x1, regi, q=q, yend=x2, w=w, regime_lag_sep=True, regime_err_sep=True
-        )
-        w1 = libpysal.weights.util.lat2W(latt // 2, latt)
-        w1.transform = "r"
-        model1 = GM_Lag(
-            y[0 : (n // 2)].reshape((n // 2), 1),
-            x1[0 : (n // 2)],
-            yend=x2[0 : (n // 2)],
-            q=q[0 : (n // 2)],
-            w=w1,
-        )
-        model2 = GM_Lag(
-            y[(n // 2) : n].reshape((n // 2), 1),
-            x1[(n // 2) : n],
-            yend=x2[(n // 2) : n],
-            q=q[(n // 2) : n],
-            w=w1,
-        )
+        w = libpysal.weights.util.lat2W(latt,latt)
+        w.transform='r'
+        regi = [0]*(n//2) + [1]*(n//2)
+        model = GM_Lag_Regimes(y, x1, regi, q=q, yend=x2, w=w, regime_lag_sep=True, regime_err_sep=True)
+        w1 = libpysal.weights.util.lat2W(latt//2,latt)
+        w1.transform='r'
+        model1 = GM_Lag(y[0:(n//2)].reshape((n//2),1), x1[0:(n//2)],yend=x2[0:(n//2)], q=q[0:(n//2)], w=w1)
+        model2 = GM_Lag(y[(n//2):n].reshape((n//2),1), x1[(n//2):n],yend=x2[(n//2):n], q=q[(n//2):n], w=w1)
         tbetas = np.vstack((model1.betas, model2.betas))
-        np.testing.assert_allclose(model.betas, tbetas)
-        vm = np.hstack((model1.vm.diagonal(), model2.vm.diagonal()))
-        np.testing.assert_allclose(model.vm.diagonal(), vm, RTOL)
-        # Columbus:
+        np.testing.assert_allclose(model.betas,tbetas)
+        vm = np.hstack((model1.vm.diagonal(),model2.vm.diagonal()))
+        np.testing.assert_allclose(model.vm.diagonal(), vm,RTOL)
+        #Columbus:
         X = np.array(self.db.by_col("INC"))
-        X = np.reshape(X, (49, 1))
+        X = np.reshape(X, (49,1))
         yd = np.array(self.db.by_col("HOVAL"))
-        yd = np.reshape(yd, (49, 1))
+        yd = np.reshape(yd, (49,1))
         q = np.array(self.db.by_col("DISCBD"))
-        q = np.reshape(q, (49, 1))
-        reg = GM_Lag_Regimes(
-            self.y,
-            X,
-            self.regimes,
-            yend=yd,
-            q=q,
-            w=self.w,
-            regime_lag_sep=True,
-            regime_err_sep=True,
-        )
-        tbetas = np.array(
-            [
-                [42.35827477],
-                [-0.09472413],
-                [-0.68794223],
-                [0.54482537],
-                [32.24228762],
-                [-0.12304063],
-                [-0.46840307],
-                [0.67108156],
-            ]
-        )
+        q = np.reshape(q, (49,1))
+        reg = GM_Lag_Regimes(self.y, X, self.regimes, yend=yd, q=q, w=self.w,regime_lag_sep=True, regime_err_sep = True) 
+        tbetas = np.array([[ 42.35827477],
+       [ -0.09472413],
+       [ -0.68794223],
+       [  0.54482537],
+       [ 32.24228762],
+       [ -0.12304063],
+       [ -0.46840307],
+       [  0.67108156]])
         np.testing.assert_allclose(tbetas, reg.betas)
-        vm = np.array(
-            [200.92894859, 4.56244927, -4.85603079, -2.9755413, 0.0, 0.0, 0.0, 0.0]
-        )
-        np.testing.assert_allclose(reg.vm[0], vm, RTOL)
-        e_3 = np.array([[-1.32209547], [-13.15611199], [-11.62357696]])
-        np.testing.assert_allclose(reg.e_pred[0:3], e_3, RTOL)
-        u_3 = np.array([[6.99250069], [-7.5665856], [-7.04753328]])
-        np.testing.assert_allclose(reg.u[0:3], u_3, RTOL)
-        predy_3 = np.array([[8.73347931], [26.3683396], [37.67431428]])
-        np.testing.assert_allclose(reg.predy[0:3], predy_3, RTOL)
-        predy_e_3 = np.array([[17.04807547], [31.95786599], [42.25035796]])
-        np.testing.assert_allclose(reg.predy_e[0:3], predy_e_3, RTOL)
-        chow_regi = np.array(
-            [
-                [1.51825373e-01, 6.96797034e-01],
-                [3.20105698e-04, 9.85725412e-01],
-                [8.58836996e-02, 7.69476896e-01],
-                [1.01357290e-01, 7.50206873e-01],
-            ]
-        )
-        np.testing.assert_allclose(reg.chow.regi, chow_regi, RTOL)
-        np.testing.assert_allclose(reg.chow.joint[0], 0.38417230022512161, RTOL)
+        vm = np.array([ 200.92894859,    4.56244927,   -4.85603079,   -2.9755413 ,
+          0.        ,    0.        ,    0.        ,    0.        ])
+        np.testing.assert_allclose(reg.vm[0], vm,RTOL)
+        e_3 = np.array([[ -1.32209547],
+       [-13.15611199],
+       [-11.62357696]])
+        np.testing.assert_allclose(reg.e_pred[0:3], e_3,RTOL)
+        u_3 = np.array([[ 6.99250069],
+       [-7.5665856 ],
+       [-7.04753328]])
+        np.testing.assert_allclose(reg.u[0:3], u_3,RTOL)
+        predy_3 = np.array([[  8.73347931],
+       [ 26.3683396 ],
+       [ 37.67431428]])
+        np.testing.assert_allclose(reg.predy[0:3], predy_3,RTOL)
+        predy_e_3 = np.array([[ 17.04807547],
+       [ 31.95786599],
+       [ 42.25035796]])
+        np.testing.assert_allclose(reg.predy_e[0:3], predy_e_3,RTOL)
+        chow_regi = np.array([[  1.51825373e-01,   6.96797034e-01],
+       [  3.20105698e-04,   9.85725412e-01],
+       [  8.58836996e-02,   7.69476896e-01],
+       [  1.01357290e-01,   7.50206873e-01]])
+        np.testing.assert_allclose(reg.chow.regi, chow_regi,RTOL)
+        np.testing.assert_allclose(reg.chow.joint[0], 0.38417230022512161,RTOL)
 
     def test_fixed_const(self):
         X = np.array(self.db.by_col("INC"))
-        X = np.reshape(X, (49, 1))
+        X = np.reshape(X, (49,1))
         yd = np.array(self.db.by_col("HOVAL"))
-        yd = np.reshape(yd, (49, 1))
+        yd = np.reshape(yd, (49,1))
         q = np.array(self.db.by_col("DISCBD"))
-        q = np.reshape(q, (49, 1))
-        reg = GM_Lag_Regimes(
-            self.y,
-            X,
-            self.regimes,
-            yend=yd,
-            q=q,
-            w=self.w,
-            constant_regi="one",
-            regime_lag_sep=False,
-            regime_err_sep=False,
-        )
-        tbetas = np.array(
-            [
-                [-0.37658823],
-                [-0.9666079],
-                [35.5445944],
-                [-0.45793559],
-                [-0.24216904],
-                [0.62500602],
-            ]
-        )
-        np.testing.assert_allclose(tbetas, reg.betas, RTOL)
-        vm = np.array(
-            [1.4183697, -0.05975784, -0.27161863, -0.62517245, 0.02266177, 0.00312976]
-        )
-        np.testing.assert_allclose(reg.vm[0], vm, RTOL)
-        e_3 = np.array([[0.17317815], [-5.53766328], [-3.82889307]])
-        np.testing.assert_allclose(reg.e_pred[0:3], e_3, RTOL)
-        u_3 = np.array([[3.10025518], [-1.83150689], [-1.49598494]])
-        np.testing.assert_allclose(reg.u[0:3], u_3, RTOL)
-        predy_3 = np.array([[12.62572482], [20.63326089], [32.12276594]])
-        np.testing.assert_allclose(reg.predy[0:3], predy_3, RTOL)
-        predy_e_3 = np.array([[15.55280185], [24.33941728], [34.45567407]])
-        np.testing.assert_allclose(reg.predy_e[0:3], predy_e_3, RTOL)
-        chow_regi = np.array(
-            [[1.85767047e-01, 6.66463269e-01], [1.19445012e01, 5.48089036e-04]]
-        )
-        np.testing.assert_allclose(reg.chow.regi, chow_regi, RTOL)
-        np.testing.assert_allclose(reg.chow.joint[0], 12.017256217621382, RTOL)
+        q = np.reshape(q, (49,1))
+        reg = GM_Lag_Regimes(self.y, X, self.regimes, yend=yd, q=q, w=self.w, constant_regi='one', regime_lag_sep=False, regime_err_sep=False) 
+        tbetas = np.array([[ -0.37658823],
+       [ -0.9666079 ],
+       [ 35.5445944 ],
+       [ -0.45793559],
+       [ -0.24216904],
+       [  0.62500602]])
+        np.testing.assert_allclose(tbetas, reg.betas,RTOL)
+        vm = np.array([ 1.4183697 , -0.05975784, -0.27161863, -0.62517245,  0.02266177,
+        0.00312976])
+        np.testing.assert_allclose(reg.vm[0], vm,RTOL)
+        e_3 = np.array([[ 0.17317815],
+       [-5.53766328],
+       [-3.82889307]])
+        np.testing.assert_allclose(reg.e_pred[0:3], e_3,RTOL)
+        u_3 = np.array([[ 3.10025518],
+       [-1.83150689],
+       [-1.49598494]])
+        np.testing.assert_allclose(reg.u[0:3], u_3,RTOL)
+        predy_3 = np.array([[ 12.62572482],
+       [ 20.63326089],
+       [ 32.12276594]])
+        np.testing.assert_allclose(reg.predy[0:3], predy_3,RTOL)
+        predy_e_3 = np.array([[ 15.55280185],
+       [ 24.33941728],
+       [ 34.45567407]])
+        np.testing.assert_allclose(reg.predy_e[0:3], predy_e_3,RTOL)
+        chow_regi = np.array([[  1.85767047e-01,   6.66463269e-01],
+       [  1.19445012e+01,   5.48089036e-04]])
+        np.testing.assert_allclose(reg.chow.regi, chow_regi,RTOL)
+        np.testing.assert_allclose(reg.chow.joint[0], 12.017256217621382,RTOL)
 
     def test_names(self):
-        y_var = "CRIME"
-        x_var = ["INC"]
+        y_var = 'CRIME'
+        x_var = ['INC']
         x = np.array([self.db.by_col(name) for name in x_var]).T
-        yd_var = ["HOVAL"]
+        yd_var = ['HOVAL']
         yd = np.array([self.db.by_col(name) for name in yd_var]).T
-        q_var = ["DISCBD"]
+        q_var = ['DISCBD']
         q = np.array([self.db.by_col(name) for name in q_var]).T
-        r_var = "NSA"
-        reg = GM_Lag_Regimes(
-            self.y,
-            x,
-            self.regimes,
-            yend=yd,
-            q=q,
-            w=self.w,
-            name_y=y_var,
-            name_x=x_var,
-            name_yend=yd_var,
-            name_q=q_var,
-            name_regimes=r_var,
-            name_ds="columbus",
-            name_w="columbus.gal",
-            regime_lag_sep=False,
-            regime_err_sep=False,
-        )
-        betas = np.array(
-            [
-                [37.87698329],
-                [-0.89426982],
-                [31.4714777],
-                [-0.71640525],
-                [-0.28494432],
-                [-0.2294271],
-                [0.62996544],
-            ]
-        )
-        np.testing.assert_allclose(reg.betas, betas, RTOL)
-        vm = np.array(
-            [
-                109.93469618,
-                -0.33407447,
-                82.05180377,
-                -1.57964725,
-                -1.50284553,
-                -0.15634575,
-                -1.43800683,
-            ]
-        )
-        np.testing.assert_allclose(reg.vm[0], vm, RTOL)
-        chow_regi = np.array(
-            [
-                [0.50743058, 0.47625326],
-                [0.02437494, 0.87593468],
-                [0.01377251, 0.9065777],
-            ]
-        )
-        np.testing.assert_allclose(reg.chow.regi, chow_regi, RTOL)
-        np.testing.assert_allclose(reg.chow.joint[0], 0.63336222761359162, RTOL)
-        self.assertListEqual(reg.name_x, ["0_CONSTANT", "0_INC", "1_CONSTANT", "1_INC"])
-        self.assertListEqual(reg.name_yend, ["0_HOVAL", "1_HOVAL", "_Global_W_CRIME"])
-        self.assertListEqual(
-            reg.name_q,
-            ["0_DISCBD", "0_W_INC", "0_W_DISCBD", "1_DISCBD", "1_W_INC", "1_W_DISCBD"],
-        )
+        r_var = 'NSA'
+        reg = GM_Lag_Regimes(self.y, x, self.regimes, yend=yd, q=q, w=self.w, name_y=y_var, name_x=x_var, name_yend=yd_var, name_q=q_var, name_regimes=r_var, name_ds='columbus', name_w='columbus.gal', regime_lag_sep=False, regime_err_sep=False)
+        betas = np.array([[ 37.87698329],
+       [ -0.89426982],
+       [ 31.4714777 ],
+       [ -0.71640525],
+       [ -0.28494432],
+       [ -0.2294271 ],
+       [  0.62996544]])
+        np.testing.assert_allclose(reg.betas, betas,RTOL)
+        vm = np.array([ 109.93469618,   -0.33407447,   82.05180377,   -1.57964725,
+         -1.50284553,   -0.15634575,   -1.43800683])
+        np.testing.assert_allclose(reg.vm[0], vm,RTOL)
+        chow_regi = np.array([[ 0.50743058,  0.47625326],
+       [ 0.02437494,  0.87593468],
+       [ 0.01377251,  0.9065777 ]])
+        np.testing.assert_allclose(reg.chow.regi, chow_regi,RTOL)
+        np.testing.assert_allclose(reg.chow.joint[0], 0.63336222761359162,RTOL)
+        self.assertListEqual(reg.name_x, ['0_CONSTANT', '0_INC', '1_CONSTANT', '1_INC'])
+        self.assertListEqual(reg.name_yend, ['0_HOVAL', '1_HOVAL', '_Global_W_CRIME'])
+        self.assertListEqual(reg.name_q, ['0_DISCBD', '0_W_INC', '0_W_DISCBD', '1_DISCBD', '1_W_INC', '1_W_DISCBD'])
         self.assertEqual(reg.name_y, y_var)
 
-
-if __name__ == "__main__":
+if __name__ == '__main__':
     unittest.main()

From 1af9b1ff24828adf85a9bd91726911680b5bac78 Mon Sep 17 00:00:00 2001
From: Pedro Amaral <pedrovma@gmail.com>
Date: Wed, 13 Sep 2023 17:34:39 -0300
Subject: [PATCH 17/28] Attempt at fixing the SUR-related failures due to
 deprecation

---
 spreg/diagnostics_sur.py |  2 +-
 spreg/sur_utils.py       | 21 ++++++++++++++-------
 2 files changed, 15 insertions(+), 8 deletions(-)

diff --git a/spreg/diagnostics_sur.py b/spreg/diagnostics_sur.py
index 8e145b54..7fdfdb1d 100644
--- a/spreg/diagnostics_sur.py
+++ b/spreg/diagnostics_sur.py
@@ -221,7 +221,7 @@ def surLMlag(n_eq, WS, bigy, bigX, bigE, bigYP, sig, varb):
 
     """
     # Score
-    Y = np.hstack((bigy[r]) for r in range(n_eq))
+    Y = np.hstack([bigy[r] for r in range(n_eq)])
     WY = WS * Y
     EWY = np.dot(bigE.T, WY)
     sigi = la.inv(sig)
diff --git a/spreg/sur_utils.py b/spreg/sur_utils.py
index 50dc7341..bb78d3c8 100644
--- a/spreg/sur_utils.py
+++ b/spreg/sur_utils.py
@@ -290,7 +290,9 @@ def sur_dict2mat(dicts):
 
     """
     n_dicts = len(dicts.keys())
-    mat = np.vstack((dicts[t] for t in range(n_dicts)))
+    #mat = np.vstack((dicts[t] for t in range(n_dicts)))
+    mat = np.vstack([dicts[t] for t in range(n_dicts)])
+
     return mat
 
 
@@ -392,10 +394,11 @@ def sur_est(bigXX, bigXy, bigE, bigK):
         for t in range(n_eq):
             sxy = sxy + sigi[r, t] * bigXy[(r, t)]
         sigiXy[r] = sxy
-    xsigy = np.vstack((sigiXy[t] for t in range(n_eq)))
-    xsigx = np.vstack(
-        ((np.hstack(sigiXX[(r, t)] for t in range(n_eq))) for r in range(n_eq))
-    )
+    #xsigy = np.vstack((sigiXy[t] for t in range(n_eq)))
+    xsigy = np.vstack(tuple(sigiXy[t] for t in range(n_eq)))
+    #xsigx = np.vstack(((np.hstack(sigiXX[(r, t)] for t in range(n_eq))) for r in range(n_eq)))
+    array_lists = [[sigiXX[(r, t)] for t in range(n_eq)] for r in range(n_eq)]
+    xsigx = np.vstack([np.hstack(arr_list) for arr_list in array_lists])    
     varb = la.inv(xsigx)
     beta = np.dot(varb, xsigy)
     bSUR = sur_mat2dict(beta, bigK)
@@ -423,7 +426,9 @@ def sur_resids(bigy, bigX, beta):
 
     """
     n_eq = len(bigy.keys())
-    bigE = np.hstack((bigy[r] - spdot(bigX[r], beta[r])) for r in range(n_eq))
+    #bigE = np.hstack((bigy[r] - spdot(bigX[r], beta[r])) for r in range(n_eq))
+    bigE = np.hstack(tuple(bigy[r] - spdot(bigX[r], beta[r]) for r in range(n_eq)))
+
     return bigE
 
 
@@ -449,7 +454,9 @@ def sur_predict(bigy, bigX, beta):
 
     """
     n_eq = len(bigy.keys())
-    bigYP = np.hstack(spdot(bigX[r], beta[r]) for r in range(n_eq))
+    #bigYP = np.hstack(spdot(bigX[r], beta[r]) for r in range(n_eq))
+    bigYP = np.hstack([spdot(bigX[r], beta[r]) for r in range(n_eq)])
+
     return bigYP
 
 

From 503316c4855a4ace846e2e8ec86601ffcb769d46 Mon Sep 17 00:00:00 2001
From: Pedro Amaral <pedrovma@gmail.com>
Date: Wed, 13 Sep 2023 17:42:40 -0300
Subject: [PATCH 18/28] Attempt at fixing last failed SUR file

---
 spreg/sur_error.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/spreg/sur_error.py b/spreg/sur_error.py
index b88e1220..37143f5d 100644
--- a/spreg/sur_error.py
+++ b/spreg/sur_error.py
@@ -553,7 +553,7 @@ def __init__(self, bigy, bigX, w, epsilon=0.0000001):
             bigE = sur_resids(self.bigy, self.bigX, b1)
             res = minimize(
                 clik,
-                lam,
+                np.array(lam).flatten(),
                 args=(self.n, self.n2, self.n_eq, bigE, I, WS),
                 method="L-BFGS-B",
                 bounds=lambdabounds,
@@ -1119,7 +1119,7 @@ def _test():
         bigy0,
         bigX0,
         w,
-        regimes=regimes,
+        #regimes=regimes,
         name_bigy=bigyvars0,
         name_bigX=bigXvars0,
         name_w="natqueen",

From e949afa260fec1558349c5d839c9b6ccc7f95cb4 Mon Sep 17 00:00:00 2001
From: Pedro Amaral <pedrovma@gmail.com>
Date: Wed, 13 Sep 2023 18:01:14 -0300
Subject: [PATCH 19/28] Following Eli's fix!

Thanks, Eli! =)
---
 .github/workflows/unittests.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/unittests.yml b/.github/workflows/unittests.yml
index 5e023225..e8db45b7 100644
--- a/.github/workflows/unittests.yml
+++ b/.github/workflows/unittests.yml
@@ -47,7 +47,7 @@
        
        - name: install bleeding edge libpysal (only Ubuntu / Python 3.9)
          shell: bash -l {0}
-         run: pip install git+https://github.com/pysal/libpysal.git@master
+         run: pip install git+https://github.com/pysal/libpysal.git@main
          if: matrix.os == 'ubuntu-latest' && contains(matrix.environment-file, 'DEV')
        
        - name: run tests - bash

From c74cf34697340bebf1a39319dad6b64eec272f22 Mon Sep 17 00:00:00 2001
From: Pedro Amaral <pedrovma@gmail.com>
Date: Thu, 14 Sep 2023 14:14:59 -0300
Subject: [PATCH 20/28] Adding message about the lack of implementation of
 latex format for the output

---
 spreg/output.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/spreg/output.py b/spreg/output.py
index c871c842..25f94e66 100644
--- a/spreg/output.py
+++ b/spreg/output.py
@@ -16,6 +16,8 @@
 ###############################################################################
 
 def output(reg, vm, other_end=False, robust=False, latex=False):
+    if latex:
+        print("Warning: Latex output not implemented yet. Using standard output instead.")
     strSummary = output_start(reg)
     for eq in reg.output['equation'].unique():
         try:

From 42b59be31fd3f2cc6d8a3e334bd9044ad7d1eac6 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Thu, 14 Sep 2023 20:24:33 -0400
Subject: [PATCH 21/28] Bump codecov/codecov-action from 3 to 4 (#119)

Bumps [codecov/codecov-action](https://github.com/codecov/codecov-action) from 3 to 4.
- [Release notes](https://github.com/codecov/codecov-action/releases)
- [Changelog](https://github.com/codecov/codecov-action/blob/main/CHANGELOG.md)
- [Commits](https://github.com/codecov/codecov-action/compare/v3...v4)

---
updated-dependencies:
- dependency-name: codecov/codecov-action
  dependency-type: direct:production
  update-type: version-update:semver-major
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 .github/workflows/unittests.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/unittests.yml b/.github/workflows/unittests.yml
index 5e023225..f0ef85f7 100644
--- a/.github/workflows/unittests.yml
+++ b/.github/workflows/unittests.yml
@@ -61,7 +61,7 @@
          if: matrix.os == 'windows-latest'
        
        - name: codecov
-         uses: codecov/codecov-action@v3
+         uses: codecov/codecov-action@v4
          with:
            token: ${{ secrets.CODECOV_TOKEN }}
            file: ./coverage.xml

From 3d0b8c05c77d95c1e705e9ffaaa7593ee075fcf4 Mon Sep 17 00:00:00 2001
From: James Gaboardi <jgaboardi@gmail.com>
Date: Sat, 16 Sep 2023 13:38:20 -0400
Subject: [PATCH 22/28] Revert "Bump codecov/codecov-action from 3 to 4 (#119)"
 (#120)

This reverts commit 42b59be31fd3f2cc6d8a3e334bd9044ad7d1eac6.
---
 .github/workflows/unittests.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/unittests.yml b/.github/workflows/unittests.yml
index f0ef85f7..5e023225 100644
--- a/.github/workflows/unittests.yml
+++ b/.github/workflows/unittests.yml
@@ -61,7 +61,7 @@
          if: matrix.os == 'windows-latest'
        
        - name: codecov
-         uses: codecov/codecov-action@v4
+         uses: codecov/codecov-action@v3
          with:
            token: ${{ secrets.CODECOV_TOKEN }}
            file: ./coverage.xml

From 0fe03b2e6435e03fc64e7dcf0778e62d14a71a46 Mon Sep 17 00:00:00 2001
From: eli knaap <ek@knaaptime.com>
Date: Wed, 20 Sep 2023 20:17:13 -0700
Subject: [PATCH 23/28] modernize infrastructure

---
 .gitattributes                            |    1 -
 .github/release.yml                       |   16 +
 .github/workflows/build_docs.yml          |   63 +-
 .github/workflows/release_and_publish.yml |  103 +-
 .github/workflows/unittests.yml           |    2 +-
 MANIFEST.in                               |    3 -
 Makefile                                  |   24 -
 ci/{38.yaml => 311.yaml}                  |    2 +-
 docs/conf.py                              |    4 -
 environment.yml                           |    4 +-
 pyproject.toml                            |  105 +
 requirements.txt                          |    5 -
 requirements_docs.txt                     |    5 -
 requirements_plus.txt                     |   11 -
 requirements_tests.txt                    |    8 -
 setup.cfg                                 |    7 -
 setup.py                                  |   71 -
 spreg/__init__.py                         |   45 +-
 spreg/_version.py                         |  686 -------
 spreg/diagnostics_panel.py                |    2 +-
 versioneer.py                             | 2189 ---------------------
 21 files changed, 231 insertions(+), 3125 deletions(-)
 delete mode 100644 .gitattributes
 create mode 100644 .github/release.yml
 delete mode 100644 MANIFEST.in
 delete mode 100644 Makefile
 rename ci/{38.yaml => 311.yaml} (96%)
 create mode 100644 pyproject.toml
 delete mode 100644 requirements.txt
 delete mode 100644 requirements_docs.txt
 delete mode 100644 requirements_plus.txt
 delete mode 100644 requirements_tests.txt
 delete mode 100644 setup.cfg
 delete mode 100644 setup.py
 delete mode 100644 spreg/_version.py
 delete mode 100644 versioneer.py

diff --git a/.gitattributes b/.gitattributes
deleted file mode 100644
index f606b65c..00000000
--- a/.gitattributes
+++ /dev/null
@@ -1 +0,0 @@
-spreg/_version.py export-subst
diff --git a/.github/release.yml b/.github/release.yml
new file mode 100644
index 00000000..f5435449
--- /dev/null
+++ b/.github/release.yml
@@ -0,0 +1,16 @@
+changelog:
+  exclude:
+    labels:
+      - ignore-for-release
+    authors:
+      - dependabot
+  categories:
+    - title: Bug Fixes
+      labels:
+        - bug
+    - title: Enhancements
+      labels:
+        - enhancement
+    - title: Other Changes
+      labels:
+        - "*"
diff --git a/.github/workflows/build_docs.yml b/.github/workflows/build_docs.yml
index dda4d0b0..af0611af 100644
--- a/.github/workflows/build_docs.yml
+++ b/.github/workflows/build_docs.yml
@@ -1,44 +1,48 @@
  name: Build Docs
- 
  on:
    push:
-     # Sequence of patterns matched against refs/tags
-     tags:
-       - 'v*' # Push events to matching v*, i.e. v1.0, v20.15.10
-   workflow_dispatch:
-     inputs:
-       version:
-         description: Manual Doc Build Reason
-         default: test
-         required: false
+     branches:
+     - master
+     - main
  jobs:
    docs:
-     name: build & push docs
+     name: CI (${{ matrix.os }}-${{ matrix.environment-file }})
      runs-on: ${{ matrix.os }}
-     timeout-minutes: 90
+     continue-on-error: ${{ matrix.experimental }}
+     timeout-minutes: 20
      strategy:
        matrix:
          os: ['ubuntu-latest']
-         environment-file: [ci/310.yaml]
+         environment-file: [.ci/39.yml]
          experimental: [false]
      defaults:
        run:
          shell: bash -l {0}
-     
      steps:
-       - name: checkout repo
-         uses: actions/checkout@v4
-       
-       - name: setup micromamba
-         uses: mamba-org/provision-with-micromamba@main
+       - uses: actions/checkout@v2
+       - uses: actions/cache@v2
+         env:
+           CACHE_NUMBER: 0
          with:
-           environment-file: ${{ matrix.environment-file }}
-           micromamba-version: 'latest'
-       
-       - name: make docs
-         run: cd docs; make html
-       
-       - name: commit docs
+           path: ~/conda_pkgs_dir
+           key: ${{ matrix.os }}-conda-${{ env.CACHE_NUMBER }}-${{ hashFiles(matrix.environment-file) }}
+       - uses: conda-incubator/setup-miniconda@v2
+         with:
+            miniconda-version: 'latest'
+            channels: conda-forge
+            channel-priority: true
+            auto-update-conda: true
+            auto-activate-base: false
+            environment-file: ${{ matrix.environment-file }}
+            activate-environment: test
+            use-only-tar-bz2: true
+       - run: conda info --all
+       - run: conda list
+       - run: conda config --show-sources
+       - run: conda config --show
+       - run: pip install -e . --no-deps --force-reinstall
+       - run: cd docs; make html
+       - name: Commit documentation changes
          run: |
            git clone https://github.com/ammaraskar/sphinx-action-test.git --branch gh-pages --single-branch gh-pages
            cp -r docs/_build/html/* gh-pages/
@@ -47,10 +51,9 @@
            git config --local user.name "GitHub Action"
            git add .
            git commit -m "Update documentation" -a || true
-           # The above command will fail if no changes were present,
-           # so we ignore the return code.
-       
-       - name: push to gh-pages
+           # The above command will fail if no changes were present, so we ignore
+           # the return code.
+       - name: Push changes
          uses: ad-m/github-push-action@master
          with:
             branch: gh-pages
diff --git a/.github/workflows/release_and_publish.yml b/.github/workflows/release_and_publish.yml
index 8d4da65f..420633db 100644
--- a/.github/workflows/release_and_publish.yml
+++ b/.github/workflows/release_and_publish.yml
@@ -7,59 +7,52 @@
  # under the user's name, not the organzation.
  
  #--------------------------------------------------
- name: Release & Publish
+name: Release & Publish
 
- on:
-   push:
-     # Sequence of patterns matched against refs/tags
-     tags:
-       - "v*" # Push events to matching v*, i.e. v1.0, v20.15.10
- 
- jobs:
-   build:
-     name: Create release & publish to PyPI
-     runs-on: ubuntu-latest
-     steps:
-       - name: Checkout repo
-         uses: actions/checkout@v4
- 
-       - name: Set up python
-         uses: actions/setup-python@v4
-         with:
-           python-version: "3.x"
- 
-       - name: Install Dependencies
-         run: |
-           python -m pip install --upgrade pip
-           pip install setuptools wheel twine jupyter urllib3 pandas pyyaml versioneer
-           python setup.py sdist bdist_wheel
- 
-       - name: run Changelog
-         run: |
-           jupyter nbconvert --to notebook --execute --inplace  --ExecutePreprocessor.timeout=-1  --ExecutePreprocessor.kernel_name=python3 tools/gitcount.ipynb
- 
-       - name: cat Changelog
-         uses: pCYSl5EDgo/cat@master
-         id: changetxt
-         with:
-           path: ./tools/changelog.md
-         env:
-           TEXT: ${{ steps.changetxt.outputs.text }}
- 
-       - name: Get the tag name
-         run: echo "TAG=${GITHUB_REF/refs\/tags\//}" >> $GITHUB_ENV
- 
-       - name: Release
-         uses: softprops/action-gh-release@v1
-         with:
-           body: ${{ steps.changetxt.outputs.text }}
-           body_path: ${{ steps.changetxt.outputs.path }}
-           name: Release ${{ env.TAG }}
-         env:
-           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
- 
-       - name: Publish distribution 📦 to PyPI
-         uses: pypa/gh-action-pypi-publish@master
-         with:
-           user: __token__
-           password: ${{ secrets.PYPI_PASSWORD }}
+on:
+  push:
+    # Sequence of patterns matched against refs/tags
+    tags:
+      - "v*" # Push events to matching v*, i.e. v1.0, v20.15.10
+  workflow_dispatch:
+    inputs:
+      version:
+        description: Manual Release
+        default: test
+        required: false
+
+jobs:
+  build:
+    name: Create release & publish to PyPI
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout repo
+        uses: actions/checkout@v3
+      
+      - name: Set up python
+        uses: actions/setup-python@v4
+        with:
+          python-version: "3.x"
+      
+      - name: Install Dependencies
+        run: |
+          python -m pip install --upgrade pip
+          python -m pip install --upgrade build twine
+          python -m build
+          twine check --strict dist/*
+      
+      - name: Create Release Notes
+        uses: actions/github-script@v6
+        with:
+          github-token: ${{secrets.GITHUB_TOKEN}}
+          script: |
+            await github.request(`POST /repos/${{ github.repository }}/releases`, {
+              tag_name: "${{ github.ref }}",
+              generate_release_notes: true
+            });
+      
+      - name: Publish distribution 📦 to PyPI
+        uses: pypa/gh-action-pypi-publish@release/v1
+        with:
+          user: __token__
+          password: ${{ secrets.PYPI_PASSWORD }}
\ No newline at end of file
diff --git a/.github/workflows/unittests.yml b/.github/workflows/unittests.yml
index e8db45b7..8c5c5f7f 100644
--- a/.github/workflows/unittests.yml
+++ b/.github/workflows/unittests.yml
@@ -24,11 +24,11 @@
        matrix:
          os: [ubuntu-latest]
          environment-file:
-           - ci/38.yaml
            - ci/39.yaml
            - ci/310.yaml
            - ci/310-BASE.yaml
            - ci/310-DEV.yaml
+           - ci/311.yaml
          include:
            - environment-file: ci/310.yaml
              os: macos-latest
diff --git a/MANIFEST.in b/MANIFEST.in
deleted file mode 100644
index 386dbca8..00000000
--- a/MANIFEST.in
+++ /dev/null
@@ -1,3 +0,0 @@
-include LICENSE.txt MANIFEST.in requirements_tests.txt requirements_docs.txt requirements_plus.txt requirements.txt
-include versioneer.py
-include spreg/_version.py
diff --git a/Makefile b/Makefile
deleted file mode 100644
index dafea398..00000000
--- a/Makefile
+++ /dev/null
@@ -1,24 +0,0 @@
-# developer Makefile for repeated tasks
-# 
-.PHONY: clean
-
-test:
-	nosetests 
-
-doctest:
-	cd doc; make pickle; make doctest
-
-install:
-	python setup.py install >/dev/null
-
-src:
-	python setup.py sdist >/dev/null
-
-win:
-	python setup.py bdist_wininst >/dev/null
-
-clean: 
-	find . -name "*.pyc" -exec rm '{}' ';'
-	find spreg -name "__pycache__" -exec rm -rf '{}' ';'
-	rm -rf dist
-	rm -rf build
diff --git a/ci/38.yaml b/ci/311.yaml
similarity index 96%
rename from ci/38.yaml
rename to ci/311.yaml
index ead6ee36..bc295d06 100644
--- a/ci/38.yaml
+++ b/ci/311.yaml
@@ -2,7 +2,7 @@ name: test
 channels:
   - conda-forge
 dependencies:
-  - python=3.8
+  - python=3.11
   # required
   - libpysal
   - numpy>=1.3
diff --git a/docs/conf.py b/docs/conf.py
index 9e1d3fb8..3bcf078d 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -16,12 +16,8 @@
 # add these directories to sys.path here. If the directory is relative to the
 # documentation root, use os.path.abspath to make it absolute, like shown here.
 #
-import sys, os
 import sphinx_bootstrap_theme
 
-
-sys.path.insert(0, os.path.abspath("../"))
-
 # import your package to obtain the version info to display on the docs website
 import spreg
 
diff --git a/environment.yml b/environment.yml
index 09680823..4ecc2762 100644
--- a/environment.yml
+++ b/environment.yml
@@ -1,8 +1,8 @@
-name: notebooks-environment
+name: spreg
 channels:
   - conda-forge
 dependencies:
-  - python=3.10
+  - python>=3.10
   - bokeh
   - folium
   - geojson
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 00000000..a469cb3b
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,105 @@
+[build-system]
+requires = ["setuptools>=61.0", "setuptools_scm[toml]>=6.2"]
+build-backend = "setuptools.build_meta"
+
+[tool.setuptools_scm]
+
+[project]
+name = "spreg"
+dynamic = ["version"]
+authors = [
+    { name = "Luc Anselin", email = "anselin@uchicago.edu" },
+    { name = "Serge Rey", email = "sjsrey@gmail.com" },
+    { name = "Pedo Amaral", email = "pedrovma@gmail.com" },
+]
+maintainers = [{ name = "pysal contributors" }]
+license = { text = "BSD 3-Clause" }
+description = "PySAL Spatial Econometric Regression in Python"
+keywords = [
+    "spatial econometrics, regression, statistics, spatial modeling"
+]
+readme = { text = """\
+Spatial Econometric Regression in Python"
+
+""", content-type = "text/x-rst" }
+classifiers = [
+    "Programming Language :: Python :: 3",
+    "License :: OSI Approved :: BSD License",
+    "Operating System :: OS Independent",
+    "Intended Audience :: Science/Research",
+    "Topic :: Scientific/Engineering :: GIS",
+]
+requires-python = ">=3.8"
+dependencies = [
+"scipy>=0.11",
+"numpy>=1.3",
+"pandas",
+"libpysal>=4.0.0",
+"scikit-learn>=0.22",
+]
+
+[project.urls]
+Home = "https://github.com/pysal/spreg/"
+Repository = "https://github.com/pysal/spreg"
+
+[project.optional-dependencies]
+dev = ["pre-commit"]
+docs = [
+    "nbsphinx",
+    "numpydoc",
+    "pandoc",
+    "sphinx",
+    "sphinxcontrib-bibtex",
+    "sphinx_bootstrap_theme",
+    "mkdocs-jupyter",
+    "myst-parser"
+]
+tests = [
+    "codecov",
+    "coverage",
+    "pytest",
+    "pytest-mpl",
+    "pytest-cov",
+    "watermark",
+
+]
+
+[tool.setuptools.packages.find]
+include = ["spreg", "spreg.*"]
+
+[tool.black]
+line-length = 88
+
+[tool.ruff]
+line-length = 88
+select = ["E", "F", "W", "I", "UP", "N", "B", "A", "C4", "SIM", "ARG"]
+target-version = "py39"
+ignore = [
+    "B006",
+    "B008",
+    "B009",
+    "B010",
+    "C408",
+    "E731",
+    "F401",
+    "F403",
+    "N803",
+    "N806",
+    "N999",
+    "UP007"
+]
+exclude = ["spreg/tests/*", "docs/*"]
+
+[tool.coverage.run]
+source = ["./spreg"]
+
+[tool.coverage.report]
+exclude_lines = [
+    "if self.debug:",
+    "pragma: no cover",
+    "raise NotImplementedError",
+    "except ModuleNotFoundError:",
+    "except ImportError",
+]
+ignore_errors = true
+omit = ["spreg/tests/*", "docs/conf.py"]
diff --git a/requirements.txt b/requirements.txt
deleted file mode 100644
index d8a091d6..00000000
--- a/requirements.txt
+++ /dev/null
@@ -1,5 +0,0 @@
-scipy>=0.11
-numpy>=1.3
-pandas
-libpysal>=4.0.0
-scikit-learn>=0.22
diff --git a/requirements_docs.txt b/requirements_docs.txt
deleted file mode 100644
index 8e82663f..00000000
--- a/requirements_docs.txt
+++ /dev/null
@@ -1,5 +0,0 @@
-sphinx>=1.4.3
-sphinxcontrib-bibtex
-sphinx_bootstrap_theme>=0.7.0
-numpydoc
-nbsphinx
\ No newline at end of file
diff --git a/requirements_plus.txt b/requirements_plus.txt
deleted file mode 100644
index ac1e32bc..00000000
--- a/requirements_plus.txt
+++ /dev/null
@@ -1,11 +0,0 @@
-matplotlib>=1.5.1
-seaborn>=0.7.0
-geopandas>=0.2
-bokeh>=0.11.1
-geojson>=1.3.2
-folium>=0.2.1
-mplleaflet>=0.0.5
-statsmodels>=0.6.1
-numba
-numexpr
-watermark
diff --git a/requirements_tests.txt b/requirements_tests.txt
deleted file mode 100644
index 7c8c886f..00000000
--- a/requirements_tests.txt
+++ /dev/null
@@ -1,8 +0,0 @@
-codecov
-coverage
-pytest
-pytest-cov
-pytest-xdist
-twine
-versioneer
-wheel
diff --git a/setup.cfg b/setup.cfg
deleted file mode 100644
index 60b4c73e..00000000
--- a/setup.cfg
+++ /dev/null
@@ -1,7 +0,0 @@
-[versioneer]
-VCS = git
-style = pep440
-versionfile_source = spreg/_version.py
-versionfile_build = spreg/_version.py
-tag_prefix = v
-parentdir_prefix = spreg-
diff --git a/setup.py b/setup.py
deleted file mode 100644
index 7bedc601..00000000
--- a/setup.py
+++ /dev/null
@@ -1,71 +0,0 @@
-# coding: utf-8
-from distutils.command.build_py import build_py
-from setuptools import setup
-import versioneer
-
-package = "spreg"
-
-with open("README.md", encoding="utf8") as file:
-    long_description = file.read()
-
-
-def _get_requirements_from_files(groups_files):
-    groups_reqlist = {}
-
-    for k, v in groups_files.items():
-        with open(v, "r") as f:
-            pkg_list = f.read().splitlines()
-        groups_reqlist[k] = pkg_list
-
-    return groups_reqlist
-
-
-def setup_package():
-    # get all file endings and copy whole file names without a file suffix
-    # assumes nested directories are only down one level
-    _groups_files = {
-        "base": "requirements.txt",
-        "tests": "requirements_tests.txt",
-        "plus": "requirements_plus.txt",
-        "docs": "requirements_docs.txt",
-    }
-
-    reqs = _get_requirements_from_files(_groups_files)
-    install_reqs = reqs.pop("base")
-    extras_reqs = reqs
-
-    setup(
-        name=package,
-        version=versioneer.get_version(),
-        description="PySAL Spatial Econometrics Package",
-        long_description=long_description,
-        long_description_content_type="text/markdown",
-        maintainer="PySAL Developers",
-        maintainer_email="pysal-dev@googlegroups.com",
-        url=f"https://github.com/pysal/{package}",
-        download_url=f"https://pypi.python.org/pypi/{package}",
-        license="BSD",
-        py_modules=[package],
-        packages=[package],
-        keywords="spatial statistics",
-        classifiers=[
-            "Development Status :: 5 - Production/Stable",
-            "Intended Audience :: Science/Research",
-            "Intended Audience :: Developers",
-            "Intended Audience :: Education",
-            "Topic :: Scientific/Engineering",
-            "Topic :: Scientific/Engineering :: GIS",
-            "License :: OSI Approved :: BSD License",
-            "Programming Language :: Python",
-            "Programming Language :: Python :: 3.8",
-            "Programming Language :: Python :: 3.9",
-            "Programming Language :: Python :: 3.10",
-        ],
-        install_requires=install_reqs,
-        extras_require=extras_reqs,
-        cmdclass=versioneer.get_cmdclass({"build_py": build_py}),
-    )
-
-
-if __name__ == "__main__":
-    setup_package()
diff --git a/spreg/__init__.py b/spreg/__init__.py
index 2e7e01f8..845e95e8 100644
--- a/spreg/__init__.py
+++ b/spreg/__init__.py
@@ -1,38 +1,41 @@
-from .ols import *
+
+import contextlib
+from importlib.metadata import PackageNotFoundError, version
+
 from .diagnostics import *
 from .diagnostics_panel import *
 from .diagnostics_sp import *
 from .diagnostics_sur import *
 from .diagnostics_tsls import *
-from .user_output import *
-from .twosls import *
-from .twosls_sp import *
 from .error_sp import *
 from .error_sp_het import *
-from .error_sp_hom import *
-from .ols_regimes import *
-from .twosls_regimes import *
-from .twosls_sp_regimes import *
-from .error_sp_regimes import *
 from .error_sp_het_regimes import *
+from .error_sp_hom import *
 from .error_sp_hom_regimes import *
-from .probit import *
-from .ml_lag import *
-from .ml_lag_regimes import *
+from .error_sp_regimes import *
 from .ml_error import *
 from .ml_error_regimes import *
+from .ml_lag import *
+from .ml_lag_regimes import *
+from .ols import *
+from .ols_regimes import *
+from .panel_fe import *
+from .panel_re import *
+from .probit import *
+from .regimes import *
+from .skater_reg import *
+from .sp_panels import *
+from .sputils import *
 from .sur import *
 from .sur_error import *
 from .sur_lag import *
 from .sur_utils import *
+from .twosls import *
+from .twosls_regimes import *
+from .twosls_sp import *
+from .twosls_sp_regimes import *
+from .user_output import *
 from .utils import *
-from .regimes import *
-from .sputils import *
-from .sp_panels import *
-from .panel_fe import *
-from .panel_re import *
-from .skater_reg import *
-
-from . import _version
 
-__version__ = _version.get_versions()["version"]
+with contextlib.suppress(PackageNotFoundError):
+    __version__ = version("spreg")
diff --git a/spreg/_version.py b/spreg/_version.py
deleted file mode 100644
index db0ad52e..00000000
--- a/spreg/_version.py
+++ /dev/null
@@ -1,686 +0,0 @@
-# This file helps to compute a version number in source trees obtained from
-# git-archive tarball (such as those provided by githubs download-from-tag
-# feature). Distribution tarballs (built by setup.py sdist) and build
-# directories (produced by setup.py build) will contain a much shorter file
-# that just contains the computed version number.
-
-# This file is released into the public domain. Generated by
-# versioneer-0.22 (https://github.com/python-versioneer/python-versioneer)
-
-"""Git implementation of _version.py."""
-
-import errno
-import os
-import re
-import subprocess
-import sys
-from typing import Callable, Dict
-import functools
-
-
-def get_keywords():
-    """Get the keywords needed to look up the version information."""
-    # these strings will be replaced by git during git-archive.
-    # setup.py/versioneer.py will grep for the variable names, so they must
-    # each be defined on a line of their own. _version.py will just call
-    # get_keywords().
-    git_refnames = "$Format:%d$"
-    git_full = "$Format:%H$"
-    git_date = "$Format:%ci$"
-    keywords = {"refnames": git_refnames, "full": git_full, "date": git_date}
-    return keywords
-
-
-class VersioneerConfig:
-    """Container for Versioneer configuration parameters."""
-
-
-def get_config():
-    """Create, populate and return the VersioneerConfig() object."""
-    # these strings are filled in when 'setup.py versioneer' creates
-    # _version.py
-    cfg = VersioneerConfig()
-    cfg.VCS = "git"
-    cfg.style = "pep440"
-    cfg.tag_prefix = "v"
-    cfg.parentdir_prefix = "spreg-"
-    cfg.versionfile_source = "spreg/_version.py"
-    cfg.verbose = False
-    return cfg
-
-
-class NotThisMethod(Exception):
-    """Exception raised if a method is not valid for the current scenario."""
-
-
-LONG_VERSION_PY: Dict[str, str] = {}
-HANDLERS: Dict[str, Dict[str, Callable]] = {}
-
-
-def register_vcs_handler(vcs, method):  # decorator
-    """Create decorator to mark a method as the handler of a VCS."""
-
-    def decorate(f):
-        """Store f in HANDLERS[vcs][method]."""
-        if vcs not in HANDLERS:
-            HANDLERS[vcs] = {}
-        HANDLERS[vcs][method] = f
-        return f
-
-    return decorate
-
-
-def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False, env=None):
-    """Call the given command(s)."""
-    assert isinstance(commands, list)
-    process = None
-
-    popen_kwargs = {}
-    if sys.platform == "win32":
-        # This hides the console window if pythonw.exe is used
-        startupinfo = subprocess.STARTUPINFO()
-        startupinfo.dwFlags |= subprocess.STARTF_USESHOWWINDOW
-        popen_kwargs["startupinfo"] = startupinfo
-
-    for command in commands:
-        try:
-            dispcmd = str([command] + args)
-            # remember shell=False, so use git.cmd on windows, not just git
-            process = subprocess.Popen(
-                [command] + args,
-                cwd=cwd,
-                env=env,
-                stdout=subprocess.PIPE,
-                stderr=(subprocess.PIPE if hide_stderr else None),
-                **popen_kwargs
-            )
-            break
-        except OSError:
-            e = sys.exc_info()[1]
-            if e.errno == errno.ENOENT:
-                continue
-            if verbose:
-                print("unable to run %s" % dispcmd)
-                print(e)
-            return None, None
-    else:
-        if verbose:
-            print("unable to find command, tried %s" % (commands,))
-        return None, None
-    stdout = process.communicate()[0].strip().decode()
-    if process.returncode != 0:
-        if verbose:
-            print("unable to run %s (error)" % dispcmd)
-            print("stdout was %s" % stdout)
-        return None, process.returncode
-    return stdout, process.returncode
-
-
-def versions_from_parentdir(parentdir_prefix, root, verbose):
-    """Try to determine the version from the parent directory name.
-
-    Source tarballs conventionally unpack into a directory that includes both
-    the project name and a version string. We will also support searching up
-    two directory levels for an appropriately named parent directory
-    """
-    rootdirs = []
-
-    for _ in range(3):
-        dirname = os.path.basename(root)
-        if dirname.startswith(parentdir_prefix):
-            return {
-                "version": dirname[len(parentdir_prefix) :],
-                "full-revisionid": None,
-                "dirty": False,
-                "error": None,
-                "date": None,
-            }
-        rootdirs.append(root)
-        root = os.path.dirname(root)  # up a level
-
-    if verbose:
-        print(
-            "Tried directories %s but none started with prefix %s"
-            % (str(rootdirs), parentdir_prefix)
-        )
-    raise NotThisMethod("rootdir doesn't start with parentdir_prefix")
-
-
-@register_vcs_handler("git", "get_keywords")
-def git_get_keywords(versionfile_abs):
-    """Extract version information from the given file."""
-    # the code embedded in _version.py can just fetch the value of these
-    # keywords. When used from setup.py, we don't want to import _version.py,
-    # so we do it with a regexp instead. This function is not used from
-    # _version.py.
-    keywords = {}
-    try:
-        with open(versionfile_abs, "r") as fobj:
-            for line in fobj:
-                if line.strip().startswith("git_refnames ="):
-                    mo = re.search(r'=\s*"(.*)"', line)
-                    if mo:
-                        keywords["refnames"] = mo.group(1)
-                if line.strip().startswith("git_full ="):
-                    mo = re.search(r'=\s*"(.*)"', line)
-                    if mo:
-                        keywords["full"] = mo.group(1)
-                if line.strip().startswith("git_date ="):
-                    mo = re.search(r'=\s*"(.*)"', line)
-                    if mo:
-                        keywords["date"] = mo.group(1)
-    except OSError:
-        pass
-    return keywords
-
-
-@register_vcs_handler("git", "keywords")
-def git_versions_from_keywords(keywords, tag_prefix, verbose):
-    """Get version information from git keywords."""
-    if "refnames" not in keywords:
-        raise NotThisMethod("Short version file found")
-    date = keywords.get("date")
-    if date is not None:
-        # Use only the last line.  Previous lines may contain GPG signature
-        # information.
-        date = date.splitlines()[-1]
-
-        # git-2.2.0 added "%cI", which expands to an ISO-8601 -compliant
-        # datestamp. However we prefer "%ci" (which expands to an "ISO-8601
-        # -like" string, which we must then edit to make compliant), because
-        # it's been around since git-1.5.3, and it's too difficult to
-        # discover which version we're using, or to work around using an
-        # older one.
-        date = date.strip().replace(" ", "T", 1).replace(" ", "", 1)
-    refnames = keywords["refnames"].strip()
-    if refnames.startswith("$Format"):
-        if verbose:
-            print("keywords are unexpanded, not using")
-        raise NotThisMethod("unexpanded keywords, not a git-archive tarball")
-    refs = {r.strip() for r in refnames.strip("()").split(",")}
-    # starting in git-1.8.3, tags are listed as "tag: foo-1.0" instead of
-    # just "foo-1.0". If we see a "tag: " prefix, prefer those.
-    TAG = "tag: "
-    tags = {r[len(TAG) :] for r in refs if r.startswith(TAG)}
-    if not tags:
-        # Either we're using git < 1.8.3, or there really are no tags. We use
-        # a heuristic: assume all version tags have a digit. The old git %d
-        # expansion behaves like git log --decorate=short and strips out the
-        # refs/heads/ and refs/tags/ prefixes that would let us distinguish
-        # between branches and tags. By ignoring refnames without digits, we
-        # filter out many common branch names like "release" and
-        # "stabilization", as well as "HEAD" and "master".
-        tags = {r for r in refs if re.search(r"\d", r)}
-        if verbose:
-            print("discarding '%s', no digits" % ",".join(refs - tags))
-    if verbose:
-        print("likely tags: %s" % ",".join(sorted(tags)))
-    for ref in sorted(tags):
-        # sorting will prefer e.g. "2.0" over "2.0rc1"
-        if ref.startswith(tag_prefix):
-            r = ref[len(tag_prefix) :]
-            # Filter out refs that exactly match prefix or that don't start
-            # with a number once the prefix is stripped (mostly a concern
-            # when prefix is '')
-            if not re.match(r"\d", r):
-                continue
-            if verbose:
-                print("picking %s" % r)
-            return {
-                "version": r,
-                "full-revisionid": keywords["full"].strip(),
-                "dirty": False,
-                "error": None,
-                "date": date,
-            }
-    # no suitable tags, so version is "0+unknown", but full hex is still there
-    if verbose:
-        print("no suitable tags, using unknown + full revision id")
-    return {
-        "version": "0+unknown",
-        "full-revisionid": keywords["full"].strip(),
-        "dirty": False,
-        "error": "no suitable tags",
-        "date": None,
-    }
-
-
-@register_vcs_handler("git", "pieces_from_vcs")
-def git_pieces_from_vcs(tag_prefix, root, verbose, runner=run_command):
-    """Get version from 'git describe' in the root of the source tree.
-
-    This only gets called if the git-archive 'subst' keywords were *not*
-    expanded, and _version.py hasn't already been rewritten with a short
-    version string, meaning we're inside a checked out source tree.
-    """
-    GITS = ["git"]
-    if sys.platform == "win32":
-        GITS = ["git.cmd", "git.exe"]
-
-    # GIT_DIR can interfere with correct operation of Versioneer.
-    # It may be intended to be passed to the Versioneer-versioned project,
-    # but that should not change where we get our version from.
-    env = os.environ.copy()
-    env.pop("GIT_DIR", None)
-    runner = functools.partial(runner, env=env)
-
-    _, rc = runner(GITS, ["rev-parse", "--git-dir"], cwd=root, hide_stderr=True)
-    if rc != 0:
-        if verbose:
-            print("Directory %s not under git control" % root)
-        raise NotThisMethod("'git rev-parse --git-dir' returned error")
-
-    MATCH_ARGS = ["--match", "%s*" % tag_prefix] if tag_prefix else []
-
-    # if there is a tag matching tag_prefix, this yields TAG-NUM-gHEX[-dirty]
-    # if there isn't one, this yields HEX[-dirty] (no NUM)
-    describe_out, rc = runner(
-        GITS,
-        ["describe", "--tags", "--dirty", "--always", "--long", *MATCH_ARGS],
-        cwd=root,
-    )
-    # --long was added in git-1.5.5
-    if describe_out is None:
-        raise NotThisMethod("'git describe' failed")
-    describe_out = describe_out.strip()
-    full_out, rc = runner(GITS, ["rev-parse", "HEAD"], cwd=root)
-    if full_out is None:
-        raise NotThisMethod("'git rev-parse' failed")
-    full_out = full_out.strip()
-
-    pieces = {}
-    pieces["long"] = full_out
-    pieces["short"] = full_out[:7]  # maybe improved later
-    pieces["error"] = None
-
-    branch_name, rc = runner(GITS, ["rev-parse", "--abbrev-ref", "HEAD"], cwd=root)
-    # --abbrev-ref was added in git-1.6.3
-    if rc != 0 or branch_name is None:
-        raise NotThisMethod("'git rev-parse --abbrev-ref' returned error")
-    branch_name = branch_name.strip()
-
-    if branch_name == "HEAD":
-        # If we aren't exactly on a branch, pick a branch which represents
-        # the current commit. If all else fails, we are on a branchless
-        # commit.
-        branches, rc = runner(GITS, ["branch", "--contains"], cwd=root)
-        # --contains was added in git-1.5.4
-        if rc != 0 or branches is None:
-            raise NotThisMethod("'git branch --contains' returned error")
-        branches = branches.split("\n")
-
-        # Remove the first line if we're running detached
-        if "(" in branches[0]:
-            branches.pop(0)
-
-        # Strip off the leading "* " from the list of branches.
-        branches = [branch[2:] for branch in branches]
-        if "master" in branches:
-            branch_name = "master"
-        elif not branches:
-            branch_name = None
-        else:
-            # Pick the first branch that is returned. Good or bad.
-            branch_name = branches[0]
-
-    pieces["branch"] = branch_name
-
-    # parse describe_out. It will be like TAG-NUM-gHEX[-dirty] or HEX[-dirty]
-    # TAG might have hyphens.
-    git_describe = describe_out
-
-    # look for -dirty suffix
-    dirty = git_describe.endswith("-dirty")
-    pieces["dirty"] = dirty
-    if dirty:
-        git_describe = git_describe[: git_describe.rindex("-dirty")]
-
-    # now we have TAG-NUM-gHEX or HEX
-
-    if "-" in git_describe:
-        # TAG-NUM-gHEX
-        mo = re.search(r"^(.+)-(\d+)-g([0-9a-f]+)$", git_describe)
-        if not mo:
-            # unparsable. Maybe git-describe is misbehaving?
-            pieces["error"] = "unable to parse git-describe output: '%s'" % describe_out
-            return pieces
-
-        # tag
-        full_tag = mo.group(1)
-        if not full_tag.startswith(tag_prefix):
-            if verbose:
-                fmt = "tag '%s' doesn't start with prefix '%s'"
-                print(fmt % (full_tag, tag_prefix))
-            pieces["error"] = "tag '%s' doesn't start with prefix '%s'" % (
-                full_tag,
-                tag_prefix,
-            )
-            return pieces
-        pieces["closest-tag"] = full_tag[len(tag_prefix) :]
-
-        # distance: number of commits since tag
-        pieces["distance"] = int(mo.group(2))
-
-        # commit: short hex revision ID
-        pieces["short"] = mo.group(3)
-
-    else:
-        # HEX: no tags
-        pieces["closest-tag"] = None
-        count_out, rc = runner(GITS, ["rev-list", "HEAD", "--count"], cwd=root)
-        pieces["distance"] = int(count_out)  # total number of commits
-
-    # commit date: see ISO-8601 comment in git_versions_from_keywords()
-    date = runner(GITS, ["show", "-s", "--format=%ci", "HEAD"], cwd=root)[0].strip()
-    # Use only the last line.  Previous lines may contain GPG signature
-    # information.
-    date = date.splitlines()[-1]
-    pieces["date"] = date.strip().replace(" ", "T", 1).replace(" ", "", 1)
-
-    return pieces
-
-
-def plus_or_dot(pieces):
-    """Return a + if we don't already have one, else return a ."""
-    if "+" in pieces.get("closest-tag", ""):
-        return "."
-    return "+"
-
-
-def render_pep440(pieces):
-    """Build up version string, with post-release "local version identifier".
-
-    Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you
-    get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty
-
-    Exceptions:
-    1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty]
-    """
-    if pieces["closest-tag"]:
-        rendered = pieces["closest-tag"]
-        if pieces["distance"] or pieces["dirty"]:
-            rendered += plus_or_dot(pieces)
-            rendered += "%d.g%s" % (pieces["distance"], pieces["short"])
-            if pieces["dirty"]:
-                rendered += ".dirty"
-    else:
-        # exception #1
-        rendered = "0+untagged.%d.g%s" % (pieces["distance"], pieces["short"])
-        if pieces["dirty"]:
-            rendered += ".dirty"
-    return rendered
-
-
-def render_pep440_branch(pieces):
-    """TAG[[.dev0]+DISTANCE.gHEX[.dirty]] .
-
-    The ".dev0" means not master branch. Note that .dev0 sorts backwards
-    (a feature branch will appear "older" than the master branch).
-
-    Exceptions:
-    1: no tags. 0[.dev0]+untagged.DISTANCE.gHEX[.dirty]
-    """
-    if pieces["closest-tag"]:
-        rendered = pieces["closest-tag"]
-        if pieces["distance"] or pieces["dirty"]:
-            if pieces["branch"] != "master":
-                rendered += ".dev0"
-            rendered += plus_or_dot(pieces)
-            rendered += "%d.g%s" % (pieces["distance"], pieces["short"])
-            if pieces["dirty"]:
-                rendered += ".dirty"
-    else:
-        # exception #1
-        rendered = "0"
-        if pieces["branch"] != "master":
-            rendered += ".dev0"
-        rendered += "+untagged.%d.g%s" % (pieces["distance"], pieces["short"])
-        if pieces["dirty"]:
-            rendered += ".dirty"
-    return rendered
-
-
-def pep440_split_post(ver):
-    """Split pep440 version string at the post-release segment.
-
-    Returns the release segments before the post-release and the
-    post-release version number (or -1 if no post-release segment is present).
-    """
-    vc = str.split(ver, ".post")
-    return vc[0], int(vc[1] or 0) if len(vc) == 2 else None
-
-
-def render_pep440_pre(pieces):
-    """TAG[.postN.devDISTANCE] -- No -dirty.
-
-    Exceptions:
-    1: no tags. 0.post0.devDISTANCE
-    """
-    if pieces["closest-tag"]:
-        if pieces["distance"]:
-            # update the post release segment
-            tag_version, post_version = pep440_split_post(pieces["closest-tag"])
-            rendered = tag_version
-            if post_version is not None:
-                rendered += ".post%d.dev%d" % (post_version + 1, pieces["distance"])
-            else:
-                rendered += ".post0.dev%d" % (pieces["distance"])
-        else:
-            # no commits, use the tag as the version
-            rendered = pieces["closest-tag"]
-    else:
-        # exception #1
-        rendered = "0.post0.dev%d" % pieces["distance"]
-    return rendered
-
-
-def render_pep440_post(pieces):
-    """TAG[.postDISTANCE[.dev0]+gHEX] .
-
-    The ".dev0" means dirty. Note that .dev0 sorts backwards
-    (a dirty tree will appear "older" than the corresponding clean one),
-    but you shouldn't be releasing software with -dirty anyways.
-
-    Exceptions:
-    1: no tags. 0.postDISTANCE[.dev0]
-    """
-    if pieces["closest-tag"]:
-        rendered = pieces["closest-tag"]
-        if pieces["distance"] or pieces["dirty"]:
-            rendered += ".post%d" % pieces["distance"]
-            if pieces["dirty"]:
-                rendered += ".dev0"
-            rendered += plus_or_dot(pieces)
-            rendered += "g%s" % pieces["short"]
-    else:
-        # exception #1
-        rendered = "0.post%d" % pieces["distance"]
-        if pieces["dirty"]:
-            rendered += ".dev0"
-        rendered += "+g%s" % pieces["short"]
-    return rendered
-
-
-def render_pep440_post_branch(pieces):
-    """TAG[.postDISTANCE[.dev0]+gHEX[.dirty]] .
-
-    The ".dev0" means not master branch.
-
-    Exceptions:
-    1: no tags. 0.postDISTANCE[.dev0]+gHEX[.dirty]
-    """
-    if pieces["closest-tag"]:
-        rendered = pieces["closest-tag"]
-        if pieces["distance"] or pieces["dirty"]:
-            rendered += ".post%d" % pieces["distance"]
-            if pieces["branch"] != "master":
-                rendered += ".dev0"
-            rendered += plus_or_dot(pieces)
-            rendered += "g%s" % pieces["short"]
-            if pieces["dirty"]:
-                rendered += ".dirty"
-    else:
-        # exception #1
-        rendered = "0.post%d" % pieces["distance"]
-        if pieces["branch"] != "master":
-            rendered += ".dev0"
-        rendered += "+g%s" % pieces["short"]
-        if pieces["dirty"]:
-            rendered += ".dirty"
-    return rendered
-
-
-def render_pep440_old(pieces):
-    """TAG[.postDISTANCE[.dev0]] .
-
-    The ".dev0" means dirty.
-
-    Exceptions:
-    1: no tags. 0.postDISTANCE[.dev0]
-    """
-    if pieces["closest-tag"]:
-        rendered = pieces["closest-tag"]
-        if pieces["distance"] or pieces["dirty"]:
-            rendered += ".post%d" % pieces["distance"]
-            if pieces["dirty"]:
-                rendered += ".dev0"
-    else:
-        # exception #1
-        rendered = "0.post%d" % pieces["distance"]
-        if pieces["dirty"]:
-            rendered += ".dev0"
-    return rendered
-
-
-def render_git_describe(pieces):
-    """TAG[-DISTANCE-gHEX][-dirty].
-
-    Like 'git describe --tags --dirty --always'.
-
-    Exceptions:
-    1: no tags. HEX[-dirty]  (note: no 'g' prefix)
-    """
-    if pieces["closest-tag"]:
-        rendered = pieces["closest-tag"]
-        if pieces["distance"]:
-            rendered += "-%d-g%s" % (pieces["distance"], pieces["short"])
-    else:
-        # exception #1
-        rendered = pieces["short"]
-    if pieces["dirty"]:
-        rendered += "-dirty"
-    return rendered
-
-
-def render_git_describe_long(pieces):
-    """TAG-DISTANCE-gHEX[-dirty].
-
-    Like 'git describe --tags --dirty --always -long'.
-    The distance/hash is unconditional.
-
-    Exceptions:
-    1: no tags. HEX[-dirty]  (note: no 'g' prefix)
-    """
-    if pieces["closest-tag"]:
-        rendered = pieces["closest-tag"]
-        rendered += "-%d-g%s" % (pieces["distance"], pieces["short"])
-    else:
-        # exception #1
-        rendered = pieces["short"]
-    if pieces["dirty"]:
-        rendered += "-dirty"
-    return rendered
-
-
-def render(pieces, style):
-    """Render the given version pieces into the requested style."""
-    if pieces["error"]:
-        return {
-            "version": "unknown",
-            "full-revisionid": pieces.get("long"),
-            "dirty": None,
-            "error": pieces["error"],
-            "date": None,
-        }
-
-    if not style or style == "default":
-        style = "pep440"  # the default
-
-    if style == "pep440":
-        rendered = render_pep440(pieces)
-    elif style == "pep440-branch":
-        rendered = render_pep440_branch(pieces)
-    elif style == "pep440-pre":
-        rendered = render_pep440_pre(pieces)
-    elif style == "pep440-post":
-        rendered = render_pep440_post(pieces)
-    elif style == "pep440-post-branch":
-        rendered = render_pep440_post_branch(pieces)
-    elif style == "pep440-old":
-        rendered = render_pep440_old(pieces)
-    elif style == "git-describe":
-        rendered = render_git_describe(pieces)
-    elif style == "git-describe-long":
-        rendered = render_git_describe_long(pieces)
-    else:
-        raise ValueError("unknown style '%s'" % style)
-
-    return {
-        "version": rendered,
-        "full-revisionid": pieces["long"],
-        "dirty": pieces["dirty"],
-        "error": None,
-        "date": pieces.get("date"),
-    }
-
-
-def get_versions():
-    """Get version information or return default if unable to do so."""
-    # I am in _version.py, which lives at ROOT/VERSIONFILE_SOURCE. If we have
-    # __file__, we can work backwards from there to the root. Some
-    # py2exe/bbfreeze/non-CPython implementations don't do __file__, in which
-    # case we can only use expanded keywords.
-
-    cfg = get_config()
-    verbose = cfg.verbose
-
-    try:
-        return git_versions_from_keywords(get_keywords(), cfg.tag_prefix, verbose)
-    except NotThisMethod:
-        pass
-
-    try:
-        root = os.path.realpath(__file__)
-        # versionfile_source is the relative path from the top of the source
-        # tree (where the .git directory might live) to this file. Invert
-        # this to find the root from __file__.
-        for _ in cfg.versionfile_source.split("/"):
-            root = os.path.dirname(root)
-    except NameError:
-        return {
-            "version": "0+unknown",
-            "full-revisionid": None,
-            "dirty": None,
-            "error": "unable to find root of source tree",
-            "date": None,
-        }
-
-    try:
-        pieces = git_pieces_from_vcs(cfg.tag_prefix, root, verbose)
-        return render(pieces, cfg.style)
-    except NotThisMethod:
-        pass
-
-    try:
-        if cfg.parentdir_prefix:
-            return versions_from_parentdir(cfg.parentdir_prefix, root, verbose)
-    except NotThisMethod:
-        pass
-
-    return {
-        "version": "0+unknown",
-        "full-revisionid": None,
-        "dirty": None,
-        "error": "unable to compute version",
-        "date": None,
-    }
diff --git a/spreg/diagnostics_panel.py b/spreg/diagnostics_panel.py
index 730135cc..5b299d34 100644
--- a/spreg/diagnostics_panel.py
+++ b/spreg/diagnostics_panel.py
@@ -10,7 +10,7 @@
 import numpy.linalg as la
 from scipy import sparse as sp
 from . import user_output as USER
-from . import OLS
+from .ols import OLS
 from .utils import spdot
 from scipy import stats
 from .panel_utils import check_panel
diff --git a/versioneer.py b/versioneer.py
deleted file mode 100644
index f1c45727..00000000
--- a/versioneer.py
+++ /dev/null
@@ -1,2189 +0,0 @@
-# Version: 0.22
-
-"""The Versioneer - like a rocketeer, but for versions.
-
-The Versioneer
-==============
-
-* like a rocketeer, but for versions!
-* https://github.com/python-versioneer/python-versioneer
-* Brian Warner
-* License: Public Domain
-* Compatible with: Python 3.6, 3.7, 3.8, 3.9, 3.10 and pypy3
-* [![Latest Version][pypi-image]][pypi-url]
-* [![Build Status][travis-image]][travis-url]
-
-This is a tool for managing a recorded version number in distutils/setuptools-based
-python projects. The goal is to remove the tedious and error-prone "update
-the embedded version string" step from your release process. Making a new
-release should be as easy as recording a new tag in your version-control
-system, and maybe making new tarballs.
-
-
-## Quick Install
-
-* `pip install versioneer` to somewhere in your $PATH
-* add a `[versioneer]` section to your setup.cfg (see [Install](INSTALL.md))
-* run `versioneer install` in your source tree, commit the results
-* Verify version information with `python setup.py version`
-
-## Version Identifiers
-
-Source trees come from a variety of places:
-
-* a version-control system checkout (mostly used by developers)
-* a nightly tarball, produced by build automation
-* a snapshot tarball, produced by a web-based VCS browser, like github's
-  "tarball from tag" feature
-* a release tarball, produced by "setup.py sdist", distributed through PyPI
-
-Within each source tree, the version identifier (either a string or a number,
-this tool is format-agnostic) can come from a variety of places:
-
-* ask the VCS tool itself, e.g. "git describe" (for checkouts), which knows
-  about recent "tags" and an absolute revision-id
-* the name of the directory into which the tarball was unpacked
-* an expanded VCS keyword ($Id$, etc)
-* a `_version.py` created by some earlier build step
-
-For released software, the version identifier is closely related to a VCS
-tag. Some projects use tag names that include more than just the version
-string (e.g. "myproject-1.2" instead of just "1.2"), in which case the tool
-needs to strip the tag prefix to extract the version identifier. For
-unreleased software (between tags), the version identifier should provide
-enough information to help developers recreate the same tree, while also
-giving them an idea of roughly how old the tree is (after version 1.2, before
-version 1.3). Many VCS systems can report a description that captures this,
-for example `git describe --tags --dirty --always` reports things like
-"0.7-1-g574ab98-dirty" to indicate that the checkout is one revision past the
-0.7 tag, has a unique revision id of "574ab98", and is "dirty" (it has
-uncommitted changes).
-
-The version identifier is used for multiple purposes:
-
-* to allow the module to self-identify its version: `myproject.__version__`
-* to choose a name and prefix for a 'setup.py sdist' tarball
-
-## Theory of Operation
-
-Versioneer works by adding a special `_version.py` file into your source
-tree, where your `__init__.py` can import it. This `_version.py` knows how to
-dynamically ask the VCS tool for version information at import time.
-
-`_version.py` also contains `$Revision$` markers, and the installation
-process marks `_version.py` to have this marker rewritten with a tag name
-during the `git archive` command. As a result, generated tarballs will
-contain enough information to get the proper version.
-
-To allow `setup.py` to compute a version too, a `versioneer.py` is added to
-the top level of your source tree, next to `setup.py` and the `setup.cfg`
-that configures it. This overrides several distutils/setuptools commands to
-compute the version when invoked, and changes `setup.py build` and `setup.py
-sdist` to replace `_version.py` with a small static file that contains just
-the generated version data.
-
-## Installation
-
-See [INSTALL.md](./INSTALL.md) for detailed installation instructions.
-
-## Version-String Flavors
-
-Code which uses Versioneer can learn about its version string at runtime by
-importing `_version` from your main `__init__.py` file and running the
-`get_versions()` function. From the "outside" (e.g. in `setup.py`), you can
-import the top-level `versioneer.py` and run `get_versions()`.
-
-Both functions return a dictionary with different flavors of version
-information:
-
-* `['version']`: A condensed version string, rendered using the selected
-  style. This is the most commonly used value for the project's version
-  string. The default "pep440" style yields strings like `0.11`,
-  `0.11+2.g1076c97`, or `0.11+2.g1076c97.dirty`. See the "Styles" section
-  below for alternative styles.
-
-* `['full-revisionid']`: detailed revision identifier. For Git, this is the
-  full SHA1 commit id, e.g. "1076c978a8d3cfc70f408fe5974aa6c092c949ac".
-
-* `['date']`: Date and time of the latest `HEAD` commit. For Git, it is the
-  commit date in ISO 8601 format. This will be None if the date is not
-  available.
-
-* `['dirty']`: a boolean, True if the tree has uncommitted changes. Note that
-  this is only accurate if run in a VCS checkout, otherwise it is likely to
-  be False or None
-
-* `['error']`: if the version string could not be computed, this will be set
-  to a string describing the problem, otherwise it will be None. It may be
-  useful to throw an exception in setup.py if this is set, to avoid e.g.
-  creating tarballs with a version string of "unknown".
-
-Some variants are more useful than others. Including `full-revisionid` in a
-bug report should allow developers to reconstruct the exact code being tested
-(or indicate the presence of local changes that should be shared with the
-developers). `version` is suitable for display in an "about" box or a CLI
-`--version` output: it can be easily compared against release notes and lists
-of bugs fixed in various releases.
-
-The installer adds the following text to your `__init__.py` to place a basic
-version in `YOURPROJECT.__version__`:
-
-    from ._version import get_versions
-    __version__ = get_versions()['version']
-    del get_versions
-
-## Styles
-
-The setup.cfg `style=` configuration controls how the VCS information is
-rendered into a version string.
-
-The default style, "pep440", produces a PEP440-compliant string, equal to the
-un-prefixed tag name for actual releases, and containing an additional "local
-version" section with more detail for in-between builds. For Git, this is
-TAG[+DISTANCE.gHEX[.dirty]] , using information from `git describe --tags
---dirty --always`. For example "0.11+2.g1076c97.dirty" indicates that the
-tree is like the "1076c97" commit but has uncommitted changes (".dirty"), and
-that this commit is two revisions ("+2") beyond the "0.11" tag. For released
-software (exactly equal to a known tag), the identifier will only contain the
-stripped tag, e.g. "0.11".
-
-Other styles are available. See [details.md](details.md) in the Versioneer
-source tree for descriptions.
-
-## Debugging
-
-Versioneer tries to avoid fatal errors: if something goes wrong, it will tend
-to return a version of "0+unknown". To investigate the problem, run `setup.py
-version`, which will run the version-lookup code in a verbose mode, and will
-display the full contents of `get_versions()` (including the `error` string,
-which may help identify what went wrong).
-
-## Known Limitations
-
-Some situations are known to cause problems for Versioneer. This details the
-most significant ones. More can be found on Github
-[issues page](https://github.com/python-versioneer/python-versioneer/issues).
-
-### Subprojects
-
-Versioneer has limited support for source trees in which `setup.py` is not in
-the root directory (e.g. `setup.py` and `.git/` are *not* siblings). The are
-two common reasons why `setup.py` might not be in the root:
-
-* Source trees which contain multiple subprojects, such as
-  [Buildbot](https://github.com/buildbot/buildbot), which contains both
-  "master" and "slave" subprojects, each with their own `setup.py`,
-  `setup.cfg`, and `tox.ini`. Projects like these produce multiple PyPI
-  distributions (and upload multiple independently-installable tarballs).
-* Source trees whose main purpose is to contain a C library, but which also
-  provide bindings to Python (and perhaps other languages) in subdirectories.
-
-Versioneer will look for `.git` in parent directories, and most operations
-should get the right version string. However `pip` and `setuptools` have bugs
-and implementation details which frequently cause `pip install .` from a
-subproject directory to fail to find a correct version string (so it usually
-defaults to `0+unknown`).
-
-`pip install --editable .` should work correctly. `setup.py install` might
-work too.
-
-Pip-8.1.1 is known to have this problem, but hopefully it will get fixed in
-some later version.
-
-[Bug #38](https://github.com/python-versioneer/python-versioneer/issues/38) is tracking
-this issue. The discussion in
-[PR #61](https://github.com/python-versioneer/python-versioneer/pull/61) describes the
-issue from the Versioneer side in more detail.
-[pip PR#3176](https://github.com/pypa/pip/pull/3176) and
-[pip PR#3615](https://github.com/pypa/pip/pull/3615) contain work to improve
-pip to let Versioneer work correctly.
-
-Versioneer-0.16 and earlier only looked for a `.git` directory next to the
-`setup.cfg`, so subprojects were completely unsupported with those releases.
-
-### Editable installs with setuptools <= 18.5
-
-`setup.py develop` and `pip install --editable .` allow you to install a
-project into a virtualenv once, then continue editing the source code (and
-test) without re-installing after every change.
-
-"Entry-point scripts" (`setup(entry_points={"console_scripts": ..})`) are a
-convenient way to specify executable scripts that should be installed along
-with the python package.
-
-These both work as expected when using modern setuptools. When using
-setuptools-18.5 or earlier, however, certain operations will cause
-`pkg_resources.DistributionNotFound` errors when running the entrypoint
-script, which must be resolved by re-installing the package. This happens
-when the install happens with one version, then the egg_info data is
-regenerated while a different version is checked out. Many setup.py commands
-cause egg_info to be rebuilt (including `sdist`, `wheel`, and installing into
-a different virtualenv), so this can be surprising.
-
-[Bug #83](https://github.com/python-versioneer/python-versioneer/issues/83) describes
-this one, but upgrading to a newer version of setuptools should probably
-resolve it.
-
-
-## Updating Versioneer
-
-To upgrade your project to a new release of Versioneer, do the following:
-
-* install the new Versioneer (`pip install -U versioneer` or equivalent)
-* edit `setup.cfg`, if necessary, to include any new configuration settings
-  indicated by the release notes. See [UPGRADING](./UPGRADING.md) for details.
-* re-run `versioneer install` in your source tree, to replace
-  `SRC/_version.py`
-* commit any changed files
-
-## Future Directions
-
-This tool is designed to make it easily extended to other version-control
-systems: all VCS-specific components are in separate directories like
-src/git/ . The top-level `versioneer.py` script is assembled from these
-components by running make-versioneer.py . In the future, make-versioneer.py
-will take a VCS name as an argument, and will construct a version of
-`versioneer.py` that is specific to the given VCS. It might also take the
-configuration arguments that are currently provided manually during
-installation by editing setup.py . Alternatively, it might go the other
-direction and include code from all supported VCS systems, reducing the
-number of intermediate scripts.
-
-## Similar projects
-
-* [setuptools_scm](https://github.com/pypa/setuptools_scm/) - a non-vendored build-time
-  dependency
-* [minver](https://github.com/jbweston/miniver) - a lightweight reimplementation of
-  versioneer
-* [versioningit](https://github.com/jwodder/versioningit) - a PEP 518-based setuptools
-  plugin
-
-## License
-
-To make Versioneer easier to embed, all its code is dedicated to the public
-domain. The `_version.py` that it creates is also in the public domain.
-Specifically, both are released under the Creative Commons "Public Domain
-Dedication" license (CC0-1.0), as described in
-https://creativecommons.org/publicdomain/zero/1.0/ .
-
-[pypi-image]: https://img.shields.io/pypi/v/versioneer.svg
-[pypi-url]: https://pypi.python.org/pypi/versioneer/
-[travis-image]:
-https://img.shields.io/travis/com/python-versioneer/python-versioneer.svg
-[travis-url]: https://travis-ci.com/github/python-versioneer/python-versioneer
-
-"""
-# pylint:disable=invalid-name,import-outside-toplevel,missing-function-docstring
-# pylint:disable=missing-class-docstring,too-many-branches,too-many-statements
-# pylint:disable=raise-missing-from,too-many-lines,too-many-locals,import-error
-# pylint:disable=too-few-public-methods,redefined-outer-name,consider-using-with
-# pylint:disable=attribute-defined-outside-init,too-many-arguments
-
-import configparser
-import errno
-import json
-import os
-import re
-import subprocess
-import sys
-from typing import Callable, Dict
-import functools
-
-
-class VersioneerConfig:
-    """Container for Versioneer configuration parameters."""
-
-
-def get_root():
-    """Get the project root directory.
-
-    We require that all commands are run from the project root, i.e. the
-    directory that contains setup.py, setup.cfg, and versioneer.py .
-    """
-    root = os.path.realpath(os.path.abspath(os.getcwd()))
-    setup_py = os.path.join(root, "setup.py")
-    versioneer_py = os.path.join(root, "versioneer.py")
-    if not (os.path.exists(setup_py) or os.path.exists(versioneer_py)):
-        # allow 'python path/to/setup.py COMMAND'
-        root = os.path.dirname(os.path.realpath(os.path.abspath(sys.argv[0])))
-        setup_py = os.path.join(root, "setup.py")
-        versioneer_py = os.path.join(root, "versioneer.py")
-    if not (os.path.exists(setup_py) or os.path.exists(versioneer_py)):
-        err = (
-            "Versioneer was unable to run the project root directory. "
-            "Versioneer requires setup.py to be executed from "
-            "its immediate directory (like 'python setup.py COMMAND'), "
-            "or in a way that lets it use sys.argv[0] to find the root "
-            "(like 'python path/to/setup.py COMMAND')."
-        )
-        raise VersioneerBadRootError(err)
-    try:
-        # Certain runtime workflows (setup.py install/develop in a setuptools
-        # tree) execute all dependencies in a single python process, so
-        # "versioneer" may be imported multiple times, and python's shared
-        # module-import table will cache the first one. So we can't use
-        # os.path.dirname(__file__), as that will find whichever
-        # versioneer.py was first imported, even in later projects.
-        my_path = os.path.realpath(os.path.abspath(__file__))
-        me_dir = os.path.normcase(os.path.splitext(my_path)[0])
-        vsr_dir = os.path.normcase(os.path.splitext(versioneer_py)[0])
-        if me_dir != vsr_dir:
-            print(
-                "Warning: build in %s is using versioneer.py from %s"
-                % (os.path.dirname(my_path), versioneer_py)
-            )
-    except NameError:
-        pass
-    return root
-
-
-def get_config_from_root(root):
-    """Read the project setup.cfg file to determine Versioneer config."""
-    # This might raise OSError (if setup.cfg is missing), or
-    # configparser.NoSectionError (if it lacks a [versioneer] section), or
-    # configparser.NoOptionError (if it lacks "VCS="). See the docstring at
-    # the top of versioneer.py for instructions on writing your setup.cfg .
-    setup_cfg = os.path.join(root, "setup.cfg")
-    parser = configparser.ConfigParser()
-    with open(setup_cfg, "r") as cfg_file:
-        parser.read_file(cfg_file)
-    VCS = parser.get("versioneer", "VCS")  # mandatory
-
-    # Dict-like interface for non-mandatory entries
-    section = parser["versioneer"]
-
-    cfg = VersioneerConfig()
-    cfg.VCS = VCS
-    cfg.style = section.get("style", "")
-    cfg.versionfile_source = section.get("versionfile_source")
-    cfg.versionfile_build = section.get("versionfile_build")
-    cfg.tag_prefix = section.get("tag_prefix")
-    if cfg.tag_prefix in ("''", '""'):
-        cfg.tag_prefix = ""
-    cfg.parentdir_prefix = section.get("parentdir_prefix")
-    cfg.verbose = section.get("verbose")
-    return cfg
-
-
-class NotThisMethod(Exception):
-    """Exception raised if a method is not valid for the current scenario."""
-
-
-# these dictionaries contain VCS-specific tools
-LONG_VERSION_PY: Dict[str, str] = {}
-HANDLERS: Dict[str, Dict[str, Callable]] = {}
-
-
-def register_vcs_handler(vcs, method):  # decorator
-    """Create decorator to mark a method as the handler of a VCS."""
-
-    def decorate(f):
-        """Store f in HANDLERS[vcs][method]."""
-        HANDLERS.setdefault(vcs, {})[method] = f
-        return f
-
-    return decorate
-
-
-def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False, env=None):
-    """Call the given command(s)."""
-    assert isinstance(commands, list)
-    process = None
-
-    popen_kwargs = {}
-    if sys.platform == "win32":
-        # This hides the console window if pythonw.exe is used
-        startupinfo = subprocess.STARTUPINFO()
-        startupinfo.dwFlags |= subprocess.STARTF_USESHOWWINDOW
-        popen_kwargs["startupinfo"] = startupinfo
-
-    for command in commands:
-        try:
-            dispcmd = str([command] + args)
-            # remember shell=False, so use git.cmd on windows, not just git
-            process = subprocess.Popen(
-                [command] + args,
-                cwd=cwd,
-                env=env,
-                stdout=subprocess.PIPE,
-                stderr=(subprocess.PIPE if hide_stderr else None),
-                **popen_kwargs,
-            )
-            break
-        except OSError:
-            e = sys.exc_info()[1]
-            if e.errno == errno.ENOENT:
-                continue
-            if verbose:
-                print("unable to run %s" % dispcmd)
-                print(e)
-            return None, None
-    else:
-        if verbose:
-            print("unable to find command, tried %s" % (commands,))
-        return None, None
-    stdout = process.communicate()[0].strip().decode()
-    if process.returncode != 0:
-        if verbose:
-            print("unable to run %s (error)" % dispcmd)
-            print("stdout was %s" % stdout)
-        return None, process.returncode
-    return stdout, process.returncode
-
-
-LONG_VERSION_PY[
-    "git"
-] = r'''
-# This file helps to compute a version number in source trees obtained from
-# git-archive tarball (such as those provided by githubs download-from-tag
-# feature). Distribution tarballs (built by setup.py sdist) and build
-# directories (produced by setup.py build) will contain a much shorter file
-# that just contains the computed version number.
-
-# This file is released into the public domain. Generated by
-# versioneer-0.22 (https://github.com/python-versioneer/python-versioneer)
-
-"""Git implementation of _version.py."""
-
-import errno
-import os
-import re
-import subprocess
-import sys
-from typing import Callable, Dict
-import functools
-
-
-def get_keywords():
-    """Get the keywords needed to look up the version information."""
-    # these strings will be replaced by git during git-archive.
-    # setup.py/versioneer.py will grep for the variable names, so they must
-    # each be defined on a line of their own. _version.py will just call
-    # get_keywords().
-    git_refnames = "%(DOLLAR)sFormat:%%d%(DOLLAR)s"
-    git_full = "%(DOLLAR)sFormat:%%H%(DOLLAR)s"
-    git_date = "%(DOLLAR)sFormat:%%ci%(DOLLAR)s"
-    keywords = {"refnames": git_refnames, "full": git_full, "date": git_date}
-    return keywords
-
-
-class VersioneerConfig:
-    """Container for Versioneer configuration parameters."""
-
-
-def get_config():
-    """Create, populate and return the VersioneerConfig() object."""
-    # these strings are filled in when 'setup.py versioneer' creates
-    # _version.py
-    cfg = VersioneerConfig()
-    cfg.VCS = "git"
-    cfg.style = "%(STYLE)s"
-    cfg.tag_prefix = "%(TAG_PREFIX)s"
-    cfg.parentdir_prefix = "%(PARENTDIR_PREFIX)s"
-    cfg.versionfile_source = "%(VERSIONFILE_SOURCE)s"
-    cfg.verbose = False
-    return cfg
-
-
-class NotThisMethod(Exception):
-    """Exception raised if a method is not valid for the current scenario."""
-
-
-LONG_VERSION_PY: Dict[str, str] = {}
-HANDLERS: Dict[str, Dict[str, Callable]] = {}
-
-
-def register_vcs_handler(vcs, method):  # decorator
-    """Create decorator to mark a method as the handler of a VCS."""
-    def decorate(f):
-        """Store f in HANDLERS[vcs][method]."""
-        if vcs not in HANDLERS:
-            HANDLERS[vcs] = {}
-        HANDLERS[vcs][method] = f
-        return f
-    return decorate
-
-
-def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False,
-                env=None):
-    """Call the given command(s)."""
-    assert isinstance(commands, list)
-    process = None
-
-    popen_kwargs = {}
-    if sys.platform == "win32":
-        # This hides the console window if pythonw.exe is used
-        startupinfo = subprocess.STARTUPINFO()
-        startupinfo.dwFlags |= subprocess.STARTF_USESHOWWINDOW
-        popen_kwargs["startupinfo"] = startupinfo
-
-    for command in commands:
-        try:
-            dispcmd = str([command] + args)
-            # remember shell=False, so use git.cmd on windows, not just git
-            process = subprocess.Popen([command] + args, cwd=cwd, env=env,
-                                       stdout=subprocess.PIPE,
-                                       stderr=(subprocess.PIPE if hide_stderr
-                                               else None), **popen_kwargs)
-            break
-        except OSError:
-            e = sys.exc_info()[1]
-            if e.errno == errno.ENOENT:
-                continue
-            if verbose:
-                print("unable to run %%s" %% dispcmd)
-                print(e)
-            return None, None
-    else:
-        if verbose:
-            print("unable to find command, tried %%s" %% (commands,))
-        return None, None
-    stdout = process.communicate()[0].strip().decode()
-    if process.returncode != 0:
-        if verbose:
-            print("unable to run %%s (error)" %% dispcmd)
-            print("stdout was %%s" %% stdout)
-        return None, process.returncode
-    return stdout, process.returncode
-
-
-def versions_from_parentdir(parentdir_prefix, root, verbose):
-    """Try to determine the version from the parent directory name.
-
-    Source tarballs conventionally unpack into a directory that includes both
-    the project name and a version string. We will also support searching up
-    two directory levels for an appropriately named parent directory
-    """
-    rootdirs = []
-
-    for _ in range(3):
-        dirname = os.path.basename(root)
-        if dirname.startswith(parentdir_prefix):
-            return {"version": dirname[len(parentdir_prefix):],
-                    "full-revisionid": None,
-                    "dirty": False, "error": None, "date": None}
-        rootdirs.append(root)
-        root = os.path.dirname(root)  # up a level
-
-    if verbose:
-        print("Tried directories %%s but none started with prefix %%s" %%
-              (str(rootdirs), parentdir_prefix))
-    raise NotThisMethod("rootdir doesn't start with parentdir_prefix")
-
-
-@register_vcs_handler("git", "get_keywords")
-def git_get_keywords(versionfile_abs):
-    """Extract version information from the given file."""
-    # the code embedded in _version.py can just fetch the value of these
-    # keywords. When used from setup.py, we don't want to import _version.py,
-    # so we do it with a regexp instead. This function is not used from
-    # _version.py.
-    keywords = {}
-    try:
-        with open(versionfile_abs, "r") as fobj:
-            for line in fobj:
-                if line.strip().startswith("git_refnames ="):
-                    mo = re.search(r'=\s*"(.*)"', line)
-                    if mo:
-                        keywords["refnames"] = mo.group(1)
-                if line.strip().startswith("git_full ="):
-                    mo = re.search(r'=\s*"(.*)"', line)
-                    if mo:
-                        keywords["full"] = mo.group(1)
-                if line.strip().startswith("git_date ="):
-                    mo = re.search(r'=\s*"(.*)"', line)
-                    if mo:
-                        keywords["date"] = mo.group(1)
-    except OSError:
-        pass
-    return keywords
-
-
-@register_vcs_handler("git", "keywords")
-def git_versions_from_keywords(keywords, tag_prefix, verbose):
-    """Get version information from git keywords."""
-    if "refnames" not in keywords:
-        raise NotThisMethod("Short version file found")
-    date = keywords.get("date")
-    if date is not None:
-        # Use only the last line.  Previous lines may contain GPG signature
-        # information.
-        date = date.splitlines()[-1]
-
-        # git-2.2.0 added "%%cI", which expands to an ISO-8601 -compliant
-        # datestamp. However we prefer "%%ci" (which expands to an "ISO-8601
-        # -like" string, which we must then edit to make compliant), because
-        # it's been around since git-1.5.3, and it's too difficult to
-        # discover which version we're using, or to work around using an
-        # older one.
-        date = date.strip().replace(" ", "T", 1).replace(" ", "", 1)
-    refnames = keywords["refnames"].strip()
-    if refnames.startswith("$Format"):
-        if verbose:
-            print("keywords are unexpanded, not using")
-        raise NotThisMethod("unexpanded keywords, not a git-archive tarball")
-    refs = {r.strip() for r in refnames.strip("()").split(",")}
-    # starting in git-1.8.3, tags are listed as "tag: foo-1.0" instead of
-    # just "foo-1.0". If we see a "tag: " prefix, prefer those.
-    TAG = "tag: "
-    tags = {r[len(TAG):] for r in refs if r.startswith(TAG)}
-    if not tags:
-        # Either we're using git < 1.8.3, or there really are no tags. We use
-        # a heuristic: assume all version tags have a digit. The old git %%d
-        # expansion behaves like git log --decorate=short and strips out the
-        # refs/heads/ and refs/tags/ prefixes that would let us distinguish
-        # between branches and tags. By ignoring refnames without digits, we
-        # filter out many common branch names like "release" and
-        # "stabilization", as well as "HEAD" and "master".
-        tags = {r for r in refs if re.search(r'\d', r)}
-        if verbose:
-            print("discarding '%%s', no digits" %% ",".join(refs - tags))
-    if verbose:
-        print("likely tags: %%s" %% ",".join(sorted(tags)))
-    for ref in sorted(tags):
-        # sorting will prefer e.g. "2.0" over "2.0rc1"
-        if ref.startswith(tag_prefix):
-            r = ref[len(tag_prefix):]
-            # Filter out refs that exactly match prefix or that don't start
-            # with a number once the prefix is stripped (mostly a concern
-            # when prefix is '')
-            if not re.match(r'\d', r):
-                continue
-            if verbose:
-                print("picking %%s" %% r)
-            return {"version": r,
-                    "full-revisionid": keywords["full"].strip(),
-                    "dirty": False, "error": None,
-                    "date": date}
-    # no suitable tags, so version is "0+unknown", but full hex is still there
-    if verbose:
-        print("no suitable tags, using unknown + full revision id")
-    return {"version": "0+unknown",
-            "full-revisionid": keywords["full"].strip(),
-            "dirty": False, "error": "no suitable tags", "date": None}
-
-
-@register_vcs_handler("git", "pieces_from_vcs")
-def git_pieces_from_vcs(tag_prefix, root, verbose, runner=run_command):
-    """Get version from 'git describe' in the root of the source tree.
-
-    This only gets called if the git-archive 'subst' keywords were *not*
-    expanded, and _version.py hasn't already been rewritten with a short
-    version string, meaning we're inside a checked out source tree.
-    """
-    GITS = ["git"]
-    if sys.platform == "win32":
-        GITS = ["git.cmd", "git.exe"]
-
-    # GIT_DIR can interfere with correct operation of Versioneer.
-    # It may be intended to be passed to the Versioneer-versioned project,
-    # but that should not change where we get our version from.
-    env = os.environ.copy()
-    env.pop("GIT_DIR", None)
-    runner = functools.partial(runner, env=env)
-
-    _, rc = runner(GITS, ["rev-parse", "--git-dir"], cwd=root,
-                   hide_stderr=True)
-    if rc != 0:
-        if verbose:
-            print("Directory %%s not under git control" %% root)
-        raise NotThisMethod("'git rev-parse --git-dir' returned error")
-
-    MATCH_ARGS = ["--match", "%%s*" %% tag_prefix] if tag_prefix else []
-
-    # if there is a tag matching tag_prefix, this yields TAG-NUM-gHEX[-dirty]
-    # if there isn't one, this yields HEX[-dirty] (no NUM)
-    describe_out, rc = runner(GITS, ["describe", "--tags", "--dirty",
-                                     "--always", "--long", *MATCH_ARGS],
-                              cwd=root)
-    # --long was added in git-1.5.5
-    if describe_out is None:
-        raise NotThisMethod("'git describe' failed")
-    describe_out = describe_out.strip()
-    full_out, rc = runner(GITS, ["rev-parse", "HEAD"], cwd=root)
-    if full_out is None:
-        raise NotThisMethod("'git rev-parse' failed")
-    full_out = full_out.strip()
-
-    pieces = {}
-    pieces["long"] = full_out
-    pieces["short"] = full_out[:7]  # maybe improved later
-    pieces["error"] = None
-
-    branch_name, rc = runner(GITS, ["rev-parse", "--abbrev-ref", "HEAD"],
-                             cwd=root)
-    # --abbrev-ref was added in git-1.6.3
-    if rc != 0 or branch_name is None:
-        raise NotThisMethod("'git rev-parse --abbrev-ref' returned error")
-    branch_name = branch_name.strip()
-
-    if branch_name == "HEAD":
-        # If we aren't exactly on a branch, pick a branch which represents
-        # the current commit. If all else fails, we are on a branchless
-        # commit.
-        branches, rc = runner(GITS, ["branch", "--contains"], cwd=root)
-        # --contains was added in git-1.5.4
-        if rc != 0 or branches is None:
-            raise NotThisMethod("'git branch --contains' returned error")
-        branches = branches.split("\n")
-
-        # Remove the first line if we're running detached
-        if "(" in branches[0]:
-            branches.pop(0)
-
-        # Strip off the leading "* " from the list of branches.
-        branches = [branch[2:] for branch in branches]
-        if "master" in branches:
-            branch_name = "master"
-        elif not branches:
-            branch_name = None
-        else:
-            # Pick the first branch that is returned. Good or bad.
-            branch_name = branches[0]
-
-    pieces["branch"] = branch_name
-
-    # parse describe_out. It will be like TAG-NUM-gHEX[-dirty] or HEX[-dirty]
-    # TAG might have hyphens.
-    git_describe = describe_out
-
-    # look for -dirty suffix
-    dirty = git_describe.endswith("-dirty")
-    pieces["dirty"] = dirty
-    if dirty:
-        git_describe = git_describe[:git_describe.rindex("-dirty")]
-
-    # now we have TAG-NUM-gHEX or HEX
-
-    if "-" in git_describe:
-        # TAG-NUM-gHEX
-        mo = re.search(r'^(.+)-(\d+)-g([0-9a-f]+)$', git_describe)
-        if not mo:
-            # unparsable. Maybe git-describe is misbehaving?
-            pieces["error"] = ("unable to parse git-describe output: '%%s'"
-                               %% describe_out)
-            return pieces
-
-        # tag
-        full_tag = mo.group(1)
-        if not full_tag.startswith(tag_prefix):
-            if verbose:
-                fmt = "tag '%%s' doesn't start with prefix '%%s'"
-                print(fmt %% (full_tag, tag_prefix))
-            pieces["error"] = ("tag '%%s' doesn't start with prefix '%%s'"
-                               %% (full_tag, tag_prefix))
-            return pieces
-        pieces["closest-tag"] = full_tag[len(tag_prefix):]
-
-        # distance: number of commits since tag
-        pieces["distance"] = int(mo.group(2))
-
-        # commit: short hex revision ID
-        pieces["short"] = mo.group(3)
-
-    else:
-        # HEX: no tags
-        pieces["closest-tag"] = None
-        count_out, rc = runner(GITS, ["rev-list", "HEAD", "--count"], cwd=root)
-        pieces["distance"] = int(count_out)  # total number of commits
-
-    # commit date: see ISO-8601 comment in git_versions_from_keywords()
-    date = runner(GITS, ["show", "-s", "--format=%%ci", "HEAD"], cwd=root)[0].strip()
-    # Use only the last line.  Previous lines may contain GPG signature
-    # information.
-    date = date.splitlines()[-1]
-    pieces["date"] = date.strip().replace(" ", "T", 1).replace(" ", "", 1)
-
-    return pieces
-
-
-def plus_or_dot(pieces):
-    """Return a + if we don't already have one, else return a ."""
-    if "+" in pieces.get("closest-tag", ""):
-        return "."
-    return "+"
-
-
-def render_pep440(pieces):
-    """Build up version string, with post-release "local version identifier".
-
-    Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you
-    get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty
-
-    Exceptions:
-    1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty]
-    """
-    if pieces["closest-tag"]:
-        rendered = pieces["closest-tag"]
-        if pieces["distance"] or pieces["dirty"]:
-            rendered += plus_or_dot(pieces)
-            rendered += "%%d.g%%s" %% (pieces["distance"], pieces["short"])
-            if pieces["dirty"]:
-                rendered += ".dirty"
-    else:
-        # exception #1
-        rendered = "0+untagged.%%d.g%%s" %% (pieces["distance"],
-                                          pieces["short"])
-        if pieces["dirty"]:
-            rendered += ".dirty"
-    return rendered
-
-
-def render_pep440_branch(pieces):
-    """TAG[[.dev0]+DISTANCE.gHEX[.dirty]] .
-
-    The ".dev0" means not master branch. Note that .dev0 sorts backwards
-    (a feature branch will appear "older" than the master branch).
-
-    Exceptions:
-    1: no tags. 0[.dev0]+untagged.DISTANCE.gHEX[.dirty]
-    """
-    if pieces["closest-tag"]:
-        rendered = pieces["closest-tag"]
-        if pieces["distance"] or pieces["dirty"]:
-            if pieces["branch"] != "master":
-                rendered += ".dev0"
-            rendered += plus_or_dot(pieces)
-            rendered += "%%d.g%%s" %% (pieces["distance"], pieces["short"])
-            if pieces["dirty"]:
-                rendered += ".dirty"
-    else:
-        # exception #1
-        rendered = "0"
-        if pieces["branch"] != "master":
-            rendered += ".dev0"
-        rendered += "+untagged.%%d.g%%s" %% (pieces["distance"],
-                                          pieces["short"])
-        if pieces["dirty"]:
-            rendered += ".dirty"
-    return rendered
-
-
-def pep440_split_post(ver):
-    """Split pep440 version string at the post-release segment.
-
-    Returns the release segments before the post-release and the
-    post-release version number (or -1 if no post-release segment is present).
-    """
-    vc = str.split(ver, ".post")
-    return vc[0], int(vc[1] or 0) if len(vc) == 2 else None
-
-
-def render_pep440_pre(pieces):
-    """TAG[.postN.devDISTANCE] -- No -dirty.
-
-    Exceptions:
-    1: no tags. 0.post0.devDISTANCE
-    """
-    if pieces["closest-tag"]:
-        if pieces["distance"]:
-            # update the post release segment
-            tag_version, post_version = pep440_split_post(pieces["closest-tag"])
-            rendered = tag_version
-            if post_version is not None:
-                rendered += ".post%%d.dev%%d" %% (post_version+1, pieces["distance"])
-            else:
-                rendered += ".post0.dev%%d" %% (pieces["distance"])
-        else:
-            # no commits, use the tag as the version
-            rendered = pieces["closest-tag"]
-    else:
-        # exception #1
-        rendered = "0.post0.dev%%d" %% pieces["distance"]
-    return rendered
-
-
-def render_pep440_post(pieces):
-    """TAG[.postDISTANCE[.dev0]+gHEX] .
-
-    The ".dev0" means dirty. Note that .dev0 sorts backwards
-    (a dirty tree will appear "older" than the corresponding clean one),
-    but you shouldn't be releasing software with -dirty anyways.
-
-    Exceptions:
-    1: no tags. 0.postDISTANCE[.dev0]
-    """
-    if pieces["closest-tag"]:
-        rendered = pieces["closest-tag"]
-        if pieces["distance"] or pieces["dirty"]:
-            rendered += ".post%%d" %% pieces["distance"]
-            if pieces["dirty"]:
-                rendered += ".dev0"
-            rendered += plus_or_dot(pieces)
-            rendered += "g%%s" %% pieces["short"]
-    else:
-        # exception #1
-        rendered = "0.post%%d" %% pieces["distance"]
-        if pieces["dirty"]:
-            rendered += ".dev0"
-        rendered += "+g%%s" %% pieces["short"]
-    return rendered
-
-
-def render_pep440_post_branch(pieces):
-    """TAG[.postDISTANCE[.dev0]+gHEX[.dirty]] .
-
-    The ".dev0" means not master branch.
-
-    Exceptions:
-    1: no tags. 0.postDISTANCE[.dev0]+gHEX[.dirty]
-    """
-    if pieces["closest-tag"]:
-        rendered = pieces["closest-tag"]
-        if pieces["distance"] or pieces["dirty"]:
-            rendered += ".post%%d" %% pieces["distance"]
-            if pieces["branch"] != "master":
-                rendered += ".dev0"
-            rendered += plus_or_dot(pieces)
-            rendered += "g%%s" %% pieces["short"]
-            if pieces["dirty"]:
-                rendered += ".dirty"
-    else:
-        # exception #1
-        rendered = "0.post%%d" %% pieces["distance"]
-        if pieces["branch"] != "master":
-            rendered += ".dev0"
-        rendered += "+g%%s" %% pieces["short"]
-        if pieces["dirty"]:
-            rendered += ".dirty"
-    return rendered
-
-
-def render_pep440_old(pieces):
-    """TAG[.postDISTANCE[.dev0]] .
-
-    The ".dev0" means dirty.
-
-    Exceptions:
-    1: no tags. 0.postDISTANCE[.dev0]
-    """
-    if pieces["closest-tag"]:
-        rendered = pieces["closest-tag"]
-        if pieces["distance"] or pieces["dirty"]:
-            rendered += ".post%%d" %% pieces["distance"]
-            if pieces["dirty"]:
-                rendered += ".dev0"
-    else:
-        # exception #1
-        rendered = "0.post%%d" %% pieces["distance"]
-        if pieces["dirty"]:
-            rendered += ".dev0"
-    return rendered
-
-
-def render_git_describe(pieces):
-    """TAG[-DISTANCE-gHEX][-dirty].
-
-    Like 'git describe --tags --dirty --always'.
-
-    Exceptions:
-    1: no tags. HEX[-dirty]  (note: no 'g' prefix)
-    """
-    if pieces["closest-tag"]:
-        rendered = pieces["closest-tag"]
-        if pieces["distance"]:
-            rendered += "-%%d-g%%s" %% (pieces["distance"], pieces["short"])
-    else:
-        # exception #1
-        rendered = pieces["short"]
-    if pieces["dirty"]:
-        rendered += "-dirty"
-    return rendered
-
-
-def render_git_describe_long(pieces):
-    """TAG-DISTANCE-gHEX[-dirty].
-
-    Like 'git describe --tags --dirty --always -long'.
-    The distance/hash is unconditional.
-
-    Exceptions:
-    1: no tags. HEX[-dirty]  (note: no 'g' prefix)
-    """
-    if pieces["closest-tag"]:
-        rendered = pieces["closest-tag"]
-        rendered += "-%%d-g%%s" %% (pieces["distance"], pieces["short"])
-    else:
-        # exception #1
-        rendered = pieces["short"]
-    if pieces["dirty"]:
-        rendered += "-dirty"
-    return rendered
-
-
-def render(pieces, style):
-    """Render the given version pieces into the requested style."""
-    if pieces["error"]:
-        return {"version": "unknown",
-                "full-revisionid": pieces.get("long"),
-                "dirty": None,
-                "error": pieces["error"],
-                "date": None}
-
-    if not style or style == "default":
-        style = "pep440"  # the default
-
-    if style == "pep440":
-        rendered = render_pep440(pieces)
-    elif style == "pep440-branch":
-        rendered = render_pep440_branch(pieces)
-    elif style == "pep440-pre":
-        rendered = render_pep440_pre(pieces)
-    elif style == "pep440-post":
-        rendered = render_pep440_post(pieces)
-    elif style == "pep440-post-branch":
-        rendered = render_pep440_post_branch(pieces)
-    elif style == "pep440-old":
-        rendered = render_pep440_old(pieces)
-    elif style == "git-describe":
-        rendered = render_git_describe(pieces)
-    elif style == "git-describe-long":
-        rendered = render_git_describe_long(pieces)
-    else:
-        raise ValueError("unknown style '%%s'" %% style)
-
-    return {"version": rendered, "full-revisionid": pieces["long"],
-            "dirty": pieces["dirty"], "error": None,
-            "date": pieces.get("date")}
-
-
-def get_versions():
-    """Get version information or return default if unable to do so."""
-    # I am in _version.py, which lives at ROOT/VERSIONFILE_SOURCE. If we have
-    # __file__, we can work backwards from there to the root. Some
-    # py2exe/bbfreeze/non-CPython implementations don't do __file__, in which
-    # case we can only use expanded keywords.
-
-    cfg = get_config()
-    verbose = cfg.verbose
-
-    try:
-        return git_versions_from_keywords(get_keywords(), cfg.tag_prefix,
-                                          verbose)
-    except NotThisMethod:
-        pass
-
-    try:
-        root = os.path.realpath(__file__)
-        # versionfile_source is the relative path from the top of the source
-        # tree (where the .git directory might live) to this file. Invert
-        # this to find the root from __file__.
-        for _ in cfg.versionfile_source.split('/'):
-            root = os.path.dirname(root)
-    except NameError:
-        return {"version": "0+unknown", "full-revisionid": None,
-                "dirty": None,
-                "error": "unable to find root of source tree",
-                "date": None}
-
-    try:
-        pieces = git_pieces_from_vcs(cfg.tag_prefix, root, verbose)
-        return render(pieces, cfg.style)
-    except NotThisMethod:
-        pass
-
-    try:
-        if cfg.parentdir_prefix:
-            return versions_from_parentdir(cfg.parentdir_prefix, root, verbose)
-    except NotThisMethod:
-        pass
-
-    return {"version": "0+unknown", "full-revisionid": None,
-            "dirty": None,
-            "error": "unable to compute version", "date": None}
-'''
-
-
-@register_vcs_handler("git", "get_keywords")
-def git_get_keywords(versionfile_abs):
-    """Extract version information from the given file."""
-    # the code embedded in _version.py can just fetch the value of these
-    # keywords. When used from setup.py, we don't want to import _version.py,
-    # so we do it with a regexp instead. This function is not used from
-    # _version.py.
-    keywords = {}
-    try:
-        with open(versionfile_abs, "r") as fobj:
-            for line in fobj:
-                if line.strip().startswith("git_refnames ="):
-                    mo = re.search(r'=\s*"(.*)"', line)
-                    if mo:
-                        keywords["refnames"] = mo.group(1)
-                if line.strip().startswith("git_full ="):
-                    mo = re.search(r'=\s*"(.*)"', line)
-                    if mo:
-                        keywords["full"] = mo.group(1)
-                if line.strip().startswith("git_date ="):
-                    mo = re.search(r'=\s*"(.*)"', line)
-                    if mo:
-                        keywords["date"] = mo.group(1)
-    except OSError:
-        pass
-    return keywords
-
-
-@register_vcs_handler("git", "keywords")
-def git_versions_from_keywords(keywords, tag_prefix, verbose):
-    """Get version information from git keywords."""
-    if "refnames" not in keywords:
-        raise NotThisMethod("Short version file found")
-    date = keywords.get("date")
-    if date is not None:
-        # Use only the last line.  Previous lines may contain GPG signature
-        # information.
-        date = date.splitlines()[-1]
-
-        # git-2.2.0 added "%cI", which expands to an ISO-8601 -compliant
-        # datestamp. However we prefer "%ci" (which expands to an "ISO-8601
-        # -like" string, which we must then edit to make compliant), because
-        # it's been around since git-1.5.3, and it's too difficult to
-        # discover which version we're using, or to work around using an
-        # older one.
-        date = date.strip().replace(" ", "T", 1).replace(" ", "", 1)
-    refnames = keywords["refnames"].strip()
-    if refnames.startswith("$Format"):
-        if verbose:
-            print("keywords are unexpanded, not using")
-        raise NotThisMethod("unexpanded keywords, not a git-archive tarball")
-    refs = {r.strip() for r in refnames.strip("()").split(",")}
-    # starting in git-1.8.3, tags are listed as "tag: foo-1.0" instead of
-    # just "foo-1.0". If we see a "tag: " prefix, prefer those.
-    TAG = "tag: "
-    tags = {r[len(TAG) :] for r in refs if r.startswith(TAG)}
-    if not tags:
-        # Either we're using git < 1.8.3, or there really are no tags. We use
-        # a heuristic: assume all version tags have a digit. The old git %d
-        # expansion behaves like git log --decorate=short and strips out the
-        # refs/heads/ and refs/tags/ prefixes that would let us distinguish
-        # between branches and tags. By ignoring refnames without digits, we
-        # filter out many common branch names like "release" and
-        # "stabilization", as well as "HEAD" and "master".
-        tags = {r for r in refs if re.search(r"\d", r)}
-        if verbose:
-            print("discarding '%s', no digits" % ",".join(refs - tags))
-    if verbose:
-        print("likely tags: %s" % ",".join(sorted(tags)))
-    for ref in sorted(tags):
-        # sorting will prefer e.g. "2.0" over "2.0rc1"
-        if ref.startswith(tag_prefix):
-            r = ref[len(tag_prefix) :]
-            # Filter out refs that exactly match prefix or that don't start
-            # with a number once the prefix is stripped (mostly a concern
-            # when prefix is '')
-            if not re.match(r"\d", r):
-                continue
-            if verbose:
-                print("picking %s" % r)
-            return {
-                "version": r,
-                "full-revisionid": keywords["full"].strip(),
-                "dirty": False,
-                "error": None,
-                "date": date,
-            }
-    # no suitable tags, so version is "0+unknown", but full hex is still there
-    if verbose:
-        print("no suitable tags, using unknown + full revision id")
-    return {
-        "version": "0+unknown",
-        "full-revisionid": keywords["full"].strip(),
-        "dirty": False,
-        "error": "no suitable tags",
-        "date": None,
-    }
-
-
-@register_vcs_handler("git", "pieces_from_vcs")
-def git_pieces_from_vcs(tag_prefix, root, verbose, runner=run_command):
-    """Get version from 'git describe' in the root of the source tree.
-
-    This only gets called if the git-archive 'subst' keywords were *not*
-    expanded, and _version.py hasn't already been rewritten with a short
-    version string, meaning we're inside a checked out source tree.
-    """
-    GITS = ["git"]
-    if sys.platform == "win32":
-        GITS = ["git.cmd", "git.exe"]
-
-    # GIT_DIR can interfere with correct operation of Versioneer.
-    # It may be intended to be passed to the Versioneer-versioned project,
-    # but that should not change where we get our version from.
-    env = os.environ.copy()
-    env.pop("GIT_DIR", None)
-    runner = functools.partial(runner, env=env)
-
-    _, rc = runner(GITS, ["rev-parse", "--git-dir"], cwd=root, hide_stderr=True)
-    if rc != 0:
-        if verbose:
-            print("Directory %s not under git control" % root)
-        raise NotThisMethod("'git rev-parse --git-dir' returned error")
-
-    MATCH_ARGS = ["--match", "%s*" % tag_prefix] if tag_prefix else []
-
-    # if there is a tag matching tag_prefix, this yields TAG-NUM-gHEX[-dirty]
-    # if there isn't one, this yields HEX[-dirty] (no NUM)
-    describe_out, rc = runner(
-        GITS,
-        ["describe", "--tags", "--dirty", "--always", "--long", *MATCH_ARGS],
-        cwd=root,
-    )
-    # --long was added in git-1.5.5
-    if describe_out is None:
-        raise NotThisMethod("'git describe' failed")
-    describe_out = describe_out.strip()
-    full_out, rc = runner(GITS, ["rev-parse", "HEAD"], cwd=root)
-    if full_out is None:
-        raise NotThisMethod("'git rev-parse' failed")
-    full_out = full_out.strip()
-
-    pieces = {}
-    pieces["long"] = full_out
-    pieces["short"] = full_out[:7]  # maybe improved later
-    pieces["error"] = None
-
-    branch_name, rc = runner(GITS, ["rev-parse", "--abbrev-ref", "HEAD"], cwd=root)
-    # --abbrev-ref was added in git-1.6.3
-    if rc != 0 or branch_name is None:
-        raise NotThisMethod("'git rev-parse --abbrev-ref' returned error")
-    branch_name = branch_name.strip()
-
-    if branch_name == "HEAD":
-        # If we aren't exactly on a branch, pick a branch which represents
-        # the current commit. If all else fails, we are on a branchless
-        # commit.
-        branches, rc = runner(GITS, ["branch", "--contains"], cwd=root)
-        # --contains was added in git-1.5.4
-        if rc != 0 or branches is None:
-            raise NotThisMethod("'git branch --contains' returned error")
-        branches = branches.split("\n")
-
-        # Remove the first line if we're running detached
-        if "(" in branches[0]:
-            branches.pop(0)
-
-        # Strip off the leading "* " from the list of branches.
-        branches = [branch[2:] for branch in branches]
-        if "master" in branches:
-            branch_name = "master"
-        elif not branches:
-            branch_name = None
-        else:
-            # Pick the first branch that is returned. Good or bad.
-            branch_name = branches[0]
-
-    pieces["branch"] = branch_name
-
-    # parse describe_out. It will be like TAG-NUM-gHEX[-dirty] or HEX[-dirty]
-    # TAG might have hyphens.
-    git_describe = describe_out
-
-    # look for -dirty suffix
-    dirty = git_describe.endswith("-dirty")
-    pieces["dirty"] = dirty
-    if dirty:
-        git_describe = git_describe[: git_describe.rindex("-dirty")]
-
-    # now we have TAG-NUM-gHEX or HEX
-
-    if "-" in git_describe:
-        # TAG-NUM-gHEX
-        mo = re.search(r"^(.+)-(\d+)-g([0-9a-f]+)$", git_describe)
-        if not mo:
-            # unparsable. Maybe git-describe is misbehaving?
-            pieces["error"] = "unable to parse git-describe output: '%s'" % describe_out
-            return pieces
-
-        # tag
-        full_tag = mo.group(1)
-        if not full_tag.startswith(tag_prefix):
-            if verbose:
-                fmt = "tag '%s' doesn't start with prefix '%s'"
-                print(fmt % (full_tag, tag_prefix))
-            pieces["error"] = "tag '%s' doesn't start with prefix '%s'" % (
-                full_tag,
-                tag_prefix,
-            )
-            return pieces
-        pieces["closest-tag"] = full_tag[len(tag_prefix) :]
-
-        # distance: number of commits since tag
-        pieces["distance"] = int(mo.group(2))
-
-        # commit: short hex revision ID
-        pieces["short"] = mo.group(3)
-
-    else:
-        # HEX: no tags
-        pieces["closest-tag"] = None
-        count_out, rc = runner(GITS, ["rev-list", "HEAD", "--count"], cwd=root)
-        pieces["distance"] = int(count_out)  # total number of commits
-
-    # commit date: see ISO-8601 comment in git_versions_from_keywords()
-    date = runner(GITS, ["show", "-s", "--format=%ci", "HEAD"], cwd=root)[0].strip()
-    # Use only the last line.  Previous lines may contain GPG signature
-    # information.
-    date = date.splitlines()[-1]
-    pieces["date"] = date.strip().replace(" ", "T", 1).replace(" ", "", 1)
-
-    return pieces
-
-
-def do_vcs_install(manifest_in, versionfile_source, ipy):
-    """Git-specific installation logic for Versioneer.
-
-    For Git, this means creating/changing .gitattributes to mark _version.py
-    for export-subst keyword substitution.
-    """
-    GITS = ["git"]
-    if sys.platform == "win32":
-        GITS = ["git.cmd", "git.exe"]
-    files = [manifest_in, versionfile_source]
-    if ipy:
-        files.append(ipy)
-    try:
-        my_path = __file__
-        if my_path.endswith(".pyc") or my_path.endswith(".pyo"):
-            my_path = os.path.splitext(my_path)[0] + ".py"
-        versioneer_file = os.path.relpath(my_path)
-    except NameError:
-        versioneer_file = "versioneer.py"
-    files.append(versioneer_file)
-    present = False
-    try:
-        with open(".gitattributes", "r") as fobj:
-            for line in fobj:
-                if line.strip().startswith(versionfile_source):
-                    if "export-subst" in line.strip().split()[1:]:
-                        present = True
-                        break
-    except OSError:
-        pass
-    if not present:
-        with open(".gitattributes", "a+") as fobj:
-            fobj.write(f"{versionfile_source} export-subst\n")
-        files.append(".gitattributes")
-    run_command(GITS, ["add", "--"] + files)
-
-
-def versions_from_parentdir(parentdir_prefix, root, verbose):
-    """Try to determine the version from the parent directory name.
-
-    Source tarballs conventionally unpack into a directory that includes both
-    the project name and a version string. We will also support searching up
-    two directory levels for an appropriately named parent directory
-    """
-    rootdirs = []
-
-    for _ in range(3):
-        dirname = os.path.basename(root)
-        if dirname.startswith(parentdir_prefix):
-            return {
-                "version": dirname[len(parentdir_prefix) :],
-                "full-revisionid": None,
-                "dirty": False,
-                "error": None,
-                "date": None,
-            }
-        rootdirs.append(root)
-        root = os.path.dirname(root)  # up a level
-
-    if verbose:
-        print(
-            "Tried directories %s but none started with prefix %s"
-            % (str(rootdirs), parentdir_prefix)
-        )
-    raise NotThisMethod("rootdir doesn't start with parentdir_prefix")
-
-
-SHORT_VERSION_PY = """
-# This file was generated by 'versioneer.py' (0.22) from
-# revision-control system data, or from the parent directory name of an
-# unpacked source archive. Distribution tarballs contain a pre-generated copy
-# of this file.
-
-import json
-
-version_json = '''
-%s
-'''  # END VERSION_JSON
-
-
-def get_versions():
-    return json.loads(version_json)
-"""
-
-
-def versions_from_file(filename):
-    """Try to determine the version from _version.py if present."""
-    try:
-        with open(filename) as f:
-            contents = f.read()
-    except OSError:
-        raise NotThisMethod("unable to read _version.py")
-    mo = re.search(
-        r"version_json = '''\n(.*)'''  # END VERSION_JSON", contents, re.M | re.S
-    )
-    if not mo:
-        mo = re.search(
-            r"version_json = '''\r\n(.*)'''  # END VERSION_JSON", contents, re.M | re.S
-        )
-    if not mo:
-        raise NotThisMethod("no version_json in _version.py")
-    return json.loads(mo.group(1))
-
-
-def write_to_version_file(filename, versions):
-    """Write the given version number to the given _version.py file."""
-    os.unlink(filename)
-    contents = json.dumps(versions, sort_keys=True, indent=1, separators=(",", ": "))
-    with open(filename, "w") as f:
-        f.write(SHORT_VERSION_PY % contents)
-
-    print("set %s to '%s'" % (filename, versions["version"]))
-
-
-def plus_or_dot(pieces):
-    """Return a + if we don't already have one, else return a ."""
-    if "+" in pieces.get("closest-tag", ""):
-        return "."
-    return "+"
-
-
-def render_pep440(pieces):
-    """Build up version string, with post-release "local version identifier".
-
-    Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you
-    get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty
-
-    Exceptions:
-    1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty]
-    """
-    if pieces["closest-tag"]:
-        rendered = pieces["closest-tag"]
-        if pieces["distance"] or pieces["dirty"]:
-            rendered += plus_or_dot(pieces)
-            rendered += "%d.g%s" % (pieces["distance"], pieces["short"])
-            if pieces["dirty"]:
-                rendered += ".dirty"
-    else:
-        # exception #1
-        rendered = "0+untagged.%d.g%s" % (pieces["distance"], pieces["short"])
-        if pieces["dirty"]:
-            rendered += ".dirty"
-    return rendered
-
-
-def render_pep440_branch(pieces):
-    """TAG[[.dev0]+DISTANCE.gHEX[.dirty]] .
-
-    The ".dev0" means not master branch. Note that .dev0 sorts backwards
-    (a feature branch will appear "older" than the master branch).
-
-    Exceptions:
-    1: no tags. 0[.dev0]+untagged.DISTANCE.gHEX[.dirty]
-    """
-    if pieces["closest-tag"]:
-        rendered = pieces["closest-tag"]
-        if pieces["distance"] or pieces["dirty"]:
-            if pieces["branch"] != "master":
-                rendered += ".dev0"
-            rendered += plus_or_dot(pieces)
-            rendered += "%d.g%s" % (pieces["distance"], pieces["short"])
-            if pieces["dirty"]:
-                rendered += ".dirty"
-    else:
-        # exception #1
-        rendered = "0"
-        if pieces["branch"] != "master":
-            rendered += ".dev0"
-        rendered += "+untagged.%d.g%s" % (pieces["distance"], pieces["short"])
-        if pieces["dirty"]:
-            rendered += ".dirty"
-    return rendered
-
-
-def pep440_split_post(ver):
-    """Split pep440 version string at the post-release segment.
-
-    Returns the release segments before the post-release and the
-    post-release version number (or -1 if no post-release segment is present).
-    """
-    vc = str.split(ver, ".post")
-    return vc[0], int(vc[1] or 0) if len(vc) == 2 else None
-
-
-def render_pep440_pre(pieces):
-    """TAG[.postN.devDISTANCE] -- No -dirty.
-
-    Exceptions:
-    1: no tags. 0.post0.devDISTANCE
-    """
-    if pieces["closest-tag"]:
-        if pieces["distance"]:
-            # update the post release segment
-            tag_version, post_version = pep440_split_post(pieces["closest-tag"])
-            rendered = tag_version
-            if post_version is not None:
-                rendered += ".post%d.dev%d" % (post_version + 1, pieces["distance"])
-            else:
-                rendered += ".post0.dev%d" % (pieces["distance"])
-        else:
-            # no commits, use the tag as the version
-            rendered = pieces["closest-tag"]
-    else:
-        # exception #1
-        rendered = "0.post0.dev%d" % pieces["distance"]
-    return rendered
-
-
-def render_pep440_post(pieces):
-    """TAG[.postDISTANCE[.dev0]+gHEX] .
-
-    The ".dev0" means dirty. Note that .dev0 sorts backwards
-    (a dirty tree will appear "older" than the corresponding clean one),
-    but you shouldn't be releasing software with -dirty anyways.
-
-    Exceptions:
-    1: no tags. 0.postDISTANCE[.dev0]
-    """
-    if pieces["closest-tag"]:
-        rendered = pieces["closest-tag"]
-        if pieces["distance"] or pieces["dirty"]:
-            rendered += ".post%d" % pieces["distance"]
-            if pieces["dirty"]:
-                rendered += ".dev0"
-            rendered += plus_or_dot(pieces)
-            rendered += "g%s" % pieces["short"]
-    else:
-        # exception #1
-        rendered = "0.post%d" % pieces["distance"]
-        if pieces["dirty"]:
-            rendered += ".dev0"
-        rendered += "+g%s" % pieces["short"]
-    return rendered
-
-
-def render_pep440_post_branch(pieces):
-    """TAG[.postDISTANCE[.dev0]+gHEX[.dirty]] .
-
-    The ".dev0" means not master branch.
-
-    Exceptions:
-    1: no tags. 0.postDISTANCE[.dev0]+gHEX[.dirty]
-    """
-    if pieces["closest-tag"]:
-        rendered = pieces["closest-tag"]
-        if pieces["distance"] or pieces["dirty"]:
-            rendered += ".post%d" % pieces["distance"]
-            if pieces["branch"] != "master":
-                rendered += ".dev0"
-            rendered += plus_or_dot(pieces)
-            rendered += "g%s" % pieces["short"]
-            if pieces["dirty"]:
-                rendered += ".dirty"
-    else:
-        # exception #1
-        rendered = "0.post%d" % pieces["distance"]
-        if pieces["branch"] != "master":
-            rendered += ".dev0"
-        rendered += "+g%s" % pieces["short"]
-        if pieces["dirty"]:
-            rendered += ".dirty"
-    return rendered
-
-
-def render_pep440_old(pieces):
-    """TAG[.postDISTANCE[.dev0]] .
-
-    The ".dev0" means dirty.
-
-    Exceptions:
-    1: no tags. 0.postDISTANCE[.dev0]
-    """
-    if pieces["closest-tag"]:
-        rendered = pieces["closest-tag"]
-        if pieces["distance"] or pieces["dirty"]:
-            rendered += ".post%d" % pieces["distance"]
-            if pieces["dirty"]:
-                rendered += ".dev0"
-    else:
-        # exception #1
-        rendered = "0.post%d" % pieces["distance"]
-        if pieces["dirty"]:
-            rendered += ".dev0"
-    return rendered
-
-
-def render_git_describe(pieces):
-    """TAG[-DISTANCE-gHEX][-dirty].
-
-    Like 'git describe --tags --dirty --always'.
-
-    Exceptions:
-    1: no tags. HEX[-dirty]  (note: no 'g' prefix)
-    """
-    if pieces["closest-tag"]:
-        rendered = pieces["closest-tag"]
-        if pieces["distance"]:
-            rendered += "-%d-g%s" % (pieces["distance"], pieces["short"])
-    else:
-        # exception #1
-        rendered = pieces["short"]
-    if pieces["dirty"]:
-        rendered += "-dirty"
-    return rendered
-
-
-def render_git_describe_long(pieces):
-    """TAG-DISTANCE-gHEX[-dirty].
-
-    Like 'git describe --tags --dirty --always -long'.
-    The distance/hash is unconditional.
-
-    Exceptions:
-    1: no tags. HEX[-dirty]  (note: no 'g' prefix)
-    """
-    if pieces["closest-tag"]:
-        rendered = pieces["closest-tag"]
-        rendered += "-%d-g%s" % (pieces["distance"], pieces["short"])
-    else:
-        # exception #1
-        rendered = pieces["short"]
-    if pieces["dirty"]:
-        rendered += "-dirty"
-    return rendered
-
-
-def render(pieces, style):
-    """Render the given version pieces into the requested style."""
-    if pieces["error"]:
-        return {
-            "version": "unknown",
-            "full-revisionid": pieces.get("long"),
-            "dirty": None,
-            "error": pieces["error"],
-            "date": None,
-        }
-
-    if not style or style == "default":
-        style = "pep440"  # the default
-
-    if style == "pep440":
-        rendered = render_pep440(pieces)
-    elif style == "pep440-branch":
-        rendered = render_pep440_branch(pieces)
-    elif style == "pep440-pre":
-        rendered = render_pep440_pre(pieces)
-    elif style == "pep440-post":
-        rendered = render_pep440_post(pieces)
-    elif style == "pep440-post-branch":
-        rendered = render_pep440_post_branch(pieces)
-    elif style == "pep440-old":
-        rendered = render_pep440_old(pieces)
-    elif style == "git-describe":
-        rendered = render_git_describe(pieces)
-    elif style == "git-describe-long":
-        rendered = render_git_describe_long(pieces)
-    else:
-        raise ValueError("unknown style '%s'" % style)
-
-    return {
-        "version": rendered,
-        "full-revisionid": pieces["long"],
-        "dirty": pieces["dirty"],
-        "error": None,
-        "date": pieces.get("date"),
-    }
-
-
-class VersioneerBadRootError(Exception):
-    """The project root directory is unknown or missing key files."""
-
-
-def get_versions(verbose=False):
-    """Get the project version from whatever source is available.
-
-    Returns dict with two keys: 'version' and 'full'.
-    """
-    if "versioneer" in sys.modules:
-        # see the discussion in cmdclass.py:get_cmdclass()
-        del sys.modules["versioneer"]
-
-    root = get_root()
-    cfg = get_config_from_root(root)
-
-    assert cfg.VCS is not None, "please set [versioneer]VCS= in setup.cfg"
-    handlers = HANDLERS.get(cfg.VCS)
-    assert handlers, "unrecognized VCS '%s'" % cfg.VCS
-    verbose = verbose or cfg.verbose
-    assert (
-        cfg.versionfile_source is not None
-    ), "please set versioneer.versionfile_source"
-    assert cfg.tag_prefix is not None, "please set versioneer.tag_prefix"
-
-    versionfile_abs = os.path.join(root, cfg.versionfile_source)
-
-    # extract version from first of: _version.py, VCS command (e.g. 'git
-    # describe'), parentdir. This is meant to work for developers using a
-    # source checkout, for users of a tarball created by 'setup.py sdist',
-    # and for users of a tarball/zipball created by 'git archive' or github's
-    # download-from-tag feature or the equivalent in other VCSes.
-
-    get_keywords_f = handlers.get("get_keywords")
-    from_keywords_f = handlers.get("keywords")
-    if get_keywords_f and from_keywords_f:
-        try:
-            keywords = get_keywords_f(versionfile_abs)
-            ver = from_keywords_f(keywords, cfg.tag_prefix, verbose)
-            if verbose:
-                print("got version from expanded keyword %s" % ver)
-            return ver
-        except NotThisMethod:
-            pass
-
-    try:
-        ver = versions_from_file(versionfile_abs)
-        if verbose:
-            print("got version from file %s %s" % (versionfile_abs, ver))
-        return ver
-    except NotThisMethod:
-        pass
-
-    from_vcs_f = handlers.get("pieces_from_vcs")
-    if from_vcs_f:
-        try:
-            pieces = from_vcs_f(cfg.tag_prefix, root, verbose)
-            ver = render(pieces, cfg.style)
-            if verbose:
-                print("got version from VCS %s" % ver)
-            return ver
-        except NotThisMethod:
-            pass
-
-    try:
-        if cfg.parentdir_prefix:
-            ver = versions_from_parentdir(cfg.parentdir_prefix, root, verbose)
-            if verbose:
-                print("got version from parentdir %s" % ver)
-            return ver
-    except NotThisMethod:
-        pass
-
-    if verbose:
-        print("unable to compute version")
-
-    return {
-        "version": "0+unknown",
-        "full-revisionid": None,
-        "dirty": None,
-        "error": "unable to compute version",
-        "date": None,
-    }
-
-
-def get_version():
-    """Get the short version string for this project."""
-    return get_versions()["version"]
-
-
-def get_cmdclass(cmdclass=None):
-    """Get the custom setuptools/distutils subclasses used by Versioneer.
-
-    If the package uses a different cmdclass (e.g. one from numpy), it
-    should be provide as an argument.
-    """
-    if "versioneer" in sys.modules:
-        del sys.modules["versioneer"]
-        # this fixes the "python setup.py develop" case (also 'install' and
-        # 'easy_install .'), in which subdependencies of the main project are
-        # built (using setup.py bdist_egg) in the same python process. Assume
-        # a main project A and a dependency B, which use different versions
-        # of Versioneer. A's setup.py imports A's Versioneer, leaving it in
-        # sys.modules by the time B's setup.py is executed, causing B to run
-        # with the wrong versioneer. Setuptools wraps the sub-dep builds in a
-        # sandbox that restores sys.modules to it's pre-build state, so the
-        # parent is protected against the child's "import versioneer". By
-        # removing ourselves from sys.modules here, before the child build
-        # happens, we protect the child from the parent's versioneer too.
-        # Also see https://github.com/python-versioneer/python-versioneer/issues/52
-
-    cmds = {} if cmdclass is None else cmdclass.copy()
-
-    # we add "version" to both distutils and setuptools
-    try:
-        from setuptools import Command
-    except ImportError:
-        from distutils.core import Command
-
-    class cmd_version(Command):
-        description = "report generated version string"
-        user_options = []
-        boolean_options = []
-
-        def initialize_options(self):
-            pass
-
-        def finalize_options(self):
-            pass
-
-        def run(self):
-            vers = get_versions(verbose=True)
-            print("Version: %s" % vers["version"])
-            print(" full-revisionid: %s" % vers.get("full-revisionid"))
-            print(" dirty: %s" % vers.get("dirty"))
-            print(" date: %s" % vers.get("date"))
-            if vers["error"]:
-                print(" error: %s" % vers["error"])
-
-    cmds["version"] = cmd_version
-
-    # we override "build_py" in both distutils and setuptools
-    #
-    # most invocation pathways end up running build_py:
-    #  distutils/build -> build_py
-    #  distutils/install -> distutils/build ->..
-    #  setuptools/bdist_wheel -> distutils/install ->..
-    #  setuptools/bdist_egg -> distutils/install_lib -> build_py
-    #  setuptools/install -> bdist_egg ->..
-    #  setuptools/develop -> ?
-    #  pip install:
-    #   copies source tree to a tempdir before running egg_info/etc
-    #   if .git isn't copied too, 'git describe' will fail
-    #   then does setup.py bdist_wheel, or sometimes setup.py install
-    #  setup.py egg_info -> ?
-
-    # we override different "build_py" commands for both environments
-    if "build_py" in cmds:
-        _build_py = cmds["build_py"]
-    elif "setuptools" in sys.modules:
-        from setuptools.command.build_py import build_py as _build_py
-    else:
-        from distutils.command.build_py import build_py as _build_py
-
-    class cmd_build_py(_build_py):
-        def run(self):
-            root = get_root()
-            cfg = get_config_from_root(root)
-            versions = get_versions()
-            _build_py.run(self)
-            # now locate _version.py in the new build/ directory and replace
-            # it with an updated value
-            if cfg.versionfile_build:
-                target_versionfile = os.path.join(self.build_lib, cfg.versionfile_build)
-                print("UPDATING %s" % target_versionfile)
-                write_to_version_file(target_versionfile, versions)
-
-    cmds["build_py"] = cmd_build_py
-
-    if "build_ext" in cmds:
-        _build_ext = cmds["build_ext"]
-    elif "setuptools" in sys.modules:
-        from setuptools.command.build_ext import build_ext as _build_ext
-    else:
-        from distutils.command.build_ext import build_ext as _build_ext
-
-    class cmd_build_ext(_build_ext):
-        def run(self):
-            root = get_root()
-            cfg = get_config_from_root(root)
-            versions = get_versions()
-            _build_ext.run(self)
-            if self.inplace:
-                # build_ext --inplace will only build extensions in
-                # build/lib<..> dir with no _version.py to write to.
-                # As in place builds will already have a _version.py
-                # in the module dir, we do not need to write one.
-                return
-            # now locate _version.py in the new build/ directory and replace
-            # it with an updated value
-            target_versionfile = os.path.join(self.build_lib, cfg.versionfile_build)
-            print("UPDATING %s" % target_versionfile)
-            write_to_version_file(target_versionfile, versions)
-
-    cmds["build_ext"] = cmd_build_ext
-
-    if "cx_Freeze" in sys.modules:  # cx_freeze enabled?
-        from cx_Freeze.dist import build_exe as _build_exe
-
-        # nczeczulin reports that py2exe won't like the pep440-style string
-        # as FILEVERSION, but it can be used for PRODUCTVERSION, e.g.
-        # setup(console=[{
-        #   "version": versioneer.get_version().split("+", 1)[0], # FILEVERSION
-        #   "product_version": versioneer.get_version(),
-        #   ...
-
-        class cmd_build_exe(_build_exe):
-            def run(self):
-                root = get_root()
-                cfg = get_config_from_root(root)
-                versions = get_versions()
-                target_versionfile = cfg.versionfile_source
-                print("UPDATING %s" % target_versionfile)
-                write_to_version_file(target_versionfile, versions)
-
-                _build_exe.run(self)
-                os.unlink(target_versionfile)
-                with open(cfg.versionfile_source, "w") as f:
-                    LONG = LONG_VERSION_PY[cfg.VCS]
-                    f.write(
-                        LONG
-                        % {
-                            "DOLLAR": "$",
-                            "STYLE": cfg.style,
-                            "TAG_PREFIX": cfg.tag_prefix,
-                            "PARENTDIR_PREFIX": cfg.parentdir_prefix,
-                            "VERSIONFILE_SOURCE": cfg.versionfile_source,
-                        }
-                    )
-
-        cmds["build_exe"] = cmd_build_exe
-        del cmds["build_py"]
-
-    if "py2exe" in sys.modules:  # py2exe enabled?
-        from py2exe.distutils_buildexe import py2exe as _py2exe
-
-        class cmd_py2exe(_py2exe):
-            def run(self):
-                root = get_root()
-                cfg = get_config_from_root(root)
-                versions = get_versions()
-                target_versionfile = cfg.versionfile_source
-                print("UPDATING %s" % target_versionfile)
-                write_to_version_file(target_versionfile, versions)
-
-                _py2exe.run(self)
-                os.unlink(target_versionfile)
-                with open(cfg.versionfile_source, "w") as f:
-                    LONG = LONG_VERSION_PY[cfg.VCS]
-                    f.write(
-                        LONG
-                        % {
-                            "DOLLAR": "$",
-                            "STYLE": cfg.style,
-                            "TAG_PREFIX": cfg.tag_prefix,
-                            "PARENTDIR_PREFIX": cfg.parentdir_prefix,
-                            "VERSIONFILE_SOURCE": cfg.versionfile_source,
-                        }
-                    )
-
-        cmds["py2exe"] = cmd_py2exe
-
-    # we override different "sdist" commands for both environments
-    if "sdist" in cmds:
-        _sdist = cmds["sdist"]
-    elif "setuptools" in sys.modules:
-        from setuptools.command.sdist import sdist as _sdist
-    else:
-        from distutils.command.sdist import sdist as _sdist
-
-    class cmd_sdist(_sdist):
-        def run(self):
-            versions = get_versions()
-            self._versioneer_generated_versions = versions
-            # unless we update this, the command will keep using the old
-            # version
-            self.distribution.metadata.version = versions["version"]
-            return _sdist.run(self)
-
-        def make_release_tree(self, base_dir, files):
-            root = get_root()
-            cfg = get_config_from_root(root)
-            _sdist.make_release_tree(self, base_dir, files)
-            # now locate _version.py in the new base_dir directory
-            # (remembering that it may be a hardlink) and replace it with an
-            # updated value
-            target_versionfile = os.path.join(base_dir, cfg.versionfile_source)
-            print("UPDATING %s" % target_versionfile)
-            write_to_version_file(
-                target_versionfile, self._versioneer_generated_versions
-            )
-
-    cmds["sdist"] = cmd_sdist
-
-    return cmds
-
-
-CONFIG_ERROR = """
-setup.cfg is missing the necessary Versioneer configuration. You need
-a section like:
-
- [versioneer]
- VCS = git
- style = pep440
- versionfile_source = src/myproject/_version.py
- versionfile_build = myproject/_version.py
- tag_prefix =
- parentdir_prefix = myproject-
-
-You will also need to edit your setup.py to use the results:
-
- import versioneer
- setup(version=versioneer.get_version(),
-       cmdclass=versioneer.get_cmdclass(), ...)
-
-Please read the docstring in ./versioneer.py for configuration instructions,
-edit setup.cfg, and re-run the installer or 'python versioneer.py setup'.
-"""
-
-SAMPLE_CONFIG = """
-# See the docstring in versioneer.py for instructions. Note that you must
-# re-run 'versioneer.py setup' after changing this section, and commit the
-# resulting files.
-
-[versioneer]
-#VCS = git
-#style = pep440
-#versionfile_source =
-#versionfile_build =
-#tag_prefix =
-#parentdir_prefix =
-
-"""
-
-OLD_SNIPPET = """
-from ._version import get_versions
-__version__ = get_versions()['version']
-del get_versions
-"""
-
-INIT_PY_SNIPPET = """
-from . import {0}
-__version__ = {0}.get_versions()['version']
-"""
-
-
-def do_setup():
-    """Do main VCS-independent setup function for installing Versioneer."""
-    root = get_root()
-    try:
-        cfg = get_config_from_root(root)
-    except (OSError, configparser.NoSectionError, configparser.NoOptionError) as e:
-        if isinstance(e, (OSError, configparser.NoSectionError)):
-            print("Adding sample versioneer config to setup.cfg", file=sys.stderr)
-            with open(os.path.join(root, "setup.cfg"), "a") as f:
-                f.write(SAMPLE_CONFIG)
-        print(CONFIG_ERROR, file=sys.stderr)
-        return 1
-
-    print(" creating %s" % cfg.versionfile_source)
-    with open(cfg.versionfile_source, "w") as f:
-        LONG = LONG_VERSION_PY[cfg.VCS]
-        f.write(
-            LONG
-            % {
-                "DOLLAR": "$",
-                "STYLE": cfg.style,
-                "TAG_PREFIX": cfg.tag_prefix,
-                "PARENTDIR_PREFIX": cfg.parentdir_prefix,
-                "VERSIONFILE_SOURCE": cfg.versionfile_source,
-            }
-        )
-
-    ipy = os.path.join(os.path.dirname(cfg.versionfile_source), "__init__.py")
-    if os.path.exists(ipy):
-        try:
-            with open(ipy, "r") as f:
-                old = f.read()
-        except OSError:
-            old = ""
-        module = os.path.splitext(os.path.basename(cfg.versionfile_source))[0]
-        snippet = INIT_PY_SNIPPET.format(module)
-        if OLD_SNIPPET in old:
-            print(" replacing boilerplate in %s" % ipy)
-            with open(ipy, "w") as f:
-                f.write(old.replace(OLD_SNIPPET, snippet))
-        elif snippet not in old:
-            print(" appending to %s" % ipy)
-            with open(ipy, "a") as f:
-                f.write(snippet)
-        else:
-            print(" %s unmodified" % ipy)
-    else:
-        print(" %s doesn't exist, ok" % ipy)
-        ipy = None
-
-    # Make sure both the top-level "versioneer.py" and versionfile_source
-    # (PKG/_version.py, used by runtime code) are in MANIFEST.in, so
-    # they'll be copied into source distributions. Pip won't be able to
-    # install the package without this.
-    manifest_in = os.path.join(root, "MANIFEST.in")
-    simple_includes = set()
-    try:
-        with open(manifest_in, "r") as f:
-            for line in f:
-                if line.startswith("include "):
-                    for include in line.split()[1:]:
-                        simple_includes.add(include)
-    except OSError:
-        pass
-    # That doesn't cover everything MANIFEST.in can do
-    # (http://docs.python.org/2/distutils/sourcedist.html#commands), so
-    # it might give some false negatives. Appending redundant 'include'
-    # lines is safe, though.
-    if "versioneer.py" not in simple_includes:
-        print(" appending 'versioneer.py' to MANIFEST.in")
-        with open(manifest_in, "a") as f:
-            f.write("include versioneer.py\n")
-    else:
-        print(" 'versioneer.py' already in MANIFEST.in")
-    if cfg.versionfile_source not in simple_includes:
-        print(
-            " appending versionfile_source ('%s') to MANIFEST.in"
-            % cfg.versionfile_source
-        )
-        with open(manifest_in, "a") as f:
-            f.write("include %s\n" % cfg.versionfile_source)
-    else:
-        print(" versionfile_source already in MANIFEST.in")
-
-    # Make VCS-specific changes. For git, this means creating/changing
-    # .gitattributes to mark _version.py for export-subst keyword
-    # substitution.
-    do_vcs_install(manifest_in, cfg.versionfile_source, ipy)
-    return 0
-
-
-def scan_setup_py():
-    """Validate the contents of setup.py against Versioneer's expectations."""
-    found = set()
-    setters = False
-    errors = 0
-    with open("setup.py", "r") as f:
-        for line in f.readlines():
-            if "import versioneer" in line:
-                found.add("import")
-            if "versioneer.get_cmdclass()" in line:
-                found.add("cmdclass")
-            if "versioneer.get_version()" in line:
-                found.add("get_version")
-            if "versioneer.VCS" in line:
-                setters = True
-            if "versioneer.versionfile_source" in line:
-                setters = True
-    if len(found) != 3:
-        print("")
-        print("Your setup.py appears to be missing some important items")
-        print("(but I might be wrong). Please make sure it has something")
-        print("roughly like the following:")
-        print("")
-        print(" import versioneer")
-        print(" setup( version=versioneer.get_version(),")
-        print("        cmdclass=versioneer.get_cmdclass(),  ...)")
-        print("")
-        errors += 1
-    if setters:
-        print("You should remove lines like 'versioneer.VCS = ' and")
-        print("'versioneer.versionfile_source = ' . This configuration")
-        print("now lives in setup.cfg, and should be removed from setup.py")
-        print("")
-        errors += 1
-    return errors
-
-
-if __name__ == "__main__":
-    cmd = sys.argv[1]
-    if cmd == "setup":
-        errors = do_setup()
-        errors += scan_setup_py()
-        if errors:
-            sys.exit(1)

From ee14315430a4c42519b486e421a5c3f6971a395c Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Thu, 21 Sep 2023 22:29:56 -0400
Subject: [PATCH 24/28] Bump actions/cache from 2 to 3 (#123)

Bumps [actions/cache](https://github.com/actions/cache) from 2 to 3.
- [Release notes](https://github.com/actions/cache/releases)
- [Changelog](https://github.com/actions/cache/blob/main/RELEASES.md)
- [Commits](https://github.com/actions/cache/compare/v2...v3)

---
updated-dependencies:
- dependency-name: actions/cache
  dependency-type: direct:production
  update-type: version-update:semver-major
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 .github/workflows/build_docs.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/build_docs.yml b/.github/workflows/build_docs.yml
index af0611af..cfc46ef9 100644
--- a/.github/workflows/build_docs.yml
+++ b/.github/workflows/build_docs.yml
@@ -20,7 +20,7 @@
          shell: bash -l {0}
      steps:
        - uses: actions/checkout@v2
-       - uses: actions/cache@v2
+       - uses: actions/cache@v3
          env:
            CACHE_NUMBER: 0
          with:

From cd1a77bb4dc20fca64e98744a16add4f78089dbc Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Thu, 21 Sep 2023 22:40:54 -0400
Subject: [PATCH 25/28] Bump actions/checkout from 2 to 4 (#122)

Bumps [actions/checkout](https://github.com/actions/checkout) from 2 to 4.
- [Release notes](https://github.com/actions/checkout/releases)
- [Changelog](https://github.com/actions/checkout/blob/main/CHANGELOG.md)
- [Commits](https://github.com/actions/checkout/compare/v2...v4)

---
updated-dependencies:
- dependency-name: actions/checkout
  dependency-type: direct:production
  update-type: version-update:semver-major
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 .github/workflows/build_docs.yml          | 2 +-
 .github/workflows/release_and_publish.yml | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/build_docs.yml b/.github/workflows/build_docs.yml
index cfc46ef9..e5391750 100644
--- a/.github/workflows/build_docs.yml
+++ b/.github/workflows/build_docs.yml
@@ -19,7 +19,7 @@
        run:
          shell: bash -l {0}
      steps:
-       - uses: actions/checkout@v2
+       - uses: actions/checkout@v4
        - uses: actions/cache@v3
          env:
            CACHE_NUMBER: 0
diff --git a/.github/workflows/release_and_publish.yml b/.github/workflows/release_and_publish.yml
index 420633db..4993b64d 100644
--- a/.github/workflows/release_and_publish.yml
+++ b/.github/workflows/release_and_publish.yml
@@ -27,7 +27,7 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Checkout repo
-        uses: actions/checkout@v3
+        uses: actions/checkout@v4
       
       - name: Set up python
         uses: actions/setup-python@v4

From 809c072110d6b0026e086530aa430d7caa1c48c5 Mon Sep 17 00:00:00 2001
From: eli knaap <ek@knaaptime.com>
Date: Tue, 26 Sep 2023 07:40:39 -0700
Subject: [PATCH 26/28] Update build_docs.yml

rm leading period in .ci
---
 .github/workflows/build_docs.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/build_docs.yml b/.github/workflows/build_docs.yml
index e5391750..58c77f00 100644
--- a/.github/workflows/build_docs.yml
+++ b/.github/workflows/build_docs.yml
@@ -13,7 +13,7 @@
      strategy:
        matrix:
          os: ['ubuntu-latest']
-         environment-file: [.ci/39.yml]
+         environment-file: [ci/39.yml]
          experimental: [false]
      defaults:
        run:

From e57cac0e2244602f9977304600e0007b6f97b83b Mon Sep 17 00:00:00 2001
From: eli knaap <ek@knaaptime.com>
Date: Tue, 26 Sep 2023 07:43:13 -0700
Subject: [PATCH 27/28] Update build_docs.yml

yml-->yaml
---
 .github/workflows/build_docs.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/build_docs.yml b/.github/workflows/build_docs.yml
index 58c77f00..fe8fbedd 100644
--- a/.github/workflows/build_docs.yml
+++ b/.github/workflows/build_docs.yml
@@ -13,7 +13,7 @@
      strategy:
        matrix:
          os: ['ubuntu-latest']
-         environment-file: [ci/39.yml]
+         environment-file: [ci/39.yaml]
          experimental: [false]
      defaults:
        run:

From c0ead26df0938ea523a9804d6900abebb934a50a Mon Sep 17 00:00:00 2001
From: James Gaboardi <jgaboardi@gmail.com>
Date: Tue, 3 Oct 2023 11:43:04 -0400
Subject: [PATCH 28/28] Update pyproject.toml (#124)

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index a469cb3b..8fadf3f5 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -32,7 +32,7 @@ classifiers = [
 requires-python = ">=3.8"
 dependencies = [
 "scipy>=0.11",
-"numpy>=1.3",
+"numpy>=1.23",
 "pandas",
 "libpysal>=4.0.0",
 "scikit-learn>=0.22",